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2026[ to top ]

Unmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classification.Rauch, Lukas; Heinrich, René; Ghaffari, Houtan; Miklautz, Lukas; Moummad, Ilyass; Sick, Bernhard; Scholz, Christoph. In International Conference on Learning Representations (ICLR). 2026.
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Although probing frozen models has become a standard evaluation paradigm, self-supervised learning in audio defaults to fine-tuning when pursuing state-of-the-art on AudioSet. A key reason is that global pooling creates an information bottleneck causing linear probes to misrepresent the embedding quality: The \texttt{cls-token discards crucial token information about dispersed, localized events in audio. This weakness is rooted in the mismatch between the pretraining objective (globally) and the downstream task (localized). Across a comprehensive benchmark of 13 datasets and 6 spectrogram-based encoders, we investigate the global pooling bottleneck. We introduce binarized prototypical probes: a lightweight and simple pooling method that learns prototypes to perform class-wise information aggregation. Despite its simplicity, our method notably outperforms linear and attentive probing. Our work establishes probing as a competitive and efficient paradigm for evaluating audio SSL models, challenging the reliance on costly fine-tuning.
@inproceedings{rauch2026unmute, abstract = {Although probing frozen models has become a standard evaluation paradigm, self-supervised learning in audio defaults to fine-tuning when pursuing state-of-the-art on AudioSet. A key reason is that global pooling creates an information bottleneck causing linear probes to misrepresent the embedding quality: The \texttt{cls}-token discards crucial token information about dispersed, localized events in audio. This weakness is rooted in the mismatch between the pretraining objective (globally) and the downstream task (localized). Across a comprehensive benchmark of 13 datasets and 6 spectrogram-based encoders, we investigate the global pooling bottleneck. We introduce binarized prototypical probes: a lightweight and simple pooling method that learns prototypes to perform class-wise information aggregation. Despite its simplicity, our method notably outperforms linear and attentive probing. Our work establishes probing as a competitive and efficient paradigm for evaluating audio SSL models, challenging the reliance on costly fine-tuning.}, author = {Rauch, Lukas and Heinrich, René and Ghaffari, Houtan and Miklautz, Lukas and Moummad, Ilyass and Sick, Bernhard and Scholz, Christoph}, booktitle = {International Conference on Learning Representations (ICLR)}, keywords = {itegpub}, note = {(accepted)}, title = {Unmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classification}, year = 2026 }
%0 Conference Paper %1 rauch2026unmute %A Rauch, Lukas %A Heinrich, René %A Ghaffari, Houtan %A Miklautz, Lukas %A Moummad, Ilyass %A Sick, Bernhard %A Scholz, Christoph %B International Conference on Learning Representations (ICLR) %D 2026 %T Unmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classification %X Although probing frozen models has become a standard evaluation paradigm, self-supervised learning in audio defaults to fine-tuning when pursuing state-of-the-art on AudioSet. A key reason is that global pooling creates an information bottleneck causing linear probes to misrepresent the embedding quality: The \texttt{cls-token discards crucial token information about dispersed, localized events in audio. This weakness is rooted in the mismatch between the pretraining objective (globally) and the downstream task (localized). Across a comprehensive benchmark of 13 datasets and 6 spectrogram-based encoders, we investigate the global pooling bottleneck. We introduce binarized prototypical probes: a lightweight and simple pooling method that learns prototypes to perform class-wise information aggregation. Despite its simplicity, our method notably outperforms linear and attentive probing. Our work establishes probing as a competitive and efficient paradigm for evaluating audio SSL models, challenging the reliance on costly fine-tuning.
TRACE: Time series representation learning with contrastive embeddings for anomaly detection in photovoltaic systems.Nivarthi, Chandana Priya; Huang, Zhixin; Gruhl, Christian; Sick, Bernhard. In Energy and AI, 23, bl. 100670. Elsevier, 2026.
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Reliable anomaly detection in photovoltaic (PV) inverters is critical for ensuring operational efficiency and reducing maintenance costs in renewable energy systems. We introduce TRACE (Time series Representation learning with Autoencoder-based Contrastive Embeddings), a self-supervised contrastive learning framework for multivariate time series anomaly detection in PV systems. TRACE employs a two-stage architecture: autoencoder-based representation learning with interchangeable backbones followed by contrastive training through a Siamese network. The framework generates semantically coherent augmentations by perturbing autoencoder reconstructions and applies three negative mining strategies to create challenging contrastive pairs. Comprehensive experiments on a real-world PV inverter dataset and two industrial benchmarks demonstrate TRACE’s superiority. Autoencoder-based augmentations deliver a 21.3% relative improvement in mean F1 (0.616 vs. 0.508) over traditional perturbation methods, with TransformerAE emerging as the optimal backbone architecture. While negative sampling strategies show dataset-specific advantages, their impact remains secondary to encoder capacity. TRACE with TransformerAE and reconstruction-error negatives consistently outperforms fourteen state-of-the-art time series anomaly detection methods, achieving highest F1 scores on all the three datasets while maintaining exceptional precision up to 0.99. Visualization analysis confirms TRACE’s capacity for early fault detection up to three days before failure and interpretable embedding separation. The framework addresses the fundamental challenge of label scarcity in industrial monitoring through self-supervised learning, providing a practical and transparent solution for predictive maintenance in PV systems and broader industrial applications.
@article{nivarthi2026trace, abstract = {Reliable anomaly detection in photovoltaic (PV) inverters is critical for ensuring operational efficiency and reducing maintenance costs in renewable energy systems. We introduce TRACE (Time series Representation learning with Autoencoder-based Contrastive Embeddings), a self-supervised contrastive learning framework for multivariate time series anomaly detection in PV systems. TRACE employs a two-stage architecture: autoencoder-based representation learning with interchangeable backbones followed by contrastive training through a Siamese network. The framework generates semantically coherent augmentations by perturbing autoencoder reconstructions and applies three negative mining strategies to create challenging contrastive pairs. Comprehensive experiments on a real-world PV inverter dataset and two industrial benchmarks demonstrate TRACE’s superiority. Autoencoder-based augmentations deliver a 21.3% relative improvement in mean F1 (0.616 vs. 0.508) over traditional perturbation methods, with TransformerAE emerging as the optimal backbone architecture. While negative sampling strategies show dataset-specific advantages, their impact remains secondary to encoder capacity. TRACE with TransformerAE and reconstruction-error negatives consistently outperforms fourteen state-of-the-art time series anomaly detection methods, achieving highest F1 scores on all the three datasets while maintaining exceptional precision up to 0.99. Visualization analysis confirms TRACE’s capacity for early fault detection up to three days before failure and interpretable embedding separation. The framework addresses the fundamental challenge of label scarcity in industrial monitoring through self-supervised learning, providing a practical and transparent solution for predictive maintenance in PV systems and broader industrial applications.}, author = {Nivarthi, Chandana Priya and Huang, Zhixin and Gruhl, Christian and Sick, Bernhard}, journal = {Energy and AI}, keywords = {itegpub}, pages = 100670, publisher = {Elsevier}, title = {TRACE: Time series representation learning with contrastive embeddings for anomaly detection in photovoltaic systems}, volume = 23, year = 2026 }
%0 Journal Article %1 nivarthi2026trace %A Nivarthi, Chandana Priya %A Huang, Zhixin %A Gruhl, Christian %A Sick, Bernhard %D 2026 %I Elsevier %J Energy and AI %P 100670 %R 10.1016/j.egyai.2025.100670 %T TRACE: Time series representation learning with contrastive embeddings for anomaly detection in photovoltaic systems %U https://www.sciencedirect.com/science/article/pii/S2666546825002022 %V 23 %X Reliable anomaly detection in photovoltaic (PV) inverters is critical for ensuring operational efficiency and reducing maintenance costs in renewable energy systems. We introduce TRACE (Time series Representation learning with Autoencoder-based Contrastive Embeddings), a self-supervised contrastive learning framework for multivariate time series anomaly detection in PV systems. TRACE employs a two-stage architecture: autoencoder-based representation learning with interchangeable backbones followed by contrastive training through a Siamese network. The framework generates semantically coherent augmentations by perturbing autoencoder reconstructions and applies three negative mining strategies to create challenging contrastive pairs. Comprehensive experiments on a real-world PV inverter dataset and two industrial benchmarks demonstrate TRACE’s superiority. Autoencoder-based augmentations deliver a 21.3% relative improvement in mean F1 (0.616 vs. 0.508) over traditional perturbation methods, with TransformerAE emerging as the optimal backbone architecture. While negative sampling strategies show dataset-specific advantages, their impact remains secondary to encoder capacity. TRACE with TransformerAE and reconstruction-error negatives consistently outperforms fourteen state-of-the-art time series anomaly detection methods, achieving highest F1 scores on all the three datasets while maintaining exceptional precision up to 0.99. Visualization analysis confirms TRACE’s capacity for early fault detection up to three days before failure and interpretable embedding separation. The framework addresses the fundamental challenge of label scarcity in industrial monitoring through self-supervised learning, providing a practical and transparent solution for predictive maintenance in PV systems and broader industrial applications.
Graph reinforcement learning for power grids: A comprehensive survey.Hassouna, Mohamed; Holzhüter, Clara; Lytaev, Pawel; Thomas, Josephine; Sick, Bernhard; Scholz, Christoph. In Energy and AI, 23, bl. 100671. Elsevier, 2026.
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The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in traditional power grid methods. In this context, Graph Neural Networks are a promising solution due to their ability to learn from graph-structured data. Combined with Reinforcement Learning, they can be used as control approaches to determine remedial actions. This review analyzes how Graph Reinforcement Learning can improve representation learning and decision-making in power grid applications, particularly transmission and distribution grids. We analyze the reviewed approaches in terms of the graph structure, the Graph Neural Network architecture, and the Reinforcement Learning approach. Although Graph Reinforcement Learning has demonstrated adaptability to unpredictable events and noisy data, its current stage is primarily proof-of-concept, and it is not yet deployable to real-world applications. We highlight the open challenges and limitations for real-world applications..
@article{hassouna2026graph, abstract = {The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in traditional power grid methods. In this context, Graph Neural Networks are a promising solution due to their ability to learn from graph-structured data. Combined with Reinforcement Learning, they can be used as control approaches to determine remedial actions. This review analyzes how Graph Reinforcement Learning can improve representation learning and decision-making in power grid applications, particularly transmission and distribution grids. We analyze the reviewed approaches in terms of the graph structure, the Graph Neural Network architecture, and the Reinforcement Learning approach. Although Graph Reinforcement Learning has demonstrated adaptability to unpredictable events and noisy data, its current stage is primarily proof-of-concept, and it is not yet deployable to real-world applications. We highlight the open challenges and limitations for real-world applications..}, author = {Hassouna, Mohamed and Holzhüter, Clara and Lytaev, Pawel and Thomas, Josephine and Sick, Bernhard and Scholz, Christoph}, journal = {Energy and AI}, keywords = {itegpub}, pages = 100671, publisher = {Elsevier}, title = {Graph reinforcement learning for power grids: A comprehensive survey}, volume = 23, year = 2026 }
%0 Journal Article %1 hassouna2026graph %A Hassouna, Mohamed %A Holzhüter, Clara %A Lytaev, Pawel %A Thomas, Josephine %A Sick, Bernhard %A Scholz, Christoph %D 2026 %I Elsevier %J Energy and AI %P 100671 %R 10.1016/j.egyai.2025.100671 %T Graph reinforcement learning for power grids: A comprehensive survey %U https://www.sciencedirect.com/science/article/pii/S2666546825002034 %V 23 %X The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in traditional power grid methods. In this context, Graph Neural Networks are a promising solution due to their ability to learn from graph-structured data. Combined with Reinforcement Learning, they can be used as control approaches to determine remedial actions. This review analyzes how Graph Reinforcement Learning can improve representation learning and decision-making in power grid applications, particularly transmission and distribution grids. We analyze the reviewed approaches in terms of the graph structure, the Graph Neural Network architecture, and the Reinforcement Learning approach. Although Graph Reinforcement Learning has demonstrated adaptability to unpredictable events and noisy data, its current stage is primarily proof-of-concept, and it is not yet deployable to real-world applications. We highlight the open challenges and limitations for real-world applications..
Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach.Hassouna, Mohamed; Holzhüter, Clara; Lehna, Malte; de Jong, Matthijs; Viebahn, Jan; Sick, Bernhard; Scholz, Christoph. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), ble. 129–146. Springer, 2026.
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The rising proportion of renewable energy in the electricity mix introduces significant operational challenges for power grid operators. Effective power grid management demands adaptive decision-making strategies capable of handling dynamic conditions. With the increase in complexity, more and more Deep Learning (DL) approaches have been proposed to find suitable grid topologies for congestion management. In this work, we contribute to this research by introducing a novel Imitation Learning (IL) approach that leverages soft labels derived from simulated topological action outcomes, thereby capturing multiple viable actions per state. Unlike traditional IL methods that rely on hard labels to enforce a single optimal action, our method constructs soft labels that capture the effectiveness of actions that prove suitable in resolving grid congestion. To further enhance decision-making, we integrate Graph Neural Networks (GNNs) to encode the structural properties of power grids, ensuring that the topology-aware representations contribute to better agent performance. Our approach significantly outperforms its hard-label counterparts as well as state-of-the-art Deep Reinforcement Learning (DRL) baseline agents. Most notably, it achieves a 17% better performance compared to the greedy expert agent from which the imitation targets were derived.
@inproceedings{hassouna2025learning, abstract = {The rising proportion of renewable energy in the electricity mix introduces significant operational challenges for power grid operators. Effective power grid management demands adaptive decision-making strategies capable of handling dynamic conditions. With the increase in complexity, more and more Deep Learning (DL) approaches have been proposed to find suitable grid topologies for congestion management. In this work, we contribute to this research by introducing a novel Imitation Learning (IL) approach that leverages soft labels derived from simulated topological action outcomes, thereby capturing multiple viable actions per state. Unlike traditional IL methods that rely on hard labels to enforce a single optimal action, our method constructs soft labels that capture the effectiveness of actions that prove suitable in resolving grid congestion. To further enhance decision-making, we integrate Graph Neural Networks (GNNs) to encode the structural properties of power grids, ensuring that the topology-aware representations contribute to better agent performance. Our approach significantly outperforms its hard-label counterparts as well as state-of-the-art Deep Reinforcement Learning (DRL) baseline agents. Most notably, it achieves a 17% better performance compared to the greedy expert agent from which the imitation targets were derived.}, author = {Hassouna, Mohamed and Holzhüter, Clara and Lehna, Malte and de Jong, Matthijs and Viebahn, Jan and Sick, Bernhard and Scholz, Christoph}, booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)}, keywords = {itegpub}, pages = {129–146}, publisher = {Springer}, title = {Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach}, year = 2026 }
%0 Conference Paper %1 hassouna2025learning %A Hassouna, Mohamed %A Holzhüter, Clara %A Lehna, Malte %A de Jong, Matthijs %A Viebahn, Jan %A Sick, Bernhard %A Scholz, Christoph %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) %D 2026 %I Springer %P 129--146 %R 10.1007/978-3-032-06129-4_8 %T Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach %U https://link.springer.com/chapter/10.1007/978-3-032-06129-4_8 %X The rising proportion of renewable energy in the electricity mix introduces significant operational challenges for power grid operators. Effective power grid management demands adaptive decision-making strategies capable of handling dynamic conditions. With the increase in complexity, more and more Deep Learning (DL) approaches have been proposed to find suitable grid topologies for congestion management. In this work, we contribute to this research by introducing a novel Imitation Learning (IL) approach that leverages soft labels derived from simulated topological action outcomes, thereby capturing multiple viable actions per state. Unlike traditional IL methods that rely on hard labels to enforce a single optimal action, our method constructs soft labels that capture the effectiveness of actions that prove suitable in resolving grid congestion. To further enhance decision-making, we integrate Graph Neural Networks (GNNs) to encode the structural properties of power grids, ensuring that the topology-aware representations contribute to better agent performance. Our approach significantly outperforms its hard-label counterparts as well as state-of-the-art Deep Reinforcement Learning (DRL) baseline agents. Most notably, it achieves a 17% better performance compared to the greedy expert agent from which the imitation targets were derived.

2025[ to top ]

AudioProtoPNet: An interpretable deep learning model for bird sound classification.Heinrich, René; Rauch, Lukas; Sick, Bernhard; Scholz, Christoph. In Ecological Informatics, 87, bl. 103081. Elsevier, 2025.
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Deep learning models have significantly advanced acoustic bird monitoring by recognizing numerous bird species based on their vocalizations. However, traditional deep learning models are black boxes that provide no insight into their underlying computations, limiting their usefulness to ornithologists and machine learning engineers. Explainable models could facilitate debugging, knowledge discovery, trust, and interdisciplinary collaboration. We introduce AudioProtoPNet, an adaptation of the Prototypical Part Network (ProtoPNet) for multi-label bird sound classification. It is inherently interpretable, leveraging a ConvNeXt backbone to extract embeddings and a prototype learning classifier trained on these embeddings. The classifier learns prototypical patterns of each bird species’ vocalizations from spectrograms of instances in the training data. During inference, recordings are classified by comparing them to learned prototypes in the embedding space, providing explanations for the model’s decisions and insights into the most informative embeddings of each bird species. The model was trained on the BirdSet training dataset, which consists of 9734 bird species and over 6800 h of recordings. Its performance was evaluated on the seven BirdSet test datasets, covering different geographical regions. AudioProtoPNet outperformed the state-of-the-art bird sound classification model Perch, which is superior to the more popular BirdNet, achieving an average AUROC of 0.90 and a cmAP of 0.42, with relative improvements of 7.1% and 16.7% over Perch, respectively. These results demonstrate that even for the challenging task of multi-label bird sound classification, it is possible to develop powerful yet interpretable deep learning models that provide valuable insights for professionals in ornithology and machine learning.
@article{heinrich2025audioprotopnet, abstract = {Deep learning models have significantly advanced acoustic bird monitoring by recognizing numerous bird species based on their vocalizations. However, traditional deep learning models are black boxes that provide no insight into their underlying computations, limiting their usefulness to ornithologists and machine learning engineers. Explainable models could facilitate debugging, knowledge discovery, trust, and interdisciplinary collaboration. We introduce AudioProtoPNet, an adaptation of the Prototypical Part Network (ProtoPNet) for multi-label bird sound classification. It is inherently interpretable, leveraging a ConvNeXt backbone to extract embeddings and a prototype learning classifier trained on these embeddings. The classifier learns prototypical patterns of each bird species’ vocalizations from spectrograms of instances in the training data. During inference, recordings are classified by comparing them to learned prototypes in the embedding space, providing explanations for the model’s decisions and insights into the most informative embeddings of each bird species. The model was trained on the BirdSet training dataset, which consists of 9734 bird species and over 6800 h of recordings. Its performance was evaluated on the seven BirdSet test datasets, covering different geographical regions. AudioProtoPNet outperformed the state-of-the-art bird sound classification model Perch, which is superior to the more popular BirdNet, achieving an average AUROC of 0.90 and a cmAP of 0.42, with relative improvements of 7.1% and 16.7% over Perch, respectively. These results demonstrate that even for the challenging task of multi-label bird sound classification, it is possible to develop powerful yet interpretable deep learning models that provide valuable insights for professionals in ornithology and machine learning.}, author = {Heinrich, René and Rauch, Lukas and Sick, Bernhard and Scholz, Christoph}, journal = {Ecological Informatics}, keywords = {itegpub}, pages = 103081, publisher = {Elsevier}, title = {AudioProtoPNet: An interpretable deep learning model for bird sound classification}, volume = 87, year = 2025 }
%0 Journal Article %1 heinrich2025audioprotopnet %A Heinrich, René %A Rauch, Lukas %A Sick, Bernhard %A Scholz, Christoph %D 2025 %I Elsevier %J Ecological Informatics %P 103081 %R 10.1016/j.ecoinf.2025.103081 %T AudioProtoPNet: An interpretable deep learning model for bird sound classification %U https://www.sciencedirect.com/science/article/pii/S1574954125000901 %V 87 %X Deep learning models have significantly advanced acoustic bird monitoring by recognizing numerous bird species based on their vocalizations. However, traditional deep learning models are black boxes that provide no insight into their underlying computations, limiting their usefulness to ornithologists and machine learning engineers. Explainable models could facilitate debugging, knowledge discovery, trust, and interdisciplinary collaboration. We introduce AudioProtoPNet, an adaptation of the Prototypical Part Network (ProtoPNet) for multi-label bird sound classification. It is inherently interpretable, leveraging a ConvNeXt backbone to extract embeddings and a prototype learning classifier trained on these embeddings. The classifier learns prototypical patterns of each bird species’ vocalizations from spectrograms of instances in the training data. During inference, recordings are classified by comparing them to learned prototypes in the embedding space, providing explanations for the model’s decisions and insights into the most informative embeddings of each bird species. The model was trained on the BirdSet training dataset, which consists of 9734 bird species and over 6800 h of recordings. Its performance was evaluated on the seven BirdSet test datasets, covering different geographical regions. AudioProtoPNet outperformed the state-of-the-art bird sound classification model Perch, which is superior to the more popular BirdNet, achieving an average AUROC of 0.90 and a cmAP of 0.42, with relative improvements of 7.1% and 16.7% over Perch, respectively. These results demonstrate that even for the challenging task of multi-label bird sound classification, it is possible to develop powerful yet interpretable deep learning models that provide valuable insights for professionals in ornithology and machine learning.
Human-AI interaction in safety-critical network infrastructures.Mussi, Marco; Metelli, Alberto Maria; Restelli, Marcello; Losapio, Gianvito; Bessa, Ricardo J.; Boos, Daniel; Borst, Clark; Leto, Giulia; Castagna, Alberto; Chavarriaga, Ricardo; Dias, Duarte; Egli, Adrian; Eisenegger, Andrina; El Manyari, Yassine; Fuxjäger, Anton; Geraldes, Joaquim; Hamouche, Samira; Hassouna, Mohamed; Lemetayer, Bruno; Leyli-Abadi, Milad; Liessner, Roman; Lundberg, Jonas; Marot, Antoine; Meddeb, Maroua; Schiaffonati, Viola; Schneider, Manuel; Stadelmann, Thilo; Usher, Julia; Van Hoof, Herke; Viebahn, Jan; Waefler, Toni; Zanotti, Giacomo. In iScience, 28(9), bl. 113400. Elsevier, 2025.
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Artificial Intelligence (AI) is transforming every aspect of modern society. It demonstrates a high potential to contribute to more flexible operations of safety-critical network infrastructures under deep transformation to tackle global challenges, such as climate change, energy transition, efficiency, and digital transformation, including increasing infrastructure resilience to natural and human-made hazards. The widespread adoption of AI creates the conditions for a new and inevitable interaction between humans and AI-based decision systems. In such a scenario, creating an ecosystem in which humans and AI interact healthily, where the roles and positions of both actors are well-defined, is a critical challenge for research and industry in the coming years. This perspective article outlines the challenges and requirements for effective human-AI interaction by taking an interdisciplinary point of view that merges computer science, decision-making sciences, psychological constructs, and industrial practices. The work focuses on three emblematic safety-critical scenarios from two different domains: energy (power grids) and mobility (railway networks and air traffic management)
@article{mussi2025human, abstract = {Artificial Intelligence (AI) is transforming every aspect of modern society. It demonstrates a high potential to contribute to more flexible operations of safety-critical network infrastructures under deep transformation to tackle global challenges, such as climate change, energy transition, efficiency, and digital transformation, including increasing infrastructure resilience to natural and human-made hazards. The widespread adoption of AI creates the conditions for a new and inevitable interaction between humans and AI-based decision systems. In such a scenario, creating an ecosystem in which humans and AI interact healthily, where the roles and positions of both actors are well-defined, is a critical challenge for research and industry in the coming years. This perspective article outlines the challenges and requirements for effective human-AI interaction by taking an interdisciplinary point of view that merges computer science, decision-making sciences, psychological constructs, and industrial practices. The work focuses on three emblematic safety-critical scenarios from two different domains: energy (power grids) and mobility (railway networks and air traffic management)}, author = {Mussi, Marco and Metelli, Alberto Maria and Restelli, Marcello and Losapio, Gianvito and Bessa, Ricardo J. and Boos, Daniel and Borst, Clark and Leto, Giulia and Castagna, Alberto and Chavarriaga, Ricardo and Dias, Duarte and Egli, Adrian and Eisenegger, Andrina and El Manyari, Yassine and Fuxjäger, Anton and Geraldes, Joaquim and Hamouche, Samira and Hassouna, Mohamed and Lemetayer, Bruno and Leyli-Abadi, Milad and Liessner, Roman and Lundberg, Jonas and Marot, Antoine and Meddeb, Maroua and Schiaffonati, Viola and Schneider, Manuel and Stadelmann, Thilo and Usher, Julia and Van Hoof, Herke and Viebahn, Jan and Waefler, Toni and Zanotti, Giacomo}, journal = {iScience}, keywords = {itegpub}, number = 9, pages = 113400, publisher = {Elsevier}, title = {Human-AI interaction in safety-critical network infrastructures}, volume = 28, year = 2025 }
%0 Journal Article %1 mussi2025human %A Mussi, Marco %A Metelli, Alberto Maria %A Restelli, Marcello %A Losapio, Gianvito %A Bessa, Ricardo J. %A Boos, Daniel %A Borst, Clark %A Leto, Giulia %A Castagna, Alberto %A Chavarriaga, Ricardo %A Dias, Duarte %A Egli, Adrian %A Eisenegger, Andrina %A El Manyari, Yassine %A Fuxjäger, Anton %A Geraldes, Joaquim %A Hamouche, Samira %A Hassouna, Mohamed %A Lemetayer, Bruno %A Leyli-Abadi, Milad %A Liessner, Roman %A Lundberg, Jonas %A Marot, Antoine %A Meddeb, Maroua %A Schiaffonati, Viola %A Schneider, Manuel %A Stadelmann, Thilo %A Usher, Julia %A Van Hoof, Herke %A Viebahn, Jan %A Waefler, Toni %A Zanotti, Giacomo %D 2025 %I Elsevier %J iScience %N 9 %P 113400 %R 10.1016/j.isci.2025.113400 %T Human-AI interaction in safety-critical network infrastructures %U https://www.cell.com/iscience/fulltext/S2589-0042(25)01661-X %V 28 %X Artificial Intelligence (AI) is transforming every aspect of modern society. It demonstrates a high potential to contribute to more flexible operations of safety-critical network infrastructures under deep transformation to tackle global challenges, such as climate change, energy transition, efficiency, and digital transformation, including increasing infrastructure resilience to natural and human-made hazards. The widespread adoption of AI creates the conditions for a new and inevitable interaction between humans and AI-based decision systems. In such a scenario, creating an ecosystem in which humans and AI interact healthily, where the roles and positions of both actors are well-defined, is a critical challenge for research and industry in the coming years. This perspective article outlines the challenges and requirements for effective human-AI interaction by taking an interdisciplinary point of view that merges computer science, decision-making sciences, psychological constructs, and industrial practices. The work focuses on three emblematic safety-critical scenarios from two different domains: energy (power grids) and mobility (railway networks and air traffic management)
A Conceptual Framework for AI-based Decision Systems in Critical Infrastructures.Leyli abadi, Milad; Bessa, Ricardo J.; Viebahn, Jan; Boos, Daniel; Borst, Clark; Castagna, Alberto; Chavarriaga, Ricardo; Hassouna, Mohamed; Lemetayer, Bruno; Leto, Giulia; Marot, Antoine; Meddeb, Maroua; Meyer, Manuel; Schiaffonati, Viola; Schneider, Manuel; Waefler, Toni. In IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2025.
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The interaction between humans and AI in safety-critical systems presents a unique set of challenges that remain partially addressed by existing frameworks. These challenges stem from the complex interplay of requirements for transparency, trust, and explainability, coupled with the necessity for robust and safe decision-making. A framework that holistically integrates human and AI capabilities while addressing these concerns is notably required, bridging the critical gaps in designing, deploying, and maintaining safe and effective systems. This paper proposes a holistic conceptual framework for critical infrastructures by adopting an interdisciplinary approach. It integrates traditionally distinct fields such as mathematics, decision theory, computer science, philosophy, psychology, and cognitive engineering and draws on specialized engineering domains, particularly energy, mobility, and aeronautics. Its flexibility is further demonstrated through a case study on power grid management.
@inproceedings{leyliabadi2025conceptual, abstract = {The interaction between humans and AI in safety-critical systems presents a unique set of challenges that remain partially addressed by existing frameworks. These challenges stem from the complex interplay of requirements for transparency, trust, and explainability, coupled with the necessity for robust and safe decision-making. A framework that holistically integrates human and AI capabilities while addressing these concerns is notably required, bridging the critical gaps in designing, deploying, and maintaining safe and effective systems. This paper proposes a holistic conceptual framework for critical infrastructures by adopting an interdisciplinary approach. It integrates traditionally distinct fields such as mathematics, decision theory, computer science, philosophy, psychology, and cognitive engineering and draws on specialized engineering domains, particularly energy, mobility, and aeronautics. Its flexibility is further demonstrated through a case study on power grid management.}, author = {Leyli abadi, Milad and Bessa, Ricardo J. and Viebahn, Jan and Boos, Daniel and Borst, Clark and Castagna, Alberto and Chavarriaga, Ricardo and Hassouna, Mohamed and Lemetayer, Bruno and Leto, Giulia and Marot, Antoine and Meddeb, Maroua and Meyer, Manuel and Schiaffonati, Viola and Schneider, Manuel and Waefler, Toni}, booktitle = {IEEE International Conference on Systems, Man and Cybernetics (SMC)}, keywords = {itegpub}, note = {(accepted)}, publisher = {IEEE}, title = {A Conceptual Framework for AI-based Decision Systems in Critical Infrastructures}, year = 2025 }
%0 Conference Paper %1 leyliabadi2025conceptual %A Leyli abadi, Milad %A Bessa, Ricardo J. %A Viebahn, Jan %A Boos, Daniel %A Borst, Clark %A Castagna, Alberto %A Chavarriaga, Ricardo %A Hassouna, Mohamed %A Lemetayer, Bruno %A Leto, Giulia %A Marot, Antoine %A Meddeb, Maroua %A Meyer, Manuel %A Schiaffonati, Viola %A Schneider, Manuel %A Waefler, Toni %B IEEE International Conference on Systems, Man and Cybernetics (SMC) %D 2025 %I IEEE %R 10.48550/arXiv.2504.16133 %T A Conceptual Framework for AI-based Decision Systems in Critical Infrastructures %U https://arxiv.org/abs/2504.16133 %X The interaction between humans and AI in safety-critical systems presents a unique set of challenges that remain partially addressed by existing frameworks. These challenges stem from the complex interplay of requirements for transparency, trust, and explainability, coupled with the necessity for robust and safe decision-making. A framework that holistically integrates human and AI capabilities while addressing these concerns is notably required, bridging the critical gaps in designing, deploying, and maintaining safe and effective systems. This paper proposes a holistic conceptual framework for critical infrastructures by adopting an interdisciplinary approach. It integrates traditionally distinct fields such as mathematics, decision theory, computer science, philosophy, psychology, and cognitive engineering and draws on specialized engineering domains, particularly energy, mobility, and aeronautics. Its flexibility is further demonstrated through a case study on power grid management.
BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics.Rauch, Lukas; Schwinger, Raphael; Wirth, Moritz; Heinrich, René; Huselji, Denis; Herde, Marek; Lange, Jonas; Kahl, Stefan; Sick, Bernhard; Tomforde, Sven; Scholz, Christoph. In International Conference on Learning Representations (ICLR). 2025.
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Deep learning (DL) has greatly advanced audio classification, yet the field is limited by the scarcity of large-scale benchmark datasets that have propelled progress in other domains. While AudioSet is a pivotal step to bridge this gap as a universal-domain dataset, its restricted accessibility and limited range of evaluation use cases challenge its role as the sole resource. Therefore, we introduce BirdSet, a large-scale benchmark dataset for audio classification focusing on avian bioacoustics. BirdSet surpasses AudioSet with over 6,800 recording hours (\\\\uparrow\\\\!17\\\\%) from nearly 10,000 classes (\\\\uparrow\\\\!18\\\\times) for training and more than 400 hours (\\\\uparrow\\\\!7\\\\times) across eight strongly labeled evaluation datasets. It serves as a versatile resource for use cases such as multi-label classification, covariate shift or self-supervised learning. We benchmark six well-known DL models in multi-label classification across three distinct training scenarios and outline further evaluation use cases in audio classification. We host our dataset on Hugging Face for easy accessibility and offer an extensive codebase to reproduce our results.
@inproceedings{rauch2025birdset, abstract = {Deep learning (DL) has greatly advanced audio classification, yet the field is limited by the scarcity of large-scale benchmark datasets that have propelled progress in other domains. While AudioSet is a pivotal step to bridge this gap as a universal-domain dataset, its restricted accessibility and limited range of evaluation use cases challenge its role as the sole resource. Therefore, we introduce BirdSet, a large-scale benchmark dataset for audio classification focusing on avian bioacoustics. BirdSet surpasses AudioSet with over 6,800 recording hours (\\\\uparrow\\\\!17\\\\%) from nearly 10,000 classes (\\\\uparrow\\\\!18\\\\times) for training and more than 400 hours (\\\\uparrow\\\\!7\\\\times) across eight strongly labeled evaluation datasets. It serves as a versatile resource for use cases such as multi-label classification, covariate shift or self-supervised learning. We benchmark six well-known DL models in multi-label classification across three distinct training scenarios and outline further evaluation use cases in audio classification. We host our dataset on Hugging Face for easy accessibility and offer an extensive codebase to reproduce our results.}, author = {Rauch, Lukas and Schwinger, Raphael and Wirth, Moritz and Heinrich, René and Huselji, Denis and Herde, Marek and Lange, Jonas and Kahl, Stefan and Sick, Bernhard and Tomforde, Sven and Scholz, Christoph}, booktitle = {International Conference on Learning Representations (ICLR)}, keywords = {itegpub}, note = {(accepted)}, title = {BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics}, year = 2025 }
%0 Conference Paper %1 rauch2025birdset %A Rauch, Lukas %A Schwinger, Raphael %A Wirth, Moritz %A Heinrich, René %A Huselji, Denis %A Herde, Marek %A Lange, Jonas %A Kahl, Stefan %A Sick, Bernhard %A Tomforde, Sven %A Scholz, Christoph %B International Conference on Learning Representations (ICLR) %D 2025 %T BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics %X Deep learning (DL) has greatly advanced audio classification, yet the field is limited by the scarcity of large-scale benchmark datasets that have propelled progress in other domains. While AudioSet is a pivotal step to bridge this gap as a universal-domain dataset, its restricted accessibility and limited range of evaluation use cases challenge its role as the sole resource. Therefore, we introduce BirdSet, a large-scale benchmark dataset for audio classification focusing on avian bioacoustics. BirdSet surpasses AudioSet with over 6,800 recording hours (\\\\uparrow\\\\!17\\\\%) from nearly 10,000 classes (\\\\uparrow\\\\!18\\\\times) for training and more than 400 hours (\\\\uparrow\\\\!7\\\\times) across eight strongly labeled evaluation datasets. It serves as a versatile resource for use cases such as multi-label classification, covariate shift or self-supervised learning. We benchmark six well-known DL models in multi-label classification across three distinct training scenarios and outline further evaluation use cases in audio classification. We host our dataset on Hugging Face for easy accessibility and offer an extensive codebase to reproduce our results.
Improving Fast 6D Pose Estimation with Adaptive Model Fusion.Hubert, Andreas; Guo, Chenxi; Doll, Konrad; Sick, Bernhard. In International Conference on Machine Learning and Intelligent Systems Engineering (MLISE), ble. 58–63. IEEE, 2025.
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Object tracking in manual assembly cells presents unique challenges due to the wide variation in motion speeds, ranging from slow, precise movements to rapid changes of direction. Traditional tracking models face a fundamental trade-off: models specialized for slow motion achieve high accuracy at low speeds but fail at higher velocities, while fast-motion models track rapid movements effectively but lose precision for slow moving objects. To address this limitation, we propose an Adaptive Fusion Tracker (AFT) approach that combines a Slow Motion Tracker (SMT) and a Fast Motion Tracker (FMT) to improve tracking robustness across diverse motion ranges. Our experimental evaluation demonstrates that AFT configurations, fusing SMT and FMT results, enhance performance beyond the individual models' operational limits. Among them, AFT with averages the results by weight consistently achieves the highest tracking accuracy, doubling the performance for certain tracking motion speeds, while a state-based AFT offers a computationally efficient alternative. These findings suggest that adaptive fusion extends the effective operational range of tracking models, making them more reliable for real-world applications.
@inproceedings{hubert2025improving, abstract = {Object tracking in manual assembly cells presents unique challenges due to the wide variation in motion speeds, ranging from slow, precise movements to rapid changes of direction. Traditional tracking models face a fundamental trade-off: models specialized for slow motion achieve high accuracy at low speeds but fail at higher velocities, while fast-motion models track rapid movements effectively but lose precision for slow moving objects. To address this limitation, we propose an Adaptive Fusion Tracker (AFT) approach that combines a Slow Motion Tracker (SMT) and a Fast Motion Tracker (FMT) to improve tracking robustness across diverse motion ranges. Our experimental evaluation demonstrates that AFT configurations, fusing SMT and FMT results, enhance performance beyond the individual models' operational limits. Among them, AFT with averages the results by weight consistently achieves the highest tracking accuracy, doubling the performance for certain tracking motion speeds, while a state-based AFT offers a computationally efficient alternative. These findings suggest that adaptive fusion extends the effective operational range of tracking models, making them more reliable for real-world applications.}, author = {Hubert, Andreas and Guo, Chenxi and Doll, Konrad and Sick, Bernhard}, booktitle = {International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)}, keywords = {itegpub}, pages = {58–63}, publisher = {IEEE}, title = {Improving Fast 6D Pose Estimation with Adaptive Model Fusion}, year = 2025 }
%0 Conference Paper %1 hubert2025improving %A Hubert, Andreas %A Guo, Chenxi %A Doll, Konrad %A Sick, Bernhard %B International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) %D 2025 %I IEEE %P 58--63 %R 10.1109/MLISE66443.2025.11100239 %T Improving Fast 6D Pose Estimation with Adaptive Model Fusion %X Object tracking in manual assembly cells presents unique challenges due to the wide variation in motion speeds, ranging from slow, precise movements to rapid changes of direction. Traditional tracking models face a fundamental trade-off: models specialized for slow motion achieve high accuracy at low speeds but fail at higher velocities, while fast-motion models track rapid movements effectively but lose precision for slow moving objects. To address this limitation, we propose an Adaptive Fusion Tracker (AFT) approach that combines a Slow Motion Tracker (SMT) and a Fast Motion Tracker (FMT) to improve tracking robustness across diverse motion ranges. Our experimental evaluation demonstrates that AFT configurations, fusing SMT and FMT results, enhance performance beyond the individual models' operational limits. Among them, AFT with averages the results by weight consistently achieves the highest tracking accuracy, doubling the performance for certain tracking motion speeds, while a state-based AFT offers a computationally efficient alternative. These findings suggest that adaptive fusion extends the effective operational range of tracking models, making them more reliable for real-world applications.
Reconstructing time-of-flight detector values of angular streaking using machine learning.Meier, David; Viefhaus, Jens; Hartmann, Gregor; Helml, Wolfram; Otto, Thorsten; Sick, Bernhard. In Physical Review Accelerators and Beams, 28(7), bl. 074601. American Physical Society, 2025.
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Angular streaking experiments enable for experimentation in the attosecond regions. However, the deployed time-of-flight (TOF) detectors are susceptible to noise and failure. These shortcomings make the outputs of the TOF detectors hard to understand for humans and further processing, such as, for example, the extraction of beam properties. In this article, we present an approach to remove high noise levels and reconstruct up to three failed TOF detectors from an arrangement of 16 TOF detectors. Due to its fast evaluation time, the presented method is applicable online during a running experiment. It is trained with simulation data, and we show the results of denoising and reconstruction of our method on real-world experiment data.
@article{meier2025reconstructing, abstract = {Angular streaking experiments enable for experimentation in the attosecond regions. However, the deployed time-of-flight (TOF) detectors are susceptible to noise and failure. These shortcomings make the outputs of the TOF detectors hard to understand for humans and further processing, such as, for example, the extraction of beam properties. In this article, we present an approach to remove high noise levels and reconstruct up to three failed TOF detectors from an arrangement of 16 TOF detectors. Due to its fast evaluation time, the presented method is applicable online during a running experiment. It is trained with simulation data, and we show the results of denoising and reconstruction of our method on real-world experiment data.}, author = {Meier, David and Viefhaus, Jens and Hartmann, Gregor and Helml, Wolfram and Otto, Thorsten and Sick, Bernhard}, journal = {Physical Review Accelerators and Beams}, keywords = {itegpub}, number = 7, pages = {074601}, publisher = {American Physical Society}, title = {Reconstructing time-of-flight detector values of angular streaking using machine learning}, volume = 28, year = 2025 }
%0 Journal Article %1 meier2025reconstructing %A Meier, David %A Viefhaus, Jens %A Hartmann, Gregor %A Helml, Wolfram %A Otto, Thorsten %A Sick, Bernhard %D 2025 %I American Physical Society %J Physical Review Accelerators and Beams %N 7 %P 074601 %R 10.1103/csvm-858f %T Reconstructing time-of-flight detector values of angular streaking using machine learning %U https://journals.aps.org/prab/abstract/10.1103/csvm-858f %V 28 %X Angular streaking experiments enable for experimentation in the attosecond regions. However, the deployed time-of-flight (TOF) detectors are susceptible to noise and failure. These shortcomings make the outputs of the TOF detectors hard to understand for humans and further processing, such as, for example, the extraction of beam properties. In this article, we present an approach to remove high noise levels and reconstruct up to three failed TOF detectors from an arrangement of 16 TOF detectors. Due to its fast evaluation time, the presented method is applicable online during a running experiment. It is trained with simulation data, and we show the results of denoising and reconstruction of our method on real-world experiment data.
Graph Neural Networks for Grid Control: Prospects in AI-assisted Transmission Grid Operation.Holzhueter, Clara; Lytaev, Pawel; Dipp, Marcel; Hassouna, Mohamed; Brendlinger, Kurt; Viebahn, Jan; Gegelman, Wiktor; Merz, Christian. In ETG Kongress 2025; Voller Energie – heute und morgen., ble. 874–881. VDE, 2025.
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Transmission grid congestion management and outage planning are critical tasks in modern grid operation due to the non-linear nature of power flows and the large-scale optimization challenges faced by operators. Traditionally, overloads are addressed through generator redispatch, a costly and therefore suboptimal measure. In the project "Graph Neural Networks for Grid Control" (GNN4GC), we investigate alternative strategies, focusing on topological remedial actions that could minimize or even completely eliminate redispatch costs. Topology optimization, a core aspect of this project, presents significant challenges due to its combinatorial nature, requiring extensive computational resources for power flow calculations. To address this, GNN4GC is split into three stages. In the first stage, we explore the use of Graph Neural Networks (GNNs) to accelerate these calculations and benchmark their performance against established tools like pandapower and a DC power flow solver developed by 50Hertz Transmission GmbH and TenneT TSO GmbH. In the second stage, we use Reinforcement Learning and other heuristics to select suitable topologies and solve the topology optimization problem. As a third stage, we test the respective agent on real-life grids to benchmark the methodology. The aim of the final stage is to build a recommender system that can be used in a control room in the future.
@inproceedings{holzhueter2025graph, abstract = {Transmission grid congestion management and outage planning are critical tasks in modern grid operation due to the non-linear nature of power flows and the large-scale optimization challenges faced by operators. Traditionally, overloads are addressed through generator redispatch, a costly and therefore suboptimal measure. In the project "Graph Neural Networks for Grid Control" (GNN4GC), we investigate alternative strategies, focusing on topological remedial actions that could minimize or even completely eliminate redispatch costs. Topology optimization, a core aspect of this project, presents significant challenges due to its combinatorial nature, requiring extensive computational resources for power flow calculations. To address this, GNN4GC is split into three stages. In the first stage, we explore the use of Graph Neural Networks (GNNs) to accelerate these calculations and benchmark their performance against established tools like pandapower and a DC power flow solver developed by 50Hertz Transmission GmbH and TenneT TSO GmbH. In the second stage, we use Reinforcement Learning and other heuristics to select suitable topologies and solve the topology optimization problem. As a third stage, we test the respective agent on real-life grids to benchmark the methodology. The aim of the final stage is to build a recommender system that can be used in a control room in the future.}, author = {Holzhueter, Clara and Lytaev, Pawel and Dipp, Marcel and Hassouna, Mohamed and Brendlinger, Kurt and Viebahn, Jan and Gegelman, Wiktor and Merz, Christian}, booktitle = {ETG Kongress 2025; Voller Energie – heute und morgen.}, keywords = {itegpub}, pages = {874–881}, publisher = {VDE}, title = {Graph Neural Networks for Grid Control: Prospects in AI-assisted Transmission Grid Operation}, year = 2025 }
%0 Conference Paper %1 holzhueter2025graph %A Holzhueter, Clara %A Lytaev, Pawel %A Dipp, Marcel %A Hassouna, Mohamed %A Brendlinger, Kurt %A Viebahn, Jan %A Gegelman, Wiktor %A Merz, Christian %B ETG Kongress 2025; Voller Energie – heute und morgen. %D 2025 %I VDE %P 874--881 %T Graph Neural Networks for Grid Control: Prospects in AI-assisted Transmission Grid Operation %U https://ieeexplore.ieee.org/abstract/document/11201946 %X Transmission grid congestion management and outage planning are critical tasks in modern grid operation due to the non-linear nature of power flows and the large-scale optimization challenges faced by operators. Traditionally, overloads are addressed through generator redispatch, a costly and therefore suboptimal measure. In the project "Graph Neural Networks for Grid Control" (GNN4GC), we investigate alternative strategies, focusing on topological remedial actions that could minimize or even completely eliminate redispatch costs. Topology optimization, a core aspect of this project, presents significant challenges due to its combinatorial nature, requiring extensive computational resources for power flow calculations. To address this, GNN4GC is split into three stages. In the first stage, we explore the use of Graph Neural Networks (GNNs) to accelerate these calculations and benchmark their performance against established tools like pandapower and a DC power flow solver developed by 50Hertz Transmission GmbH and TenneT TSO GmbH. In the second stage, we use Reinforcement Learning and other heuristics to select suitable topologies and solve the topology optimization problem. As a third stage, we test the respective agent on real-life grids to benchmark the methodology. The aim of the final stage is to build a recommender system that can be used in a control room in the future.
Can Masked Autoencoders Also Listen to Birds?Rauch, Lukas; Heinrich, René; Moummad, Ilyass; Joly, Alexis; Sick, Bernhard; Scholz, Christoph. In Transactions on Machine Learning Research. 2025.
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Masked Autoencoders (MAEs) learn rich representations in audio classification through an efficient self-supervised reconstruction task. Yet, general-purpose models struggle in fine-grained audio domains such as bird sound classification, which demands distinguishing subtle inter-species differences under high intra-species variability. We show that bridging this domain gap requires full-pipeline adaptation beyond domain-specific pretraining data. Using BirdSet, a large-scale bioacoustic benchmark, we systematically adapt pretraining, fine-tuning, and frozen feature utilization. Our Bird-MAE sets new state-of-the-art results on BirdSet’s multi-label classification benchmark. Additionally, we introduce the parameter-efficient prototypical probing, which boosts the utility of frozen MAE features by achieving up to 37 mAP points over linear probes and narrowing the gap to fine-tuning in low-resource settings. Bird-MAE also exhibits strong few-shot generalization with prototypical probes on our newly established few-shot benchmark on BirdSet, underscoring the importance of tailored self-supervised learning pipelines for fine-grained audio domains.
@article{rauch2025can, abstract = {Masked Autoencoders (MAEs) learn rich representations in audio classification through an efficient self-supervised reconstruction task. Yet, general-purpose models struggle in fine-grained audio domains such as bird sound classification, which demands distinguishing subtle inter-species differences under high intra-species variability. We show that bridging this domain gap requires full-pipeline adaptation beyond domain-specific pretraining data. Using BirdSet, a large-scale bioacoustic benchmark, we systematically adapt pretraining, fine-tuning, and frozen feature utilization. Our Bird-MAE sets new state-of-the-art results on BirdSet’s multi-label classification benchmark. Additionally, we introduce the parameter-efficient prototypical probing, which boosts the utility of frozen MAE features by achieving up to 37 mAP points over linear probes and narrowing the gap to fine-tuning in low-resource settings. Bird-MAE also exhibits strong few-shot generalization with prototypical probes on our newly established few-shot benchmark on BirdSet, underscoring the importance of tailored self-supervised learning pipelines for fine-grained audio domains.}, author = {Rauch, Lukas and Heinrich, René and Moummad, Ilyass and Joly, Alexis and Sick, Bernhard and Scholz, Christoph}, journal = {Transactions on Machine Learning Research}, keywords = {itegpub}, title = {Can Masked Autoencoders Also Listen to Birds?}, year = 2025 }
%0 Journal Article %1 rauch2025can %A Rauch, Lukas %A Heinrich, René %A Moummad, Ilyass %A Joly, Alexis %A Sick, Bernhard %A Scholz, Christoph %D 2025 %J Transactions on Machine Learning Research %T Can Masked Autoencoders Also Listen to Birds? %U https://openreview.net/forum?id=GIBWR0Xo2J %X Masked Autoencoders (MAEs) learn rich representations in audio classification through an efficient self-supervised reconstruction task. Yet, general-purpose models struggle in fine-grained audio domains such as bird sound classification, which demands distinguishing subtle inter-species differences under high intra-species variability. We show that bridging this domain gap requires full-pipeline adaptation beyond domain-specific pretraining data. Using BirdSet, a large-scale bioacoustic benchmark, we systematically adapt pretraining, fine-tuning, and frozen feature utilization. Our Bird-MAE sets new state-of-the-art results on BirdSet’s multi-label classification benchmark. Additionally, we introduce the parameter-efficient prototypical probing, which boosts the utility of frozen MAE features by achieving up to 37 mAP points over linear probes and narrowing the gap to fine-tuning in low-resource settings. Bird-MAE also exhibits strong few-shot generalization with prototypical probes on our newly established few-shot benchmark on BirdSet, underscoring the importance of tailored self-supervised learning pipelines for fine-grained audio domains.
Graph Neural Networks for Automatic Addition of Optimizing Components in Printed Circuit Board Schematics.Plettenberg, Pascal; Alcalde, André; Sick, Bernhard; Thomas, Josephine M. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), ble. 508–524. Springer, 2025.
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The design and optimization of Printed Circuit Board (PCB) schematics is crucial for the development of high-quality electronic devices. Thereby, an important task is to optimize drafts by adding components that improve the robustness and reliability of the circuit, e.g., pull-up resistors or decoupling capacitors. Since there is a shortage of skilled engineers and manual optimizations are very time-consuming, these best practices are often neglected. However, this typically leads to higher costs for troubleshooting in later development stages as well as shortened product life cycles, resulting in an increased amount of electronic waste that is difficult to recycle. Here, we present an approach for automating the addition of new components into PCB schematics by representing them as bipartite graphs and utilizing a node pair prediction model based on Graph Neural Networks (GNNs). We apply our approach to three highly relevant PCB design optimization tasks and compare the performance of several popular GNN architectures on realworld datasets labeled by human experts. We show that GNNs can solve these problems with high accuracy and demonstrate that our approach offers the potential to automate PCB design optimizations in a timeand cost-efficient manner.
@inproceedings{plettenberg2025graph, abstract = {The design and optimization of Printed Circuit Board (PCB) schematics is crucial for the development of high-quality electronic devices. Thereby, an important task is to optimize drafts by adding components that improve the robustness and reliability of the circuit, e.g., pull-up resistors or decoupling capacitors. Since there is a shortage of skilled engineers and manual optimizations are very time-consuming, these best practices are often neglected. However, this typically leads to higher costs for troubleshooting in later development stages as well as shortened product life cycles, resulting in an increased amount of electronic waste that is difficult to recycle. Here, we present an approach for automating the addition of new components into PCB schematics by representing them as bipartite graphs and utilizing a node pair prediction model based on Graph Neural Networks (GNNs). We apply our approach to three highly relevant PCB design optimization tasks and compare the performance of several popular GNN architectures on realworld datasets labeled by human experts. We show that GNNs can solve these problems with high accuracy and demonstrate that our approach offers the potential to automate PCB design optimizations in a timeand cost-efficient manner.}, author = {Plettenberg, Pascal and Alcalde, André and Sick, Bernhard and Thomas, Josephine M.}, booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)}, keywords = {PrintedCircuitBoards}, pages = {508–524}, publisher = {Springer}, title = {Graph Neural Networks for Automatic Addition of Optimizing Components in Printed Circuit Board Schematics}, year = 2025 }
%0 Conference Paper %1 plettenberg2025graph %A Plettenberg, Pascal %A Alcalde, André %A Sick, Bernhard %A Thomas, Josephine M. %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) %D 2025 %I Springer %P 508--524 %R 10.1007/978-3-662-72243-5_29 %T Graph Neural Networks for Automatic Addition of Optimizing Components in Printed Circuit Board Schematics %U https://link.springer.com/chapter/10.1007/978-3-662-72243-5_29 %X The design and optimization of Printed Circuit Board (PCB) schematics is crucial for the development of high-quality electronic devices. Thereby, an important task is to optimize drafts by adding components that improve the robustness and reliability of the circuit, e.g., pull-up resistors or decoupling capacitors. Since there is a shortage of skilled engineers and manual optimizations are very time-consuming, these best practices are often neglected. However, this typically leads to higher costs for troubleshooting in later development stages as well as shortened product life cycles, resulting in an increased amount of electronic waste that is difficult to recycle. Here, we present an approach for automating the addition of new components into PCB schematics by representing them as bipartite graphs and utilizing a node pair prediction model based on Graph Neural Networks (GNNs). We apply our approach to three highly relevant PCB design optimization tasks and compare the performance of several popular GNN architectures on realworld datasets labeled by human experts. We show that GNNs can solve these problems with high accuracy and demonstrate that our approach offers the potential to automate PCB design optimizations in a timeand cost-efficient manner.
Location-based Probabilistic Load Forecasting of Electric Vehicle Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network.Ali, Mohammad Wazed; Mustafa, Mohammad Asif Ibna; Shuvo, Md. Aukerul Moin; Sick, Bernhard. In International Conference on Architecture of Computing Systems (ARCS), ble. 205–219. Springer, 2025.
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Electrification of vehicles is a potential way of reducing fossil fuel usage and thus lessening environmental pollution. Electric Vehicles (EVs) of various types for different transport modes (including air, water, and land) are evolving. Moreover, different EV user groups (commuters, commercial or domestic users, drivers) may use different charging infrastructures (public, private, home, and workplace) at various times. Therefore, usage patterns and energy demand are very stochastic. Characterizing and forecasting the charging demand of these diverse EV usage profiles is essential in preventing power outages. Previously developed data-driven load models are limited to specific use cases and locations. None of these models are simultaneously adaptive enough to transfer knowledge of day-ahead forecasting among EV charging sites of diverse locations, trained with limited data, and cost-effective. This article presents a location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models. We conducted our experiments on data from four charging sites, namely Caltech, JPL, Office-1, and NREL, which have diverse EV user types like students, full-time and part-time employees, random visitors, etc. With a Prediction Interval Coverage Probability (PICP) score of 93.62%, our proposed deep MQ-TCN model exhibited a remarkable 28.93% improvement over the XGBoost model for a day-ahead load forecasting at the JPL charging site. By transferring knowledge with the inductive Transfer Learning (TL) approach, the MQ-TCN model achieved a 96.88% PICP score for the load forecasting task at the NREL site using only two weeks of data.
@inproceedings{ali2025location, abstract = {Electrification of vehicles is a potential way of reducing fossil fuel usage and thus lessening environmental pollution. Electric Vehicles (EVs) of various types for different transport modes (including air, water, and land) are evolving. Moreover, different EV user groups (commuters, commercial or domestic users, drivers) may use different charging infrastructures (public, private, home, and workplace) at various times. Therefore, usage patterns and energy demand are very stochastic. Characterizing and forecasting the charging demand of these diverse EV usage profiles is essential in preventing power outages. Previously developed data-driven load models are limited to specific use cases and locations. None of these models are simultaneously adaptive enough to transfer knowledge of day-ahead forecasting among EV charging sites of diverse locations, trained with limited data, and cost-effective. This article presents a location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models. We conducted our experiments on data from four charging sites, namely Caltech, JPL, Office-1, and NREL, which have diverse EV user types like students, full-time and part-time employees, random visitors, etc. With a Prediction Interval Coverage Probability (PICP) score of 93.62%, our proposed deep MQ-TCN model exhibited a remarkable 28.93% improvement over the XGBoost model for a day-ahead load forecasting at the JPL charging site. By transferring knowledge with the inductive Transfer Learning (TL) approach, the MQ-TCN model achieved a 96.88% PICP score for the load forecasting task at the NREL site using only two weeks of data.}, author = {Ali, Mohammad Wazed and Mustafa, Mohammad Asif Ibna and Shuvo, Md. Aukerul Moin and Sick, Bernhard}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {itegpub}, pages = {205–219}, publisher = {Springer}, title = {Location-based Probabilistic Load Forecasting of Electric Vehicle Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network}, year = 2025 }
%0 Conference Paper %1 ali2025location %A Ali, Mohammad Wazed %A Mustafa, Mohammad Asif Ibna %A Shuvo, Md. Aukerul Moin %A Sick, Bernhard %B International Conference on Architecture of Computing Systems (ARCS) %D 2025 %I Springer %P 205--219 %R 10.1007/978-3-032-03281-2_14 %T Location-based Probabilistic Load Forecasting of Electric Vehicle Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network %X Electrification of vehicles is a potential way of reducing fossil fuel usage and thus lessening environmental pollution. Electric Vehicles (EVs) of various types for different transport modes (including air, water, and land) are evolving. Moreover, different EV user groups (commuters, commercial or domestic users, drivers) may use different charging infrastructures (public, private, home, and workplace) at various times. Therefore, usage patterns and energy demand are very stochastic. Characterizing and forecasting the charging demand of these diverse EV usage profiles is essential in preventing power outages. Previously developed data-driven load models are limited to specific use cases and locations. None of these models are simultaneously adaptive enough to transfer knowledge of day-ahead forecasting among EV charging sites of diverse locations, trained with limited data, and cost-effective. This article presents a location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models. We conducted our experiments on data from four charging sites, namely Caltech, JPL, Office-1, and NREL, which have diverse EV user types like students, full-time and part-time employees, random visitors, etc. With a Prediction Interval Coverage Probability (PICP) score of 93.62%, our proposed deep MQ-TCN model exhibited a remarkable 28.93% improvement over the XGBoost model for a day-ahead load forecasting at the JPL charging site. By transferring knowledge with the inductive Transfer Learning (TL) approach, the MQ-TCN model achieved a 96.88% PICP score for the load forecasting task at the NREL site using only two weeks of data.
Unify: Uncertainty incorporated Federated Learning for Object Detection.Gao, Shang; Sick, Bernhard; Götz-Hahn, Franz. In International Conference on Architecture of Computing Systems (ARCS), ble. 330–344. Springer, 2025.
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Federated Learning (FL) has demonstrated promise for collaborative training on decentralized data while preserving privacy. However, applying FL to object detection remains challenging due to non-IID data distributions and domain shifts that can degrade performance with standard aggregation methods. To address these issues, we propose the Uncertainty Incorporated Federated Learning for Object Detection (Unify) framework, a novel FL framework that leverages Evidential Learning (EL) to estimate both epistemic and aleatoric uncertainties. By integrating these uncertainty estimates into the aggregation process, Unify down-weights unreliable client updates, leading to improved cross-domain generalization and enhanced stability. Built upon a lightweight YOLOX detector, our approach achieves detection performance that closely approaches centralized training while significantly outperforming conventional FedAvg under heterogeneous conditions. Evaluations on the KITTI and nuImages datasets demonstrate that Unify improves mean Average Precision (mAP) and yields uncertainties. The findings highlight the potential of uncertainty-incorporated FL in developing resilient, adaptive, and self-organizing computing systems.
@inproceedings{gao2025unify, abstract = {Federated Learning (FL) has demonstrated promise for collaborative training on decentralized data while preserving privacy. However, applying FL to object detection remains challenging due to non-IID data distributions and domain shifts that can degrade performance with standard aggregation methods. To address these issues, we propose the Uncertainty Incorporated Federated Learning for Object Detection (Unify) framework, a novel FL framework that leverages Evidential Learning (EL) to estimate both epistemic and aleatoric uncertainties. By integrating these uncertainty estimates into the aggregation process, Unify down-weights unreliable client updates, leading to improved cross-domain generalization and enhanced stability. Built upon a lightweight YOLOX detector, our approach achieves detection performance that closely approaches centralized training while significantly outperforming conventional FedAvg under heterogeneous conditions. Evaluations on the KITTI and nuImages datasets demonstrate that Unify improves mean Average Precision (mAP) and yields uncertainties. The findings highlight the potential of uncertainty-incorporated FL in developing resilient, adaptive, and self-organizing computing systems.}, author = {Gao, Shang and Sick, Bernhard and Götz-Hahn, Franz}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {itegpub}, pages = {330–344}, publisher = {Springer}, title = {Unify: Uncertainty incorporated Federated Learning for Object Detection}, year = 2025 }
%0 Conference Paper %1 gao2025unify %A Gao, Shang %A Sick, Bernhard %A Götz-Hahn, Franz %B International Conference on Architecture of Computing Systems (ARCS) %D 2025 %I Springer %P 330--344 %R 10.1007/978-3-032-03281-2_22 %T Unify: Uncertainty incorporated Federated Learning for Object Detection %U https://link.springer.com/chapter/10.1007/978-3-032-03281-2_22 %X Federated Learning (FL) has demonstrated promise for collaborative training on decentralized data while preserving privacy. However, applying FL to object detection remains challenging due to non-IID data distributions and domain shifts that can degrade performance with standard aggregation methods. To address these issues, we propose the Uncertainty Incorporated Federated Learning for Object Detection (Unify) framework, a novel FL framework that leverages Evidential Learning (EL) to estimate both epistemic and aleatoric uncertainties. By integrating these uncertainty estimates into the aggregation process, Unify down-weights unreliable client updates, leading to improved cross-domain generalization and enhanced stability. Built upon a lightweight YOLOX detector, our approach achieves detection performance that closely approaches centralized training while significantly outperforming conventional FedAvg under heterogeneous conditions. Evaluations on the KITTI and nuImages datasets demonstrate that Unify improves mean Average Precision (mAP) and yields uncertainties. The findings highlight the potential of uncertainty-incorporated FL in developing resilient, adaptive, and self-organizing computing systems.
Towards Cognitive Situational Awareness in Maritime Traffic Using Federated Evidential Learning.Gao, Shang; Huang, Zhixin; Al-Falouji, Ghassan; Sick, Bernhard; Tomforde, Sven. In IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), ble. 9–16. IEEE, 2025.
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The increasing complexity of maritime navigation and the shift towards (semi)autonomous systems necessitate enhanced situational awareness (SA) to ensure maritime safety. This transition introduces new requirements for situation mod- elling and SA, particularly in busy waterways. To address these challenges, we present the Federated Evidential Learning for Anomaly Detection of Ship Trajectories (FEAST) framework, which integrates Federated Learning and Evidential Learning to provide a privacy-preserving, collaborative, and uncertainty- aware approach to out-of-distribution (OOD) anomaly detection in maritime traffic. FEAST uses data from the Automatic Identification System from the Kiel region, Germany, which exhibits unique characteristics of dynamic and heterogeneous maritime activity due to its connection with the traffic-dense Kiel Canal. Our extensive evaluations demonstrate that FEAST improves OOD anomaly detection by leveraging epistemic and aleatoric uncertainty estimates, outperforming baseline methods such as Denoise AutoEncoders and Variational AutoEncoders. Consequently, FEAST forms a solution to reliable and inter- pretable maritime traffic anomaly detection, supporting enhanced SA in maritime operations.
@inproceedings{gao2025towards, abstract = {The increasing complexity of maritime navigation and the shift towards (semi)autonomous systems necessitate enhanced situational awareness (SA) to ensure maritime safety. This transition introduces new requirements for situation mod- elling and SA, particularly in busy waterways. To address these challenges, we present the Federated Evidential Learning for Anomaly Detection of Ship Trajectories (FEAST) framework, which integrates Federated Learning and Evidential Learning to provide a privacy-preserving, collaborative, and uncertainty- aware approach to out-of-distribution (OOD) anomaly detection in maritime traffic. FEAST uses data from the Automatic Identification System from the Kiel region, Germany, which exhibits unique characteristics of dynamic and heterogeneous maritime activity due to its connection with the traffic-dense Kiel Canal. Our extensive evaluations demonstrate that FEAST improves OOD anomaly detection by leveraging epistemic and aleatoric uncertainty estimates, outperforming baseline methods such as Denoise AutoEncoders and Variational AutoEncoders. Consequently, FEAST forms a solution to reliable and inter- pretable maritime traffic anomaly detection, supporting enhanced SA in maritime operations.}, author = {Gao, Shang and Huang, Zhixin and Al-Falouji, Ghassan and Sick, Bernhard and Tomforde, Sven}, booktitle = {IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)}, keywords = {itegpub}, pages = {9–16}, publisher = {IEEE}, title = {Towards Cognitive Situational Awareness in Maritime Traffic Using Federated Evidential Learning}, year = 2025 }
%0 Conference Paper %1 gao2025towards %A Gao, Shang %A Huang, Zhixin %A Al-Falouji, Ghassan %A Sick, Bernhard %A Tomforde, Sven %B IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) %D 2025 %I IEEE %P 9--16 %R 10.1109/CogSIMA64436.2025.11079521 %T Towards Cognitive Situational Awareness in Maritime Traffic Using Federated Evidential Learning %X The increasing complexity of maritime navigation and the shift towards (semi)autonomous systems necessitate enhanced situational awareness (SA) to ensure maritime safety. This transition introduces new requirements for situation mod- elling and SA, particularly in busy waterways. To address these challenges, we present the Federated Evidential Learning for Anomaly Detection of Ship Trajectories (FEAST) framework, which integrates Federated Learning and Evidential Learning to provide a privacy-preserving, collaborative, and uncertainty- aware approach to out-of-distribution (OOD) anomaly detection in maritime traffic. FEAST uses data from the Automatic Identification System from the Kiel region, Germany, which exhibits unique characteristics of dynamic and heterogeneous maritime activity due to its connection with the traffic-dense Kiel Canal. Our extensive evaluations demonstrate that FEAST improves OOD anomaly detection by leveraging epistemic and aleatoric uncertainty estimates, outperforming baseline methods such as Denoise AutoEncoders and Variational AutoEncoders. Consequently, FEAST forms a solution to reliable and inter- pretable maritime traffic anomaly detection, supporting enhanced SA in maritime operations.
Data selection strategies for minimizing measurement time in materials characterization.Liehr, Alexander; Dingel, Kristina; Kottke, Daniel; Degener, Sebastian; Meier, David; Sick, Bernhard; Niendorf, Thomas. In Scientific Reports, 15(1), bl. 15182. Springer Nature, 2025.
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Every new material needs to be assessed and qualified for an envisaged application. A steadily increasing number of new alloys, designed to address challenges in terms of reliability and sustainability, poses significant demands on well-known analysis methods in terms of their efficiency, e.g., in X-ray diffraction analysis. Particularly in laboratory measurements, where the intensities in diffraction experiments tend to be low, a possibility to adapt the exposure time to the prevailing boundary conditions, i.e., the investigated microstructure, is seen to be a very effective approach. The counting time is decisive for, e.g., complex texture, phase, and residual stress measurements. Traditionally, more measurement points and, thus, longer data collection times lead to more accurate information. Here, too short counting times result in poor signal-to-background ratios and dominant signal noise, respectively, rendering subsequent evaluation more difficult or even impossible. Then, it is necessary to repeat experiments with adjusted, usually significantly longer counting time. To prevent redundant measurements, it is state-of-the-art to always consider the entire measurement range, regardless of whether the investigated points are relevant and contribute to the subsequent materials characterization, respectively. Obviously, this kind of approach is extremely time-consuming and, eventually, not efficient. The present study highlights that specific selection strategies, taking into account the prevailing microstructure of the alloy in focus, can decrease counting times in X-ray energy dispersive diffraction experiments without any detrimental effect on data quality for the subsequent analysis. All relevant data, including the code, are carefully assessed and will be the basis for a widely adapted strategy enabling efficient measurements not only in lab environments but also in large-scale facilities.
@article{liehr2025data, abstract = {Every new material needs to be assessed and qualified for an envisaged application. A steadily increasing number of new alloys, designed to address challenges in terms of reliability and sustainability, poses significant demands on well-known analysis methods in terms of their efficiency, e.g., in X-ray diffraction analysis. Particularly in laboratory measurements, where the intensities in diffraction experiments tend to be low, a possibility to adapt the exposure time to the prevailing boundary conditions, i.e., the investigated microstructure, is seen to be a very effective approach. The counting time is decisive for, e.g., complex texture, phase, and residual stress measurements. Traditionally, more measurement points and, thus, longer data collection times lead to more accurate information. Here, too short counting times result in poor signal-to-background ratios and dominant signal noise, respectively, rendering subsequent evaluation more difficult or even impossible. Then, it is necessary to repeat experiments with adjusted, usually significantly longer counting time. To prevent redundant measurements, it is state-of-the-art to always consider the entire measurement range, regardless of whether the investigated points are relevant and contribute to the subsequent materials characterization, respectively. Obviously, this kind of approach is extremely time-consuming and, eventually, not efficient. The present study highlights that specific selection strategies, taking into account the prevailing microstructure of the alloy in focus, can decrease counting times in X-ray energy dispersive diffraction experiments without any detrimental effect on data quality for the subsequent analysis. All relevant data, including the code, are carefully assessed and will be the basis for a widely adapted strategy enabling efficient measurements not only in lab environments but also in large-scale facilities.}, author = {Liehr, Alexander and Dingel, Kristina and Kottke, Daniel and Degener, Sebastian and Meier, David and Sick, Bernhard and Niendorf, Thomas}, journal = {Scientific Reports}, keywords = {itegpub}, number = 1, pages = 15182, publisher = {Springer Nature}, title = {Data selection strategies for minimizing measurement time in materials characterization}, volume = 15, year = 2025 }
%0 Journal Article %1 liehr2025data %A Liehr, Alexander %A Dingel, Kristina %A Kottke, Daniel %A Degener, Sebastian %A Meier, David %A Sick, Bernhard %A Niendorf, Thomas %D 2025 %I Springer Nature %J Scientific Reports %N 1 %P 15182 %R 10.1038/s41598-025-96221-1 %T Data selection strategies for minimizing measurement time in materials characterization %V 15 %X Every new material needs to be assessed and qualified for an envisaged application. A steadily increasing number of new alloys, designed to address challenges in terms of reliability and sustainability, poses significant demands on well-known analysis methods in terms of their efficiency, e.g., in X-ray diffraction analysis. Particularly in laboratory measurements, where the intensities in diffraction experiments tend to be low, a possibility to adapt the exposure time to the prevailing boundary conditions, i.e., the investigated microstructure, is seen to be a very effective approach. The counting time is decisive for, e.g., complex texture, phase, and residual stress measurements. Traditionally, more measurement points and, thus, longer data collection times lead to more accurate information. Here, too short counting times result in poor signal-to-background ratios and dominant signal noise, respectively, rendering subsequent evaluation more difficult or even impossible. Then, it is necessary to repeat experiments with adjusted, usually significantly longer counting time. To prevent redundant measurements, it is state-of-the-art to always consider the entire measurement range, regardless of whether the investigated points are relevant and contribute to the subsequent materials characterization, respectively. Obviously, this kind of approach is extremely time-consuming and, eventually, not efficient. The present study highlights that specific selection strategies, taking into account the prevailing microstructure of the alloy in focus, can decrease counting times in X-ray energy dispersive diffraction experiments without any detrimental effect on data quality for the subsequent analysis. All relevant data, including the code, are carefully assessed and will be the basis for a widely adapted strategy enabling efficient measurements not only in lab environments but also in large-scale facilities.
Efficient Bayesian Updates for Deep Active Learning via Laplace Approximations.Huseljic, Denis; Herde, Marek; Rauch, Lukas; Hahn, Paul; Huang, Zhixin; Kottke, Daniel; Vogt, Stephan; Sick, Bernhard. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), ble. 20–35. Springer, 2025.
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Deep active learning (AL) selects batches of instances for annotation to avoid retraining deep neural networks (DNNs) after each new label. Employing a naive top-b selection can result in a batch of redundant (similar) instances. To address this, various AL strategies employ clustering techniques that ensure diversity within a batch. We approach this issue by substituting the costly retraining with an efficient Bayesian update. Our proposed update represents a second-order optimization step using the Gaussian posterior from a last-layer Laplace approximation. Thereby, we achieve low computational complexity by computing the inverse Hessian in closed form. We demonstrate that in typical AL settings, our update closely approximates retraining while being considerably faster. Leveraging our update, we introduce a new framework for batch selection through sequential construction, updating the DNN after each label acquisition. Furthermore, we incorporate our update into a look-ahead selection strategy as a feasible upper baseline approximating optimal batch selection. Our results highlight the potential of efficient updates to advance deep AL research.
@inproceedings{huseljic2025efficient, abstract = {Deep active learning (AL) selects batches of instances for annotation to avoid retraining deep neural networks (DNNs) after each new label. Employing a naive top-b selection can result in a batch of redundant (similar) instances. To address this, various AL strategies employ clustering techniques that ensure diversity within a batch. We approach this issue by substituting the costly retraining with an efficient Bayesian update. Our proposed update represents a second-order optimization step using the Gaussian posterior from a last-layer Laplace approximation. Thereby, we achieve low computational complexity by computing the inverse Hessian in closed form. We demonstrate that in typical AL settings, our update closely approximates retraining while being considerably faster. Leveraging our update, we introduce a new framework for batch selection through sequential construction, updating the DNN after each label acquisition. Furthermore, we incorporate our update into a look-ahead selection strategy as a feasible upper baseline approximating optimal batch selection. Our results highlight the potential of efficient updates to advance deep AL research.}, author = {Huseljic, Denis and Herde, Marek and Rauch, Lukas and Hahn, Paul and Huang, Zhixin and Kottke, Daniel and Vogt, Stephan and Sick, Bernhard}, booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)}, keywords = {itegpub}, pages = {20–35}, publisher = {Springer}, title = {Efficient Bayesian Updates for Deep Active Learning via Laplace Approximations}, year = 2025 }
%0 Conference Paper %1 huseljic2025efficient %A Huseljic, Denis %A Herde, Marek %A Rauch, Lukas %A Hahn, Paul %A Huang, Zhixin %A Kottke, Daniel %A Vogt, Stephan %A Sick, Bernhard %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) %D 2025 %I Springer %P 20--35 %R 10.1007/978-3-032-05981-9_2 %T Efficient Bayesian Updates for Deep Active Learning via Laplace Approximations %U https://link.springer.com/chapter/10.1007/978-3-032-05981-9_2 %X Deep active learning (AL) selects batches of instances for annotation to avoid retraining deep neural networks (DNNs) after each new label. Employing a naive top-b selection can result in a batch of redundant (similar) instances. To address this, various AL strategies employ clustering techniques that ensure diversity within a batch. We approach this issue by substituting the costly retraining with an efficient Bayesian update. Our proposed update represents a second-order optimization step using the Gaussian posterior from a last-layer Laplace approximation. Thereby, we achieve low computational complexity by computing the inverse Hessian in closed form. We demonstrate that in typical AL settings, our update closely approximates retraining while being considerably faster. Leveraging our update, we introduce a new framework for batch selection through sequential construction, updating the DNN after each label acquisition. Furthermore, we incorporate our update into a look-ahead selection strategy as a feasible upper baseline approximating optimal batch selection. Our results highlight the potential of efficient updates to advance deep AL research.
Flow-Attentional Graph Neural Networks.Plettenberg, Pascal; Köhler, Dominik; Sick, Bernhard; Thomas, Josephine M. In Transactions on Machine Learning Research. 2025.
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Graph Neural Networks (GNNs) have become essential for learning from graph-structured data. However, existing GNNs do not consider the conservation law inherent in graphs associated with a flow of physical resources, such as electrical current in power grids or traffic in transportation networks, which can lead to reduced model performance. To address this, we propose flow attention, which adapts existing graph attention mechanisms to satisfy Kirchhoff’s first law. Furthermore, we discuss how this modification influences the expressivity and identify sets of non-isomorphic graphs that can be discriminated by flow attention but not by standard attention. Through extensive experiments on two flow graph datasets (electronic circuits and power grids), we demonstrate that flow attention enhances the performance of attention-based GNNs on both graph-level classification and regression tasks.
@article{plettenberg2025flow, abstract = {Graph Neural Networks (GNNs) have become essential for learning from graph-structured data. However, existing GNNs do not consider the conservation law inherent in graphs associated with a flow of physical resources, such as electrical current in power grids or traffic in transportation networks, which can lead to reduced model performance. To address this, we propose flow attention, which adapts existing graph attention mechanisms to satisfy Kirchhoff’s first law. Furthermore, we discuss how this modification influences the expressivity and identify sets of non-isomorphic graphs that can be discriminated by flow attention but not by standard attention. Through extensive experiments on two flow graph datasets (electronic circuits and power grids), we demonstrate that flow attention enhances the performance of attention-based GNNs on both graph-level classification and regression tasks.}, author = {Plettenberg, Pascal and Köhler, Dominik and Sick, Bernhard and Thomas, Josephine M.}, journal = {Transactions on Machine Learning Research}, keywords = {itegpub}, title = {Flow-Attentional Graph Neural Networks}, year = 2025 }
%0 Journal Article %1 plettenberg2025flow %A Plettenberg, Pascal %A Köhler, Dominik %A Sick, Bernhard %A Thomas, Josephine M. %D 2025 %J Transactions on Machine Learning Research %T Flow-Attentional Graph Neural Networks %U https://openreview.net/forum?id=tOzg7UxTPD %X Graph Neural Networks (GNNs) have become essential for learning from graph-structured data. However, existing GNNs do not consider the conservation law inherent in graphs associated with a flow of physical resources, such as electrical current in power grids or traffic in transportation networks, which can lead to reduced model performance. To address this, we propose flow attention, which adapts existing graph attention mechanisms to satisfy Kirchhoff’s first law. Furthermore, we discuss how this modification influences the expressivity and identify sets of non-isomorphic graphs that can be discriminated by flow attention but not by standard attention. Through extensive experiments on two flow graph datasets (electronic circuits and power grids), we demonstrate that flow attention enhances the performance of attention-based GNNs on both graph-level classification and regression tasks.
crowd-hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labels.Herde, Marek; Lührs, Lukas; Huseljic, Denis; Sick, Bernhard. In Transactions on Machine Learning Research. 2025.
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Crowdworking is a cost-efficient solution for acquiring class labels. Since these labels are subject to noise, various approaches to learning from crowds have been proposed. Typically, these approaches are evaluated using default hyperparameter configurations, which often result in unfair and suboptimal performance, or using hyperparameter configurations tuned via a validation set with ground truth class labels, which represents an often unrealistic scenario. Moreover, both setups can yield different approach rankings, complicating study comparisons. Therefore, we introduce crowd-hpo as a framework for evaluating approaches to learning from crowds, together with criteria for selecting well-performing hyperparameter configurations using only noisy crowd-labeled validation data. Extensive experiments with neural networks demonstrate that these criteria select hyperparameter configurations that improve the learning from crowds approaches' generalization performances, measured on separate test sets with ground truth labels. Hence, incorporating such criteria into experimental studies is essential for enabling fairer and more realistic benchmarking.
@article{herde2025crowd, abstract = {Crowdworking is a cost-efficient solution for acquiring class labels. Since these labels are subject to noise, various approaches to learning from crowds have been proposed. Typically, these approaches are evaluated using default hyperparameter configurations, which often result in unfair and suboptimal performance, or using hyperparameter configurations tuned via a validation set with ground truth class labels, which represents an often unrealistic scenario. Moreover, both setups can yield different approach rankings, complicating study comparisons. Therefore, we introduce crowd-hpo as a framework for evaluating approaches to learning from crowds, together with criteria for selecting well-performing hyperparameter configurations using only noisy crowd-labeled validation data. Extensive experiments with neural networks demonstrate that these criteria select hyperparameter configurations that improve the learning from crowds approaches' generalization performances, measured on separate test sets with ground truth labels. Hence, incorporating such criteria into experimental studies is essential for enabling fairer and more realistic benchmarking.}, author = {Herde, Marek and Lührs, Lukas and Huseljic, Denis and Sick, Bernhard}, journal = {Transactions on Machine Learning Research}, keywords = {itegpub}, title = {crowd-hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labels}, year = 2025 }
%0 Journal Article %1 herde2025crowd %A Herde, Marek %A Lührs, Lukas %A Huseljic, Denis %A Sick, Bernhard %D 2025 %J Transactions on Machine Learning Research %T crowd-hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labels %U https://openreview.net/forum?id=SaKfhylVLK %X Crowdworking is a cost-efficient solution for acquiring class labels. Since these labels are subject to noise, various approaches to learning from crowds have been proposed. Typically, these approaches are evaluated using default hyperparameter configurations, which often result in unfair and suboptimal performance, or using hyperparameter configurations tuned via a validation set with ground truth class labels, which represents an often unrealistic scenario. Moreover, both setups can yield different approach rankings, complicating study comparisons. Therefore, we introduce crowd-hpo as a framework for evaluating approaches to learning from crowds, together with criteria for selecting well-performing hyperparameter configurations using only noisy crowd-labeled validation data. Extensive experiments with neural networks demonstrate that these criteria select hyperparameter configurations that improve the learning from crowds approaches' generalization performances, measured on separate test sets with ground truth labels. Hence, incorporating such criteria into experimental studies is essential for enabling fairer and more realistic benchmarking.
Expressive Pooling for Graph Neural Networks.Lachi, Veronica; Moallemy-Oureh, Alice; Roth, Andreas; Welke, Pascal. In Transactions on Machine Learning Research. 2025.
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Considerable efforts have been dedicated to exploring methods that enhance the expressiveness of graph neural networks. Current endeavors primarily focus on modifying the message-passing process to overcome limitations imposed by the Weisfeiler-Leman test, often at the expense of increasing computational cost. In practical applications, message-passing layers are interleaved with pooling layers for graph-level tasks, enabling the learning of increasingly abstract and coarser representations of input graphs. In this work, we formally prove two directions that allow pooling methods to increase the expressive power of a graph neural network while keeping the message-passing method unchanged. We systemically assign eight frequently used pooling operators to our theoretical conditions for increasing expressivity and introduce a novel pooling method XP, short for eXpressive Pooling, as an additional simple method that satisfies our theoretical conditions. Experiments conducted on the Brec dataset confirm that those pooling methods that satisfy our conditions empirically increase the expressivity of graph neural networks.
@article{lachi2025expressive, abstract = {Considerable efforts have been dedicated to exploring methods that enhance the expressiveness of graph neural networks. Current endeavors primarily focus on modifying the message-passing process to overcome limitations imposed by the Weisfeiler-Leman test, often at the expense of increasing computational cost. In practical applications, message-passing layers are interleaved with pooling layers for graph-level tasks, enabling the learning of increasingly abstract and coarser representations of input graphs. In this work, we formally prove two directions that allow pooling methods to increase the expressive power of a graph neural network while keeping the message-passing method unchanged. We systemically assign eight frequently used pooling operators to our theoretical conditions for increasing expressivity and introduce a novel pooling method XP, short for eXpressive Pooling, as an additional simple method that satisfies our theoretical conditions. Experiments conducted on the Brec dataset confirm that those pooling methods that satisfy our conditions empirically increase the expressivity of graph neural networks.}, author = {Lachi, Veronica and Moallemy-Oureh, Alice and Roth, Andreas and Welke, Pascal}, journal = {Transactions on Machine Learning Research}, keywords = {itegpub}, title = {Expressive Pooling for Graph Neural Networks}, year = 2025 }
%0 Journal Article %1 lachi2025expressive %A Lachi, Veronica %A Moallemy-Oureh, Alice %A Roth, Andreas %A Welke, Pascal %D 2025 %J Transactions on Machine Learning Research %T Expressive Pooling for Graph Neural Networks %U https://openreview.net/forum?id=xGADInGWMt %X Considerable efforts have been dedicated to exploring methods that enhance the expressiveness of graph neural networks. Current endeavors primarily focus on modifying the message-passing process to overcome limitations imposed by the Weisfeiler-Leman test, often at the expense of increasing computational cost. In practical applications, message-passing layers are interleaved with pooling layers for graph-level tasks, enabling the learning of increasingly abstract and coarser representations of input graphs. In this work, we formally prove two directions that allow pooling methods to increase the expressive power of a graph neural network while keeping the message-passing method unchanged. We systemically assign eight frequently used pooling operators to our theoretical conditions for increasing expressivity and introduce a novel pooling method XP, short for eXpressive Pooling, as an additional simple method that satisfies our theoretical conditions. Experiments conducted on the Brec dataset confirm that those pooling methods that satisfy our conditions empirically increase the expressivity of graph neural networks.
PPTopoGym: Towards an RL Environment for Topology Actions on Power Grids.Köhler, Dominik; Hassouna, Mohamed; Degtyar, Dmitry; Krauß, Jonas; Brendlicger, Kurt; Scholz, Christoph. In Workshop on Machine Learning for Sustainable Power Systems (ML4SPS 2025), ECML PKDD. Springer, 2025.
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Power grids are facing growing challenges, arising from the increasing share of renewable energy sources and highly variable loads, in- troducing significant variability into both power generation and demand. This variability can create transmission line bottlenecks, which in turn lead to overheating. Transmission system operators address this prob- lem by rerouting the power flow via topological control, which is more cost-effective than redispatch measures. While reinforcement learning has shown promise in optimizing such topology changes, existing reinforce- ment learning environments exhibit notable differences from real-world operations. To support research and bridge the gap to real-world ap- plications, we present PPTopoGym—a reinforcement learning environ- ment for evaluating the impact of topology actions on the power flow in pandapower-based grids. It allows for simulations with variable load and generation profiles, and supports direct computation of key grid security metrics, i.e., N−0 and N−1 security constraints for each time step. PP- TopoGym addresses key limitations of existing environments by enabling realistic grid modeling and the integration of real-world data. The code is publicly available.
@inproceedings{kohler2025pptopogym, abstract = {Power grids are facing growing challenges, arising from the increasing share of renewable energy sources and highly variable loads, in- troducing significant variability into both power generation and demand. This variability can create transmission line bottlenecks, which in turn lead to overheating. Transmission system operators address this prob- lem by rerouting the power flow via topological control, which is more cost-effective than redispatch measures. While reinforcement learning has shown promise in optimizing such topology changes, existing reinforce- ment learning environments exhibit notable differences from real-world operations. To support research and bridge the gap to real-world ap- plications, we present PPTopoGym—a reinforcement learning environ- ment for evaluating the impact of topology actions on the power flow in pandapower-based grids. It allows for simulations with variable load and generation profiles, and supports direct computation of key grid security metrics, i.e., N−0 and N−1 security constraints for each time step. PP- TopoGym addresses key limitations of existing environments by enabling realistic grid modeling and the integration of real-world data. The code is publicly available.}, author = {Köhler, Dominik and Hassouna, Mohamed and Degtyar, Dmitry and Krauß, Jonas and Brendlicger, Kurt and Scholz, Christoph}, booktitle = {Workshop on Machine Learning for Sustainable Power Systems (ML4SPS 2025), ECML PKDD}, keywords = {itegpub}, note = {(accepted)}, publisher = {Springer}, title = {PPTopoGym: Towards an RL Environment for Topology Actions on Power Grids}, year = 2025 }
%0 Conference Paper %1 kohler2025pptopogym %A Köhler, Dominik %A Hassouna, Mohamed %A Degtyar, Dmitry %A Krauß, Jonas %A Brendlicger, Kurt %A Scholz, Christoph %B Workshop on Machine Learning for Sustainable Power Systems (ML4SPS 2025), ECML PKDD %D 2025 %I Springer %T PPTopoGym: Towards an RL Environment for Topology Actions on Power Grids %X Power grids are facing growing challenges, arising from the increasing share of renewable energy sources and highly variable loads, in- troducing significant variability into both power generation and demand. This variability can create transmission line bottlenecks, which in turn lead to overheating. Transmission system operators address this prob- lem by rerouting the power flow via topological control, which is more cost-effective than redispatch measures. While reinforcement learning has shown promise in optimizing such topology changes, existing reinforce- ment learning environments exhibit notable differences from real-world operations. To support research and bridge the gap to real-world ap- plications, we present PPTopoGym—a reinforcement learning environ- ment for evaluating the impact of topology actions on the power flow in pandapower-based grids. It allows for simulations with variable load and generation profiles, and supports direct computation of key grid security metrics, i.e., N−0 and N−1 security constraints for each time step. PP- TopoGym addresses key limitations of existing environments by enabling realistic grid modeling and the integration of real-world data. The code is publicly available.
Offset Finding of Beamline Parameters on the METRIXS Beamline at BESSY II Using Machine Learning.Meier, David; Zeschke, Thomas; Feuer-Forson, Peter; Sick, Bernhard; Viefhaus, Jens; Hartmann, Gregor. In Journal of Physics: Conference Series, bl. 012130. IOP Publishing, 2025.
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Beamline alignment is challenging as the beamline components must be set up ideally so that the rays follow the desired optical path. Automated methods using a digital twin allow for faster diagnostics and improved beam properties compared to manual tuning. We introduce an automated method of finding the offsets to improve this digital twin model. These offsets represent the unknown but constant differences between the beamline parameter positions as set up at the physical beamline and the corresponding parameter positions of its digital twin. Our method assumes the capability to execute precise relative movements with a known step size for these parameters, although the absolute position information is unknown. By combining the surrogate model with a global optimizer, we successfully determine offsets for 34 beamline parameters on a simulated METRIXS beamline at the BESSY II synchrotron radiation source in Berlin.
@inproceedings{meier2025offset, abstract = {Beamline alignment is challenging as the beamline components must be set up ideally so that the rays follow the desired optical path. Automated methods using a digital twin allow for faster diagnostics and improved beam properties compared to manual tuning. We introduce an automated method of finding the offsets to improve this digital twin model. These offsets represent the unknown but constant differences between the beamline parameter positions as set up at the physical beamline and the corresponding parameter positions of its digital twin. Our method assumes the capability to execute precise relative movements with a known step size for these parameters, although the absolute position information is unknown. By combining the surrogate model with a global optimizer, we successfully determine offsets for 34 beamline parameters on a simulated METRIXS beamline at the BESSY II synchrotron radiation source in Berlin.}, author = {Meier, David and Zeschke, Thomas and Feuer-Forson, Peter and Sick, Bernhard and Viefhaus, Jens and Hartmann, Gregor}, booktitle = {Journal of Physics: Conference Series}, keywords = {itegpub}, pages = {012130}, publisher = {IOP Publishing}, title = {Offset Finding of Beamline Parameters on the METRIXS Beamline at BESSY II Using Machine Learning}, year = 2025 }
%0 Conference Paper %1 meier2025offset %A Meier, David %A Zeschke, Thomas %A Feuer-Forson, Peter %A Sick, Bernhard %A Viefhaus, Jens %A Hartmann, Gregor %B Journal of Physics: Conference Series %D 2025 %I IOP Publishing %P 012130 %R 10.1088/1742-6596/3010/1/012130 %T Offset Finding of Beamline Parameters on the METRIXS Beamline at BESSY II Using Machine Learning %U https://iopscience.iop.org/article/10.1088/1742-6596/3010/1/012130 %X Beamline alignment is challenging as the beamline components must be set up ideally so that the rays follow the desired optical path. Automated methods using a digital twin allow for faster diagnostics and improved beam properties compared to manual tuning. We introduce an automated method of finding the offsets to improve this digital twin model. These offsets represent the unknown but constant differences between the beamline parameter positions as set up at the physical beamline and the corresponding parameter positions of its digital twin. Our method assumes the capability to execute precise relative movements with a known step size for these parameters, although the absolute position information is unknown. By combining the surrogate model with a global optimizer, we successfully determine offsets for 34 beamline parameters on a simulated METRIXS beamline at the BESSY II synchrotron radiation source in Berlin.
Dynamic Multimodal Cyclist Behaviour Modelling: From Representation Insights to Federated Collaboration.Gao, Shang. In Organic Computing -- Doctoral Dissertation Colloquium 2025, S. Tomforde, C. Krupitzer (reds.). kassel university press, 2025.
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In the field of intelligent transportation systems, accurately modelling cyclist behaviour is crucial to enhancing traffic safety and efficiency, particularly as road environments are increasingly shared with automated vehicles. Modelling cyclist behaviour is intrinsically complex due to its dynamic, context-dependent nature, shaped by diverse environmental and individual factors. These complexities pose significant challenges for traditional centralized learning approaches, particularly regarding data privacy concerns and heterogeneity across distributed data sources. Federated learning, as an emerging framework, offers the potential to address these challenges by enabling collaborative model training without sharing raw data. However, in real-world traffic scenarios, federated learning must tackle the complexities of non-independent and identically distributed data and the need for real-time adaptability in dynamic traffic environments. This work explores an uncertainty-aware federated learning framework, incorporating multimodal data fusion, to support modelling cyclist behaviour in traffic environments. This research addresses key challenges in federated learning for cyclist behaviour modelling and explores potential solutions, offering both theoretical and practical insights for future applications in intelligent transportation sys- tems.
@incollection{gao2025dynamic, abstract = {In the field of intelligent transportation systems, accurately modelling cyclist behaviour is crucial to enhancing traffic safety and efficiency, particularly as road environments are increasingly shared with automated vehicles. Modelling cyclist behaviour is intrinsically complex due to its dynamic, context-dependent nature, shaped by diverse environmental and individual factors. These complexities pose significant challenges for traditional centralized learning approaches, particularly regarding data privacy concerns and heterogeneity across distributed data sources. Federated learning, as an emerging framework, offers the potential to address these challenges by enabling collaborative model training without sharing raw data. However, in real-world traffic scenarios, federated learning must tackle the complexities of non-independent and identically distributed data and the need for real-time adaptability in dynamic traffic environments. This work explores an uncertainty-aware federated learning framework, incorporating multimodal data fusion, to support modelling cyclist behaviour in traffic environments. This research addresses key challenges in federated learning for cyclist behaviour modelling and explores potential solutions, offering both theoretical and practical insights for future applications in intelligent transportation sys- tems.}, author = {Gao, Shang}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2025}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, note = {(accepted)}, publisher = {kassel university press}, title = {Dynamic Multimodal Cyclist Behaviour Modelling: From Representation Insights to Federated Collaboration}, year = 2025 }
%0 Book Section %1 gao2025dynamic %A Gao, Shang %B Organic Computing -- Doctoral Dissertation Colloquium 2025 %D 2025 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %T Dynamic Multimodal Cyclist Behaviour Modelling: From Representation Insights to Federated Collaboration %X In the field of intelligent transportation systems, accurately modelling cyclist behaviour is crucial to enhancing traffic safety and efficiency, particularly as road environments are increasingly shared with automated vehicles. Modelling cyclist behaviour is intrinsically complex due to its dynamic, context-dependent nature, shaped by diverse environmental and individual factors. These complexities pose significant challenges for traditional centralized learning approaches, particularly regarding data privacy concerns and heterogeneity across distributed data sources. Federated learning, as an emerging framework, offers the potential to address these challenges by enabling collaborative model training without sharing raw data. However, in real-world traffic scenarios, federated learning must tackle the complexities of non-independent and identically distributed data and the need for real-time adaptability in dynamic traffic environments. This work explores an uncertainty-aware federated learning framework, incorporating multimodal data fusion, to support modelling cyclist behaviour in traffic environments. This research addresses key challenges in federated learning for cyclist behaviour modelling and explores potential solutions, offering both theoretical and practical insights for future applications in intelligent transportation sys- tems.

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Steering Towards Maritime Safety with True Motion Predictions Ensemble.Al-Falouji, Ghassan; Beyer, Tom; Gao, Shang; Tomforde, Sven. In Workshop on self-improving system integration (SISSY), ACSOS, ble. 7–12. IEEE, 2024.
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Maritime transportation is vital for global trade, yet safety challenges persist, especially with increasing vessel density and narrow waterways. The standard Closest Point of Approach (CPA) method, used to estimate collision risk, has limitations due to its reliance on constant Course Over Ground (COG) and Speed Over Ground (SOG) assumptions, neglecting dynamic vessel behaviours, true ship dimensions, and environmental influences. Addressing these limitations, this paper introduces an enhanced CPA (eCPA) method that counters these deficits by incorporating ship trajectory predictions, probabilistic risk assessment, and real-time data integration. The eCPA method includes user- adjustable tools for trajectory interpolation, prediction, and anomaly elimination, making it adaptable to various maritime scenarios. Extensive testing in encounter scenarios demonstrates the effectiveness of our approach in improving maritime safety through robust and probabilistic estimation of encounter points. This method aligns with the Self-Integration and Self-Organising Systems (SISSY) initiative, exhibiting how autonomous maritime operations can benefit from self-improving and self-integrating systems to enhance navigation safety and efficiency.
@inproceedings{alfalouji2024steering, abstract = {Maritime transportation is vital for global trade, yet safety challenges persist, especially with increasing vessel density and narrow waterways. The standard Closest Point of Approach (CPA) method, used to estimate collision risk, has limitations due to its reliance on constant Course Over Ground (COG) and Speed Over Ground (SOG) assumptions, neglecting dynamic vessel behaviours, true ship dimensions, and environmental influences. Addressing these limitations, this paper introduces an enhanced CPA (eCPA) method that counters these deficits by incorporating ship trajectory predictions, probabilistic risk assessment, and real-time data integration. The eCPA method includes user- adjustable tools for trajectory interpolation, prediction, and anomaly elimination, making it adaptable to various maritime scenarios. Extensive testing in encounter scenarios demonstrates the effectiveness of our approach in improving maritime safety through robust and probabilistic estimation of encounter points. This method aligns with the Self-Integration and Self-Organising Systems (SISSY) initiative, exhibiting how autonomous maritime operations can benefit from self-improving and self-integrating systems to enhance navigation safety and efficiency.}, author = {Al-Falouji, Ghassan and Beyer, Tom and Gao, Shang and Tomforde, Sven}, booktitle = {Workshop on self-improving system integration (SISSY), ACSOS}, keywords = {Trajectory}, pages = {7–12}, publisher = {IEEE}, title = {Steering Towards Maritime Safety with True Motion Predictions Ensemble}, year = 2024 }
%0 Conference Paper %1 alfalouji2024steering %A Al-Falouji, Ghassan %A Beyer, Tom %A Gao, Shang %A Tomforde, Sven %B Workshop on self-improving system integration (SISSY), ACSOS %D 2024 %I IEEE %P 7--12 %R 10.1109/ACSOS-C63493.2024.00021 %T Steering Towards Maritime Safety with True Motion Predictions Ensemble %X Maritime transportation is vital for global trade, yet safety challenges persist, especially with increasing vessel density and narrow waterways. The standard Closest Point of Approach (CPA) method, used to estimate collision risk, has limitations due to its reliance on constant Course Over Ground (COG) and Speed Over Ground (SOG) assumptions, neglecting dynamic vessel behaviours, true ship dimensions, and environmental influences. Addressing these limitations, this paper introduces an enhanced CPA (eCPA) method that counters these deficits by incorporating ship trajectory predictions, probabilistic risk assessment, and real-time data integration. The eCPA method includes user- adjustable tools for trajectory interpolation, prediction, and anomaly elimination, making it adaptable to various maritime scenarios. Extensive testing in encounter scenarios demonstrates the effectiveness of our approach in improving maritime safety through robust and probabilistic estimation of encounter points. This method aligns with the Self-Integration and Self-Organising Systems (SISSY) initiative, exhibiting how autonomous maritime operations can benefit from self-improving and self-integrating systems to enhance navigation safety and efficiency.
Spatial-Temporal Attention Graph Neural Network with Uncertainty Estimation for Remaining Useful Life Prediction.Huang, Zhixin; Nivarthi, Chandana Priya; Gruhl, Christian; Sick, Bernhard. In International Joint Conference on Neural Networks (IJCNN), ble. 1–9. IEEE, 2024.
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In the increasingly complex industrial system health management domain, accurate prediction of remaining useful life plays an essential role. This paper analyzes the methods to improve the predictive performance of remaining useful life from three aspects: optimizing model structures, augmenting uncertainty estimation in predictions, and transitioning normalization methods. Based on our analysis, we propose a novel model, the Uncertainty Spatial-Temporal Attention Graph Neural Network (USTAGNN), which consists of three primary components: sensor graph construction, a spatio-temporal feature extractor, and a probabilistic prediction module. The feature extractor leverages graph neural networks and temporal convolutional networks as a foundation to extract spatial and temporal features, further enhanced by attention mechanisms, spectral normalization, and residual connections to bolster its distance awareness. Following extensive experimental comparisons, we utilized the parameter-driven dynamic adjacency matrix for sensor graph construction and the deep kernel Gaussian process for precise uncertainty estimation. USTAGNN tries to resolve issues not thoroughly addressed in existing research, such as comparative analyses of sensor graph construction methods, accurate uncertainty estimation, and the model’s generalization under different preprocessing conditions. The proposed model demonstrated state-of-the-art performance on various subsets of the C-MAPSS dataset, achieving up to a 35.9% improvement in prediction score.
@inproceedings{huang2024spatial, abstract = {In the increasingly complex industrial system health management domain, accurate prediction of remaining useful life plays an essential role. This paper analyzes the methods to improve the predictive performance of remaining useful life from three aspects: optimizing model structures, augmenting uncertainty estimation in predictions, and transitioning normalization methods. Based on our analysis, we propose a novel model, the Uncertainty Spatial-Temporal Attention Graph Neural Network (USTAGNN), which consists of three primary components: sensor graph construction, a spatio-temporal feature extractor, and a probabilistic prediction module. The feature extractor leverages graph neural networks and temporal convolutional networks as a foundation to extract spatial and temporal features, further enhanced by attention mechanisms, spectral normalization, and residual connections to bolster its distance awareness. Following extensive experimental comparisons, we utilized the parameter-driven dynamic adjacency matrix for sensor graph construction and the deep kernel Gaussian process for precise uncertainty estimation. USTAGNN tries to resolve issues not thoroughly addressed in existing research, such as comparative analyses of sensor graph construction methods, accurate uncertainty estimation, and the model’s generalization under different preprocessing conditions. The proposed model demonstrated state-of-the-art performance on various subsets of the C-MAPSS dataset, achieving up to a 35.9% improvement in prediction score.}, author = {Huang, Zhixin and Nivarthi, Chandana Priya and Gruhl, Christian and Sick, Bernhard}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {itegpub}, pages = {1–9}, publisher = {IEEE}, title = {Spatial-Temporal Attention Graph Neural Network with Uncertainty Estimation for Remaining Useful Life Prediction}, year = 2024 }
%0 Conference Paper %1 huang2024spatial %A Huang, Zhixin %A Nivarthi, Chandana Priya %A Gruhl, Christian %A Sick, Bernhard %B International Joint Conference on Neural Networks (IJCNN) %D 2024 %I IEEE %P 1--9 %R 10.1109/IJCNN60899.2024.10650622 %T Spatial-Temporal Attention Graph Neural Network with Uncertainty Estimation for Remaining Useful Life Prediction %X In the increasingly complex industrial system health management domain, accurate prediction of remaining useful life plays an essential role. This paper analyzes the methods to improve the predictive performance of remaining useful life from three aspects: optimizing model structures, augmenting uncertainty estimation in predictions, and transitioning normalization methods. Based on our analysis, we propose a novel model, the Uncertainty Spatial-Temporal Attention Graph Neural Network (USTAGNN), which consists of three primary components: sensor graph construction, a spatio-temporal feature extractor, and a probabilistic prediction module. The feature extractor leverages graph neural networks and temporal convolutional networks as a foundation to extract spatial and temporal features, further enhanced by attention mechanisms, spectral normalization, and residual connections to bolster its distance awareness. Following extensive experimental comparisons, we utilized the parameter-driven dynamic adjacency matrix for sensor graph construction and the deep kernel Gaussian process for precise uncertainty estimation. USTAGNN tries to resolve issues not thoroughly addressed in existing research, such as comparative analyses of sensor graph construction methods, accurate uncertainty estimation, and the model’s generalization under different preprocessing conditions. The proposed model demonstrated state-of-the-art performance on various subsets of the C-MAPSS dataset, achieving up to a 35.9% improvement in prediction score.
LiST: An All-Linear-Layer Spatial-Temporal Feature Extractor with Uncertainty Estimation for RUL Prediction.Huang, Zhixin; Gruhl, Christian; Sick, Bernhard. In IEEE Conference on Industrial Electronics and Applications (ICIEA), ble. 1–7. IEEE, 2024.
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In the context of Remaining Useful Life (RUL) prediction for industrial systems, the pursuit of prediction accuracy must be balanced against the hardware costs of model operation and the reliability of prediction results. To resolve these challenges, we introduce LiST, an all-linear-layer spatial-temporal feature extractor integrated with uncertainty estimation, specifically designed for processing sensor multivariate time series (MTS) data. Unlike traditional linear models that flatten MTS and thus neglect their spatial-temporal dependencies, LiST's linear layers act on both the sensor and time dimensions of MTS that can extract spatial and temporal features like GNN and RNN models. Through performance comparisons on four RUL prediction datasets, LiST uses only 66.1% of the parameters, achieves comparable accuracy to state-of-the-art GNN and RNN models, obtains the best results on two datasets with up to a 21.6% improvement in Score, and enhances training efficiency by 3.2 times. Additionally, LiST can predict RUL with uncertainty estimation and precisely disentangle epistemic and aleatoric uncertainties, thus enhancing the model's practicality and reliability in real-world industrial applications.
@inproceedings{huang2024list, abstract = {In the context of Remaining Useful Life (RUL) prediction for industrial systems, the pursuit of prediction accuracy must be balanced against the hardware costs of model operation and the reliability of prediction results. To resolve these challenges, we introduce LiST, an all-linear-layer spatial-temporal feature extractor integrated with uncertainty estimation, specifically designed for processing sensor multivariate time series (MTS) data. Unlike traditional linear models that flatten MTS and thus neglect their spatial-temporal dependencies, LiST's linear layers act on both the sensor and time dimensions of MTS that can extract spatial and temporal features like GNN and RNN models. Through performance comparisons on four RUL prediction datasets, LiST uses only 66.1% of the parameters, achieves comparable accuracy to state-of-the-art GNN and RNN models, obtains the best results on two datasets with up to a 21.6% improvement in Score, and enhances training efficiency by 3.2 times. Additionally, LiST can predict RUL with uncertainty estimation and precisely disentangle epistemic and aleatoric uncertainties, thus enhancing the model's practicality and reliability in real-world industrial applications.}, author = {Huang, Zhixin and Gruhl, Christian and Sick, Bernhard}, booktitle = {IEEE Conference on Industrial Electronics and Applications (ICIEA)}, keywords = {itegpub}, pages = {1–7}, publisher = {IEEE}, title = {LiST: An All-Linear-Layer Spatial-Temporal Feature Extractor with Uncertainty Estimation for RUL Prediction}, year = 2024 }
%0 Conference Paper %1 huang2024list %A Huang, Zhixin %A Gruhl, Christian %A Sick, Bernhard %B IEEE Conference on Industrial Electronics and Applications (ICIEA) %D 2024 %I IEEE %P 1--7 %R 10.1109/ICIEA61579.2024.10665236 %T LiST: An All-Linear-Layer Spatial-Temporal Feature Extractor with Uncertainty Estimation for RUL Prediction %X In the context of Remaining Useful Life (RUL) prediction for industrial systems, the pursuit of prediction accuracy must be balanced against the hardware costs of model operation and the reliability of prediction results. To resolve these challenges, we introduce LiST, an all-linear-layer spatial-temporal feature extractor integrated with uncertainty estimation, specifically designed for processing sensor multivariate time series (MTS) data. Unlike traditional linear models that flatten MTS and thus neglect their spatial-temporal dependencies, LiST's linear layers act on both the sensor and time dimensions of MTS that can extract spatial and temporal features like GNN and RNN models. Through performance comparisons on four RUL prediction datasets, LiST uses only 66.1% of the parameters, achieves comparable accuracy to state-of-the-art GNN and RNN models, obtains the best results on two datasets with up to a 21.6% improvement in Score, and enhances training efficiency by 3.2 times. Additionally, LiST can predict RUL with uncertainty estimation and precisely disentangle epistemic and aleatoric uncertainties, thus enhancing the model's practicality and reliability in real-world industrial applications.
Sensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models.Reichert, Hannes; Hetzel, Manuel; Hubert, Andreas; Doll, Konrad; Sick, Bernhard. In Workshop on Omnidirectional Computer Vision (OmniCV), ICVPR, ble. 1254–1261. IEEE, 2024.
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Objects are represented differently in projection-based sensors such as cameras depending on sensor resolution, field of view, and distortion, leading to distorted physical and geometric properties. As a result, sensor data process- ing depend on these properties. With the large variations of sensors on the market, an equivariant representation and suitable processing are necessary to become independent of the sensor used. In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. Further- more, we introduce a SensorConv layer as an extension to the conventional convolution layer. SensorConv enable us- ing projection properties in convolutional neural networks. To that end, we propose an architecture for using the Sen- sorConv layer in the Detectron2 [21] framework. We col- lected a dataset of equirectangular images for our experi- ments with the CARLA [3] simulator. To analyze multiple sensor models (i.e., sensor intrinsic), we created an aug- mentation method to emulate a high variability of sensors from the collected equirectangular panoramas. In our ex- periment, we show that our method can generalize better across different camera sensors.
@inproceedings{reichert2024sensor, abstract = {Objects are represented differently in projection-based sensors such as cameras depending on sensor resolution, field of view, and distortion, leading to distorted physical and geometric properties. As a result, sensor data process- ing depend on these properties. With the large variations of sensors on the market, an equivariant representation and suitable processing are necessary to become independent of the sensor used. In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. Further- more, we introduce a SensorConv layer as an extension to the conventional convolution layer. SensorConv enable us- ing projection properties in convolutional neural networks. To that end, we propose an architecture for using the Sen- sorConv layer in the Detectron2 [21] framework. We col- lected a dataset of equirectangular images for our experi- ments with the CARLA [3] simulator. To analyze multiple sensor models (i.e., sensor intrinsic), we created an aug- mentation method to emulate a high variability of sensors from the collected equirectangular panoramas. In our ex- periment, we show that our method can generalize better across different camera sensors.}, author = {Reichert, Hannes and Hetzel, Manuel and Hubert, Andreas and Doll, Konrad and Sick, Bernhard}, booktitle = {Workshop on Omnidirectional Computer Vision (OmniCV), ICVPR}, keywords = {itegpub}, pages = {1254–1261}, publisher = {IEEE}, title = {Sensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models}, year = 2024 }
%0 Conference Paper %1 reichert2024sensor %A Reichert, Hannes %A Hetzel, Manuel %A Hubert, Andreas %A Doll, Konrad %A Sick, Bernhard %B Workshop on Omnidirectional Computer Vision (OmniCV), ICVPR %D 2024 %I IEEE %P 1254--1261 %R 10.1109/CVPRW63382.2024.00132 %T Sensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models %X Objects are represented differently in projection-based sensors such as cameras depending on sensor resolution, field of view, and distortion, leading to distorted physical and geometric properties. As a result, sensor data process- ing depend on these properties. With the large variations of sensors on the market, an equivariant representation and suitable processing are necessary to become independent of the sensor used. In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. Further- more, we introduce a SensorConv layer as an extension to the conventional convolution layer. SensorConv enable us- ing projection properties in convolutional neural networks. To that end, we propose an architecture for using the Sen- sorConv layer in the Detectron2 [21] framework. We col- lected a dataset of equirectangular images for our experi- ments with the CARLA [3] simulator. To analyze multiple sensor models (i.e., sensor intrinsic), we created an aug- mentation method to emulate a high variability of sensors from the collected equirectangular panoramas. In our ex- periment, we show that our method can generalize better across different camera sensors.
Adaptive Shapley: Using Explainable AI with Large Datasets to Quantify the Impact of Arbitrary Error Sources.Magnussen, Birk Martin; Jessulat, Maik; Stern, Claudius; Sick, Bernhard. In International Conference on Big Data Analytics (ICBDA), ble. 305–310. IEEE, 2024.
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Almost all sensors suffer from some level of uncertainty introduced from production inaccuracies. When the sensor data is processed by machine learning, quantifying the impact of such production inaccuracies on the output of the machine learning model becomes difficult. Certain neural network architectures, such as continuous feature networks, allow individual features and data to be omitted while still being able to correctly predict the result without the need for retraining. Such features can, for example, be individual channels of a sensor. This article proposes a method to use the capability to omit arbitrary features or sensor channels to calculate Shapley values for each sensor channel. These Shapley values represent the contribution of each individual channel to the measurement. They are defined using an arbitrary function called the ``value function''. If the value function is defined as the error of the current measurement, the Shapley values will represent the contribution of each sensor channel to the error of the measurement result. By calculating Shapley values like this for a large unlabelled dataset of measurements, it is possible to understand how much measurement error was introduced by which channel of which sensor in each measurement. Averaging the Shapley values for each sensor in the dataset will then result in a metric for each channel of that sensor, which represents a contribution to measurement errors. By comparing these values to any arbitrary quality metrics for the sensor channels obtained in a calibration process or similar step, it is possible to correlate and quantify which value in the quality metric will cause how much of a measurement error, or whether the quality metric is even relevant for the measurement accuracy. This article will show the efficacy and use case of the method on an example of the production and quality control of optical sensors based on multiple spatially resolved reflection spectroscopy.
@inproceedings{magnussen2024adaptive, abstract = {Almost all sensors suffer from some level of uncertainty introduced from production inaccuracies. When the sensor data is processed by machine learning, quantifying the impact of such production inaccuracies on the output of the machine learning model becomes difficult. Certain neural network architectures, such as continuous feature networks, allow individual features and data to be omitted while still being able to correctly predict the result without the need for retraining. Such features can, for example, be individual channels of a sensor. This article proposes a method to use the capability to omit arbitrary features or sensor channels to calculate Shapley values for each sensor channel. These Shapley values represent the contribution of each individual channel to the measurement. They are defined using an arbitrary function called the ``value function''. If the value function is defined as the error of the current measurement, the Shapley values will represent the contribution of each sensor channel to the error of the measurement result. By calculating Shapley values like this for a large unlabelled dataset of measurements, it is possible to understand how much measurement error was introduced by which channel of which sensor in each measurement. Averaging the Shapley values for each sensor in the dataset will then result in a metric for each channel of that sensor, which represents a contribution to measurement errors. By comparing these values to any arbitrary quality metrics for the sensor channels obtained in a calibration process or similar step, it is possible to correlate and quantify which value in the quality metric will cause how much of a measurement error, or whether the quality metric is even relevant for the measurement accuracy. This article will show the efficacy and use case of the method on an example of the production and quality control of optical sensors based on multiple spatially resolved reflection spectroscopy.}, author = {Magnussen, Birk Martin and Jessulat, Maik and Stern, Claudius and Sick, Bernhard}, booktitle = {International Conference on Big Data Analytics (ICBDA)}, keywords = {itegpub}, pages = {305–310}, publisher = {IEEE}, title = {Adaptive Shapley: Using Explainable AI with Large Datasets to Quantify the Impact of Arbitrary Error Sources}, year = 2024 }
%0 Conference Paper %1 magnussen2024adaptive %A Magnussen, Birk Martin %A Jessulat, Maik %A Stern, Claudius %A Sick, Bernhard %B International Conference on Big Data Analytics (ICBDA) %D 2024 %I IEEE %P 305--310 %R 10.1109/ICBDA61153.2024.10607312 %T Adaptive Shapley: Using Explainable AI with Large Datasets to Quantify the Impact of Arbitrary Error Sources %X Almost all sensors suffer from some level of uncertainty introduced from production inaccuracies. When the sensor data is processed by machine learning, quantifying the impact of such production inaccuracies on the output of the machine learning model becomes difficult. Certain neural network architectures, such as continuous feature networks, allow individual features and data to be omitted while still being able to correctly predict the result without the need for retraining. Such features can, for example, be individual channels of a sensor. This article proposes a method to use the capability to omit arbitrary features or sensor channels to calculate Shapley values for each sensor channel. These Shapley values represent the contribution of each individual channel to the measurement. They are defined using an arbitrary function called the ``value function''. If the value function is defined as the error of the current measurement, the Shapley values will represent the contribution of each sensor channel to the error of the measurement result. By calculating Shapley values like this for a large unlabelled dataset of measurements, it is possible to understand how much measurement error was introduced by which channel of which sensor in each measurement. Averaging the Shapley values for each sensor in the dataset will then result in a metric for each channel of that sensor, which represents a contribution to measurement errors. By comparing these values to any arbitrary quality metrics for the sensor channels obtained in a calibration process or similar step, it is possible to correlate and quantify which value in the quality metric will cause how much of a measurement error, or whether the quality metric is even relevant for the measurement accuracy. This article will show the efficacy and use case of the method on an example of the production and quality control of optical sensors based on multiple spatially resolved reflection spectroscopy.
Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network.Ali, Mohammad Wazed; Mustafa, Mohammed Asif bin; Shuvo, Md. Aukerul Moin; Sick, Bernhard. In arXiv e-prints, bl. arXiv:2409.11862. 2024.
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Electrification of vehicles is a potential way of reducing fossil fuel usage and thus lessening environmental pollution. Electric Vehicles (EVs) of various types for different transport modes (including air, water, and land) are evolving. Moreover, different EV user groups (commuters, commercial or domestic users, drivers) may use different charging infrastructures (public, private, home, and workplace) at various times. Therefore, usage patterns and energy demand are very stochastic. Characterizing and forecasting the charging demand of these diverse EV usage profiles is essential in preventing power outages. Previously developed data-driven load models are limited to specific use cases and locations. None of these models are simultaneously adaptive enough to transfer knowledge of day-ahead forecasting among EV charging sites of diverse locations, trained with limited data, and cost-effective. This article presents a location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models. We conducted our experiments on data from four charging sites, namely Caltech, JPL, Office-1, and NREL, which have diverse EV user types like students, full-time and part-time employees, random visitors, etc. With a Prediction Interval Coverage Probability (PICP) score of 93.62%, our proposed deep MQ-TCN model exhibited a remarkable 28.93% improvement over the XGBoost model for a day-ahead load forecasting at the JPL charging site. By transferring knowledge with the inductive Transfer Learning (TL) approach, the MQ-TCN model achieved a 96.88% PICP score for the load forecasting task at the NREL site using only two weeks of data.
@article{ali2024location, abstract = {Electrification of vehicles is a potential way of reducing fossil fuel usage and thus lessening environmental pollution. Electric Vehicles (EVs) of various types for different transport modes (including air, water, and land) are evolving. Moreover, different EV user groups (commuters, commercial or domestic users, drivers) may use different charging infrastructures (public, private, home, and workplace) at various times. Therefore, usage patterns and energy demand are very stochastic. Characterizing and forecasting the charging demand of these diverse EV usage profiles is essential in preventing power outages. Previously developed data-driven load models are limited to specific use cases and locations. None of these models are simultaneously adaptive enough to transfer knowledge of day-ahead forecasting among EV charging sites of diverse locations, trained with limited data, and cost-effective. This article presents a location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models. We conducted our experiments on data from four charging sites, namely Caltech, JPL, Office-1, and NREL, which have diverse EV user types like students, full-time and part-time employees, random visitors, etc. With a Prediction Interval Coverage Probability (PICP) score of 93.62%, our proposed deep MQ-TCN model exhibited a remarkable 28.93% improvement over the XGBoost model for a day-ahead load forecasting at the JPL charging site. By transferring knowledge with the inductive Transfer Learning (TL) approach, the MQ-TCN model achieved a 96.88% PICP score for the load forecasting task at the NREL site using only two weeks of data.}, author = {Ali, Mohammad Wazed and Mustafa, Mohammed Asif bin and Shuvo, Md. Aukerul Moin and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2409.11862}, title = {Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network.}, year = 2024 }
%0 Journal Article %1 ali2024location %A Ali, Mohammad Wazed %A Mustafa, Mohammed Asif bin %A Shuvo, Md. Aukerul Moin %A Sick, Bernhard %D 2024 %J arXiv e-prints %P arXiv:2409.11862 %T Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network. %U https://arxiv.org/abs/2409.11862 %X Electrification of vehicles is a potential way of reducing fossil fuel usage and thus lessening environmental pollution. Electric Vehicles (EVs) of various types for different transport modes (including air, water, and land) are evolving. Moreover, different EV user groups (commuters, commercial or domestic users, drivers) may use different charging infrastructures (public, private, home, and workplace) at various times. Therefore, usage patterns and energy demand are very stochastic. Characterizing and forecasting the charging demand of these diverse EV usage profiles is essential in preventing power outages. Previously developed data-driven load models are limited to specific use cases and locations. None of these models are simultaneously adaptive enough to transfer knowledge of day-ahead forecasting among EV charging sites of diverse locations, trained with limited data, and cost-effective. This article presents a location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models. We conducted our experiments on data from four charging sites, namely Caltech, JPL, Office-1, and NREL, which have diverse EV user types like students, full-time and part-time employees, random visitors, etc. With a Prediction Interval Coverage Probability (PICP) score of 93.62%, our proposed deep MQ-TCN model exhibited a remarkable 28.93% improvement over the XGBoost model for a day-ahead load forecasting at the JPL charging site. By transferring knowledge with the inductive Transfer Learning (TL) approach, the MQ-TCN model achieved a 96.88% PICP score for the load forecasting task at the NREL site using only two weeks of data.
Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence as a Basis for Automated Driving.Zernetsch, Stefan; Kress, Viktor; Bieshaar, Maarten; Schneegans, Jan; Reitberger, Günther; Fuchs, Erich; Sick, Bernhard; Doll, Konrad. In Cooperatively Interacting Vehicles: Methods and Effects of Automated Cooperation in Traffic, C. Stiller, M. Althoff, C. Burger, B. Deml, L. Eckstein, F. Flemisch (reds.), ble. 35–87. Springer, 2024.
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The project Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence as a Basis for Automated Driving (DeCoInt{\$$}{\$$}^2{\$$}{\$$}2) focuses on detecting the intentions of vulnerable road users (VRUs) in automated driving using cooperative technologies. Especially in urban areas, VRUs, e.g., pedestrians and cyclists, will continue to play an essential role in mixed traffic. For an accident-free and highly efficient traffic flow with automated vehicles, it is vital to perceive VRUs and their intentions and analyze them similarly to humans when driving and forecasting VRU trajectories. Doing this reliably and robustly with a multimodal sensor system (e.g., cameras, LiDARs, accelerometers, and gyroscopes in mobile devices) in real-time is a big challenge. We follow a holistic, cooperative approach to recognize humans' movements and forecast their trajectories. Heterogeneous open sets of agents, i.e., collaboratively interacting vehicles, infrastructure, and VRUs equipped with mobile devices, exchange information to determine individual models of their surrounding environment, allowing an accurate and reliable forecast of VRU basic movements and trajectories. The collective intelligence of cooperating agents resolves occlusions, implausibilities, and inconsistencies. We developed new methods by considering and combining novel signal processing and modeling techniques with machine learning-based pattern recognition approaches. The cooperation between agents happens on several levels: the VRU perception level, the level of recognized trajectories, or the level of already detected intentions.
@inbook{zernetsch2024detecting, abstract = {The project Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence as a Basis for Automated Driving (DeCoInt{\$}{\$}^2{\$}{\$}2) focuses on detecting the intentions of vulnerable road users (VRUs) in automated driving using cooperative technologies. Especially in urban areas, VRUs, e.g., pedestrians and cyclists, will continue to play an essential role in mixed traffic. For an accident-free and highly efficient traffic flow with automated vehicles, it is vital to perceive VRUs and their intentions and analyze them similarly to humans when driving and forecasting VRU trajectories. Doing this reliably and robustly with a multimodal sensor system (e.g., cameras, LiDARs, accelerometers, and gyroscopes in mobile devices) in real-time is a big challenge. We follow a holistic, cooperative approach to recognize humans' movements and forecast their trajectories. Heterogeneous open sets of agents, i.e., collaboratively interacting vehicles, infrastructure, and VRUs equipped with mobile devices, exchange information to determine individual models of their surrounding environment, allowing an accurate and reliable forecast of VRU basic movements and trajectories. The collective intelligence of cooperating agents resolves occlusions, implausibilities, and inconsistencies. We developed new methods by considering and combining novel signal processing and modeling techniques with machine learning-based pattern recognition approaches. The cooperation between agents happens on several levels: the VRU perception level, the level of recognized trajectories, or the level of already detected intentions.}, author = {Zernetsch, Stefan and Kress, Viktor and Bieshaar, Maarten and Schneegans, Jan and Reitberger, G{\"u}nther and Fuchs, Erich and Sick, Bernhard and Doll, Konrad}, booktitle = {Cooperatively Interacting Vehicles: Methods and Effects of Automated Cooperation in Traffic}, editor = {Stiller, Christoph and Althoff, Matthias and Burger, Christoph and Deml, Barbara and Eckstein, Lutz and Flemisch, Frank}, keywords = {itegpub}, pages = {35–87}, publisher = {Springer}, title = {Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence as a Basis for Automated Driving}, year = 2024 }
%0 Book Section %1 zernetsch2024detecting %A Zernetsch, Stefan %A Kress, Viktor %A Bieshaar, Maarten %A Schneegans, Jan %A Reitberger, Günther %A Fuchs, Erich %A Sick, Bernhard %A Doll, Konrad %B Cooperatively Interacting Vehicles: Methods and Effects of Automated Cooperation in Traffic %D 2024 %E Stiller, Christoph %E Althoff, Matthias %E Burger, Christoph %E Deml, Barbara %E Eckstein, Lutz %E Flemisch, Frank %I Springer %P 35--87 %R 10.1007/978-3-031-60494-2_3 %T Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence as a Basis for Automated Driving %X The project Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence as a Basis for Automated Driving (DeCoInt{\$$}{\$$}^2{\$$}{\$$}2) focuses on detecting the intentions of vulnerable road users (VRUs) in automated driving using cooperative technologies. Especially in urban areas, VRUs, e.g., pedestrians and cyclists, will continue to play an essential role in mixed traffic. For an accident-free and highly efficient traffic flow with automated vehicles, it is vital to perceive VRUs and their intentions and analyze them similarly to humans when driving and forecasting VRU trajectories. Doing this reliably and robustly with a multimodal sensor system (e.g., cameras, LiDARs, accelerometers, and gyroscopes in mobile devices) in real-time is a big challenge. We follow a holistic, cooperative approach to recognize humans' movements and forecast their trajectories. Heterogeneous open sets of agents, i.e., collaboratively interacting vehicles, infrastructure, and VRUs equipped with mobile devices, exchange information to determine individual models of their surrounding environment, allowing an accurate and reliable forecast of VRU basic movements and trajectories. The collective intelligence of cooperating agents resolves occlusions, implausibilities, and inconsistencies. We developed new methods by considering and combining novel signal processing and modeling techniques with machine learning-based pattern recognition approaches. The cooperation between agents happens on several levels: the VRU perception level, the level of recognized trajectories, or the level of already detected intentions. %@ 978-3-031-60494-2
Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. Bunse, Mirko; Herde, Marek; Krempl, Georg; Lemaire, Vincent; Tharwat, Alaa; Pham, Minh Tuan; Saadallah, Amal. In Vol. 3770CEUR Workshop Proceedings. 2024.
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@proceedings{bunse2024workshop, editor = {Bunse, Mirko and Herde, Marek and Krempl, Georg and Lemaire, Vincent and Tharwat, Alaa and Pham, Minh Tuan and Saadallah, Amal}, keywords = {itegpub}, series = {{CEUR} Workshop Proceedings}, title = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, volume = 3770, year = 2024 }
%0 Conference Proceedings %1 bunse2024workshop %B CEUR Workshop Proceedings %D 2024 %E Bunse, Mirko %E Herde, Marek %E Krempl, Georg %E Lemaire, Vincent %E Tharwat, Alaa %E Pham, Minh Tuan %E Saadallah, Amal %T Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %U http://ceur-ws.org/Vol-3770 %V 3770
Systematic Evaluation of Uncertainty Calibration in Pretrained Object Detectors.Huseljic, Denis; Herde, Marek; Hahn, Paul; Müjde, Mehmet; Sick, Bernhard. In International Journal of Computer Vision, 133(3), ble. 1033–1047. Springer, 2024.
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In the field of deep learning based computer vision, the development of deep object detection has led to unique paradigms (e.g., two-stage or set-based) and architectures (e.g., Faster-RCNN or DETR) which enable outstanding performance on challenging benchmark datasets. Despite this, the trained object detectors typically do not reliably assess uncertainty regarding their own knowledge, and the quality of their probabilistic predictions is usually poor. As these are often used to make subsequent decisions, such inaccurate probabilistic predictions must be avoided. In this work, we investigate the uncertainty calibration properties of different pretrained object detection architectures in a multi-class setting. We propose a framework to ensure a fair, unbiased, and repeatable evaluation and conduct detailed analyses assessing the calibration under distributional changes (e.g., distributional shift and application to out-of-distribution data). Furthermore, by investigating the influence of different detector paradigms, post-processing steps, and suitable choices of metrics, we deliver novel insights into why poor detector calibration emerges. Based on these insights, we are able to improve the calibration of a detector by simply finetuning its last layer.
@article{huseljic2024systematic, abstract = {In the field of deep learning based computer vision, the development of deep object detection has led to unique paradigms (e.g., two-stage or set-based) and architectures (e.g., Faster-RCNN or DETR) which enable outstanding performance on challenging benchmark datasets. Despite this, the trained object detectors typically do not reliably assess uncertainty regarding their own knowledge, and the quality of their probabilistic predictions is usually poor. As these are often used to make subsequent decisions, such inaccurate probabilistic predictions must be avoided. In this work, we investigate the uncertainty calibration properties of different pretrained object detection architectures in a multi-class setting. We propose a framework to ensure a fair, unbiased, and repeatable evaluation and conduct detailed analyses assessing the calibration under distributional changes (e.g., distributional shift and application to out-of-distribution data). Furthermore, by investigating the influence of different detector paradigms, post-processing steps, and suitable choices of metrics, we deliver novel insights into why poor detector calibration emerges. Based on these insights, we are able to improve the calibration of a detector by simply finetuning its last layer.}, author = {Huseljic, Denis and Herde, Marek and Hahn, Paul and Müjde, Mehmet and Sick, Bernhard}, journal = {International Journal of Computer Vision}, keywords = {itegpub}, number = 3, pages = {1033–1047}, publisher = {Springer}, title = {Systematic Evaluation of Uncertainty Calibration in Pretrained Object Detectors}, volume = 133, year = 2024 }
%0 Journal Article %1 huseljic2024systematic %A Huseljic, Denis %A Herde, Marek %A Hahn, Paul %A Müjde, Mehmet %A Sick, Bernhard %D 2024 %I Springer %J International Journal of Computer Vision %N 3 %P 1033--1047 %R 10.1007/s11263-024-02219-z %T Systematic Evaluation of Uncertainty Calibration in Pretrained Object Detectors %U https://link.springer.com/article/10.1007/s11263-024-02219-z %V 133 %X In the field of deep learning based computer vision, the development of deep object detection has led to unique paradigms (e.g., two-stage or set-based) and architectures (e.g., Faster-RCNN or DETR) which enable outstanding performance on challenging benchmark datasets. Despite this, the trained object detectors typically do not reliably assess uncertainty regarding their own knowledge, and the quality of their probabilistic predictions is usually poor. As these are often used to make subsequent decisions, such inaccurate probabilistic predictions must be avoided. In this work, we investigate the uncertainty calibration properties of different pretrained object detection architectures in a multi-class setting. We propose a framework to ensure a fair, unbiased, and repeatable evaluation and conduct detailed analyses assessing the calibration under distributional changes (e.g., distributional shift and application to out-of-distribution data). Furthermore, by investigating the influence of different detector paradigms, post-processing steps, and suitable choices of metrics, we deliver novel insights into why poor detector calibration emerges. Based on these insights, we are able to improve the calibration of a detector by simply finetuning its last layer.
From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs.Decke, Jens; Wünsch, Olaf; Sick, Bernhard; Gruhl, Christian. In International Conference on Architecture of Computing Systems (ARCS), ble. 82–96. Springer, 2024.
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This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded structured, and unstructured meshes, the study compares the performance and computational efficiency of three computer vision-based models against three graph-based models across three datasets. The research aims to identify the most suitable models for different mesh topographies, particularly highlighting the exploration of graded meshes, a less studied area. Results demonstrate that computer vision-based models, notably U-Net, outperform the graph models in prediction performance and efficiency in two (structured and graded) out of three mesh topographies. The study also reveals the unexpected effectiveness of computer vision-based models in handling unstructured meshes, suggesting a potential shift in methodological approaches for data-driven partial differential equation learning. The article underscores deep learning as a viable and potentially sustainable way to enhance traditional high-performance computing methods, advocating for informed model selection based on the topography of the mesh.
@inproceedings{decke2024structured, abstract = {This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded structured, and unstructured meshes, the study compares the performance and computational efficiency of three computer vision-based models against three graph-based models across three datasets. The research aims to identify the most suitable models for different mesh topographies, particularly highlighting the exploration of graded meshes, a less studied area. Results demonstrate that computer vision-based models, notably U-Net, outperform the graph models in prediction performance and efficiency in two (structured and graded) out of three mesh topographies. The study also reveals the unexpected effectiveness of computer vision-based models in handling unstructured meshes, suggesting a potential shift in methodological approaches for data-driven partial differential equation learning. The article underscores deep learning as a viable and potentially sustainable way to enhance traditional high-performance computing methods, advocating for informed model selection based on the topography of the mesh.}, author = {Decke, Jens and Wünsch, Olaf and Sick, Bernhard and Gruhl, Christian}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {itegpub}, pages = {82–96}, publisher = {Springer}, title = {From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs}, year = 2024 }
%0 Conference Paper %1 decke2024structured %A Decke, Jens %A Wünsch, Olaf %A Sick, Bernhard %A Gruhl, Christian %B International Conference on Architecture of Computing Systems (ARCS) %D 2024 %I Springer %P 82--96 %R 10.1007/978-3-031-66146-4_6 %T From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs %X This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded structured, and unstructured meshes, the study compares the performance and computational efficiency of three computer vision-based models against three graph-based models across three datasets. The research aims to identify the most suitable models for different mesh topographies, particularly highlighting the exploration of graded meshes, a less studied area. Results demonstrate that computer vision-based models, notably U-Net, outperform the graph models in prediction performance and efficiency in two (structured and graded) out of three mesh topographies. The study also reveals the unexpected effectiveness of computer vision-based models in handling unstructured meshes, suggesting a potential shift in methodological approaches for data-driven partial differential equation learning. The article underscores deep learning as a viable and potentially sustainable way to enhance traditional high-performance computing methods, advocating for informed model selection based on the topography of the mesh.
Self-Aware Control for Autonomous Underwater Vehicles.Decke, Jens; Gerland, Florian; Schomberg, Thomas; Wünsch, Olaf; Sick, Bernhard; Gruhl, Christian. In Workshop on self-improving system integration (SISSY), ACSOS, ble. 13–14. IEEE, 2024.
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We present a concept to research the implementation of a self-aware control system for underwater cleaning robots. Multiple robots cooperate as a swarm to clean the hull of large container ships while in transit. We utilise physics-informed neural networks to implement an autonomous steering system and combine it with a deep reinforcement learning-based decision and scheduling system. This approach ensures efficient navigation and cleaning operations, adapting to dynamic environmental conditions in real-time. The proposed control system allows the cleaner robots, to enhance fuel efficiency, reduce CO2 emissions, and minimize the environmental impact of maritime transportation.
@inproceedings{decke2024self, abstract = {We present a concept to research the implementation of a self-aware control system for underwater cleaning robots. Multiple robots cooperate as a swarm to clean the hull of large container ships while in transit. We utilise physics-informed neural networks to implement an autonomous steering system and combine it with a deep reinforcement learning-based decision and scheduling system. This approach ensures efficient navigation and cleaning operations, adapting to dynamic environmental conditions in real-time. The proposed control system allows the cleaner robots, to enhance fuel efficiency, reduce CO2 emissions, and minimize the environmental impact of maritime transportation.}, author = {Decke, Jens and Gerland, Florian and Schomberg, Thomas and Wünsch, Olaf and Sick, Bernhard and Gruhl, Christian}, booktitle = {Workshop on self-improving system integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {13–14}, publisher = {IEEE}, title = {Self-Aware Control for Autonomous Underwater Vehicles}, year = 2024 }
%0 Conference Paper %1 decke2024self %A Decke, Jens %A Gerland, Florian %A Schomberg, Thomas %A Wünsch, Olaf %A Sick, Bernhard %A Gruhl, Christian %B Workshop on self-improving system integration (SISSY), ACSOS %D 2024 %I IEEE %P 13--14 %R 10.1109/ACSOS-C63493.2024.00022 %T Self-Aware Control for Autonomous Underwater Vehicles %X We present a concept to research the implementation of a self-aware control system for underwater cleaning robots. Multiple robots cooperate as a swarm to clean the hull of large container ships while in transit. We utilise physics-informed neural networks to implement an autonomous steering system and combine it with a deep reinforcement learning-based decision and scheduling system. This approach ensures efficient navigation and cleaning operations, adapting to dynamic environmental conditions in real-time. The proposed control system allows the cleaner robots, to enhance fuel efficiency, reduce CO2 emissions, and minimize the environmental impact of maritime transportation.
An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing.Decke, Jens; Jenß, Arne; Sick, Bernhard; Gruhl, Christian. In International Conference on Architecture of Computing Systems (ARCS), ble. 51–66. Springer, 2024.
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This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.
@inproceedings{decke2024efficient, abstract = {This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.}, author = {Decke, Jens and Jenß, Arne and Sick, Bernhard and Gruhl, Christian}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {itegpub}, pages = {51–66}, publisher = {Springer}, title = {An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing}, year = 2024 }
%0 Conference Paper %1 decke2024efficient %A Decke, Jens %A Jenß, Arne %A Sick, Bernhard %A Gruhl, Christian %B International Conference on Architecture of Computing Systems (ARCS) %D 2024 %I Springer %P 51--66 %R 10.1007/978-3-031-66146-4_4 %T An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing %X This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.
Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension.Herde, Marek; Lührs, Lukas; Huseljic, Denis; Sick, Bernhard. In arXiv e-prints, bl. arXiv:2405.0338. 2024.
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Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by making memorizing false class labels more difficult. However, mixup neglects that, typically, multiple annotators, e.g., crowdworkers, provide class labels. Therefore, we propose an extension of mixup, which handles multiple class labels per instance while considering which class label originates from which annotator. Integrated into our multi-annotator classification framework annot-mix, it performs superiorly to eight state-of-the-art approaches on eleven datasets with noisy class labels provided either by human or simulated annotators. Our code is publicly available through our repository at this https://github.com/ies-research/annot-mix..
@article{herde2024annot, abstract = {Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by making memorizing false class labels more difficult. However, mixup neglects that, typically, multiple annotators, e.g., crowdworkers, provide class labels. Therefore, we propose an extension of mixup, which handles multiple class labels per instance while considering which class label originates from which annotator. Integrated into our multi-annotator classification framework annot-mix, it performs superiorly to eight state-of-the-art approaches on eleven datasets with noisy class labels provided either by human or simulated annotators. Our code is publicly available through our repository at this https://github.com/ies-research/annot-mix..}, author = {Herde, Marek and Lührs, Lukas and Huseljic, Denis and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2405.0338}, title = {Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension}, year = 2024 }
%0 Journal Article %1 herde2024annot %A Herde, Marek %A Lührs, Lukas %A Huseljic, Denis %A Sick, Bernhard %D 2024 %J arXiv e-prints %P arXiv:2405.0338 %T Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension %U https://arxiv.org/abs/2405.03386 %X Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by making memorizing false class labels more difficult. However, mixup neglects that, typically, multiple annotators, e.g., crowdworkers, provide class labels. Therefore, we propose an extension of mixup, which handles multiple class labels per instance while considering which class label originates from which annotator. Integrated into our multi-annotator classification framework annot-mix, it performs superiorly to eight state-of-the-art approaches on eleven datasets with noisy class labels provided either by human or simulated annotators. Our code is publicly available through our repository at this https://github.com/ies-research/annot-mix..
Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension.Herde, Marek; Lührs, Lukas; Huseljic, Denis; Sick, Bernhard. In European Conference on Artificial Intelligence (ECAI), ble. 2910–2918. IOS Press, 2024.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
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Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by making memorizing false class labels more difficult. However, mixup neglects that multiple annotators, e.g., crowdworkers, typically provide class labels. Therefore, we propose an extension of mixup, which handles multiple class labels per instance while considering which class label originates from which annotator. Integrated into our multi-annotator classification framework annot-mix, it performs superiorly to eleven (mostly state-of-the-art) approaches in an evaluation study with eleven datasets comprising noisy class labels from either human or simulated annotators. Our code is publicly available through our GitHub repository at https://github.com/ies-research/multi-annotator-machine-learning/tree/annot-mix.
@inproceedings{herde2024annota, abstract = {Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by making memorizing false class labels more difficult. However, mixup neglects that multiple annotators, e.g., crowdworkers, typically provide class labels. Therefore, we propose an extension of mixup, which handles multiple class labels per instance while considering which class label originates from which annotator. Integrated into our multi-annotator classification framework annot-mix, it performs superiorly to eleven (mostly state-of-the-art) approaches in an evaluation study with eleven datasets comprising noisy class labels from either human or simulated annotators. Our code is publicly available through our GitHub repository at https://github.com/ies-research/multi-annotator-machine-learning/tree/annot-mix.}, author = {Herde, Marek and Lührs, Lukas and Huseljic, Denis and Sick, Bernhard}, booktitle = {European Conference on Artificial Intelligence (ECAI)}, keywords = {itegpub}, pages = {2910–2918}, publisher = {IOS Press}, title = {Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension}, year = 2024 }
%0 Conference Paper %1 herde2024annota %A Herde, Marek %A Lührs, Lukas %A Huseljic, Denis %A Sick, Bernhard %B European Conference on Artificial Intelligence (ECAI) %D 2024 %I IOS Press %P 2910--2918 %R 10.3233/FAIA240829 %T Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension %U https://ebooks.iospress.nl/doi/10.3233/FAIA240829 %X Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by making memorizing false class labels more difficult. However, mixup neglects that multiple annotators, e.g., crowdworkers, typically provide class labels. Therefore, we propose an extension of mixup, which handles multiple class labels per instance while considering which class label originates from which annotator. Integrated into our multi-annotator classification framework annot-mix, it performs superiorly to eleven (mostly state-of-the-art) approaches in an evaluation study with eleven datasets comprising noisy class labels from either human or simulated annotators. Our code is publicly available through our GitHub repository at https://github.com/ies-research/multi-annotator-machine-learning/tree/annot-mix.
The Interplay of Uncertainty Modeling and Deep Active Learning: An Empirical Analysis in Image Classification.Huseljic, Denis; Herde, Marek; Nagel, Yannick; Rauch, Lukas; Strimaitis, Paulius; Sick, Bernhard. In Transactions on Machine Learning Research. 2024.
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Deep active learning (AL) seeks to reduce the annotation costs required for training deep neural networks (DNNs). Often, deep AL strategies focus on instances where the predictive uncertainty of a DNN is high. Furthermore, Bayesian concepts to model uncertainty are frequently adopted. Despite considerable research, a detailed analysis of the role of uncertainty in deep AL is still missing, especially regarding aleatoric and epistemic uncertainty, both related to predictive uncertainty. This article provides an in-depth empirical study analyzing the interplay of uncertainty and deep AL in image classification. Our study investigates four hypotheses that provide an intuitive understanding of the effects of accurately estimating aleatoric and epistemic uncertainty on existing uncertainty-based AL strategies but also, in the opposite direction, the impact of uncertainty-based AL on the quality of uncertainty estimates that are needed in many applications. By analyzing these hypotheses on synthetic and real-world data, we find that accurate aleatoric estimates can even impair instance selection, while accurate epistemic estimates have negligible effects. Moreover, we provide a publicly available toolbox for deep AL with various models and strategies to facilitate further research and practical applications. Code is available at https://github.com/dhuseljic/dal-toolbox.
@article{huseljic2024interplay, abstract = {Deep active learning (AL) seeks to reduce the annotation costs required for training deep neural networks (DNNs). Often, deep AL strategies focus on instances where the predictive uncertainty of a DNN is high. Furthermore, Bayesian concepts to model uncertainty are frequently adopted. Despite considerable research, a detailed analysis of the role of uncertainty in deep AL is still missing, especially regarding aleatoric and epistemic uncertainty, both related to predictive uncertainty. This article provides an in-depth empirical study analyzing the interplay of uncertainty and deep AL in image classification. Our study investigates four hypotheses that provide an intuitive understanding of the effects of accurately estimating aleatoric and epistemic uncertainty on existing uncertainty-based AL strategies but also, in the opposite direction, the impact of uncertainty-based AL on the quality of uncertainty estimates that are needed in many applications. By analyzing these hypotheses on synthetic and real-world data, we find that accurate aleatoric estimates can even impair instance selection, while accurate epistemic estimates have negligible effects. Moreover, we provide a publicly available toolbox for deep AL with various models and strategies to facilitate further research and practical applications. Code is available at https://github.com/dhuseljic/dal-toolbox.}, author = {Huseljic, Denis and Herde, Marek and Nagel, Yannick and Rauch, Lukas and Strimaitis, Paulius and Sick, Bernhard}, journal = {Transactions on Machine Learning Research}, keywords = {itegpub}, title = {The Interplay of Uncertainty Modeling and Deep Active Learning: An Empirical Analysis in Image Classification}, year = 2024 }
%0 Journal Article %1 huseljic2024interplay %A Huseljic, Denis %A Herde, Marek %A Nagel, Yannick %A Rauch, Lukas %A Strimaitis, Paulius %A Sick, Bernhard %D 2024 %J Transactions on Machine Learning Research %T The Interplay of Uncertainty Modeling and Deep Active Learning: An Empirical Analysis in Image Classification %U https://openreview.net/forum?id=KLBD13bsVl %X Deep active learning (AL) seeks to reduce the annotation costs required for training deep neural networks (DNNs). Often, deep AL strategies focus on instances where the predictive uncertainty of a DNN is high. Furthermore, Bayesian concepts to model uncertainty are frequently adopted. Despite considerable research, a detailed analysis of the role of uncertainty in deep AL is still missing, especially regarding aleatoric and epistemic uncertainty, both related to predictive uncertainty. This article provides an in-depth empirical study analyzing the interplay of uncertainty and deep AL in image classification. Our study investigates four hypotheses that provide an intuitive understanding of the effects of accurately estimating aleatoric and epistemic uncertainty on existing uncertainty-based AL strategies but also, in the opposite direction, the impact of uncertainty-based AL on the quality of uncertainty estimates that are needed in many applications. By analyzing these hypotheses on synthetic and real-world data, we find that accurate aleatoric estimates can even impair instance selection, while accurate epistemic estimates have negligible effects. Moreover, we provide a publicly available toolbox for deep AL with various models and strategies to facilitate further research and practical applications. Code is available at https://github.com/dhuseljic/dal-toolbox.
Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications.Hetzel, Manuel; Reichert, Hannes; Doll, Konrad; Sick, Bernhard. In Workshop on Uncertainty Quantification for Computer Vision (UNCV), ECCV, ble. 135–152. Springer, 2024.
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Autonomous systems, like vehicles or robots, require reliable, accurate, fast, resource-efficient, scalable, and low-latency trajectory predictions to get initial knowledge about future locations and movements of surrounding objects for safe human-machine interaction. Furthermore, they need to know the uncertainty of the predictions for risk assessment to provide safe path planning. This paper presents a lightweight method to address these requirements, combining Long Short-Term Memory and Mixture Density Networks. Our method predicts probability distributions, including confidence level estimations for positional uncertainty to support subsequent risk management applications and runs on a lowpower embedded platform. We discuss essential requirements for human trajectory prediction in autonomous vehicle applications and demonstrate our method’s performance using multiple traffic-related datasets. Furthermore, we explain reliability and sharpness metrics and show how important they are to guarantee the correctness and robustness of a model’s predictions and uncertainty assessments. These essential evaluations have so far received little attention for no good reason. Our approach focuses entirely on real-world applicability. Verifying prediction uncertainties and a model’s reliability are central to autonomous realworld applications. Our framework and code are available at: https://github.com/kav-institute/mdn_trajectory_forecasting.
@inproceedings{hetzel2024reliable, abstract = {Autonomous systems, like vehicles or robots, require reliable, accurate, fast, resource-efficient, scalable, and low-latency trajectory predictions to get initial knowledge about future locations and movements of surrounding objects for safe human-machine interaction. Furthermore, they need to know the uncertainty of the predictions for risk assessment to provide safe path planning. This paper presents a lightweight method to address these requirements, combining Long Short-Term Memory and Mixture Density Networks. Our method predicts probability distributions, including confidence level estimations for positional uncertainty to support subsequent risk management applications and runs on a lowpower embedded platform. We discuss essential requirements for human trajectory prediction in autonomous vehicle applications and demonstrate our method’s performance using multiple traffic-related datasets. Furthermore, we explain reliability and sharpness metrics and show how important they are to guarantee the correctness and robustness of a model’s predictions and uncertainty assessments. These essential evaluations have so far received little attention for no good reason. Our approach focuses entirely on real-world applicability. Verifying prediction uncertainties and a model’s reliability are central to autonomous realworld applications. Our framework and code are available at: https://github.com/kav-institute/mdn_trajectory_forecasting.}, author = {Hetzel, Manuel and Reichert, Hannes and Doll, Konrad and Sick, Bernhard}, booktitle = {Workshop on Uncertainty Quantification for Computer Vision (UNCV), ECCV}, keywords = {itegpub}, pages = {135–152}, publisher = {Springer}, title = {Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications}, year = 2024 }
%0 Conference Paper %1 hetzel2024reliable %A Hetzel, Manuel %A Reichert, Hannes %A Doll, Konrad %A Sick, Bernhard %B Workshop on Uncertainty Quantification for Computer Vision (UNCV), ECCV %D 2024 %I Springer %P 135--152 %R 10.1007/978-3-031-91585-7_9 %T Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications %X Autonomous systems, like vehicles or robots, require reliable, accurate, fast, resource-efficient, scalable, and low-latency trajectory predictions to get initial knowledge about future locations and movements of surrounding objects for safe human-machine interaction. Furthermore, they need to know the uncertainty of the predictions for risk assessment to provide safe path planning. This paper presents a lightweight method to address these requirements, combining Long Short-Term Memory and Mixture Density Networks. Our method predicts probability distributions, including confidence level estimations for positional uncertainty to support subsequent risk management applications and runs on a lowpower embedded platform. We discuss essential requirements for human trajectory prediction in autonomous vehicle applications and demonstrate our method’s performance using multiple traffic-related datasets. Furthermore, we explain reliability and sharpness metrics and show how important they are to guarantee the correctness and robustness of a model’s predictions and uncertainty assessments. These essential evaluations have so far received little attention for no good reason. Our approach focuses entirely on real-world applicability. Verifying prediction uncertainties and a model’s reliability are central to autonomous realworld applications. Our framework and code are available at: https://github.com/kav-institute/mdn_trajectory_forecasting.
Capturing Nonlinear Electron Dynamics with Fully Characterised Attosecond X-ray Pulses.Funke, Lars; Ilchen, Markus; Dingel, Kristina; Mazza, Tommaso; Mullins, Terence; Otto, Thorsten; Rivas, Daniel; Savio, Sara; Serkez, Svitozar; Walter, Peter; Wieland, Niclas; Wülfing, Lasse; Bari, Sadia; Boll, Rebecca; Braune, Markus; Calegari, Francesca; De Fanis, Alberto; Decking, Winfried; Duensing, Andreas; Düsterer, Stefan; Ehresmann, Arno; Erk, Benjamin; Ferreira de Lima, Danilo Enoque; Galler, Andreas; Geloni, Gianluca; Grünert, Jan; Guetg, Marc; Grychtol, Patrik; Hans, Andreas; Held, Arne; Hindriksson, Ruda; Inhester, Ludger; Jahnke, Till; Laksman, Joakim; Larsson, Mats; Liu, Jia; Marangos, Jon P.; Marder, Lutz; Meier, David; Meyer, Michael; Mirian, Najmeh; Ott, Christian; Passow, Christopher; Pfeifer, Thomas; Rupprecht, Patrick; Schletter, Albert; Schmidt, Philipp; Scholz, Frank; Schott, Simon; Schneidmiller, Evgeny; Sick, Bernhard; Son, Sang-Kil; Tiedtke, Kai; Usenko, Sergey; Wanie, Vincent; Wurzer, Markus; Yurkov, Mikhail; Zhaunerchyk, Vitali; Helml, Wolfram. In arXiv e-prints, bl. arXiv:2408.03858. 2024.
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Attosecond X-ray pulses are the key to studying electron dynamics at their natural time scale involving specific electronic states. They are promising to build the conceptual bridge between physical and chemical photo-reaction processes. Free-electron lasers have demonstrated their capability of generating intense attosecond X-ray pulses. However, harnessing them for time-resolving experiments and investigations of nonlinear X-ray absorption mechanisms remains a cutting-edge challenge. We have characterised X-ray pulses with durations of down to 700attoseconds and peak powers up to 200GW at ∼ 1keV photon energy via angular streaking at the SQS instrument of the European XFEL. As direct application, we present results of nonlinear X-ray-matter interaction via state-specific spectroscopy on a transient system. Using the derived spectral and temporal information of each pulse, we deliberately steer the probability for formation of double-core vacancies in neon gas atoms through excitation or ionisation of the second inner-shell electron after K-shell ionisation. Our results advance the field of attosecond science with highly intense and fully characterised X-ray pulses to the site-specific investigation of electronic motion in transient media.
@article{funke2024capturing, abstract = {Attosecond X-ray pulses are the key to studying electron dynamics at their natural time scale involving specific electronic states. They are promising to build the conceptual bridge between physical and chemical photo-reaction processes. Free-electron lasers have demonstrated their capability of generating intense attosecond X-ray pulses. However, harnessing them for time-resolving experiments and investigations of nonlinear X-ray absorption mechanisms remains a cutting-edge challenge. We have characterised X-ray pulses with durations of down to 700attoseconds and peak powers up to 200GW at ∼ 1keV photon energy via angular streaking at the SQS instrument of the European XFEL. As direct application, we present results of nonlinear X-ray-matter interaction via state-specific spectroscopy on a transient system. Using the derived spectral and temporal information of each pulse, we deliberately steer the probability for formation of double-core vacancies in neon gas atoms through excitation or ionisation of the second inner-shell electron after K-shell ionisation. Our results advance the field of attosecond science with highly intense and fully characterised X-ray pulses to the site-specific investigation of electronic motion in transient media.}, author = {Funke, Lars and Ilchen, Markus and Dingel, Kristina and Mazza, Tommaso and Mullins, Terence and Otto, Thorsten and Rivas, Daniel and Savio, Sara and Serkez, Svitozar and Walter, Peter and Wieland, Niclas and Wülfing, Lasse and Bari, Sadia and Boll, Rebecca and Braune, Markus and Calegari, Francesca and De Fanis, Alberto and Decking, Winfried and Duensing, Andreas and Düsterer, Stefan and Ehresmann, Arno and Erk, Benjamin and Ferreira de Lima, Danilo Enoque and Galler, Andreas and Geloni, Gianluca and Grünert, Jan and Guetg, Marc and Grychtol, Patrik and Hans, Andreas and Held, Arne and Hindriksson, Ruda and Inhester, Ludger and Jahnke, Till and Laksman, Joakim and Larsson, Mats and Liu, Jia and Marangos, Jon P. and Marder, Lutz and Meier, David and Meyer, Michael and Mirian, Najmeh and Ott, Christian and Passow, Christopher and Pfeifer, Thomas and Rupprecht, Patrick and Schletter, Albert and Schmidt, Philipp and Scholz, Frank and Schott, Simon and Schneidmiller, Evgeny and Sick, Bernhard and Son, Sang-Kil and Tiedtke, Kai and Usenko, Sergey and Wanie, Vincent and Wurzer, Markus and Yurkov, Mikhail and Zhaunerchyk, Vitali and Helml, Wolfram}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2408.03858}, title = {Capturing Nonlinear Electron Dynamics with Fully Characterised Attosecond X-ray Pulses}, year = 2024 }
%0 Journal Article %1 funke2024capturing %A Funke, Lars %A Ilchen, Markus %A Dingel, Kristina %A Mazza, Tommaso %A Mullins, Terence %A Otto, Thorsten %A Rivas, Daniel %A Savio, Sara %A Serkez, Svitozar %A Walter, Peter %A Wieland, Niclas %A Wülfing, Lasse %A Bari, Sadia %A Boll, Rebecca %A Braune, Markus %A Calegari, Francesca %A De Fanis, Alberto %A Decking, Winfried %A Duensing, Andreas %A Düsterer, Stefan %A Ehresmann, Arno %A Erk, Benjamin %A Ferreira de Lima, Danilo Enoque %A Galler, Andreas %A Geloni, Gianluca %A Grünert, Jan %A Guetg, Marc %A Grychtol, Patrik %A Hans, Andreas %A Held, Arne %A Hindriksson, Ruda %A Inhester, Ludger %A Jahnke, Till %A Laksman, Joakim %A Larsson, Mats %A Liu, Jia %A Marangos, Jon P. %A Marder, Lutz %A Meier, David %A Meyer, Michael %A Mirian, Najmeh %A Ott, Christian %A Passow, Christopher %A Pfeifer, Thomas %A Rupprecht, Patrick %A Schletter, Albert %A Schmidt, Philipp %A Scholz, Frank %A Schott, Simon %A Schneidmiller, Evgeny %A Sick, Bernhard %A Son, Sang-Kil %A Tiedtke, Kai %A Usenko, Sergey %A Wanie, Vincent %A Wurzer, Markus %A Yurkov, Mikhail %A Zhaunerchyk, Vitali %A Helml, Wolfram %D 2024 %J arXiv e-prints %P arXiv:2408.03858 %T Capturing Nonlinear Electron Dynamics with Fully Characterised Attosecond X-ray Pulses %U https://arxiv.org/abs/2408.03858 %X Attosecond X-ray pulses are the key to studying electron dynamics at their natural time scale involving specific electronic states. They are promising to build the conceptual bridge between physical and chemical photo-reaction processes. Free-electron lasers have demonstrated their capability of generating intense attosecond X-ray pulses. However, harnessing them for time-resolving experiments and investigations of nonlinear X-ray absorption mechanisms remains a cutting-edge challenge. We have characterised X-ray pulses with durations of down to 700attoseconds and peak powers up to 200GW at ∼ 1keV photon energy via angular streaking at the SQS instrument of the European XFEL. As direct application, we present results of nonlinear X-ray-matter interaction via state-specific spectroscopy on a transient system. Using the derived spectral and temporal information of each pulse, we deliberately steer the probability for formation of double-core vacancies in neon gas atoms through excitation or ionisation of the second inner-shell electron after K-shell ionisation. Our results advance the field of attosecond science with highly intense and fully characterised X-ray pulses to the site-specific investigation of electronic motion in transient media.
Casimir Effect in MEMS: Materials, Geometries, and Metrologies—A Review.Elsaka, Basma; Yang, Xiaohui; Kästner, Philipp; Dingel, Kristina; Sick, Bernhard; Lehmann, Peter; Buhmann, Stefan Yoshi; Hillmer, Hartmut. In Materials, 17(14), bl. 3393. 2024.
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Casimir force densities, i.e., force per area, become very large if two solid material surfaces come closer together to each other than 10 nm. In most cases, the forces are attractive. In some cases, they can be repulsive depending on the solid materials and the fluid medium in between. This review provides an overview of experimental and theoretical studies that have been performed and focuses on four main aspects: (i) the combinations of different materials, (ii) the considered geometries, (iii) the applied experimental measurement methodologies and (iv) a novel self-assembly methodology based on Casimir forces. Briefly reviewed is also the influence of additional parameters such as temperature, conductivity, and surface roughness. The Casimir effect opens many application possibilities in microelectromechanical systems (MEMS) and nanoelectromechanical systems (NEMS), where an overview is also provided. The knowledge generation in this fascinating field requires interdisciplinary approaches to generate synergetic effects between technological fabrication metrology, theoretical simulations, the establishment of adequate models, artificial intelligence, and machine learning. Finally, multiple applications are addressed as a research roadmap.
@article{elsaka2024casimir, abstract = {Casimir force densities, i.e., force per area, become very large if two solid material surfaces come closer together to each other than 10 nm. In most cases, the forces are attractive. In some cases, they can be repulsive depending on the solid materials and the fluid medium in between. This review provides an overview of experimental and theoretical studies that have been performed and focuses on four main aspects: (i) the combinations of different materials, (ii) the considered geometries, (iii) the applied experimental measurement methodologies and (iv) a novel self-assembly methodology based on Casimir forces. Briefly reviewed is also the influence of additional parameters such as temperature, conductivity, and surface roughness. The Casimir effect opens many application possibilities in microelectromechanical systems (MEMS) and nanoelectromechanical systems (NEMS), where an overview is also provided. The knowledge generation in this fascinating field requires interdisciplinary approaches to generate synergetic effects between technological fabrication metrology, theoretical simulations, the establishment of adequate models, artificial intelligence, and machine learning. Finally, multiple applications are addressed as a research roadmap.}, author = {Elsaka, Basma and Yang, Xiaohui and Kästner, Philipp and Dingel, Kristina and Sick, Bernhard and Lehmann, Peter and Buhmann, Stefan Yoshi and Hillmer, Hartmut}, journal = {Materials}, keywords = {itegpub}, number = 14, pages = 3393, title = {Casimir Effect in MEMS: Materials, Geometries, and Metrologies—A Review}, volume = 17, year = 2024 }
%0 Journal Article %1 elsaka2024casimir %A Elsaka, Basma %A Yang, Xiaohui %A Kästner, Philipp %A Dingel, Kristina %A Sick, Bernhard %A Lehmann, Peter %A Buhmann, Stefan Yoshi %A Hillmer, Hartmut %D 2024 %J Materials %N 14 %P 3393 %R 10.3390/ma17143393 %T Casimir Effect in MEMS: Materials, Geometries, and Metrologies—A Review %U https://www.mdpi.com/1996-1944/17/14/3393 %V 17 %X Casimir force densities, i.e., force per area, become very large if two solid material surfaces come closer together to each other than 10 nm. In most cases, the forces are attractive. In some cases, they can be repulsive depending on the solid materials and the fluid medium in between. This review provides an overview of experimental and theoretical studies that have been performed and focuses on four main aspects: (i) the combinations of different materials, (ii) the considered geometries, (iii) the applied experimental measurement methodologies and (iv) a novel self-assembly methodology based on Casimir forces. Briefly reviewed is also the influence of additional parameters such as temperature, conductivity, and surface roughness. The Casimir effect opens many application possibilities in microelectromechanical systems (MEMS) and nanoelectromechanical systems (NEMS), where an overview is also provided. The knowledge generation in this fascinating field requires interdisciplinary approaches to generate synergetic effects between technological fabrication metrology, theoretical simulations, the establishment of adequate models, artificial intelligence, and machine learning. Finally, multiple applications are addressed as a research roadmap.
Multi-Task Representation Learning with Temporal Attention for Zero-Shot Time Series Anomaly Detection.Nivarthi, Chandana Priya; Huang, Zhixin; Gruhl, Christian; Sick, Bernhard. In International Joint Conference on Neural Networks (IJCNN), ble. 1–10. IEEE, 2024.
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Ensuring the reliability of critical industrial systems across various sectors is crucial. It is essential to detect deviations from regular behaviour to mitigate disruptions and preserve infrastructure integrity. However, accurately labelling anomaly datasets is challenging due to their rarity and manual annotation subjectivity. The conventional approach of training separate models for each dataset entity further complicates model development. This paper presents a novel Multi-task Learning framework combining LSTM Autoencoder with temporal attention mechanism (MTL-LATAM) for effective time series anomaly detection. Multitask learning models improve adaptability and generalizability, leading to reduced runtime and compute power while supporting zero-shot evaluation. These models offer flexibility in detecting emerging anomalies. Additionally, we introduce a dynamic thresholding mechanism to incorporate temporal context for anomaly detection and provide visualizations of attention weights to enhance interpretability. The study compares MTL- LATAM, with other multi-task models, evaluates multi-task versus single-task models and assesses the performance of the proposed frame- work in zero-shot learning scenarios. The findings indicate MTL- LATAM’s effectiveness across real-world and open-source datasets, achieving 95% and 97% task synergy. The results underscore the superior performance of multi-task models in zero-shot tasks compared to individual models trained exclusively on their respective datasets.
@inproceedings{nivarthi2024multi, abstract = {Ensuring the reliability of critical industrial systems across various sectors is crucial. It is essential to detect deviations from regular behaviour to mitigate disruptions and preserve infrastructure integrity. However, accurately labelling anomaly datasets is challenging due to their rarity and manual annotation subjectivity. The conventional approach of training separate models for each dataset entity further complicates model development. This paper presents a novel Multi-task Learning framework combining LSTM Autoencoder with temporal attention mechanism (MTL-LATAM) for effective time series anomaly detection. Multitask learning models improve adaptability and generalizability, leading to reduced runtime and compute power while supporting zero-shot evaluation. These models offer flexibility in detecting emerging anomalies. Additionally, we introduce a dynamic thresholding mechanism to incorporate temporal context for anomaly detection and provide visualizations of attention weights to enhance interpretability. The study compares MTL- LATAM, with other multi-task models, evaluates multi-task versus single-task models and assesses the performance of the proposed frame- work in zero-shot learning scenarios. The findings indicate MTL- LATAM’s effectiveness across real-world and open-source datasets, achieving 95% and 97% task synergy. The results underscore the superior performance of multi-task models in zero-shot tasks compared to individual models trained exclusively on their respective datasets.}, author = {Nivarthi, Chandana Priya and Huang, Zhixin and Gruhl, Christian and Sick, Bernhard}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {itegpub}, pages = {1–10}, publisher = {IEEE}, title = {Multi-Task Representation Learning with Temporal Attention for Zero-Shot Time Series Anomaly Detection}, year = 2024 }
%0 Conference Paper %1 nivarthi2024multi %A Nivarthi, Chandana Priya %A Huang, Zhixin %A Gruhl, Christian %A Sick, Bernhard %B International Joint Conference on Neural Networks (IJCNN) %D 2024 %I IEEE %P 1--10 %R 10.1109/IJCNN60899.2024.10651344 %T Multi-Task Representation Learning with Temporal Attention for Zero-Shot Time Series Anomaly Detection %X Ensuring the reliability of critical industrial systems across various sectors is crucial. It is essential to detect deviations from regular behaviour to mitigate disruptions and preserve infrastructure integrity. However, accurately labelling anomaly datasets is challenging due to their rarity and manual annotation subjectivity. The conventional approach of training separate models for each dataset entity further complicates model development. This paper presents a novel Multi-task Learning framework combining LSTM Autoencoder with temporal attention mechanism (MTL-LATAM) for effective time series anomaly detection. Multitask learning models improve adaptability and generalizability, leading to reduced runtime and compute power while supporting zero-shot evaluation. These models offer flexibility in detecting emerging anomalies. Additionally, we introduce a dynamic thresholding mechanism to incorporate temporal context for anomaly detection and provide visualizations of attention weights to enhance interpretability. The study compares MTL- LATAM, with other multi-task models, evaluates multi-task versus single-task models and assesses the performance of the proposed frame- work in zero-shot learning scenarios. The findings indicate MTL- LATAM’s effectiveness across real-world and open-source datasets, achieving 95% and 97% task synergy. The results underscore the superior performance of multi-task models in zero-shot tasks compared to individual models trained exclusively on their respective datasets.
PrOuD: Probabilistic Outlier Detection Solution for Time Series Analysis on Real-world Photovoltaic Inverters.He, Yujiang; Huang, Zhixin; Vogt, Stephan; Sick, Bernhard. In Energies (MDPI), 17(1), bl. 64. MDPI, 2024.
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Anomaly detection methods applied to time series are mostly viewed as a black box, which solely provides a deterministic answer to the detected target. Without a convincing explanation, domain experts can hardly trust the detection results and conduct a further time series diagnosis in real-world applications. To overcome the challenge, we mathematically analyzed the sources of anomalies and novelties in multivariate time series as well as their relationships from the perspective of Gaussian-distributed non-stationary noise. Furthermore, we proposed mathematical methods to generate artificial time series and synthetic anomalies, with the goal of solving the problem that it is difficult to train and evaluate models in real-world applications due to the lack of sufficient data. In addition, we designed extbfProbabilistic extbfOutlier extbfDetection (PrOuD), which is a general solution to provide interpretable detection results to assist domain experts in time series analysis. PrOuD can convert a predictive uncertainty of a trained model about a time series value into an estimated uncertainty of the detected outlier through Monte Carlo Estimation. The experimental results obtained on both artificial time series and real-world photovoltaic inverter data demonstrated that the proposed solution could detect emerging anomalies accurately and quickly. The implemented PrOuD demo case shows the potential to makes the detection results of the existing detection methods more convincing so that domain experts can more efficiently complete their tasks, such as time series diagnosis and anomalous pattern clustering.
@article{he2023proud, abstract = {Anomaly detection methods applied to time series are mostly viewed as a black box, which solely provides a deterministic answer to the detected target. Without a convincing explanation, domain experts can hardly trust the detection results and conduct a further time series diagnosis in real-world applications. To overcome the challenge, we mathematically analyzed the sources of anomalies and novelties in multivariate time series as well as their relationships from the perspective of Gaussian-distributed non-stationary noise. Furthermore, we proposed mathematical methods to generate artificial time series and synthetic anomalies, with the goal of solving the problem that it is difficult to train and evaluate models in real-world applications due to the lack of sufficient data. In addition, we designed extbf{Pr}obabilistic extbf{Ou}tlier extbf{D}etection (PrOuD), which is a general solution to provide interpretable detection results to assist domain experts in time series analysis. PrOuD can convert a predictive uncertainty of a trained model about a time series value into an estimated uncertainty of the detected outlier through Monte Carlo Estimation. The experimental results obtained on both artificial time series and real-world photovoltaic inverter data demonstrated that the proposed solution could detect emerging anomalies accurately and quickly. The implemented PrOuD demo case shows the potential to makes the detection results of the existing detection methods more convincing so that domain experts can more efficiently complete their tasks, such as time series diagnosis and anomalous pattern clustering.}, author = {He, Yujiang and Huang, Zhixin and Vogt, Stephan and Sick, Bernhard}, journal = {Energies (MDPI)}, keywords = {itegpub}, number = 1, pages = 64, publisher = {MDPI}, title = {PrOuD: Probabilistic Outlier Detection Solution for Time Series Analysis on Real-world Photovoltaic Inverters}, volume = 17, year = 2024 }
%0 Journal Article %1 he2023proud %A He, Yujiang %A Huang, Zhixin %A Vogt, Stephan %A Sick, Bernhard %D 2024 %I MDPI %J Energies (MDPI) %N 1 %P 64 %R 10.3390/en17010064 %T PrOuD: Probabilistic Outlier Detection Solution for Time Series Analysis on Real-world Photovoltaic Inverters %U https://www.mdpi.com/1996-1073/17/1/64 %V 17 %X Anomaly detection methods applied to time series are mostly viewed as a black box, which solely provides a deterministic answer to the detected target. Without a convincing explanation, domain experts can hardly trust the detection results and conduct a further time series diagnosis in real-world applications. To overcome the challenge, we mathematically analyzed the sources of anomalies and novelties in multivariate time series as well as their relationships from the perspective of Gaussian-distributed non-stationary noise. Furthermore, we proposed mathematical methods to generate artificial time series and synthetic anomalies, with the goal of solving the problem that it is difficult to train and evaluate models in real-world applications due to the lack of sufficient data. In addition, we designed extbfProbabilistic extbfOutlier extbfDetection (PrOuD), which is a general solution to provide interpretable detection results to assist domain experts in time series analysis. PrOuD can convert a predictive uncertainty of a trained model about a time series value into an estimated uncertainty of the detected outlier through Monte Carlo Estimation. The experimental results obtained on both artificial time series and real-world photovoltaic inverter data demonstrated that the proposed solution could detect emerging anomalies accurately and quickly. The implemented PrOuD demo case shows the potential to makes the detection results of the existing detection methods more convincing so that domain experts can more efficiently complete their tasks, such as time series diagnosis and anomalous pattern clustering.
General Reusability: Ensuring Long-Term Benefits of Deep Active Learning.Hahn, Paul; Huseljic, Denis; Herde, Marek; Sick, Bernhard. In Workshop on Interactive Adapative Learning (IAL), ECML PKDD, ble. 33–46. 2024.
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Deep active learning (DAL) provides a promising framework for reducing labeling costs for data-hungry deep neural networks. In DAL research, experiments are evaluated based on the performance of the model that is responsible for querying data, which can be interpreted as a model-centric evaluation. However, the data queried by DAL should be model agnostic so that new models can be effectively trained on it. An example can be derived from the rapid progress in deep learning, where new architectures are proposed in quick succession. Naturally, we want to employ these new and more effective models while still using the queried data from DAL. This results in the need to ensure high performance for both current and future models, which we refer to as reusability. In line with this topic, we propose general reusability, a novel, data-centric evaluation metric that measures the long-term value of queried data. Our experiments demonstrate that the current evaluation protocol in DAL research does not assess the reusability of data and that GR can fill this gap.
@inproceedings{hahn2024general, abstract = {Deep active learning (DAL) provides a promising framework for reducing labeling costs for data-hungry deep neural networks. In DAL research, experiments are evaluated based on the performance of the model that is responsible for querying data, which can be interpreted as a model-centric evaluation. However, the data queried by DAL should be model agnostic so that new models can be effectively trained on it. An example can be derived from the rapid progress in deep learning, where new architectures are proposed in quick succession. Naturally, we want to employ these new and more effective models while still using the queried data from DAL. This results in the need to ensure high performance for both current and future models, which we refer to as reusability. In line with this topic, we propose general reusability, a novel, data-centric evaluation metric that measures the long-term value of queried data. Our experiments demonstrate that the current evaluation protocol in DAL research does not assess the reusability of data and that GR can fill this gap.}, author = {Hahn, Paul and Huseljic, Denis and Herde, Marek and Sick, Bernhard}, booktitle = {Workshop on Interactive Adapative Learning (IAL), ECML PKDD}, keywords = {itegpub}, pages = {33–46}, title = {General Reusability: Ensuring Long-Term Benefits of Deep Active Learning}, year = 2024 }
%0 Conference Paper %1 hahn2024general %A Hahn, Paul %A Huseljic, Denis %A Herde, Marek %A Sick, Bernhard %B Workshop on Interactive Adapative Learning (IAL), ECML PKDD %D 2024 %P 33--46 %T General Reusability: Ensuring Long-Term Benefits of Deep Active Learning %U https://ceur-ws.org/Vol-3770/paper5.pdf %X Deep active learning (DAL) provides a promising framework for reducing labeling costs for data-hungry deep neural networks. In DAL research, experiments are evaluated based on the performance of the model that is responsible for querying data, which can be interpreted as a model-centric evaluation. However, the data queried by DAL should be model agnostic so that new models can be effectively trained on it. An example can be derived from the rapid progress in deep learning, where new architectures are proposed in quick succession. Naturally, we want to employ these new and more effective models while still using the queried data from DAL. This results in the need to ensure high performance for both current and future models, which we refer to as reusability. In line with this topic, we propose general reusability, a novel, data-centric evaluation metric that measures the long-term value of queried data. Our experiments demonstrate that the current evaluation protocol in DAL research does not assess the reusability of data and that GR can fill this gap.
Predictions and Decision Making for Resilient Intelligent Sustainable Energy Systems.Braun, Martin; Gruhl, Christian; Hans, Christian Andreas; Härtel, Philipp; Scholz, Christoph; Sick, Bernhard; Siefert, Malte; Steinke, Florian; Stursberg, Olaf; Wende-von Berg, Sebastia. In Innovative Smart Grid Technologies Europe (ISGT EUROPE), ble. 1–5. IEEE, 2024.
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Future energy systems are subject to various uncertain influences. As resilient systems they should maintain a constantly high operational performance whatever happens. We explore different levels and timescales of decision making in energy systems, highlighting different uncertainty sources in different domains and discuss how uncertainties can be represented and how to react to them. Moreover, we summarize, which uncertainties are already well examined and which ones still need further inquiry to obtain resilient energy systems.
@inproceedings{braun2024predictions, abstract = {Future energy systems are subject to various uncertain influences. As resilient systems they should maintain a constantly high operational performance whatever happens. We explore different levels and timescales of decision making in energy systems, highlighting different uncertainty sources in different domains and discuss how uncertainties can be represented and how to react to them. Moreover, we summarize, which uncertainties are already well examined and which ones still need further inquiry to obtain resilient energy systems.}, author = {Braun, Martin and Gruhl, Christian and Hans, Christian Andreas and Härtel, Philipp and Scholz, Christoph and Sick, Bernhard and Siefert, Malte and Steinke, Florian and Stursberg, Olaf and Wende-von Berg, Sebastia}, booktitle = {Innovative Smart Grid Technologies Europe (ISGT EUROPE)}, keywords = {itegpub}, pages = {1–5}, publisher = {IEEE}, title = {Predictions and Decision Making for Resilient Intelligent Sustainable Energy Systems}, year = 2024 }
%0 Conference Paper %1 braun2024predictions %A Braun, Martin %A Gruhl, Christian %A Hans, Christian Andreas %A Härtel, Philipp %A Scholz, Christoph %A Sick, Bernhard %A Siefert, Malte %A Steinke, Florian %A Stursberg, Olaf %A Wende-von Berg, Sebastia %B Innovative Smart Grid Technologies Europe (ISGT EUROPE) %D 2024 %I IEEE %P 1--5 %R 10.1109/ISGTEUROPE62998.2024.10863480 %T Predictions and Decision Making for Resilient Intelligent Sustainable Energy Systems %U https://ieeexplore.ieee.org/abstract/document/10863480 %X Future energy systems are subject to various uncertain influences. As resilient systems they should maintain a constantly high operational performance whatever happens. We explore different levels and timescales of decision making in energy systems, highlighting different uncertainty sources in different domains and discuss how uncertainties can be represented and how to react to them. Moreover, we summarize, which uncertainties are already well examined and which ones still need further inquiry to obtain resilient energy systems.
Organic Computing -- Doctoral Dissertation Colloquium 2023. Tomforde, Sven; Krupitzer, Christian. In Vol. 26Intelligent Embedded Systems. kassel university press, 2024.
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@book{tomforde2024organic, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Organic Computing – Doctoral Dissertation Colloquium 2023}, volume = 26, year = 2024 }
%0 Book %1 tomforde2024organic %B Intelligent Embedded Systems %D 2024 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %R 10.17170/kobra-202402269661 %T Organic Computing -- Doctoral Dissertation Colloquium 2023 %V 26
Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation.Heidecker, Florian; El-Khateeb, Ahmad; Bieshaar, Maarten; Sick, Bernhard. In arXiv e-prints, bl. arXiv:2404.11266. 2024.
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The operating environment of a highly automated vehicle is subject to change, e.g., weather, illumination, or the scenario containing different objects and other participants in which the highly automated vehicle has to navigate its passengers safely. These situations must be considered when developing and validating highly automated driving functions. This already poses a problem for training and evaluating deep learning models because without the costly labeling of thousands of recordings, not knowing whether the data contains relevant, interesting data for further model training, it is a guess under which conditions and situations the model performs poorly. For this purpose, we present corner case criteria based on the predictive uncertainty. With our corner case criteria, we are able to detect uncertainty-based corner cases of an object instance segmentation model without relying on ground truth (GT) data. We evaluated each corner case criterion using the COCO and the NuImages dataset to analyze the potential of our approach. We also provide a corner case decision function that allows us to distinguish each object into True Positive (TP), localization and/or classification corner case, or False Positive (FP). We also present our first results of an iterative training cycle that outperforms the baseline and where the data added to the training dataset is selected based on the corner case decision function.
@article{heidecker2024criteria, abstract = {The operating environment of a highly automated vehicle is subject to change, e.g., weather, illumination, or the scenario containing different objects and other participants in which the highly automated vehicle has to navigate its passengers safely. These situations must be considered when developing and validating highly automated driving functions. This already poses a problem for training and evaluating deep learning models because without the costly labeling of thousands of recordings, not knowing whether the data contains relevant, interesting data for further model training, it is a guess under which conditions and situations the model performs poorly. For this purpose, we present corner case criteria based on the predictive uncertainty. With our corner case criteria, we are able to detect uncertainty-based corner cases of an object instance segmentation model without relying on ground truth (GT) data. We evaluated each corner case criterion using the COCO and the NuImages dataset to analyze the potential of our approach. We also provide a corner case decision function that allows us to distinguish each object into True Positive (TP), localization and/or classification corner case, or False Positive (FP). We also present our first results of an iterative training cycle that outperforms the baseline and where the data added to the training dataset is selected based on the corner case decision function.}, author = {Heidecker, Florian and El-Khateeb, Ahmad and Bieshaar, Maarten and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2404.11266}, title = {Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation}, year = 2024 }
%0 Journal Article %1 heidecker2024criteria %A Heidecker, Florian %A El-Khateeb, Ahmad %A Bieshaar, Maarten %A Sick, Bernhard %D 2024 %J arXiv e-prints %P arXiv:2404.11266 %T Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation %U https://arxiv.org/abs/2404.11266 %X The operating environment of a highly automated vehicle is subject to change, e.g., weather, illumination, or the scenario containing different objects and other participants in which the highly automated vehicle has to navigate its passengers safely. These situations must be considered when developing and validating highly automated driving functions. This already poses a problem for training and evaluating deep learning models because without the costly labeling of thousands of recordings, not knowing whether the data contains relevant, interesting data for further model training, it is a guess under which conditions and situations the model performs poorly. For this purpose, we present corner case criteria based on the predictive uncertainty. With our corner case criteria, we are able to detect uncertainty-based corner cases of an object instance segmentation model without relying on ground truth (GT) data. We evaluated each corner case criterion using the COCO and the NuImages dataset to analyze the potential of our approach. We also provide a corner case decision function that allows us to distinguish each object into True Positive (TP), localization and/or classification corner case, or False Positive (FP). We also present our first results of an iterative training cycle that outperforms the baseline and where the data added to the training dataset is selected based on the corner case decision function.
Optical Detection of the Body Mass Index and Related Parameters Using Multiple Spatially Resolved Reflection Spectroscopy.Magnussen, Birk Martin; Möckel, Frank; Jessulat, Maik; Stern, Claudius; Sick, Bernhard. In International Conference on Bioinformatics and Computational Biology (ICBCB), ble. 132–136. IEEE, 2024.
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Obesity is a common problem for many people. In order to assist people combating obesity, providing methods to quickly, easily, and inexpensively assess their body composition is important. This article investigates how noninvasive, optical sensors based on multiple spatially resolved reflection spectroscopy can be used to measure the body mass index and body composition parameters. Using machine learning to train continuous feature networks, it is possible to predict the body mass index of a subject, with a correlation of $$R=0.61$$ with $$p<0.0001$$. Similarly, the predicted body mass index shows correlations to both the subject's visceral fat ($$R=0.44,p=0.0023$$) and skeletal muscle mass index ($$R=0.52,p=0.0003$$), indicating that the trained neural network is capable of identifying both types of tissue. Strategies to independently detect either type of tissue are discussed.
@inproceedings{magnussen2024optical, abstract = {Obesity is a common problem for many people. In order to assist people combating obesity, providing methods to quickly, easily, and inexpensively assess their body composition is important. This article investigates how noninvasive, optical sensors based on multiple spatially resolved reflection spectroscopy can be used to measure the body mass index and body composition parameters. Using machine learning to train continuous feature networks, it is possible to predict the body mass index of a subject, with a correlation of $R=0.61$ with $p<0.0001$. Similarly, the predicted body mass index shows correlations to both the subject's visceral fat ($R=0.44,p=0.0023$) and skeletal muscle mass index ($R=0.52,p=0.0003$), indicating that the trained neural network is capable of identifying both types of tissue. Strategies to independently detect either type of tissue are discussed.}, author = {Magnussen, Birk Martin and Möckel, Frank and Jessulat, Maik and Stern, Claudius and Sick, Bernhard}, booktitle = {International Conference on Bioinformatics and Computational Biology (ICBCB)}, keywords = {itegpub}, pages = {132–136}, publisher = {IEEE}, title = {Optical Detection of the Body Mass Index and Related Parameters Using Multiple Spatially Resolved Reflection Spectroscopy}, year = 2024 }
%0 Conference Paper %1 magnussen2024optical %A Magnussen, Birk Martin %A Möckel, Frank %A Jessulat, Maik %A Stern, Claudius %A Sick, Bernhard %B International Conference on Bioinformatics and Computational Biology (ICBCB) %D 2024 %I IEEE %P 132--136 %R 10.1109/ICBCB61507.2024.11012023 %T Optical Detection of the Body Mass Index and Related Parameters Using Multiple Spatially Resolved Reflection Spectroscopy %U https://ieeexplore.ieee.org/abstract/document/11012023 %X Obesity is a common problem for many people. In order to assist people combating obesity, providing methods to quickly, easily, and inexpensively assess their body composition is important. This article investigates how noninvasive, optical sensors based on multiple spatially resolved reflection spectroscopy can be used to measure the body mass index and body composition parameters. Using machine learning to train continuous feature networks, it is possible to predict the body mass index of a subject, with a correlation of $$R=0.61$$ with $$p<0.0001$$. Similarly, the predicted body mass index shows correlations to both the subject's visceral fat ($$R=0.44,p=0.0023$$) and skeletal muscle mass index ($$R=0.52,p=0.0003$$), indicating that the trained neural network is capable of identifying both types of tissue. Strategies to independently detect either type of tissue are discussed.
It is the habit not the handle that affects tooth brushing - a randomised counterbalanced cross over study with young and healthy adults.Deinzer, Renate; Eidenhardt, Zdenka; Sohrabi, Keywan; Stenger, Manuel; Kraft, Dominik; Sick, Bernhard; Götz-Hahn, Franz; Bottenbruch, Carlotta; Berneburg, Nils; Weik, Ulrike. In BMC Oral Health, 24(1), bl. 757. Springer, 2024.
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Objective To assess the effect of the toothbrush handle on video-observed toothbrushing behaviour and toothbrushing effectiveness. Methods This is a randomized counterbalanced cross-over study. N = 50 university students and employees brushed their teeth at two occasions, one week apart, using either a commercial ergonomically designed manual toothbrush (MT) or Brushalyze V1 (BV1), a manual toothbrush with a thick cylindrical handle without any specific ergonomic features. Brushing behaviour was video-analysed. Plaque was assessed at the second occasion immediately after brushing. Participants also rated their self-perceived oral cleanliness and directly compared the two brushes regarding their handling and compared them to the brushed they used at home. Results The study participants found the BV1 significantly more cumbersome than the M1 or their brush at home. (p < 0.05). However, correlation analyses revealed a strong consistency of brushing behavior with the two brushes (0.71 < r < 0.91). Means differed only slightly (all d < 0.36). These differences became statistically significant only for the brushing time at inner surfaces (d = 0.31 p = 0.03) and horizontal movements at inner surfaces (d = 0.35, p = 0.02). Plaque levels at the gingival margins did not differ while slightly more plaque persisted at the more coronal aspects of the crown after brushing with BV1 (d = 0.592; p 0.042). Discussion The results of the study indicate that the brushing handle does not play a major role in brushing behavior or brushing effectiveness.
@article{deinzer2024it, abstract = {Objective To assess the effect of the toothbrush handle on video-observed toothbrushing behaviour and toothbrushing effectiveness. Methods This is a randomized counterbalanced cross-over study. N = 50 university students and employees brushed their teeth at two occasions, one week apart, using either a commercial ergonomically designed manual toothbrush (MT) or Brushalyze V1 (BV1), a manual toothbrush with a thick cylindrical handle without any specific ergonomic features. Brushing behaviour was video-analysed. Plaque was assessed at the second occasion immediately after brushing. Participants also rated their self-perceived oral cleanliness and directly compared the two brushes regarding their handling and compared them to the brushed they used at home. Results The study participants found the BV1 significantly more cumbersome than the M1 or their brush at home. (p < 0.05). However, correlation analyses revealed a strong consistency of brushing behavior with the two brushes (0.71 < r < 0.91). Means differed only slightly (all d < 0.36). These differences became statistically significant only for the brushing time at inner surfaces (d = 0.31 p = 0.03) and horizontal movements at inner surfaces (d = 0.35, p = 0.02). Plaque levels at the gingival margins did not differ while slightly more plaque persisted at the more coronal aspects of the crown after brushing with BV1 (d = 0.592; p 0.042). Discussion The results of the study indicate that the brushing handle does not play a major role in brushing behavior or brushing effectiveness.}, author = {Deinzer, Renate and Eidenhardt, Zdenka and Sohrabi, Keywan and Stenger, Manuel and Kraft, Dominik and Sick, Bernhard and Götz-Hahn, Franz and Bottenbruch, Carlotta and Berneburg, Nils and Weik, Ulrike}, journal = {BMC Oral Health}, keywords = {itegpub}, number = 1, pages = 757, publisher = {Springer}, title = {It is the habit not the handle that affects tooth brushing - a randomised counterbalanced cross over study with young and healthy adults}, volume = 24, year = 2024 }
%0 Journal Article %1 deinzer2024it %A Deinzer, Renate %A Eidenhardt, Zdenka %A Sohrabi, Keywan %A Stenger, Manuel %A Kraft, Dominik %A Sick, Bernhard %A Götz-Hahn, Franz %A Bottenbruch, Carlotta %A Berneburg, Nils %A Weik, Ulrike %D 2024 %I Springer %J BMC Oral Health %N 1 %P 757 %R 10.1186/s12903-024-04538-6 %T It is the habit not the handle that affects tooth brushing - a randomised counterbalanced cross over study with young and healthy adults %V 24 %X Objective To assess the effect of the toothbrush handle on video-observed toothbrushing behaviour and toothbrushing effectiveness. Methods This is a randomized counterbalanced cross-over study. N = 50 university students and employees brushed their teeth at two occasions, one week apart, using either a commercial ergonomically designed manual toothbrush (MT) or Brushalyze V1 (BV1), a manual toothbrush with a thick cylindrical handle without any specific ergonomic features. Brushing behaviour was video-analysed. Plaque was assessed at the second occasion immediately after brushing. Participants also rated their self-perceived oral cleanliness and directly compared the two brushes regarding their handling and compared them to the brushed they used at home. Results The study participants found the BV1 significantly more cumbersome than the M1 or their brush at home. (p < 0.05). However, correlation analyses revealed a strong consistency of brushing behavior with the two brushes (0.71 < r < 0.91). Means differed only slightly (all d < 0.36). These differences became statistically significant only for the brushing time at inner surfaces (d = 0.31 p = 0.03) and horizontal movements at inner surfaces (d = 0.35, p = 0.02). Plaque levels at the gingival margins did not differ while slightly more plaque persisted at the more coronal aspects of the crown after brushing with BV1 (d = 0.592; p 0.042). Discussion The results of the study indicate that the brushing handle does not play a major role in brushing behavior or brushing effectiveness.
dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans.Herde, Marek; Huseljic, Denis; Rauch, Lukas; Sick, Bernhard. In Conference on Neural Information Processing Systems (NeurIPS 2024) Track on Datasets and Benchmarks, ble. 1–16. 2024.
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Human annotators typically provide annotated data for training machine learning models, such as neural networks. Yet, human annotations are subject to noise, impairing generalization performances. Methodological research on approaches counteracting noisy annotations requires corresponding datasets for a meaningful empirical evaluation. Consequently, we introduce a novel benchmark dataset, dopanim, consisting of about 15,750 animal images of 15 classes with ground truth labels. For approximately 10,500 of these images, 20 humans provided over 52,000 annotations with an accuracy of circa 67%. Its key attributes include (1) the challenging task of classifying doppelganger animals, (2) human-estimated likelihoods as annotations, and (3) annotator metadata. We benchmark well-known multi-annotator learning approaches using seven variants of this dataset and outline further evaluation use cases such as learning beyond hard class labels and active learning. Our dataset and a comprehensive codebase are publicly available to emulate the data collection process and to reproduce all empirical results.
@inproceedings{herde2024dopanim, abstract = {Human annotators typically provide annotated data for training machine learning models, such as neural networks. Yet, human annotations are subject to noise, impairing generalization performances. Methodological research on approaches counteracting noisy annotations requires corresponding datasets for a meaningful empirical evaluation. Consequently, we introduce a novel benchmark dataset, dopanim, consisting of about 15,750 animal images of 15 classes with ground truth labels. For approximately 10,500 of these images, 20 humans provided over 52,000 annotations with an accuracy of circa 67%. Its key attributes include (1) the challenging task of classifying doppelganger animals, (2) human-estimated likelihoods as annotations, and (3) annotator metadata. We benchmark well-known multi-annotator learning approaches using seven variants of this dataset and outline further evaluation use cases such as learning beyond hard class labels and active learning. Our dataset and a comprehensive codebase are publicly available to emulate the data collection process and to reproduce all empirical results.}, author = {Herde, Marek and Huseljic, Denis and Rauch, Lukas and Sick, Bernhard}, booktitle = {Conference on Neural Information Processing Systems (NeurIPS 2024) Track on Datasets and Benchmarks}, keywords = {itegpub}, pages = {1–16}, title = {dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans}, year = 2024 }
%0 Conference Paper %1 herde2024dopanim %A Herde, Marek %A Huseljic, Denis %A Rauch, Lukas %A Sick, Bernhard %B Conference on Neural Information Processing Systems (NeurIPS 2024) Track on Datasets and Benchmarks %D 2024 %P 1--16 %T dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans %U https://openreview.net/forum?id=XOGosbxLrz %X Human annotators typically provide annotated data for training machine learning models, such as neural networks. Yet, human annotations are subject to noise, impairing generalization performances. Methodological research on approaches counteracting noisy annotations requires corresponding datasets for a meaningful empirical evaluation. Consequently, we introduce a novel benchmark dataset, dopanim, consisting of about 15,750 animal images of 15 classes with ground truth labels. For approximately 10,500 of these images, 20 humans provided over 52,000 annotations with an accuracy of circa 67%. Its key attributes include (1) the challenging task of classifying doppelganger animals, (2) human-estimated likelihoods as annotations, and (3) annotator metadata. We benchmark well-known multi-annotator learning approaches using seven variants of this dataset and outline further evaluation use cases such as learning beyond hard class labels and active learning. Our dataset and a comprehensive codebase are publicly available to emulate the data collection process and to reproduce all empirical results.
Weisfeiler–Lehman goes dynamic: An analysis of the expressive power of Graph Neural Networks for attributed and dynamic graphs.Beddar-Wiesing, Silvia; D’Inverno, Alessio; Graziani, Caterina; Lachi, Veronica; Moallemy-Oureh, Alice; Scarselli, Franco; Thomas, Josephine. In Neural Networks, 173, bl. 106213. Elsevier, 2024.
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Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On the one hand, it has been proven that GNNs are as powerful as the Weisfeiler–Lehman test (1-WL) in their ability to distinguish graphs. Moreover, it has been shown that the equivalence enforced by 1-WL equals unfolding equivalence. On the other hand, GNNs turned out to be universal approximators on graphs modulo the constraints enforced by 1-WL/unfolding equivalence. However, these results only apply to Static Attributed Undirected Homogeneous Graphs (SAUHG) with node attributes. In contrast, real-life applications often involve a much larger variety of graph types. In this paper, we conduct a theoretical analysis of the expressive power of GNNs for two other graph domains that are particularly interesting in practical applications, namely dynamic graphs and SAUGHs with edge attributes. Dynamic graphs are widely used in modern applications; hence, the study of the expressive capability of GNNs in this domain is essential for practical reasons and, in addition, it requires a new analyzing approach due to the difference in the architecture of dynamic GNNs compared to static ones. On the other hand, the examination of SAUHGs is of particular relevance since they act as a standard form for all graph types: it has been shown that all graph types can be transformed without loss of information to SAUHGs with both attributes on nodes and edges. This paper considers generic GNN models and appropriate 1-WL tests for those domains. Then, the known results on the expressive power of GNNs are extended to the mentioned domains: it is proven that GNNs have the same capability as the 1-WL test, the 1-WL equivalence equals unfolding equivalence and that GNNs are universal approximators modulo 1-WL/unfolding equivalence. Moreover, the proof of the approximation capability is mostly constructive and allows us to deduce hints on the architecture of GNNs that can achieve the desired approximation.
@article{beddarwiesing2024weisfeiler, abstract = {Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On the one hand, it has been proven that GNNs are as powerful as the Weisfeiler–Lehman test (1-WL) in their ability to distinguish graphs. Moreover, it has been shown that the equivalence enforced by 1-WL equals unfolding equivalence. On the other hand, GNNs turned out to be universal approximators on graphs modulo the constraints enforced by 1-WL/unfolding equivalence. However, these results only apply to Static Attributed Undirected Homogeneous Graphs (SAUHG) with node attributes. In contrast, real-life applications often involve a much larger variety of graph types. In this paper, we conduct a theoretical analysis of the expressive power of GNNs for two other graph domains that are particularly interesting in practical applications, namely dynamic graphs and SAUGHs with edge attributes. Dynamic graphs are widely used in modern applications; hence, the study of the expressive capability of GNNs in this domain is essential for practical reasons and, in addition, it requires a new analyzing approach due to the difference in the architecture of dynamic GNNs compared to static ones. On the other hand, the examination of SAUHGs is of particular relevance since they act as a standard form for all graph types: it has been shown that all graph types can be transformed without loss of information to SAUHGs with both attributes on nodes and edges. This paper considers generic GNN models and appropriate 1-WL tests for those domains. Then, the known results on the expressive power of GNNs are extended to the mentioned domains: it is proven that GNNs have the same capability as the 1-WL test, the 1-WL equivalence equals unfolding equivalence and that GNNs are universal approximators modulo 1-WL/unfolding equivalence. Moreover, the proof of the approximation capability is mostly constructive and allows us to deduce hints on the architecture of GNNs that can achieve the desired approximation.}, author = {Beddar-Wiesing, Silvia and D'Inverno, Alessio and Graziani, Caterina and Lachi, Veronica and Moallemy-Oureh, Alice and Scarselli, Franco and Thomas, Josephine}, journal = {Neural Networks}, keywords = {Graph_Neural_Network}, pages = 106213, publisher = {Elsevier}, title = {Weisfeiler–Lehman goes dynamic: An analysis of the expressive power of Graph Neural Networks for attributed and dynamic graphs}, volume = 173, year = 2024 }
%0 Journal Article %1 beddarwiesing2024weisfeiler %A Beddar-Wiesing, Silvia %A D'Inverno, Alessio %A Graziani, Caterina %A Lachi, Veronica %A Moallemy-Oureh, Alice %A Scarselli, Franco %A Thomas, Josephine %D 2024 %I Elsevier %J Neural Networks %P 106213 %R 10.1016/j.neunet.2024.106213 %T Weisfeiler–Lehman goes dynamic: An analysis of the expressive power of Graph Neural Networks for attributed and dynamic graphs %U https://www.sciencedirect.com/science/article/pii/S0893608024001370 %V 173 %X Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On the one hand, it has been proven that GNNs are as powerful as the Weisfeiler–Lehman test (1-WL) in their ability to distinguish graphs. Moreover, it has been shown that the equivalence enforced by 1-WL equals unfolding equivalence. On the other hand, GNNs turned out to be universal approximators on graphs modulo the constraints enforced by 1-WL/unfolding equivalence. However, these results only apply to Static Attributed Undirected Homogeneous Graphs (SAUHG) with node attributes. In contrast, real-life applications often involve a much larger variety of graph types. In this paper, we conduct a theoretical analysis of the expressive power of GNNs for two other graph domains that are particularly interesting in practical applications, namely dynamic graphs and SAUGHs with edge attributes. Dynamic graphs are widely used in modern applications; hence, the study of the expressive capability of GNNs in this domain is essential for practical reasons and, in addition, it requires a new analyzing approach due to the difference in the architecture of dynamic GNNs compared to static ones. On the other hand, the examination of SAUHGs is of particular relevance since they act as a standard form for all graph types: it has been shown that all graph types can be transformed without loss of information to SAUHGs with both attributes on nodes and edges. This paper considers generic GNN models and appropriate 1-WL tests for those domains. Then, the known results on the expressive power of GNNs are extended to the mentioned domains: it is proven that GNNs have the same capability as the 1-WL test, the 1-WL equivalence equals unfolding equivalence and that GNNs are universal approximators modulo 1-WL/unfolding equivalence. Moreover, the proof of the approximation capability is mostly constructive and allows us to deduce hints on the architecture of GNNs that can achieve the desired approximation.
Tutorial: Interactive Adaptive Learning.Herde, Marek; Pham, Minh Tuan; Tharwat, Alaa; Sick, Bernhard. In Workshop on Interactive Adapative Learning (IAL), ECML PKDD, ble. 1–6. 2024.
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We summarize the contents of the tutorial we present as a part of the 8th Interactive Adaptive Learning workshop. This workshop is co-located with the ECML-PKDD conference, where it takes place on September 9th, 2024 in Vilnius, Lithuania.
@inproceedings{herde2024tutorial, abstract = {We summarize the contents of the tutorial we present as a part of the 8th Interactive Adaptive Learning workshop. This workshop is co-located with the ECML-PKDD conference, where it takes place on September 9th, 2024 in Vilnius, Lithuania.}, author = {Herde, Marek and Pham, Minh Tuan and Tharwat, Alaa and Sick, Bernhard}, booktitle = {Workshop on Interactive Adapative Learning (IAL), ECML PKDD}, keywords = {itegpub}, pages = {1–6}, title = {Tutorial: Interactive Adaptive Learning}, year = 2024 }
%0 Conference Paper %1 herde2024tutorial %A Herde, Marek %A Pham, Minh Tuan %A Tharwat, Alaa %A Sick, Bernhard %B Workshop on Interactive Adapative Learning (IAL), ECML PKDD %D 2024 %P 1--6 %T Tutorial: Interactive Adaptive Learning %U https://ceur-ws.org/Vol-3770/paper1.pdf %X We summarize the contents of the tutorial we present as a part of the 8th Interactive Adaptive Learning workshop. This workshop is co-located with the ECML-PKDD conference, where it takes place on September 9th, 2024 in Vilnius, Lithuania.
Exploring 3D Object Detection for Autonomous Factory Driving: Advanced Research on Handling Limited Annotations with Ground Truth Sampling Augmentation.Reuse, Matthias; Amende, Karl; Simon, Martin; Sick, Bernhard. In Computer Sciences & Mathematics Forum, 9(1), bl. 5. MDPI, 2024.
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Autonomously driving vehicles in car factories and parking spaces can represent a competitive advantage in the logistics industry. However, the real-world application is challenging in many ways. First of all, there are no publicly available datasets for this specific task. Therefore, we equipped two industrial production sites with up to 11 LiDAR sensors to collect and annotate our own data for infrastructural 3D object detection. These form the basis for extensive experiments. Due to the still limited amount of labeled data, the commonly used ground truth sampling augmentation is the core of research in this work. Several variations of this augmentation method are explored, revealing that in our case, the most commonly used is not necessarily the best. We show that an easy-to-create polygon can noticeably improve the detection results in this application scenario. By using these augmentation methods, it is even possible to achieve moderate detection results when only empty frames without any objects and a database with only a few labeled objects are used.
@article{reuse2024exploring, abstract = {Autonomously driving vehicles in car factories and parking spaces can represent a competitive advantage in the logistics industry. However, the real-world application is challenging in many ways. First of all, there are no publicly available datasets for this specific task. Therefore, we equipped two industrial production sites with up to 11 LiDAR sensors to collect and annotate our own data for infrastructural 3D object detection. These form the basis for extensive experiments. Due to the still limited amount of labeled data, the commonly used ground truth sampling augmentation is the core of research in this work. Several variations of this augmentation method are explored, revealing that in our case, the most commonly used is not necessarily the best. We show that an easy-to-create polygon can noticeably improve the detection results in this application scenario. By using these augmentation methods, it is even possible to achieve moderate detection results when only empty frames without any objects and a database with only a few labeled objects are used.}, author = {Reuse, Matthias and Amende, Karl and Simon, Martin and Sick, Bernhard}, journal = {Computer Sciences \& Mathematics Forum}, keywords = {itegpub}, number = 1, pages = 5, publisher = {MDPI}, title = {Exploring 3D Object Detection for Autonomous Factory Driving: Advanced Research on Handling Limited Annotations with Ground Truth Sampling Augmentation}, volume = 9, year = 2024 }
%0 Journal Article %1 reuse2024exploring %A Reuse, Matthias %A Amende, Karl %A Simon, Martin %A Sick, Bernhard %D 2024 %I MDPI %J Computer Sciences & Mathematics Forum %N 1 %P 5 %R 10.3390/cmsf2024009005 %T Exploring 3D Object Detection for Autonomous Factory Driving: Advanced Research on Handling Limited Annotations with Ground Truth Sampling Augmentation %V 9 %X Autonomously driving vehicles in car factories and parking spaces can represent a competitive advantage in the logistics industry. However, the real-world application is challenging in many ways. First of all, there are no publicly available datasets for this specific task. Therefore, we equipped two industrial production sites with up to 11 LiDAR sensors to collect and annotate our own data for infrastructural 3D object detection. These form the basis for extensive experiments. Due to the still limited amount of labeled data, the commonly used ground truth sampling augmentation is the core of research in this work. Several variations of this augmentation method are explored, revealing that in our case, the most commonly used is not necessarily the best. We show that an easy-to-create polygon can noticeably improve the detection results in this application scenario. By using these augmentation methods, it is even possible to achieve moderate detection results when only empty frames without any objects and a database with only a few labeled objects are used.
Influence of Background Color on 6D Pose Tracking Accuracy.Hubert, Andreas; Doll, Konrad; Sick, Bernhard. In International Conference on Engineering and Emerging Technologies (ICEET), ble. 1–6. IEEE, 2024.
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Fast 6D pose tracking is a critical component in numerous applications ranging from robotics to augmented reality. A notable method for addressing this challenge involves simulating the last known pose and comparing it with the current one, a process central to the SE(3)-TrackNet approach, which is known for its reliability. Traditionally, this method employs a uniform black background for the simulated input. This study challenges the standard practice by demonstrating that the choice of background color can significantly influences the accuracy of 6D pose estimation. Through a series of experiments, we provide results showing that background color is a critical factor for the effectiveness of the SE(3) TrackNet approach.
@inproceedings{hubert2024influence, abstract = {Fast 6D pose tracking is a critical component in numerous applications ranging from robotics to augmented reality. A notable method for addressing this challenge involves simulating the last known pose and comparing it with the current one, a process central to the SE(3)-TrackNet approach, which is known for its reliability. Traditionally, this method employs a uniform black background for the simulated input. This study challenges the standard practice by demonstrating that the choice of background color can significantly influences the accuracy of 6D pose estimation. Through a series of experiments, we provide results showing that background color is a critical factor for the effectiveness of the SE(3) TrackNet approach.}, author = {Hubert, Andreas and Doll, Konrad and Sick, Bernhard}, booktitle = {International Conference on Engineering and Emerging Technologies (ICEET)}, keywords = {itegpub}, pages = {1–6}, publisher = {IEEE}, title = {Influence of Background Color on 6D Pose Tracking Accuracy}, year = 2024 }
%0 Conference Paper %1 hubert2024influence %A Hubert, Andreas %A Doll, Konrad %A Sick, Bernhard %B International Conference on Engineering and Emerging Technologies (ICEET) %D 2024 %I IEEE %P 1--6 %R 10.1109/ICEET65156.2024.10913824 %T Influence of Background Color on 6D Pose Tracking Accuracy %X Fast 6D pose tracking is a critical component in numerous applications ranging from robotics to augmented reality. A notable method for addressing this challenge involves simulating the last known pose and comparing it with the current one, a process central to the SE(3)-TrackNet approach, which is known for its reliability. Traditionally, this method employs a uniform black background for the simulated input. This study challenges the standard practice by demonstrating that the choice of background color can significantly influences the accuracy of 6D pose estimation. Through a series of experiments, we provide results showing that background color is a critical factor for the effectiveness of the SE(3) TrackNet approach.
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification.Huseljic, Denis; Hahn, Paul; Herde, Marek; Rauch, Lukas; Sick, Bernhard. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), ble. 280–296. 2024.
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Deep active learning (AL) seeks to minimize the annotation costs for training deep neural networks. \\textsc{Bait, a recently proposed AL strategy based on the Fisher Information, has demonstrated impressive performance across various datasets. However, \\textsc{Bait's high computational and memory requirements hinder its applicability on large-scale classification tasks, resulting in current research neglecting \\textsc{Bait in their evaluation. This paper introduces two methods to enhance \\textsc{Bait's computational efficiency and scalability. Notably, we significantly reduce its time complexity by approximating the Fisher Information. In particular, we adapt the original formulation by i) taking the expectation over the most probable classes, and ii) constructing a binary classification task, leading to an alternative likelihood for gradient computations. Consequently, this allows the efficient use of \\textsc{Bait on large-scale datasets, including ImageNet. Our unified and comprehensive evaluation across a variety of datasets demonstrates that our approximations achieve strong performance with considerably reduced time complexity. Furthermore, we provide an extensive open-source toolbox that implements recent state-of-the-art AL strategies, available at \\url{https://github.com/dhuseljic/dal-toolbox.
@inproceedings{huseljic2024fast, abstract = {Deep active learning (AL) seeks to minimize the annotation costs for training deep neural networks. \\textsc{Bait}, a recently proposed AL strategy based on the Fisher Information, has demonstrated impressive performance across various datasets. However, \\textsc{Bait}'s high computational and memory requirements hinder its applicability on large-scale classification tasks, resulting in current research neglecting \\textsc{Bait} in their evaluation. This paper introduces two methods to enhance \\textsc{Bait}'s computational efficiency and scalability. Notably, we significantly reduce its time complexity by approximating the Fisher Information. In particular, we adapt the original formulation by i) taking the expectation over the most probable classes, and ii) constructing a binary classification task, leading to an alternative likelihood for gradient computations. Consequently, this allows the efficient use of \\textsc{Bait} on large-scale datasets, including ImageNet. Our unified and comprehensive evaluation across a variety of datasets demonstrates that our approximations achieve strong performance with considerably reduced time complexity. Furthermore, we provide an extensive open-source toolbox that implements recent state-of-the-art AL strategies, available at \\url{https://github.com/dhuseljic/dal-toolbox}.}, author = {Huseljic, Denis and Hahn, Paul and Herde, Marek and Rauch, Lukas and Sick, Bernhard}, booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)}, keywords = {itegpub}, pages = {280–296}, title = {Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification}, year = 2024 }
%0 Conference Paper %1 huseljic2024fast %A Huseljic, Denis %A Hahn, Paul %A Herde, Marek %A Rauch, Lukas %A Sick, Bernhard %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) %D 2024 %P 280--296 %R 10.1007/978-3-031-70368-3_17 %T Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification %X Deep active learning (AL) seeks to minimize the annotation costs for training deep neural networks. \\textsc{Bait, a recently proposed AL strategy based on the Fisher Information, has demonstrated impressive performance across various datasets. However, \\textsc{Bait's high computational and memory requirements hinder its applicability on large-scale classification tasks, resulting in current research neglecting \\textsc{Bait in their evaluation. This paper introduces two methods to enhance \\textsc{Bait's computational efficiency and scalability. Notably, we significantly reduce its time complexity by approximating the Fisher Information. In particular, we adapt the original formulation by i) taking the expectation over the most probable classes, and ii) constructing a binary classification task, leading to an alternative likelihood for gradient computations. Consequently, this allows the efficient use of \\textsc{Bait on large-scale datasets, including ImageNet. Our unified and comprehensive evaluation across a variety of datasets demonstrates that our approximations achieve strong performance with considerably reduced time complexity. Furthermore, we provide an extensive open-source toolbox that implements recent state-of-the-art AL strategies, available at \\url{https://github.com/dhuseljic/dal-toolbox.
Fault Detection for Agents in Power Grid Topology Optimization: A Comprehensive Analysis.Lehna, Malte; Hassouna, Mohamed; Degtyar, Dmitry; Tomforde, Sven; Scholz, Christoph. In Machine Learning for Sustainable Power Systems (ML4SPS), ECML. Springer, 2024.
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Optimizing the topology of transmission networks using Deep Reinforcement Learning (DRL) has increasingly come into focus. Various DRL agents have been proposed, which are mostly benchmarked on the Grid2Op environment from the Learning to Run a Power Network (L2RPN) challenges. The environments have many advantages with their realistic chronics and underlying power flow backends. However, the interpretation of agent survival or failure is not always clear, as there are a variety of potential causes. In this work, we focus on the failures of the power grid simulation to identify patterns and detect them in advance. We collect the failed chronics of three different agents on the WCCI 2022 L2RPN environment, totaling about 40k data points. By clustering, we are able to detect five distinct clusters, identifying common failure types. Further, we propose a multi-class prediction approach to detect failures beforehand and evaluate five different prediction models. Here, the Light Gradient-Boosting Machine (LightGBM) shows the best failure detection performance, with an accuracy of 86%. It also correctly identifies in 91% of the time failure and survival observations. Finally, we provide a detailed feature importance analysis that identifies critical features and regions in the grid.
@inproceedings{lehna2024fault, abstract = {Optimizing the topology of transmission networks using Deep Reinforcement Learning (DRL) has increasingly come into focus. Various DRL agents have been proposed, which are mostly benchmarked on the Grid2Op environment from the Learning to Run a Power Network (L2RPN) challenges. The environments have many advantages with their realistic chronics and underlying power flow backends. However, the interpretation of agent survival or failure is not always clear, as there are a variety of potential causes. In this work, we focus on the failures of the power grid simulation to identify patterns and detect them in advance. We collect the failed chronics of three different agents on the WCCI 2022 L2RPN environment, totaling about 40k data points. By clustering, we are able to detect five distinct clusters, identifying common failure types. Further, we propose a multi-class prediction approach to detect failures beforehand and evaluate five different prediction models. Here, the Light Gradient-Boosting Machine (LightGBM) shows the best failure detection performance, with an accuracy of 86%. It also correctly identifies in 91% of the time failure and survival observations. Finally, we provide a detailed feature importance analysis that identifies critical features and regions in the grid.}, author = {Lehna, Malte and Hassouna, Mohamed and Degtyar, Dmitry and Tomforde, Sven and Scholz, Christoph}, booktitle = {Machine Learning for Sustainable Power Systems (ML4SPS), ECML}, keywords = {itegpub}, note = {(accepted)}, publisher = {Springer}, title = {Fault Detection for Agents in Power Grid Topology Optimization: A Comprehensive Analysis}, year = 2024 }
%0 Conference Paper %1 lehna2024fault %A Lehna, Malte %A Hassouna, Mohamed %A Degtyar, Dmitry %A Tomforde, Sven %A Scholz, Christoph %B Machine Learning for Sustainable Power Systems (ML4SPS), ECML %D 2024 %I Springer %T Fault Detection for Agents in Power Grid Topology Optimization: A Comprehensive Analysis %X Optimizing the topology of transmission networks using Deep Reinforcement Learning (DRL) has increasingly come into focus. Various DRL agents have been proposed, which are mostly benchmarked on the Grid2Op environment from the Learning to Run a Power Network (L2RPN) challenges. The environments have many advantages with their realistic chronics and underlying power flow backends. However, the interpretation of agent survival or failure is not always clear, as there are a variety of potential causes. In this work, we focus on the failures of the power grid simulation to identify patterns and detect them in advance. We collect the failed chronics of three different agents on the WCCI 2022 L2RPN environment, totaling about 40k data points. By clustering, we are able to detect five distinct clusters, identifying common failure types. Further, we propose a multi-class prediction approach to detect failures beforehand and evaluate five different prediction models. Here, the Light Gradient-Boosting Machine (LightGBM) shows the best failure detection performance, with an accuracy of 86%. It also correctly identifies in 91% of the time failure and survival observations. Finally, we provide a detailed feature importance analysis that identifies critical features and regions in the grid.
Enhancing Multi-objective Optimisation Through Machine Learning-Supported Multiphysics Simulation.Botache, Diego; Decke, Jens; Ripken, Winfried; Dornipati, Abhinay; Götz-Hahn, Franz; Ayeb, Mohamed; Sick, Bernhard. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), ble. 297–312. Springer, 2024.
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This paper presents a methodological framework for training, self-optimising, and self-organising surrogate models to approximate and speed up multiobjective optimisation of technical systems based on multiphysics simulations. At the hand of two real-world datasets, we illustrate that surrogate models can be trained on relatively small amounts of data to approximate the underlying simulations accurately. Including explainable AI techniques allow for highlighting feature relevancy or dependencies and supporting the possible extension of the used datasets. One of the datasets was created for this paper and is made publicly available for the broader scientific community. Extensive experiments combine four machine learning and deep learning algorithms with an evolutionary optimisation algorithm. The performance of the combined training and optimisation pipeline is evaluated by verifying the generated Pareto-optimal results using the ground truth simulations. The results from our pipeline and a comprehensive evaluation strategy show the potential for efficiently acquiring solution candidates in multiobjective optimisation tasks by reducing the number of simulations and conserving a higher prediction accuracy, i.e., with a MAPE score under 5% for one of the presented use cases.
@inproceedings{botache2024enhancinga, abstract = {This paper presents a methodological framework for training, self-optimising, and self-organising surrogate models to approximate and speed up multiobjective optimisation of technical systems based on multiphysics simulations. At the hand of two real-world datasets, we illustrate that surrogate models can be trained on relatively small amounts of data to approximate the underlying simulations accurately. Including explainable AI techniques allow for highlighting feature relevancy or dependencies and supporting the possible extension of the used datasets. One of the datasets was created for this paper and is made publicly available for the broader scientific community. Extensive experiments combine four machine learning and deep learning algorithms with an evolutionary optimisation algorithm. The performance of the combined training and optimisation pipeline is evaluated by verifying the generated Pareto-optimal results using the ground truth simulations. The results from our pipeline and a comprehensive evaluation strategy show the potential for efficiently acquiring solution candidates in multiobjective optimisation tasks by reducing the number of simulations and conserving a higher prediction accuracy, i.e., with a MAPE score under 5% for one of the presented use cases.}, author = {Botache, Diego and Decke, Jens and Ripken, Winfried and Dornipati, Abhinay and Götz-Hahn, Franz and Ayeb, Mohamed and Sick, Bernhard}, booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)}, keywords = {itegpub}, pages = {297–312}, publisher = {Springer}, title = {Enhancing Multi-objective Optimisation Through Machine Learning-Supported Multiphysics Simulation}, year = 2024 }
%0 Conference Paper %1 botache2024enhancinga %A Botache, Diego %A Decke, Jens %A Ripken, Winfried %A Dornipati, Abhinay %A Götz-Hahn, Franz %A Ayeb, Mohamed %A Sick, Bernhard %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) %D 2024 %I Springer %P 297--312 %R 10.1007/978-3-031-70381-2_19 %T Enhancing Multi-objective Optimisation Through Machine Learning-Supported Multiphysics Simulation %X This paper presents a methodological framework for training, self-optimising, and self-organising surrogate models to approximate and speed up multiobjective optimisation of technical systems based on multiphysics simulations. At the hand of two real-world datasets, we illustrate that surrogate models can be trained on relatively small amounts of data to approximate the underlying simulations accurately. Including explainable AI techniques allow for highlighting feature relevancy or dependencies and supporting the possible extension of the used datasets. One of the datasets was created for this paper and is made publicly available for the broader scientific community. Extensive experiments combine four machine learning and deep learning algorithms with an evolutionary optimisation algorithm. The performance of the combined training and optimisation pipeline is evaluated by verifying the generated Pareto-optimal results using the ground truth simulations. The results from our pipeline and a comprehensive evaluation strategy show the potential for efficiently acquiring solution candidates in multiobjective optimisation tasks by reducing the number of simulations and conserving a higher prediction accuracy, i.e., with a MAPE score under 5% for one of the presented use cases.
Enhancing Maritime Behaviour Analysis through Novel Feature Engineering and Digital Shadow Modelling: A Case Study in Kiel Fjord.Al-Falouji, Ghassan; Gao, Shang; Haschke, Lukas; Nowotka, Dirk; Tomforde, Sven. In International Conference on Architecture of Computing Systems (ARCS), ble. 97–111. Springer, 2024.
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With the continuous evolution of maritime technology, there is a growing need for analysing and modelling vessel behaviour in complex waterway systems. This paper presents an extension and utilisation of the Surface Vessel Nautical Behaviour Analysis (SV-NBA) framework for in-depth spatio-temporal analysis of maritime surface vessels’ behaviour in Kiel Fjord. Leveraging one year of collected Automatic Identification System (AIS) data, we extracted features from the recorded data. Three feature sets are generated and compared using expert-knowledge fea- tures, methods for feature selection, and Denoising Autoencoder latent space representation. Behaviour modelling and analysis utilises clustered data from the three feature sets, employing Gaussian Mixture Models (GMM). The trained GMM models serve as digital shadows, enabling storage-efficient representation of vessel behaviour and facilitates appli- cations such as online situational awareness and marine-traffic manage- ment. These digital shadows serve as observer-layer in a dynamic au- tonomous system of system, offering insights into maritime activities and enhancing navigation safety in busy waterways. This research contributes to the advancement of autonomous navigation systems and supports ef- ficient maritime traffic management strategies.
@inproceedings{AlFalouji2024Behaviour, abstract = {With the continuous evolution of maritime technology, there is a growing need for analysing and modelling vessel behaviour in complex waterway systems. This paper presents an extension and utilisation of the Surface Vessel Nautical Behaviour Analysis (SV-NBA) framework for in-depth spatio-temporal analysis of maritime surface vessels’ behaviour in Kiel Fjord. Leveraging one year of collected Automatic Identification System (AIS) data, we extracted features from the recorded data. Three feature sets are generated and compared using expert-knowledge fea- tures, methods for feature selection, and Denoising Autoencoder latent space representation. Behaviour modelling and analysis utilises clustered data from the three feature sets, employing Gaussian Mixture Models (GMM). The trained GMM models serve as digital shadows, enabling storage-efficient representation of vessel behaviour and facilitates appli- cations such as online situational awareness and marine-traffic manage- ment. These digital shadows serve as observer-layer in a dynamic au- tonomous system of system, offering insights into maritime activities and enhancing navigation safety in busy waterways. This research contributes to the advancement of autonomous navigation systems and supports ef- ficient maritime traffic management strategies.}, author = {Al-Falouji, Ghassan and Gao, Shang and Haschke, Lukas and Nowotka, Dirk and Tomforde, Sven}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {DigitalShadows}, pages = {97–111}, publisher = {Springer}, title = {Enhancing Maritime Behaviour Analysis through Novel Feature Engineering and Digital Shadow Modelling: A Case Study in Kiel Fjord}, year = 2024 }
%0 Conference Paper %1 AlFalouji2024Behaviour %A Al-Falouji, Ghassan %A Gao, Shang %A Haschke, Lukas %A Nowotka, Dirk %A Tomforde, Sven %B International Conference on Architecture of Computing Systems (ARCS) %D 2024 %I Springer %P 97--111 %R 10.1007/978-3-031-66146-4_7 %T Enhancing Maritime Behaviour Analysis through Novel Feature Engineering and Digital Shadow Modelling: A Case Study in Kiel Fjord %X With the continuous evolution of maritime technology, there is a growing need for analysing and modelling vessel behaviour in complex waterway systems. This paper presents an extension and utilisation of the Surface Vessel Nautical Behaviour Analysis (SV-NBA) framework for in-depth spatio-temporal analysis of maritime surface vessels’ behaviour in Kiel Fjord. Leveraging one year of collected Automatic Identification System (AIS) data, we extracted features from the recorded data. Three feature sets are generated and compared using expert-knowledge fea- tures, methods for feature selection, and Denoising Autoencoder latent space representation. Behaviour modelling and analysis utilises clustered data from the three feature sets, employing Gaussian Mixture Models (GMM). The trained GMM models serve as digital shadows, enabling storage-efficient representation of vessel behaviour and facilitates appli- cations such as online situational awareness and marine-traffic manage- ment. These digital shadows serve as observer-layer in a dynamic au- tonomous system of system, offering insights into maritime activities and enhancing navigation safety in busy waterways. This research contributes to the advancement of autonomous navigation systems and supports ef- ficient maritime traffic management strategies.
Using Machine Learning for Optical Spectroscopy Data Analysis - Processing Multiple Spatially Resolved Reflection Spectroscopy Data with Continuous Feature Networks. Magnussen, Birk Martin. kassel university press, 2024.
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Living a healthy lifestyle is an ever-increasing priority. To facilitate such a healthy lifestyle, accurate, quick, and inexpensive feedback on diet quality is essential. Sensors based on multiple spatially resolved reflection spectroscopy aim to provide such feedback. However, current data processing algorithms require highly accurate hardware. This requirement for accuracy causes production costs of the sensors to be too expensive, while the application scope is too small to be viable for end-customers. In order to keep production costs low, new algorithms capable of handling production inaccuracies need to be developed. This thesis proposes such a novel neural network architecture called a continuous feature network. In addition to being well suited for the sensor data at hand, continuous feature networks are capable of compensating for sensor inaccuracies. A continuous feature network is also capable of predicting results from an input sample with partially missing data, allowing it to ignore certain production defects. In this thesis, continuous feature networks are proposed, implemented, trained, and investigated using real-world sensor data. To improve training, a novel method for semi-supervised learning based on the available datasets is introduced and evaluated. Based on the ability of the continuous feature network to operate on partially missing data, a novel explainable AI method is introduced, allowing to accurately quantify possible error sources for a measurement. The newly introduced methods are applied to the processing of sensor data, relaxing the requirement for highly accurate sensor hardware while increasing prediction accuracy. This enables a significant reduction in production rejects and thus sensor cost, while also allowing for the detection and prediction of new vitality parameters.
@phdthesis{magnussen2024using, abstract = {Living a healthy lifestyle is an ever-increasing priority. To facilitate such a healthy lifestyle, accurate, quick, and inexpensive feedback on diet quality is essential. Sensors based on multiple spatially resolved reflection spectroscopy aim to provide such feedback. However, current data processing algorithms require highly accurate hardware. This requirement for accuracy causes production costs of the sensors to be too expensive, while the application scope is too small to be viable for end-customers. In order to keep production costs low, new algorithms capable of handling production inaccuracies need to be developed. This thesis proposes such a novel neural network architecture called a continuous feature network. In addition to being well suited for the sensor data at hand, continuous feature networks are capable of compensating for sensor inaccuracies. A continuous feature network is also capable of predicting results from an input sample with partially missing data, allowing it to ignore certain production defects. In this thesis, continuous feature networks are proposed, implemented, trained, and investigated using real-world sensor data. To improve training, a novel method for semi-supervised learning based on the available datasets is introduced and evaluated. Based on the ability of the continuous feature network to operate on partially missing data, a novel explainable AI method is introduced, allowing to accurately quantify possible error sources for a measurement. The newly introduced methods are applied to the processing of sensor data, relaxing the requirement for highly accurate sensor hardware while increasing prediction accuracy. This enables a significant reduction in production rejects and thus sensor cost, while also allowing for the detection and prediction of new vitality parameters.}, author = {Magnussen, Birk Martin}, keywords = {itegpub}, publisher = {kassel university press}, school = {University of Kassel}, title = {Using Machine Learning for Optical Spectroscopy Data Analysis - Processing Multiple Spatially Resolved Reflection Spectroscopy Data with Continuous Feature Networks}, year = 2024 }
%0 Thesis %1 magnussen2024using %A Magnussen, Birk Martin %D 2024 %I kassel university press %R 10.17170/kobra-2025011410812 %T Using Machine Learning for Optical Spectroscopy Data Analysis - Processing Multiple Spatially Resolved Reflection Spectroscopy Data with Continuous Feature Networks %X Living a healthy lifestyle is an ever-increasing priority. To facilitate such a healthy lifestyle, accurate, quick, and inexpensive feedback on diet quality is essential. Sensors based on multiple spatially resolved reflection spectroscopy aim to provide such feedback. However, current data processing algorithms require highly accurate hardware. This requirement for accuracy causes production costs of the sensors to be too expensive, while the application scope is too small to be viable for end-customers. In order to keep production costs low, new algorithms capable of handling production inaccuracies need to be developed. This thesis proposes such a novel neural network architecture called a continuous feature network. In addition to being well suited for the sensor data at hand, continuous feature networks are capable of compensating for sensor inaccuracies. A continuous feature network is also capable of predicting results from an input sample with partially missing data, allowing it to ignore certain production defects. In this thesis, continuous feature networks are proposed, implemented, trained, and investigated using real-world sensor data. To improve training, a novel method for semi-supervised learning based on the available datasets is introduced and evaluated. Based on the ability of the continuous feature network to operate on partially missing data, a novel explainable AI method is introduced, allowing to accurately quantify possible error sources for a measurement. The newly introduced methods are applied to the processing of sensor data, relaxing the requirement for highly accurate sensor hardware while increasing prediction accuracy. This enables a significant reduction in production rejects and thus sensor cost, while also allowing for the detection and prediction of new vitality parameters.
Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation.Botache, Diego; Decke, Jens; Ripken, Winfried; Dornipati, Abhinay; Götz-Hahn, Franz; Ayeb, Mohamed; Sick, Bernhard. In arXiv e-prints, bl. arXiv:2309.13179v2. 2024.
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This paper presents a methodological framework for training, self-optimising, and self-organising surrogate models to approximate and speed up multiobjective optimisation of technical systems based on multiphysics simulations. At the hand of two real-world datasets, we illustrate that surrogate models can be trained on relatively small amounts of data to approximate the underlying simulations accurately. Including explainable AI techniques allow for highlighting feature relevancy or dependencies and supporting the possible extension of the used datasets. One of the datasets was created for this paper and is made publicly available for the broader scientific community. Extensive experiments combine four machine learning and deep learning algorithms with an evolutionary optimisation algorithm. The performance of the combined training and optimisation pipeline is evaluated by verifying the generated Pareto-optimal results using the ground truth simulations. The results from our pipeline and a comprehensive evaluation strategy show the potential for efficiently acquiring solution candidates in multiobjective optimisation tasks by reducing the number of simulations and conserving a higher prediction accuracy, i.e., with a MAPE score under 5% for one of the presented use cases.
@article{botache2024enhancing, abstract = {This paper presents a methodological framework for training, self-optimising, and self-organising surrogate models to approximate and speed up multiobjective optimisation of technical systems based on multiphysics simulations. At the hand of two real-world datasets, we illustrate that surrogate models can be trained on relatively small amounts of data to approximate the underlying simulations accurately. Including explainable AI techniques allow for highlighting feature relevancy or dependencies and supporting the possible extension of the used datasets. One of the datasets was created for this paper and is made publicly available for the broader scientific community. Extensive experiments combine four machine learning and deep learning algorithms with an evolutionary optimisation algorithm. The performance of the combined training and optimisation pipeline is evaluated by verifying the generated Pareto-optimal results using the ground truth simulations. The results from our pipeline and a comprehensive evaluation strategy show the potential for efficiently acquiring solution candidates in multiobjective optimisation tasks by reducing the number of simulations and conserving a higher prediction accuracy, i.e., with a MAPE score under 5% for one of the presented use cases.}, author = {Botache, Diego and Decke, Jens and Ripken, Winfried and Dornipati, Abhinay and Götz-Hahn, Franz and Ayeb, Mohamed and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2309.13179v2}, title = {Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation}, year = 2024 }
%0 Journal Article %1 botache2024enhancing %A Botache, Diego %A Decke, Jens %A Ripken, Winfried %A Dornipati, Abhinay %A Götz-Hahn, Franz %A Ayeb, Mohamed %A Sick, Bernhard %D 2024 %J arXiv e-prints %P arXiv:2309.13179v2 %T Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation %U https://arxiv.org/abs/2309.13179 %X This paper presents a methodological framework for training, self-optimising, and self-organising surrogate models to approximate and speed up multiobjective optimisation of technical systems based on multiphysics simulations. At the hand of two real-world datasets, we illustrate that surrogate models can be trained on relatively small amounts of data to approximate the underlying simulations accurately. Including explainable AI techniques allow for highlighting feature relevancy or dependencies and supporting the possible extension of the used datasets. One of the datasets was created for this paper and is made publicly available for the broader scientific community. Extensive experiments combine four machine learning and deep learning algorithms with an evolutionary optimisation algorithm. The performance of the combined training and optimisation pipeline is evaluated by verifying the generated Pareto-optimal results using the ground truth simulations. The results from our pipeline and a comprehensive evaluation strategy show the potential for efficiently acquiring solution candidates in multiobjective optimisation tasks by reducing the number of simulations and conserving a higher prediction accuracy, i.e., with a MAPE score under 5% for one of the presented use cases.
Time-Series Representation Learning via Heterogeneous Spatial-Temporal Contrasting for Remaining Useful Life Prediction.Huang, Zhixin; He, Yujiang; Nivarthi, Chandana Priya; Gruhl, Christian; Sick, Bernhard. In International Conference on Pattern Recognition (ICPR), ble. 1–21. Springer, 2024.
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Classical contrastive learning paradigms rely on manual augmentations like cropping, masking, dropping, or adding noise randomly to create divergent sample views from original data. However, the choice of which method to manipulate samples is often subjective and may destroy the latent pattern of the sample. In response, this paper introduces a novel contrastive learning paradigm without choosing sample view augmentation methods, termed Heterogeneous Spatial-Temporal Representation Contrasting (HSTRC). Instead of sample view augmentation, we employ dual branches with a heterogeneous spatial-temporal flipped structure to extract two distinct hidden feature views from the same source data, which avoids disturbing the original time series. Leveraging a combination of cross-branch spatial-temporal contrastive and projected feature contrastive loss functions, HSTRC can effectively extract robust representations from unlabeled time series data. Remarkably, by only fine-tuning the fully connected layers on top of extracted representations by HSTRC, we achieve the best performance across several Remaining Useful Life prediction datasets, showing up to 19.2% improvements over the state-of-the-art supervised learning methods and classical contrastive learning paradigms. Besides, further intensive experiments demonstrate HSTRC's effectiveness in active learning, out-of-distribution testing, and transfer learning scenarios.
@inproceedings{huang2024time, abstract = {Classical contrastive learning paradigms rely on manual augmentations like cropping, masking, dropping, or adding noise randomly to create divergent sample views from original data. However, the choice of which method to manipulate samples is often subjective and may destroy the latent pattern of the sample. In response, this paper introduces a novel contrastive learning paradigm without choosing sample view augmentation methods, termed Heterogeneous Spatial-Temporal Representation Contrasting (HSTRC). Instead of sample view augmentation, we employ dual branches with a heterogeneous spatial-temporal flipped structure to extract two distinct hidden feature views from the same source data, which avoids disturbing the original time series. Leveraging a combination of cross-branch spatial-temporal contrastive and projected feature contrastive loss functions, HSTRC can effectively extract robust representations from unlabeled time series data. Remarkably, by only fine-tuning the fully connected layers on top of extracted representations by HSTRC, we achieve the best performance across several Remaining Useful Life prediction datasets, showing up to 19.2% improvements over the state-of-the-art supervised learning methods and classical contrastive learning paradigms. Besides, further intensive experiments demonstrate HSTRC's effectiveness in active learning, out-of-distribution testing, and transfer learning scenarios.}, author = {Huang, Zhixin and He, Yujiang and Nivarthi, Chandana Priya and Gruhl, Christian and Sick, Bernhard}, booktitle = {International Conference on Pattern Recognition (ICPR)}, keywords = {itegpub}, pages = {1–21}, publisher = {Springer}, title = {Time-Series Representation Learning via Heterogeneous Spatial-Temporal Contrasting for Remaining Useful Life Prediction}, year = 2024 }
%0 Conference Paper %1 huang2024time %A Huang, Zhixin %A He, Yujiang %A Nivarthi, Chandana Priya %A Gruhl, Christian %A Sick, Bernhard %B International Conference on Pattern Recognition (ICPR) %D 2024 %I Springer %P 1--21 %R 10.1007/978-3-031-78398-2_1 %T Time-Series Representation Learning via Heterogeneous Spatial-Temporal Contrasting for Remaining Useful Life Prediction %X Classical contrastive learning paradigms rely on manual augmentations like cropping, masking, dropping, or adding noise randomly to create divergent sample views from original data. However, the choice of which method to manipulate samples is often subjective and may destroy the latent pattern of the sample. In response, this paper introduces a novel contrastive learning paradigm without choosing sample view augmentation methods, termed Heterogeneous Spatial-Temporal Representation Contrasting (HSTRC). Instead of sample view augmentation, we employ dual branches with a heterogeneous spatial-temporal flipped structure to extract two distinct hidden feature views from the same source data, which avoids disturbing the original time series. Leveraging a combination of cross-branch spatial-temporal contrastive and projected feature contrastive loss functions, HSTRC can effectively extract robust representations from unlabeled time series data. Remarkably, by only fine-tuning the fully connected layers on top of extracted representations by HSTRC, we achieve the best performance across several Remaining Useful Life prediction datasets, showing up to 19.2% improvements over the state-of-the-art supervised learning methods and classical contrastive learning paradigms. Besides, further intensive experiments demonstrate HSTRC's effectiveness in active learning, out-of-distribution testing, and transfer learning scenarios.

2023[ to top ]

Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering.Aßenmacher, Matthias; Rauch, Lukas; Goschenhofer, Jann; Stephan, Andreas; Bischl, Bernd; Roth, Benjamin; Sick, Bernhard. In Workshop on Interactive Adapative Learning (IAL), ECML PKDD, ble. 65–73. 2023.
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Three fields revolving around the question of how to cope with limited amounts of labeled data are Deep Active Learning (DAL), deep Constrained Clustering (CC), and Weakly Supervised Learning (WSL). DAL tackles the problem by adaptively posing the question of which data samples to annotate next in order to achieve the best incremental learning improvement, although it suffers from several limitations that hinder its deployment in practical settings. We point out how CC algorithms and WSL could be employed to overcome these limitations and increase the practical applicability of DAL research. Specifically, we discuss the opportunities to use the class discovery capabilities of CC and the possibility of further reducing human annotation efforts by utilizing WSL. We argue that the practical applicability of DAL algorithms will benefit from employing CC and WSL methods for the learning and labeling process. We inspect the overlaps between the three research areas and identify relevant and exciting research questions at the intersection of these areas.
@inproceedings{assenmacher2023towards, abstract = {Three fields revolving around the question of how to cope with limited amounts of labeled data are Deep Active Learning (DAL), deep Constrained Clustering (CC), and Weakly Supervised Learning (WSL). DAL tackles the problem by adaptively posing the question of which data samples to annotate next in order to achieve the best incremental learning improvement, although it suffers from several limitations that hinder its deployment in practical settings. We point out how CC algorithms and WSL could be employed to overcome these limitations and increase the practical applicability of DAL research. Specifically, we discuss the opportunities to use the class discovery capabilities of CC and the possibility of further reducing human annotation efforts by utilizing WSL. We argue that the practical applicability of DAL algorithms will benefit from employing CC and WSL methods for the learning and labeling process. We inspect the overlaps between the three research areas and identify relevant and exciting research questions at the intersection of these areas.}, author = {Aßenmacher, Matthias and Rauch, Lukas and Goschenhofer, Jann and Stephan, Andreas and Bischl, Bernd and Roth, Benjamin and Sick, Bernhard}, booktitle = {Workshop on Interactive Adapative Learning (IAL), ECML PKDD}, keywords = {itegpub}, pages = {65–73}, title = {Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering}, year = 2023 }
%0 Conference Paper %1 assenmacher2023towards %A Aßenmacher, Matthias %A Rauch, Lukas %A Goschenhofer, Jann %A Stephan, Andreas %A Bischl, Bernd %A Roth, Benjamin %A Sick, Bernhard %B Workshop on Interactive Adapative Learning (IAL), ECML PKDD %D 2023 %P 65--73 %T Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering %U https://ceur-ws.org/Vol-3470/paper7.pdf %X Three fields revolving around the question of how to cope with limited amounts of labeled data are Deep Active Learning (DAL), deep Constrained Clustering (CC), and Weakly Supervised Learning (WSL). DAL tackles the problem by adaptively posing the question of which data samples to annotate next in order to achieve the best incremental learning improvement, although it suffers from several limitations that hinder its deployment in practical settings. We point out how CC algorithms and WSL could be employed to overcome these limitations and increase the practical applicability of DAL research. Specifically, we discuss the opportunities to use the class discovery capabilities of CC and the possibility of further reducing human annotation efforts by utilizing WSL. We argue that the practical applicability of DAL algorithms will benefit from employing CC and WSL methods for the learning and labeling process. We inspect the overlaps between the three research areas and identify relevant and exciting research questions at the intersection of these areas.
Sensor Equivariance by LiDAR Projection Images.Reichert, Hannes; Hetzel, Manuel; Schreck, Steven; Doll, Konrad; Sick, Bernhard. In IEEE Intelligent Vehicles Symposium (IV), ble. 1–6. IEEE, 2023.
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In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. This addresses the issue of sensor-dependent object representation in projection-based sensors, such as LiDAR, which can lead to distorted physical and geometric properties due to variations in sensor resolution and field of view. To that end, we propose an architecture for processing this data in an instance segmentation framework. We focus specifically on LiDAR as a key sensor modality for machine vision tasks and highly automated driving (HAD). Through an experimental setup in a controlled synthetic environment, we identify a bias on sensor resolution and field of view and demonstrate that our proposed method can reduce said bias for the task of LiDAR instance segmentation. Furthermore, we define our method such that it can be applied to other projection-based sensors, such as cameras. To promote transparency, we make our code and dataset publicly available. This method shows the potential to improve performance and robustness in various machine vision tasks that utilize projection-based sensors.
@inproceedings{reichert2023sensor, abstract = {In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. This addresses the issue of sensor-dependent object representation in projection-based sensors, such as LiDAR, which can lead to distorted physical and geometric properties due to variations in sensor resolution and field of view. To that end, we propose an architecture for processing this data in an instance segmentation framework. We focus specifically on LiDAR as a key sensor modality for machine vision tasks and highly automated driving (HAD). Through an experimental setup in a controlled synthetic environment, we identify a bias on sensor resolution and field of view and demonstrate that our proposed method can reduce said bias for the task of LiDAR instance segmentation. Furthermore, we define our method such that it can be applied to other projection-based sensors, such as cameras. To promote transparency, we make our code and dataset publicly available. This method shows the potential to improve performance and robustness in various machine vision tasks that utilize projection-based sensors.}, author = {Reichert, Hannes and Hetzel, Manuel and Schreck, Steven and Doll, Konrad and Sick, Bernhard}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {itegpub}, pages = {1–6}, publisher = {IEEE}, title = {Sensor Equivariance by {LiDAR} Projection Images}, year = 2023 }
%0 Conference Paper %1 reichert2023sensor %A Reichert, Hannes %A Hetzel, Manuel %A Schreck, Steven %A Doll, Konrad %A Sick, Bernhard %B IEEE Intelligent Vehicles Symposium (IV) %D 2023 %I IEEE %P 1--6 %R 10.1109/IV55152.2023.10186817 %T Sensor Equivariance by LiDAR Projection Images %X In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. This addresses the issue of sensor-dependent object representation in projection-based sensors, such as LiDAR, which can lead to distorted physical and geometric properties due to variations in sensor resolution and field of view. To that end, we propose an architecture for processing this data in an instance segmentation framework. We focus specifically on LiDAR as a key sensor modality for machine vision tasks and highly automated driving (HAD). Through an experimental setup in a controlled synthetic environment, we identify a bias on sensor resolution and field of view and demonstrate that our proposed method can reduce said bias for the task of LiDAR instance segmentation. Furthermore, we define our method such that it can be applied to other projection-based sensors, such as cameras. To promote transparency, we make our code and dataset publicly available. This method shows the potential to improve performance and robustness in various machine vision tasks that utilize projection-based sensors.
Towards Few-Shot Time Series Anomaly Detection with Temporal Attention and Dynamic Thresholding.Nivarthi, Chandana Priya; Sick, Bernhard. In International Conference on Machine Learning and Applications (ICMLA), ble. 1444–1450. IEEE, 2023.
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Anomaly detection plays a pivotal role in diverse realworld applications such as cybersecurity, fault detection, network monitoring, predictive maintenance, and highly automated driving. However, obtaining labeled anomalous data can be a formidable challenge, especially when anomalies exhibit temporal evolution. This paper introduces LATAM (Long short-term memory Autoencoder with Temporal Attention Mechanism) for few-shot anomaly detection, with the aim of enhancing detection performance in scenarios with limited labeled anomaly data. LATAM effectively captures temporal dependencies and emphasizes significant patterns in multivariate time series data. In our investigation, we comprehensively evaluate LATAM against other anomaly detection models, particularly assessing its capability in few-shot learning scenarios where we have minimal examples from the normal class and none from the anomalous class in the training data. Our experimental results, derived from real-world photovoltaic inverter data, highlight LATAM’s superiority, showcasing a substantial 27% mean F1 score improvement, even when trained on a mere two-week dataset. Furthermore, LATAM demonstrates remarkable results on the open-source SWaT dataset, achieving a 12% boost in accuracy with only two days of training data. Moreover, we introduce a simple yet effective dynamic thresholding mechanism, further enhancing the anomaly detection capabilities of LATAM. This underscores LATAM’s efficacy in addressing the challenges posed by limited labeled anomalies in practical scenarios and it proves valuable for downstream tasks involving temporal representation and time series prediction, extending its utility beyond anomaly detection applications.
@inproceedings{nivarthi2023towards, abstract = {Anomaly detection plays a pivotal role in diverse realworld applications such as cybersecurity, fault detection, network monitoring, predictive maintenance, and highly automated driving. However, obtaining labeled anomalous data can be a formidable challenge, especially when anomalies exhibit temporal evolution. This paper introduces LATAM (Long short-term memory Autoencoder with Temporal Attention Mechanism) for few-shot anomaly detection, with the aim of enhancing detection performance in scenarios with limited labeled anomaly data. LATAM effectively captures temporal dependencies and emphasizes significant patterns in multivariate time series data. In our investigation, we comprehensively evaluate LATAM against other anomaly detection models, particularly assessing its capability in few-shot learning scenarios where we have minimal examples from the normal class and none from the anomalous class in the training data. Our experimental results, derived from real-world photovoltaic inverter data, highlight LATAM’s superiority, showcasing a substantial 27% mean F1 score improvement, even when trained on a mere two-week dataset. Furthermore, LATAM demonstrates remarkable results on the open-source SWaT dataset, achieving a 12% boost in accuracy with only two days of training data. Moreover, we introduce a simple yet effective dynamic thresholding mechanism, further enhancing the anomaly detection capabilities of LATAM. This underscores LATAM’s efficacy in addressing the challenges posed by limited labeled anomalies in practical scenarios and it proves valuable for downstream tasks involving temporal representation and time series prediction, extending its utility beyond anomaly detection applications.}, author = {Nivarthi, Chandana Priya and Sick, Bernhard}, booktitle = {International Conference on Machine Learning and Applications (ICMLA)}, keywords = {itegpub}, pages = {1444–1450}, publisher = {IEEE}, title = {Towards Few-Shot Time Series Anomaly Detection with Temporal Attention and Dynamic Thresholding}, year = 2023 }
%0 Conference Paper %1 nivarthi2023towards %A Nivarthi, Chandana Priya %A Sick, Bernhard %B International Conference on Machine Learning and Applications (ICMLA) %D 2023 %I IEEE %P 1444--1450 %R 10.1109/ICMLA58977.2023.00218 %T Towards Few-Shot Time Series Anomaly Detection with Temporal Attention and Dynamic Thresholding %X Anomaly detection plays a pivotal role in diverse realworld applications such as cybersecurity, fault detection, network monitoring, predictive maintenance, and highly automated driving. However, obtaining labeled anomalous data can be a formidable challenge, especially when anomalies exhibit temporal evolution. This paper introduces LATAM (Long short-term memory Autoencoder with Temporal Attention Mechanism) for few-shot anomaly detection, with the aim of enhancing detection performance in scenarios with limited labeled anomaly data. LATAM effectively captures temporal dependencies and emphasizes significant patterns in multivariate time series data. In our investigation, we comprehensively evaluate LATAM against other anomaly detection models, particularly assessing its capability in few-shot learning scenarios where we have minimal examples from the normal class and none from the anomalous class in the training data. Our experimental results, derived from real-world photovoltaic inverter data, highlight LATAM’s superiority, showcasing a substantial 27% mean F1 score improvement, even when trained on a mere two-week dataset. Furthermore, LATAM demonstrates remarkable results on the open-source SWaT dataset, achieving a 12% boost in accuracy with only two days of training data. Moreover, we introduce a simple yet effective dynamic thresholding mechanism, further enhancing the anomaly detection capabilities of LATAM. This underscores LATAM’s efficacy in addressing the challenges posed by limited labeled anomalies in practical scenarios and it proves valuable for downstream tasks involving temporal representation and time series prediction, extending its utility beyond anomaly detection applications.
Self-Integration and Agent Compatibility.Gruhl, Christian; Sick, Bernhard. In Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), ble. 71–73. IEEE, 2023.
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This position paper addresses the issue of agent compatibility in the context of self-aware microsystems (SAMs), which are SISSY systems with self-awareness capabilities. SAMs are designed to adapt their behaviour and collaborate with other agents in multi-agent settings. Achieving compatibility and effective collaboration between agents pose significant challenges, such as detecting incompatibility and discerning it from malicious behaviour. We present our concept of agent compatibility, highlight future challenges, and propose directions for future research in this area. By focusing on agent compatibility, we aim to advance the understanding of SAMs and promote successful collaboration among agents.
@inproceedings{gruhl2023self, abstract = {This position paper addresses the issue of agent compatibility in the context of self-aware microsystems (SAMs), which are SISSY systems with self-awareness capabilities. SAMs are designed to adapt their behaviour and collaborate with other agents in multi-agent settings. Achieving compatibility and effective collaboration between agents pose significant challenges, such as detecting incompatibility and discerning it from malicious behaviour. We present our concept of agent compatibility, highlight future challenges, and propose directions for future research in this area. By focusing on agent compatibility, we aim to advance the understanding of SAMs and promote successful collaboration among agents.}, author = {Gruhl, Christian and Sick, Bernhard}, booktitle = {Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)}, keywords = {itegpub}, pages = {71–73}, publisher = {IEEE}, title = {Self-Integration and Agent Compatibility}, year = 2023 }
%0 Conference Paper %1 gruhl2023self %A Gruhl, Christian %A Sick, Bernhard %B Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) %D 2023 %I IEEE %P 71--73 %R 10.1109/ACSOS-C58168.2023.00039 %T Self-Integration and Agent Compatibility %U https://ieeexplore.ieee.org/abstract/document/10336212 %X This position paper addresses the issue of agent compatibility in the context of self-aware microsystems (SAMs), which are SISSY systems with self-awareness capabilities. SAMs are designed to adapt their behaviour and collaborate with other agents in multi-agent settings. Achieving compatibility and effective collaboration between agents pose significant challenges, such as detecting incompatibility and discerning it from malicious behaviour. We present our concept of agent compatibility, highlight future challenges, and propose directions for future research in this area. By focusing on agent compatibility, we aim to advance the understanding of SAMs and promote successful collaboration among agents.
Height Change Feature Based Free Space Detection.Schreck, Steven; Reichert, Hannes; Hetzel, Manuel; Doll, Konrad; Sick, Bernhard. In International Conference on Control, Mechatronics and Automation (ICCMA), ble. 171–176. IEEE, 2023.
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We present a novel method for free space detection in dynamic environments like factory sites, crucial for autonomous forklifts to avoid collisions. It introduces a technique for fast surface normal estimation using spherical projected LiDAR data, which helps in detecting free space efficiently and in real-time. The method's effectiveness is proven with a 50.90% mIoU score on the Semantic KITTI dataset at 105 Hz, and a 63.30% mIoU score on a factory site dataset at 54 Hz.
@inproceedings{schreck2023height, abstract = {We present a novel method for free space detection in dynamic environments like factory sites, crucial for autonomous forklifts to avoid collisions. It introduces a technique for fast surface normal estimation using spherical projected LiDAR data, which helps in detecting free space efficiently and in real-time. The method's effectiveness is proven with a 50.90% mIoU score on the Semantic KITTI dataset at 105 Hz, and a 63.30% mIoU score on a factory site dataset at 54 Hz.}, author = {Schreck, Steven and Reichert, Hannes and Hetzel, Manuel and Doll, Konrad and Sick, Bernhard}, booktitle = {International Conference on Control, Mechatronics and Automation (ICCMA)}, keywords = {itegpub}, pages = {171–176}, publisher = {IEEE}, title = {Height Change Feature Based Free Space Detection}, year = 2023 }
%0 Conference Paper %1 schreck2023height %A Schreck, Steven %A Reichert, Hannes %A Hetzel, Manuel %A Doll, Konrad %A Sick, Bernhard %B International Conference on Control, Mechatronics and Automation (ICCMA) %D 2023 %I IEEE %P 171--176 %R 10.1109/ICCMA59762.2023.10374705 %T Height Change Feature Based Free Space Detection %X We present a novel method for free space detection in dynamic environments like factory sites, crucial for autonomous forklifts to avoid collisions. It introduces a technique for fast surface normal estimation using spherical projected LiDAR data, which helps in detecting free space efficiently and in real-time. The method's effectiveness is proven with a 50.90% mIoU score on the Semantic KITTI dataset at 105 Hz, and a 63.30% mIoU score on a factory site dataset at 54 Hz.
Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy.Sandrock, Christoph; Herde, Marek; Kottke, Daniel; Sick, Bernhard. In Discovery Science (DS), ble. 265–276. Springer, 2023.
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Active learning aims to reduce the amount of labeled data while maximizing machine learning models’ performances. Currently, there is sparse research on the potential of an optimal active learning strategy. Therefore, we propose a non-myopic oracle policy that accesses the true labels of the data pool to approximate an optimal active learning strategy. We evaluate how the hyperparameters of this oracle policy influence its performance and empirically demonstrate that it is an upper baseline for common active learning strategies while being faster than a state-of-the-art oracle policy. For the sake of reproducibility, all the code related to our research is publicly available on our GitHub repository at https://github.com/ies-research/non-myopic-oracle-policy.
@inproceedings{sandrock2023exploring, abstract = {Active learning aims to reduce the amount of labeled data while maximizing machine learning models’ performances. Currently, there is sparse research on the potential of an optimal active learning strategy. Therefore, we propose a non-myopic oracle policy that accesses the true labels of the data pool to approximate an optimal active learning strategy. We evaluate how the hyperparameters of this oracle policy influence its performance and empirically demonstrate that it is an upper baseline for common active learning strategies while being faster than a state-of-the-art oracle policy. For the sake of reproducibility, all the code related to our research is publicly available on our GitHub repository at https://github.com/ies-research/non-myopic-oracle-policy.}, author = {Sandrock, Christoph and Herde, Marek and Kottke, Daniel and Sick, Bernhard}, booktitle = {Discovery Science (DS)}, keywords = {itegpub}, pages = {265–276}, publisher = {Springer}, title = {Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy}, year = 2023 }
%0 Conference Paper %1 sandrock2023exploring %A Sandrock, Christoph %A Herde, Marek %A Kottke, Daniel %A Sick, Bernhard %B Discovery Science (DS) %D 2023 %I Springer %P 265--276 %R 10.1007/978-3-031-45275-8_18 %T Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy %U https://link.springer.com/chapter/10.1007/978-3-031-45275-8_18 %X Active learning aims to reduce the amount of labeled data while maximizing machine learning models’ performances. Currently, there is sparse research on the potential of an optimal active learning strategy. Therefore, we propose a non-myopic oracle policy that accesses the true labels of the data pool to approximate an optimal active learning strategy. We evaluate how the hyperparameters of this oracle policy influence its performance and empirically demonstrate that it is an upper baseline for common active learning strategies while being faster than a state-of-the-art oracle policy. For the sake of reproducibility, all the code related to our research is publicly available on our GitHub repository at https://github.com/ies-research/non-myopic-oracle-policy.
Machine Learning Supported Optimisation and Experimental Evaluation of Electrical Motors for Small Urban Passenger Vehicles.Botache, Diego. In Organic Computing -- Doctoral Dissertation Colloquium 2023, S. Tomforde, C. Krupitzer (reds.), ble. 14–26. kassel university press, 2023.
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The increasing environmental pollution and rising global energy demands require the rapid development of novel and sustainable traction machines from urban transport up to large-scale transport vehicles. The optimisation of the development cycle of electrical traction machines plays a vital role in sustainable mobility. Still, developers have to face the difficulty of analysing time-consuming multiphysics simulations to assess the performance of many prototype variants. Accelerating this process involves implementing machine learning-supported strategies that can effectively acquire results and predictions about the performance and efficiency of prototypes in an iterative optimisation process and detect possible issues during the experimental procedure. The following research project streamlines the development cycle by identifying and presenting multiple strategies for applying and evaluating machine learning and deep learning techniques in different stages and areas in the development cycle including self-optimisation and self-adaptation capabilities. By implementing these strategies, the optimisation process can be further improved and become more efficient and robust, reducing the time and cost required for testing and validation. Therefore, this proposal addresses two application areas. The first area consists of the supported machine learning motor performance prediction at the topology design and optimisation stage of structural components. The second area corresponds to the machine learning-supported experimental evaluation process of prototypes at desired test benches.
@incollection{botache2023machine, abstract = {The increasing environmental pollution and rising global energy demands require the rapid development of novel and sustainable traction machines from urban transport up to large-scale transport vehicles. The optimisation of the development cycle of electrical traction machines plays a vital role in sustainable mobility. Still, developers have to face the difficulty of analysing time-consuming multiphysics simulations to assess the performance of many prototype variants. Accelerating this process involves implementing machine learning-supported strategies that can effectively acquire results and predictions about the performance and efficiency of prototypes in an iterative optimisation process and detect possible issues during the experimental procedure. The following research project streamlines the development cycle by identifying and presenting multiple strategies for applying and evaluating machine learning and deep learning techniques in different stages and areas in the development cycle including self-optimisation and self-adaptation capabilities. By implementing these strategies, the optimisation process can be further improved and become more efficient and robust, reducing the time and cost required for testing and validation. Therefore, this proposal addresses two application areas. The first area consists of the supported machine learning motor performance prediction at the topology design and optimisation stage of structural components. The second area corresponds to the machine learning-supported experimental evaluation process of prototypes at desired test benches.}, author = {Botache, Diego}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2023}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {14–26}, publisher = {kassel university press}, title = {Machine Learning Supported Optimisation and Experimental Evaluation of Electrical Motors for Small Urban Passenger Vehicles}, year = 2023 }
%0 Book Section %1 botache2023machine %A Botache, Diego %B Organic Computing -- Doctoral Dissertation Colloquium 2023 %D 2023 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 14--26 %T Machine Learning Supported Optimisation and Experimental Evaluation of Electrical Motors for Small Urban Passenger Vehicles %X The increasing environmental pollution and rising global energy demands require the rapid development of novel and sustainable traction machines from urban transport up to large-scale transport vehicles. The optimisation of the development cycle of electrical traction machines plays a vital role in sustainable mobility. Still, developers have to face the difficulty of analysing time-consuming multiphysics simulations to assess the performance of many prototype variants. Accelerating this process involves implementing machine learning-supported strategies that can effectively acquire results and predictions about the performance and efficiency of prototypes in an iterative optimisation process and detect possible issues during the experimental procedure. The following research project streamlines the development cycle by identifying and presenting multiple strategies for applying and evaluating machine learning and deep learning techniques in different stages and areas in the development cycle including self-optimisation and self-adaptation capabilities. By implementing these strategies, the optimisation process can be further improved and become more efficient and robust, reducing the time and cost required for testing and validation. Therefore, this proposal addresses two application areas. The first area consists of the supported machine learning motor performance prediction at the topology design and optimisation stage of structural components. The second area corresponds to the machine learning-supported experimental evaluation process of prototypes at desired test benches.
Self-awareness in Cyber-Physical Systems: Recent Developments and Open Challenges.Esterle, Lukas; Dutt, Nikil; Gruhl, Christian; Lewis, Peter R.; Marcenaro, Lucio; Regazzoni, Carlo; Jantsch, Axel. In Design, Automation & Test in Europe Conference & Exhibition (DATE), ble. 1–6. IEEE, 2023.
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Self-aware computing systems enable computing systems to reflect on their actions and behavior. This becomes even more relevant in Cyber-Physical Systems where computing systems have to control and interact with elements in the real world. This paper reports on recent advances made in computational self-awareness for cyber-physical systems.
@inproceedings{esterle2023self, abstract = {Self-aware computing systems enable computing systems to reflect on their actions and behavior. This becomes even more relevant in Cyber-Physical Systems where computing systems have to control and interact with elements in the real world. This paper reports on recent advances made in computational self-awareness for cyber-physical systems.}, author = {Esterle, Lukas and Dutt, Nikil and Gruhl, Christian and Lewis, Peter R. and Marcenaro, Lucio and Regazzoni, Carlo and Jantsch, Axel}, booktitle = {Design, Automation & Test in Europe Conference & Exhibition (DATE)}, keywords = {itegpub}, pages = {1–6}, publisher = {IEEE}, title = {Self-awareness in Cyber-Physical Systems: Recent Developments and Open Challenges}, year = 2023 }
%0 Conference Paper %1 esterle2023self %A Esterle, Lukas %A Dutt, Nikil %A Gruhl, Christian %A Lewis, Peter R. %A Marcenaro, Lucio %A Regazzoni, Carlo %A Jantsch, Axel %B Design, Automation & Test in Europe Conference & Exhibition (DATE) %D 2023 %I IEEE %P 1--6 %R 10.23919/DATE56975.2023.10137197 %T Self-awareness in Cyber-Physical Systems: Recent Developments and Open Challenges %U https://ieeexplore.ieee.org/abstract/document/10137197 %X Self-aware computing systems enable computing systems to reflect on their actions and behavior. This becomes even more relevant in Cyber-Physical Systems where computing systems have to control and interact with elements in the real world. This paper reports on recent advances made in computational self-awareness for cyber-physical systems.
Utilizing Continuous Kernels for Processing Irregularly and Inconsistently Sampled Data With Position-Dependent Features.Magnussen, Birk Martin; Stern, Claudius; Sick, Bernhard. In International Conference on Autonomic and Autonomous Systems (ICAS), ble. 49–53. ThinkMind, 2023.
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Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable for certain types of data, where the position of detected features is relevant. However, the capabilities of continuous kernels to process irregularly sampled data are still desired. This article introduces a novel method utilizing continuous kernels for detecting global features at absolute positions in the data domain. Through a use case in processing multiple spatially resolved reflection spectroscopy data, which is sampled irregularly and inconsistently, we show that the proposed method is capable of processing such data natively without additional preprocessing as is needed using comparable methods. In addition, we show that the proposed method is able to achieve a higher prediction accuracy than a comparable network on a dataset with position-dependent features. Furthermore, a higher robustness to missing data compared to a benchmark network using data interpolation is observed, which allows the network to adapt to sensors with individual failed components without the need for retraining.
@inproceedings{magnussen2023utilizing, abstract = {Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable for certain types of data, where the position of detected features is relevant. However, the capabilities of continuous kernels to process irregularly sampled data are still desired. This article introduces a novel method utilizing continuous kernels for detecting global features at absolute positions in the data domain. Through a use case in processing multiple spatially resolved reflection spectroscopy data, which is sampled irregularly and inconsistently, we show that the proposed method is capable of processing such data natively without additional preprocessing as is needed using comparable methods. In addition, we show that the proposed method is able to achieve a higher prediction accuracy than a comparable network on a dataset with position-dependent features. Furthermore, a higher robustness to missing data compared to a benchmark network using data interpolation is observed, which allows the network to adapt to sensors with individual failed components without the need for retraining.}, author = {Magnussen, Birk Martin and Stern, Claudius and Sick, Bernhard}, booktitle = {International Conference on Autonomic and Autonomous Systems (ICAS)}, keywords = {itegpub}, pages = {49–53}, publisher = {ThinkMind}, title = {Utilizing Continuous Kernels for Processing Irregularly and Inconsistently Sampled Data With Position-Dependent Features}, year = 2023 }
%0 Conference Paper %1 magnussen2023utilizing %A Magnussen, Birk Martin %A Stern, Claudius %A Sick, Bernhard %B International Conference on Autonomic and Autonomous Systems (ICAS) %D 2023 %I ThinkMind %P 49--53 %T Utilizing Continuous Kernels for Processing Irregularly and Inconsistently Sampled Data With Position-Dependent Features %U http://www.thinkmind.org/index.php?view=article&articleid=icas_2023_1_90_20043 %X Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable for certain types of data, where the position of detected features is relevant. However, the capabilities of continuous kernels to process irregularly sampled data are still desired. This article introduces a novel method utilizing continuous kernels for detecting global features at absolute positions in the data domain. Through a use case in processing multiple spatially resolved reflection spectroscopy data, which is sampled irregularly and inconsistently, we show that the proposed method is capable of processing such data natively without additional preprocessing as is needed using comparable methods. In addition, we show that the proposed method is able to achieve a higher prediction accuracy than a comparable network on a dataset with position-dependent features. Furthermore, a higher robustness to missing data compared to a benchmark network using data interpolation is observed, which allows the network to adapt to sensors with individual failed components without the need for retraining.
Sampling-based Uncertainty Estimation for an Instance Segmentation Network.Heidecker, Florian; El-Khateeb, Ahmad; Sick, Bernhard. In arXiv e-prints, bl. arXiv:2305.14977. 2023.
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The examination of uncertainty in the predictions of machine learning (ML) models is receiving increasing attention. One uncertainty modeling technique used for this purpose is Monte-Carlo (MC)-Dropout, where repeated predictions are generated for a single input. Therefore, clustering is required to describe the resulting uncertainty, but only through efficient clustering is it possible to describe the uncertainty from the model attached to each object. This article uses Bayesian Gaussian Mixture (BGM) to solve this problem. In addition, we investigate different values for the dropout rate and other techniques, such as focal loss and calibration, which we integrate into the Mask-RCNN model to obtain the most accurate uncertainty approximation of each instance and showcase it graphically.
@article{heidecker2023sampling, abstract = {The examination of uncertainty in the predictions of machine learning (ML) models is receiving increasing attention. One uncertainty modeling technique used for this purpose is Monte-Carlo (MC)-Dropout, where repeated predictions are generated for a single input. Therefore, clustering is required to describe the resulting uncertainty, but only through efficient clustering is it possible to describe the uncertainty from the model attached to each object. This article uses Bayesian Gaussian Mixture (BGM) to solve this problem. In addition, we investigate different values for the dropout rate and other techniques, such as focal loss and calibration, which we integrate into the Mask-RCNN model to obtain the most accurate uncertainty approximation of each instance and showcase it graphically.}, author = {Heidecker, Florian and El-Khateeb, Ahmad and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2305.14977}, title = {Sampling-based Uncertainty Estimation for an Instance Segmentation Network}, year = 2023 }
%0 Journal Article %1 heidecker2023sampling %A Heidecker, Florian %A El-Khateeb, Ahmad %A Sick, Bernhard %D 2023 %J arXiv e-prints %P arXiv:2305.14977 %T Sampling-based Uncertainty Estimation for an Instance Segmentation Network %U https://arxiv.org/abs/2305.14977 %X The examination of uncertainty in the predictions of machine learning (ML) models is receiving increasing attention. One uncertainty modeling technique used for this purpose is Monte-Carlo (MC)-Dropout, where repeated predictions are generated for a single input. Therefore, clustering is required to describe the resulting uncertainty, but only through efficient clustering is it possible to describe the uncertainty from the model attached to each object. This article uses Bayesian Gaussian Mixture (BGM) to solve this problem. In addition, we investigate different values for the dropout rate and other techniques, such as focal loss and calibration, which we integrate into the Mask-RCNN model to obtain the most accurate uncertainty approximation of each instance and showcase it graphically.
Dataset of a parameterized U-bend flow for deep learning application.Decke, Jens; Wünsch, Olaf; Sick, Bernhard. In Data in Brief, 50(1), bl. 109477. 2023.
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This dataset contains 10,000 fluid flow and heat transfer simulations in U-bend shapes. Each of them is described by 28 design parameters, which are processed with the help of Computational Fluid Dynamics methods. The dataset provides a comprehensive benchmark for investigating various problems and methods from the field of design optimization. For these investigations supervised, semi-supervised and unsupervised deep learning approaches can be employed. One unique feature of this dataset is that each shape can be represented by three distinct data types including design parameter and objective combinations, five different resolutions of 2D images from the geometry and the solution variables of the numerical simulation, as well as a representation using the cell values of the numerical mesh. This third representation enables considering the specific data structure of numerical simulations for deep learning approaches. The source code and the container used to generate the data are published as part of this work.
@article{decke2023dataset, abstract = {This dataset contains 10,000 fluid flow and heat transfer simulations in U-bend shapes. Each of them is described by 28 design parameters, which are processed with the help of Computational Fluid Dynamics methods. The dataset provides a comprehensive benchmark for investigating various problems and methods from the field of design optimization. For these investigations supervised, semi-supervised and unsupervised deep learning approaches can be employed. One unique feature of this dataset is that each shape can be represented by three distinct data types including design parameter and objective combinations, five different resolutions of 2D images from the geometry and the solution variables of the numerical simulation, as well as a representation using the cell values of the numerical mesh. This third representation enables considering the specific data structure of numerical simulations for deep learning approaches. The source code and the container used to generate the data are published as part of this work.}, author = {Decke, Jens and Wünsch, Olaf and Sick, Bernhard}, journal = {Data in Brief}, keywords = {MachineLearning}, number = 1, pages = 109477, title = {Dataset of a parameterized U-bend flow for deep learning application}, volume = 50, year = 2023 }
%0 Journal Article %1 decke2023dataset %A Decke, Jens %A Wünsch, Olaf %A Sick, Bernhard %D 2023 %J Data in Brief %N 1 %P 109477 %R https://doi.org/10.1016/j.dib.2023.109477 %T Dataset of a parameterized U-bend flow for deep learning application %U https://www.sciencedirect.com/science/article/pii/S2352340923005772 %V 50 %X This dataset contains 10,000 fluid flow and heat transfer simulations in U-bend shapes. Each of them is described by 28 design parameters, which are processed with the help of Computational Fluid Dynamics methods. The dataset provides a comprehensive benchmark for investigating various problems and methods from the field of design optimization. For these investigations supervised, semi-supervised and unsupervised deep learning approaches can be employed. One unique feature of this dataset is that each shape can be represented by three distinct data types including design parameter and objective combinations, five different resolutions of 2D images from the geometry and the solution variables of the numerical simulation, as well as a representation using the cell values of the numerical mesh. This third representation enables considering the specific data structure of numerical simulations for deep learning approaches. The source code and the container used to generate the data are published as part of this work.
Role of Hyperparameters in Deep Active Learning.Huseljic, Denis; Herde, Marek; Hahn, Paul; Sick, Bernhard. In Workshop on Interactive Adaptive Learning (IAL), ECML PKDD, ble. 19–24. 2023.
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Despite considerable research on pool-based deep active learning (DAL), deploying DAL into real applications still has several challenges. A frequently neglected aspect is the choice of training hyperparameters (HPs), such as the learning rate. Since these HPs determine how the deep neural network learns in each cycle, they must be chosen carefully. In this article, we analyze the role of HPs in DAL. We find that optimizing HPs reduces the performance gap between DAL strategies. Conversely, we highlight challenges in finding optimal HPs when using datasets selected via DAL strategies.
@inproceedings{huseljic2023role, abstract = {Despite considerable research on pool-based deep active learning (DAL), deploying DAL into real applications still has several challenges. A frequently neglected aspect is the choice of training hyperparameters (HPs), such as the learning rate. Since these HPs determine how the deep neural network learns in each cycle, they must be chosen carefully. In this article, we analyze the role of HPs in DAL. We find that optimizing HPs reduces the performance gap between DAL strategies. Conversely, we highlight challenges in finding optimal HPs when using datasets selected via DAL strategies.}, author = {Huseljic, Denis and Herde, Marek and Hahn, Paul and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {itegpub}, pages = {19–24}, title = {Role of Hyperparameters in Deep Active Learning}, year = 2023 }
%0 Conference Paper %1 huseljic2023role %A Huseljic, Denis %A Herde, Marek %A Hahn, Paul %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2023 %P 19--24 %T Role of Hyperparameters in Deep Active Learning %U https://ceur-ws.org/Vol-3470/paper4.pdf %X Despite considerable research on pool-based deep active learning (DAL), deploying DAL into real applications still has several challenges. A frequently neglected aspect is the choice of training hyperparameters (HPs), such as the learning rate. Since these HPs determine how the deep neural network learns in each cycle, they must be chosen carefully. In this article, we analyze the role of HPs in DAL. We find that optimizing HPs reduces the performance gap between DAL strategies. Conversely, we highlight challenges in finding optimal HPs when using datasets selected via DAL strategies.
Graph Neural Networks Designed for Different Graph Types: A Survey.Thomas, Josephine; Moallemy-Oureh, Alice; Beddar-Wiesing, Silvia; Holzhüter, Clara. In Transactions on Machine Learning Research. 2023.
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Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. For this purpose, they can be defined as many different types which suitably reflect the individual contexts of the represented problem. To address cutting-edge problems based on graph data, the research field of Graph Neural Networks (GNNs) has emerged. Despite the field’s youth and the speed at which new models are developed, many recent surveys have been published to keep track of them. Nevertheless, it has not yet been gathered which GNN can process what kind of graph types. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static and dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover, we distinguish between GNN models for discrete-time or continuous-time dynamic graphs and group the models according to their architecture. We find that there are still graph types that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.
@article{thomas2023graph, abstract = {Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. For this purpose, they can be defined as many different types which suitably reflect the individual contexts of the represented problem. To address cutting-edge problems based on graph data, the research field of Graph Neural Networks (GNNs) has emerged. Despite the field’s youth and the speed at which new models are developed, many recent surveys have been published to keep track of them. Nevertheless, it has not yet been gathered which GNN can process what kind of graph types. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static and dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover, we distinguish between GNN models for discrete-time or continuous-time dynamic graphs and group the models according to their architecture. We find that there are still graph types that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.}, author = {Thomas, Josephine and Moallemy-Oureh, Alice and Beddar-Wiesing, Silvia and Holzhüter, Clara}, journal = {Transactions on Machine Learning Research}, keywords = {itegpub}, title = {Graph Neural Networks Designed for Different Graph Types: A Survey}, year = 2023 }
%0 Journal Article %1 thomas2023graph %A Thomas, Josephine %A Moallemy-Oureh, Alice %A Beddar-Wiesing, Silvia %A Holzhüter, Clara %D 2023 %J Transactions on Machine Learning Research %T Graph Neural Networks Designed for Different Graph Types: A Survey %U https://openreview.net/pdf?id=h4BYtZ79uy %X Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. For this purpose, they can be defined as many different types which suitably reflect the individual contexts of the represented problem. To address cutting-edge problems based on graph data, the research field of Graph Neural Networks (GNNs) has emerged. Despite the field’s youth and the speed at which new models are developed, many recent surveys have been published to keep track of them. Nevertheless, it has not yet been gathered which GNN can process what kind of graph types. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static and dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover, we distinguish between GNN models for discrete-time or continuous-time dynamic graphs and group the models according to their architecture. We find that there are still graph types that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.
An Examination of Organic Computing Strategies in Design Optimization.Decke, Jens. In Organic Computing -- Doctoral Dissertation Colloquium 2023, S. Tomforde, C. Krupitzer (reds.), ble. 41–54. kassel university press, 2023.
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In the field of design optimization, numerical equation solvers, commonly used for finite element analysis and computational fluid dy- namics, impose significant computational demands. The iterative nature of design optimization, involving numerous simulations, magnifies re- source requirements in terms of time, energy, costs, and greenhouse gas emissions. Accelerating this process has been a long-standing research challenge, driven by the potential for resource savings and the ability to tackle increasingly complex problems. Recently, organic computing methods have gained prominence as promising approaches to address this challenge. This study aims to bridge the gap by integrating tech- niques from deep learning, active learning, and generative learning into the field of design optimization. The goal is to accelerate the design op- timization process while addressing specific challenges such as exploring large and complex design spaces and evaluating the advantages and con- straints of these methods. This research has the potential to significantly impact the industrial use of design optimization by providing faster and more efficient tools.
@incollection{decke2023examination, abstract = {In the field of design optimization, numerical equation solvers, commonly used for finite element analysis and computational fluid dy- namics, impose significant computational demands. The iterative nature of design optimization, involving numerous simulations, magnifies re- source requirements in terms of time, energy, costs, and greenhouse gas emissions. Accelerating this process has been a long-standing research challenge, driven by the potential for resource savings and the ability to tackle increasingly complex problems. Recently, organic computing methods have gained prominence as promising approaches to address this challenge. This study aims to bridge the gap by integrating tech- niques from deep learning, active learning, and generative learning into the field of design optimization. The goal is to accelerate the design op- timization process while addressing specific challenges such as exploring large and complex design spaces and evaluating the advantages and con- straints of these methods. This research has the potential to significantly impact the industrial use of design optimization by providing faster and more efficient tools.}, author = {Decke, Jens}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2023}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {41–54}, publisher = {kassel university press}, title = {An Examination of Organic Computing Strategies in Design Optimization}, year = 2023 }
%0 Book Section %1 decke2023examination %A Decke, Jens %B Organic Computing -- Doctoral Dissertation Colloquium 2023 %D 2023 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 41--54 %T An Examination of Organic Computing Strategies in Design Optimization %X In the field of design optimization, numerical equation solvers, commonly used for finite element analysis and computational fluid dy- namics, impose significant computational demands. The iterative nature of design optimization, involving numerous simulations, magnifies re- source requirements in terms of time, energy, costs, and greenhouse gas emissions. Accelerating this process has been a long-standing research challenge, driven by the potential for resource savings and the ability to tackle increasingly complex problems. Recently, organic computing methods have gained prominence as promising approaches to address this challenge. This study aims to bridge the gap by integrating tech- niques from deep learning, active learning, and generative learning into the field of design optimization. The goal is to accelerate the design op- timization process while addressing specific challenges such as exploring large and complex design spaces and evaluating the advantages and con- straints of these methods. This research has the potential to significantly impact the industrial use of design optimization by providing faster and more efficient tools.
Multi-annotator Deep Learning: A Probabilistic Framework for Classification.Herde, Marek; Huseljic, Denis; Sick, Bernhard. In Transactions on Machine Learning Research. 2023.
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Solving complex classification tasks using deep neural networks typically requires large amounts of annotated data. However, corresponding class labels are noisy when provided by error-prone annotators, e.g., crowdworkers. Training standard deep neural networks leads to subpar performances in such multi-annotator supervised learning settings. We address this issue by presenting a probabilistic training framework named multi-annotator deep learning (MaDL). A downstream ground truth and an annotator performance model are jointly trained in an end-to-end learning approach. The ground truth model learns to predict instances' true class labels, while the annotator performance model infers probabilistic estimates of annotators' performances. A modular network architecture enables us to make varying assumptions regarding annotators' performances, e.g., an optional class or instance dependency. Further, we learn annotator embeddings to estimate annotators' densities within a latent space as proxies of their potentially correlated annotations. Together with a weighted loss function, we improve the learning from correlated annotation patterns. In a comprehensive evaluation, we examine three research questions about multi-annotator supervised learning. Our findings show MaDL's state-of-the-art performance and robustness against many correlated, spamming annotators.
@article{herde2023multi, abstract = {Solving complex classification tasks using deep neural networks typically requires large amounts of annotated data. However, corresponding class labels are noisy when provided by error-prone annotators, e.g., crowdworkers. Training standard deep neural networks leads to subpar performances in such multi-annotator supervised learning settings. We address this issue by presenting a probabilistic training framework named multi-annotator deep learning (MaDL). A downstream ground truth and an annotator performance model are jointly trained in an end-to-end learning approach. The ground truth model learns to predict instances' true class labels, while the annotator performance model infers probabilistic estimates of annotators' performances. A modular network architecture enables us to make varying assumptions regarding annotators' performances, e.g., an optional class or instance dependency. Further, we learn annotator embeddings to estimate annotators' densities within a latent space as proxies of their potentially correlated annotations. Together with a weighted loss function, we improve the learning from correlated annotation patterns. In a comprehensive evaluation, we examine three research questions about multi-annotator supervised learning. Our findings show MaDL's state-of-the-art performance and robustness against many correlated, spamming annotators.}, author = {Herde, Marek and Huseljic, Denis and Sick, Bernhard}, journal = {Transactions on Machine Learning Research}, keywords = {itegpub}, title = {Multi-annotator Deep Learning: A Probabilistic Framework for Classification}, year = 2023 }
%0 Journal Article %1 herde2023multi %A Herde, Marek %A Huseljic, Denis %A Sick, Bernhard %D 2023 %J Transactions on Machine Learning Research %T Multi-annotator Deep Learning: A Probabilistic Framework for Classification %U https://openreview.net/forum?id=MgdoxzImlK %X Solving complex classification tasks using deep neural networks typically requires large amounts of annotated data. However, corresponding class labels are noisy when provided by error-prone annotators, e.g., crowdworkers. Training standard deep neural networks leads to subpar performances in such multi-annotator supervised learning settings. We address this issue by presenting a probabilistic training framework named multi-annotator deep learning (MaDL). A downstream ground truth and an annotator performance model are jointly trained in an end-to-end learning approach. The ground truth model learns to predict instances' true class labels, while the annotator performance model infers probabilistic estimates of annotators' performances. A modular network architecture enables us to make varying assumptions regarding annotators' performances, e.g., an optional class or instance dependency. Further, we learn annotator embeddings to estimate annotators' densities within a latent space as proxies of their potentially correlated annotations. Together with a weighted loss function, we improve the learning from correlated annotation patterns. In a comprehensive evaluation, we examine three research questions about multi-annotator supervised learning. Our findings show MaDL's state-of-the-art performance and robustness against many correlated, spamming annotators.
Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis.Botache, Diego; Dingel, Kristina; Huhnstock, Rico; Ehresmann, Arno; Sick, Bernhard. In arXiv e-prints, bl. arXiv:2307.14294. 2023.
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Splitting of sequential data, such as videos and time series, is an essential step in various data analysis tasks, including object tracking and anomaly detection. However, splitting sequential data presents a variety of challenges that can impact the accuracy and reliability of subsequent analyses. This concept article examines the challenges associated with splitting sequential data, including data acquisition, data representation, split ratio selection, setting up quality criteria, and choosing suitable selection strategies. We explore these challenges through two real-world examples: motor test benches and particle tracking in liquids.
@article{botache2023unraveling, abstract = {Splitting of sequential data, such as videos and time series, is an essential step in various data analysis tasks, including object tracking and anomaly detection. However, splitting sequential data presents a variety of challenges that can impact the accuracy and reliability of subsequent analyses. This concept article examines the challenges associated with splitting sequential data, including data acquisition, data representation, split ratio selection, setting up quality criteria, and choosing suitable selection strategies. We explore these challenges through two real-world examples: motor test benches and particle tracking in liquids.}, author = {Botache, Diego and Dingel, Kristina and Huhnstock, Rico and Ehresmann, Arno and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2307.14294}, title = {Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis}, year = 2023 }
%0 Journal Article %1 botache2023unraveling %A Botache, Diego %A Dingel, Kristina %A Huhnstock, Rico %A Ehresmann, Arno %A Sick, Bernhard %D 2023 %J arXiv e-prints %P arXiv:2307.14294 %T Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis %U https://arxiv.org/abs/2307.14294 %X Splitting of sequential data, such as videos and time series, is an essential step in various data analysis tasks, including object tracking and anomaly detection. However, splitting sequential data presents a variety of challenges that can impact the accuracy and reliability of subsequent analyses. This concept article examines the challenges associated with splitting sequential data, including data acquisition, data representation, split ratio selection, setting up quality criteria, and choosing suitable selection strategies. We explore these challenges through two real-world examples: motor test benches and particle tracking in liquids.
Model selection, adaptation, and combination for transfer learning in wind and photovoltaic power forecasts.Schreiber, Jens; Sick, Bernhard. In Energy and AI, 14, bl. 100249. 2023.
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There is recent interest in using model hubs – a collection of pre-trained models – in computer vision tasks. To employ a model hub, we first select a source model and then adapt the model for the target to compensate for differences. There still needs to be more research on model selection and adaption for renewable power forecasts. In particular, none of the related work examines different model selection and adaptation strategies for neural network architectures. Also, none of the current studies investigates the influence of available training samples and considers seasonality in the evaluation. We close these gaps by conducting the first thorough experiment for model selection and adaptation for transfer learning in renewable power forecast, adopting recent developments from the field of computer vision on 667 wind and photovoltaic parks from six datasets. We simulate different amounts of training samples for each season to calculate informative forecast errors. We examine the marginal likelihood and forecast error for model selection for those amounts. Furthermore, we study four adaption strategies. As an extension of the current state of the art, we utilize a Bayesian linear regression for forecasting the response based on features extracted from a neural network. This approach outperforms the baseline with only seven days of training data and shows that fine-tuning is not beneficial with less than three months of data. We further show how combining multiple models through ensembles can significantly improve the model selection and adaptation approach such that we have a similar mean error with only 30 days of training data which is otherwise only possible with an entire year of training data. We achieve a mean error of 9.8 and 14 percent for the most realistic dataset for PV and wind with only seven days of training data.
@article{schreiber2023model, abstract = {There is recent interest in using model hubs – a collection of pre-trained models – in computer vision tasks. To employ a model hub, we first select a source model and then adapt the model for the target to compensate for differences. There still needs to be more research on model selection and adaption for renewable power forecasts. In particular, none of the related work examines different model selection and adaptation strategies for neural network architectures. Also, none of the current studies investigates the influence of available training samples and considers seasonality in the evaluation. We close these gaps by conducting the first thorough experiment for model selection and adaptation for transfer learning in renewable power forecast, adopting recent developments from the field of computer vision on 667 wind and photovoltaic parks from six datasets. We simulate different amounts of training samples for each season to calculate informative forecast errors. We examine the marginal likelihood and forecast error for model selection for those amounts. Furthermore, we study four adaption strategies. As an extension of the current state of the art, we utilize a Bayesian linear regression for forecasting the response based on features extracted from a neural network. This approach outperforms the baseline with only seven days of training data and shows that fine-tuning is not beneficial with less than three months of data. We further show how combining multiple models through ensembles can significantly improve the model selection and adaptation approach such that we have a similar mean error with only 30 days of training data which is otherwise only possible with an entire year of training data. We achieve a mean error of 9.8 and 14 percent for the most realistic dataset for PV and wind with only seven days of training data.}, author = {Schreiber, Jens and Sick, Bernhard}, journal = {Energy and AI}, keywords = {itegpub}, pages = 100249, title = {Model selection, adaptation, and combination for transfer learning in wind and photovoltaic power forecasts}, volume = 14, year = 2023 }
%0 Journal Article %1 schreiber2023model %A Schreiber, Jens %A Sick, Bernhard %D 2023 %J Energy and AI %P 100249 %R 10.1016/j.egyai.2023.100249 %T Model selection, adaptation, and combination for transfer learning in wind and photovoltaic power forecasts %U https://www.sciencedirect.com/science/article/pii/S2666546823000216 %V 14 %X There is recent interest in using model hubs – a collection of pre-trained models – in computer vision tasks. To employ a model hub, we first select a source model and then adapt the model for the target to compensate for differences. There still needs to be more research on model selection and adaption for renewable power forecasts. In particular, none of the related work examines different model selection and adaptation strategies for neural network architectures. Also, none of the current studies investigates the influence of available training samples and considers seasonality in the evaluation. We close these gaps by conducting the first thorough experiment for model selection and adaptation for transfer learning in renewable power forecast, adopting recent developments from the field of computer vision on 667 wind and photovoltaic parks from six datasets. We simulate different amounts of training samples for each season to calculate informative forecast errors. We examine the marginal likelihood and forecast error for model selection for those amounts. Furthermore, we study four adaption strategies. As an extension of the current state of the art, we utilize a Bayesian linear regression for forecasting the response based on features extracted from a neural network. This approach outperforms the baseline with only seven days of training data and shows that fine-tuning is not beneficial with less than three months of data. We further show how combining multiple models through ensembles can significantly improve the model selection and adaptation approach such that we have a similar mean error with only 30 days of training data which is otherwise only possible with an entire year of training data. We achieve a mean error of 9.8 and 14 percent for the most realistic dataset for PV and wind with only seven days of training data.
Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network.Moallemy-Oureh, Alice; Beddar-Wiesing, Silvia; Nather, Rüdiger; Thomas, Josephine. In Workshop on Temporal Graph Learning (TGL), NeurIPS, ble. 1–7. 2023.
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Temporal Point Processes (TPPs) have recently become increasingly interesting for learning dynamics in graph data. A reason for this is that learning on dynamic graph data is becoming more relevant, since data from many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, is naturally related and inherently dynamic. In addition, TPPs provide a meaningful characterization of event streams and a prediction mechanism for future events. Therefore, (semi-)parameterized Neural TPPs have been introduced whose characterization can be (partially) learned and, thus, enable the representation of more complex phenomena. However, the research on modeling dynamic graphs with TPPs is relatively young, and only a few models for node attribute changes or evolving edges have been proposed yet. To allow for learning on fully dynamic graph streams, i.e., graphs that can change in their structure (addition/deletion of nodes/edge) and in their node/edge attributes, we propose a Marked Neural Spatio-Temporal Point Process (MNSTPP). It leverages a Dynamic Graph Neural Network to learn a Marked TPP that handles attributes and spatial data to model and predict any event in a graph stream.
@inproceedings{moallemyoureh2023marked, abstract = {Temporal Point Processes (TPPs) have recently become increasingly interesting for learning dynamics in graph data. A reason for this is that learning on dynamic graph data is becoming more relevant, since data from many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, is naturally related and inherently dynamic. In addition, TPPs provide a meaningful characterization of event streams and a prediction mechanism for future events. Therefore, (semi-)parameterized Neural TPPs have been introduced whose characterization can be (partially) learned and, thus, enable the representation of more complex phenomena. However, the research on modeling dynamic graphs with TPPs is relatively young, and only a few models for node attribute changes or evolving edges have been proposed yet. To allow for learning on fully dynamic graph streams, i.e., graphs that can change in their structure (addition/deletion of nodes/edge) and in their node/edge attributes, we propose a Marked Neural Spatio-Temporal Point Process (MNSTPP). It leverages a Dynamic Graph Neural Network to learn a Marked TPP that handles attributes and spatial data to model and predict any event in a graph stream.}, author = {Moallemy-Oureh, Alice and Beddar-Wiesing, Silvia and Nather, Rüdiger and Thomas, Josephine}, booktitle = {Workshop on Temporal Graph Learning (TGL), NeurIPS}, keywords = {itegpub}, pages = {1–7}, title = {Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network}, year = 2023 }
%0 Conference Paper %1 moallemyoureh2023marked %A Moallemy-Oureh, Alice %A Beddar-Wiesing, Silvia %A Nather, Rüdiger %A Thomas, Josephine %B Workshop on Temporal Graph Learning (TGL), NeurIPS %D 2023 %P 1--7 %T Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network %U https://openreview.net/forum?id=QJx3Cmddsy %X Temporal Point Processes (TPPs) have recently become increasingly interesting for learning dynamics in graph data. A reason for this is that learning on dynamic graph data is becoming more relevant, since data from many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, is naturally related and inherently dynamic. In addition, TPPs provide a meaningful characterization of event streams and a prediction mechanism for future events. Therefore, (semi-)parameterized Neural TPPs have been introduced whose characterization can be (partially) learned and, thus, enable the representation of more complex phenomena. However, the research on modeling dynamic graphs with TPPs is relatively young, and only a few models for node attribute changes or evolving edges have been proposed yet. To allow for learning on fully dynamic graph streams, i.e., graphs that can change in their structure (addition/deletion of nodes/edge) and in their node/edge attributes, we propose a Marked Neural Spatio-Temporal Point Process (MNSTPP). It leverages a Dynamic Graph Neural Network to learn a Marked TPP that handles attributes and spatial data to model and predict any event in a graph stream.
Multi-Task Representation Learning for Renewable-Power Forecasting: A Comparative Analysis of Unified Autoencoder Variants and Task-Embedding Dimensions.Nivarthi, Chandana Priya; Vogt, Stephan; Sick, Bernhard. In Machine Learning and Knowledge Extraction (MAKE), 5(3), ble. 1214–1233. MDPI, 2023.
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Typically, renewable-power-generation forecasting using machine learning involves creating separate models for each photovoltaic or wind park, known as single-task learning models. However, transfer learning has gained popularity in recent years, as it allows for the transfer of knowledge from source parks to target parks. Nevertheless, determining the most similar source park(s) for transfer learning can be challenging, particularly when the target park has limited or no historical data samples. To address this issue, we propose a multi-task learning architecture that employs a Unified Autoencoder (UAE) to initially learn a common representation of input weather features among tasks and then utilizes a Task-Embedding layer in a Neural Network (TENN) to learn task-specific information. This proposed UAE-TENN architecture can be easily extended to new parks with or without historical data. We evaluate the performance of our proposed architecture and compare it to single-task learning models on six photovoltaic and wind farm datasets consisting of a total of 529 parks. Our results show that the UAE-TENN architecture significantly improves power-forecasting performance by 10 to 19% for photovoltaic parks and 5 to 15% for wind parks compared to baseline models. We also demonstrate that UAE-TENN improves forecast accuracy for a new park by 19% for photovoltaic parks, even in a zero-shot learning scenario where there is no historical data. Additionally, we propose variants of the Unified Autoencoder with convolutional and LSTM layers, compare their performance, and provide a comparison among architectures with different numbers of task-embedding dimensions. Finally, we demonstrate the utility of trained task embeddings for interpretation and visualization purposes.
@article{nivarthi2023multi, abstract = {Typically, renewable-power-generation forecasting using machine learning involves creating separate models for each photovoltaic or wind park, known as single-task learning models. However, transfer learning has gained popularity in recent years, as it allows for the transfer of knowledge from source parks to target parks. Nevertheless, determining the most similar source park(s) for transfer learning can be challenging, particularly when the target park has limited or no historical data samples. To address this issue, we propose a multi-task learning architecture that employs a Unified Autoencoder (UAE) to initially learn a common representation of input weather features among tasks and then utilizes a Task-Embedding layer in a Neural Network (TENN) to learn task-specific information. This proposed UAE-TENN architecture can be easily extended to new parks with or without historical data. We evaluate the performance of our proposed architecture and compare it to single-task learning models on six photovoltaic and wind farm datasets consisting of a total of 529 parks. Our results show that the UAE-TENN architecture significantly improves power-forecasting performance by 10 to 19% for photovoltaic parks and 5 to 15% for wind parks compared to baseline models. We also demonstrate that UAE-TENN improves forecast accuracy for a new park by 19% for photovoltaic parks, even in a zero-shot learning scenario where there is no historical data. Additionally, we propose variants of the Unified Autoencoder with convolutional and LSTM layers, compare their performance, and provide a comparison among architectures with different numbers of task-embedding dimensions. Finally, we demonstrate the utility of trained task embeddings for interpretation and visualization purposes.}, author = {Nivarthi, Chandana Priya and Vogt, Stephan and Sick, Bernhard}, journal = {Machine Learning and Knowledge Extraction (MAKE)}, keywords = {itegpub}, number = 3, pages = {1214–1233}, publisher = {MDPI}, title = {Multi-Task Representation Learning for Renewable-Power Forecasting: A Comparative Analysis of Unified Autoencoder Variants and Task-Embedding Dimensions}, volume = 5, year = 2023 }
%0 Journal Article %1 nivarthi2023multi %A Nivarthi, Chandana Priya %A Vogt, Stephan %A Sick, Bernhard %D 2023 %I MDPI %J Machine Learning and Knowledge Extraction (MAKE) %N 3 %P 1214--1233 %R 10.3390/make5030062 %T Multi-Task Representation Learning for Renewable-Power Forecasting: A Comparative Analysis of Unified Autoencoder Variants and Task-Embedding Dimensions %U https://www.mdpi.com/2504-4990/5/3/62 %V 5 %X Typically, renewable-power-generation forecasting using machine learning involves creating separate models for each photovoltaic or wind park, known as single-task learning models. However, transfer learning has gained popularity in recent years, as it allows for the transfer of knowledge from source parks to target parks. Nevertheless, determining the most similar source park(s) for transfer learning can be challenging, particularly when the target park has limited or no historical data samples. To address this issue, we propose a multi-task learning architecture that employs a Unified Autoencoder (UAE) to initially learn a common representation of input weather features among tasks and then utilizes a Task-Embedding layer in a Neural Network (TENN) to learn task-specific information. This proposed UAE-TENN architecture can be easily extended to new parks with or without historical data. We evaluate the performance of our proposed architecture and compare it to single-task learning models on six photovoltaic and wind farm datasets consisting of a total of 529 parks. Our results show that the UAE-TENN architecture significantly improves power-forecasting performance by 10 to 19% for photovoltaic parks and 5 to 15% for wind parks compared to baseline models. We also demonstrate that UAE-TENN improves forecast accuracy for a new park by 19% for photovoltaic parks, even in a zero-shot learning scenario where there is no historical data. Additionally, we propose variants of the Unified Autoencoder with convolutional and LSTM layers, compare their performance, and provide a comparison among architectures with different numbers of task-embedding dimensions. Finally, we demonstrate the utility of trained task embeddings for interpretation and visualization purposes.
Graph Pooling Provably Improves Expressivity.Lachi, Veronica; Moallemy-Oureh, Alice; Roth, Andreas; Welke, Pascal. In Workshop on New Frontiers in Graph Learning, NeurIPS, ble. 1–7. 2023.
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In the domain of graph neural networks (GNNs), pooling operators are fundamental to reduce the size of the graph by simplifying graph structures and vertex features. Recent advances have shown that well-designed pooling operators, coupled with message-passing layers, can endow hierarchical GNNs with an expressive power regarding the graph isomorphism test that is equal to the Weisfeiler-Leman test. However, the ability of hierarchical GNNs to increase expressive power by utilizing graph coarsening was not yet explored. This results in uncertainties about the benefits of pooling operators and a lack of sufficient properties to guide their design. In this work, we identify conditions for pooling operators to generate WL-distinguishable coarsened graphs from originally WL-indistinguishable but non-isomorphic graphs. Our conditions are versatile and can be tailored to specific tasks and data characteristics, offering a promising avenue for further research.
@inproceedings{lachi2023graph, abstract = {In the domain of graph neural networks (GNNs), pooling operators are fundamental to reduce the size of the graph by simplifying graph structures and vertex features. Recent advances have shown that well-designed pooling operators, coupled with message-passing layers, can endow hierarchical GNNs with an expressive power regarding the graph isomorphism test that is equal to the Weisfeiler-Leman test. However, the ability of hierarchical GNNs to increase expressive power by utilizing graph coarsening was not yet explored. This results in uncertainties about the benefits of pooling operators and a lack of sufficient properties to guide their design. In this work, we identify conditions for pooling operators to generate WL-distinguishable coarsened graphs from originally WL-indistinguishable but non-isomorphic graphs. Our conditions are versatile and can be tailored to specific tasks and data characteristics, offering a promising avenue for further research.}, author = {Lachi, Veronica and Moallemy-Oureh, Alice and Roth, Andreas and Welke, Pascal}, booktitle = {Workshop on New Frontiers in Graph Learning, NeurIPS}, keywords = {itegpub}, pages = {1–7}, title = {Graph Pooling Provably Improves Expressivity}, year = 2023 }
%0 Conference Paper %1 lachi2023graph %A Lachi, Veronica %A Moallemy-Oureh, Alice %A Roth, Andreas %A Welke, Pascal %B Workshop on New Frontiers in Graph Learning, NeurIPS %D 2023 %P 1--7 %T Graph Pooling Provably Improves Expressivity %U https://openreview.net/forum?id=lR5NYB9zrv %X In the domain of graph neural networks (GNNs), pooling operators are fundamental to reduce the size of the graph by simplifying graph structures and vertex features. Recent advances have shown that well-designed pooling operators, coupled with message-passing layers, can endow hierarchical GNNs with an expressive power regarding the graph isomorphism test that is equal to the Weisfeiler-Leman test. However, the ability of hierarchical GNNs to increase expressive power by utilizing graph coarsening was not yet explored. This results in uncertainties about the benefits of pooling operators and a lack of sufficient properties to guide their design. In this work, we identify conditions for pooling operators to generate WL-distinguishable coarsened graphs from originally WL-indistinguishable but non-isomorphic graphs. Our conditions are versatile and can be tailored to specific tasks and data characteristics, offering a promising avenue for further research.
Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents.Lehna, Malte; Viebahn, Jan; Marot, Antoine; Tomforde, Sven; Scholz, Christoph. In Energy and AI, 14, bl. 100276. Energy and AI, 2023.
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The operation of electricity grids has become increasingly complex due to the current upheaval and the increase in renewable energy production. As a consequence, active grid management is reaching its limits with conventional approaches. In the context of the Learning to Run a Power Network (L2RPN) challenge, it has been shown that Reinforcement Learning (RL) is an efficient and reliable approach with considerable potential for automatic grid operation. In this article, we analyse the submitted agent from Binbinchen and provide novel strategies to improve the agent, both for the RL and the rule-based approach. The main improvement is a N-1 strategy, where we consider topology actions that keep the grid stable, even if one line is disconnected. More, we also propose a topology reversion to the original grid, which proved to be beneficial. The improvements are tested against reference approaches on the challenge test sets and are able to increase the performance of the rule-based agent by 27%. In direct comparison between rule-based and RL agent we find similar performance. However, the RL agent has a clear computational advantage. We also analyse the behaviour in an exemplary case in more detail to provide additional insights. Here, we observe that through the N-1 strategy, the actions of both the rule-based and the RL agent become more diversified.
@article{lehna2023managing, abstract = {The operation of electricity grids has become increasingly complex due to the current upheaval and the increase in renewable energy production. As a consequence, active grid management is reaching its limits with conventional approaches. In the context of the Learning to Run a Power Network (L2RPN) challenge, it has been shown that Reinforcement Learning (RL) is an efficient and reliable approach with considerable potential for automatic grid operation. In this article, we analyse the submitted agent from Binbinchen and provide novel strategies to improve the agent, both for the RL and the rule-based approach. The main improvement is a N-1 strategy, where we consider topology actions that keep the grid stable, even if one line is disconnected. More, we also propose a topology reversion to the original grid, which proved to be beneficial. The improvements are tested against reference approaches on the challenge test sets and are able to increase the performance of the rule-based agent by 27%. In direct comparison between rule-based and RL agent we find similar performance. However, the RL agent has a clear computational advantage. We also analyse the behaviour in an exemplary case in more detail to provide additional insights. Here, we observe that through the N-1 strategy, the actions of both the rule-based and the RL agent become more diversified.}, author = {Lehna, Malte and Viebahn, Jan and Marot, Antoine and Tomforde, Sven and Scholz, Christoph}, journal = {Energy and AI}, keywords = {itegpub}, pages = 100276, publisher = {Energy and AI}, title = {Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents}, volume = 14, year = 2023 }
%0 Journal Article %1 lehna2023managing %A Lehna, Malte %A Viebahn, Jan %A Marot, Antoine %A Tomforde, Sven %A Scholz, Christoph %D 2023 %I Energy and AI %J Energy and AI %P 100276 %R 10.1016/j.egyai.2023.100276 %T Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents %U https://www.sciencedirect.com/science/article/pii/S2666546823000484?via%3Dihub %V 14 %X The operation of electricity grids has become increasingly complex due to the current upheaval and the increase in renewable energy production. As a consequence, active grid management is reaching its limits with conventional approaches. In the context of the Learning to Run a Power Network (L2RPN) challenge, it has been shown that Reinforcement Learning (RL) is an efficient and reliable approach with considerable potential for automatic grid operation. In this article, we analyse the submitted agent from Binbinchen and provide novel strategies to improve the agent, both for the RL and the rule-based approach. The main improvement is a N-1 strategy, where we consider topology actions that keep the grid stable, even if one line is disconnected. More, we also propose a topology reversion to the original grid, which proved to be beneficial. The improvements are tested against reference approaches on the challenge test sets and are able to increase the performance of the rule-based agent by 27%. In direct comparison between rule-based and RL agent we find similar performance. However, the RL agent has a clear computational advantage. We also analyse the behaviour in an exemplary case in more detail to provide additional insights. Here, we observe that through the N-1 strategy, the actions of both the rule-based and the RL agent become more diversified.
Power flow forecasts at transmission grid nodes using Graph Neural Networks.Beinert, Dominik; Holzhüter, Clara; Thomas, Josephine; Vogt, Stephan. In Energy and AI, 14(1), bl. 100262. Elsevier, 2023.
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The increasing share of renewable energy in the electricity grid and progressing changes in power consumption have led to fluctuating, and weather-dependent power flows. To ensure grid stability, grid operators rely on power forecasts which are crucial for grid calculations and planning. In this paper, a Multi-Task Learning approach is combined with a Graph Neural Network (GNN) to predict vertical power flows at transformers connecting high and extra-high voltage levels. The proposed method accounts for local differences in power flow characteristics by using an Embedding Multi-Task Learning approach. The use of a Bayesian embedding to capture the latent node characteristics allows to share the weights across all transformers in the subsequent node-invariant GNN while still allowing the individual behavioral patterns of the transformers to be distinguished. At the same time, dependencies between transformers are considered by the GNN architecture which can learn relationships between different transformers and thus take into account that power flows in an electricity network are not independent from each other. The effectiveness of the proposed method is demonstrated through evaluation on two real-world data sets provided by two of four German Transmission System Operators, comprising large portions of the operated German transmission grid. The results show that the proposed Multi-Task Graph Neural Network is a suitable representation learner for electricity networks with a clear advantage provided by the preceding embedding layer. It is able to capture interconnections between correlated transformers and indeed improves the performance in power flow prediction compared to standard Neural Networks. A sign test shows that the proposed model reduces the test RMSE on both data sets compared to the benchmark models significantly.
@article{beinert2023power, abstract = {The increasing share of renewable energy in the electricity grid and progressing changes in power consumption have led to fluctuating, and weather-dependent power flows. To ensure grid stability, grid operators rely on power forecasts which are crucial for grid calculations and planning. In this paper, a Multi-Task Learning approach is combined with a Graph Neural Network (GNN) to predict vertical power flows at transformers connecting high and extra-high voltage levels. The proposed method accounts for local differences in power flow characteristics by using an Embedding Multi-Task Learning approach. The use of a Bayesian embedding to capture the latent node characteristics allows to share the weights across all transformers in the subsequent node-invariant GNN while still allowing the individual behavioral patterns of the transformers to be distinguished. At the same time, dependencies between transformers are considered by the GNN architecture which can learn relationships between different transformers and thus take into account that power flows in an electricity network are not independent from each other. The effectiveness of the proposed method is demonstrated through evaluation on two real-world data sets provided by two of four German Transmission System Operators, comprising large portions of the operated German transmission grid. The results show that the proposed Multi-Task Graph Neural Network is a suitable representation learner for electricity networks with a clear advantage provided by the preceding embedding layer. It is able to capture interconnections between correlated transformers and indeed improves the performance in power flow prediction compared to standard Neural Networks. A sign test shows that the proposed model reduces the test RMSE on both data sets compared to the benchmark models significantly.}, author = {Beinert, Dominik and Holzhüter, Clara and Thomas, Josephine and Vogt, Stephan}, journal = {Energy and AI}, keywords = {itegpub}, number = 1, pages = 100262, publisher = {Elsevier}, title = {Power flow forecasts at transmission grid nodes using Graph Neural Networks}, volume = 14, year = 2023 }
%0 Journal Article %1 beinert2023power %A Beinert, Dominik %A Holzhüter, Clara %A Thomas, Josephine %A Vogt, Stephan %D 2023 %I Elsevier %J Energy and AI %N 1 %P 100262 %R 10.1016/j.egyai.2023.100262 %T Power flow forecasts at transmission grid nodes using Graph Neural Networks %U https://www.sciencedirect.com/science/article/pii/S2666546823000344?via%3Dihub %V 14 %X The increasing share of renewable energy in the electricity grid and progressing changes in power consumption have led to fluctuating, and weather-dependent power flows. To ensure grid stability, grid operators rely on power forecasts which are crucial for grid calculations and planning. In this paper, a Multi-Task Learning approach is combined with a Graph Neural Network (GNN) to predict vertical power flows at transformers connecting high and extra-high voltage levels. The proposed method accounts for local differences in power flow characteristics by using an Embedding Multi-Task Learning approach. The use of a Bayesian embedding to capture the latent node characteristics allows to share the weights across all transformers in the subsequent node-invariant GNN while still allowing the individual behavioral patterns of the transformers to be distinguished. At the same time, dependencies between transformers are considered by the GNN architecture which can learn relationships between different transformers and thus take into account that power flows in an electricity network are not independent from each other. The effectiveness of the proposed method is demonstrated through evaluation on two real-world data sets provided by two of four German Transmission System Operators, comprising large portions of the operated German transmission grid. The results show that the proposed Multi-Task Graph Neural Network is a suitable representation learner for electricity networks with a clear advantage provided by the preceding embedding layer. It is able to capture interconnections between correlated transformers and indeed improves the performance in power flow prediction compared to standard Neural Networks. A sign test shows that the proposed model reduces the test RMSE on both data sets compared to the benchmark models significantly.
Context Information for Corner Case Detection in Highly Automated Driving.Heidecker, Florian; Susetzky, Tobias; Fuchs, Erich; Sick, Bernhard. In IEEE International Conference on Intelligent Transportation Systems (ITSC), ble. 1522–1529. IEEE, 2023.
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Context information provided along with a dataset can be very helpful for solving a problem because the additional knowledge is already available and does not need to be extracted. Moreover, the context indicates how diverse a dataset is, i.e., how many samples per context category are available to train and test machine learning (ML) models. In this article, we present context annotations for the BDD100k image dataset. The annotations comprise, for instance, information about daytime, road condition (dry/wet), and dirt on the windshield. Sometimes, no or only little data are available for unique or rare combinations of these context attributes. However, data that matches these context conditions is crucial when discussing corner cases: Firstly, most ML models, e.g., object detectors, are not trained on such data, which leads to the assumption that they will perform poorly in those situations. Secondly, data containing corner cases are required for validating ML models. With this in mind, separate ML models dedicated to context detection are useful for expanding the training set with additional data of special interest, such as corner cases.
@inproceedings{heidecker2023context, abstract = {Context information provided along with a dataset can be very helpful for solving a problem because the additional knowledge is already available and does not need to be extracted. Moreover, the context indicates how diverse a dataset is, i.e., how many samples per context category are available to train and test machine learning (ML) models. In this article, we present context annotations for the BDD100k image dataset. The annotations comprise, for instance, information about daytime, road condition (dry/wet), and dirt on the windshield. Sometimes, no or only little data are available for unique or rare combinations of these context attributes. However, data that matches these context conditions is crucial when discussing corner cases: Firstly, most ML models, e.g., object detectors, are not trained on such data, which leads to the assumption that they will perform poorly in those situations. Secondly, data containing corner cases are required for validating ML models. With this in mind, separate ML models dedicated to context detection are useful for expanding the training set with additional data of special interest, such as corner cases.}, author = {Heidecker, Florian and Susetzky, Tobias and Fuchs, Erich and Sick, Bernhard}, booktitle = {IEEE International Conference on Intelligent Transportation Systems (ITSC)}, keywords = {itegpub}, pages = {1522–1529}, publisher = {IEEE}, title = {Context Information for Corner Case Detection in Highly Automated Driving}, year = 2023 }
%0 Conference Paper %1 heidecker2023context %A Heidecker, Florian %A Susetzky, Tobias %A Fuchs, Erich %A Sick, Bernhard %B IEEE International Conference on Intelligent Transportation Systems (ITSC) %D 2023 %I IEEE %P 1522--1529 %R 10.1109/ITSC57777.2023.10422414 %T Context Information for Corner Case Detection in Highly Automated Driving %X Context information provided along with a dataset can be very helpful for solving a problem because the additional knowledge is already available and does not need to be extracted. Moreover, the context indicates how diverse a dataset is, i.e., how many samples per context category are available to train and test machine learning (ML) models. In this article, we present context annotations for the BDD100k image dataset. The annotations comprise, for instance, information about daytime, road condition (dry/wet), and dirt on the windshield. Sometimes, no or only little data are available for unique or rare combinations of these context attributes. However, data that matches these context conditions is crucial when discussing corner cases: Firstly, most ML models, e.g., object detectors, are not trained on such data, which leads to the assumption that they will perform poorly in those situations. Secondly, data containing corner cases are required for validating ML models. With this in mind, separate ML models dedicated to context detection are useful for expanding the training set with additional data of special interest, such as corner cases.
It is the habit not the handle that affects tooth brushing. Results of a randomised counterbalanced cross over study.Deinzer, Renate; Eidenhardt, Zdenka; Sohrabi, Keywan; Stenger, Manuel; Kraft, Dominik; Sick, Bernhard; Götz-Hahn, Franz; Bottenbruch, Carlotta; Berneburg, Nils; Weik, Ulrike. In Research Square. 2023.
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Objective: To assess the effect of the toothbrush handle on video-observed toothbrushing behaviour and toothbrushing effectiveness Methods: This is a randomized counterbalanced cross-over study. N = 50 university students brushed their teeth at two occasions, one week apart, using either a commercial ergonomically designed manual toothbrush (MT) or Brushalyze V1 (BV1), a manual toothbrush with a thick cylindrical handle without any specific ergonomic features. Brushing behaviour was video-analysed. Plaque was assessed at the second occasion immediately after brushing. Participants directly compared the two brushes regarding their handling and compared them to the brushed they used at home. Results: The study participants found the BV1 significantly more cumbersome than the M1 or their brush at home. (p < 0.05). However, correlation analyses revealed a strong consistency of brushing behavior with the two brushes (0.71 < r < 0.91). Means differed only slightly (all d < 0.36). These differences became statistically significant only for the brushing time at inner surfaces (d = 0.31 p = 0.03) and horizontal movements at inner surfaces (d = 0.35, p = 0.02). Plaque levels at the gingival margins did not differ while slightly more plaque persisted at the more coronal aspects of the crown after brushing with BV1 (d = 0.592; p 0.042) Discussion: The results of the study indicate that the brushing handle does not play a major role in brushing behavior or brushing effectiveness.
@article{deinzer2023it, abstract = {Objective: To assess the effect of the toothbrush handle on video-observed toothbrushing behaviour and toothbrushing effectiveness Methods: This is a randomized counterbalanced cross-over study. N = 50 university students brushed their teeth at two occasions, one week apart, using either a commercial ergonomically designed manual toothbrush (MT) or Brushalyze V1 (BV1), a manual toothbrush with a thick cylindrical handle without any specific ergonomic features. Brushing behaviour was video-analysed. Plaque was assessed at the second occasion immediately after brushing. Participants directly compared the two brushes regarding their handling and compared them to the brushed they used at home. Results: The study participants found the BV1 significantly more cumbersome than the M1 or their brush at home. (p < 0.05). However, correlation analyses revealed a strong consistency of brushing behavior with the two brushes (0.71 < r < 0.91). Means differed only slightly (all d < 0.36). These differences became statistically significant only for the brushing time at inner surfaces (d = 0.31 p = 0.03) and horizontal movements at inner surfaces (d = 0.35, p = 0.02). Plaque levels at the gingival margins did not differ while slightly more plaque persisted at the more coronal aspects of the crown after brushing with BV1 (d = 0.592; p 0.042) Discussion: The results of the study indicate that the brushing handle does not play a major role in brushing behavior or brushing effectiveness.}, author = {Deinzer, Renate and Eidenhardt, Zdenka and Sohrabi, Keywan and Stenger, Manuel and Kraft, Dominik and Sick, Bernhard and Götz-Hahn, Franz and Bottenbruch, Carlotta and Berneburg, Nils and Weik, Ulrike}, journal = {Research Square}, keywords = {itegpub}, note = {(preprint)}, title = {It is the habit not the handle that affects tooth brushing. Results of a randomised counterbalanced cross over study}, year = 2023 }
%0 Journal Article %1 deinzer2023it %A Deinzer, Renate %A Eidenhardt, Zdenka %A Sohrabi, Keywan %A Stenger, Manuel %A Kraft, Dominik %A Sick, Bernhard %A Götz-Hahn, Franz %A Bottenbruch, Carlotta %A Berneburg, Nils %A Weik, Ulrike %D 2023 %J Research Square %R 10.21203/rs.3.rs-3491691/v1 %T It is the habit not the handle that affects tooth brushing. Results of a randomised counterbalanced cross over study %U https://www.researchsquare.com/article/rs-3491691/v1 %X Objective: To assess the effect of the toothbrush handle on video-observed toothbrushing behaviour and toothbrushing effectiveness Methods: This is a randomized counterbalanced cross-over study. N = 50 university students brushed their teeth at two occasions, one week apart, using either a commercial ergonomically designed manual toothbrush (MT) or Brushalyze V1 (BV1), a manual toothbrush with a thick cylindrical handle without any specific ergonomic features. Brushing behaviour was video-analysed. Plaque was assessed at the second occasion immediately after brushing. Participants directly compared the two brushes regarding their handling and compared them to the brushed they used at home. Results: The study participants found the BV1 significantly more cumbersome than the M1 or their brush at home. (p < 0.05). However, correlation analyses revealed a strong consistency of brushing behavior with the two brushes (0.71 < r < 0.91). Means differed only slightly (all d < 0.36). These differences became statistically significant only for the brushing time at inner surfaces (d = 0.31 p = 0.03) and horizontal movements at inner surfaces (d = 0.35, p = 0.02). Plaque levels at the gingival margins did not differ while slightly more plaque persisted at the more coronal aspects of the crown after brushing with BV1 (d = 0.592; p 0.042) Discussion: The results of the study indicate that the brushing handle does not play a major role in brushing behavior or brushing effectiveness.
Time-aware Robustness of Temporal Graph Neural Networks for Link Prediction.Sälzer, Marco; Beddar-Wiesing, Silvia. In International Symposium on Temporal Representation and Reasoning (TIME), ble. 19:1–19:3. 2023.
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Graph Neural Networks (GNNs) provide a framework for computing functions over graphs based on learnable parameters, which gained much attention in recent years. The most popular GNN models, so-called convolutional GNN or message-passing GNN apply a neighborhood aggregation procedure to each node in a graph to compute its output. Usually, such GNNs are used for classification or prediction tasks over static graphs. However, this limits their applicability in contexts like social networks or knowledge graphs, where underlying graphs change stepwise or time-continuously. Temporal Graph Neural Networks (TGNN) try to close this gap. The general idea of TGNN is to generalize the neighborhood aggregation procedure mentioned above to temporal graphs, usually represented as a tuple of a base graph with a series of time-stamped observed changes. In most applications involving Neural Network based models, giving reliable safety certificates are highly desirable but also a significant challenge, especially because of the blackbox nature of neural models. In this extended abstract, we address the topic of verifying TGNN, which is an unexplored area of research. We present a first notion of a time-aware robustness property for TGNN used for link prediction tasks, motivated by recent work on similar time-aware attacks. Furthermore, we discuss our ongoing work regarding promising verification approaches for the presented or similar safety properties and possible next steps in this research direction.
@inproceedings{salzer2023time, abstract = {Graph Neural Networks (GNNs) provide a framework for computing functions over graphs based on learnable parameters, which gained much attention in recent years. The most popular GNN models, so-called convolutional GNN or message-passing GNN apply a neighborhood aggregation procedure to each node in a graph to compute its output. Usually, such GNNs are used for classification or prediction tasks over static graphs. However, this limits their applicability in contexts like social networks or knowledge graphs, where underlying graphs change stepwise or time-continuously. Temporal Graph Neural Networks (TGNN) try to close this gap. The general idea of TGNN is to generalize the neighborhood aggregation procedure mentioned above to temporal graphs, usually represented as a tuple of a base graph with a series of time-stamped observed changes. In most applications involving Neural Network based models, giving reliable safety certificates are highly desirable but also a significant challenge, especially because of the blackbox nature of neural models. In this extended abstract, we address the topic of verifying TGNN, which is an unexplored area of research. We present a first notion of a time-aware robustness property for TGNN used for link prediction tasks, motivated by recent work on similar time-aware attacks. Furthermore, we discuss our ongoing work regarding promising verification approaches for the presented or similar safety properties and possible next steps in this research direction.}, author = {Sälzer, Marco and Beddar-Wiesing, Silvia}, booktitle = {International Symposium on Temporal Representation and Reasoning (TIME)}, keywords = {itegpub}, pages = {19:1–19:3}, title = {Time-aware Robustness of Temporal Graph Neural Networks for Link Prediction}, year = 2023 }
%0 Conference Paper %1 salzer2023time %A Sälzer, Marco %A Beddar-Wiesing, Silvia %B International Symposium on Temporal Representation and Reasoning (TIME) %D 2023 %P 19:1--19:3 %R 10.4230/LIPIcs.TIME.2023.19 %T Time-aware Robustness of Temporal Graph Neural Networks for Link Prediction %X Graph Neural Networks (GNNs) provide a framework for computing functions over graphs based on learnable parameters, which gained much attention in recent years. The most popular GNN models, so-called convolutional GNN or message-passing GNN apply a neighborhood aggregation procedure to each node in a graph to compute its output. Usually, such GNNs are used for classification or prediction tasks over static graphs. However, this limits their applicability in contexts like social networks or knowledge graphs, where underlying graphs change stepwise or time-continuously. Temporal Graph Neural Networks (TGNN) try to close this gap. The general idea of TGNN is to generalize the neighborhood aggregation procedure mentioned above to temporal graphs, usually represented as a tuple of a base graph with a series of time-stamped observed changes. In most applications involving Neural Network based models, giving reliable safety certificates are highly desirable but also a significant challenge, especially because of the blackbox nature of neural models. In this extended abstract, we address the topic of verifying TGNN, which is an unexplored area of research. We present a first notion of a time-aware robustness property for TGNN used for link prediction tasks, motivated by recent work on similar time-aware attacks. Furthermore, we discuss our ongoing work regarding promising verification approaches for the presented or similar safety properties and possible next steps in this research direction.
The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset.Hetzel, Manuel; Reichert, Hannes; Reitberger, Günther; Doll, Konrad; Sick, Bernhard; Fuchs, Erich. In IEEE Intelligent Vehicles Symposium (IV), ble. 1–7. IEEE, 2023.
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Inner-city intersections are among the most critical traffic areas for injury and fatal accidents. Automated vehicles struggle with the complex and hectic everyday life within those areas. Sensor-equipped smart infrastructures, which can cooperate with vehicles, can benefit automated traffic by extending the perception capabilities of drivers and vehicle perception systems. Additionally, they offer the opportunity to gather reproducible and precise data of a holistic scene understanding, including context information as a basis for training algorithms for various applications in automated traffic. Therefore, we introduce the Infrastructural Multi-Person Trajectory and Context Dataset (IMPTC). We use an intelligent public inner-city intersection in Germany with visual sensor technology. A multi-view camera and LiDAR system perceives traffic situations and road users’ behavior. Additional sensors monitor contextual information like weather, lighting, and traffic light signal status. The data acquisition system focuses on Vulnerable Road Users (VRUs) and multiagent interaction. The resulting dataset consists of eight hours of measurement data. It contains over 2,500 VRU trajectories, including pedestrians, cyclists, e-scooter riders, strollers, and wheelchair users, and over 20,000 vehicle trajectories at different day times, weather conditions, and seasons. In addition, to enable the entire stack of research capabilities, the dataset includes all data, starting from the sensor-, calibration- and detection data until trajectory and context data. The dataset is continuously expanded and is available online for non-commercial research at https://github.com/kav-institute/imptc-dataset
@inproceedings{hetzel2023imptc, abstract = {Inner-city intersections are among the most critical traffic areas for injury and fatal accidents. Automated vehicles struggle with the complex and hectic everyday life within those areas. Sensor-equipped smart infrastructures, which can cooperate with vehicles, can benefit automated traffic by extending the perception capabilities of drivers and vehicle perception systems. Additionally, they offer the opportunity to gather reproducible and precise data of a holistic scene understanding, including context information as a basis for training algorithms for various applications in automated traffic. Therefore, we introduce the Infrastructural Multi-Person Trajectory and Context Dataset (IMPTC). We use an intelligent public inner-city intersection in Germany with visual sensor technology. A multi-view camera and LiDAR system perceives traffic situations and road users’ behavior. Additional sensors monitor contextual information like weather, lighting, and traffic light signal status. The data acquisition system focuses on Vulnerable Road Users (VRUs) and multiagent interaction. The resulting dataset consists of eight hours of measurement data. It contains over 2,500 VRU trajectories, including pedestrians, cyclists, e-scooter riders, strollers, and wheelchair users, and over 20,000 vehicle trajectories at different day times, weather conditions, and seasons. In addition, to enable the entire stack of research capabilities, the dataset includes all data, starting from the sensor-, calibration- and detection data until trajectory and context data. The dataset is continuously expanded and is available online for non-commercial research at https://github.com/kav-institute/imptc-dataset}, author = {Hetzel, Manuel and Reichert, Hannes and Reitberger, Günther and Doll, Konrad and Sick, Bernhard and Fuchs, Erich}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {itegpub}, pages = {1–7}, publisher = {IEEE}, title = {The {IMPTC} Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset}, year = 2023 }
%0 Conference Paper %1 hetzel2023imptc %A Hetzel, Manuel %A Reichert, Hannes %A Reitberger, Günther %A Doll, Konrad %A Sick, Bernhard %A Fuchs, Erich %B IEEE Intelligent Vehicles Symposium (IV) %D 2023 %I IEEE %P 1--7 %R 10.1109/IV55152.2023.10186776 %T The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset %X Inner-city intersections are among the most critical traffic areas for injury and fatal accidents. Automated vehicles struggle with the complex and hectic everyday life within those areas. Sensor-equipped smart infrastructures, which can cooperate with vehicles, can benefit automated traffic by extending the perception capabilities of drivers and vehicle perception systems. Additionally, they offer the opportunity to gather reproducible and precise data of a holistic scene understanding, including context information as a basis for training algorithms for various applications in automated traffic. Therefore, we introduce the Infrastructural Multi-Person Trajectory and Context Dataset (IMPTC). We use an intelligent public inner-city intersection in Germany with visual sensor technology. A multi-view camera and LiDAR system perceives traffic situations and road users’ behavior. Additional sensors monitor contextual information like weather, lighting, and traffic light signal status. The data acquisition system focuses on Vulnerable Road Users (VRUs) and multiagent interaction. The resulting dataset consists of eight hours of measurement data. It contains over 2,500 VRU trajectories, including pedestrians, cyclists, e-scooter riders, strollers, and wheelchair users, and over 20,000 vehicle trajectories at different day times, weather conditions, and seasons. In addition, to enable the entire stack of research capabilities, the dataset includes all data, starting from the sensor-, calibration- and detection data until trajectory and context data. The dataset is continuously expanded and is available online for non-commercial research at https://github.com/kav-institute/imptc-dataset
Organic Computing -- Doctoral Dissertation Colloquium 2022. Tomforde, Sven; Krupitzer, Christian. In Vol. 24Intelligent Embedded Systems. kassel university press, 2023.
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@book{tomforde2023organic, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Organic Computing – Doctoral Dissertation Colloquium 2022}, volume = 24, year = 2023 }
%0 Book %1 tomforde2023organic %B Intelligent Embedded Systems %D 2023 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %R 10.17170/kobra-202302107484 %T Organic Computing -- Doctoral Dissertation Colloquium 2022 %V 24
Active Bird2Vec: Towards End-To-End Bird Sound Monitoring with Transformers.Rauch, Lukas; Schwinger, Raphael; Wirth, Moritz; Sick, Bernhard; Tomforde, Sven; Scholz, Christoph. In Workshop on Artificial Intelligence for Sustainability (AI4S), ECAI, ble. 1–6. 2023.
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We propose a shift towards end-to-end learning in bird sound monitoring by combining self-supervised (SSL) and deep active learning (DAL). Leveraging transformer models, we aim to bypass traditional spectrogram conversions, enabling direct raw audio processing. ACTIVE BIRD2VEC is set to generate high-quality bird sound representations through SSL, potentially accelerating the assessment of environmental changes and decision-making processes for wind farms. Additionally, we seek to utilize the wide variety of bird vocalizations through DAL, reducing the reliance on extensively labeled datasets by human experts. We plan to curate a comprehensive set of tasks through Huggingface Datasets, enhancing future comparability and reproducibility of bioacoustic research. A comparative analysis between various transformer models will be conducted to evaluate their proficiency in bird sound recognition tasks. We aim to accelerate the progression of avian bioacoustic research and contribute to more effective conservation strategies.
@inproceedings{rauch2023active, abstract = {We propose a shift towards end-to-end learning in bird sound monitoring by combining self-supervised (SSL) and deep active learning (DAL). Leveraging transformer models, we aim to bypass traditional spectrogram conversions, enabling direct raw audio processing. ACTIVE BIRD2VEC is set to generate high-quality bird sound representations through SSL, potentially accelerating the assessment of environmental changes and decision-making processes for wind farms. Additionally, we seek to utilize the wide variety of bird vocalizations through DAL, reducing the reliance on extensively labeled datasets by human experts. We plan to curate a comprehensive set of tasks through Huggingface Datasets, enhancing future comparability and reproducibility of bioacoustic research. A comparative analysis between various transformer models will be conducted to evaluate their proficiency in bird sound recognition tasks. We aim to accelerate the progression of avian bioacoustic research and contribute to more effective conservation strategies.}, author = {Rauch, Lukas and Schwinger, Raphael and Wirth, Moritz and Sick, Bernhard and Tomforde, Sven and Scholz, Christoph}, booktitle = {Workshop on Artificial Intelligence for Sustainability (AI4S), ECAI}, keywords = {itegpub}, pages = {1–6}, title = {Active Bird2Vec: Towards End-To-End Bird Sound Monitoring with Transformers}, year = 2023 }
%0 Conference Paper %1 rauch2023active %A Rauch, Lukas %A Schwinger, Raphael %A Wirth, Moritz %A Sick, Bernhard %A Tomforde, Sven %A Scholz, Christoph %B Workshop on Artificial Intelligence for Sustainability (AI4S), ECAI %D 2023 %P 1--6 %T Active Bird2Vec: Towards End-To-End Bird Sound Monitoring with Transformers %U https://arxiv.org/abs/2308.07121 %X We propose a shift towards end-to-end learning in bird sound monitoring by combining self-supervised (SSL) and deep active learning (DAL). Leveraging transformer models, we aim to bypass traditional spectrogram conversions, enabling direct raw audio processing. ACTIVE BIRD2VEC is set to generate high-quality bird sound representations through SSL, potentially accelerating the assessment of environmental changes and decision-making processes for wind farms. Additionally, we seek to utilize the wide variety of bird vocalizations through DAL, reducing the reliance on extensively labeled datasets by human experts. We plan to curate a comprehensive set of tasks through Huggingface Datasets, enhancing future comparability and reproducibility of bioacoustic research. A comparative analysis between various transformer models will be conducted to evaluate their proficiency in bird sound recognition tasks. We aim to accelerate the progression of avian bioacoustic research and contribute to more effective conservation strategies.
Targeted Adversarial Attacks on Wind Power Forecasts.Heinrich, René; Scholz, Christoph; Vogt, Stephan; Lehna, Malte. In Machine Learning, 113(2), ble. 863–889. Springer, 2023.
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In recent years, researchers proposed a variety of deep learning models for wind power forecasting. These models predict the wind power generation of wind farms or entire regions more accurately than traditional machine learning algorithms or physical models. However, latest research has shown that deep learning models can often be manipulated by adversarial attacks. Since wind power forecasts are essential for the stability of modern power systems, it is important to protect them from this threat. In this work, we investigate the vulnerability of two different forecasting models to targeted, semi-targeted, and untargeted adversarial attacks. We consider a Long Short-Term Memory (LSTM) network for predicting the power generation of individual wind farms and a Convolutional Neural Network (CNN) for forecasting the wind power generation throughout Germany. Moreover, we propose the Total Adversarial Robustness Score (TARS), an evaluation metric for quantifying the robustness of regression models to targeted and semi-targeted adversarial attacks. It assesses the impact of attacks on the model's performance, as well as the extent to which the attacker's goal was achieved, by assigning a score between 0 (very vulnerable) and 1 (very robust). In our experiments, the LSTM forecasting model was fairly robust and achieved a TARS value of over 0.78 for all adversarial attacks investigated. The CNN forecasting model only achieved TARS values below 0.10 when trained ordinarily, and was thus very vulnerable. Yet, its robustness could be significantly improved by adversarial training, which always resulted in a TARS above 0.46.
@article{heinrich2023targeted, abstract = {In recent years, researchers proposed a variety of deep learning models for wind power forecasting. These models predict the wind power generation of wind farms or entire regions more accurately than traditional machine learning algorithms or physical models. However, latest research has shown that deep learning models can often be manipulated by adversarial attacks. Since wind power forecasts are essential for the stability of modern power systems, it is important to protect them from this threat. In this work, we investigate the vulnerability of two different forecasting models to targeted, semi-targeted, and untargeted adversarial attacks. We consider a Long Short-Term Memory (LSTM) network for predicting the power generation of individual wind farms and a Convolutional Neural Network (CNN) for forecasting the wind power generation throughout Germany. Moreover, we propose the Total Adversarial Robustness Score (TARS), an evaluation metric for quantifying the robustness of regression models to targeted and semi-targeted adversarial attacks. It assesses the impact of attacks on the model's performance, as well as the extent to which the attacker's goal was achieved, by assigning a score between 0 (very vulnerable) and 1 (very robust). In our experiments, the LSTM forecasting model was fairly robust and achieved a TARS value of over 0.78 for all adversarial attacks investigated. The CNN forecasting model only achieved TARS values below 0.10 when trained ordinarily, and was thus very vulnerable. Yet, its robustness could be significantly improved by adversarial training, which always resulted in a TARS above 0.46.}, author = {Heinrich, René and Scholz, Christoph and Vogt, Stephan and Lehna, Malte}, journal = {Machine Learning}, keywords = {itegpub}, number = 2, pages = {863–889}, publisher = {Springer}, title = {Targeted Adversarial Attacks on Wind Power Forecasts}, volume = 113, year = 2023 }
%0 Journal Article %1 heinrich2023targeted %A Heinrich, René %A Scholz, Christoph %A Vogt, Stephan %A Lehna, Malte %D 2023 %I Springer %J Machine Learning %N 2 %P 863--889 %R 10.1007/s10994-023-06396-9 %T Targeted Adversarial Attacks on Wind Power Forecasts %V 113 %X In recent years, researchers proposed a variety of deep learning models for wind power forecasting. These models predict the wind power generation of wind farms or entire regions more accurately than traditional machine learning algorithms or physical models. However, latest research has shown that deep learning models can often be manipulated by adversarial attacks. Since wind power forecasts are essential for the stability of modern power systems, it is important to protect them from this threat. In this work, we investigate the vulnerability of two different forecasting models to targeted, semi-targeted, and untargeted adversarial attacks. We consider a Long Short-Term Memory (LSTM) network for predicting the power generation of individual wind farms and a Convolutional Neural Network (CNN) for forecasting the wind power generation throughout Germany. Moreover, we propose the Total Adversarial Robustness Score (TARS), an evaluation metric for quantifying the robustness of regression models to targeted and semi-targeted adversarial attacks. It assesses the impact of attacks on the model's performance, as well as the extent to which the attacker's goal was achieved, by assigning a score between 0 (very vulnerable) and 1 (very robust). In our experiments, the LSTM forecasting model was fairly robust and achieved a TARS value of over 0.78 for all adversarial attacks investigated. The CNN forecasting model only achieved TARS values below 0.10 when trained ordinarily, and was thus very vulnerable. Yet, its robustness could be significantly improved by adversarial training, which always resulted in a TARS above 0.46.
Active Label Refinement for Semantic Segmentation of Satellite Images.Pham, Minh Tuan; Wijesingha, Jayan; Kottke, Daniel; Herde, Marek; Huseljic, Denis; Sick, Bernhard; Wachendorf, Michael; Esch, Thomas. In arXiv e-prints, bl. arXiv:2309.06159. 2023.
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Remote sensing through semantic segmentation of satellite images contributes to the understanding and utilisation of the earth's surface. For this purpose, semantic segmentation networks are typically trained on large sets of labelled satellite images. However, obtaining expert labels for these images is costly. Therefore, we propose to rely on a low-cost approach, e.g. crowdsourcing or pretrained networks, to label the images in the first step. Since these initial labels are partially erroneous, we use active learning strategies to cost-efficiently refine the labels in the second step. We evaluate the active learning strategies using satellite images of Bengaluru in India, labelled with land cover and land use labels. Our experimental results suggest that an active label refinement to improve the semantic segmentation network's performance is beneficial.
@article{pham2023active, abstract = {Remote sensing through semantic segmentation of satellite images contributes to the understanding and utilisation of the earth's surface. For this purpose, semantic segmentation networks are typically trained on large sets of labelled satellite images. However, obtaining expert labels for these images is costly. Therefore, we propose to rely on a low-cost approach, e.g. crowdsourcing or pretrained networks, to label the images in the first step. Since these initial labels are partially erroneous, we use active learning strategies to cost-efficiently refine the labels in the second step. We evaluate the active learning strategies using satellite images of Bengaluru in India, labelled with land cover and land use labels. Our experimental results suggest that an active label refinement to improve the semantic segmentation network's performance is beneficial.}, author = {Pham, Minh Tuan and Wijesingha, Jayan and Kottke, Daniel and Herde, Marek and Huseljic, Denis and Sick, Bernhard and Wachendorf, Michael and Esch, Thomas}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2309.06159}, title = {Active Label Refinement for Semantic Segmentation of Satellite Images}, year = 2023 }
%0 Journal Article %1 pham2023active %A Pham, Minh Tuan %A Wijesingha, Jayan %A Kottke, Daniel %A Herde, Marek %A Huseljic, Denis %A Sick, Bernhard %A Wachendorf, Michael %A Esch, Thomas %D 2023 %J arXiv e-prints %P arXiv:2309.06159 %T Active Label Refinement for Semantic Segmentation of Satellite Images %U https://arxiv.org/abs/2309.06159 %X Remote sensing through semantic segmentation of satellite images contributes to the understanding and utilisation of the earth's surface. For this purpose, semantic segmentation networks are typically trained on large sets of labelled satellite images. However, obtaining expert labels for these images is costly. Therefore, we propose to rely on a low-cost approach, e.g. crowdsourcing or pretrained networks, to label the images in the first step. Since these initial labels are partially erroneous, we use active learning strategies to cost-efficiently refine the labels in the second step. We evaluate the active learning strategies using satellite images of Bengaluru in India, labelled with land cover and land use labels. Our experimental results suggest that an active label refinement to improve the semantic segmentation network's performance is beneficial.
Who knows best? A Case Study on Intelligent Crowdworker Selection via Deep Learning.Herde, Marek; Huseljic, Denis; Sick, Bernhard; Bretschneider, Ulrich; Oeste-Reiß, Sarah. In Workshop on Interactive Adapative Learning (IAL), ECML PKDD, ble. 14–18. 2023.
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Crowdworking is a popular approach for annotating large amounts of data to train deep neural networks. However, parts of the annotations are often erroneous. In a case study, we demonstrate how an intelligent crowdworker selection via deep learning reduces the number of erroneous annotations and, thus, the annotation costs of obtaining reliable data for training deep neural networks.
@inproceedings{herde2023who, abstract = {Crowdworking is a popular approach for annotating large amounts of data to train deep neural networks. However, parts of the annotations are often erroneous. In a case study, we demonstrate how an intelligent crowdworker selection via deep learning reduces the number of erroneous annotations and, thus, the annotation costs of obtaining reliable data for training deep neural networks.}, author = {Herde, Marek and Huseljic, Denis and Sick, Bernhard and Bretschneider, Ulrich and Oeste-Reiß, Sarah}, booktitle = {Workshop on Interactive Adapative Learning (IAL), ECML PKDD}, keywords = {itegpub}, pages = {14–18}, title = {Who knows best? A Case Study on Intelligent Crowdworker Selection via Deep Learning}, year = 2023 }
%0 Conference Paper %1 herde2023who %A Herde, Marek %A Huseljic, Denis %A Sick, Bernhard %A Bretschneider, Ulrich %A Oeste-Reiß, Sarah %B Workshop on Interactive Adapative Learning (IAL), ECML PKDD %D 2023 %P 14--18 %T Who knows best? A Case Study on Intelligent Crowdworker Selection via Deep Learning %U https://ceur-ws.org/Vol-3470/paper3.pdf %X Crowdworking is a popular approach for annotating large amounts of data to train deep neural networks. However, parts of the annotations are often erroneous. In a case study, we demonstrate how an intelligent crowdworker selection via deep learning reduces the number of erroneous annotations and, thus, the annotation costs of obtaining reliable data for training deep neural networks.
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers.Rauch, Lukas; Aßenmacher, Matthias; Huseljic, Denis; Wirth, Moritz; Bischl, Bernd; Sick, Bernhard. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), ble. 55–74. Springer, 2023.
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Deep active learning (DAL) seeks to reduce annotation costs by enabling the model to actively query instance annotations from which it expects to learn the most. Despite extensive research, there is cur- rently no standardized evaluation protocol for transformer-based lan- guage models in the field of DAL. Diverse experimental settings lead to difficulties in comparing research and deriving recommendations for prac- titioners. To tackle this challenge, we propose the ActiveGLAE bench- mark, a comprehensive collection of data sets and evaluation guidelines for assessing DAL. Our benchmark aims to facilitate and streamline the evaluation process of novel DAL strategies. Additionally, we provide an extensive overview of current practice in DAL with transformer-based language models. We identify three key challenges - data set selection, model training, and DAL settings - that pose difficulties in comparing query strategies. We establish baseline results through an extensive set of experiments as a reference point for evaluating future work. Based on our findings, we provide guidelines for researchers and practitioners.
@inproceedings{rauch2023activeglae, abstract = {Deep active learning (DAL) seeks to reduce annotation costs by enabling the model to actively query instance annotations from which it expects to learn the most. Despite extensive research, there is cur- rently no standardized evaluation protocol for transformer-based lan- guage models in the field of DAL. Diverse experimental settings lead to difficulties in comparing research and deriving recommendations for prac- titioners. To tackle this challenge, we propose the ActiveGLAE bench- mark, a comprehensive collection of data sets and evaluation guidelines for assessing DAL. Our benchmark aims to facilitate and streamline the evaluation process of novel DAL strategies. Additionally, we provide an extensive overview of current practice in DAL with transformer-based language models. We identify three key challenges - data set selection, model training, and DAL settings - that pose difficulties in comparing query strategies. We establish baseline results through an extensive set of experiments as a reference point for evaluating future work. Based on our findings, we provide guidelines for researchers and practitioners.}, author = {Rauch, Lukas and Aßenmacher, Matthias and Huseljic, Denis and Wirth, Moritz and Bischl, Bernd and Sick, Bernhard}, booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)}, keywords = {itegpub}, pages = {55–74}, publisher = {Springer}, title = {ActiveGLAE: A Benchmark for Deep Active Learning with Transformers}, year = 2023 }
%0 Conference Paper %1 rauch2023activeglae %A Rauch, Lukas %A Aßenmacher, Matthias %A Huseljic, Denis %A Wirth, Moritz %A Bischl, Bernd %A Sick, Bernhard %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) %D 2023 %I Springer %P 55--74 %R 10.1007/978-3-031-43412-9_4 %T ActiveGLAE: A Benchmark for Deep Active Learning with Transformers %U https://link.springer.com/chapter/10.1007/978-3-031-43412-9_4 %X Deep active learning (DAL) seeks to reduce annotation costs by enabling the model to actively query instance annotations from which it expects to learn the most. Despite extensive research, there is cur- rently no standardized evaluation protocol for transformer-based lan- guage models in the field of DAL. Diverse experimental settings lead to difficulties in comparing research and deriving recommendations for prac- titioners. To tackle this challenge, we propose the ActiveGLAE bench- mark, a comprehensive collection of data sets and evaluation guidelines for assessing DAL. Our benchmark aims to facilitate and streamline the evaluation process of novel DAL strategies. Additionally, we provide an extensive overview of current practice in DAL with transformer-based language models. We identify three key challenges - data set selection, model training, and DAL settings - that pose difficulties in comparing query strategies. We establish baseline results through an extensive set of experiments as a reference point for evaluating future work. Based on our findings, we provide guidelines for researchers and practitioners.
Continuous Feature Networks: A Novel Method to Process Irregularly and Inconsistently Sampled Data With Position-Dependent Features.Magnussen, Birk Martin; Stern, Claudius; Sick, Bernhard. In International Journal On Advances in Intelligent Systems, 16(3&4), ble. 43–50. ThinkMind, 2023.
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Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable if the position of detected features is relevant for the prediction task. However, the capabilities of continuous kernels to process irregularly sampled data are still desired. This article introduces the continuous feature network, a novel method utilizing continuous kernels, for detecting global features at absolute positions in the data domain. Through a use case in processing multiple spatially resolved reflection spectroscopy data, which is sampled irregularly and inconsistently, we show that the proposed method is capable of processing such data directly without additional preprocessing or augmentation as is needed using comparable methods. In addition, we show that the proposed method is able to achieve a higher prediction accuracy than a comparable network on a dataset with position-dependent features. Furthermore, a higher robustness to missing data compared to a benchmark network using data interpolation is observed, which allows the network to adapt to sensors with a failure of individual light emitters or detectors without the need for retraining. The article shows how these capabilities stem from the continuous kernels used and how the number of available kernels to be trained affects the model. Finally, the article proposes a method to utilize the introduced method as a base for an interpretable model usable for explainable AI.
@article{magnussen2023continuous, abstract = {Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable if the position of detected features is relevant for the prediction task. However, the capabilities of continuous kernels to process irregularly sampled data are still desired. This article introduces the continuous feature network, a novel method utilizing continuous kernels, for detecting global features at absolute positions in the data domain. Through a use case in processing multiple spatially resolved reflection spectroscopy data, which is sampled irregularly and inconsistently, we show that the proposed method is capable of processing such data directly without additional preprocessing or augmentation as is needed using comparable methods. In addition, we show that the proposed method is able to achieve a higher prediction accuracy than a comparable network on a dataset with position-dependent features. Furthermore, a higher robustness to missing data compared to a benchmark network using data interpolation is observed, which allows the network to adapt to sensors with a failure of individual light emitters or detectors without the need for retraining. The article shows how these capabilities stem from the continuous kernels used and how the number of available kernels to be trained affects the model. Finally, the article proposes a method to utilize the introduced method as a base for an interpretable model usable for explainable AI.}, author = {Magnussen, Birk Martin and Stern, Claudius and Sick, Bernhard}, journal = {International Journal On Advances in Intelligent Systems}, keywords = {itegpub}, number = {3&4}, pages = {43–50}, publisher = {ThinkMind}, title = {Continuous Feature Networks: A Novel Method to Process Irregularly and Inconsistently Sampled Data With Position-Dependent Features}, volume = 16, year = 2023 }
%0 Journal Article %1 magnussen2023continuous %A Magnussen, Birk Martin %A Stern, Claudius %A Sick, Bernhard %D 2023 %I ThinkMind %J International Journal On Advances in Intelligent Systems %N 3&4 %P 43--50 %T Continuous Feature Networks: A Novel Method to Process Irregularly and Inconsistently Sampled Data With Position-Dependent Features %U http://www.thinkmind.org/index.php?view=article&articleid=intsys_v16_n34_2023_3 %V 16 %X Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable if the position of detected features is relevant for the prediction task. However, the capabilities of continuous kernels to process irregularly sampled data are still desired. This article introduces the continuous feature network, a novel method utilizing continuous kernels, for detecting global features at absolute positions in the data domain. Through a use case in processing multiple spatially resolved reflection spectroscopy data, which is sampled irregularly and inconsistently, we show that the proposed method is capable of processing such data directly without additional preprocessing or augmentation as is needed using comparable methods. In addition, we show that the proposed method is able to achieve a higher prediction accuracy than a comparable network on a dataset with position-dependent features. Furthermore, a higher robustness to missing data compared to a benchmark network using data interpolation is observed, which allows the network to adapt to sensors with a failure of individual light emitters or detectors without the need for retraining. The article shows how these capabilities stem from the continuous kernels used and how the number of available kernels to be trained affects the model. Finally, the article proposes a method to utilize the introduced method as a base for an interpretable model usable for explainable AI.
Context-aware recommendations for extended electric vehicle battery lifetime.Eider, Markus; Sick, Bernhard; Berl, Andreas. In Sustainable Computing: Informatics and Systems (SUSCOM), 37, bl. 100845. Elsevier, 2023.
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Electric vehicles are a means of reducing CO2 emissions in transportation. However, the sustainability of electric vehicle batteries is affected by battery health degradation, which decreases their overall lifetime. This results in a substantial amount of depleted batteries due to replacements. Users have a major impact on battery health degradation through their actions while handling electric vehicles, such as the use of fast charging. To mitigate this problem, this article presents a methodology to generate user guidance for batteryfriendly actions in the upcoming use. Therefore, we first identify general recommendations from related work and combine them with the vehicle context in order to define context-aware recommendations in the form of if–then rules. These context-aware recommendations are then used to generate user advice. Second, the article covers how to predict the vehicle context in order to determine necessary recommendations. Third, a prescriptive recommendation system architecture is proposed, which takes vehicle context information, and produces user guidance. Finally, we test the proposed architecture using fuzzy logic as decision system. Overall, the architecture provides satisfactory user advice
@article{eider2023context, abstract = {Electric vehicles are a means of reducing CO2 emissions in transportation. However, the sustainability of electric vehicle batteries is affected by battery health degradation, which decreases their overall lifetime. This results in a substantial amount of depleted batteries due to replacements. Users have a major impact on battery health degradation through their actions while handling electric vehicles, such as the use of fast charging. To mitigate this problem, this article presents a methodology to generate user guidance for batteryfriendly actions in the upcoming use. Therefore, we first identify general recommendations from related work and combine them with the vehicle context in order to define context-aware recommendations in the form of if–then rules. These context-aware recommendations are then used to generate user advice. Second, the article covers how to predict the vehicle context in order to determine necessary recommendations. Third, a prescriptive recommendation system architecture is proposed, which takes vehicle context information, and produces user guidance. Finally, we test the proposed architecture using fuzzy logic as decision system. Overall, the architecture provides satisfactory user advice}, author = {Eider, Markus and Sick, Bernhard and Berl, Andreas}, journal = {Sustainable Computing: Informatics and Systems (SUSCOM)}, keywords = {itegpub}, pages = 100845, publisher = {Elsevier}, title = {Context-aware recommendations for extended electric vehicle battery lifetime}, volume = 37, year = 2023 }
%0 Journal Article %1 eider2023context %A Eider, Markus %A Sick, Bernhard %A Berl, Andreas %D 2023 %I Elsevier %J Sustainable Computing: Informatics and Systems (SUSCOM) %P 100845 %R 10.1016/j.suscom.2022.100845 %T Context-aware recommendations for extended electric vehicle battery lifetime %V 37 %X Electric vehicles are a means of reducing CO2 emissions in transportation. However, the sustainability of electric vehicle batteries is affected by battery health degradation, which decreases their overall lifetime. This results in a substantial amount of depleted batteries due to replacements. Users have a major impact on battery health degradation through their actions while handling electric vehicles, such as the use of fast charging. To mitigate this problem, this article presents a methodology to generate user guidance for batteryfriendly actions in the upcoming use. Therefore, we first identify general recommendations from related work and combine them with the vehicle context in order to define context-aware recommendations in the form of if–then rules. These context-aware recommendations are then used to generate user advice. Second, the article covers how to predict the vehicle context in order to determine necessary recommendations. Third, a prescriptive recommendation system architecture is proposed, which takes vehicle context information, and produces user guidance. Finally, we test the proposed architecture using fuzzy logic as decision system. Overall, the architecture provides satisfactory user advice
On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision-Based Tool.Engelhardt, Anna; Decke, Jens; Meier, David; Dulig, Franz; Ragunathan, Rishan; Wegener, Thomas; Sick, Bernhard; Niendorf, Thomas. In Advanced Engineering Materials, 25(21), bl. 2300876. Wiley, 2023.
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Fracture surface analysis is of utmost importance with respect to structural integrity of metallic materials. This especially holds true for additively manufactured materials. Despite an increasing trend of automatization of testing methods, the analysis and classification of fatigue fracture surface images is commonly done manually by experts. Although this leads to correct results in most cases, it has several disadvantages, e.g., the need of a huge knowledge base to interpret images correctly. In present work, an unsupervised tool for analysis of overview images of fatigue fracture surface images is developed to support nonexperienced users to identify the origin of the fracture. The tool is developed using fracture surface images of additively manufactured Ti6Al4V specimens fatigued in the high-cycle-fatigue regime and is based on the identification of river marks. Several recording parameters seem to have no significant influence on the results as long as preprocessing settings are adapted. Moreover, it is possible to analyze images of other materials with the tool as long as the fracture surfaces contain river marks. However, special features like multiple origins or origins located in direct vicinity to the surface, e.g., caused by increased plastic strains, require a further tool development or alternative approaches.
@article{engelhardt2023reliability, abstract = {Fracture surface analysis is of utmost importance with respect to structural integrity of metallic materials. This especially holds true for additively manufactured materials. Despite an increasing trend of automatization of testing methods, the analysis and classification of fatigue fracture surface images is commonly done manually by experts. Although this leads to correct results in most cases, it has several disadvantages, e.g., the need of a huge knowledge base to interpret images correctly. In present work, an unsupervised tool for analysis of overview images of fatigue fracture surface images is developed to support nonexperienced users to identify the origin of the fracture. The tool is developed using fracture surface images of additively manufactured Ti6Al4V specimens fatigued in the high-cycle-fatigue regime and is based on the identification of river marks. Several recording parameters seem to have no significant influence on the results as long as preprocessing settings are adapted. Moreover, it is possible to analyze images of other materials with the tool as long as the fracture surfaces contain river marks. However, special features like multiple origins or origins located in direct vicinity to the surface, e.g., caused by increased plastic strains, require a further tool development or alternative approaches.}, author = {Engelhardt, Anna and Decke, Jens and Meier, David and Dulig, Franz and Ragunathan, Rishan and Wegener, Thomas and Sick, Bernhard and Niendorf, Thomas}, journal = {Advanced Engineering Materials}, keywords = {ImageRecognition}, number = 21, pages = 2300876, publisher = {Wiley}, title = {On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision-Based Tool}, volume = 25, year = 2023 }
%0 Journal Article %1 engelhardt2023reliability %A Engelhardt, Anna %A Decke, Jens %A Meier, David %A Dulig, Franz %A Ragunathan, Rishan %A Wegener, Thomas %A Sick, Bernhard %A Niendorf, Thomas %D 2023 %I Wiley %J Advanced Engineering Materials %N 21 %P 2300876 %R https://doi.org/10.1002/adem.202300876 %T On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision-Based Tool %U https://onlinelibrary.wiley.com/doi/full/10.1002/adem.202300876 %V 25 %X Fracture surface analysis is of utmost importance with respect to structural integrity of metallic materials. This especially holds true for additively manufactured materials. Despite an increasing trend of automatization of testing methods, the analysis and classification of fatigue fracture surface images is commonly done manually by experts. Although this leads to correct results in most cases, it has several disadvantages, e.g., the need of a huge knowledge base to interpret images correctly. In present work, an unsupervised tool for analysis of overview images of fatigue fracture surface images is developed to support nonexperienced users to identify the origin of the fracture. The tool is developed using fracture surface images of additively manufactured Ti6Al4V specimens fatigued in the high-cycle-fatigue regime and is based on the identification of river marks. Several recording parameters seem to have no significant influence on the results as long as preprocessing settings are adapted. Moreover, it is possible to analyze images of other materials with the tool as long as the fracture surfaces contain river marks. However, special features like multiple origins or origins located in direct vicinity to the surface, e.g., caused by increased plastic strains, require a further tool development or alternative approaches.
Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning.Meier, David; Ragunathan, Rishan; Degener, Sebastian; Liehr, Alexander; Vollmer, Malte; Niendorf, Thomas; Sick, Bernhard. In Scientific Reports, 13(1), bl. 5410. Springer Nature, 2023.
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X‑ray diffraction crystallography allows non‑destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X‑ray diffraction has been highly time‑ consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep‑learning‑based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU‑based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.
@article{meier2023reconstruction, abstract = {X‑ray diffraction crystallography allows non‑destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X‑ray diffraction has been highly time‑ consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep‑learning‑based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU‑based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.}, author = {Meier, David and Ragunathan, Rishan and Degener, Sebastian and Liehr, Alexander and Vollmer, Malte and Niendorf, Thomas and Sick, Bernhard}, journal = {Scientific Reports}, keywords = {itegpub}, number = 1, pages = 5410, publisher = {Springer Nature}, title = {Reconstruction of incomplete {X}-ray diffraction pole figures of oligocrystalline materials using deep learning}, volume = 13, year = 2023 }
%0 Journal Article %1 meier2023reconstruction %A Meier, David %A Ragunathan, Rishan %A Degener, Sebastian %A Liehr, Alexander %A Vollmer, Malte %A Niendorf, Thomas %A Sick, Bernhard %D 2023 %I Springer Nature %J Scientific Reports %N 1 %P 5410 %R 10.1038/s41598-023-31580-1 %T Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning %U https://www.nature.com/articles/s41598-023-31580-1 %V 13 %X X‑ray diffraction crystallography allows non‑destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X‑ray diffraction has been highly time‑ consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep‑learning‑based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU‑based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.
What Does Really Count? Estimating Relevance of Corner Cases for Semantic Segmentation in Automated Driving.Breitenstein, Jasmin; Heidecker, Florian; Lyssenko, Maria; Bogdoll, Daniel; Bieshaar, Maarten; Zöllner, J. Marius; Sick, Bernhard; Fingscheidt, Tim. In Workshop on roBustness and Reliability of Autonomous Vehicles in the Open-world (BRAVO), ICCV, ble. 3991–4000. 2023.
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In safety-critical applications such as automated driving, perception errors may create an imminent risk to vulnerable road users (VRU). To mitigate the occurrence of unexpected and potentially dangerous situations, so-called corner cases, perception models are trained on a huge amount of data. However, the models are typically evaluated using task-agnostic metrics, which do not reflect the severity of safety-critical misdetections. Consequently, misdetections with particular relevance for the safe driving task should entail a more severe penalty during evaluation to pinpoint corner cases in large-scale datasets. In this work, we propose a novel metric IoUw that exploits relevance on the pixel level of the semantic segmentation output to extend the notion of the intersection over union (IoU) by emphasizing small areas of an image affected by corner cases. We (i) employ IoUw to measure the effect of pre-defined relevance criteria on the segmentation evaluation, and (ii) use the relevance-adapted IoUw to refine the identification of corner cases. In our experiments, we investigate vision-based relevance criteria and physical attributes as per-pixel criticality to factor in the imminent risk, showing that IoUw precisely accentuates the relevance of corner cases.
@inproceedings{breitenstein2023what, abstract = {In safety-critical applications such as automated driving, perception errors may create an imminent risk to vulnerable road users (VRU). To mitigate the occurrence of unexpected and potentially dangerous situations, so-called corner cases, perception models are trained on a huge amount of data. However, the models are typically evaluated using task-agnostic metrics, which do not reflect the severity of safety-critical misdetections. Consequently, misdetections with particular relevance for the safe driving task should entail a more severe penalty during evaluation to pinpoint corner cases in large-scale datasets. In this work, we propose a novel metric IoUw that exploits relevance on the pixel level of the semantic segmentation output to extend the notion of the intersection over union (IoU) by emphasizing small areas of an image affected by corner cases. We (i) employ IoUw to measure the effect of pre-defined relevance criteria on the segmentation evaluation, and (ii) use the relevance-adapted IoUw to refine the identification of corner cases. In our experiments, we investigate vision-based relevance criteria and physical attributes as per-pixel criticality to factor in the imminent risk, showing that IoUw precisely accentuates the relevance of corner cases.}, author = {Breitenstein, Jasmin and Heidecker, Florian and Lyssenko, Maria and Bogdoll, Daniel and Bieshaar, Maarten and Zöllner, J. Marius and Sick, Bernhard and Fingscheidt, Tim}, booktitle = {Workshop on roBustness and Reliability of Autonomous Vehicles in the Open-world (BRAVO), ICCV}, keywords = {itegpub}, pages = {3991–4000}, title = {What Does Really Count? Estimating Relevance of Corner Cases for Semantic Segmentation in Automated Driving}, year = 2023 }
%0 Conference Paper %1 breitenstein2023what %A Breitenstein, Jasmin %A Heidecker, Florian %A Lyssenko, Maria %A Bogdoll, Daniel %A Bieshaar, Maarten %A Zöllner, J. Marius %A Sick, Bernhard %A Fingscheidt, Tim %B Workshop on roBustness and Reliability of Autonomous Vehicles in the Open-world (BRAVO), ICCV %D 2023 %P 3991--4000 %T What Does Really Count? Estimating Relevance of Corner Cases for Semantic Segmentation in Automated Driving %U https://openaccess.thecvf.com/content/ICCV2023W/BRAVO/html/Breitenstein_What_Does_Really_Count_Estimating_Relevance_of_Corner_Cases_for_ICCVW_2023_paper.html %X In safety-critical applications such as automated driving, perception errors may create an imminent risk to vulnerable road users (VRU). To mitigate the occurrence of unexpected and potentially dangerous situations, so-called corner cases, perception models are trained on a huge amount of data. However, the models are typically evaluated using task-agnostic metrics, which do not reflect the severity of safety-critical misdetections. Consequently, misdetections with particular relevance for the safe driving task should entail a more severe penalty during evaluation to pinpoint corner cases in large-scale datasets. In this work, we propose a novel metric IoUw that exploits relevance on the pixel level of the semantic segmentation output to extend the notion of the intersection over union (IoU) by emphasizing small areas of an image affected by corner cases. We (i) employ IoUw to measure the effect of pre-defined relevance criteria on the segmentation evaluation, and (ii) use the relevance-adapted IoUw to refine the identification of corner cases. In our experiments, we investigate vision-based relevance criteria and physical attributes as per-pixel criticality to factor in the imminent risk, showing that IoUw precisely accentuates the relevance of corner cases.
DADO – Low-Cost Query Strategies for Deep Active Design Optimization.Decke, Jens; Gruhl, Christian; Rauch, Lukas; Sick, Bernhard. In International Conference on Machine Learning and Applications (ICMLA), ble. 1611–1618. IEEE, 2023.
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In this work, we apply deep active learning to the field of design optimization to reduce the number of computationally expensive numerical simulations widely used in industry and engineering. We are interested in optimizing the design of structural components, where a set of parameters describes the shape. If we can predict the performance based on these parameters and consider only the promising candidates for simulation, there is an enormous potential for saving computing power. We present two query strategies for self-optimization to reduce the computational cost in multi-objective design optimization problems. Our proposed methodology provides an intuitive approach that is easy to apply, offers significant improvements over random sampling, and circumvents the need for uncertainty estimation. We evaluate our strategies on a large dataset from the domain of fluid dynamics and introduce two new evaluation metrics to determine the model's performance. Findings from our evaluation highlights the effectiveness of our query strategies in accelerating design optimization. Furthermore, the introduced method is easily transferable to other self-optimization problems in industry and engineering.
@inproceedings{decke2023dado, abstract = {In this work, we apply deep active learning to the field of design optimization to reduce the number of computationally expensive numerical simulations widely used in industry and engineering. We are interested in optimizing the design of structural components, where a set of parameters describes the shape. If we can predict the performance based on these parameters and consider only the promising candidates for simulation, there is an enormous potential for saving computing power. We present two query strategies for self-optimization to reduce the computational cost in multi-objective design optimization problems. Our proposed methodology provides an intuitive approach that is easy to apply, offers significant improvements over random sampling, and circumvents the need for uncertainty estimation. We evaluate our strategies on a large dataset from the domain of fluid dynamics and introduce two new evaluation metrics to determine the model's performance. Findings from our evaluation highlights the effectiveness of our query strategies in accelerating design optimization. Furthermore, the introduced method is easily transferable to other self-optimization problems in industry and engineering.}, author = {Decke, Jens and Gruhl, Christian and Rauch, Lukas and Sick, Bernhard}, booktitle = {International Conference on Machine Learning and Applications (ICMLA)}, keywords = {itegpub}, pages = {1611–1618}, publisher = {IEEE}, title = {DADO – Low-Cost Query Strategies for Deep Active Design Optimization}, year = 2023 }
%0 Conference Paper %1 decke2023dado %A Decke, Jens %A Gruhl, Christian %A Rauch, Lukas %A Sick, Bernhard %B International Conference on Machine Learning and Applications (ICMLA) %D 2023 %I IEEE %P 1611--1618 %R 10.1109/ICMLA58977.2023.00244 %T DADO – Low-Cost Query Strategies for Deep Active Design Optimization %X In this work, we apply deep active learning to the field of design optimization to reduce the number of computationally expensive numerical simulations widely used in industry and engineering. We are interested in optimizing the design of structural components, where a set of parameters describes the shape. If we can predict the performance based on these parameters and consider only the promising candidates for simulation, there is an enormous potential for saving computing power. We present two query strategies for self-optimization to reduce the computational cost in multi-objective design optimization problems. Our proposed methodology provides an intuitive approach that is easy to apply, offers significant improvements over random sampling, and circumvents the need for uncertainty estimation. We evaluate our strategies on a large dataset from the domain of fluid dynamics and introduce two new evaluation metrics to determine the model's performance. Findings from our evaluation highlights the effectiveness of our query strategies in accelerating design optimization. Furthermore, the introduced method is easily transferable to other self-optimization problems in industry and engineering.
Active Learning with Fast Model Updates and Class-Balanced Selection for Imbalanced Datasets.Huang, Zhixin; He, Yujiang; Herde, Marek; Huseljic, Denis; Sick, Bernhard. In Workshop on Interactive Adapative Learning (IAL), ECML PKDD, ble. 28–45. 2023.
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Active learning strategies aim to minimize the number of queried samples for model training. However, two challenges in pool-based deep active learning on imbalanced datasets are observed in experiments: (1) the declining performance of active learning strategies due to imbalanced class distribution; (2)~the lack of sample diversity in acquisition batches due to the absence of timely model updates. This paper proposes the AL-FaMoUS, a general solution combining fast model updates and class-balanced minibatch selection to the active learning process. Furthermore, a simplification of the AL-FaMoUS, which selects one single sample in each acquisition minibatch, is experimentally evaluated on four image and three time-series imbalanced datasets. The results demonstrate that the implemented AL-FaMoUS outperforms the other adopted AL strategies, including uncertainty sampling and BALD solely combined with either the fast model update or the class balance selection strategy, in terms of accuracy and Macro F1 score.
@inproceedings{huang2023active, abstract = {Active learning strategies aim to minimize the number of queried samples for model training. However, two challenges in pool-based deep active learning on imbalanced datasets are observed in experiments: (1) the declining performance of active learning strategies due to imbalanced class distribution; (2) the lack of sample diversity in acquisition batches due to the absence of timely model updates. This paper proposes the AL-FaMoUS, a general solution combining fast model updates and class-balanced minibatch selection to the active learning process. Furthermore, a simplification of the AL-FaMoUS, which selects one single sample in each acquisition minibatch, is experimentally evaluated on four image and three time-series imbalanced datasets. The results demonstrate that the implemented AL-FaMoUS outperforms the other adopted AL strategies, including uncertainty sampling and BALD solely combined with either the fast model update or the class balance selection strategy, in terms of accuracy and Macro F1 score.}, author = {Huang, Zhixin and He, Yujiang and Herde, Marek and Huseljic, Denis and Sick, Bernhard}, booktitle = {Workshop on Interactive Adapative Learning (IAL), ECML PKDD}, keywords = {itegpub}, pages = {28–45}, title = {Active Learning with Fast Model Updates and Class-Balanced Selection for Imbalanced Datasets}, year = 2023 }
%0 Conference Paper %1 huang2023active %A Huang, Zhixin %A He, Yujiang %A Herde, Marek %A Huseljic, Denis %A Sick, Bernhard %B Workshop on Interactive Adapative Learning (IAL), ECML PKDD %D 2023 %P 28--45 %T Active Learning with Fast Model Updates and Class-Balanced Selection for Imbalanced Datasets %U https://ceur-ws.org/Vol-3470/paper5.pdf %X Active learning strategies aim to minimize the number of queried samples for model training. However, two challenges in pool-based deep active learning on imbalanced datasets are observed in experiments: (1) the declining performance of active learning strategies due to imbalanced class distribution; (2)~the lack of sample diversity in acquisition batches due to the absence of timely model updates. This paper proposes the AL-FaMoUS, a general solution combining fast model updates and class-balanced minibatch selection to the active learning process. Furthermore, a simplification of the AL-FaMoUS, which selects one single sample in each acquisition minibatch, is experimentally evaluated on four image and three time-series imbalanced datasets. The results demonstrate that the implemented AL-FaMoUS outperforms the other adopted AL strategies, including uncertainty sampling and BALD solely combined with either the fast model update or the class balance selection strategy, in terms of accuracy and Macro F1 score.
Leveraging Repeated Unlabelled Noisy Measurements to Augment Supervised Learning.Magnussen, Birk Martin; Stern, Claudius; Sick, Bernhard. In International Conference on Computational Intelligence and Intelligent Systems (CIIS), ble. 1–6. ACM, 2023.
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Often, producing large labelled datasets for supervised machine learning is difficult and expensive. In cases where the expensive part is due to labelling and obtaining ground truth, it is often comparably easy to acquire large datasets containing unlabelled data points. For reproducible measurements, it is possible to record information on multiple data points being from the same reproducible measurement series, which should thus have an equal but unknown ground truth. In this article, we propose a method to incorporate a dataset of such unlabelled data points for which some data points are known to be equal in end-to-end training of otherwise labelled data. We show that, with the example of predicting the carotenoid concentration in human skin from optical multiple spatially resolved reflection spectroscopy data, the proposed method is capable of reducing the required number of labelled data points to achieve the same prediction accuracy for different model architectures. In addition, we show that the proposed method is capable of reducing the negative impact of noisy data when performing a repeated measurement of the same sample.
@inproceedings{magnussen2023leveraging, abstract = {Often, producing large labelled datasets for supervised machine learning is difficult and expensive. In cases where the expensive part is due to labelling and obtaining ground truth, it is often comparably easy to acquire large datasets containing unlabelled data points. For reproducible measurements, it is possible to record information on multiple data points being from the same reproducible measurement series, which should thus have an equal but unknown ground truth. In this article, we propose a method to incorporate a dataset of such unlabelled data points for which some data points are known to be equal in end-to-end training of otherwise labelled data. We show that, with the example of predicting the carotenoid concentration in human skin from optical multiple spatially resolved reflection spectroscopy data, the proposed method is capable of reducing the required number of labelled data points to achieve the same prediction accuracy for different model architectures. In addition, we show that the proposed method is capable of reducing the negative impact of noisy data when performing a repeated measurement of the same sample.}, author = {Magnussen, Birk Martin and Stern, Claudius and Sick, Bernhard}, booktitle = {International Conference on Computational Intelligence and Intelligent Systems (CIIS)}, keywords = {itegpub}, pages = {1–6}, publisher = {ACM}, title = {Leveraging Repeated Unlabelled Noisy Measurements to Augment Supervised Learning}, year = 2023 }
%0 Conference Paper %1 magnussen2023leveraging %A Magnussen, Birk Martin %A Stern, Claudius %A Sick, Bernhard %B International Conference on Computational Intelligence and Intelligent Systems (CIIS) %D 2023 %I ACM %P 1--6 %R 10.1145/3638209.3638210 %T Leveraging Repeated Unlabelled Noisy Measurements to Augment Supervised Learning %U https://dl.acm.org/doi/10.1145/3638209.3638210 %X Often, producing large labelled datasets for supervised machine learning is difficult and expensive. In cases where the expensive part is due to labelling and obtaining ground truth, it is often comparably easy to acquire large datasets containing unlabelled data points. For reproducible measurements, it is possible to record information on multiple data points being from the same reproducible measurement series, which should thus have an equal but unknown ground truth. In this article, we propose a method to incorporate a dataset of such unlabelled data points for which some data points are known to be equal in end-to-end training of otherwise labelled data. We show that, with the example of predicting the carotenoid concentration in human skin from optical multiple spatially resolved reflection spectroscopy data, the proposed method is capable of reducing the required number of labelled data points to achieve the same prediction accuracy for different model architectures. In addition, we show that the proposed method is capable of reducing the negative impact of noisy data when performing a repeated measurement of the same sample.
Corner Cases in Machine Learning Processes.Heidecker, Florian; Bieshaar, Maarten; Sick, Bernhard. In AI Perspectives & Advances, 6(1), ble. 1–17. 2023.
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Applications using machine learning (ML), such as highly autonomous driving, depend highly on the performance of the ML model. The data amount and quality used for model training and validation are crucial. If the model cannot detect and interpret a new, rare, or perhaps dangerous situation, often referred to as a corner case, we will likely blame the data for not being good enough or too small in number. However, the implemented ML model and its associated architecture also influence the behavior. Therefore, the occurrence of prediction errors resulting from the ML model itself is not surprising. This work addresses a corner case definition from an ML model's perspective to determine which aspects must be considered. To achieve this goal, we present an overview of properties for corner cases that are beneficial for the description, explanation, reproduction, or synthetic generation of corner cases. To define ML corner cases, we review different considerations in the literature and summarize them in a general description and mathematical formulation, whereby the expected relevance-weighted loss is the key to distinguishing corner cases from common data. Moreover, we show how to operationalize the corner case characteristics to determine the value of a corner case. To conclude, we present the extended taxonomy for ML corner cases by adding the input, model, and deployment levels, considering the influence of the corner case properties.
@article{heidecker2023corner, abstract = {Applications using machine learning (ML), such as highly autonomous driving, depend highly on the performance of the ML model. The data amount and quality used for model training and validation are crucial. If the model cannot detect and interpret a new, rare, or perhaps dangerous situation, often referred to as a corner case, we will likely blame the data for not being good enough or too small in number. However, the implemented ML model and its associated architecture also influence the behavior. Therefore, the occurrence of prediction errors resulting from the ML model itself is not surprising. This work addresses a corner case definition from an ML model's perspective to determine which aspects must be considered. To achieve this goal, we present an overview of properties for corner cases that are beneficial for the description, explanation, reproduction, or synthetic generation of corner cases. To define ML corner cases, we review different considerations in the literature and summarize them in a general description and mathematical formulation, whereby the expected relevance-weighted loss is the key to distinguishing corner cases from common data. Moreover, we show how to operationalize the corner case characteristics to determine the value of a corner case. To conclude, we present the extended taxonomy for ML corner cases by adding the input, model, and deployment levels, considering the influence of the corner case properties.}, author = {Heidecker, Florian and Bieshaar, Maarten and Sick, Bernhard}, journal = {AI Perspectives & Advances}, keywords = {itegpub}, number = 1, pages = {1–17}, title = {Corner Cases in Machine Learning Processes}, volume = 6, year = 2023 }
%0 Journal Article %1 heidecker2023corner %A Heidecker, Florian %A Bieshaar, Maarten %A Sick, Bernhard %D 2023 %J AI Perspectives & Advances %N 1 %P 1--17 %R 10.1186/s42467-023-00015-y %T Corner Cases in Machine Learning Processes %U https://aiperspectives.springeropen.com/articles/10.1186/s42467-023-00015-y %V 6 %X Applications using machine learning (ML), such as highly autonomous driving, depend highly on the performance of the ML model. The data amount and quality used for model training and validation are crucial. If the model cannot detect and interpret a new, rare, or perhaps dangerous situation, often referred to as a corner case, we will likely blame the data for not being good enough or too small in number. However, the implemented ML model and its associated architecture also influence the behavior. Therefore, the occurrence of prediction errors resulting from the ML model itself is not surprising. This work addresses a corner case definition from an ML model's perspective to determine which aspects must be considered. To achieve this goal, we present an overview of properties for corner cases that are beneficial for the description, explanation, reproduction, or synthetic generation of corner cases. To define ML corner cases, we review different considerations in the literature and summarize them in a general description and mathematical formulation, whereby the expected relevance-weighted loss is the key to distinguishing corner cases from common data. Moreover, we show how to operationalize the corner case characteristics to determine the value of a corner case. To conclude, we present the extended taxonomy for ML corner cases by adding the input, model, and deployment levels, considering the influence of the corner case properties.
Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction.Huang, Zhixin; He, Yujiang; Sick, Bernhard. In Computational Science and Computational Intelligence (CSCI), ble. 99–105. IEEE, 2023.
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RUL prediction plays a crucial role in the health management of industrial systems. Given the increasing complexity of systems, data-driven predictive models have attracted significant research interest. Upon reviewing the existing literature, it appears that many studies either do not fully integrate both spatial and temporal features or employ only a single attention mechanism. Furthermore, there seems to be inconsistency in the choice of data normalization methods, particularly concerning operating conditions, which might influence predictive performance. To bridge these observations, this study presents the Spatio-Temporal Attention Graph Neural Network. Our model combines graph neural networks and temporal convolutional neural networks for spatial and temporal feature extraction, respectively. The cascade of these extractors, combined with multihead attention mechanisms for both spatio-temporal dimensions, aims to improve predictive precision and refine model explainability. Comprehensive experiments were conducted on the CMAPSS dataset to evaluate the impact of unified versus clustering normalization. The findings suggest that our model performs state-of-the-art results using only the unified normalization. Additionally, when dealing with datasets with multiple operating conditions, cluster normalization enhances the performance of our proposed model by up to 27%.
@inproceedings{huang2023spatio, abstract = {RUL prediction plays a crucial role in the health management of industrial systems. Given the increasing complexity of systems, data-driven predictive models have attracted significant research interest. Upon reviewing the existing literature, it appears that many studies either do not fully integrate both spatial and temporal features or employ only a single attention mechanism. Furthermore, there seems to be inconsistency in the choice of data normalization methods, particularly concerning operating conditions, which might influence predictive performance. To bridge these observations, this study presents the Spatio-Temporal Attention Graph Neural Network. Our model combines graph neural networks and temporal convolutional neural networks for spatial and temporal feature extraction, respectively. The cascade of these extractors, combined with multihead attention mechanisms for both spatio-temporal dimensions, aims to improve predictive precision and refine model explainability. Comprehensive experiments were conducted on the CMAPSS dataset to evaluate the impact of unified versus clustering normalization. The findings suggest that our model performs state-of-the-art results using only the unified normalization. Additionally, when dealing with datasets with multiple operating conditions, cluster normalization enhances the performance of our proposed model by up to 27%.}, author = {Huang, Zhixin and He, Yujiang and Sick, Bernhard}, booktitle = {Computational Science and Computational Intelligence (CSCI)}, keywords = {Graph_Neural_Network}, pages = {99–105}, publisher = {IEEE}, title = {Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction}, year = 2023 }
%0 Conference Paper %1 huang2023spatio %A Huang, Zhixin %A He, Yujiang %A Sick, Bernhard %B Computational Science and Computational Intelligence (CSCI) %D 2023 %I IEEE %P 99--105 %R 10.1109/CSCI62032.2023.00022 %T Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction %X RUL prediction plays a crucial role in the health management of industrial systems. Given the increasing complexity of systems, data-driven predictive models have attracted significant research interest. Upon reviewing the existing literature, it appears that many studies either do not fully integrate both spatial and temporal features or employ only a single attention mechanism. Furthermore, there seems to be inconsistency in the choice of data normalization methods, particularly concerning operating conditions, which might influence predictive performance. To bridge these observations, this study presents the Spatio-Temporal Attention Graph Neural Network. Our model combines graph neural networks and temporal convolutional neural networks for spatial and temporal feature extraction, respectively. The cascade of these extractors, combined with multihead attention mechanisms for both spatio-temporal dimensions, aims to improve predictive precision and refine model explainability. Comprehensive experiments were conducted on the CMAPSS dataset to evaluate the impact of unified versus clustering normalization. The findings suggest that our model performs state-of-the-art results using only the unified normalization. Additionally, when dealing with datasets with multiple operating conditions, cluster normalization enhances the performance of our proposed model by up to 27%.
Domain Imaging in Periodic Submicron Wide Nanostructures by Digital Drift Correction in Kerr Microscopy.Akhundzada, Sapida; Dingel, Kristina; Bischof, David; Janzen, Christian; Sick, Bernhard; Ehresmann, Arno. In Advanced Photonics Research, 4(10), bl. 2300170. Wiley, 2023.
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Magneto-optical Kerr microscopy is a powerful method for imaging magnetic domains. Even though domain imaging below the diffractive resolution limit is possible, such investigations are getting increasingly complex with decreasing structure size due to the decreasing Kerr contrast. As magnetic domain images free of topographical artifacts are obtained by subtracting a reference image from the actual image, the corresponding challenges are additionally increased by unavoidable sample motion in the time interval between acquiring the two images. Software-based drift corrections typically rely on a unique structure in the image's region of interest (ROI), recognized automatically or selected manually by the user. By digital image shifting, the ROI positions in the actual and reference images are aligned, and the sample motion is compensated. For magnetic domain imaging in periodically arranged micro- or nano-objects, unique topographical features are not given, making the drift correction by ROIs difficult, often even impossible. Herein, a novel software-based approach is presented for drift corrections to image domains with features close/below the optical resolution limit and for investigating periodically arranged micro- or nano-objects without utilizing ROIs. High-contrast images are obtained, enabling the characterization of periodically arranged 1D, 2D, and 3D magnetic objects with lateral dimensions below 100 nm.
@article{akhundzada2023domain, abstract = {Magneto-optical Kerr microscopy is a powerful method for imaging magnetic domains. Even though domain imaging below the diffractive resolution limit is possible, such investigations are getting increasingly complex with decreasing structure size due to the decreasing Kerr contrast. As magnetic domain images free of topographical artifacts are obtained by subtracting a reference image from the actual image, the corresponding challenges are additionally increased by unavoidable sample motion in the time interval between acquiring the two images. Software-based drift corrections typically rely on a unique structure in the image's region of interest (ROI), recognized automatically or selected manually by the user. By digital image shifting, the ROI positions in the actual and reference images are aligned, and the sample motion is compensated. For magnetic domain imaging in periodically arranged micro- or nano-objects, unique topographical features are not given, making the drift correction by ROIs difficult, often even impossible. Herein, a novel software-based approach is presented for drift corrections to image domains with features close/below the optical resolution limit and for investigating periodically arranged micro- or nano-objects without utilizing ROIs. High-contrast images are obtained, enabling the characterization of periodically arranged 1D, 2D, and 3D magnetic objects with lateral dimensions below 100 nm.}, author = {Akhundzada, Sapida and Dingel, Kristina and Bischof, David and Janzen, Christian and Sick, Bernhard and Ehresmann, Arno}, journal = {Advanced Photonics Research}, keywords = {itegpub}, number = 10, pages = 2300170, publisher = {Wiley}, title = {Domain Imaging in Periodic Submicron Wide Nanostructures by Digital Drift Correction in Kerr Microscopy}, volume = 4, year = 2023 }
%0 Journal Article %1 akhundzada2023domain %A Akhundzada, Sapida %A Dingel, Kristina %A Bischof, David %A Janzen, Christian %A Sick, Bernhard %A Ehresmann, Arno %D 2023 %I Wiley %J Advanced Photonics Research %N 10 %P 2300170 %R https://doi.org/10.1002/adpr.202300170 %T Domain Imaging in Periodic Submicron Wide Nanostructures by Digital Drift Correction in Kerr Microscopy %U https://onlinelibrary.wiley.com/doi/abs/10.1002/adpr.20230017 %V 4 %X Magneto-optical Kerr microscopy is a powerful method for imaging magnetic domains. Even though domain imaging below the diffractive resolution limit is possible, such investigations are getting increasingly complex with decreasing structure size due to the decreasing Kerr contrast. As magnetic domain images free of topographical artifacts are obtained by subtracting a reference image from the actual image, the corresponding challenges are additionally increased by unavoidable sample motion in the time interval between acquiring the two images. Software-based drift corrections typically rely on a unique structure in the image's region of interest (ROI), recognized automatically or selected manually by the user. By digital image shifting, the ROI positions in the actual and reference images are aligned, and the sample motion is compensated. For magnetic domain imaging in periodically arranged micro- or nano-objects, unique topographical features are not given, making the drift correction by ROIs difficult, often even impossible. Herein, a novel software-based approach is presented for drift corrections to image domains with features close/below the optical resolution limit and for investigating periodically arranged micro- or nano-objects without utilizing ROIs. High-contrast images are obtained, enabling the characterization of periodically arranged 1D, 2D, and 3D magnetic objects with lateral dimensions below 100 nm.

2022[ to top ]

A Concept for Automated Polarized Web Content Annotation based on Multimodal Active Learning.Herde, Marek; Huseljic, Denis; Mitrovic, Jelena; Granitzer, Michael; Sick, Bernhard. In Workshop on Interactive Adaptive Learning (IAL), ECML PKDD, ble. 1–6. 2022.
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Active learning (AL) techniques hardly cope with complex annotations tasks, where, for example, annotations might express relationships across data modalities. As a use case, we consider the task of automatically detecting and reporting multimodal, polarized web content (PWC). Samples of this content type emerge dynamically, covering a broad spectrum of topics. Thus, training machine learning systems for detecting PWC is challenging, particularly if it needs to be done with minimum annotation cost. In this article, we propose the concept of multimodal AL for complex annotations in the context of PWC detection and formulate the resulting challenges as questions for future research.
@inproceedings{herde2022concept, abstract = {Active learning (AL) techniques hardly cope with complex annotations tasks, where, for example, annotations might express relationships across data modalities. As a use case, we consider the task of automatically detecting and reporting multimodal, polarized web content (PWC). Samples of this content type emerge dynamically, covering a broad spectrum of topics. Thus, training machine learning systems for detecting PWC is challenging, particularly if it needs to be done with minimum annotation cost. In this article, we propose the concept of multimodal AL for complex annotations in the context of PWC detection and formulate the resulting challenges as questions for future research.}, author = {Herde, Marek and Huseljic, Denis and Mitrovic, Jelena and Granitzer, Michael and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {itegpub}, pages = {1–6}, title = {A Concept for Automated Polarized Web Content Annotation based on Multimodal Active Learning}, year = 2022 }
%0 Conference Paper %1 herde2022concept %A Herde, Marek %A Huseljic, Denis %A Mitrovic, Jelena %A Granitzer, Michael %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2022 %P 1--6 %T A Concept for Automated Polarized Web Content Annotation based on Multimodal Active Learning %U http://ceur-ws.org/Vol-3259/ialatecml_paper1.pdf %X Active learning (AL) techniques hardly cope with complex annotations tasks, where, for example, annotations might express relationships across data modalities. As a use case, we consider the task of automatically detecting and reporting multimodal, polarized web content (PWC). Samples of this content type emerge dynamically, covering a broad spectrum of topics. Thus, training machine learning systems for detecting PWC is challenging, particularly if it needs to be done with minimum annotation cost. In this article, we propose the concept of multimodal AL for complex annotations in the context of PWC detection and formulate the resulting challenges as questions for future research.
Generating Synthetic Time Series for Machine-Learning-Empowered Monitoring of Electric Motor Test Benches.Westmeier, Tobias; Botache, Diego; Bieshaar, Maarten; Sick, Bernhard. In IEEE International Conference on Data Science and Advanced Analytics (DSAA), ble. 513–522. IEEE, 2022.
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The development of new electric traction machines is a time-consuming process as it involves intensive testing on motor test benches. Machine-Learning-empowered monitoring offers the opportunity to anticipate costly failures early and hence reducing development time. However, machine learning (ML) for process monitoring requires large amounts of training data, especially as the targeted fault states are scarce and yet diverse in their appearances. Therefore, we propose to use synthetic time series data to leverage the high cost of acquiring training data from experiments in real test benches. In this article, we present a novel scheme to generate synthetic data based on a sub-dimensional time series representation. We introduce a highly flexible model by mapping the data to a latent representation and approximating the latent data distribution by a Gaussian Mixture Model. In addition, we propose the Frechet InceptionTime Distance (FITD) as a new distance measure to evaluate the generated data. It allows extracting characteristics at different scales by using multiple kernel sizes. In this way, we ensure that the synthesized data contains characteristics similar to those present in the real data. In our experiment, we train two types of fault detectors, one based on real data of a motor test bench and the other based on synthetic data. We also consider employing fault-aware conditional architectures to generate training data for different fault types explicitly. Our final results show that using synthesized data in the training process increases the performance in terms of classification accuracy score (CAS) up to 29%.
@inproceedings{westmeier2022generating, abstract = {The development of new electric traction machines is a time-consuming process as it involves intensive testing on motor test benches. Machine-Learning-empowered monitoring offers the opportunity to anticipate costly failures early and hence reducing development time. However, machine learning (ML) for process monitoring requires large amounts of training data, especially as the targeted fault states are scarce and yet diverse in their appearances. Therefore, we propose to use synthetic time series data to leverage the high cost of acquiring training data from experiments in real test benches. In this article, we present a novel scheme to generate synthetic data based on a sub-dimensional time series representation. We introduce a highly flexible model by mapping the data to a latent representation and approximating the latent data distribution by a Gaussian Mixture Model. In addition, we propose the Frechet InceptionTime Distance (FITD) as a new distance measure to evaluate the generated data. It allows extracting characteristics at different scales by using multiple kernel sizes. In this way, we ensure that the synthesized data contains characteristics similar to those present in the real data. In our experiment, we train two types of fault detectors, one based on real data of a motor test bench and the other based on synthetic data. We also consider employing fault-aware conditional architectures to generate training data for different fault types explicitly. Our final results show that using synthesized data in the training process increases the performance in terms of classification accuracy score (CAS) up to 29%.}, author = {Westmeier, Tobias and Botache, Diego and Bieshaar, Maarten and Sick, Bernhard}, booktitle = {IEEE International Conference on Data Science and Advanced Analytics (DSAA)}, keywords = {Variational-Autoencoder}, pages = {513–522}, publisher = {IEEE}, title = {Generating Synthetic Time Series for Machine-Learning-Empowered Monitoring of Electric Motor Test Benches}, year = 2022 }
%0 Conference Paper %1 westmeier2022generating %A Westmeier, Tobias %A Botache, Diego %A Bieshaar, Maarten %A Sick, Bernhard %B IEEE International Conference on Data Science and Advanced Analytics (DSAA) %D 2022 %I IEEE %P 513--522 %R 10.1109/DSAA54385.2022.10032385 %T Generating Synthetic Time Series for Machine-Learning-Empowered Monitoring of Electric Motor Test Benches %U https://ieeexplore.ieee.org/document/10032385 %X The development of new electric traction machines is a time-consuming process as it involves intensive testing on motor test benches. Machine-Learning-empowered monitoring offers the opportunity to anticipate costly failures early and hence reducing development time. However, machine learning (ML) for process monitoring requires large amounts of training data, especially as the targeted fault states are scarce and yet diverse in their appearances. Therefore, we propose to use synthetic time series data to leverage the high cost of acquiring training data from experiments in real test benches. In this article, we present a novel scheme to generate synthetic data based on a sub-dimensional time series representation. We introduce a highly flexible model by mapping the data to a latent representation and approximating the latent data distribution by a Gaussian Mixture Model. In addition, we propose the Frechet InceptionTime Distance (FITD) as a new distance measure to evaluate the generated data. It allows extracting characteristics at different scales by using multiple kernel sizes. In this way, we ensure that the synthesized data contains characteristics similar to those present in the real data. In our experiment, we train two types of fault detectors, one based on real data of a motor test bench and the other based on synthetic data. We also consider employing fault-aware conditional architectures to generate training data for different fault types explicitly. Our final results show that using synthesized data in the training process increases the performance in terms of classification accuracy score (CAS) up to 29%.
Organic Computing -- Doctoral Dissertation Colloquium 2021. Tomforde, Sven; Krupitzer, Christian. In Vol. 20Intelligent Embedded Systems. kassel university press, 2022.
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@book{tomforde2022organic, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Organic Computing – Doctoral Dissertation Colloquium 2021}, volume = 20, year = 2022 }
%0 Book %1 tomforde2022organic %B Intelligent Embedded Systems %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %R 10.17170/kobra-202202215780 %T Organic Computing -- Doctoral Dissertation Colloquium 2021 %V 20
Predicting flow stress behavior of an AA7075 alloy using machine learning methods.Decke, Jens; Engelhardt, Anna; Rauch, Lukas; Degener, Sebastian; Sajjadifar, Seyedvahid; Scharifi, Emad; Steinhoff, Kurt; Niendorf, Thomas; Sick, Bernhard. In Crystals, 9(12), ble. 1–19. MDPI, 2022.
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This work focuses on the prediction of hot deformation behavior of thermo-mechanically processed precipitation hardenable aluminum alloy AA7075. Data available are focusing on a novel hot forming process at different tool temperatures ranging from 24°C to 350°C to set different cooling rates after solution-heat-treatment. Isothermal uniaxial tensile tests in the temperature range from 200°C to 400°C and at strain rates ranging from 0.001 s^-1 to 0.1 s^-1 were carried out on four different material conditions. The present paper mainly focuses on a comparative study of modeling techniques based on Machine Learning (ML) and the well-known Zerilli-Armstrong model (Z-A) as an empirically based reference. Work focused on predicting single data points of curves that the model was trained on. Due to the novel way data were split with respect to training and testing data, it becomes possible to predict entire stress-strain curves which leads to a reduction in the number of required laboratory experiments, finally saving costs and time in future experiments. While all investigated ML methods showed a higher performance than the Z-A model, the extreme Gradient Boosting model (XGB) showed the superior results, i.e., highest error reduction of 91% with respect to the Mean Squared Error.
@article{decke2022predicting, abstract = {This work focuses on the prediction of hot deformation behavior of thermo-mechanically processed precipitation hardenable aluminum alloy AA7075. Data available are focusing on a novel hot forming process at different tool temperatures ranging from 24°C to 350°C to set different cooling rates after solution-heat-treatment. Isothermal uniaxial tensile tests in the temperature range from 200°C to 400°C and at strain rates ranging from 0.001 s^{-1} to 0.1 s^{-1} were carried out on four different material conditions. The present paper mainly focuses on a comparative study of modeling techniques based on Machine Learning (ML) and the well-known Zerilli-Armstrong model (Z-A) as an empirically based reference. Work focused on predicting single data points of curves that the model was trained on. Due to the novel way data were split with respect to training and testing data, it becomes possible to predict entire stress-strain curves which leads to a reduction in the number of required laboratory experiments, finally saving costs and time in future experiments. While all investigated ML methods showed a higher performance than the Z-A model, the extreme Gradient Boosting model (XGB) showed the superior results, i.e., highest error reduction of 91% with respect to the Mean Squared Error.}, author = {Decke, Jens and Engelhardt, Anna and Rauch, Lukas and Degener, Sebastian and Sajjadifar, Seyedvahid and Scharifi, Emad and Steinhoff, Kurt and Niendorf, Thomas and Sick, Bernhard}, journal = {Crystals}, keywords = {itegpub}, number = 12, pages = {1–19}, publisher = {MDPI}, title = {Predicting flow stress behavior of an AA7075 alloy using machine learning methods}, volume = 9, year = 2022 }
%0 Journal Article %1 decke2022predicting %A Decke, Jens %A Engelhardt, Anna %A Rauch, Lukas %A Degener, Sebastian %A Sajjadifar, Seyedvahid %A Scharifi, Emad %A Steinhoff, Kurt %A Niendorf, Thomas %A Sick, Bernhard %D 2022 %I MDPI %J Crystals %N 12 %P 1--19 %R 10.3390/cryst12091281 %T Predicting flow stress behavior of an AA7075 alloy using machine learning methods %U https://www.mdpi.com/2073-4352/12/9/1281 %V 9 %X This work focuses on the prediction of hot deformation behavior of thermo-mechanically processed precipitation hardenable aluminum alloy AA7075. Data available are focusing on a novel hot forming process at different tool temperatures ranging from 24°C to 350°C to set different cooling rates after solution-heat-treatment. Isothermal uniaxial tensile tests in the temperature range from 200°C to 400°C and at strain rates ranging from 0.001 s^-1 to 0.1 s^-1 were carried out on four different material conditions. The present paper mainly focuses on a comparative study of modeling techniques based on Machine Learning (ML) and the well-known Zerilli-Armstrong model (Z-A) as an empirically based reference. Work focused on predicting single data points of curves that the model was trained on. Due to the novel way data were split with respect to training and testing data, it becomes possible to predict entire stress-strain curves which leads to a reduction in the number of required laboratory experiments, finally saving costs and time in future experiments. While all investigated ML methods showed a higher performance than the Z-A model, the extreme Gradient Boosting model (XGB) showed the superior results, i.e., highest error reduction of 91% with respect to the Mean Squared Error.
Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users Trajectories.Kress, Viktor; Jeske, Fabian; Zernetsch, Stefan; Doll, Konrad; Sick, Bernhard. In IEEE Transactions on Intelligent Vehicles, 8(3), ble. 2592–2603. IEEE, 2022.
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In this article, an approach for probabilistic trajectory forecasting of vulnerable road users (VRUs) is presented, taking into consideration past movements and the surrounding environment. Past movements are represented by 3D poses reflecting the posture and movements of individual body parts. The surrounding environment is modeled in the form of semantic maps showing, e.g., the course of streets, sidewalks, and the occurrence of obstacles. Forecasts are generated in grids discretizing the space and in the form of arbitrary discrete probability distributions. The distributions are evaluated for their reliability, sharpness, and positional accuracy. We compare our method with two approaches providing forecasts in the form of continuous probability distributions, and we discuss their respective advantages and disadvantages. We thereby investigate the impact of poses and semantic maps. Using a technique we refer to as spatial label smoothing, our approach is able to achieve reliable forecasts. Overall, the 3D poses have a positive impact on the forecasts. The semantic maps facilitate the adaptation of the probability distributions to the individual situation and prevent forecasts of trajectories leading through obstacles. Our method is evaluated on a dataset recorded in inner-city traffic using a research vehicle. The dataset has been made publicly available.
@article{kress2022pose, abstract = {In this article, an approach for probabilistic trajectory forecasting of vulnerable road users (VRUs) is presented, taking into consideration past movements and the surrounding environment. Past movements are represented by 3D poses reflecting the posture and movements of individual body parts. The surrounding environment is modeled in the form of semantic maps showing, e.g., the course of streets, sidewalks, and the occurrence of obstacles. Forecasts are generated in grids discretizing the space and in the form of arbitrary discrete probability distributions. The distributions are evaluated for their reliability, sharpness, and positional accuracy. We compare our method with two approaches providing forecasts in the form of continuous probability distributions, and we discuss their respective advantages and disadvantages. We thereby investigate the impact of poses and semantic maps. Using a technique we refer to as spatial label smoothing, our approach is able to achieve reliable forecasts. Overall, the 3D poses have a positive impact on the forecasts. The semantic maps facilitate the adaptation of the probability distributions to the individual situation and prevent forecasts of trajectories leading through obstacles. Our method is evaluated on a dataset recorded in inner-city traffic using a research vehicle. The dataset has been made publicly available.}, author = {Kress, Viktor and Jeske, Fabian and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, journal = {IEEE Transactions on Intelligent Vehicles}, keywords = {itegpub}, number = 3, pages = {2592–2603}, publisher = {IEEE}, title = {Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users Trajectories}, volume = 8, year = 2022 }
%0 Journal Article %1 kress2022pose %A Kress, Viktor %A Jeske, Fabian %A Zernetsch, Stefan %A Doll, Konrad %A Sick, Bernhard %D 2022 %I IEEE %J IEEE Transactions on Intelligent Vehicles %N 3 %P 2592--2603 %R 10.1109/TIV.2022.3149624 %T Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users Trajectories %V 8 %X In this article, an approach for probabilistic trajectory forecasting of vulnerable road users (VRUs) is presented, taking into consideration past movements and the surrounding environment. Past movements are represented by 3D poses reflecting the posture and movements of individual body parts. The surrounding environment is modeled in the form of semantic maps showing, e.g., the course of streets, sidewalks, and the occurrence of obstacles. Forecasts are generated in grids discretizing the space and in the form of arbitrary discrete probability distributions. The distributions are evaluated for their reliability, sharpness, and positional accuracy. We compare our method with two approaches providing forecasts in the form of continuous probability distributions, and we discuss their respective advantages and disadvantages. We thereby investigate the impact of poses and semantic maps. Using a technique we refer to as spatial label smoothing, our approach is able to achieve reliable forecasts. Overall, the 3D poses have a positive impact on the forecasts. The semantic maps facilitate the adaptation of the probability distributions to the individual situation and prevent forecasts of trajectories leading through obstacles. Our method is evaluated on a dataset recorded in inner-city traffic using a research vehicle. The dataset has been made publicly available.
Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs.Beddar-Wiesing, Silvia; D’Inverno, Giuseppe Alessio; Graziani, Caterina; Lachi, Veronica; Moallemy-Oureh, Alice; Scarselli, Franco; Thomas, Josephine. In arXiv e-prints, bl. arXiv:2210.03990. 2022.
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Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On the one hand, it has been proven that GNNs are as powerful as the Weisfeiler-Lehman test (1-WL) in their ability to distinguish graphs. Moreover, it has been shown that the equivalence enforced by 1-WL equals unfolding equivalence. On the other hand, GNNs turned out to be universal approximators on graphs modulo the constraints enforced by 1-WL/unfolding equivalence. However, these results only apply to Static Undirected Homogeneous Graphs with node attributes. In contrast, real-life applications often involve a variety of graph properties, such as, e.g., dynamics or node and edge attributes. In this paper, we conduct a theoretical analysis of the expressive power of GNNs for these two graph types that are particularly of interest. Dynamic graphs are widely used in modern applications, and its theoretical analysis requires new approaches. The attributed type acts as a standard form for all graph types since it has been shown that all graph types can be transformed without loss to Static Undirected Homogeneous Graphs with attributes on nodes and edges (SAUHG). The study considers generic GNN models and proposes appropriate 1-WL tests for those domains. Then, the results on the expressive power of GNNs are extended by proving that GNNs have the same capability as the 1-WL test in distinguishing dynamic and attributed graphs, the 1-WL equivalence equals unfolding equivalence and that GNNs are universal approximators modulo 1-WL/unfolding equivalence. Moreover, the proof of the approximation capability holds for SAUHGs, which include most of those used in practical applications, and it is constructive in nature allowing to deduce hints on the architecture of GNNs that can achieve the desired accuracy.
@article{beddarwiesing2022weisfeiler, abstract = {Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On the one hand, it has been proven that GNNs are as powerful as the Weisfeiler-Lehman test (1-WL) in their ability to distinguish graphs. Moreover, it has been shown that the equivalence enforced by 1-WL equals unfolding equivalence. On the other hand, GNNs turned out to be universal approximators on graphs modulo the constraints enforced by 1-WL/unfolding equivalence. However, these results only apply to Static Undirected Homogeneous Graphs with node attributes. In contrast, real-life applications often involve a variety of graph properties, such as, e.g., dynamics or node and edge attributes. In this paper, we conduct a theoretical analysis of the expressive power of GNNs for these two graph types that are particularly of interest. Dynamic graphs are widely used in modern applications, and its theoretical analysis requires new approaches. The attributed type acts as a standard form for all graph types since it has been shown that all graph types can be transformed without loss to Static Undirected Homogeneous Graphs with attributes on nodes and edges (SAUHG). The study considers generic GNN models and proposes appropriate 1-WL tests for those domains. Then, the results on the expressive power of GNNs are extended by proving that GNNs have the same capability as the 1-WL test in distinguishing dynamic and attributed graphs, the 1-WL equivalence equals unfolding equivalence and that GNNs are universal approximators modulo 1-WL/unfolding equivalence. Moreover, the proof of the approximation capability holds for SAUHGs, which include most of those used in practical applications, and it is constructive in nature allowing to deduce hints on the architecture of GNNs that can achieve the desired accuracy.}, author = {Beddar-Wiesing, Silvia and D'Inverno, Giuseppe Alessio and Graziani, Caterina and Lachi, Veronica and Moallemy-Oureh, Alice and Scarselli, Franco and Thomas, Josephine}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2210.03990}, title = {{W}eisfeiler-{L}ehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs}, year = 2022 }
%0 Journal Article %1 beddarwiesing2022weisfeiler %A Beddar-Wiesing, Silvia %A D'Inverno, Giuseppe Alessio %A Graziani, Caterina %A Lachi, Veronica %A Moallemy-Oureh, Alice %A Scarselli, Franco %A Thomas, Josephine %D 2022 %J arXiv e-prints %P arXiv:2210.03990 %T Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs %U https://arxiv.org/abs/2210.03990 %X Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On the one hand, it has been proven that GNNs are as powerful as the Weisfeiler-Lehman test (1-WL) in their ability to distinguish graphs. Moreover, it has been shown that the equivalence enforced by 1-WL equals unfolding equivalence. On the other hand, GNNs turned out to be universal approximators on graphs modulo the constraints enforced by 1-WL/unfolding equivalence. However, these results only apply to Static Undirected Homogeneous Graphs with node attributes. In contrast, real-life applications often involve a variety of graph properties, such as, e.g., dynamics or node and edge attributes. In this paper, we conduct a theoretical analysis of the expressive power of GNNs for these two graph types that are particularly of interest. Dynamic graphs are widely used in modern applications, and its theoretical analysis requires new approaches. The attributed type acts as a standard form for all graph types since it has been shown that all graph types can be transformed without loss to Static Undirected Homogeneous Graphs with attributes on nodes and edges (SAUHG). The study considers generic GNN models and proposes appropriate 1-WL tests for those domains. Then, the results on the expressive power of GNNs are extended by proving that GNNs have the same capability as the 1-WL test in distinguishing dynamic and attributed graphs, the 1-WL equivalence equals unfolding equivalence and that GNNs are universal approximators modulo 1-WL/unfolding equivalence. Moreover, the proof of the approximation capability holds for SAUHGs, which include most of those used in practical applications, and it is constructive in nature allowing to deduce hints on the architecture of GNNs that can achieve the desired accuracy.
Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario.Krupitzer, Christian; Gruhl, Christian; Sick, Bernhard; Tomforde, Sven. In Information and Software Technology, 145, bl. 106826. Elsevier, 2022.
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Context: Smart and adaptive Systems, such as self-adaptive and self-organising (SASO) systems, typically consist of a large set of highly autonomous and heterogeneous subsystems that are able to adapt their behaviour to the requirements of ever-changing, dynamic environments. Their successful operation is based on appropriate modelling of the internal and external conditions. Objective: The control problem for establishing a near-to-optimal coordinated behaviour of systems with multiple, potentially conflicting objectives is either approached in a distributed (i.e., fully autonomous by the autonomous subsystems) or in a centralised way (i.e. one instance controlling the optimisation and planning process). In the distributed approach, selfish behaviour and being limited to local knowledge may lead to sub-optimal system behaviour, while the centralised approach ignores the autonomy and the coordination efforts of parts of the system. Method: In this article, we present a concept for a hybrid (i.e., integrating a central optimisation with a distributed decision-making process) system management that combines local reinforcement learning and self-awareness mechanisms of fully autonomous subsystems with external system-wide planning and optimisation of adaptation freedom that steers the behaviour dynamically by issuing plans and guidelines augmented with incentivisation schemes. Results: This work addresses the inherent uncertainty of the dynamic system behaviour, the local autonomous and context-aware learning of subsystems, and proactive control based on adaptiveness. We provide the ‘swarm-fleet infrastructure’—a self-organised taxi service established by autonomous, privately-owned cars—as a testbed for structured comparison of systems. Conclusion: The ‘swarm-fleet infrastructure’ supports the advantages of a proactive hybrid self-adaptive and self-organising system operation. Further, we provide a system model to combine the system-wide optimisation while ensuring local decision-making through reinforcement learning for individualised configurations.
@article{krupitzer2022proactive, abstract = {Context: Smart and adaptive Systems, such as self-adaptive and self-organising (SASO) systems, typically consist of a large set of highly autonomous and heterogeneous subsystems that are able to adapt their behaviour to the requirements of ever-changing, dynamic environments. Their successful operation is based on appropriate modelling of the internal and external conditions. Objective: The control problem for establishing a near-to-optimal coordinated behaviour of systems with multiple, potentially conflicting objectives is either approached in a distributed (i.e., fully autonomous by the autonomous subsystems) or in a centralised way (i.e. one instance controlling the optimisation and planning process). In the distributed approach, selfish behaviour and being limited to local knowledge may lead to sub-optimal system behaviour, while the centralised approach ignores the autonomy and the coordination efforts of parts of the system. Method: In this article, we present a concept for a hybrid (i.e., integrating a central optimisation with a distributed decision-making process) system management that combines local reinforcement learning and self-awareness mechanisms of fully autonomous subsystems with external system-wide planning and optimisation of adaptation freedom that steers the behaviour dynamically by issuing plans and guidelines augmented with incentivisation schemes. Results: This work addresses the inherent uncertainty of the dynamic system behaviour, the local autonomous and context-aware learning of subsystems, and proactive control based on adaptiveness. We provide the ‘swarm-fleet infrastructure’—a self-organised taxi service established by autonomous, privately-owned cars—as a testbed for structured comparison of systems. Conclusion: The ‘swarm-fleet infrastructure’ supports the advantages of a proactive hybrid self-adaptive and self-organising system operation. Further, we provide a system model to combine the system-wide optimisation while ensuring local decision-making through reinforcement learning for individualised configurations.}, author = {Krupitzer, Christian and Gruhl, Christian and Sick, Bernhard and Tomforde, Sven}, journal = {Information and Software Technology}, keywords = {itegpub}, pages = 106826, publisher = {Elsevier}, title = {Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario}, volume = 145, year = 2022 }
%0 Journal Article %1 krupitzer2022proactive %A Krupitzer, Christian %A Gruhl, Christian %A Sick, Bernhard %A Tomforde, Sven %D 2022 %I Elsevier %J Information and Software Technology %P 106826 %R 10.1016/j.infsof.2022.106826 %T Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario %U https://www.sciencedirect.com/science/article/abs/pii/S0950584922000052 %V 145 %X Context: Smart and adaptive Systems, such as self-adaptive and self-organising (SASO) systems, typically consist of a large set of highly autonomous and heterogeneous subsystems that are able to adapt their behaviour to the requirements of ever-changing, dynamic environments. Their successful operation is based on appropriate modelling of the internal and external conditions. Objective: The control problem for establishing a near-to-optimal coordinated behaviour of systems with multiple, potentially conflicting objectives is either approached in a distributed (i.e., fully autonomous by the autonomous subsystems) or in a centralised way (i.e. one instance controlling the optimisation and planning process). In the distributed approach, selfish behaviour and being limited to local knowledge may lead to sub-optimal system behaviour, while the centralised approach ignores the autonomy and the coordination efforts of parts of the system. Method: In this article, we present a concept for a hybrid (i.e., integrating a central optimisation with a distributed decision-making process) system management that combines local reinforcement learning and self-awareness mechanisms of fully autonomous subsystems with external system-wide planning and optimisation of adaptation freedom that steers the behaviour dynamically by issuing plans and guidelines augmented with incentivisation schemes. Results: This work addresses the inherent uncertainty of the dynamic system behaviour, the local autonomous and context-aware learning of subsystems, and proactive control based on adaptiveness. We provide the ‘swarm-fleet infrastructure’—a self-organised taxi service established by autonomous, privately-owned cars—as a testbed for structured comparison of systems. Conclusion: The ‘swarm-fleet infrastructure’ supports the advantages of a proactive hybrid self-adaptive and self-organising system operation. Further, we provide a system model to combine the system-wide optimisation while ensuring local decision-making through reinforcement learning for individualised configurations.
Graph Neural Networks Designed for Different Graph Types: A Survey.Thomas, Josephine M.; Moallemy-Oureh, Alice; Beddar-Wiesing, Silvia; Holzhüter, Clara. In arXiv e-prints, bl. arXiv:2204.03080. 2022.
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Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. Based on this, the young research field of Graph Neural Networks (GNNs) has emerged. Despite the youth of the field and the speed in which new models are developed, many good surveys have been published in the last years. Nevertheless, an overview on which graph types can be modeled by GNNs is missing. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static as well as on dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover in the dynamic case, we separate the models in discrete-time and continuous-time dynamic graphs based on their architecture. While ordering the existing GNN models, we find, that there are still graph types, that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.
@article{thomas2022graph, abstract = {Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. Based on this, the young research field of Graph Neural Networks (GNNs) has emerged. Despite the youth of the field and the speed in which new models are developed, many good surveys have been published in the last years. Nevertheless, an overview on which graph types can be modeled by GNNs is missing. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static as well as on dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover in the dynamic case, we separate the models in discrete-time and continuous-time dynamic graphs based on their architecture. While ordering the existing GNN models, we find, that there are still graph types, that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.}, author = {Thomas, Josephine M. and Moallemy-Oureh, Alice and Beddar-Wiesing, Silvia and Holzhüter, Clara}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2204.03080}, title = {Graph Neural Networks Designed for Different Graph Types: A Survey}, year = 2022 }
%0 Journal Article %1 thomas2022graph %A Thomas, Josephine M. %A Moallemy-Oureh, Alice %A Beddar-Wiesing, Silvia %A Holzhüter, Clara %D 2022 %J arXiv e-prints %P arXiv:2204.03080 %T Graph Neural Networks Designed for Different Graph Types: A Survey %U https://arxiv.org/abs/2204.03080 %X Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. Based on this, the young research field of Graph Neural Networks (GNNs) has emerged. Despite the youth of the field and the speed in which new models are developed, many good surveys have been published in the last years. Nevertheless, an overview on which graph types can be modeled by GNNs is missing. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static as well as on dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover in the dynamic case, we separate the models in discrete-time and continuous-time dynamic graphs based on their architecture. While ordering the existing GNN models, we find, that there are still graph types, that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.
NDNET: A Unified Framework for Anomaly and Novelty Detection.Decke, Jens; Schmeißing, Jörn; Botache, Diego; Bieshaar, Maarten; Sick, Bernhard; Gruhl, Christian. In International Conference on Architecture of Computing Systems (ARCS), ble. 197–210. Springer, 2022.
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We introduce NDNET (https://novelty-detection.net/p/ndnet), an anomaly and novelty detection library that implements various detection algorithms adjusted for online processing of data streams. The intention of this library is threefold: 1) Make experimentation with different anomaly and novelty detection algorithms simple. 2) Support the development of new novelty detection approaches by providing the mCANDIES framework. 3) Provide fundamentals to analyze and evaluate novelty detection algorithms on data streams. The library is freely available and developed as open-source software.
@inproceedings{decke2022ndnet, abstract = {We introduce NDNET (https://novelty-detection.net/p/ndnet), an anomaly and novelty detection library that implements various detection algorithms adjusted for online processing of data streams. The intention of this library is threefold: 1) Make experimentation with different anomaly and novelty detection algorithms simple. 2) Support the development of new novelty detection approaches by providing the mCANDIES framework. 3) Provide fundamentals to analyze and evaluate novelty detection algorithms on data streams. The library is freely available and developed as open-source software.}, author = {Decke, Jens and Schmeißing, Jörn and Botache, Diego and Bieshaar, Maarten and Sick, Bernhard and Gruhl, Christian}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {itegpub}, pages = {197–210}, publisher = {Springer}, title = {{NDNET}: {A} Unified Framework for Anomaly and Novelty Detection}, year = 2022 }
%0 Conference Paper %1 decke2022ndnet %A Decke, Jens %A Schmeißing, Jörn %A Botache, Diego %A Bieshaar, Maarten %A Sick, Bernhard %A Gruhl, Christian %B International Conference on Architecture of Computing Systems (ARCS) %D 2022 %I Springer %P 197--210 %R 10.1007/978-3-031-21867-5_13 %T NDNET: A Unified Framework for Anomaly and Novelty Detection %X We introduce NDNET (https://novelty-detection.net/p/ndnet), an anomaly and novelty detection library that implements various detection algorithms adjusted for online processing of data streams. The intention of this library is threefold: 1) Make experimentation with different anomaly and novelty detection algorithms simple. 2) Support the development of new novelty detection approaches by providing the mCANDIES framework. 3) Provide fundamentals to analyze and evaluate novelty detection algorithms on data streams. The library is freely available and developed as open-source software.
FDGNN: Fully Dynamic Graph Neural Network.Moallemy-Oureh, Alice; Beddar-Wiesing, Silcia; Nather, Rüdiger; Thomas, Josephine M. In arXiv e-prints, bl. arXiv:2206.03469. 2022.
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Dynamic Graph Neural Networks recently became more and more important as graphs from many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, are dynamic by nature. While temporal changes (dynamics) play an essential role in many real-world applications, most of the models in the literature on Graph Neural Networks (GNN) process static graphs. The few GNN models on dynamic graphs only consider exceptional cases of dynamics, e.g., node attribute-dynamic graphs or structure-dynamic graphs limited to additions or changes to the graph's edges, etc. Therefore, we present a novel Fully Dynamic Graph Neural Network (FDGNN) that can handle fully-dynamic graphs in continuous time. The proposed method provides a node and an edge embedding that includes their activity to address added and deleted nodes or edges, and possible attributes. Furthermore, the embeddings specify Temporal Point Processes for each event to encode the distributions of the structure- and attribute-related incoming graph events. In addition, our model can be updated efficiently by considering single events for local retraining.
@article{moallemyoureh2022fdgnn, abstract = {Dynamic Graph Neural Networks recently became more and more important as graphs from many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, are dynamic by nature. While temporal changes (dynamics) play an essential role in many real-world applications, most of the models in the literature on Graph Neural Networks (GNN) process static graphs. The few GNN models on dynamic graphs only consider exceptional cases of dynamics, e.g., node attribute-dynamic graphs or structure-dynamic graphs limited to additions or changes to the graph's edges, etc. Therefore, we present a novel Fully Dynamic Graph Neural Network (FDGNN) that can handle fully-dynamic graphs in continuous time. The proposed method provides a node and an edge embedding that includes their activity to address added and deleted nodes or edges, and possible attributes. Furthermore, the embeddings specify Temporal Point Processes for each event to encode the distributions of the structure- and attribute-related incoming graph events. In addition, our model can be updated efficiently by considering single events for local retraining.}, author = {Moallemy-Oureh, Alice and Beddar-Wiesing, Silcia and Nather, Rüdiger and Thomas, Josephine M.}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2206.03469}, title = {FDGNN: Fully Dynamic Graph Neural Network}, year = 2022 }
%0 Journal Article %1 moallemyoureh2022fdgnn %A Moallemy-Oureh, Alice %A Beddar-Wiesing, Silcia %A Nather, Rüdiger %A Thomas, Josephine M. %D 2022 %J arXiv e-prints %P arXiv:2206.03469 %T FDGNN: Fully Dynamic Graph Neural Network %U https://arxiv.org/abs/2206.03469 %X Dynamic Graph Neural Networks recently became more and more important as graphs from many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, are dynamic by nature. While temporal changes (dynamics) play an essential role in many real-world applications, most of the models in the literature on Graph Neural Networks (GNN) process static graphs. The few GNN models on dynamic graphs only consider exceptional cases of dynamics, e.g., node attribute-dynamic graphs or structure-dynamic graphs limited to additions or changes to the graph's edges, etc. Therefore, we present a novel Fully Dynamic Graph Neural Network (FDGNN) that can handle fully-dynamic graphs in continuous time. The proposed method provides a node and an edge embedding that includes their activity to address added and deleted nodes or edges, and possible attributes. Furthermore, the embeddings specify Temporal Point Processes for each event to encode the distributions of the structure- and attribute-related incoming graph events. In addition, our model can be updated efficiently by considering single events for local retraining.
On the Extension of the Weisfeiler-Lehman Hierarchy by WL Tests for Arbitrary Graphs.Beddar-Wiesing, Silvia; D’Inverno, Giuseppe Alessio; Graziani, Caterina; Lachi, Veronica; Moallemy-Oureh, Alice; Scarselli, Franco. In Workshp on Mining and Learning on Graphs (MLG), ECML PKDD, ble. 1–13. 2022.
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Graph isomorphism (GI) has occupied both theoreticians and applied scientists since the early 1950s. Over the years, several approaches and algorithms with which an isomorphism between two graphs can be tested have been developed. A general approach is the Weisfeiler–Lehman (WL) test, which is based on a coloring algorithm and provides a necessary criterion for graph isomorphism. However, the WL test is restricted to examining graphs with only node attributes. Therefore, this paper presents two extensions of the WL algorithm to allow for testing on arbitrary graphs. One considers edge attributes, and the other tests the isomorphism on dynamic graphs. Additionally, we extend the WL-hierarchy by the attributed and dynamic WL tests and show that it is a partial order, which induces a lattice. In the future, this may allow for practical implications coming from lattice theory.
@inproceedings{beddarwiesing2022extension, abstract = {Graph isomorphism (GI) has occupied both theoreticians and applied scientists since the early 1950s. Over the years, several approaches and algorithms with which an isomorphism between two graphs can be tested have been developed. A general approach is the Weisfeiler–Lehman (WL) test, which is based on a coloring algorithm and provides a necessary criterion for graph isomorphism. However, the WL test is restricted to examining graphs with only node attributes. Therefore, this paper presents two extensions of the WL algorithm to allow for testing on arbitrary graphs. One considers edge attributes, and the other tests the isomorphism on dynamic graphs. Additionally, we extend the WL-hierarchy by the attributed and dynamic WL tests and show that it is a partial order, which induces a lattice. In the future, this may allow for practical implications coming from lattice theory.}, author = {Beddar-Wiesing, Silvia and D'Inverno, Giuseppe Alessio and Graziani, Caterina and Lachi, Veronica and Moallemy-Oureh, Alice and Scarselli, Franco}, booktitle = {Workshp on Mining and Learning on Graphs (MLG), ECML PKDD}, keywords = {itegpub}, pages = {1–13}, title = {On the Extension of the {W}eisfeiler-{L}ehman Hierarchy by WL Tests for Arbitrary Graphs}, year = 2022 }
%0 Conference Paper %1 beddarwiesing2022extension %A Beddar-Wiesing, Silvia %A D'Inverno, Giuseppe Alessio %A Graziani, Caterina %A Lachi, Veronica %A Moallemy-Oureh, Alice %A Scarselli, Franco %B Workshp on Mining and Learning on Graphs (MLG), ECML PKDD %D 2022 %P 1--13 %T On the Extension of the Weisfeiler-Lehman Hierarchy by WL Tests for Arbitrary Graphs %U https://openreview.net/forum?id=Qt6GrgDz2y5 %X Graph isomorphism (GI) has occupied both theoreticians and applied scientists since the early 1950s. Over the years, several approaches and algorithms with which an isomorphism between two graphs can be tested have been developed. A general approach is the Weisfeiler–Lehman (WL) test, which is based on a coloring algorithm and provides a necessary criterion for graph isomorphism. However, the WL test is restricted to examining graphs with only node attributes. Therefore, this paper presents two extensions of the WL algorithm to allow for testing on arbitrary graphs. One considers edge attributes, and the other tests the isomorphism on dynamic graphs. Additionally, we extend the WL-hierarchy by the attributed and dynamic WL tests and show that it is a partial order, which induces a lattice. In the future, this may allow for practical implications coming from lattice theory.
Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting.Nivarthi, Chandana Priya; Vogt, Stephan; Sick, Bernhard. In International Conference on Machine Learning and Applications (ICMLA), ble. 1530–1536. IEEE, 2022.
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Renewable power generation forecasts using machine learning are typically implemented as single-task learning models, where a separate model is trained for each photovoltaic or wind park. In recent years, transfer learning is gaining popularity in these systems, as it can be used to transfer the knowledge gained from source parks to a target park. However, for transferring the knowledge to a target park, there is a need to determine the most similar source park(s) among the existing parks. This similarity determination using historical power measurements is challenging when the target park has limited to no historical data samples. Therefore, we propose a simple multi-task learning architecture that initially learns a common representation of input weather features among the tasks, using a Unified Autoencoder (UAE) and then learns the task specific information utilizing a Task Embedding layer in a Neural Network (TENN). This proposed architecture, UAE-TENN, can be easily extended to new parks with or without historical data. An elaborate performance comparison of single and multi-task learning models is performed on six photovoltaic and wind farm datasets comprising a total of 529 parks. UAE-TENN significantly improves the performance of power forecasting by 10 to 19% for photovoltaic parks and 5 to 22% for wind parks compared to the baseline models. Even in the zero-shot learning scenario, when there is no historical data, we successfully demonstrate that the UAE-TENN improves the forecast accuracy for a new park by 19% for photovoltaic parks.
@inproceedings{nivarthi2022unified, abstract = {Renewable power generation forecasts using machine learning are typically implemented as single-task learning models, where a separate model is trained for each photovoltaic or wind park. In recent years, transfer learning is gaining popularity in these systems, as it can be used to transfer the knowledge gained from source parks to a target park. However, for transferring the knowledge to a target park, there is a need to determine the most similar source park(s) among the existing parks. This similarity determination using historical power measurements is challenging when the target park has limited to no historical data samples. Therefore, we propose a simple multi-task learning architecture that initially learns a common representation of input weather features among the tasks, using a Unified Autoencoder (UAE) and then learns the task specific information utilizing a Task Embedding layer in a Neural Network (TENN). This proposed architecture, UAE-TENN, can be easily extended to new parks with or without historical data. An elaborate performance comparison of single and multi-task learning models is performed on six photovoltaic and wind farm datasets comprising a total of 529 parks. UAE-TENN significantly improves the performance of power forecasting by 10 to 19% for photovoltaic parks and 5 to 22% for wind parks compared to the baseline models. Even in the zero-shot learning scenario, when there is no historical data, we successfully demonstrate that the UAE-TENN improves the forecast accuracy for a new park by 19% for photovoltaic parks.}, author = {Nivarthi, Chandana Priya and Vogt, Stephan and Sick, Bernhard}, booktitle = {International Conference on Machine Learning and Applications (ICMLA)}, keywords = {itegpub}, pages = {1530–1536}, publisher = {IEEE}, title = {Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting}, year = 2022 }
%0 Conference Paper %1 nivarthi2022unified %A Nivarthi, Chandana Priya %A Vogt, Stephan %A Sick, Bernhard %B International Conference on Machine Learning and Applications (ICMLA) %D 2022 %I IEEE %P 1530--1536 %R 10.1109/ICMLA55696.2022.00240 %T Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting %U https://ieeexplore.ieee.org/document/10068974 %X Renewable power generation forecasts using machine learning are typically implemented as single-task learning models, where a separate model is trained for each photovoltaic or wind park. In recent years, transfer learning is gaining popularity in these systems, as it can be used to transfer the knowledge gained from source parks to a target park. However, for transferring the knowledge to a target park, there is a need to determine the most similar source park(s) among the existing parks. This similarity determination using historical power measurements is challenging when the target park has limited to no historical data samples. Therefore, we propose a simple multi-task learning architecture that initially learns a common representation of input weather features among the tasks, using a Unified Autoencoder (UAE) and then learns the task specific information utilizing a Task Embedding layer in a Neural Network (TENN). This proposed architecture, UAE-TENN, can be easily extended to new parks with or without historical data. An elaborate performance comparison of single and multi-task learning models is performed on six photovoltaic and wind farm datasets comprising a total of 529 parks. UAE-TENN significantly improves the performance of power forecasting by 10 to 19% for photovoltaic parks and 5 to 22% for wind parks compared to the baseline models. Even in the zero-shot learning scenario, when there is no historical data, we successfully demonstrate that the UAE-TENN improves the forecast accuracy for a new park by 19% for photovoltaic parks.
Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes.Huhnstock, Rico; Reginka, Meike; Sonntag, Claudius; Merkel, Maximilian; Dingel, Kristina; Sick, Bernhard; Vogel, Michael; Ehresmann, Arno. In Scientific Reports, 12(1), ble. 1–10. Springer Nature, 2022.
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The transport of magnetic particles (MPs) by dynamic magnetic field landscapes (MFLs) using magnetically patterned substrates is promising for the development of Lab-on-a-chip (LOC) systems. The inherent close-to-substrate MP motion is sensitive to changing particle–substrate interactions. Thus, the detection of a modified particle–substrate separation distance caused by surface binding of an analyte is expected to be a promising probe in analytics and diagnostics. Here, we present an essential prerequisite for such an application, namely the label-free quantitative experimental determination of the three-dimensional trajectories of superparamagnetic particles (SPPs) transported by a dynamically changing MFL. The evaluation of defocused SPP images from optical bright-field microscopy revealed a “hopping”-like motion of the magnetic particles, previously predicted by theory, additionally allowing a quantification of maximum jump heights. As our findings pave the way towards precise determination of particle–substrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy.
@article{huhnstock2022three, abstract = {The transport of magnetic particles (MPs) by dynamic magnetic field landscapes (MFLs) using magnetically patterned substrates is promising for the development of Lab-on-a-chip (LOC) systems. The inherent close-to-substrate MP motion is sensitive to changing particle–substrate interactions. Thus, the detection of a modified particle–substrate separation distance caused by surface binding of an analyte is expected to be a promising probe in analytics and diagnostics. Here, we present an essential prerequisite for such an application, namely the label-free quantitative experimental determination of the three-dimensional trajectories of superparamagnetic particles (SPPs) transported by a dynamically changing MFL. The evaluation of defocused SPP images from optical bright-field microscopy revealed a “hopping”-like motion of the magnetic particles, previously predicted by theory, additionally allowing a quantification of maximum jump heights. As our findings pave the way towards precise determination of particle–substrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy.}, author = {Huhnstock, Rico and Reginka, Meike and Sonntag, Claudius and Merkel, Maximilian and Dingel, Kristina and Sick, Bernhard and Vogel, Michael and Ehresmann, Arno}, journal = {Scientific Reports}, keywords = {itegpub}, number = 1, pages = {1–10}, publisher = {Springer Nature}, title = {Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes}, volume = 12, year = 2022 }
%0 Journal Article %1 huhnstock2022three %A Huhnstock, Rico %A Reginka, Meike %A Sonntag, Claudius %A Merkel, Maximilian %A Dingel, Kristina %A Sick, Bernhard %A Vogel, Michael %A Ehresmann, Arno %D 2022 %I Springer Nature %J Scientific Reports %N 1 %P 1--10 %R 10.1038/s41598-022-25391-z %T Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes %U https://www.nature.com/articles/s41598-022-25391-z %V 12 %X The transport of magnetic particles (MPs) by dynamic magnetic field landscapes (MFLs) using magnetically patterned substrates is promising for the development of Lab-on-a-chip (LOC) systems. The inherent close-to-substrate MP motion is sensitive to changing particle–substrate interactions. Thus, the detection of a modified particle–substrate separation distance caused by surface binding of an analyte is expected to be a promising probe in analytics and diagnostics. Here, we present an essential prerequisite for such an application, namely the label-free quantitative experimental determination of the three-dimensional trajectories of superparamagnetic particles (SPPs) transported by a dynamically changing MFL. The evaluation of defocused SPP images from optical bright-field microscopy revealed a “hopping”-like motion of the magnetic particles, previously predicted by theory, additionally allowing a quantification of maximum jump heights. As our findings pave the way towards precise determination of particle–substrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy.
Multi-Task Autoencoders and Transfer Learning for Day-Ahead Wind and Photovoltaic Power Forecasts.Schreiber, Jens; Sick, Bernhard. In Energies, 15(21), bl. 8062. MDPI, 2022.
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Integrating new renewable energy resources requires robust and reliable forecasts to ensure a stable electrical grid and avoid blackouts. Sophisticated representation learning techniques, such as autoencoders, play an essential role, as they allow for the extraction of latent features to forecast the expected generated wind and photovoltaic power for the next seconds up to days. Thereby, autoencoders reduce the required training time and the time spent in manual feature engineering and often improve the forecast error. However, most current renewable energy forecasting research on autoencoders focuses on smaller forecast horizons for the following seconds and hours based on meteorological measurements. At the same time, larger forecast horizons, such as day-ahead power forecasts based on numerical weather predictions, are crucial for planning loads and demands within the electrical grid to prevent power failures. There is little evidence on the ability of autoencoders and their respective forecasting models to improve through multi-task learning and time series autoencoders for day-ahead power forecasts. We can close these gaps by proposing a multi-task learning autoencoder based on the recently introduced temporal convolution network. This approach reduces the number of trainable parameters by 38 for photovoltaic data and 202 for wind data while having the best reconstruction error compared to nine other representation learning techniques. At the same time, this model decreases the day-ahead forecast error up to 18.3% for photovoltaic parks and 1.5% for wind parks. We round off these results by analyzing the influences of the latent size and the number of layers to fine-tune the encoder for wind and photovoltaic power forecasts.
@article{schreiber2022multi, abstract = {Integrating new renewable energy resources requires robust and reliable forecasts to ensure a stable electrical grid and avoid blackouts. Sophisticated representation learning techniques, such as autoencoders, play an essential role, as they allow for the extraction of latent features to forecast the expected generated wind and photovoltaic power for the next seconds up to days. Thereby, autoencoders reduce the required training time and the time spent in manual feature engineering and often improve the forecast error. However, most current renewable energy forecasting research on autoencoders focuses on smaller forecast horizons for the following seconds and hours based on meteorological measurements. At the same time, larger forecast horizons, such as day-ahead power forecasts based on numerical weather predictions, are crucial for planning loads and demands within the electrical grid to prevent power failures. There is little evidence on the ability of autoencoders and their respective forecasting models to improve through multi-task learning and time series autoencoders for day-ahead power forecasts. We can close these gaps by proposing a multi-task learning autoencoder based on the recently introduced temporal convolution network. This approach reduces the number of trainable parameters by 38 for photovoltaic data and 202 for wind data while having the best reconstruction error compared to nine other representation learning techniques. At the same time, this model decreases the day-ahead forecast error up to 18.3% for photovoltaic parks and 1.5% for wind parks. We round off these results by analyzing the influences of the latent size and the number of layers to fine-tune the encoder for wind and photovoltaic power forecasts.}, author = {Schreiber, Jens and Sick, Bernhard}, journal = {Energies}, keywords = {itegpub}, number = 21, pages = 8062, publisher = {MDPI}, title = {Multi-Task Autoencoders and Transfer Learning for Day-Ahead Wind and Photovoltaic Power Forecasts}, volume = 15, year = 2022 }
%0 Journal Article %1 schreiber2022multi %A Schreiber, Jens %A Sick, Bernhard %D 2022 %I MDPI %J Energies %N 21 %P 8062 %R 10.3390/en15218062 %T Multi-Task Autoencoders and Transfer Learning for Day-Ahead Wind and Photovoltaic Power Forecasts %V 15 %X Integrating new renewable energy resources requires robust and reliable forecasts to ensure a stable electrical grid and avoid blackouts. Sophisticated representation learning techniques, such as autoencoders, play an essential role, as they allow for the extraction of latent features to forecast the expected generated wind and photovoltaic power for the next seconds up to days. Thereby, autoencoders reduce the required training time and the time spent in manual feature engineering and often improve the forecast error. However, most current renewable energy forecasting research on autoencoders focuses on smaller forecast horizons for the following seconds and hours based on meteorological measurements. At the same time, larger forecast horizons, such as day-ahead power forecasts based on numerical weather predictions, are crucial for planning loads and demands within the electrical grid to prevent power failures. There is little evidence on the ability of autoencoders and their respective forecasting models to improve through multi-task learning and time series autoencoders for day-ahead power forecasts. We can close these gaps by proposing a multi-task learning autoencoder based on the recently introduced temporal convolution network. This approach reduces the number of trainable parameters by 38 for photovoltaic data and 202 for wind data while having the best reconstruction error compared to nine other representation learning techniques. At the same time, this model decreases the day-ahead forecast error up to 18.3% for photovoltaic parks and 1.5% for wind parks. We round off these results by analyzing the influences of the latent size and the number of layers to fine-tune the encoder for wind and photovoltaic power forecasts.
Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts.He, Yujiang. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), ble. 125–140. kassel university press, 2022.
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Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively. Deep neural networks have to learn unseen tasks and overcome forgetting the knowledge obtained from previously learned tasks as the amount of data keeps increasing in applications. This article proposes two continual learning application scenarios, i.e., the target-domain incremental scenario and the data-domain incremental scenario, to describe the potential challenges in this context. Based on our previous work regarding the CLeaR framework, which is short for continual learning for regression tasks, models will be enabled to extend themselves and to learn data successively. Research topics are related, but not limited, to developing continual deep learning algorithms, strategies for non-stationarity detection in data streams, explainable and visualizable artificial intelligence, etc. Moreover, the framework- and algorithm-related hyperparameters should be dynamically updated in applications. Forecasting experiments will be conducted based on power generation and consumption data collected from real-world applications. A series of comprehensive evaluation metrics and visualization tools are applied to access the experimental results. The proposed framework is expected to be generally applied to other constantly changing scenarios.
@incollection{he2022adaptive, abstract = {Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively. Deep neural networks have to learn unseen tasks and overcome forgetting the knowledge obtained from previously learned tasks as the amount of data keeps increasing in applications. This article proposes two continual learning application scenarios, i.e., the target-domain incremental scenario and the data-domain incremental scenario, to describe the potential challenges in this context. Based on our previous work regarding the CLeaR framework, which is short for continual learning for regression tasks, models will be enabled to extend themselves and to learn data successively. Research topics are related, but not limited, to developing continual deep learning algorithms, strategies for non-stationarity detection in data streams, explainable and visualizable artificial intelligence, etc. Moreover, the framework- and algorithm-related hyperparameters should be dynamically updated in applications. Forecasting experiments will be conducted based on power generation and consumption data collected from real-world applications. A series of comprehensive evaluation metrics and visualization tools are applied to access the experimental results. The proposed framework is expected to be generally applied to other constantly changing scenarios.}, author = {He, Yujiang}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {125–140}, publisher = {kassel university press}, title = {Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts}, year = 2022 }
%0 Book Section %1 he2022adaptive %A He, Yujiang %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 125--140 %T Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts %X Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively. Deep neural networks have to learn unseen tasks and overcome forgetting the knowledge obtained from previously learned tasks as the amount of data keeps increasing in applications. This article proposes two continual learning application scenarios, i.e., the target-domain incremental scenario and the data-domain incremental scenario, to describe the potential challenges in this context. Based on our previous work regarding the CLeaR framework, which is short for continual learning for regression tasks, models will be enabled to extend themselves and to learn data successively. Research topics are related, but not limited, to developing continual deep learning algorithms, strategies for non-stationarity detection in data streams, explainable and visualizable artificial intelligence, etc. Moreover, the framework- and algorithm-related hyperparameters should be dynamically updated in applications. Forecasting experiments will be conducted based on power generation and consumption data collected from real-world applications. A series of comprehensive evaluation metrics and visualization tools are applied to access the experimental results. The proposed framework is expected to be generally applied to other constantly changing scenarios.
A Stopping Criterion for Transductive Active Learning.Kottke, Daniel; Sandrock, Christoph; Krempl, Georg; Sick, Bernhard. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), ble. 468–484. Springer, 2022.
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In transductive active learning, the goal is to determine the correct labels for an unlabeled, known dataset. Therefore, we can either ask an oracle to provide the right label at some cost or use the prediction of a classifier which we train on the labels acquired so far. In contrast, the commonly used (inductive) active learning aims to select instances for labeling out of the unlabeled set to create a generalized classifier, which will be deployed on unknown data. This article formally defines the transductive setting and shows that it requires new solutions. Additionally, we formalize the theoretically cost-optimal stopping point for the transductive scenario. Building upon the probabilistic active learning framework, we propose a new transductive selection strategy that includes a stopping criterion and show its superiority.
@inproceedings{kottke2022stopping, abstract = {In transductive active learning, the goal is to determine the correct labels for an unlabeled, known dataset. Therefore, we can either ask an oracle to provide the right label at some cost or use the prediction of a classifier which we train on the labels acquired so far. In contrast, the commonly used (inductive) active learning aims to select instances for labeling out of the unlabeled set to create a generalized classifier, which will be deployed on unknown data. This article formally defines the transductive setting and shows that it requires new solutions. Additionally, we formalize the theoretically cost-optimal stopping point for the transductive scenario. Building upon the probabilistic active learning framework, we propose a new transductive selection strategy that includes a stopping criterion and show its superiority.}, author = {Kottke, Daniel and Sandrock, Christoph and Krempl, Georg and Sick, Bernhard}, booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)}, keywords = {itegpub}, pages = {468–484}, publisher = {Springer}, title = {A Stopping Criterion for Transductive Active Learning}, year = 2022 }
%0 Conference Paper %1 kottke2022stopping %A Kottke, Daniel %A Sandrock, Christoph %A Krempl, Georg %A Sick, Bernhard %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) %D 2022 %I Springer %P 468--484 %R 10.1007/978-3-031-26412-2_29 %T A Stopping Criterion for Transductive Active Learning %U https://link.springer.com/chapter/10.1007/978-3-031-26412-2_29 %X In transductive active learning, the goal is to determine the correct labels for an unlabeled, known dataset. Therefore, we can either ask an oracle to provide the right label at some cost or use the prediction of a classifier which we train on the labels acquired so far. In contrast, the commonly used (inductive) active learning aims to select instances for labeling out of the unlabeled set to create a generalized classifier, which will be deployed on unknown data. This article formally defines the transductive setting and shows that it requires new solutions. Additionally, we formalize the theoretically cost-optimal stopping point for the transductive scenario. Building upon the probabilistic active learning framework, we propose a new transductive selection strategy that includes a stopping criterion and show its superiority.
Efficient SVDD sampling with approximation guarantees for the decision boundary.Englhardt, Adrian; Trittenbach, Holger; Kottke, Daniel; Sick, Bernhard; Böhm, Klemens. In Machine Learning, 111(4), ble. 1349–1375. Springer, 2022.
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Support Vector Data Description (SVDD) is a popular one-class classifier for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size. To avoid prohibitive training times, sampling methods select small subsets of the training data on which SVDD trains a decision boundary hopefully equivalent to the one obtained on the full data set. According to the literature, a good sample should therefore contain so-called boundary observations that SVDD would select as support vectors on the full data set. However, non-boundary observations also are essential to not fragment contiguous inlier regions and avoid poor classification accuracy. Other aspects, such as selecting a sufficiently representative sample, are important as well. But existing sampling methods largely overlook them, resulting in poor classification accuracy. In this article, we study how to select a sample considering these points. Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary. We then propose RAPID, an efficient algorithm to solve this optimization problem. RAPID does not require any tuning of parameters, is easy to implement and scales well to large data sets. We evaluate our approach on real-world and synthetic data. Our evaluation is the most comprehensive one for SVDD sampling so far. Our results show that RAPID outperforms its competitors in classification accuracy, in sample size, and in runtime.
@article{englhardt2022efficient, abstract = {Support Vector Data Description (SVDD) is a popular one-class classifier for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size. To avoid prohibitive training times, sampling methods select small subsets of the training data on which SVDD trains a decision boundary hopefully equivalent to the one obtained on the full data set. According to the literature, a good sample should therefore contain so-called boundary observations that SVDD would select as support vectors on the full data set. However, non-boundary observations also are essential to not fragment contiguous inlier regions and avoid poor classification accuracy. Other aspects, such as selecting a sufficiently representative sample, are important as well. But existing sampling methods largely overlook them, resulting in poor classification accuracy. In this article, we study how to select a sample considering these points. Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary. We then propose RAPID, an efficient algorithm to solve this optimization problem. RAPID does not require any tuning of parameters, is easy to implement and scales well to large data sets. We evaluate our approach on real-world and synthetic data. Our evaluation is the most comprehensive one for SVDD sampling so far. Our results show that RAPID outperforms its competitors in classification accuracy, in sample size, and in runtime.}, author = {Englhardt, Adrian and Trittenbach, Holger and Kottke, Daniel and Sick, Bernhard and Böhm, Klemens}, journal = {Machine Learning}, keywords = {itegpub}, number = 4, pages = {1349–1375}, publisher = {Springer}, title = {Efficient {SVDD} sampling with approximation guarantees for the decision boundary}, volume = 111, year = 2022 }
%0 Journal Article %1 englhardt2022efficient %A Englhardt, Adrian %A Trittenbach, Holger %A Kottke, Daniel %A Sick, Bernhard %A Böhm, Klemens %D 2022 %I Springer %J Machine Learning %N 4 %P 1349--1375 %R 10.1007/s10994-022-06149-0 %T Efficient SVDD sampling with approximation guarantees for the decision boundary %U https://link.springer.com/article/10.1007/s10994-022-06149-0 %V 111 %X Support Vector Data Description (SVDD) is a popular one-class classifier for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size. To avoid prohibitive training times, sampling methods select small subsets of the training data on which SVDD trains a decision boundary hopefully equivalent to the one obtained on the full data set. According to the literature, a good sample should therefore contain so-called boundary observations that SVDD would select as support vectors on the full data set. However, non-boundary observations also are essential to not fragment contiguous inlier regions and avoid poor classification accuracy. Other aspects, such as selecting a sufficiently representative sample, are important as well. But existing sampling methods largely overlook them, resulting in poor classification accuracy. In this article, we study how to select a sample considering these points. Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary. We then propose RAPID, an efficient algorithm to solve this optimization problem. RAPID does not require any tuning of parameters, is easy to implement and scales well to large data sets. We evaluate our approach on real-world and synthetic data. Our evaluation is the most comprehensive one for SVDD sampling so far. Our results show that RAPID outperforms its competitors in classification accuracy, in sample size, and in runtime.
Enhancing Active Learning with Weak Supervision and Transfer Learning by Leveraging Information and Knowledge Sources.Rauch, Lukas; Huseljic, Denis; Sick, Bernhard. In Workshop on Interactive Adaptive Learning (IAL), ECML PKDD, ble. 27–42. 2022.
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One of the major limitations of deploying a machine learning model is the availability of labeled training data and the resulting expensive annotation process. Although active learning (AL) methods may reduce the annotation cost by actively selecting the most-useful instances, a costly human annotator usually provides the labels. Therefore, even with AL, we still consider the annotation process to be time-consuming and expensive. Besides human annotators, though, companies often have a vast amount of information and knowledge sources available that can generate low-cost labels (e.g., a black-box model) or improve the learning process (e.g., a pre-trained model). We present a novel approach that enhances AL with weak supervision (WS) and transfer learning (TL) to reduce the annotation cost by leveraging these sources. Specifically, we consider a black-box model like a rule-based system as an error-prone and weakly-supervised annotator that inexpensively provides labels. We estimate its performance with an annotator model to decide whether a human annotation is required. Additionally, we utilize unlabeled internal and external data by transferring knowledge from a pre-trained model to the AL cycle. We sequentially investigate the impact of WS and TL on annotation cost and model performance in an AL cycle through a use case. Our evaluation shows that our approach can reduce annotation cost by 51% while achieving nearly identical model performance compared to a traditional AL approach.
@inproceedings{rauch2022enhancing, abstract = {One of the major limitations of deploying a machine learning model is the availability of labeled training data and the resulting expensive annotation process. Although active learning (AL) methods may reduce the annotation cost by actively selecting the most-useful instances, a costly human annotator usually provides the labels. Therefore, even with AL, we still consider the annotation process to be time-consuming and expensive. Besides human annotators, though, companies often have a vast amount of information and knowledge sources available that can generate low-cost labels (e.g., a black-box model) or improve the learning process (e.g., a pre-trained model). We present a novel approach that enhances AL with weak supervision (WS) and transfer learning (TL) to reduce the annotation cost by leveraging these sources. Specifically, we consider a black-box model like a rule-based system as an error-prone and weakly-supervised annotator that inexpensively provides labels. We estimate its performance with an annotator model to decide whether a human annotation is required. Additionally, we utilize unlabeled internal and external data by transferring knowledge from a pre-trained model to the AL cycle. We sequentially investigate the impact of WS and TL on annotation cost and model performance in an AL cycle through a use case. Our evaluation shows that our approach can reduce annotation cost by 51% while achieving nearly identical model performance compared to a traditional AL approach.}, author = {Rauch, Lukas and Huseljic, Denis and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {itegpub}, pages = {27–42}, title = {Enhancing Active Learning with Weak Supervision and Transfer Learning by Leveraging Information and Knowledge Sources}, year = 2022 }
%0 Conference Paper %1 rauch2022enhancing %A Rauch, Lukas %A Huseljic, Denis %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2022 %P 27--42 %T Enhancing Active Learning with Weak Supervision and Transfer Learning by Leveraging Information and Knowledge Sources %U http://ceur-ws.org/Vol-3259/ialatecml_paper3.pdf %X One of the major limitations of deploying a machine learning model is the availability of labeled training data and the resulting expensive annotation process. Although active learning (AL) methods may reduce the annotation cost by actively selecting the most-useful instances, a costly human annotator usually provides the labels. Therefore, even with AL, we still consider the annotation process to be time-consuming and expensive. Besides human annotators, though, companies often have a vast amount of information and knowledge sources available that can generate low-cost labels (e.g., a black-box model) or improve the learning process (e.g., a pre-trained model). We present a novel approach that enhances AL with weak supervision (WS) and transfer learning (TL) to reduce the annotation cost by leveraging these sources. Specifically, we consider a black-box model like a rule-based system as an error-prone and weakly-supervised annotator that inexpensively provides labels. We estimate its performance with an annotator model to decide whether a human annotation is required. Additionally, we utilize unlabeled internal and external data by transferring knowledge from a pre-trained model to the AL cycle. We sequentially investigate the impact of WS and TL on annotation cost and model performance in an AL cycle through a use case. Our evaluation shows that our approach can reduce annotation cost by 51% while achieving nearly identical model performance compared to a traditional AL approach.
Student Research Abstract: Using Local Activity Encoding for Dynamic Graph Pooling in Stuctural-Dynamic Graphs.Beddar-Wiesing, Silvia. In ACM/SIGAPP Symposium on Applied Computing (SAC), ble. 604–609. ACM, 2022.
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Graphs have recently become more popular for modeling real-world as well as theoretical problems, and several Machine Learning algorithms for learning on them have developed. For graphs that change over time, however, research is still at an early stage. This article proposes a framework DynHeatmaP that generates a constant-size graph sequence representation of graphs whose structure changes over time (structural-dynamic, SDG) in linear time. The framework uses a representation of additions and deletions of nodes and edges (events) to perform dynamic graph pooling. Our algorithm translates the dynamic changes of the nodes and edges to a heatmap representation of the current node activity, regarding additions and deletions in the neighborhood, respectively. The created activity heatmap is utilized as probabilities of the nodes to be sampled in the subsequent pooling step. In addition, the neighborhood of selected nodes has been sampled such that the ratio of additions and deletions is represented accordingly. The obtained downsampled graph sequence can then be used representatively as input to a GNN for learning on the original stream. Focussing on the active regions in a graph, the fast pooling process can reduce the training time significantly and addresses the problem of unbalanced datasets in real-world applications modeled as sparse graphs.
@inproceedings{beddarwiesing2022student, abstract = {Graphs have recently become more popular for modeling real-world as well as theoretical problems, and several Machine Learning algorithms for learning on them have developed. For graphs that change over time, however, research is still at an early stage. This article proposes a framework DynHeatmaP that generates a constant-size graph sequence representation of graphs whose structure changes over time (structural-dynamic, SDG) in linear time. The framework uses a representation of additions and deletions of nodes and edges (events) to perform dynamic graph pooling. Our algorithm translates the dynamic changes of the nodes and edges to a heatmap representation of the current node activity, regarding additions and deletions in the neighborhood, respectively. The created activity heatmap is utilized as probabilities of the nodes to be sampled in the subsequent pooling step. In addition, the neighborhood of selected nodes has been sampled such that the ratio of additions and deletions is represented accordingly. The obtained downsampled graph sequence can then be used representatively as input to a GNN for learning on the original stream. Focussing on the active regions in a graph, the fast pooling process can reduce the training time significantly and addresses the problem of unbalanced datasets in real-world applications modeled as sparse graphs.}, author = {Beddar-Wiesing, Silvia}, booktitle = {ACM/SIGAPP Symposium on Applied Computing (SAC)}, keywords = {itegpub}, pages = {604–609}, publisher = {ACM}, title = {Student Research Abstract: Using Local Activity Encoding for Dynamic Graph Pooling in Stuctural-Dynamic Graphs}, year = 2022 }
%0 Conference Paper %1 beddarwiesing2022student %A Beddar-Wiesing, Silvia %B ACM/SIGAPP Symposium on Applied Computing (SAC) %D 2022 %I ACM %P 604--609 %R 10.1145/3477314.3506969 %T Student Research Abstract: Using Local Activity Encoding for Dynamic Graph Pooling in Stuctural-Dynamic Graphs %U https://dl.acm.org/doi/abs/10.1145/3477314.3506969 %X Graphs have recently become more popular for modeling real-world as well as theoretical problems, and several Machine Learning algorithms for learning on them have developed. For graphs that change over time, however, research is still at an early stage. This article proposes a framework DynHeatmaP that generates a constant-size graph sequence representation of graphs whose structure changes over time (structural-dynamic, SDG) in linear time. The framework uses a representation of additions and deletions of nodes and edges (events) to perform dynamic graph pooling. Our algorithm translates the dynamic changes of the nodes and edges to a heatmap representation of the current node activity, regarding additions and deletions in the neighborhood, respectively. The created activity heatmap is utilized as probabilities of the nodes to be sampled in the subsequent pooling step. In addition, the neighborhood of selected nodes has been sampled such that the ratio of additions and deletions is represented accordingly. The obtained downsampled graph sequence can then be used representatively as input to a GNN for learning on the original stream. Focussing on the active regions in a graph, the fast pooling process can reduce the training time significantly and addresses the problem of unbalanced datasets in real-world applications modeled as sparse graphs.
Student Research Abstract: Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs.Moallemy-Oureh, Alice. In ACM/SIGAPP Symposium on Applied Computing (SAC), ble. 600–603. ACM, 2022.
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The history of neural networks dates back to the early 1940s and has not only evolved rapidly over time but they are now amongst one of the most popular and powerful machine learning techniques. In this area, graph representation learning (GRL) using graph neural networks (GNNs) has emerged during the early 90s and developed into an impactful approach for modeling graph-structured real-world data, such as social or biological networks. There are a variety of successful applications of GRL in computational neuroscience, chemistry, mathematics, and so on. While temporal changes (dynamics) play an essential role in many real-world applications, most of the literature on GNNs and GRL, deals with static graphs. Since the few GNN models on dynamic graphs only consider exceptional cases of dynamics (i.e., attribute-dynamic graphs in discrete-time representation or structure-dynamic graphs in continuous-time representation), we aim to present a novel GNN model that can handle attribute-dynamic graphs in continuous time. This model learns any kind of graph information embedding, performs node/edge attribute forecasts, and allows for attribute-dynamic graph generation in both discrete and continuous-time.
@inproceedings{moallemyoureh2022student, abstract = {The history of neural networks dates back to the early 1940s and has not only evolved rapidly over time but they are now amongst one of the most popular and powerful machine learning techniques. In this area, graph representation learning (GRL) using graph neural networks (GNNs) has emerged during the early 90s and developed into an impactful approach for modeling graph-structured real-world data, such as social or biological networks. There are a variety of successful applications of GRL in computational neuroscience, chemistry, mathematics, and so on. While temporal changes (dynamics) play an essential role in many real-world applications, most of the literature on GNNs and GRL, deals with static graphs. Since the few GNN models on dynamic graphs only consider exceptional cases of dynamics (i.e., attribute-dynamic graphs in discrete-time representation or structure-dynamic graphs in continuous-time representation), we aim to present a novel GNN model that can handle attribute-dynamic graphs in continuous time. This model learns any kind of graph information embedding, performs node/edge attribute forecasts, and allows for attribute-dynamic graph generation in both discrete and continuous-time.}, author = {Moallemy-Oureh, Alice}, booktitle = {ACM/SIGAPP Symposium on Applied Computing (SAC)}, keywords = {itegpub}, pages = {600–603}, publisher = {ACM}, title = {Student Research Abstract: Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs}, year = 2022 }
%0 Conference Paper %1 moallemyoureh2022student %A Moallemy-Oureh, Alice %B ACM/SIGAPP Symposium on Applied Computing (SAC) %D 2022 %I ACM %P 600--603 %R 10.1145/3477314.3508018 %T Student Research Abstract: Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs %U https://dl.acm.org/doi/abs/10.1145/3477314.3508018 %X The history of neural networks dates back to the early 1940s and has not only evolved rapidly over time but they are now amongst one of the most popular and powerful machine learning techniques. In this area, graph representation learning (GRL) using graph neural networks (GNNs) has emerged during the early 90s and developed into an impactful approach for modeling graph-structured real-world data, such as social or biological networks. There are a variety of successful applications of GRL in computational neuroscience, chemistry, mathematics, and so on. While temporal changes (dynamics) play an essential role in many real-world applications, most of the literature on GNNs and GRL, deals with static graphs. Since the few GNN models on dynamic graphs only consider exceptional cases of dynamics (i.e., attribute-dynamic graphs in discrete-time representation or structure-dynamic graphs in continuous-time representation), we aim to present a novel GNN model that can handle attribute-dynamic graphs in continuous time. This model learns any kind of graph information embedding, performs node/edge attribute forecasts, and allows for attribute-dynamic graph generation in both discrete and continuous-time.
Stream-Based Active Learning in Changing Environments under Verification Latency.Pham, Tuan. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), ble. 152–164. kassel university press, 2022.
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Labeling data often requires human interaction, which is slow and costly depending on the required expertise. Stream-based active learning reduces the cost of labeling for data stream mining by only querying the labels for instances that improve the classifier the most. However, state-of-the-art stream-based active learning strategies assume that verification latency, i.e., the time between a query and the availability of the queried label, is negligible. In reality, this is usually not the case as the experts who provide such labels are not available all the time, and the labeling process itself requires time. This article investigates the impact of verification latency within a stream-based active learning scenario for changing data streams. We focus on two different types of changes that occur over time, i.e., changes in the number of features and changes in the distribution of instances and/or labels. In particular, we identify challenges that occur due to verification latency and propose ideas to overcome these problems within a stream-based active learning strategy.
@incollection{pham2022stream-based, abstract = {Labeling data often requires human interaction, which is slow and costly depending on the required expertise. Stream-based active learning reduces the cost of labeling for data stream mining by only querying the labels for instances that improve the classifier the most. However, state-of-the-art stream-based active learning strategies assume that verification latency, i.e., the time between a query and the availability of the queried label, is negligible. In reality, this is usually not the case as the experts who provide such labels are not available all the time, and the labeling process itself requires time. This article investigates the impact of verification latency within a stream-based active learning scenario for changing data streams. We focus on two different types of changes that occur over time, i.e., changes in the number of features and changes in the distribution of instances and/or labels. In particular, we identify challenges that occur due to verification latency and propose ideas to overcome these problems within a stream-based active learning strategy.}, author = {Pham, Tuan}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {152–164}, publisher = {kassel university press}, title = {Stream-Based Active Learning in Changing Environments under Verification Latency}, year = 2022 }
%0 Book Section %1 pham2022stream-based %A Pham, Tuan %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 152--164 %T Stream-Based Active Learning in Changing Environments under Verification Latency %X Labeling data often requires human interaction, which is slow and costly depending on the required expertise. Stream-based active learning reduces the cost of labeling for data stream mining by only querying the labels for instances that improve the classifier the most. However, state-of-the-art stream-based active learning strategies assume that verification latency, i.e., the time between a query and the availability of the queried label, is negligible. In reality, this is usually not the case as the experts who provide such labels are not available all the time, and the labeling process itself requires time. This article investigates the impact of verification latency within a stream-based active learning scenario for changing data streams. We focus on two different types of changes that occur over time, i.e., changes in the number of features and changes in the distribution of instances and/or labels. In particular, we identify challenges that occur due to verification latency and propose ideas to overcome these problems within a stream-based active learning strategy.
Stream-based active learning for sliding windows under the influence of verification latency.Pham, Tuan; Kottke, Daniel; Krempl, Georg; Sick, Bernhard. In Machine Learning, 111(6), ble. 2011–2036. Springer, 2022.
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Stream-based active learning (AL) strategies minimize the labeling effort by querying labels that improve the classifier's performance the most. So far, these strategies neglect the fact that an oracle or expert requires time to provide a queried label. We show that existing AL methods deteriorate or even fail under the influence of such verification latency. The problem with these methods is that they estimate a label's utility on the currently available labeled data. However, when this label would arrive, some of the current data may have gotten outdated and new labels have arrived. In this article, we propose to simulate the available data at the time when the label would arrive. Therefore, our method Forgetting and Simulating (FS) forgets outdated information and simulates the delayed labels to get more realistic utility estimates. We assume to know the label's arrival date a priori and the classifier's training data to be bounded by a sliding window. Our extensive experiments show that FS improves stream-based AL strategies in settings with both, constant and variable verification latency.
@article{pham2022stream, abstract = {Stream-based active learning (AL) strategies minimize the labeling effort by querying labels that improve the classifier's performance the most. So far, these strategies neglect the fact that an oracle or expert requires time to provide a queried label. We show that existing AL methods deteriorate or even fail under the influence of such verification latency. The problem with these methods is that they estimate a label's utility on the currently available labeled data. However, when this label would arrive, some of the current data may have gotten outdated and new labels have arrived. In this article, we propose to simulate the available data at the time when the label would arrive. Therefore, our method Forgetting and Simulating (FS) forgets outdated information and simulates the delayed labels to get more realistic utility estimates. We assume to know the label's arrival date a priori and the classifier's training data to be bounded by a sliding window. Our extensive experiments show that FS improves stream-based AL strategies in settings with both, constant and variable verification latency.}, author = {Pham, Tuan and Kottke, Daniel and Krempl, Georg and Sick, Bernhard}, journal = {Machine Learning}, keywords = {itegpub}, number = 6, pages = {2011–2036}, publisher = {Springer}, title = {Stream-based active learning for sliding windows under the influence of verification latency}, volume = 111, year = 2022 }
%0 Journal Article %1 pham2022stream %A Pham, Tuan %A Kottke, Daniel %A Krempl, Georg %A Sick, Bernhard %D 2022 %I Springer %J Machine Learning %N 6 %P 2011--2036 %R doi.org/10.1007/s10994-021-06099-z %T Stream-based active learning for sliding windows under the influence of verification latency %V 111 %X Stream-based active learning (AL) strategies minimize the labeling effort by querying labels that improve the classifier's performance the most. So far, these strategies neglect the fact that an oracle or expert requires time to provide a queried label. We show that existing AL methods deteriorate or even fail under the influence of such verification latency. The problem with these methods is that they estimate a label's utility on the currently available labeled data. However, when this label would arrive, some of the current data may have gotten outdated and new labels have arrived. In this article, we propose to simulate the available data at the time when the label would arrive. Therefore, our method Forgetting and Simulating (FS) forgets outdated information and simulates the delayed labels to get more realistic utility estimates. We assume to know the label's arrival date a priori and the classifier's training data to be bounded by a sliding window. Our extensive experiments show that FS improves stream-based AL strategies in settings with both, constant and variable verification latency.
Active Learning in Multivariate Time Series Anomaly Detection.Huang, Zhixin. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), ble. 113–124. kassel university press, 2022.
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Multivariate time series anomaly detection is an important research field of industry, science, and finances. In practical applications, unlabeled data is abundant, and labeled data is rare. Experts’ experience plays a key role in data labeling, while experts often have limited time, are expensive, and are concerned primarily with finding the potential anomalies. Active learning, which has been widely used to select the most informative data for labeling through experts, can improve training with less labeling effort. However, most active learning algorithms have not been evaluated on datasets with imbalanced class frequencies, such as anomaly detection. Although some semi-supervised and active learning methods were proposed to handle univariate time series data, not much work has been done on multivariate time series classification. Therefore, this article identifies critical challenges and proposes potential solutions for active learning in multivariate time series anomaly detection. We combine all possible solutions into a framework that conforms to the scope of the observation/control system in organic computing.
@incollection{huang2022active, abstract = {Multivariate time series anomaly detection is an important research field of industry, science, and finances. In practical applications, unlabeled data is abundant, and labeled data is rare. Experts’ experience plays a key role in data labeling, while experts often have limited time, are expensive, and are concerned primarily with finding the potential anomalies. Active learning, which has been widely used to select the most informative data for labeling through experts, can improve training with less labeling effort. However, most active learning algorithms have not been evaluated on datasets with imbalanced class frequencies, such as anomaly detection. Although some semi-supervised and active learning methods were proposed to handle univariate time series data, not much work has been done on multivariate time series classification. Therefore, this article identifies critical challenges and proposes potential solutions for active learning in multivariate time series anomaly detection. We combine all possible solutions into a framework that conforms to the scope of the observation/control system in organic computing.}, author = {Huang, Zhixin}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {113–124}, publisher = {kassel university press}, title = {Active Learning in Multivariate Time Series Anomaly Detection}, year = 2022 }
%0 Book Section %1 huang2022active %A Huang, Zhixin %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 113--124 %T Active Learning in Multivariate Time Series Anomaly Detection %X Multivariate time series anomaly detection is an important research field of industry, science, and finances. In practical applications, unlabeled data is abundant, and labeled data is rare. Experts’ experience plays a key role in data labeling, while experts often have limited time, are expensive, and are concerned primarily with finding the potential anomalies. Active learning, which has been widely used to select the most informative data for labeling through experts, can improve training with less labeling effort. However, most active learning algorithms have not been evaluated on datasets with imbalanced class frequencies, such as anomaly detection. Although some semi-supervised and active learning methods were proposed to handle univariate time series data, not much work has been done on multivariate time series classification. Therefore, this article identifies critical challenges and proposes potential solutions for active learning in multivariate time series anomaly detection. We combine all possible solutions into a framework that conforms to the scope of the observation/control system in organic computing.
A Review of Uncertainty Calibration in Pretrained Object Detectors.Huseljic, Denis; Herde, Marek; Muejde, Mehmet; Sick, Bernhard. In arXiv e-prints, bl. arXiv:2210.02935. 2022.
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In the field of deep learning based computer vision, the development of deep object detection has led to unique paradigms (e.g., two-stage or set-based) and architectures (e.g., Faster-RCNN or DETR) which enable outstanding performance on challenging benchmark datasets. Despite this, the trained object detectors typically do not reliably assess uncertainty regarding their own knowledge, and the quality of their probabilistic predictions is usually poor. As these are often used to make subsequent decisions, such inaccurate probabilistic predictions must be avoided. In this work, we investigate the uncertainty calibration properties of different pretrained object detection architectures in a multi-class setting. We propose a framework to ensure a fair, unbiased, and repeatable evaluation and conduct detailed analyses assessing the calibration under distributional changes (e.g., distributional shift and application to out-of-distribution data). Furthermore, by investigating the influence of different detector paradigms, post-processing steps, and suitable choices of metrics, we deliver novel insights into why poor detector calibration emerges. Based on these insights, we are able to improve the calibration of a detector by simply finetuning its last layer.
@article{huseljic2022review, abstract = {In the field of deep learning based computer vision, the development of deep object detection has led to unique paradigms (e.g., two-stage or set-based) and architectures (e.g., Faster-RCNN or DETR) which enable outstanding performance on challenging benchmark datasets. Despite this, the trained object detectors typically do not reliably assess uncertainty regarding their own knowledge, and the quality of their probabilistic predictions is usually poor. As these are often used to make subsequent decisions, such inaccurate probabilistic predictions must be avoided. In this work, we investigate the uncertainty calibration properties of different pretrained object detection architectures in a multi-class setting. We propose a framework to ensure a fair, unbiased, and repeatable evaluation and conduct detailed analyses assessing the calibration under distributional changes (e.g., distributional shift and application to out-of-distribution data). Furthermore, by investigating the influence of different detector paradigms, post-processing steps, and suitable choices of metrics, we deliver novel insights into why poor detector calibration emerges. Based on these insights, we are able to improve the calibration of a detector by simply finetuning its last layer.}, author = {Huseljic, Denis and Herde, Marek and Muejde, Mehmet and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2210.02935}, title = {A Review of Uncertainty Calibration in Pretrained Object Detectors}, year = 2022 }
%0 Journal Article %1 huseljic2022review %A Huseljic, Denis %A Herde, Marek %A Muejde, Mehmet %A Sick, Bernhard %D 2022 %J arXiv e-prints %P arXiv:2210.02935 %T A Review of Uncertainty Calibration in Pretrained Object Detectors %U https://arxiv.org/abs/2210.02935 %X In the field of deep learning based computer vision, the development of deep object detection has led to unique paradigms (e.g., two-stage or set-based) and architectures (e.g., Faster-RCNN or DETR) which enable outstanding performance on challenging benchmark datasets. Despite this, the trained object detectors typically do not reliably assess uncertainty regarding their own knowledge, and the quality of their probabilistic predictions is usually poor. As these are often used to make subsequent decisions, such inaccurate probabilistic predictions must be avoided. In this work, we investigate the uncertainty calibration properties of different pretrained object detection architectures in a multi-class setting. We propose a framework to ensure a fair, unbiased, and repeatable evaluation and conduct detailed analyses assessing the calibration under distributional changes (e.g., distributional shift and application to out-of-distribution data). Furthermore, by investigating the influence of different detector paradigms, post-processing steps, and suitable choices of metrics, we deliver novel insights into why poor detector calibration emerges. Based on these insights, we are able to improve the calibration of a detector by simply finetuning its last layer.
A Practical Evaluation of Active Learning Approaches for Object Detection.Schneegans, Jan; Bieshaar, Maarten; Sick, Bernhard. In Workshop on Interactive Adaptive Learning (IAL), ECML PKDD, ble. 49–67. 2022.
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Deep learning models are trained most successfully in a supervised manner, which requires vast amounts of annotated data. With active learning, the annotation process can be made much more efficient by intelligently selecting the most valuable batches of samples to annotate and train on. Those samples are selected based on their utility regarding the training algorithm. In this work, we examine a wide range of such selection criteria for the task of object detection as performed by the widely applied Faster R-CNN model. We focus on the large and diverse BDD100K autonomous driving dataset, paying special attention to the model’s sub-tasks and the dataset’s meta information. Furthermore, we distinguish between approaches that select samples based on aleatoric or epistemic uncertainty. A selection of evaluation measures that cover specific error sources and the overall model performance suggests that there is little difference between the individual active learning approaches, even in regards to their specialized focus on different model parts and sub-tasks. We conclude with a detailed discussion of the implied mechanisms regarding the active learning approaches that seem to affect model performances.
@inproceedings{schneegans2022practical, abstract = {Deep learning models are trained most successfully in a supervised manner, which requires vast amounts of annotated data. With active learning, the annotation process can be made much more efficient by intelligently selecting the most valuable batches of samples to annotate and train on. Those samples are selected based on their utility regarding the training algorithm. In this work, we examine a wide range of such selection criteria for the task of object detection as performed by the widely applied Faster R-CNN model. We focus on the large and diverse BDD100K autonomous driving dataset, paying special attention to the model’s sub-tasks and the dataset’s meta information. Furthermore, we distinguish between approaches that select samples based on aleatoric or epistemic uncertainty. A selection of evaluation measures that cover specific error sources and the overall model performance suggests that there is little difference between the individual active learning approaches, even in regards to their specialized focus on different model parts and sub-tasks. We conclude with a detailed discussion of the implied mechanisms regarding the active learning approaches that seem to affect model performances.}, author = {Schneegans, Jan and Bieshaar, Maarten and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {itegpub}, pages = {49–67}, title = {A Practical Evaluation of Active Learning Approaches for Object Detection}, year = 2022 }
%0 Conference Paper %1 schneegans2022practical %A Schneegans, Jan %A Bieshaar, Maarten %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2022 %P 49--67 %T A Practical Evaluation of Active Learning Approaches for Object Detection %U http://ceur-ws.org/Vol-3259/ialatecml_paper5.pdf %X Deep learning models are trained most successfully in a supervised manner, which requires vast amounts of annotated data. With active learning, the annotation process can be made much more efficient by intelligently selecting the most valuable batches of samples to annotate and train on. Those samples are selected based on their utility regarding the training algorithm. In this work, we examine a wide range of such selection criteria for the task of object detection as performed by the widely applied Faster R-CNN model. We focus on the large and diverse BDD100K autonomous driving dataset, paying special attention to the model’s sub-tasks and the dataset’s meta information. Furthermore, we distinguish between approaches that select samples based on aleatoric or epistemic uncertainty. A selection of evaluation measures that cover specific error sources and the overall model performance suggests that there is little difference between the individual active learning approaches, even in regards to their specialized focus on different model parts and sub-tasks. We conclude with a detailed discussion of the implied mechanisms regarding the active learning approaches that seem to affect model performances.
Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving.Rösch, Kevin; Heidecker, Florian; Truetsch, Julian; Kowol, Kamil; Schicktanz, Clemens; Bieshaar, Maarten; Sick, Bernhard; Stiller, Christoph. In IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (IEEE CIVTS), IEEE SSCI, ble. 86–93. IEEE, 2022.
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Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users’ movement and behavior patterns. Based on these predictions — and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users — the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy.
@inproceedings{rosch2022space, abstract = {Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users’ movement and behavior patterns. Based on these predictions — and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users — the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy.}, author = {Rösch, Kevin and Heidecker, Florian and Truetsch, Julian and Kowol, Kamil and Schicktanz, Clemens and Bieshaar, Maarten and Sick, Bernhard and Stiller, Christoph}, booktitle = {IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (IEEE CIVTS), IEEE SSCI}, keywords = {itegpub}, pages = {86–93}, publisher = {IEEE}, title = {Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving}, year = 2022 }
%0 Conference Paper %1 rosch2022space %A Rösch, Kevin %A Heidecker, Florian %A Truetsch, Julian %A Kowol, Kamil %A Schicktanz, Clemens %A Bieshaar, Maarten %A Sick, Bernhard %A Stiller, Christoph %B IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (IEEE CIVTS), IEEE SSCI %D 2022 %I IEEE %P 86--93 %R 10.1109/SSCI51031.2022.10022241 %T Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving %U https://ieeexplore.ieee.org/document/10022241 %X Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users’ movement and behavior patterns. Based on these predictions — and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users — the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy.
Social Machines.Draude, Claude; Gruhl, Christian; Hornung, Gerrit; Kropf, Jonathan; Lamla, Jörn; Leimeister, Jan Marco; Sick, Bernhard; Stumme, Gerd. In Informatik Spektrum, 45(1), ble. 38–42. Springer, 2022.
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Social Machines sind ein Paradigma für die Gestaltung soziotechnischer Systeme, die unter Verwendung von Web- und Plattformlösungen das Potenzial digitaler Technologien mit der Eigenlogik sozialer Interaktion, Organisation und Struk- turbildung auf neue Weise zusammenführen. Im Folgenden diskutieren wir das Paradigma der Social Machine aus den Perspektiven der Informatik, der Wirtschaftsinformatik, der Soziologie und des Rechts, um Orientierungspunkte für seine Gestaltung zu identifizieren. Der Begriff ist in der Literatur jedoch bisher nicht abschließend definiert sondern nur durch Beispiele illustriert. In diesem Artikel stellen wir zunächst die folgende Definition zur Diskussion: Social Machines sind soziotechnische Sys- teme, in denen die Prozesse sozialer Interaktion hybrid zwischen menschlichen und maschinellen Akteuren ablaufen und teilweise algorithmisiert sind. Im Anschluss beleuchten wir drei aktuelle, sich gegenseitig bedingende Entwicklungen von Social Machines: die immer stärkere Verschmelzung von Sozialität und Maschine, die Vermessung von Nutzeraktivitäten als Grundstoff gesellschaftlichen Zusammenhalts und die zunehmende Algorithmisierung gesellschaftlicher Prozesse. Ab- schließend diskutieren wir, dass eine teilhabeorientierte, demokratischen Werten folgende Gestaltung von Social Machines die Perspektiven der Nutzungsakzeptanz, der gesellschaftlichen Akzeptabilität und der nachhaltigen Wirtschaftlichkeit adressieren und umsetzen muss.
@article{draude2022social, abstract = {Social Machines sind ein Paradigma für die Gestaltung soziotechnischer Systeme, die unter Verwendung von Web- und Plattformlösungen das Potenzial digitaler Technologien mit der Eigenlogik sozialer Interaktion, Organisation und Struk- turbildung auf neue Weise zusammenführen. Im Folgenden diskutieren wir das Paradigma der Social Machine aus den Perspektiven der Informatik, der Wirtschaftsinformatik, der Soziologie und des Rechts, um Orientierungspunkte für seine Gestaltung zu identifizieren. Der Begriff ist in der Literatur jedoch bisher nicht abschließend definiert sondern nur durch Beispiele illustriert. In diesem Artikel stellen wir zunächst die folgende Definition zur Diskussion: Social Machines sind soziotechnische Sys- teme, in denen die Prozesse sozialer Interaktion hybrid zwischen menschlichen und maschinellen Akteuren ablaufen und teilweise algorithmisiert sind. Im Anschluss beleuchten wir drei aktuelle, sich gegenseitig bedingende Entwicklungen von Social Machines: die immer stärkere Verschmelzung von Sozialität und Maschine, die Vermessung von Nutzeraktivitäten als Grundstoff gesellschaftlichen Zusammenhalts und die zunehmende Algorithmisierung gesellschaftlicher Prozesse. Ab- schließend diskutieren wir, dass eine teilhabeorientierte, demokratischen Werten folgende Gestaltung von Social Machines die Perspektiven der Nutzungsakzeptanz, der gesellschaftlichen Akzeptabilität und der nachhaltigen Wirtschaftlichkeit adressieren und umsetzen muss.}, author = {Draude, Claude and Gruhl, Christian and Hornung, Gerrit and Kropf, Jonathan and Lamla, Jörn and Leimeister, Jan Marco and Sick, Bernhard and Stumme, Gerd}, journal = {Informatik Spektrum}, keywords = {itegpub}, number = 1, pages = {38–42}, publisher = {Springer}, title = {Social Machines}, volume = 45, year = 2022 }
%0 Journal Article %1 draude2022social %A Draude, Claude %A Gruhl, Christian %A Hornung, Gerrit %A Kropf, Jonathan %A Lamla, Jörn %A Leimeister, Jan Marco %A Sick, Bernhard %A Stumme, Gerd %D 2022 %I Springer %J Informatik Spektrum %N 1 %P 38--42 %R https://doi.org/10.1007/s00287-021-01421-4 %T Social Machines %V 45 %X Social Machines sind ein Paradigma für die Gestaltung soziotechnischer Systeme, die unter Verwendung von Web- und Plattformlösungen das Potenzial digitaler Technologien mit der Eigenlogik sozialer Interaktion, Organisation und Struk- turbildung auf neue Weise zusammenführen. Im Folgenden diskutieren wir das Paradigma der Social Machine aus den Perspektiven der Informatik, der Wirtschaftsinformatik, der Soziologie und des Rechts, um Orientierungspunkte für seine Gestaltung zu identifizieren. Der Begriff ist in der Literatur jedoch bisher nicht abschließend definiert sondern nur durch Beispiele illustriert. In diesem Artikel stellen wir zunächst die folgende Definition zur Diskussion: Social Machines sind soziotechnische Sys- teme, in denen die Prozesse sozialer Interaktion hybrid zwischen menschlichen und maschinellen Akteuren ablaufen und teilweise algorithmisiert sind. Im Anschluss beleuchten wir drei aktuelle, sich gegenseitig bedingende Entwicklungen von Social Machines: die immer stärkere Verschmelzung von Sozialität und Maschine, die Vermessung von Nutzeraktivitäten als Grundstoff gesellschaftlichen Zusammenhalts und die zunehmende Algorithmisierung gesellschaftlicher Prozesse. Ab- schließend diskutieren wir, dass eine teilhabeorientierte, demokratischen Werten folgende Gestaltung von Social Machines die Perspektiven der Nutzungsakzeptanz, der gesellschaftlichen Akzeptabilität und der nachhaltigen Wirtschaftlichkeit adressieren und umsetzen muss.
Actively Controlling and Redesigning Experiments using the Application Case of Free-Electron Laser Pulse Characterization.Dingel, Kristina. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), ble. 86–98. kassel university press, 2022.
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Experiments are the elective tool for developing new technologies, testing hypotheses, and designing new materials in many scientific fields. Thereby, it is state-of-the-art to create experiments traditionally, starting with a hypothesis leading to the product or relevant data after several experiment cycles. There are some techniques such as automation, parallelization, or inverse design to accelerate experiments. Particularly interesting, however, is the approach to integrate immediate data analysis into the experiment cycle. By analyzing experiment output data on-site using, e.g., neural networks, one can make inferences regarding the experiment's current settings at run-time and furthermore improve them. In the following research project, the goal is to develop and incorporate a data analysis stage into an experimental setup, which can be actively adapted and designed on-site. To demonstrate the developed method, the experimental design of a self-amplification of spontaneous emission pulse characterization experiment at a free-electron laser will be used to show to what extent the experiment can be adapted and optimized.
@incollection{dingel2022actively, abstract = {Experiments are the elective tool for developing new technologies, testing hypotheses, and designing new materials in many scientific fields. Thereby, it is state-of-the-art to create experiments traditionally, starting with a hypothesis leading to the product or relevant data after several experiment cycles. There are some techniques such as automation, parallelization, or inverse design to accelerate experiments. Particularly interesting, however, is the approach to integrate immediate data analysis into the experiment cycle. By analyzing experiment output data on-site using, e.g., neural networks, one can make inferences regarding the experiment's current settings at run-time and furthermore improve them. In the following research project, the goal is to develop and incorporate a data analysis stage into an experimental setup, which can be actively adapted and designed on-site. To demonstrate the developed method, the experimental design of a self-amplification of spontaneous emission pulse characterization experiment at a free-electron laser will be used to show to what extent the experiment can be adapted and optimized.}, author = {Dingel, Kristina}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {86–98}, publisher = {kassel university press}, title = {Actively Controlling and Redesigning Experiments using the Application Case of Free-Electron Laser Pulse Characterization}, year = 2022 }
%0 Book Section %1 dingel2022actively %A Dingel, Kristina %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 86--98 %T Actively Controlling and Redesigning Experiments using the Application Case of Free-Electron Laser Pulse Characterization %X Experiments are the elective tool for developing new technologies, testing hypotheses, and designing new materials in many scientific fields. Thereby, it is state-of-the-art to create experiments traditionally, starting with a hypothesis leading to the product or relevant data after several experiment cycles. There are some techniques such as automation, parallelization, or inverse design to accelerate experiments. Particularly interesting, however, is the approach to integrate immediate data analysis into the experiment cycle. By analyzing experiment output data on-site using, e.g., neural networks, one can make inferences regarding the experiment's current settings at run-time and furthermore improve them. In the following research project, the goal is to develop and incorporate a data analysis stage into an experimental setup, which can be actively adapted and designed on-site. To demonstrate the developed method, the experimental design of a self-amplification of spontaneous emission pulse characterization experiment at a free-electron laser will be used to show to what extent the experiment can be adapted and optimized.
Deep Adaptive Knowledge-Aware Learning for E-Mobility.Ali, Mohammad Wazed. In Organic Computing - Doctoral Dissertation Colloquium 2022, S. Tomforde, C. Krupitzer (reds.), ble. 139–155. kassel university press, 2022.
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Electromobility can reduce global energy demand by reducing the dependence on fossil fuels. Many different types of electric vehicles (EVs) have been produced recently and EV usage is also rising. However, there is no fixed charging behavior of these EV users. Moreover, most renewable energy sources are stochastic in nature. Thus, it is essential that EV charging demand is controlled and adapted in advance to prevent catastrophic failures such as blackouts. Deep learning models are standard technology for power forecasting in many scenarios. However, there is a lack of real-world heterogeneous multi-source data in the e-mobility domain to train a deep learning model from scratch. Moreover, deep learning (DL) models lack model explainability. Therefore, EV stakeholders can benefit from a framework that coordinates deep transfer learning, interactive machine learning, and heterogeneous graph representation learning (to ensure self-explainability). This work aims to develop a novel framework to solve e-mobility-related tasks such as forecasting EV loads efficiently in a resource-constrained environment (data, computing power, etc.). It also describe various research challenges and potential approaches to build the proposed framework. This work also present a deep generative model to synthesize EV charging session data.
@incollection{ali2022deep, abstract = {Electromobility can reduce global energy demand by reducing the dependence on fossil fuels. Many different types of electric vehicles (EVs) have been produced recently and EV usage is also rising. However, there is no fixed charging behavior of these EV users. Moreover, most renewable energy sources are stochastic in nature. Thus, it is essential that EV charging demand is controlled and adapted in advance to prevent catastrophic failures such as blackouts. Deep learning models are standard technology for power forecasting in many scenarios. However, there is a lack of real-world heterogeneous multi-source data in the e-mobility domain to train a deep learning model from scratch. Moreover, deep learning (DL) models lack model explainability. Therefore, EV stakeholders can benefit from a framework that coordinates deep transfer learning, interactive machine learning, and heterogeneous graph representation learning (to ensure self-explainability). This work aims to develop a novel framework to solve e-mobility-related tasks such as forecasting EV loads efficiently in a resource-constrained environment (data, computing power, etc.). It also describe various research challenges and potential approaches to build the proposed framework. This work also present a deep generative model to synthesize EV charging session data.}, author = {Ali, Mohammad Wazed}, booktitle = {Organic Computing - Doctoral Dissertation Colloquium 2022}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {139–155}, publisher = {kassel university press}, title = {Deep Adaptive Knowledge-Aware Learning for E-Mobility}, year = 2022 }
%0 Book Section %1 ali2022deep %A Ali, Mohammad Wazed %B Organic Computing - Doctoral Dissertation Colloquium 2022 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 139--155 %T Deep Adaptive Knowledge-Aware Learning for E-Mobility %X Electromobility can reduce global energy demand by reducing the dependence on fossil fuels. Many different types of electric vehicles (EVs) have been produced recently and EV usage is also rising. However, there is no fixed charging behavior of these EV users. Moreover, most renewable energy sources are stochastic in nature. Thus, it is essential that EV charging demand is controlled and adapted in advance to prevent catastrophic failures such as blackouts. Deep learning models are standard technology for power forecasting in many scenarios. However, there is a lack of real-world heterogeneous multi-source data in the e-mobility domain to train a deep learning model from scratch. Moreover, deep learning (DL) models lack model explainability. Therefore, EV stakeholders can benefit from a framework that coordinates deep transfer learning, interactive machine learning, and heterogeneous graph representation learning (to ensure self-explainability). This work aims to develop a novel framework to solve e-mobility-related tasks such as forecasting EV loads efficiently in a resource-constrained environment (data, computing power, etc.). It also describe various research challenges and potential approaches to build the proposed framework. This work also present a deep generative model to synthesize EV charging session data.
Optimizing a superconducting radio-frequency gun using deep reinforcement learning.Meier, David; Ramirez, Luis Vera; Völker, Jens; Viefhaus, Jens; Sick, Bernhard; Hartmann, Gregor. In Physical Review Accelerators and Beams, 25(10), bl. 104604. American Physical Society, 2022.
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Superconducting photoelectron injectors are promising for generating highly brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultrafast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimizing the beam properties is challenging due to the high number of possible machine parameter combinations. This article shows the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the required computation time, we replace the costly simulation with a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms standard optimization methods for the required function evaluations given a defined minimum accuracy.
@article{meier2022optimizing, abstract = {Superconducting photoelectron injectors are promising for generating highly brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultrafast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimizing the beam properties is challenging due to the high number of possible machine parameter combinations. This article shows the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the required computation time, we replace the costly simulation with a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms standard optimization methods for the required function evaluations given a defined minimum accuracy.}, author = {Meier, David and Ramirez, Luis Vera and Völker, Jens and Viefhaus, Jens and Sick, Bernhard and Hartmann, Gregor}, journal = {Physical Review Accelerators and Beams}, keywords = {SimulationTechniques}, number = 10, pages = 104604, publisher = {American Physical Society}, title = {Optimizing a superconducting radio-frequency gun using deep reinforcement learning}, volume = 25, year = 2022 }
%0 Journal Article %1 meier2022optimizing %A Meier, David %A Ramirez, Luis Vera %A Völker, Jens %A Viefhaus, Jens %A Sick, Bernhard %A Hartmann, Gregor %D 2022 %I American Physical Society %J Physical Review Accelerators and Beams %N 10 %P 104604 %R 10.1103/PhysRevAccelBeams.25.104604 %T Optimizing a superconducting radio-frequency gun using deep reinforcement learning %U https://journals.aps.org/prab/abstract/10.1103/PhysRevAccelBeams.25.104604 %V 25 %X Superconducting photoelectron injectors are promising for generating highly brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultrafast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimizing the beam properties is challenging due to the high number of possible machine parameter combinations. This article shows the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the required computation time, we replace the costly simulation with a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms standard optimization methods for the required function evaluations given a defined minimum accuracy.
Detecting Corner Case in the Context of Highly Automated Driving.Heidecker, Florian. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), ble. 60–73. kassel university press, 2022.
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This article introduces a research proposal that aims to deﬁne, detect, and evaluate corner cases in the context of machine learning (ML) systems and highly automated driving. Corner cases are scenarios or data in a dataset that are recorded only very rarely and therefore only available in small quantities or not at all in the dataset. However, corner cases are very important for model training, as they are needed to improve the model performance in these rare scenarios. On the other hand, in safety- and security-critical applications, such as highly automated driving, corner case data is extremely important for testing and verifying the ML system. The research proposal addresses these challenges and focuses on the deﬁnition and categorization of corner cases. Based on this, ML methods for corner case detection are developed, focusing on object detection for automated driving and trajectory data for intention detection of pedestrians and cyclists. Finally, the research proposal addresses corner case metrics and the evaluation of the developed detection models.
@incollection{florianheidecker2022detecting, abstract = {This article introduces a research proposal that aims to deﬁne, detect, and evaluate corner cases in the context of machine learning (ML) systems and highly automated driving. Corner cases are scenarios or data in a dataset that are recorded only very rarely and therefore only available in small quantities or not at all in the dataset. However, corner cases are very important for model training, as they are needed to improve the model performance in these rare scenarios. On the other hand, in safety- and security-critical applications, such as highly automated driving, corner case data is extremely important for testing and verifying the ML system. The research proposal addresses these challenges and focuses on the deﬁnition and categorization of corner cases. Based on this, ML methods for corner case detection are developed, focusing on object detection for automated driving and trajectory data for intention detection of pedestrians and cyclists. Finally, the research proposal addresses corner case metrics and the evaluation of the developed detection models.}, author = {Heidecker, Florian}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {60–73}, publisher = {kassel university press}, title = {Detecting Corner Case in the Context of Highly Automated Driving}, year = 2022 }
%0 Book Section %1 florianheidecker2022detecting %A Heidecker, Florian %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 60--73 %T Detecting Corner Case in the Context of Highly Automated Driving %X This article introduces a research proposal that aims to deﬁne, detect, and evaluate corner cases in the context of machine learning (ML) systems and highly automated driving. Corner cases are scenarios or data in a dataset that are recorded only very rarely and therefore only available in small quantities or not at all in the dataset. However, corner cases are very important for model training, as they are needed to improve the model performance in these rare scenarios. On the other hand, in safety- and security-critical applications, such as highly automated driving, corner case data is extremely important for testing and verifying the ML system. The research proposal addresses these challenges and focuses on the deﬁnition and categorization of corner cases. Based on this, ML methods for corner case detection are developed, focusing on object detection for automated driving and trajectory data for intention detection of pedestrians and cyclists. Finally, the research proposal addresses corner case metrics and the evaluation of the developed detection models.
The Vision of Self-Management in Cognitive Organic Power Distribution Systems.Loeser, Inga; Braun, Martin; Gruhl, Christian; Menke, Jan-Hendrik; Sick, Bernhard; Tomforde, Sven. In Energies, 15(3), bl. 881. MDPI, 2022.
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Due to the ongoing trend towards a decarbonisation of energy use, the power system is expected to become the backbone of all energy sectors and thus the fundamental critical infrastructure. High penetration with distributed energy resources demands the coordination of a large number of prosumers, partly controlled by home energy management systems (HEMS), to be designed in such a way that the power system’s operational limits are not violated. On the grid level, distribution management systems (DMS) seek to keep the power system in the normal operational state. On the prosumer level, distributed HEMS optimise the internal power flows by setpoint specification of batteries, photovoltaic generators, or flexible loads. The vision of the ODiS (Organic Distribution System) initiative is to develop an architecture to operate a distribution grid reliably, with high resiliency, and fully autonomously by developing “organic” HEMS and DMS which possess multiple self-x capabilities, collectively referred to as self-management. Thus, ODiS seeks answers to the following question: How can we create the most appropriate models, techniques, and algorithms to develop novel kinds of self-configuring, self-organising, self-healing, and self-optimising DMS that are integrally coupled with the distributed HEMS? In this concept paper, the vision of ODiS is presented in detail based on a thorough review of the state of the art.
@article{loeser2022vision, abstract = {Due to the ongoing trend towards a decarbonisation of energy use, the power system is expected to become the backbone of all energy sectors and thus the fundamental critical infrastructure. High penetration with distributed energy resources demands the coordination of a large number of prosumers, partly controlled by home energy management systems (HEMS), to be designed in such a way that the power system’s operational limits are not violated. On the grid level, distribution management systems (DMS) seek to keep the power system in the normal operational state. On the prosumer level, distributed HEMS optimise the internal power flows by setpoint specification of batteries, photovoltaic generators, or flexible loads. The vision of the ODiS (Organic Distribution System) initiative is to develop an architecture to operate a distribution grid reliably, with high resiliency, and fully autonomously by developing “organic” HEMS and DMS which possess multiple self-x capabilities, collectively referred to as self-management. Thus, ODiS seeks answers to the following question: How can we create the most appropriate models, techniques, and algorithms to develop novel kinds of self-configuring, self-organising, self-healing, and self-optimising DMS that are integrally coupled with the distributed HEMS? In this concept paper, the vision of ODiS is presented in detail based on a thorough review of the state of the art.}, author = {Loeser, Inga and Braun, Martin and Gruhl, Christian and Menke, Jan-Hendrik and Sick, Bernhard and Tomforde, Sven}, journal = {Energies}, keywords = {self-healing}, number = 3, pages = 881, publisher = {MDPI}, title = {The Vision of Self-Management in Cognitive Organic Power Distribution Systems}, volume = 15, year = 2022 }
%0 Journal Article %1 loeser2022vision %A Loeser, Inga %A Braun, Martin %A Gruhl, Christian %A Menke, Jan-Hendrik %A Sick, Bernhard %A Tomforde, Sven %D 2022 %I MDPI %J Energies %N 3 %P 881 %R 10.3390/en15030881 %T The Vision of Self-Management in Cognitive Organic Power Distribution Systems %U https://www.mdpi.com/1996-1073/15/3/881 %V 15 %X Due to the ongoing trend towards a decarbonisation of energy use, the power system is expected to become the backbone of all energy sectors and thus the fundamental critical infrastructure. High penetration with distributed energy resources demands the coordination of a large number of prosumers, partly controlled by home energy management systems (HEMS), to be designed in such a way that the power system’s operational limits are not violated. On the grid level, distribution management systems (DMS) seek to keep the power system in the normal operational state. On the prosumer level, distributed HEMS optimise the internal power flows by setpoint specification of batteries, photovoltaic generators, or flexible loads. The vision of the ODiS (Organic Distribution System) initiative is to develop an architecture to operate a distribution grid reliably, with high resiliency, and fully autonomously by developing “organic” HEMS and DMS which possess multiple self-x capabilities, collectively referred to as self-management. Thus, ODiS seeks answers to the following question: How can we create the most appropriate models, techniques, and algorithms to develop novel kinds of self-configuring, self-organising, self-healing, and self-optimising DMS that are integrally coupled with the distributed HEMS? In this concept paper, the vision of ODiS is presented in detail based on a thorough review of the state of the art.
Self-Aware Microsystems.Gruhl, Christian; Tomforde, Sven; Sick, Bernhard. In Workshop on Self-Improving System Integration (SISSY), ACSOS, ble. 126–127. IEEE, 2022.
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Besides infrastructure-based solutions, small-scale microsystems are increasingly connected and interoperating with others in changing environments. Following the same motivation as for self-adaptive systems, this demands novel attempts to counter complexity and provide autonomous decision freedom that paves the path towards resilient and flexible behaviour. In this article, we outline a vision for research on self-aware microsystems (SAM) that aims at providing technical solutions that are especially relevant for initiatives such as self-improving system integration.
@inproceedings{gruhl2022self, abstract = {Besides infrastructure-based solutions, small-scale microsystems are increasingly connected and interoperating with others in changing environments. Following the same motivation as for self-adaptive systems, this demands novel attempts to counter complexity and provide autonomous decision freedom that paves the path towards resilient and flexible behaviour. In this article, we outline a vision for research on self-aware microsystems (SAM) that aims at providing technical solutions that are especially relevant for initiatives such as self-improving system integration.}, author = {Gruhl, Christian and Tomforde, Sven and Sick, Bernhard}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {126–127}, publisher = {IEEE}, title = {Self-Aware Microsystems}, year = 2022 }
%0 Conference Paper %1 gruhl2022self %A Gruhl, Christian %A Tomforde, Sven %A Sick, Bernhard %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2022 %I IEEE %P 126--127 %R 10.1109/ACSOSC56246.2022.00045 %T Self-Aware Microsystems %U https://ieeexplore.ieee.org/abstract/document/9934876/ %X Besides infrastructure-based solutions, small-scale microsystems are increasingly connected and interoperating with others in changing environments. Following the same motivation as for self-adaptive systems, this demands novel attempts to counter complexity and provide autonomous decision freedom that paves the path towards resilient and flexible behaviour. In this article, we outline a vision for research on self-aware microsystems (SAM) that aims at providing technical solutions that are especially relevant for initiatives such as self-improving system integration.
Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning.Herde, Marek; Huang, Zhixin; Huseljic, Denis; Kottke, Daniel; Vogt, Stephan; Sick, Bernhard. In arXiv e-prints, bl. arXiv:2210.06112. 2022.
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Retraining deep neural networks when new data arrives is typically computationally expensive. Moreover, certain applications do not allow such costly retraining due to time or computational constraints. Fast Bayesian updates are a possible solution to this issue. Therefore, we propose a Bayesian update based on Monte-Carlo samples and a last-layer Laplace approximation for different Bayesian neural network types, i.e., Dropout, Ensemble, and Spectral Normalized Neural Gaussian Process (SNGP). In a large-scale evaluation study, we show that our updates combined with SNGP represent a fast and competitive alternative to costly retraining. As a use case, we combine the Bayesian updates for SNGP with different sequential query strategies to exemplarily demonstrate their improved selection performance in active learning.
@article{herde2022fast, abstract = {Retraining deep neural networks when new data arrives is typically computationally expensive. Moreover, certain applications do not allow such costly retraining due to time or computational constraints. Fast Bayesian updates are a possible solution to this issue. Therefore, we propose a Bayesian update based on Monte-Carlo samples and a last-layer Laplace approximation for different Bayesian neural network types, i.e., Dropout, Ensemble, and Spectral Normalized Neural Gaussian Process (SNGP). In a large-scale evaluation study, we show that our updates combined with SNGP represent a fast and competitive alternative to costly retraining. As a use case, we combine the Bayesian updates for SNGP with different sequential query strategies to exemplarily demonstrate their improved selection performance in active learning.}, author = {Herde, Marek and Huang, Zhixin and Huseljic, Denis and Kottke, Daniel and Vogt, Stephan and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2210.06112}, title = {Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning}, year = 2022 }
%0 Journal Article %1 herde2022fast %A Herde, Marek %A Huang, Zhixin %A Huseljic, Denis %A Kottke, Daniel %A Vogt, Stephan %A Sick, Bernhard %D 2022 %J arXiv e-prints %P arXiv:2210.06112 %T Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning %U https://arxiv.org/abs/2210.06112 %X Retraining deep neural networks when new data arrives is typically computationally expensive. Moreover, certain applications do not allow such costly retraining due to time or computational constraints. Fast Bayesian updates are a possible solution to this issue. Therefore, we propose a Bayesian update based on Monte-Carlo samples and a last-layer Laplace approximation for different Bayesian neural network types, i.e., Dropout, Ensemble, and Spectral Normalized Neural Gaussian Process (SNGP). In a large-scale evaluation study, we show that our updates combined with SNGP represent a fast and competitive alternative to costly retraining. As a use case, we combine the Bayesian updates for SNGP with different sequential query strategies to exemplarily demonstrate their improved selection performance in active learning.
Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems.Nivarthi, Chandana Priya. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), ble. 47–59. kassel university press, 2022.
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Transfer learning (TL), the next frontier in machine learning (ML), has gained much popularity in recent years, due to the various challenges faced in ML, like the requirement of vast amounts of training data, expensive and time-consuming labelling processes for data samples, and long training duration for models. TL is useful in tackling these problems, as it focuses on transferring knowledge from previously solved tasks to new tasks. Digital twins and other intelligent systems need to utilise TL to use the previously gained knowledge and solve new tasks in a more self-reliant way, and to incrementally increase their knowledge base. Therefore, in this article, the critical challenges in power forecasting and anomaly detection in the context of renewable energy systems are identified, and a potential TL framework to meet these challenges is proposed. This article also proposes a feature embedding approach to handle the missing sensors data. The proposed TL methods help to make a system more autonomous in the context of organic computing.
@incollection{nivarthi2022transfer, abstract = {Transfer learning (TL), the next frontier in machine learning (ML), has gained much popularity in recent years, due to the various challenges faced in ML, like the requirement of vast amounts of training data, expensive and time-consuming labelling processes for data samples, and long training duration for models. TL is useful in tackling these problems, as it focuses on transferring knowledge from previously solved tasks to new tasks. Digital twins and other intelligent systems need to utilise TL to use the previously gained knowledge and solve new tasks in a more self-reliant way, and to incrementally increase their knowledge base. Therefore, in this article, the critical challenges in power forecasting and anomaly detection in the context of renewable energy systems are identified, and a potential TL framework to meet these challenges is proposed. This article also proposes a feature embedding approach to handle the missing sensors data. The proposed TL methods help to make a system more autonomous in the context of organic computing.}, author = {Nivarthi, Chandana Priya}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {47–59}, publisher = {kassel university press}, title = {Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems}, year = 2022 }
%0 Book Section %1 nivarthi2022transfer %A Nivarthi, Chandana Priya %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 47--59 %T Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems %X Transfer learning (TL), the next frontier in machine learning (ML), has gained much popularity in recent years, due to the various challenges faced in ML, like the requirement of vast amounts of training data, expensive and time-consuming labelling processes for data samples, and long training duration for models. TL is useful in tackling these problems, as it focuses on transferring knowledge from previously solved tasks to new tasks. Digital twins and other intelligent systems need to utilise TL to use the previously gained knowledge and solve new tasks in a more self-reliant way, and to incrementally increase their knowledge base. Therefore, in this article, the critical challenges in power forecasting and anomaly detection in the context of renewable energy systems are identified, and a potential TL framework to meet these challenges is proposed. This article also proposes a feature embedding approach to handle the missing sensors data. The proposed TL methods help to make a system more autonomous in the context of organic computing.
Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning.He, Yujiang; Huang, Zhixin; Sick, Bernhard. In Workshop on Interactive Machine Learning Workshop (IMLW), AAAI, ble. 1–6. 2022.
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Continual learning can enable neural networks to evolve by learning new tasks sequentially in task-changing scenarios. However, two general challenges should be overcome in further research before we apply this technique to real-world applications. Firstly, newly collected novelties from the data stream in applications could contain anomalies that are meaningless for continual learning. Instead of viewing them as a new task for updating, we have to filter out such anomalies to reduce the disturbance of extremely high-entropy data for the progression of convergence. Secondly, fewer efforts have been put into research regarding the explainability of continual learning, which leads to a lack of transparency and credibility of the updated neural networks. Elaborated explanations about the process and result of continual learning can help experts in judgment and making decisions. Therefore, we propose the conceptual design of an explainability module with experts in the loop based on techniques, such as dimension reduction, visualization, and evaluation strategies. This work aims to overcome the mentioned challenges by sufficiently explaining and visualizing the identified anomalies and the updated neural network. With the help of this module, experts can be more confident in decision-making regarding anomaly filtering, dynamic adjustment of hyperparameters, data backup, etc.
@inproceedings{he2022design, abstract = {Continual learning can enable neural networks to evolve by learning new tasks sequentially in task-changing scenarios. However, two general challenges should be overcome in further research before we apply this technique to real-world applications. Firstly, newly collected novelties from the data stream in applications could contain anomalies that are meaningless for continual learning. Instead of viewing them as a new task for updating, we have to filter out such anomalies to reduce the disturbance of extremely high-entropy data for the progression of convergence. Secondly, fewer efforts have been put into research regarding the explainability of continual learning, which leads to a lack of transparency and credibility of the updated neural networks. Elaborated explanations about the process and result of continual learning can help experts in judgment and making decisions. Therefore, we propose the conceptual design of an explainability module with experts in the loop based on techniques, such as dimension reduction, visualization, and evaluation strategies. This work aims to overcome the mentioned challenges by sufficiently explaining and visualizing the identified anomalies and the updated neural network. With the help of this module, experts can be more confident in decision-making regarding anomaly filtering, dynamic adjustment of hyperparameters, data backup, etc.}, author = {He, Yujiang and Huang, Zhixin and Sick, Bernhard}, booktitle = {Workshop on Interactive Machine Learning Workshop (IMLW), AAAI}, keywords = {itegpub}, pages = {1–6}, title = {Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning}, year = 2022 }
%0 Conference Paper %1 he2022design %A He, Yujiang %A Huang, Zhixin %A Sick, Bernhard %B Workshop on Interactive Machine Learning Workshop (IMLW), AAAI %D 2022 %P 1--6 %T Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning %U https://arxiv.org/abs/2202.06781 %X Continual learning can enable neural networks to evolve by learning new tasks sequentially in task-changing scenarios. However, two general challenges should be overcome in further research before we apply this technique to real-world applications. Firstly, newly collected novelties from the data stream in applications could contain anomalies that are meaningless for continual learning. Instead of viewing them as a new task for updating, we have to filter out such anomalies to reduce the disturbance of extremely high-entropy data for the progression of convergence. Secondly, fewer efforts have been put into research regarding the explainability of continual learning, which leads to a lack of transparency and credibility of the updated neural networks. Elaborated explanations about the process and result of continual learning can help experts in judgment and making decisions. Therefore, we propose the conceptual design of an explainability module with experts in the loop based on techniques, such as dimension reduction, visualization, and evaluation strategies. This work aims to overcome the mentioned challenges by sufficiently explaining and visualizing the identified anomalies and the updated neural network. With the help of this module, experts can be more confident in decision-making regarding anomaly filtering, dynamic adjustment of hyperparameters, data backup, etc.
Self-Adaptive Charging Management in Electric Vehicle Infrastructures based on Reinforcement Learning.Hassouna, Mohamed. In Organic Computing -- Doctoral Dissertation Colloquium 2022, S. Tomforde, C. Krupitzer (reds.), ble. 1–16. kassel university press, 2022.
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Electromobility plays an increasingly important role in the energy transition. Electric vehicle charging poses challenges to the electricity grid due to high peak demand situations. Therefore, it is crucial to leverage the vehicle's flexibility when charging can be delayed. Conventional charging management systems cannot adapt to frequently changing conditions such as fluctuating renewable energy output, electricity prices and user behavior. In this article, we investigate the application of reinforcement learning approaches for self-adaptive charging management. In particular, we identify challenges regarding realistic environments and adaption to varying topologies and connections among charging stations. We describe related approaches and propose ideas and planned experiments to overcome these problems by utilizing generative models and graph neural networks.
@incollection{hassouna2022self, abstract = {Electromobility plays an increasingly important role in the energy transition. Electric vehicle charging poses challenges to the electricity grid due to high peak demand situations. Therefore, it is crucial to leverage the vehicle's flexibility when charging can be delayed. Conventional charging management systems cannot adapt to frequently changing conditions such as fluctuating renewable energy output, electricity prices and user behavior. In this article, we investigate the application of reinforcement learning approaches for self-adaptive charging management. In particular, we identify challenges regarding realistic environments and adaption to varying topologies and connections among charging stations. We describe related approaches and propose ideas and planned experiments to overcome these problems by utilizing generative models and graph neural networks.}, author = {Hassouna, Mohamed}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2022}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {1–16}, publisher = {kassel university press}, title = {Self-Adaptive Charging Management in Electric Vehicle Infrastructures based on Reinforcement Learning}, year = 2022 }
%0 Book Section %1 hassouna2022self %A Hassouna, Mohamed %B Organic Computing -- Doctoral Dissertation Colloquium 2022 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 1--16 %T Self-Adaptive Charging Management in Electric Vehicle Infrastructures based on Reinforcement Learning %X Electromobility plays an increasingly important role in the energy transition. Electric vehicle charging poses challenges to the electricity grid due to high peak demand situations. Therefore, it is crucial to leverage the vehicle's flexibility when charging can be delayed. Conventional charging management systems cannot adapt to frequently changing conditions such as fluctuating renewable energy output, electricity prices and user behavior. In this article, we investigate the application of reinforcement learning approaches for self-adaptive charging management. In particular, we identify challenges regarding realistic environments and adaption to varying topologies and connections among charging stations. We describe related approaches and propose ideas and planned experiments to overcome these problems by utilizing generative models and graph neural networks.
Heterogeneous Multi-Source Deep Adaptive Knowledge-Aware Learning for E-Mobility.Ali, Mohammad Wazed. In IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), ble. 57–60. IEEE, 2022.
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Electromobility (e-mobility) reduces global fossil fuel-based energy demand and promotes the usage of renewable energy sources. Different types of electric vehicle (EVs) production have increased recently due to their need. However, there is no fixed charging behavior, and demand exists. Uncontrolled EV charging may lead to a harmful impact on the power grid. Besides, most renewable energy sources depend highly on the weather and location. Thus, analyzing, forecasting, and adapting EV charging demand is essential to prevent power outages. Deep Learning (DL) models are a de facto standard technology for energy forecasting in many scenarios. Still, there is a lack of real-world data in the e-mobility domain to train a DL model from scratch. Besides, DL models have explainability issues, which can hinder decision-making. This article proposes a novel framework for addressing data scarcity issues, enabling continuous adaptation to change, and improving explainability.
@inproceedings{ali2022heterogeneous, abstract = {Electromobility (e-mobility) reduces global fossil fuel-based energy demand and promotes the usage of renewable energy sources. Different types of electric vehicle (EVs) production have increased recently due to their need. However, there is no fixed charging behavior, and demand exists. Uncontrolled EV charging may lead to a harmful impact on the power grid. Besides, most renewable energy sources depend highly on the weather and location. Thus, analyzing, forecasting, and adapting EV charging demand is essential to prevent power outages. Deep Learning (DL) models are a de facto standard technology for energy forecasting in many scenarios. Still, there is a lack of real-world data in the e-mobility domain to train a DL model from scratch. Besides, DL models have explainability issues, which can hinder decision-making. This article proposes a novel framework for addressing data scarcity issues, enabling continuous adaptation to change, and improving explainability.}, author = {Ali, Mohammad Wazed}, booktitle = {IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)}, keywords = {itegpub}, pages = {57–60}, publisher = {IEEE}, title = {Heterogeneous Multi-Source Deep Adaptive Knowledge-Aware Learning for E-Mobility}, year = 2022 }
%0 Conference Paper %1 ali2022heterogeneous %A Ali, Mohammad Wazed %B IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) %D 2022 %I IEEE %P 57--60 %R 10.1109/ACSOSC56246.2022.00030 %T Heterogeneous Multi-Source Deep Adaptive Knowledge-Aware Learning for E-Mobility %U https://ieeexplore.ieee.org/document/9935088 %X Electromobility (e-mobility) reduces global fossil fuel-based energy demand and promotes the usage of renewable energy sources. Different types of electric vehicle (EVs) production have increased recently due to their need. However, there is no fixed charging behavior, and demand exists. Uncontrolled EV charging may lead to a harmful impact on the power grid. Besides, most renewable energy sources depend highly on the weather and location. Thus, analyzing, forecasting, and adapting EV charging demand is essential to prevent power outages. Deep Learning (DL) models are a de facto standard technology for energy forecasting in many scenarios. Still, there is a lack of real-world data in the e-mobility domain to train a DL model from scratch. Besides, DL models have explainability issues, which can hinder decision-making. This article proposes a novel framework for addressing data scarcity issues, enabling continuous adaptation to change, and improving explainability.
Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses.Dingel, Kristina; Otto, Thorsten; Marder, Lutz; Funke, Lars; Held, Arne; Savio, Sara; Hans, Andreas; Hartmann, Gregor; Meier, David; Viefhaus, Jens; Sick, Bernhard; Ehresmann, Arno; Ilchen, Markus; Helml, Wolfram. In Scientific Reports, 12(1), ble. 1–14. Nature Publishing Group, 2022.
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X-ray free-electron lasers (XFELs) as the world’s brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time–energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.
@article{dingel2022artificial, abstract = {X-ray free-electron lasers (XFELs) as the world’s brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time–energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.}, author = {Dingel, Kristina and Otto, Thorsten and Marder, Lutz and Funke, Lars and Held, Arne and Savio, Sara and Hans, Andreas and Hartmann, Gregor and Meier, David and Viefhaus, Jens and Sick, Bernhard and Ehresmann, Arno and Ilchen, Markus and Helml, Wolfram}, journal = {Scientific Reports}, keywords = {itegpub}, number = 1, pages = {1–14}, publisher = {Nature Publishing Group}, title = {Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses}, volume = 12, year = 2022 }
%0 Journal Article %1 dingel2022artificial %A Dingel, Kristina %A Otto, Thorsten %A Marder, Lutz %A Funke, Lars %A Held, Arne %A Savio, Sara %A Hans, Andreas %A Hartmann, Gregor %A Meier, David %A Viefhaus, Jens %A Sick, Bernhard %A Ehresmann, Arno %A Ilchen, Markus %A Helml, Wolfram %D 2022 %I Nature Publishing Group %J Scientific Reports %N 1 %P 1--14 %R 10.1038/s41598-022-21646-x %T Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses %U https://www.nature.com/articles/s41598-022-21646-x %V 12 %X X-ray free-electron lasers (XFELs) as the world’s brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time–energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.
A Holistic View on Probabilistic Trajectory Forecasting -- Case Study: Cyclist Intention Detection.Zernetsch, Stefan; Reichert, Hannes; Kress, Viktor; Doll, Konrad; Sick, Bernhard. In IEEE Intelligent Vehicles Symposium (IV), ble. 265–272. IEEE, 2022.
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This article presents a holistic approach for probabilistic cyclist intention detection. By combining probabilities for the current cyclist motion states with a probabilistic ensemble trajectory forecast, we are able to generate reliable estimates of cyclists’ future positions. The probabilities are used as weights in the probabilistic ensemble trajectory forecast. The ensemble consists of specialized models, which produce individual forecasts in the form of Gaussian distributions under the assumption of a certain motion state of the cyclist (e.g. cyclist is starting). By weighting the specialized models, we create forecasts in the form of Gaussian mixtures that define regions within which the cyclists will reside with a certain probability. To evaluate our method, we rate the reliability, sharpness, and positional accuracy of our forecasted distributions. We compare our method to a widely used unimodal approach which produces forecasts in the form of Gaussian distributions and show that our method is able to produce more reliable and sharper outputs while retaining comparable positional accuracy. By comparing two different methods for basic movement detection, one based on the past cyclist trajectory and one using past image sequences, we demonstrate that the results can be further improved by incorporating video information. Our methods are evaluated using a dataset created at a public intersection. The code and the dataset are publicly available.
@inproceedings{zernetsch2022holistic, abstract = {This article presents a holistic approach for probabilistic cyclist intention detection. By combining probabilities for the current cyclist motion states with a probabilistic ensemble trajectory forecast, we are able to generate reliable estimates of cyclists’ future positions. The probabilities are used as weights in the probabilistic ensemble trajectory forecast. The ensemble consists of specialized models, which produce individual forecasts in the form of Gaussian distributions under the assumption of a certain motion state of the cyclist (e.g. cyclist is starting). By weighting the specialized models, we create forecasts in the form of Gaussian mixtures that define regions within which the cyclists will reside with a certain probability. To evaluate our method, we rate the reliability, sharpness, and positional accuracy of our forecasted distributions. We compare our method to a widely used unimodal approach which produces forecasts in the form of Gaussian distributions and show that our method is able to produce more reliable and sharper outputs while retaining comparable positional accuracy. By comparing two different methods for basic movement detection, one based on the past cyclist trajectory and one using past image sequences, we demonstrate that the results can be further improved by incorporating video information. Our methods are evaluated using a dataset created at a public intersection. The code and the dataset are publicly available.}, author = {Zernetsch, Stefan and Reichert, Hannes and Kress, Viktor and Doll, Konrad and Sick, Bernhard}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {itegpub}, pages = {265–272}, publisher = {IEEE}, title = {A Holistic View on Probabilistic Trajectory Forecasting – Case Study: Cyclist Intention Detection}, year = 2022 }
%0 Conference Paper %1 zernetsch2022holistic %A Zernetsch, Stefan %A Reichert, Hannes %A Kress, Viktor %A Doll, Konrad %A Sick, Bernhard %B IEEE Intelligent Vehicles Symposium (IV) %D 2022 %I IEEE %P 265--272 %R 10.1109/IV51971.2022.9827220 %T A Holistic View on Probabilistic Trajectory Forecasting -- Case Study: Cyclist Intention Detection %U https://ieeexplore.ieee.org/document/9827220 %X This article presents a holistic approach for probabilistic cyclist intention detection. By combining probabilities for the current cyclist motion states with a probabilistic ensemble trajectory forecast, we are able to generate reliable estimates of cyclists’ future positions. The probabilities are used as weights in the probabilistic ensemble trajectory forecast. The ensemble consists of specialized models, which produce individual forecasts in the form of Gaussian distributions under the assumption of a certain motion state of the cyclist (e.g. cyclist is starting). By weighting the specialized models, we create forecasts in the form of Gaussian mixtures that define regions within which the cyclists will reside with a certain probability. To evaluate our method, we rate the reliability, sharpness, and positional accuracy of our forecasted distributions. We compare our method to a widely used unimodal approach which produces forecasts in the form of Gaussian distributions and show that our method is able to produce more reliable and sharper outputs while retaining comparable positional accuracy. By comparing two different methods for basic movement detection, one based on the past cyclist trajectory and one using past image sequences, we demonstrate that the results can be further improved by incorporating video information. Our methods are evaluated using a dataset created at a public intersection. The code and the dataset are publicly available.

2021[ to top ]

Multi-annotator Probabilistic Active Learning.Herde, Marek; Kottke, Daniel; Huseljic, Denis; Sick, Bernhard. In International Conference on Pattern Recognition (ICPR), ble. 10281–10288. IEEE, 2021.
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@inproceedings{herde2021multi, author = {Herde, Marek and Kottke, Daniel and Huseljic, Denis and Sick, Bernhard}, booktitle = {International Conference on Pattern Recognition (ICPR)}, keywords = {itegpub}, pages = {10281–10288}, publisher = {IEEE}, title = {Multi-annotator Probabilistic Active Learning}, year = 2021 }
%0 Conference Paper %1 herde2021multi %A Herde, Marek %A Kottke, Daniel %A Huseljic, Denis %A Sick, Bernhard %B International Conference on Pattern Recognition (ICPR) %D 2021 %I IEEE %P 10281--10288 %R 10.1109/icpr48806.2021.9412298 %T Multi-annotator Probabilistic Active Learning
Emerging Relation Network and Task Embedding for Multi-Task Regression Problems.Schreiber, Jens; Sick, Bernhard. In International Conference on Pattern Recognition (ICPR), ble. 2663–2670. IEEE, 2021.
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@inproceedings{schreiber2021emerging, author = {Schreiber, Jens and Sick, Bernhard}, booktitle = {International Conference on Pattern Recognition (ICPR)}, keywords = {itegpub}, pages = {2663–2670}, publisher = {IEEE}, title = {Emerging Relation Network and Task Embedding for Multi-Task Regression Problems}, year = 2021 }
%0 Conference Paper %1 schreiber2021emerging %A Schreiber, Jens %A Sick, Bernhard %B International Conference on Pattern Recognition (ICPR) %D 2021 %I IEEE %P 2663--2670 %R 10.1109/icpr48806.2021.9412476 %T Emerging Relation Network and Task Embedding for Multi-Task Regression Problems
Description of Corner Cases in Automated Driving: Goals and Challenges.Bogdoll, Daniel; Breitenstein, Jasmin; Heidecker, Florian; Bieshaar, Maarten; Sick, Bernhard; Fingscheidt, Tim; Zöllner, J. Marius. In Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV, ble. 1023–1028,. IEEE, 2021.
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@inproceedings{bogdoll2021description, author = {Bogdoll, Daniel and Breitenstein, Jasmin and Heidecker, Florian and Bieshaar, Maarten and Sick, Bernhard and Fingscheidt, Tim and Zöllner, J. Marius}, booktitle = {Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV}, keywords = {itegpub}, pages = {1023–1028,}, publisher = {IEEE}, title = {Description of Corner Cases in Automated Driving: Goals and Challenges}, year = 2021 }
%0 Conference Paper %1 bogdoll2021description %A Bogdoll, Daniel %A Breitenstein, Jasmin %A Heidecker, Florian %A Bieshaar, Maarten %A Sick, Bernhard %A Fingscheidt, Tim %A Zöllner, J. Marius %B Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV %D 2021 %I IEEE %P 1023--1028, %R 10.1109/iccvw54120.2021.00119 %T Description of Corner Cases in Automated Driving: Goals and Challenges
Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks.Schneegans, Jan; Bieshaar, Maarten; Heidecker, Florian; Sick, Bernhard. In Workshop on Integrated Artificial Intelligence in Data Science, ICPR, ble. 375–389. IEEE, Milan, Italy, 2021.
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@inproceedings{schneegans2021intelligent, address = {Milan, Italy}, author = {Schneegans, Jan and Bieshaar, Maarten and Heidecker, Florian and Sick, Bernhard}, booktitle = {Workshop on Integrated Artificial Intelligence in Data Science, ICPR}, keywords = {itegpub}, pages = {375–389}, publisher = {IEEE}, title = {Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks}, year = 2021 }
%0 Conference Paper %1 schneegans2021intelligent %A Schneegans, Jan %A Bieshaar, Maarten %A Heidecker, Florian %A Sick, Bernhard %B Workshop on Integrated Artificial Intelligence in Data Science, ICPR %C Milan, Italy %D 2021 %I IEEE %P 375--389 %R 10.1007/978-3-030-68799-1_27 %T Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks
Cyclist Motion State Forecasting -- Going beyond Detection.Bieshaar, M.; Zernetsch, S.; Riepe, K.; Doll, K.; Sick, B. In IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Orlando, FL, USA, 2021.
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@inproceedings{bieshaar2021cyclist, address = {Orlando, FL, USA}, author = {Bieshaar, M. and Zernetsch, S. and Riepe, K. and Doll, K. and Sick, B.}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, keywords = {itegpub}, note = {(accepted)}, publisher = {IEEE}, title = {Cyclist Motion State Forecasting – Going beyond Detection}, year = 2021 }
%0 Conference Paper %1 bieshaar2021cyclist %A Bieshaar, M. %A Zernetsch, S. %A Riepe, K. %A Doll, K. %A Sick, B. %B IEEE Symposium Series on Computational Intelligence (SSCI) %C Orlando, FL, USA %D 2021 %I IEEE %T Cyclist Motion State Forecasting -- Going beyond Detection
OHODIN -- Online Anomaly Detection for Data Streams.Gruhl, Christian; Tomforde, Sven. In Workshop on Self-Improving System Integration (SISSY), ACSOS, ble. 193–197. IEEE, 2021.
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@inproceedings{gruhl2021ohodin, author = {Gruhl, Christian and Tomforde, Sven}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {193–197}, publisher = {IEEE}, title = {OHODIN – Online Anomaly Detection for Data Streams}, year = 2021 }
%0 Conference Paper %1 gruhl2021ohodin %A Gruhl, Christian %A Tomforde, Sven %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2021 %I IEEE %P 193--197 %R 10.1109/acsos-c52956.2021.00046 %T OHODIN -- Online Anomaly Detection for Data Streams
Novelty based Driver Identification on RR Intervals from ECG Data.Heidecker, Florian; Gruhl, Christian; Sick, Bernhard. In Workshop on Integrated Artificial Intelligence in Data Science, ICPR, ble. 407–421. IEEE, Milan, Italy, 2021.
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@inproceedings{heidecker2021novelty, address = {Milan, Italy}, author = {Heidecker, Florian and Gruhl, Christian and Sick, Bernhard}, booktitle = {Workshop on Integrated Artificial Intelligence in Data Science, ICPR}, keywords = {itegpub}, pages = {407–421}, publisher = {IEEE}, title = {Novelty based Driver Identification on RR Intervals from ECG Data}, year = 2021 }
%0 Conference Paper %1 heidecker2021novelty %A Heidecker, Florian %A Gruhl, Christian %A Sick, Bernhard %B Workshop on Integrated Artificial Intelligence in Data Science, ICPR %C Milan, Italy %D 2021 %I IEEE %P 407--421 %R 10.1007/978-3-030-68799-1_29 %T Novelty based Driver Identification on RR Intervals from ECG Data
Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information.Zernetsch, Stefan; Trupp, Oliver; Kress, Viktor; Doll, Konrad; Sick, Bernhard. In IEEE International Smart Cities Conference (ISC2), ble. 1–7. IEEE, 2021.
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@inproceedings{zernetsch2021cyclist, author = {Zernetsch, Stefan and Trupp, Oliver and Kress, Viktor and Doll, Konrad and Sick, Bernhard}, booktitle = {IEEE International Smart Cities Conference (ISC2)}, keywords = {itegpub}, pages = {1–7}, publisher = {IEEE}, title = {Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information}, year = 2021 }
%0 Conference Paper %1 zernetsch2021cyclist %A Zernetsch, Stefan %A Trupp, Oliver %A Kress, Viktor %A Doll, Konrad %A Sick, Bernhard %B IEEE International Smart Cities Conference (ISC2) %D 2021 %I IEEE %P 1--7 %R 10.1109/isc253183.2021.9562857 %T Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information
Novelty detection in continuously changing environments.Gruhl, Christian; Sick, Bernhard; Tomforde, Sven. In Future Generation Computer Systems, 114, ble. 138–154. Elsevier, 2021.
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Self-improving system integration (SISSY) aims at mastering the challenges of system organisation decisions for subsystems with highly dynamic behaviours. This is achieved by increased decision freedom of these subsystems, i.e., by moving the corresponding design decisions from design-time to run-time and from engineers to the systems themselves. As a result, a more robust and efficient constellation of the overall system organisation is maintained even under the presence of continuous change and disturbed conditions. Appropriate integration decisions require a solid foundation of assessing the current conditions and their dynamics using identifying triggers for a necessary evaluation of their own integration status. With this article, we present mCANDIES, a modular novelty detection technique especially suited for SISSY systems that fulfils exactly these identification tasks. mCANDIES tackles an anomaly or novelty detection problem in a modular way by combining the advantages of different detection approaches by exploiting locality in different regions of the input space — depending on, e.g. the density or location of expected observations. This is augmented with information about changes in the underlying model of the observations, i.e., mCANDIES identifies concept drifts. The corresponding information can then be used to trigger online learning behaviour. We analyse the behaviour of mCANDIES on artificial and real-world data sets and compare it to other techniques from the state of the art.
@article{gruhl2021novelty, abstract = {Self-improving system integration (SISSY) aims at mastering the challenges of system organisation decisions for subsystems with highly dynamic behaviours. This is achieved by increased decision freedom of these subsystems, i.e., by moving the corresponding design decisions from design-time to run-time and from engineers to the systems themselves. As a result, a more robust and efficient constellation of the overall system organisation is maintained even under the presence of continuous change and disturbed conditions. Appropriate integration decisions require a solid foundation of assessing the current conditions and their dynamics using identifying triggers for a necessary evaluation of their own integration status. With this article, we present mCANDIES, a modular novelty detection technique especially suited for SISSY systems that fulfils exactly these identification tasks. mCANDIES tackles an anomaly or novelty detection problem in a modular way by combining the advantages of different detection approaches by exploiting locality in different regions of the input space — depending on, e.g. the density or location of expected observations. This is augmented with information about changes in the underlying model of the observations, i.e., mCANDIES identifies concept drifts. The corresponding information can then be used to trigger online learning behaviour. We analyse the behaviour of mCANDIES on artificial and real-world data sets and compare it to other techniques from the state of the art.}, author = {Gruhl, Christian and Sick, Bernhard and Tomforde, Sven}, journal = {Future Generation Computer Systems}, keywords = {itegpub}, pages = {138–154}, publisher = {Elsevier}, title = {Novelty detection in continuously changing environments}, volume = 114, year = 2021 }
%0 Journal Article %1 gruhl2021novelty %A Gruhl, Christian %A Sick, Bernhard %A Tomforde, Sven %D 2021 %I Elsevier %J Future Generation Computer Systems %P 138--154 %R 10.1016/j.future.2020.07.037 %T Novelty detection in continuously changing environments %V 114 %X Self-improving system integration (SISSY) aims at mastering the challenges of system organisation decisions for subsystems with highly dynamic behaviours. This is achieved by increased decision freedom of these subsystems, i.e., by moving the corresponding design decisions from design-time to run-time and from engineers to the systems themselves. As a result, a more robust and efficient constellation of the overall system organisation is maintained even under the presence of continuous change and disturbed conditions. Appropriate integration decisions require a solid foundation of assessing the current conditions and their dynamics using identifying triggers for a necessary evaluation of their own integration status. With this article, we present mCANDIES, a modular novelty detection technique especially suited for SISSY systems that fulfils exactly these identification tasks. mCANDIES tackles an anomaly or novelty detection problem in a modular way by combining the advantages of different detection approaches by exploiting locality in different regions of the input space — depending on, e.g. the density or location of expected observations. This is augmented with information about changes in the underlying model of the observations, i.e., mCANDIES identifies concept drifts. The corresponding information can then be used to trigger online learning behaviour. We analyse the behaviour of mCANDIES on artificial and real-world data sets and compare it to other techniques from the state of the art.
Digital Shadows in Self-Improving System Integration: A Concept Using Generative Modelling.Al-Falouji, Ghassan; Gruhl, Christian; Tomforde, Sven. In Workshop on Self-Improving System Integration (SISSY), ACSOS, ble. 166–171. IEEE, 2021.
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@inproceedings{alfalouji2021digital, author = {Al-Falouji, Ghassan and Gruhl, Christian and Tomforde, Sven}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {166–171}, publisher = {IEEE}, title = {Digital Shadows in Self-Improving System Integration: A Concept Using Generative Modelling}, year = 2021 }
%0 Conference Paper %1 alfalouji2021digital %A Al-Falouji, Ghassan %A Gruhl, Christian %A Tomforde, Sven %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2021 %I IEEE %P 166--171 %R 10.1109/acsos-c52956.2021.00047 %T Digital Shadows in Self-Improving System Integration: A Concept Using Generative Modelling
Object Detection For Automotive Radar Point Clouds -- A Comparison.Scheiner, Nicolas; Kraus, Florian; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard. In AI Perspectives, 3(1), bl. 6. Springer, 2021.
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@article{scheiner2021object, author = {Scheiner, Nicolas and Kraus, Florian and Appenrodt, Nils and Dickmann, Jürgen and Sick, Bernhard}, journal = {AI Perspectives}, keywords = {itegpub}, number = 1, pages = 6, publisher = {Springer}, title = {Object Detection For Automotive Radar Point Clouds – A Comparison}, volume = 3, year = 2021 }
%0 Journal Article %1 scheiner2021object %A Scheiner, Nicolas %A Kraus, Florian %A Appenrodt, Nils %A Dickmann, Jürgen %A Sick, Bernhard %D 2021 %I Springer %J AI Perspectives %N 1 %P 6 %R 10.1186/s42467-021-00012-z %T Object Detection For Automotive Radar Point Clouds -- A Comparison %V 3
Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution.Zernetsch, Stefan; Schreck, Steven; Kress, Viktor; Doll, Konrad; Sick, Bernhard. In International Conference on Pattern Recognition (ICPR), ble. 2620–2626. IEEE, 2021.
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@inproceedings{zernetsch2021image, author = {Zernetsch, Stefan and Schreck, Steven and Kress, Viktor and Doll, Konrad and Sick, Bernhard}, booktitle = {International Conference on Pattern Recognition (ICPR)}, keywords = {itegpub}, pages = {2620–2626}, publisher = {IEEE}, title = {Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution}, year = 2021 }
%0 Conference Paper %1 zernetsch2021image %A Zernetsch, Stefan %A Schreck, Steven %A Kress, Viktor %A Doll, Konrad %A Sick, Bernhard %B International Conference on Pattern Recognition (ICPR) %D 2021 %I IEEE %P 2620--2626 %R 10.1109/icpr48806.2021.9413233 %T Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution
Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning.Haase-Schütz, Christian; Stal, Rainer; Hertlein, Heinz; Sick, Bernhard. In International Conference on Pattern Recognition (ICPR), ble. 9483–9490. IEEE, 2021.
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@inproceedings{haaseschuetz2021iterative, author = {Haase-Schütz, Christian and Stal, Rainer and Hertlein, Heinz and Sick, Bernhard}, booktitle = {International Conference on Pattern Recognition (ICPR)}, keywords = {itegpub}, pages = {9483–9490}, publisher = {IEEE}, title = {Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning}, year = 2021 }
%0 Conference Paper %1 haaseschuetz2021iterative %A Haase-Schütz, Christian %A Stal, Rainer %A Hertlein, Heinz %A Sick, Bernhard %B International Conference on Pattern Recognition (ICPR) %D 2021 %I IEEE %P 9483--9490 %R 10.1109/icpr48806.2021.9411918 %T Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning
CLeaR: An adaptive continual learning framework for regression tasks.He, Yujiang; Sick, Bernhard. In AI Perspectives, 3(1), bl. 2. Springer, 2020.
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Catastrophic forgetting means that a trained neural network model gradually forgets the previously learned tasks when being retrained on new tasks. Overcoming the forgetting problem is a major problem in machine learning. Numerous continual learning algorithms are very successful in incremental learning of classification tasks, where new samples with their labels appear frequently. However, there is currently no research that addresses the catastrophic forgetting problem in regression tasks as far as we know. This problem has emerged as one of the primary constraints in some applications, such as renewable energy forecasts. This article clarifies problem-related definitions and proposes a new methodological framework that can forecast targets and update itself by means of continual learning. The framework consists of forecasting neural networks and buffers, which store newly collected data from a non-stationary data stream in an application. The changed probability distribution of the data stream, which the framework has identified, will be learned sequentially. The framework is called CLeaR (Continual Learning for Regression Tasks), where components can be flexibly customized for a specific application scenario. We design two sets of experiments to evaluate the CLeaR framework concerning fitting error (training), prediction error (test), and forgetting ratio. The first one is based on an artificial time series to explore how hyperparameters affect the CLeaR framework. The second one is designed with data collected from European wind farms to evaluate the CLeaR framework's performance in a real-world application. The experimental results demonstrate that the CLeaR framework can continually acquire knowledge in the data stream and improve the prediction accuracy. The article concludes with further research issues arising from requirements to extend the framework.
@article{he2021clear, abstract = {Catastrophic forgetting means that a trained neural network model gradually forgets the previously learned tasks when being retrained on new tasks. Overcoming the forgetting problem is a major problem in machine learning. Numerous continual learning algorithms are very successful in incremental learning of classification tasks, where new samples with their labels appear frequently. However, there is currently no research that addresses the catastrophic forgetting problem in regression tasks as far as we know. This problem has emerged as one of the primary constraints in some applications, such as renewable energy forecasts. This article clarifies problem-related definitions and proposes a new methodological framework that can forecast targets and update itself by means of continual learning. The framework consists of forecasting neural networks and buffers, which store newly collected data from a non-stationary data stream in an application. The changed probability distribution of the data stream, which the framework has identified, will be learned sequentially. The framework is called CLeaR (Continual Learning for Regression Tasks), where components can be flexibly customized for a specific application scenario. We design two sets of experiments to evaluate the CLeaR framework concerning fitting error (training), prediction error (test), and forgetting ratio. The first one is based on an artificial time series to explore how hyperparameters affect the CLeaR framework. The second one is designed with data collected from European wind farms to evaluate the CLeaR framework's performance in a real-world application. The experimental results demonstrate that the CLeaR framework can continually acquire knowledge in the data stream and improve the prediction accuracy. The article concludes with further research issues arising from requirements to extend the framework.}, author = {He, Yujiang and Sick, Bernhard}, journal = {AI Perspectives}, keywords = {itegpub}, number = 1, pages = 2, publisher = {Springer}, title = {CLeaR: An adaptive continual learning framework for regression tasks}, volume = 3, year = 2021 }
%0 Journal Article %1 he2021clear %A He, Yujiang %A Sick, Bernhard %D 2021 %I Springer %J AI Perspectives %N 1 %P 2 %R 10.1186/s42467-021-00009-8 %T CLeaR: An adaptive continual learning framework for regression tasks %U https://doi.org/10.1186/s42467-021-00009-8 %V 3 %X Catastrophic forgetting means that a trained neural network model gradually forgets the previously learned tasks when being retrained on new tasks. Overcoming the forgetting problem is a major problem in machine learning. Numerous continual learning algorithms are very successful in incremental learning of classification tasks, where new samples with their labels appear frequently. However, there is currently no research that addresses the catastrophic forgetting problem in regression tasks as far as we know. This problem has emerged as one of the primary constraints in some applications, such as renewable energy forecasts. This article clarifies problem-related definitions and proposes a new methodological framework that can forecast targets and update itself by means of continual learning. The framework consists of forecasting neural networks and buffers, which store newly collected data from a non-stationary data stream in an application. The changed probability distribution of the data stream, which the framework has identified, will be learned sequentially. The framework is called CLeaR (Continual Learning for Regression Tasks), where components can be flexibly customized for a specific application scenario. We design two sets of experiments to evaluate the CLeaR framework concerning fitting error (training), prediction error (test), and forgetting ratio. The first one is based on an artificial time series to explore how hyperparameters affect the CLeaR framework. The second one is designed with data collected from European wind farms to evaluate the CLeaR framework's performance in a real-world application. The experimental results demonstrate that the CLeaR framework can continually acquire knowledge in the data stream and improve the prediction accuracy. The article concludes with further research issues arising from requirements to extend the framework.
Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses.Dingel, Kristina; Otto, Thorsten; Marder, Lutz; Funke, Lars; Held, Arne; Savio, Sara; Hans, Andreas; Hartmann, Gregor; Meier, David; Viefhaus, Jens; Sick, Bernhard; Ehresmann, Arno; Ilchen, Markus; Helml, Wolfram. In arXiv e-prints, bl. arXiv:2108.13979. 2021.
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X-ray free-electron lasers (XFELs) as the world`s most brilliant light sources provide ultrashort X-ray pulses with durations typically on the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena like localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes was, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time-energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence algorithms, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics at XFELs, thus enhancing and refining their scientific access in all related disciplines.
@article{dingel2021ai, abstract = {X-ray free-electron lasers (XFELs) as the world`s most brilliant light sources provide ultrashort X-ray pulses with durations typically on the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena like localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes was, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time-energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence algorithms, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics at XFELs, thus enhancing and refining their scientific access in all related disciplines.}, author = {{Dingel}, Kristina and {Otto}, Thorsten and {Marder}, Lutz and {Funke}, Lars and {Held}, Arne and {Savio}, Sara and {Hans}, Andreas and {Hartmann}, Gregor and {Meier}, David and {Viefhaus}, Jens and {Sick}, Bernhard and {Ehresmann}, Arno and {Ilchen}, Markus and {Helml}, Wolfram}, journal = {arXiv e-prints}, keywords = {Accelerator}, pages = {arXiv:2108.13979}, title = {{Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses}}, year = 2021 }
%0 Journal Article %1 dingel2021ai %A Dingel, Kristina %A Otto, Thorsten %A Marder, Lutz %A Funke, Lars %A Held, Arne %A Savio, Sara %A Hans, Andreas %A Hartmann, Gregor %A Meier, David %A Viefhaus, Jens %A Sick, Bernhard %A Ehresmann, Arno %A Ilchen, Markus %A Helml, Wolfram %D 2021 %J arXiv e-prints %P arXiv:2108.13979 %T Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses %X X-ray free-electron lasers (XFELs) as the world`s most brilliant light sources provide ultrashort X-ray pulses with durations typically on the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena like localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes was, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time-energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence algorithms, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics at XFELs, thus enhancing and refining their scientific access in all related disciplines.
An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving.Heidecker, Florian; Breitenstein, Jasmin; Rösch, Kevin; Löhdefink, Jonas; Bieshaar, Maarten; Stiller, Christoph; Fingscheidt, Tim; Sick, Bernhard. In IEEE Intelligent Vehicles Symposium (IV), ble. 644–651. IEEE, Nagoya, Japan, 2021.
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@inproceedings{heidecker2021application, address = {Nagoya, Japan}, author = {Heidecker, Florian and Breitenstein, Jasmin and Rösch, Kevin and Löhdefink, Jonas and Bieshaar, Maarten and Stiller, Christoph and Fingscheidt, Tim and Sick, Bernhard}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {itegpub}, pages = {644–651}, publisher = {IEEE}, title = {{An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving}}, year = 2021 }
%0 Conference Paper %1 heidecker2021application %A Heidecker, Florian %A Breitenstein, Jasmin %A Rösch, Kevin %A Löhdefink, Jonas %A Bieshaar, Maarten %A Stiller, Christoph %A Fingscheidt, Tim %A Sick, Bernhard %B IEEE Intelligent Vehicles Symposium (IV) %C Nagoya, Japan %D 2021 %I IEEE %P 644--651 %R 10.1109/iv48863.2021.9575933 %T An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving
Self-improving system integration: Mastering continuous change.Bellman, Kirstie; Botev, Jean; Diaconescu, Ada; Esterle, Lukas; Gruhl, Christian; Landauer, Christopher; Lewis, Peter R.; Nelson, Phyllis R.; Pournaras, Evangelos; Stein, Anthony; Tomforde, Sven. In Future Generation Computer Systems, 117, ble. 29–46. Elsevier, 2021.
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The research initiative “self-improving system integration” (SISSY) was established with the goal to master the ever-changing demands of system organisation in the presence of autonomous subsystems, evolving architectures, and highly-dynamic open environments. It aims to move integration-related decisions from design-time to run-time, implying a further shift of expertise and responsibility from human engineers to autonomous systems. This introduces a qualitative shift from existing self-adaptive and self-organising systems, moving from self-adaptation based on predefined variation types, towards more open contexts involving novel autonomous subsystems, collaborative behaviours, and emerging goals. In this article, we revisit existing SISSY research efforts and establish a corresponding terminology focusing on how SISSY relates to the broad field of integration sciences. We then investigate SISSY-related research efforts and derive a taxonomy of SISSY technology. This is concluded by establishing a research road-map for developing operational self-improving self-integrating systems.
@article{bellman2021self, abstract = {The research initiative “self-improving system integration” (SISSY) was established with the goal to master the ever-changing demands of system organisation in the presence of autonomous subsystems, evolving architectures, and highly-dynamic open environments. It aims to move integration-related decisions from design-time to run-time, implying a further shift of expertise and responsibility from human engineers to autonomous systems. This introduces a qualitative shift from existing self-adaptive and self-organising systems, moving from self-adaptation based on predefined variation types, towards more open contexts involving novel autonomous subsystems, collaborative behaviours, and emerging goals. In this article, we revisit existing SISSY research efforts and establish a corresponding terminology focusing on how SISSY relates to the broad field of integration sciences. We then investigate SISSY-related research efforts and derive a taxonomy of SISSY technology. This is concluded by establishing a research road-map for developing operational self-improving self-integrating systems.}, author = {Bellman, Kirstie and Botev, Jean and Diaconescu, Ada and Esterle, Lukas and Gruhl, Christian and Landauer, Christopher and Lewis, Peter R. and Nelson, Phyllis R. and Pournaras, Evangelos and Stein, Anthony and Tomforde, Sven}, journal = {Future Generation Computer Systems}, keywords = {System}, pages = {29–46}, publisher = {Elsevier}, title = {Self-improving system integration: Mastering continuous change}, volume = 117, year = 2021 }
%0 Journal Article %1 bellman2021self %A Bellman, Kirstie %A Botev, Jean %A Diaconescu, Ada %A Esterle, Lukas %A Gruhl, Christian %A Landauer, Christopher %A Lewis, Peter R. %A Nelson, Phyllis R. %A Pournaras, Evangelos %A Stein, Anthony %A Tomforde, Sven %D 2021 %I Elsevier %J Future Generation Computer Systems %P 29--46 %R 10.1016/j.future.2020.11.019 %T Self-improving system integration: Mastering continuous change %U http://www.sciencedirect.com/science/article/pii/S0167739X20330430 %V 117 %X The research initiative “self-improving system integration” (SISSY) was established with the goal to master the ever-changing demands of system organisation in the presence of autonomous subsystems, evolving architectures, and highly-dynamic open environments. It aims to move integration-related decisions from design-time to run-time, implying a further shift of expertise and responsibility from human engineers to autonomous systems. This introduces a qualitative shift from existing self-adaptive and self-organising systems, moving from self-adaptation based on predefined variation types, towards more open contexts involving novel autonomous subsystems, collaborative behaviours, and emerging goals. In this article, we revisit existing SISSY research efforts and establish a corresponding terminology focusing on how SISSY relates to the broad field of integration sciences. We then investigate SISSY-related research efforts and derive a taxonomy of SISSY technology. This is concluded by establishing a research road-map for developing operational self-improving self-integrating systems.
AI - Based On The Fly Design of Experiments in Physics and Engineering.Dingel, Kristina; Liehr, Alexander; Vogel, Michael; Degener, Sebastian; Meier, David; Niendorf, Thomas; Ehresmann, Arno; Sick, Bernhard. In Workshop on Self-Improving System Integration (SISSY), ACSOS, ble. 150–153. IEEE, 2021.
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@inproceedings{dingel2021aia, author = {Dingel, Kristina and Liehr, Alexander and Vogel, Michael and Degener, Sebastian and Meier, David and Niendorf, Thomas and Ehresmann, Arno and Sick, Bernhard}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {150–153}, publisher = {IEEE}, title = {AI - Based On The Fly Design of Experiments in Physics and Engineering}, year = 2021 }
%0 Conference Paper %1 dingel2021aia %A Dingel, Kristina %A Liehr, Alexander %A Vogel, Michael %A Degener, Sebastian %A Meier, David %A Niendorf, Thomas %A Ehresmann, Arno %A Sick, Bernhard %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2021 %I IEEE %P 150--153 %R 10.1109/ACSOS-C52956.2021.00048 %T AI - Based On The Fly Design of Experiments in Physics and Engineering
Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks.Huseljic, Denis; Sick, Bernhard; Herde, Marek; Kottke, Daniel. In International Conference on Pattern Recognition (ICPR), ble. 9172–9179. IEEE, 2021.
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@inproceedings{huseljic2021separation, author = {Huseljic, Denis and Sick, Bernhard and Herde, Marek and Kottke, Daniel}, booktitle = {International Conference on Pattern Recognition (ICPR)}, keywords = {itegpub}, pages = {9172–9179}, publisher = {IEEE}, title = {Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks}, year = 2021 }
%0 Conference Paper %1 huseljic2021separation %A Huseljic, Denis %A Sick, Bernhard %A Herde, Marek %A Kottke, Daniel %B International Conference on Pattern Recognition (ICPR) %D 2021 %I IEEE %P 9172--9179 %R 10.1109/icpr48806.2021.9412616 %T Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks
AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems.Dingel, Kristina; Huhnstock, Rico; Knie, André; Ehresmann, Arno; Sick, Bernhard. In Computer Physics Communications, 262, bl. 107859. Elsevier, 2021.
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Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x-y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated.
@article{dingel2021adapt, abstract = {Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x-y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated.}, author = {Dingel, Kristina and Huhnstock, Rico and Knie, Andr{\'{e}} and Ehresmann, Arno and Sick, Bernhard}, journal = {Computer Physics Communications}, keywords = {itegpub}, pages = 107859, publisher = {Elsevier}, title = {{AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems}}, volume = 262, year = 2021 }
%0 Journal Article %1 dingel2021adapt %A Dingel, Kristina %A Huhnstock, Rico %A Knie, André %A Ehresmann, Arno %A Sick, Bernhard %D 2021 %I Elsevier %J Computer Physics Communications %P 107859 %R 10.1016/j.cpc.2021.107859 %T AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems %V 262 %X Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x-y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated.
Smart Infrastructure: A Research Junction.Hetzel, Manuel; Reichert, Hannes; Doll, Konrad; Sick, Bernhard. In IEEE International Smart Cities Conference (ISC2). IEEE, 2021.
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@inproceedings{hetzel2021smart, author = {Hetzel, Manuel and Reichert, Hannes and Doll, Konrad and Sick, Bernhard}, booktitle = {IEEE International Smart Cities Conference (ISC2)}, keywords = {itegpub}, note = 1, publisher = {IEEE}, title = {Smart Infrastructure: A Research Junction}, year = 2021 }
%0 Conference Paper %1 hetzel2021smart %A Hetzel, Manuel %A Reichert, Hannes %A Doll, Konrad %A Sick, Bernhard %B IEEE International Smart Cities Conference (ISC2) %D 2021 %I IEEE %R 10.1109/isc253183.2021.9562809 %T Smart Infrastructure: A Research Junction
Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast.Schreiber, Jens; Vogt, Stephan; Sick, Bernhard. In European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD): Applied Data Science Track, ble. 118–134. Springer, 2021.
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@inproceedings{schreiber2021task, author = {Schreiber, Jens and Vogt, Stephan and Sick, Bernhard}, booktitle = {European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD): Applied Data Science Track}, keywords = {itegpub}, pages = {118–134}, publisher = {Springer}, title = {Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast}, year = 2021 }
%0 Conference Paper %1 schreiber2021task %A Schreiber, Jens %A Vogt, Stephan %A Sick, Bernhard %B European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD): Applied Data Science Track %D 2021 %I Springer %P 118--134 %R 10.1007/978-3-030-86514-6_8 %T Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast
About the Ambiguity of Data Augmentation for 3D Object Detection in Autonomous Driving.Reuse, Matthias; Simon, Martin; Sick, Bernhard. In Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV, ble. 979–987. IEEE, 2021.
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@inproceedings{reuse2021ambiguity, author = {Reuse, Matthias and Simon, Martin and Sick, Bernhard}, booktitle = {Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV}, keywords = {itegpub}, pages = {979–987}, publisher = {IEEE}, title = {About the Ambiguity of Data Augmentation for 3D Object Detection in Autonomous Driving}, year = 2021 }
%0 Conference Paper %1 reuse2021ambiguity %A Reuse, Matthias %A Simon, Martin %A Sick, Bernhard %B Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV %D 2021 %I IEEE %P 979--987 %R 10.1109/ICCVW54120.2021.00114 %T About the Ambiguity of Data Augmentation for 3D Object Detection in Autonomous Driving
A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification.Herde, Marek; Huseljic, Denis; Sick, Bernhard; Calma, Adrian. In IEEE Access, 9, ble. 166970–166989. IEEE, 2021.
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Pool-based active learning (AL) aims to optimize the annotation process (i.e., labeling) as the acquisition of annotations is often time-consuming and therefore expensive. For this purpose, an AL strategy queries annotations intelligently from annotators to train a high-performance classification model at a low annotation cost. Traditional AL strategies operate in an idealized framework. They assume a single, omniscient annotator who never gets tired and charges uniformly regardless of query difficulty. However, in real-world applications, we often face human annotators, e.g., crowd or in-house workers, who make annotation mistakes and can be reluctant to respond if tired or faced with complex queries. Recently, many novel AL strategies have been proposed to address these issues. They differ in at least one of the following three central aspects from traditional AL: 1) modeling of (multiple) human annotators whose performances can be affected by various factors, such as missing expertise; 2) generalization of the interaction with human annotators through different query and annotation types, such as asking an annotator for feedback on an inferred classification rule; 3) consideration of complex cost schemes regarding annotations and misclassification. This survey provides an overview of these AL strategies and refers to them as real-world AL. Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e.g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies. Finally, we outline possible directions for future research in the field of AL.
@article{herde2021survey, abstract = {Pool-based active learning (AL) aims to optimize the annotation process (i.e., labeling) as the acquisition of annotations is often time-consuming and therefore expensive. For this purpose, an AL strategy queries annotations intelligently from annotators to train a high-performance classification model at a low annotation cost. Traditional AL strategies operate in an idealized framework. They assume a single, omniscient annotator who never gets tired and charges uniformly regardless of query difficulty. However, in real-world applications, we often face human annotators, e.g., crowd or in-house workers, who make annotation mistakes and can be reluctant to respond if tired or faced with complex queries. Recently, many novel AL strategies have been proposed to address these issues. They differ in at least one of the following three central aspects from traditional AL: 1) modeling of (multiple) human annotators whose performances can be affected by various factors, such as missing expertise; 2) generalization of the interaction with human annotators through different query and annotation types, such as asking an annotator for feedback on an inferred classification rule; 3) consideration of complex cost schemes regarding annotations and misclassification. This survey provides an overview of these AL strategies and refers to them as real-world AL. Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e.g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies. Finally, we outline possible directions for future research in the field of AL.}, author = {Herde, Marek and Huseljic, Denis and Sick, Bernhard and Calma, Adrian}, journal = {IEEE Access}, keywords = {classification}, pages = {166970–166989}, publisher = {IEEE}, title = {A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification}, volume = 9, year = 2021 }
%0 Journal Article %1 herde2021survey %A Herde, Marek %A Huseljic, Denis %A Sick, Bernhard %A Calma, Adrian %D 2021 %I IEEE %J IEEE Access %P 166970--166989 %R 10.1109/ACCESS.2021.3135514 %T A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification %U https://ieeexplore.ieee.org/abstract/document/9650877 %V 9 %X Pool-based active learning (AL) aims to optimize the annotation process (i.e., labeling) as the acquisition of annotations is often time-consuming and therefore expensive. For this purpose, an AL strategy queries annotations intelligently from annotators to train a high-performance classification model at a low annotation cost. Traditional AL strategies operate in an idealized framework. They assume a single, omniscient annotator who never gets tired and charges uniformly regardless of query difficulty. However, in real-world applications, we often face human annotators, e.g., crowd or in-house workers, who make annotation mistakes and can be reluctant to respond if tired or faced with complex queries. Recently, many novel AL strategies have been proposed to address these issues. They differ in at least one of the following three central aspects from traditional AL: 1) modeling of (multiple) human annotators whose performances can be affected by various factors, such as missing expertise; 2) generalization of the interaction with human annotators through different query and annotation types, such as asking an annotator for feedback on an inferred classification rule; 3) consideration of complex cost schemes regarding annotations and misclassification. This survey provides an overview of these AL strategies and refers to them as real-world AL. Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e.g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies. Finally, we outline possible directions for future research in the field of AL.
The Problem with Real-World Novelty Detection -- Issues in Multivariate Probabilistic Models.Gruhl, Christian; Hannan, Abdul; Huang, Zhixin; Nivarthi, Chandana; Vogt, Stephan. In Workshop on Self-Improving System Integration (SISSY), ACSOS, ble. 204–209. IEEE, 2021.
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@inproceedings{gruhl2021problem, author = {Gruhl, Christian and Hannan, Abdul and Huang, Zhixin and Nivarthi, Chandana and Vogt, Stephan}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {204–209}, publisher = {IEEE}, title = {The Problem with Real-World Novelty Detection – Issues in Multivariate Probabilistic Models}, year = 2021 }
%0 Conference Paper %1 gruhl2021problem %A Gruhl, Christian %A Hannan, Abdul %A Huang, Zhixin %A Nivarthi, Chandana %A Vogt, Stephan %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2021 %I IEEE %P 204--209 %R 10.1109/acsos-c52956.2021.00055 %T The Problem with Real-World Novelty Detection -- Issues in Multivariate Probabilistic Models
Toward Application of Continuous Power Forecasts in a Regional Flexibility Market.He, Yujiang; Huang, Zhixin; Sick, Bernhard. In International Joint Conference on Neural Networks (IJCNN), ble. 1–8. IEEE, 2021.
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@inproceedings{he2021application, author = {He, Yujiang and Huang, Zhixin and Sick, Bernhard}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {itegpub}, pages = {1–8}, publisher = {IEEE}, title = {Toward Application of Continuous Power Forecasts in a Regional Flexibility Market}, year = 2021 }
%0 Conference Paper %1 he2021application %A He, Yujiang %A Huang, Zhixin %A Sick, Bernhard %B International Joint Conference on Neural Networks (IJCNN) %D 2021 %I IEEE %P 1--8 %R 10.1109/ijcnn52387.2021.9533626 %T Toward Application of Continuous Power Forecasts in a Regional Flexibility Market
scikit-activeml: A Library and Toolbox for Active Learning Algorithms.Kottke, Daniel; Herde, Marek; Minh, Tuan Pham; Benz, Alexander; Mergard, Pascal; Roghman, Atal; Sandrock, Christoph; Sick, Bernhard. In Preprints, bl. 2021030194. 2021.
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Machine learning applications often need large amounts of training data to perform well. Whereas unlabeled data can be easily gathered, the labeling process is difficult, time-consuming, or expensive in most applications. Active learning can help solve this problem by querying labels for those data points that will improve the performance the most. Thereby, the goal is that the learning algorithm performs sufficiently well with fewer labels. We provide a library called scikit-activeml that covers the most relevant query strategies and implements tools to work with partially labeled data. It is programmed in Python and builds on top of scikit-learn.
@article{kottke2021scikit, abstract = {Machine learning applications often need large amounts of training data to perform well. Whereas unlabeled data can be easily gathered, the labeling process is difficult, time-consuming, or expensive in most applications. Active learning can help solve this problem by querying labels for those data points that will improve the performance the most. Thereby, the goal is that the learning algorithm performs sufficiently well with fewer labels. We provide a library called scikit-activeml that covers the most relevant query strategies and implements tools to work with partially labeled data. It is programmed in Python and builds on top of scikit-learn.}, author = {Kottke, Daniel and Herde, Marek and Minh, Tuan Pham and Benz, Alexander and Mergard, Pascal and Roghman, Atal and Sandrock, Christoph and Sick, Bernhard}, journal = {Preprints}, keywords = {itegpub}, pages = 2021030194, title = {{scikit-activeml}: {A} Library and Toolbox for Active Learning Algorithms}, year = 2021 }
%0 Journal Article %1 kottke2021scikit %A Kottke, Daniel %A Herde, Marek %A Minh, Tuan Pham %A Benz, Alexander %A Mergard, Pascal %A Roghman, Atal %A Sandrock, Christoph %A Sick, Bernhard %D 2021 %J Preprints %P 2021030194 %T scikit-activeml: A Library and Toolbox for Active Learning Algorithms %U https://www.preprints.org/manuscript/202103.0194/ %X Machine learning applications often need large amounts of training data to perform well. Whereas unlabeled data can be easily gathered, the labeling process is difficult, time-consuming, or expensive in most applications. Active learning can help solve this problem by querying labels for those data points that will improve the performance the most. Thereby, the goal is that the learning algorithm performs sufficiently well with fewer labels. We provide a library called scikit-activeml that covers the most relevant query strategies and implements tools to work with partially labeled data. It is programmed in Python and builds on top of scikit-learn.
Toward optimal probabilistic active learning using a Bayesian approach.Kottke, Daniel; Herde, Marek; Sandrock, Christoph; Huseljic, Denis; Krempl, Georg; Sick, Bernhard. In Machine Learning, 110(6), ble. 1199–1231. Springer, 2021.
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Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. In this article, we propose a decision-theoretic selection strategy that (1) directly optimizes the gain in misclassification error, and (2) uses a Bayesian approach by introducing a conjugate prior distribution to determine the class posterior to deal with uncertainties. By reformulating existing selection strategies within our proposed model, we can explain which aspects are not covered in current state-of-the-art and why this leads to the superior performance of our approach. Extensive experiments on a large variety of datasets and different kernels validate our claims.
@article{kottke2021optimal, abstract = {Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. In this article, we propose a decision-theoretic selection strategy that (1) directly optimizes the gain in misclassification error, and (2) uses a Bayesian approach by introducing a conjugate prior distribution to determine the class posterior to deal with uncertainties. By reformulating existing selection strategies within our proposed model, we can explain which aspects are not covered in current state-of-the-art and why this leads to the superior performance of our approach. Extensive experiments on a large variety of datasets and different kernels validate our claims.}, author = {Kottke, Daniel and Herde, Marek and Sandrock, Christoph and Huseljic, Denis and Krempl, Georg and Sick, Bernhard}, journal = {Machine Learning}, keywords = {itegpub}, number = 6, pages = {1199–1231}, publisher = {Springer}, title = {Toward optimal probabilistic active learning using a Bayesian approach}, volume = 110, year = 2021 }
%0 Journal Article %1 kottke2021optimal %A Kottke, Daniel %A Herde, Marek %A Sandrock, Christoph %A Huseljic, Denis %A Krempl, Georg %A Sick, Bernhard %D 2021 %I Springer %J Machine Learning %N 6 %P 1199--1231 %R 10.1007/s10994-021-05986-9 %T Toward optimal probabilistic active learning using a Bayesian approach %V 110 %X Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. In this article, we propose a decision-theoretic selection strategy that (1) directly optimizes the gain in misclassification error, and (2) uses a Bayesian approach by introducing a conjugate prior distribution to determine the class posterior to deal with uncertainties. By reformulating existing selection strategies within our proposed model, we can explain which aspects are not covered in current state-of-the-art and why this leads to the superior performance of our approach. Extensive experiments on a large variety of datasets and different kernels validate our claims.
A Note on the Modeling Power of Different Graph Types.Thomas, Josephine M.; Beddar-Wiesing, Silvia; Moallemy-Oureh, Alice; Nather, Rüdiger. In arXiv e-prints, bl. arXiv:2109.10708. 2021.
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Graphs can have different properties that lead to several graph types and may allow for a varying representation of diverse information. In order to clarify the modeling power of graphs, we introduce a partial order on the most common graph types based on an expressivity relation. The expressivity relation quantifies how many properties a graph type can encode compared to another type. Additionally, we show that all attributed graph types are equally expressive and have the same modeling power.
@article{thomas2021note, abstract = {Graphs can have different properties that lead to several graph types and may allow for a varying representation of diverse information. In order to clarify the modeling power of graphs, we introduce a partial order on the most common graph types based on an expressivity relation. The expressivity relation quantifies how many properties a graph type can encode compared to another type. Additionally, we show that all attributed graph types are equally expressive and have the same modeling power.}, author = {Thomas, Josephine M. and Beddar-Wiesing, Silvia and Moallemy-Oureh, Alice and Nather, Rüdiger}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2109.10708}, title = {A Note on the Modeling Power of Different Graph Types}, year = 2021 }
%0 Journal Article %1 thomas2021note %A Thomas, Josephine M. %A Beddar-Wiesing, Silvia %A Moallemy-Oureh, Alice %A Nather, Rüdiger %D 2021 %J arXiv e-prints %P arXiv:2109.10708 %T A Note on the Modeling Power of Different Graph Types %U https://arxiv.org/abs/2109.10708 %X Graphs can have different properties that lead to several graph types and may allow for a varying representation of diverse information. In order to clarify the modeling power of graphs, we introduce a partial order on the most common graph types based on an expressivity relation. The expressivity relation quantifies how many properties a graph type can encode compared to another type. Additionally, we show that all attributed graph types are equally expressive and have the same modeling power.
Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks.Heidecker, Florian; Hannan, Abdul; Bieshaar, Maarten; Sick, Bernhard. In Workshop on Integrated Artificial Intelligence in Data Science, ICPR, ble. 361–374. IEEE, Milan, Italy, 2021.
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@inproceedings{heidecker2021towards, address = {Milan, Italy}, author = {Heidecker, Florian and Hannan, Abdul and Bieshaar, Maarten and Sick, Bernhard}, booktitle = {Workshop on Integrated Artificial Intelligence in Data Science, ICPR}, keywords = {itegpub}, pages = {361–374}, publisher = {IEEE}, title = {Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks}, year = 2021 }
%0 Conference Paper %1 heidecker2021towards %A Heidecker, Florian %A Hannan, Abdul %A Bieshaar, Maarten %A Sick, Bernhard %B Workshop on Integrated Artificial Intelligence in Data Science, ICPR %C Milan, Italy %D 2021 %I IEEE %P 361--374 %R 10.1007/978-3-030-68799-1_26 %T Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks
Towards Highly Automated Machine-Learning-Empowered Monitoring of Motor Test Stands.Botache, Diego; Bethke, Florian; Hardieck, Martin; Bieshaar, Maarten; Brabetz, Ludwig; Ayeb, Mohamed; Zipf, Peter; Sick, Bernhard. In IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS), ble. 120–130. IEEE, Washington, DC, USA, 2021.
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@inproceedings{botache2021towards, address = {Washington, DC, USA}, author = {Botache, Diego and Bethke, Florian and Hardieck, Martin and Bieshaar, Maarten and Brabetz, Ludwig and Ayeb, Mohamed and Zipf, Peter and Sick, Bernhard}, booktitle = {IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)}, keywords = {itegpub}, pages = {120–130}, publisher = {IEEE}, title = {Towards Highly Automated Machine-Learning-Empowered Monitoring of Motor Test Stands}, year = 2021 }
%0 Conference Paper %1 botache2021towards %A Botache, Diego %A Bethke, Florian %A Hardieck, Martin %A Bieshaar, Maarten %A Brabetz, Ludwig %A Ayeb, Mohamed %A Zipf, Peter %A Sick, Bernhard %B IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) %C Washington, DC, USA %D 2021 %I IEEE %P 120--130 %R 10.1109/acsos52086.2021.00031 %T Towards Highly Automated Machine-Learning-Empowered Monitoring of Motor Test Stands
Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems.Reichert, Hannes; Lang, Lukas; Rösch, Kevin; Bogdoll, Daniel; Doll, Konrad; Sick, Bernhard; Reuss, Hans-Christian; Stiller, Christoph; Zöllner, J. Marius. In IEEE International Smart Cities Conference (ISC2), ble. 1–4. IEEE, 2021.
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@inproceedings{reichert2021towards, author = {Reichert, Hannes and Lang, Lukas and Rösch, Kevin and Bogdoll, Daniel and Doll, Konrad and Sick, Bernhard and Reuss, Hans-Christian and Stiller, Christoph and Zöllner, J. Marius}, booktitle = {IEEE International Smart Cities Conference (ISC2)}, keywords = {itegpub}, pages = {1–4}, publisher = {IEEE}, title = {Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems}, year = 2021 }
%0 Conference Paper %1 reichert2021towards %A Reichert, Hannes %A Lang, Lukas %A Rösch, Kevin %A Bogdoll, Daniel %A Doll, Konrad %A Sick, Bernhard %A Reuss, Hans-Christian %A Stiller, Christoph %A Zöllner, J. Marius %B IEEE International Smart Cities Conference (ISC2) %D 2021 %I IEEE %P 1--4 %R 10.1109/isc253183.2021.9562912 %T Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems
A Holistic, Decision-Theoretic Framework for Pool-Based Active Learning. Kottke, Daniel. kassel university press, 2021.
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@phdthesis{kottke2021holistic, author = {Kottke, Daniel}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, title = {A Holistic, Decision-Theoretic Framework for Pool-Based Active Learning}, year = 2021 }
%0 Thesis %1 kottke2021holistic %A Kottke, Daniel %D 2021 %I kassel university press %R 10.17170/kobra-202109244802 %T A Holistic, Decision-Theoretic Framework for Pool-Based Active Learning
Traffic Safety in Future Cities by Using a Safety Approach Based on AI and Wireless Communications.König, I.; Bachmann, M.; Bieshaar, M.; Schindler, S.; Lambrecht, F.; David, K.; Sick, B.; Hornung, G.; Sommer, C. In ITG-Symposium on Mobile Communication - Technologies and Applications, ble. 1–6. Osnabrück, Germany, 2021.
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@inproceedings{koenig2021traffic, address = {Osnabrück, Germany}, author = {K\"onig, I. and Bachmann, M. and Bieshaar, M. and Schindler, S. and Lambrecht, F. and David, K. and Sick, B. and Hornung, G. and Sommer, C.}, booktitle = {ITG-Symposium on Mobile Communication - Technologies and Applications}, keywords = {itegpub}, pages = {1–6}, title = {Traffic Safety in Future Cities by Using a Safety Approach Based on AI and Wireless Communications}, year = 2021 }
%0 Conference Paper %1 koenig2021traffic %A König, I. %A Bachmann, M. %A Bieshaar, M. %A Schindler, S. %A Lambrecht, F. %A David, K. %A Sick, B. %A Hornung, G. %A Sommer, C. %B ITG-Symposium on Mobile Communication - Technologies and Applications %C Osnabrück, Germany %D 2021 %P 1--6 %T Traffic Safety in Future Cities by Using a Safety Approach Based on AI and Wireless Communications
Translatory and rotatory motion of exchange-bias capped Janus particles controlled by dynamic magnetic field landscapes.Huhnstock, Rico; Reginka, Meike; Tomita, Andreea; Merkel, Maximilian; Dingel, Kristina; Holzinger, Dennis; Sick, Bernhard; Vogel, Michael; Ehresmann, Arno. In Scientific Reports, 11(1), bl. 21794. Nature Publishing Group, 2021.
- [ Abstract ]
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Magnetic Janus particles (MJPs), fabricated by covering a non-magnetic spherical particle with a hemispherical magnetic in-plane exchange-bias layer system cap, display an onion magnetization state for comparably large diameters of a few microns. In this work, the motion characteristics of these MJPs will be investigated when they are steered by a magnetic field landscape over prototypical parallel-stripe domains, dynamically varied by superposed external magnetic field pulse sequences, in an aqueous medium. We demonstrate, that due to the engineered magnetization state in the hemispherical cap, a comparably fast, directed particle transport and particle rotation can be induced. Additionally, by modifying the frequency of the applied pulse sequence and the strengths of the individual field components, we observe a possible separation between a combined or an individual occurrence of these two types of motion. Our findings bear importance for lab-on-a-chip systems, where particle immobilization on a surface via analyte bridges shall be used for low concentration analyte detection and a particle rotation over a defined position of a substrate may dramatically increase the immobilization (and therefore analyte detection) probability.
@article{huhnstock2021translatory, abstract = {Magnetic Janus particles (MJPs), fabricated by covering a non-magnetic spherical particle with a hemispherical magnetic in-plane exchange-bias layer system cap, display an onion magnetization state for comparably large diameters of a few microns. In this work, the motion characteristics of these MJPs will be investigated when they are steered by a magnetic field landscape over prototypical parallel-stripe domains, dynamically varied by superposed external magnetic field pulse sequences, in an aqueous medium. We demonstrate, that due to the engineered magnetization state in the hemispherical cap, a comparably fast, directed particle transport and particle rotation can be induced. Additionally, by modifying the frequency of the applied pulse sequence and the strengths of the individual field components, we observe a possible separation between a combined or an individual occurrence of these two types of motion. Our findings bear importance for lab-on-a-chip systems, where particle immobilization on a surface via analyte bridges shall be used for low concentration analyte detection and a particle rotation over a defined position of a substrate may dramatically increase the immobilization (and therefore analyte detection) probability.}, author = {Huhnstock, Rico and Reginka, Meike and Tomita, Andreea and Merkel, Maximilian and Dingel, Kristina and Holzinger, Dennis and Sick, Bernhard and Vogel, Michael and Ehresmann, Arno}, journal = {Scientific Reports}, keywords = {itegpub}, number = 1, pages = 21794, publisher = {Nature Publishing Group}, title = {Translatory and rotatory motion of exchange-bias capped Janus particles controlled by dynamic magnetic field landscapes}, volume = 11, year = 2021 }
%0 Journal Article %1 huhnstock2021translatory %A Huhnstock, Rico %A Reginka, Meike %A Tomita, Andreea %A Merkel, Maximilian %A Dingel, Kristina %A Holzinger, Dennis %A Sick, Bernhard %A Vogel, Michael %A Ehresmann, Arno %D 2021 %I Nature Publishing Group %J Scientific Reports %N 1 %P 21794 %R 10.1038/s41598-021-01351-x %T Translatory and rotatory motion of exchange-bias capped Janus particles controlled by dynamic magnetic field landscapes %U https://www.nature.com/articles/s41598-021-01351-x %V 11 %X Magnetic Janus particles (MJPs), fabricated by covering a non-magnetic spherical particle with a hemispherical magnetic in-plane exchange-bias layer system cap, display an onion magnetization state for comparably large diameters of a few microns. In this work, the motion characteristics of these MJPs will be investigated when they are steered by a magnetic field landscape over prototypical parallel-stripe domains, dynamically varied by superposed external magnetic field pulse sequences, in an aqueous medium. We demonstrate, that due to the engineered magnetization state in the hemispherical cap, a comparably fast, directed particle transport and particle rotation can be induced. Additionally, by modifying the frequency of the applied pulse sequence and the strengths of the individual field components, we observe a possible separation between a combined or an individual occurrence of these two types of motion. Our findings bear importance for lab-on-a-chip systems, where particle immobilization on a surface via analyte bridges shall be used for low concentration analyte detection and a particle rotation over a defined position of a substrate may dramatically increase the immobilization (and therefore analyte detection) probability. %@ 0123456789
Transport efficiency of biofunctionalized magnetic particles tailored by surfactant concentration.Reginka, Meike; Hoang, Hai; Efendi, Özge; Merkel, Maximilan; Huhnstock, Rico; Holzinger, Dennis; Dingel, Kristina; Sick, Bernhard; Bertinetti, Daniela; Herberg, Friedrich; Ehresmann, Arno. In Langmuir, 37(28), ble. 8498–8507. ACS, 2021.
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@article{reginka2021transport, author = {{Reginka}, Meike and {Hoang}, Hai and {Efendi}, {\"O}zge and {Merkel}, Maximilan and {Huhnstock}, Rico and {Holzinger}, Dennis and {Dingel}, Kristina and {Sick}, Bernhard and {Bertinetti}, Daniela and {Herberg}, Friedrich and {Ehresmann}, Arno}, journal = {Langmuir}, keywords = {itegpub}, number = 28, pages = {8498–8507}, publisher = {ACS}, title = {Transport efficiency of biofunctionalized magnetic particles tailored by surfactant concentration}, volume = 37, year = 2021 }
%0 Journal Article %1 reginka2021transport %A Reginka, Meike %A Hoang, Hai %A Efendi, Özge %A Merkel, Maximilan %A Huhnstock, Rico %A Holzinger, Dennis %A Dingel, Kristina %A Sick, Bernhard %A Bertinetti, Daniela %A Herberg, Friedrich %A Ehresmann, Arno %D 2021 %I ACS %J Langmuir %N 28 %P 8498--8507 %R 10.1021/acs.langmuir.1c00900 %T Transport efficiency of biofunctionalized magnetic particles tailored by surfactant concentration %U https://pubs.acs.org/doi/abs/10.1021/acs.langmuir.1c00900 %V 37
Uncertainty and Utility Sampling with Pre-Clustering.Huang, Zhixin; He, Yujiang; Vogt, Stephan; Sick, Bernhard. In Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. 2021.
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@inproceedings{huang2021uncertainty, author = {Huang, Zhixin and He, Yujiang and Vogt, Stephan and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {itegpub}, note = {(accepted)}, title = {Uncertainty and Utility Sampling with Pre-Clustering}, year = 2021 }
%0 Conference Paper %1 huang2021uncertainty %A Huang, Zhixin %A He, Yujiang %A Vogt, Stephan %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2021 %T Uncertainty and Utility Sampling with Pre-Clustering
A Concept for Highly Automated Pre-Labeling via Cross-Domain Label Transfer for Perception in Autonomous Driving.Bieshaar, Maarten; Herde, Marek; Huselijc, Denis; Sick, Bernhard. In Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. 2021.
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@inproceedings{bieshaar2021concept, author = {Bieshaar, Maarten and Herde, Marek and Huselijc, Denis and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {itegpub}, note = {(accepted)}, title = {A Concept for Highly Automated Pre-Labeling via Cross-Domain Label Transfer for Perception in Autonomous Driving}, year = 2021 }
%0 Conference Paper %1 bieshaar2021concept %A Bieshaar, Maarten %A Herde, Marek %A Huselijc, Denis %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2021 %T A Concept for Highly Automated Pre-Labeling via Cross-Domain Label Transfer for Perception in Autonomous Driving
Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders.Hannan, Abdul; Gruhl, Christian; Sick, Bernhard. In IEEE International Conference on Cyber Security and Resilience (CSR), ble. 1–7. IEEE, 2021.
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@inproceedings{hannan2021anomaly, author = {Hannan, Abdul and Gruhl, Christian and Sick, Bernhard}, booktitle = {IEEE International Conference on Cyber Security and Resilience (CSR)}, keywords = {itegpub}, pages = {1–7}, publisher = {IEEE}, title = {{Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders}}, year = 2021 }
%0 Conference Paper %1 hannan2021anomaly %A Hannan, Abdul %A Gruhl, Christian %A Sick, Bernhard %B IEEE International Conference on Cyber Security and Resilience (CSR) %D 2021 %I IEEE %P 1--7 %R 10.1109/csr51186.2021.9527980 %T Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders
Statistical Analysis of Pairwise Connectivity.Krempl, Georg; Kottke, Daniel; Pham, Tuan. In International Conference on Discovery Science (DS), Lecture Notes in Computer Science, ble. 138–148. Springer, 2021.
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@inproceedings{krempl2021statistical, author = {Krempl, Georg and Kottke, Daniel and Pham, Tuan}, booktitle = {International Conference on Discovery Science (DS)}, keywords = {itegpub}, pages = {138–148}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Statistical Analysis of Pairwise Connectivity}, year = 2021 }
%0 Conference Paper %1 krempl2021statistical %A Krempl, Georg %A Kottke, Daniel %A Pham, Tuan %B International Conference on Discovery Science (DS) %D 2021 %I Springer %P 138--148 %R 10.1007/978-3-030-88942-5_11 %T Statistical Analysis of Pairwise Connectivity
Cooperative intention detection using machine learning. Advanced cyclist protection in the context of automated driving. Bieshaar, Maarten. kassel university press, 2021.
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@phdthesis{bieshaar2021cooperative, author = {Bieshaar, Maarten}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, title = {Cooperative intention detection using machine learning. Advanced cyclist protection in the context of automated driving}, year = 2021 }
%0 Thesis %1 bieshaar2021cooperative %A Bieshaar, Maarten %D 2021 %I kassel university press %R 10.17170/kobra-202101263056 %T Cooperative intention detection using machine learning. Advanced cyclist protection in the context of automated driving
Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning -- A Case Study: Overtaking Cyclists.Schneegans, Jan; Eilbrecht, Jan; Zernetsch, Stefan; Bieshaar, Maarten; Doll, Konrad; Stursberg, Olaf; Sick, Bernhard. In Workshop From Benchmarking Behavior Prediction to Socially Compatible Behavior Generation in Autonomous Driving, IV. 2021.
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This work considers (deep) artiﬁcial feed-forward neural networks as parametric approximators in optimal control of discrete-time switched linear systems with controlled switching. The proposed approach is based on approximate dynamic programming and allows the fast computation of (sub-)optimal discrete and continuous control inputs, either by approximating the optimal cost-to-go functions or by approximating the optimal discrete and continuous input policies. An important property of the approach is the satisfaction of polytopic state and input constraints, which is crucial for ensuring safety, as required in many control applications. A numeric example is provided for illustration and evaluation of the approaches.
@inproceedings{schneegans2021probabilistic, abstract = {This work considers (deep) artiﬁcial feed-forward neural networks as parametric approximators in optimal control of discrete-time switched linear systems with controlled switching. The proposed approach is based on approximate dynamic programming and allows the fast computation of (sub-)optimal discrete and continuous control inputs, either by approximating the optimal cost-to-go functions or by approximating the optimal discrete and continuous input policies. An important property of the approach is the satisfaction of polytopic state and input constraints, which is crucial for ensuring safety, as required in many control applications. A numeric example is provided for illustration and evaluation of the approaches.}, author = {Schneegans, Jan and Eilbrecht, Jan and Zernetsch, Stefan and Bieshaar, Maarten and Doll, Konrad and Stursberg, Olaf and Sick, Bernhard}, booktitle = {Workshop From Benchmarking Behavior Prediction to Socially Compatible Behavior Generation in Autonomous Driving, IV}, keywords = {itegpub}, note = {(accepted)}, title = {Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning – A Case Study: Overtaking Cyclists}, year = 2021 }
%0 Conference Paper %1 schneegans2021probabilistic %A Schneegans, Jan %A Eilbrecht, Jan %A Zernetsch, Stefan %A Bieshaar, Maarten %A Doll, Konrad %A Stursberg, Olaf %A Sick, Bernhard %B Workshop From Benchmarking Behavior Prediction to Socially Compatible Behavior Generation in Autonomous Driving, IV %D 2021 %T Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning -- A Case Study: Overtaking Cyclists %X This work considers (deep) artiﬁcial feed-forward neural networks as parametric approximators in optimal control of discrete-time switched linear systems with controlled switching. The proposed approach is based on approximate dynamic programming and allows the fast computation of (sub-)optimal discrete and continuous control inputs, either by approximating the optimal cost-to-go functions or by approximating the optimal discrete and continuous input policies. An important property of the approach is the satisfaction of polytopic state and input constraints, which is crucial for ensuring safety, as required in many control applications. A numeric example is provided for illustration and evaluation of the approaches.
Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders.Möller, Felix; Botache, Diego; Huseljic, Denis; Heidecker, Florian; Bieshaar, Maarten; Sick, Bernhard. In Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD), CVPR, ble. 1–10. 2021.
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@inproceedings{moeller2021out, author = {Möller, Felix and Botache, Diego and Huseljic, Denis and Heidecker, Florian and Bieshaar, Maarten and Sick, Bernhard}, booktitle = {Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD), CVPR}, keywords = {itegpub}, pages = {1–10}, title = {{Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders}}, year = 2021 }
%0 Conference Paper %1 moeller2021out %A Möller, Felix %A Botache, Diego %A Huseljic, Denis %A Heidecker, Florian %A Bieshaar, Maarten %A Sick, Bernhard %B Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD), CVPR %D 2021 %P 1--10 %T Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders %U https://openaccess.thecvf.com/content/CVPR2021W/SAIAD/papers/Moller_Out-of-Distribution_Detection_and_Generation_Using_Soft_Brownian_Offset_Sampling_and_CVPRW_2021_paper.pdf
Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks.Kress, Viktor; Zernetsch, Stefan; Doll, Konrad; Sick, Bernhard. In Workshop on Integrated Artificial Intelligence in Data Science, ICPR, ble. 57–71. Springer, 2021.
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@inproceedings{kress2021pose, author = {Kress, Viktor and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, booktitle = {Workshop on Integrated Artificial Intelligence in Data Science, ICPR}, keywords = {itegpub}, pages = {57–71}, publisher = {Springer}, title = {Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks}, year = 2021 }
%0 Conference Paper %1 kress2021pose %A Kress, Viktor %A Zernetsch, Stefan %A Doll, Konrad %A Sick, Bernhard %B Workshop on Integrated Artificial Intelligence in Data Science, ICPR %D 2021 %I Springer %P 57--71 %R 10.1007/978-3-030-68763-2_5 %T Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks

2020[ to top ]

Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets.He, Yujiang; Henze, Janosch; Sick, Bernhard. In International Federation of Automatic Control (IFAC) World Congress, ble. 12175–12182. Elsevier, 2020.
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@inproceedings{he2020continuous, author = {He, Yujiang and Henze, Janosch and Sick, Bernhard}, booktitle = {{International Federation of Automatic Control (IFAC) World Congress}}, keywords = {itegpub}, pages = {12175–12182}, publisher = {Elsevier}, title = {Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets}, year = 2020 }
%0 Conference Paper %1 he2020continuous %A He, Yujiang %A Henze, Janosch %A Sick, Bernhard %B International Federation of Automatic Control (IFAC) World Congress %D 2020 %I Elsevier %P 12175--12182 %R 10.1016/j.ifacol.2020.12.1017 %T Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets
Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks.Kress, V.; Schreck, S.; Zernetsch, S.; Doll, K.; Sick, B. In IEEE Symposium Series on Computational Intelligence (SSCI), ble. 2723–2730. IEEE, 2020.
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@inproceedings{kress2020pose, author = {{Kress}, V. and {Schreck}, S. and {Zernetsch}, S. and {Doll}, K. and {Sick}, B.}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, keywords = {itegpub}, pages = {2723–2730}, publisher = {IEEE}, title = {Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks}, year = 2020 }
%0 Conference Paper %1 kress2020pose %A Kress, V. %A Schreck, S. %A Zernetsch, S. %A Doll, K. %A Sick, B. %B IEEE Symposium Series on Computational Intelligence (SSCI) %D 2020 %I IEEE %P 2723--2730 %R 10.1109/ssci47803.2020.9308462 %T Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks
Fairness, performance, and robustness: is there a cap theorem for self-adaptive and self-organising systems?Tomforde, Sven; Gruhl, Christian. In Workshop on Self-Improving System Integration (SISSY), ACSOS, ble. 54–59. IEEE, 2020.
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@inproceedings{tomforde2020fairness, author = {Tomforde, Sven and Gruhl, Christian}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {54–59}, publisher = {IEEE}, title = {Fairness, performance, and robustness: is there a cap theorem for self-adaptive and self-organising systems?}, year = 2020 }
%0 Conference Paper %1 tomforde2020fairness %A Tomforde, Sven %A Gruhl, Christian %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2020 %I IEEE %P 54--59 %R 10.1109/acsos-c51401.2020.00029 %T Fairness, performance, and robustness: is there a cap theorem for self-adaptive and self-organising systems?
Extended Coopetitive Soft Gating Ensemble.Deist, Stephan; Schreiber, Jens; Bieshaar, Maarten; Sick, Bernhard. In arXiv e-prints, bl. arXiv:2004.14026. 2020.
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@article{deist2020extended, author = {Deist, Stephan and Schreiber, Jens and Bieshaar, Maarten and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:2004.14026}, title = {{Extended Coopetitive Soft Gating Ensemble}}, year = 2020 }
%0 Journal Article %1 deist2020extended %A Deist, Stephan %A Schreiber, Jens %A Bieshaar, Maarten %A Sick, Bernhard %D 2020 %J arXiv e-prints %P arXiv:2004.14026 %T Extended Coopetitive Soft Gating Ensemble
Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning.Pham Minh, T.; Kottke, D.; Tsarenko, A.; Gruhl, C.; Sick, B. In International Joint Conference on Neural Networks (IJCNN). IEEE, 2020.
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@inproceedings{phamminh2020improving, author = {{Pham Minh}, T. and {Kottke}, D. and {Tsarenko}, A. and {Gruhl}, C. and {Sick}, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {itegpub}, publisher = {IEEE}, title = {Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning}, year = 2020 }
%0 Conference Paper %1 phamminh2020improving %A Pham Minh, T. %A Kottke, D. %A Tsarenko, A. %A Gruhl, C. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %D 2020 %I IEEE %R 10.1109/ijcnn48605.2020.9206873 %T Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning
Toward Optimal Probabilistic Active Learning Using a Bayesian Approach.Kottke, Daniel; Herde, Marek; Sandrock, Christoph; Huseljic, Denis; Krempl, Georg; Sick, Bernhard. In arXiv e-prints, bl. arXiv:2006.01732. 2020.
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@article{kottke2020optimal, author = {Kottke, Daniel and Herde, Marek and Sandrock, Christoph and Huseljic, Denis and Krempl, Georg and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:2006.01732}, title = {{Toward Optimal Probabilistic Active Learning Using a Bayesian Approach}}, year = 2020 }
%0 Journal Article %1 kottke2020optimal %A Kottke, Daniel %A Herde, Marek %A Sandrock, Christoph %A Huseljic, Denis %A Krempl, Georg %A Sick, Bernhard %D 2020 %J arXiv e-prints %P arXiv:2006.01732 %T Toward Optimal Probabilistic Active Learning Using a Bayesian Approach
Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification?Scheiner, Nicolas; Schumann, Ole; Kraus, Florian; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard. In IEEE International Conference on Information Fusion (FUSION), ble. 1–8. IEEE, 2020.
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@inproceedings{scheiner2020shelf, author = {Scheiner, Nicolas and Schumann, Ole and Kraus, Florian and Appenrodt, Nils and Dickmann, J{\"{u}}rgen and Sick, Bernhard}, booktitle = {{IEEE} International Conference on Information Fusion (FUSION)}, keywords = {itegpub}, pages = {1–8}, publisher = {IEEE}, title = {Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification?}, year = 2020 }
%0 Conference Paper %1 scheiner2020shelf %A Scheiner, Nicolas %A Schumann, Ole %A Kraus, Florian %A Appenrodt, Nils %A Dickmann, Jürgen %A Sick, Bernhard %B IEEE International Conference on Information Fusion (FUSION) %D 2020 %I IEEE %P 1--8 %R 10.23919/fusion45008.2020.9190338 %T Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification?
Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning.Haase-Schütz, Christian; Stal, Rainer; Hertlein, Heinz; Sick, Bernhard. In arXiv e-prints, bl. arXiv:2002.02705. 2020.
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@article{haaseschuetz2020iterative, author = {{Haase-Sch{\"u}tz}, Christian and {Stal}, Rainer and {Hertlein}, Heinz and {Sick}, Bernhard}, journal = {arXiv e-prints}, keywords = {Recognition,}, pages = {arXiv:2002.02705}, title = {{Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning}}, year = 2020 }
%0 Journal Article %1 haaseschuetz2020iterative %A Haase-Schütz, Christian %A Stal, Rainer %A Hertlein, Heinz %A Sick, Bernhard %D 2020 %J arXiv e-prints %P arXiv:2002.02705 %T Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning
Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary.Englhardt, Adrian; Trittenbach, Holger; Kottke, Daniel; Sick, Bernhard; Böhm, Klemens. In arXiv e-prints, bl. arXiv:2009.13853. 2020.
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@article{englhardt2020efficient, author = {{Englhardt}, Adrian and {Trittenbach}, Holger and {Kottke}, Daniel and {Sick}, Bernhard and {B{\"o}hm}, Klemens}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:2009.13853}, title = {{Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary}}, year = 2020 }
%0 Journal Article %1 englhardt2020efficient %A Englhardt, Adrian %A Trittenbach, Holger %A Kottke, Daniel %A Sick, Bernhard %A Böhm, Klemens %D 2020 %J arXiv e-prints %P arXiv:2009.13853 %T Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary
Probabilistic upscaling and aggregation of wind power forecasts.Henze, Janosch; Siefert, Malte; Bremicker-Trübelhorn, Sascha; Asanalieva, Nazgul; Sick, Bernhard. In Energy, Sustainability and Society, 10(1), bl. 15. BMC, 2020.
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Wind power forecasts of the expected wind feed-in for the next hours or days are necessary to integrate the generated volatile wind energy into power systems. Most forecasting models predict in some sense the best value, but they ignore the other possible outcomes which may arise because of forecasting uncertainties. Probabilistic forecasts, on the other hand, also predict a distribution of possible outcomes with their respective probabilities that specific power values will occur and therefore have higher information content. In this work, we address two problems that hinder the introduction of probabilistic forecasts in practice: (1) no measurement data are available for some wind farms and (2) the flexible aggregation of probabilistic forecasts for changing wind farm portfolios.
@article{henze2020probabilistic, abstract = {Wind power forecasts of the expected wind feed-in for the next hours or days are necessary to integrate the generated volatile wind energy into power systems. Most forecasting models predict in some sense the best value, but they ignore the other possible outcomes which may arise because of forecasting uncertainties. Probabilistic forecasts, on the other hand, also predict a distribution of possible outcomes with their respective probabilities that specific power values will occur and therefore have higher information content. In this work, we address two problems that hinder the introduction of probabilistic forecasts in practice: (1) no measurement data are available for some wind farms and (2) the flexible aggregation of probabilistic forecasts for changing wind farm portfolios.}, author = {Henze, Janosch and Siefert, Malte and Bremicker-Tr{\"u}belhorn, Sascha and Asanalieva, Nazgul and Sick, Bernhard}, journal = {Energy, Sustainability and Society}, keywords = {imported}, number = 1, pages = 15, publisher = {BMC}, title = {Probabilistic upscaling and aggregation of wind power forecasts}, volume = 10, year = 2020 }
%0 Journal Article %1 henze2020probabilistic %A Henze, Janosch %A Siefert, Malte %A Bremicker-Trübelhorn, Sascha %A Asanalieva, Nazgul %A Sick, Bernhard %D 2020 %I BMC %J Energy, Sustainability and Society %N 1 %P 15 %R 10.1186/s13705-020-00247-4 %T Probabilistic upscaling and aggregation of wind power forecasts %U https://doi.org/10.1186/s13705-020-00247-4 %V 10 %X Wind power forecasts of the expected wind feed-in for the next hours or days are necessary to integrate the generated volatile wind energy into power systems. Most forecasting models predict in some sense the best value, but they ignore the other possible outcomes which may arise because of forecasting uncertainties. Probabilistic forecasts, on the other hand, also predict a distribution of possible outcomes with their respective probabilities that specific power values will occur and therefore have higher information content. In this work, we address two problems that hinder the introduction of probabilistic forecasts in practice: (1) no measurement data are available for some wind farms and (2) the flexible aggregation of probabilistic forecasts for changing wind farm portfolios.
Knowledge Representations in Technical Systems -- A Taxonomy.Scharei, Kristina; Heidecker, Florian; Bieshaar, Maarten. In arXiv e-prints, bl. arXiv:2001.04835. 2020.
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@article{scharei2020knowledge, author = {Scharei, Kristina and Heidecker, Florian and Bieshaar, Maarten}, journal = {arXiv e-prints}, keywords = {Robotics}, pages = {arXiv:2001.04835}, title = {{Knowledge Representations in Technical Systems – A Taxonomy}}, year = 2020 }
%0 Journal Article %1 scharei2020knowledge %A Scharei, Kristina %A Heidecker, Florian %A Bieshaar, Maarten %D 2020 %J arXiv e-prints %P arXiv:2001.04835 %T Knowledge Representations in Technical Systems -- A Taxonomy
Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks.He, Y.; Henze, J.; Sick, B. In International Joint Conference on Neural Networks (IJCNN). IEEE, 2020.
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@inproceedings{he2020forecasting, author = {{He}, Y. and {Henze}, J. and {Sick}, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {itegpub}, publisher = {IEEE}, title = {Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks}, year = 2020 }
%0 Conference Paper %1 he2020forecasting %A He, Y. %A Henze, J. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %D 2020 %I IEEE %R 10.1109/ijcnn48605.2020.9207536 %T Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks
Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm.Meier, David; Hartmann, Gregor; Völker, Jens; Viefhaus, Jens; Sick, Bernhard. In Advances in Computational Methods for X-Ray Optics V, ble. 71–77. SPIE, 2020.
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@inproceedings{meier2020reconstruction, author = {Meier, David and Hartmann, Gregor and Völker, Jens and Viefhaus, Jens and Sick, Bernhard}, booktitle = {Advances in Computational Methods for X-Ray Optics V}, keywords = {itegpub}, pages = {71–77}, publisher = {SPIE}, title = {Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm}, year = 2020 }
%0 Conference Paper %1 meier2020reconstruction %A Meier, David %A Hartmann, Gregor %A Völker, Jens %A Viefhaus, Jens %A Sick, Bernhard %B Advances in Computational Methods for X-Ray Optics V %D 2020 %I SPIE %P 71--77 %R 10.1117/12.2568001 %T Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm %U https://doi.org/10.1117/12.2568001
A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour.Tomforde, Sven; Gruhl, Christian; Sick, Bernhard. In Workshop on Self -Aware Computing (SeAC), ACSOS, ble. 166–169. IEEE, 2020.
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@inproceedings{tomforde2020swarm, author = {Tomforde, Sven and Gruhl, Christian and Sick, Bernhard}, booktitle = {Workshop on Self -Aware Computing (SeAC), ACSOS}, keywords = {itegpub}, pages = {166–169}, publisher = {IEEE}, title = {A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour}, year = 2020 }
%0 Conference Paper %1 tomforde2020swarm %A Tomforde, Sven %A Gruhl, Christian %A Sick, Bernhard %B Workshop on Self -Aware Computing (SeAC), ACSOS %D 2020 %I IEEE %P 166--169 %R 10.1109/acsos-c51401.2020.00050 %T A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour
Normal-Wishart clustering for novelty detection.Gruhl, Christian; Schmeißing, Jörn; Tomforde, Sven; Sick, Bernhard. In Workshop on Self-Improving System Integration (SISSY), ACSOS, ble. 64–69. IEEE, 2020.
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@inproceedings{gruhl2020normal, author = {Gruhl, Christian and Schmei{\ss}ing, J{\"o}rn and Tomforde, Sven and Sick, Bernhard}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {64–69}, publisher = {IEEE}, title = {{Normal-Wishart} clustering for novelty detection}, year = 2020 }
%0 Conference Paper %1 gruhl2020normal %A Gruhl, Christian %A Schmeißing, Jörn %A Tomforde, Sven %A Sick, Bernhard %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2020 %I IEEE %P 64--69 %R 10.1109/acsos-c51401.2020.00032 %T Normal-Wishart clustering for novelty detection
Representation Learning in Power Time Series Forecasting.Henze, Janosch; Schreiber, Jens; Sick, Bernhard. In Deep Learning: Algorithms and Applications, W. Pedrycz, S.-M. Chen (reds.), ble. 67–101. Springer, 2020.
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@incollection{henze2020representation, author = {Henze, Janosch and Schreiber, Jens and Sick, Bernhard}, booktitle = {Deep Learning: Algorithms and Applications}, editor = {Pedrycz, Witold and Chen, Shyi-Ming}, keywords = {imported}, pages = {67–101}, publisher = {Springer}, title = {Representation Learning in Power Time Series Forecasting}, year = 2020 }
%0 Book Section %1 henze2020representation %A Henze, Janosch %A Schreiber, Jens %A Sick, Bernhard %B Deep Learning: Algorithms and Applications %D 2020 %E Pedrycz, Witold %E Chen, Shyi-Ming %I Springer %P 67--101 %R 10.1007/978-3-030-31760-7_3 %T Representation Learning in Power Time Series Forecasting %U https://doi.org/10.1007/978-3-030-31760-7_3 %@ 978-3-030-31760-7
Quantile Surfaces -- Generalizing Quantile Regression to Multivariate Targets.Bieshaar, Maarten; Schreiber, Jens; Vogt, Stephan; Gensler, André; Sick, Bernhard. In arXiv e-prints, bl. arXiv:2010.05898. 2020.
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@article{bieshaar2020quantile, author = {{Bieshaar}, Maarten and {Schreiber}, Jens and {Vogt}, Stephan and {Gensler}, Andr{\'e} and {Sick}, Bernhard}, journal = {arXiv e-prints}, keywords = {Applications,}, pages = {arXiv:2010.05898}, title = {{Quantile Surfaces – Generalizing Quantile Regression to Multivariate Targets}}, year = 2020 }
%0 Journal Article %1 bieshaar2020quantile %A Bieshaar, Maarten %A Schreiber, Jens %A Vogt, Stephan %A Gensler, André %A Sick, Bernhard %D 2020 %J arXiv e-prints %P arXiv:2010.05898 %T Quantile Surfaces -- Generalizing Quantile Regression to Multivariate Targets
Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar.Scheiner, Nicolas; Kraus, Florian; Wei, Fangyin; Phan, Buu; Mannan, Fahim; Appenrodt, Nils; Ritter, Werner; Dickmann, Jurgen; Dietmayer, Klaus; Sick, Bernhard; Heide, Felix. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020.
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@inproceedings{scheiner2020seeing, author = {Scheiner, Nicolas and Kraus, Florian and Wei, Fangyin and Phan, Buu and Mannan, Fahim and Appenrodt, Nils and Ritter, Werner and Dickmann, Jurgen and Dietmayer, Klaus and Sick, Bernhard and Heide, Felix}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, keywords = {itegpub}, publisher = {IEEE}, title = {Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar}, year = 2020 }
%0 Conference Paper %1 scheiner2020seeing %A Scheiner, Nicolas %A Kraus, Florian %A Wei, Fangyin %A Phan, Buu %A Mannan, Fahim %A Appenrodt, Nils %A Ritter, Werner %A Dickmann, Jurgen %A Dietmayer, Klaus %A Sick, Bernhard %A Heide, Felix %B IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) %D 2020 %I IEEE %R 10.1109/cvpr42600.2020.00214 %T Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar

2019[ to top ]

Smart Device Based Initial Movement Detection of Cyclists Using Convolutional Neural Networks.Schneegans, Jan; Bieshaar, Maarten. In Organic Computing -- Doctoral Dissertation Colloquium 2018, S. Tomforde, B. Sick (reds.), ble. 45–60. kassel university press, Kassel, Germany, 2019.
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@incollection{schneegans2019smart, address = {Kassel, Germany}, author = {Schneegans, Jan and Bieshaar, Maarten}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2018}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {45–60}, publisher = {kassel university press}, title = {Smart Device Based Initial Movement Detection of Cyclists Using Convolutional Neural Networks}, year = 2019 }
%0 Book Section %1 schneegans2019smart %A Schneegans, Jan %A Bieshaar, Maarten %B Organic Computing -- Doctoral Dissertation Colloquium 2018 %C Kassel, Germany %D 2019 %E Tomforde, S. %E Sick, B. %I kassel university press %P 45--60 %T Smart Device Based Initial Movement Detection of Cyclists Using Convolutional Neural Networks
On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes.Rudolph, Stefan; Tomforde, Sven; Hähner, Jörg. In arXiv e-prints, bl. arXiv:1905.04205. 2019.
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@article{rudolph2019detection, author = {{Rudolph}, Stefan and {Tomforde}, Sven and {H{\"a}hner}, J{\"o}rg}, journal = {arXiv e-prints}, keywords = {Science}, pages = {arXiv:1905.04205}, title = {{On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes}}, year = 2019 }
%0 Journal Article %1 rudolph2019detection %A Rudolph, Stefan %A Tomforde, Sven %A Hähner, Jörg %D 2019 %J arXiv e-prints %P arXiv:1905.04205 %T On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes
Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic.Schreiber, Jens; Jessulat, Maik; Sick, Bernhard. In International Conference on Artificial Neural Networks and Machine Learning (ICANN): Image Processing, ble. 550–564. Springer, Cham, 2019.
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@inproceedings{schreiber2019generative, address = {Cham}, author = {Schreiber, Jens and Jessulat, Maik and Sick, Bernhard}, booktitle = {International Conference on Artificial Neural Networks and Machine Learning (ICANN): Image Processing}, keywords = {imported}, pages = {550–564}, publisher = {Springer}, title = {Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic}, year = 2019 }
%0 Conference Paper %1 schreiber2019generative %A Schreiber, Jens %A Jessulat, Maik %A Sick, Bernhard %B International Conference on Artificial Neural Networks and Machine Learning (ICANN): Image Processing %C Cham %D 2019 %I Springer %P 550--564 %R 10.1007/978-3-030-30508-6_44 %T Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic %@ 978-3-030-30508-6
Pose Based Trajectory Forecast of Vulnerable Road Users.Kress, V.; Zernetsch, S.; Doll, K.; Sick, B. In IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Xiamen, 2019.
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@inproceedings{kress2019pose, address = {Xiamen}, author = {Kress, V. and Zernetsch, S. and Doll, K. and Sick, B.}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, keywords = {imported}, publisher = {IEEE}, title = {Pose Based Trajectory Forecast of Vulnerable Road Users}, year = 2019 }
%0 Conference Paper %1 kress2019pose %A Kress, V. %A Zernetsch, S. %A Doll, K. %A Sick, B. %B IEEE Symposium Series on Computational Intelligence (SSCI) %C Xiamen %D 2019 %I IEEE %R 10.1109/ssci44817.2019.9003023 %T Pose Based Trajectory Forecast of Vulnerable Road Users
Towards Corner Case Identification in Cyclists’ Trajectories.Heidecker, F.; Bieshaar, M.; Sick, B. In ACM Computer Science in Cars Symposium (CSCS). ACM, 2019.
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@inproceedings{heidecker2019towards, author = {Heidecker, F. and Bieshaar, M. and Sick, B.}, booktitle = {ACM Computer Science in Cars Symposium (CSCS)}, keywords = {imported}, publisher = {ACM}, title = {Towards Corner Case Identification in Cyclists’ Trajectories}, year = 2019 }
%0 Conference Paper %1 heidecker2019towards %A Heidecker, F. %A Bieshaar, M. %A Sick, B. %B ACM Computer Science in Cars Symposium (CSCS) %D 2019 %I ACM %T Towards Corner Case Identification in Cyclists’ Trajectories %U https://cscs19.cispa.saarland/files/cscs19_camera_ready/28_fhmbbs_19_cscs_.pdf
Explicit Consideration of Resilience in Organic Computing Design Processes.Tomforde, S.; Gelhausen, P.; Gruhl, C.; Haering, I.; Sick, B. In International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS, ble. 1–6. VDE, 2019.
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@inproceedings{tomforde2019explicit, author = {{Tomforde}, S. and {Gelhausen}, P. and {Gruhl}, C. and {Haering}, I. and {Sick}, B.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–6}, publisher = {VDE}, title = {Explicit Consideration of Resilience in Organic Computing Design Processes}, year = 2019 }
%0 Conference Paper %1 tomforde2019explicit %A Tomforde, S. %A Gelhausen, P. %A Gruhl, C. %A Haering, I. %A Sick, B. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %D 2019 %I VDE %P 1--6 %T Explicit Consideration of Resilience in Organic Computing Design Processes %U https://ieeexplore.ieee.org/document/8836197
Pose Based Start Intention Detection of Cyclists.Kress, V.; Jung, J.; Zernetsch, S.; Doll, K.; Sick, B. In IEEE International Conference on Intelligent Transportation Systems (ITSC), ble. 2381–2386. IEEE, 2019.
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@inproceedings{kress2019posea, author = {{Kress}, V. and {Jung}, J. and {Zernetsch}, S. and {Doll}, K. and {Sick}, B.}, booktitle = {IEEE International Conference on Intelligent Transportation Systems (ITSC)}, keywords = {displays}, pages = {2381–2386}, publisher = {IEEE}, title = {Pose Based Start Intention Detection of Cyclists}, year = 2019 }
%0 Conference Paper %1 kress2019posea %A Kress, V. %A Jung, J. %A Zernetsch, S. %A Doll, K. %A Sick, B. %B IEEE International Conference on Intelligent Transportation Systems (ITSC) %D 2019 %I IEEE %P 2381--2386 %R 10.1109/ITSC.2019.8917215 %T Pose Based Start Intention Detection of Cyclists
A Multi-Stage Clustering Framework for Automotive Radar Data.Scheiner, Nicolas; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard. In IEEE International Conference on Intelligent Transportation Systems (ITSC), ble. 2060–2067. IEEE, 2019.
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@inproceedings{scheiner2019multi, author = {Scheiner, Nicolas and Appenrodt, Nils and Dickmann, J{\"{u}}rgen and Sick, Bernhard}, booktitle = {IEEE International Conference on Intelligent Transportation Systems (ITSC)}, keywords = {imported}, pages = {2060–2067}, publisher = {IEEE}, title = {A Multi-Stage Clustering Framework for Automotive Radar Data}, year = 2019 }
%0 Conference Paper %1 scheiner2019multi %A Scheiner, Nicolas %A Appenrodt, Nils %A Dickmann, Jürgen %A Sick, Bernhard %B IEEE International Conference on Intelligent Transportation Systems (ITSC) %D 2019 %I IEEE %P 2060--2067 %R 10.1109/itsc.2019.8916873 %T A Multi-Stage Clustering Framework for Automotive Radar Data
Organic Computing -- Doctoral Dissertation Colloquium 2018. Tomforde, S.; Sick, B. In Vol. 13Intelligent Embedded Systems. kassel university press, 2019.
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@book{tomforde2019organic, editor = {Tomforde, S. and Sick, B.}, keywords = {itegpub}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Organic Computing – Doctoral Dissertation Colloquium 2018}, volume = 13, year = 2019 }
%0 Book %1 tomforde2019organic %B Intelligent Embedded Systems %D 2019 %E Tomforde, S. %E Sick, B. %I kassel university press %R 10.19211/KUP9783737606974 %T Organic Computing -- Doctoral Dissertation Colloquium 2018 %V 13
Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles.Scheiner, Nicolas; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard. In IEEE Intelligent Vehicles Symposium (IV), ble. 642–649. IEEE, Paris, France, 2019.
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@inproceedings{scheiner2019radar, address = {Paris, France}, author = {Scheiner, Nicolas and Appenrodt, Nils and Dickmann, J{\"{u}}rgen and Sick, Bernhard}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {imported}, pages = {642–649}, publisher = {IEEE}, title = {Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles}, year = 2019 }
%0 Conference Paper %1 scheiner2019radar %A Scheiner, Nicolas %A Appenrodt, Nils %A Dickmann, Jürgen %A Sick, Bernhard %B IEEE Intelligent Vehicles Symposium (IV) %C Paris, France %D 2019 %I IEEE %P 642--649 %R 10.1109/ivs.2019.8813773 %T Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles
Using grid supporting flexibility in electricity distribution networks.König, Immanuel; Heilmann, Erik; Henze, Janosch; David, Klaus; Wetzel, Heike; Sick, Bernhard. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft, ble. 531–544. Gesellschaft für Informatik e.V., Bonn, 2019.
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@inproceedings{koenig2019using, address = {Bonn}, author = {König, Immanuel and Heilmann, Erik and Henze, Janosch and David, Klaus and Wetzel, Heike and Sick, Bernhard}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft}, keywords = {imported}, pages = {531–544}, publisher = {Gesellschaft für Informatik e.V.}, title = {Using grid supporting flexibility in electricity distribution networks}, year = 2019 }
%0 Conference Paper %1 koenig2019using %A König, Immanuel %A Heilmann, Erik %A Henze, Janosch %A David, Klaus %A Wetzel, Heike %A Sick, Bernhard %B INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft %C Bonn %D 2019 %I Gesellschaft für Informatik e.V. %P 531--544 %R 10.18420/inf2019_70 %T Using grid supporting flexibility in electricity distribution networks
Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning.Vogt, Stephan; Braun, Axel; Dobschinski, Jan; Sick, Bernhard. In Wind Integration Workshop. Dublin, Ireland, 2019.
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@inproceedings{vogt2019wind, address = {Dublin, Ireland}, author = {Vogt, Stephan and Braun, Axel and Dobschinski, Jan and Sick, Bernhard}, booktitle = {Wind Integration Workshop}, keywords = {imported}, title = {Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning}, year = 2019 }
%0 Conference Paper %1 vogt2019wind %A Vogt, Stephan %A Braun, Axel %A Dobschinski, Jan %A Sick, Bernhard %B Wind Integration Workshop %C Dublin, Ireland %D 2019 %R 10.24406/publica-fhg-406413 %T Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning
Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices.Scheiner, Nicolas; Haag, Stefan; Appenrodt, Nils; Duraisamy, Bharanidhar; Dickmann, Jürgen; Fritzsche, Martin; Sick, Bernhard. In International Radar Symposium (IRS), ble. 1–10. Ulm, Germany, 2019.
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@inproceedings{scheiner2019automateda, address = {Ulm, Germany}, author = {Scheiner, Nicolas and Haag, Stefan and Appenrodt, Nils and Duraisamy, Bharanidhar and Dickmann, J{\"{u}}rgen and Fritzsche, Martin and Sick, Bernhard}, booktitle = {International Radar Symposium (IRS)}, keywords = {imported}, pages = {1–10}, title = {Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices}, year = 2019 }
%0 Conference Paper %1 scheiner2019automateda %A Scheiner, Nicolas %A Haag, Stefan %A Appenrodt, Nils %A Duraisamy, Bharanidhar %A Dickmann, Jürgen %A Fritzsche, Martin %A Sick, Bernhard %B International Radar Symposium (IRS) %C Ulm, Germany %D 2019 %P 1--10 %R 10.23919/irs.2019.8768169 %T Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices %@ 9783736998605
Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS.Scheiner, Nicolas; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard. In IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), ble. 5–9. IEEE, 2019.
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@inproceedings{scheiner2019automated, author = {Scheiner, Nicolas and Appenrodt, Nils and Dickmann, J{\"{u}}rgen and Sick, Bernhard}, booktitle = {IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)}, keywords = {imported}, pages = {5–9}, publisher = {IEEE}, title = {Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS}, year = 2019 }
%0 Conference Paper %1 scheiner2019automated %A Scheiner, Nicolas %A Appenrodt, Nils %A Dickmann, Jürgen %A Sick, Bernhard %B IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM) %D 2019 %I IEEE %P 5--9 %R 10.1109/ICMIM.2019.8726801 %T Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS
Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks.Zernetsch, S.; Reichert, H.; Kress, V.; Doll, K.; Sick, B. In IEEE Intelligent Vehicles Symposium (IV), ble. 810–815. IEEE, 2019.
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@inproceedings{zernetsch2019trajectory, author = {{Zernetsch}, S. and {Reichert}, H. and {Kress}, V. and {Doll}, K. and {Sick}, B.}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {imported}, pages = {810–815}, publisher = {IEEE}, title = {Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks}, year = 2019 }
%0 Conference Paper %1 zernetsch2019trajectory %A Zernetsch, S. %A Reichert, H. %A Kress, V. %A Doll, K. %A Sick, B. %B IEEE Intelligent Vehicles Symposium (IV) %D 2019 %I IEEE %P 810--815 %R 10.1109/IVS.2019.8814258 %T Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks
Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid.Schreiber, Jens. In Organic Computing -- Doctoral Dissertation Colloquium 2018, S. Tomforde, B. Sick (reds.), ble. 75–87. kassel university press, Kassel, Germany, 2019.
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@incollection{schreiber2019transfer, address = {Kassel, Germany}, author = {Schreiber, Jens}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2018}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {75–87}, publisher = {kassel university press}, title = {Transfer Learning in the Field of Renewable Energies – {A} Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid}, year = 2019 }
%0 Book Section %1 schreiber2019transfer %A Schreiber, Jens %B Organic Computing -- Doctoral Dissertation Colloquium 2018 %C Kassel, Germany %D 2019 %E Tomforde, S. %E Sick, B. %I kassel university press %P 75--87 %T Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid
Transfer Learning is a Crucial Capability of Intelligent Systems Self-Integrating at Runtime.Stein, A.; Tomforde, S. In Workshop on Self-Improving System Integration (SISSY), FAS*W, ble. 32–35. IEEE, 2019.
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@inproceedings{stein2019transfer, author = {{Stein}, A. and {Tomforde}, S.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, pages = {32–35}, publisher = {IEEE}, title = {Transfer Learning is a Crucial Capability of Intelligent Systems Self-Integrating at Runtime}, year = 2019 }
%0 Conference Paper %1 stein2019transfer %A Stein, A. %A Tomforde, S. %B Workshop on Self-Improving System Integration (SISSY), FAS*W %D 2019 %I IEEE %P 32--35 %R 10.1109/FAS-W.2019.00021 %T Transfer Learning is a Crucial Capability of Intelligent Systems Self-Integrating at Runtime
Limitations of Assessing Active Learning Performance at Runtime.Kottke, Daniel; Schellinger, Jim; Huseljic, Denis; Sick, Bernhard. In arXiv e-prints, bl. arXiv:1901.10338. 2019.
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@article{kottke2019limitations, author = {{Kottke}, Daniel and {Schellinger}, Jim and {Huseljic}, Denis and {Sick}, Bernhard}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:1901.10338}, title = {{Limitations of Assessing Active Learning Performance at Runtime}}, year = 2019 }
%0 Journal Article %1 kottke2019limitations %A Kottke, Daniel %A Schellinger, Jim %A Huseljic, Denis %A Sick, Bernhard %D 2019 %J arXiv e-prints %P arXiv:1901.10338 %T Limitations of Assessing Active Learning Performance at Runtime
On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework.D’Angelo, M.; Gerasimou, S.; Ghahremani, S.; Grohmann, J.; Nunes, I.; Pournaras, E.; Tomforde, S. In International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), ble. 13–24. IEEE/ACM, 2019.
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@inproceedings{dangelo2019learning, author = {{D'Angelo}, M. and {Gerasimou}, S. and {Ghahremani}, S. and {Grohmann}, J. and {Nunes}, I. and {Pournaras}, E. and {Tomforde}, S.}, booktitle = {International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)}, keywords = {imported}, pages = {13–24}, publisher = {IEEE/ACM}, title = {On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework}, year = 2019 }
%0 Conference Paper %1 dangelo2019learning %A D'Angelo, M. %A Gerasimou, S. %A Ghahremani, S. %A Grohmann, J. %A Nunes, I. %A Pournaras, E. %A Tomforde, S. %B International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) %D 2019 %I IEEE/ACM %P 13--24 %R 10.1109/SEAMS.2019.00012 %T On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework
Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models.Schreiber, Jens; Buschin, Artjom; Sick, Bernhard. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft, ble. 585–598. Gesellschaft für Informatik e.V., Bonn, 2019.
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@inproceedings{schreiber2019influences, address = {Bonn}, author = {Schreiber, Jens and Buschin, Artjom and Sick, Bernhard}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft}, keywords = {imported}, pages = {585–598}, publisher = {Gesellschaft für Informatik e.V.}, title = {Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models}, year = 2019 }
%0 Conference Paper %1 schreiber2019influences %A Schreiber, Jens %A Buschin, Artjom %A Sick, Bernhard %B INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft %C Bonn %D 2019 %I Gesellschaft für Informatik e.V. %P 585--598 %R 10.18420/inf2019_74 %T Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models
Early Pedestrian Movement Detection Using Smart Devices Based on Human Activity Recognition.Botache, Diego; Dandan, Liu; Bieshaar, Maarten; Sick, Bernhard. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge), ble. 229–238. Gesellschaft für Informatik e.V., Bonn, 2019.
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@inproceedings{botache2019early, address = {Bonn}, author = {Botache, Diego and Dandan, Liu and Bieshaar, Maarten and Sick, Bernhard}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge)}, keywords = {imported}, pages = {229–238}, publisher = {Gesellschaft für Informatik e.V.}, title = {Early Pedestrian Movement Detection Using Smart Devices Based on Human Activity Recognition}, year = 2019 }
%0 Conference Paper %1 botache2019early %A Botache, Diego %A Dandan, Liu %A Bieshaar, Maarten %A Sick, Bernhard %B INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge) %C Bonn %D 2019 %I Gesellschaft für Informatik e.V. %P 229--238 %R 10.18420/inf2019_ws26 %T Early Pedestrian Movement Detection Using Smart Devices Based on Human Activity Recognition
The Organic Computing Doctoral Dissertation Colloquium: Status and Overview in 2019.Krupitzer, Christian; Tomforde, Sven. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge), ble. 545–554. Gesellschaft für Informatik e.V., Bonn, 2019.
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@inproceedings{krupitzer2019organic, address = {Bonn}, author = {Krupitzer, Christian and Tomforde, Sven}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge)}, keywords = {imported}, pages = {545–554}, publisher = {Gesellschaft für Informatik e.V.}, title = {The Organic Computing Doctoral Dissertation Colloquium: Status and Overview in 2019}, year = 2019 }
%0 Conference Paper %1 krupitzer2019organic %A Krupitzer, Christian %A Tomforde, Sven %B INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge) %C Bonn %D 2019 %I Gesellschaft für Informatik e.V. %P 545--554 %R 10.18420/inf2019_ws57 %T The Organic Computing Doctoral Dissertation Colloquium: Status and Overview in 2019
Decision Support with Hybrid Intelligence.Calma, Adrian; Dellermann, Dominik. In Organic Computing -- Doctoral Dissertation Colloquium 2018, S. Tomforde, B. Sick (reds.), ble. 143–153. kassel university press, Kassel, Germany, 2019.
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@incollection{calma2019decision, address = {Kassel, Germany}, author = {Calma, Adrian and Dellermann, Dominik}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2018}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {143–153}, publisher = {kassel university press}, title = {Decision Support with Hybrid Intelligence}, year = 2019 }
%0 Book Section %1 calma2019decision %A Calma, Adrian %A Dellermann, Dominik %B Organic Computing -- Doctoral Dissertation Colloquium 2018 %C Kassel, Germany %D 2019 %E Tomforde, S. %E Sick, B. %I kassel university press %P 143--153 %T Decision Support with Hybrid Intelligence
Start Intention Detection of Cyclists using an LSTM Network.Kress, Viktor; Jung, Janis; Zernetsch, Stefan; Doll, Konrad; Sick, Bernhard. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge), ble. 219–228. Gesellschaft für Informatik e.V., Bonn, 2019.
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@inproceedings{kress2019start, address = {Bonn}, author = {Kress, Viktor and Jung, Janis and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge)}, keywords = {imported}, pages = {219–228}, publisher = {Gesellschaft für Informatik e.V.}, title = {Start Intention Detection of Cyclists using an {LSTM} Network}, year = 2019 }
%0 Conference Paper %1 kress2019start %A Kress, Viktor %A Jung, Janis %A Zernetsch, Stefan %A Doll, Konrad %A Sick, Bernhard %B INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge) %C Bonn %D 2019 %I Gesellschaft für Informatik e.V. %P 219--228 %R 10.18420/inf2019_ws25 %T Start Intention Detection of Cyclists using an LSTM Network
CHARIOT -- Towards a Continuous High-Level Adaptive Runtime Integration Testbed.Barnes, C. M.; Bellman, K.; Botev, J.; Diaconescu, A.; Esterle, L.; Gruhl, C.; Landauer, C.; Lewis, P. R.; Nelson, P. R.; Stein, A.; Stewart, C.; Tomforde, S. In Workshop on Self-Improving System Integration (SISSY), FAS*W, ble. 52–55. IEEE, 2019.
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@inproceedings{barnes2019chariot, author = {{Barnes}, C. M. and {Bellman}, K. and {Botev}, J. and {Diaconescu}, A. and {Esterle}, L. and {Gruhl}, C. and {Landauer}, C. and {Lewis}, P. R. and {Nelson}, P. R. and {Stein}, A. and {Stewart}, C. and {Tomforde}, S.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, pages = {52–55}, publisher = {IEEE}, title = {{CHARIOT} – {T}owards a Continuous High-Level Adaptive Runtime Integration Testbed}, year = 2019 }
%0 Conference Paper %1 barnes2019chariot %A Barnes, C. M. %A Bellman, K. %A Botev, J. %A Diaconescu, A. %A Esterle, L. %A Gruhl, C. %A Landauer, C. %A Lewis, P. R. %A Nelson, P. R. %A Stein, A. %A Stewart, C. %A Tomforde, S. %B Workshop on Self-Improving System Integration (SISSY), FAS*W %D 2019 %I IEEE %P 52--55 %R 10.1109/FAS-W.2019.00026 %T CHARIOT -- Towards a Continuous High-Level Adaptive Runtime Integration Testbed
Self-Improving System Integration -- On a Definition and Characteristics of the Challenge.Bellman, K. L.; Gruhl, C.; Landauer, C.; Tomforde, S. In Workshop on Self-Improving System Integration (SISSY), FAS*W, ble. 1–3. IEEE, 2019.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
The self-improving system integration (SISSY) initiative-sometimes also called self-integration-started over six years ago. Recently, we have seen contributions approaching parts of the scope from different directions. In order to provide a better understanding how these attempts support the development of novel SISSY technology, this paper presents a definition of the term. In addition, it discusses the fundamental challenges stemming from the increasingly interconnected character of large-scale distributed and adaptive systems typically referred to as "Interwoven Systems", with these systems describing the basic motivation for SISSY research.
@inproceedings{bellman2019self, abstract = {The self-improving system integration (SISSY) initiative-sometimes also called self-integration-started over six years ago. Recently, we have seen contributions approaching parts of the scope from different directions. In order to provide a better understanding how these attempts support the development of novel SISSY technology, this paper presents a definition of the term. In addition, it discusses the fundamental challenges stemming from the increasingly interconnected character of large-scale distributed and adaptive systems typically referred to as "Interwoven Systems", with these systems describing the basic motivation for SISSY research.}, author = {{Bellman}, K. L. and {Gruhl}, C. and {Landauer}, C. and {Tomforde}, S.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {integration;self}, pages = {1–3}, publisher = {IEEE}, title = {Self-Improving System Integration – {O}n a Definition and Characteristics of the Challenge}, year = 2019 }
%0 Conference Paper %1 bellman2019self %A Bellman, K. L. %A Gruhl, C. %A Landauer, C. %A Tomforde, S. %B Workshop on Self-Improving System Integration (SISSY), FAS*W %D 2019 %I IEEE %P 1--3 %R 10.1109/FAS-W.2019.00014 %T Self-Improving System Integration -- On a Definition and Characteristics of the Challenge %X The self-improving system integration (SISSY) initiative-sometimes also called self-integration-started over six years ago. Recently, we have seen contributions approaching parts of the scope from different directions. In order to provide a better understanding how these attempts support the development of novel SISSY technology, this paper presents a definition of the term. In addition, it discusses the fundamental challenges stemming from the increasingly interconnected character of large-scale distributed and adaptive systems typically referred to as "Interwoven Systems", with these systems describing the basic motivation for SISSY research.
Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields.Hanika, Tom; Herde, Marek; Kuhn, Jochen; Leimeister, Jan Marco; Lukowicz, Paul; Oeste-Reiß, Sarah; Schmidt, Albrecht; Sick, Bernhard; Stumme, Gerd; Tomforde, Sven; Zweig, Katharina Anna. In arXiv e-prints, bl. arXiv:1905.07264. 2019.
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@article{hanika2019collaborative, author = {{Hanika}, Tom and {Herde}, Marek and {Kuhn}, Jochen and {Leimeister}, Jan Marco and {Lukowicz}, Paul and {Oeste-Rei{\ss}}, Sarah and {Schmidt}, Albrecht and {Sick}, Bernhard and {Stumme}, Gerd and {Tomforde}, Sven and {Zweig}, Katharina Anna}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:1905.07264}, title = {{Collaborative Interactive Learning – A clarification of terms and a differentiation from other research fields}}, year = 2019 }
%0 Journal Article %1 hanika2019collaborative %A Hanika, Tom %A Herde, Marek %A Kuhn, Jochen %A Leimeister, Jan Marco %A Lukowicz, Paul %A Oeste-Reiß, Sarah %A Schmidt, Albrecht %A Sick, Bernhard %A Stumme, Gerd %A Tomforde, Sven %A Zweig, Katharina Anna %D 2019 %J arXiv e-prints %P arXiv:1905.07264 %T Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields
Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers.Knauer, Uwe; Styp von Rekowski, Cornelius; Stecklina, Marianne; Krokotsch, Tilman; Pham Minh, Tuan; Hauffe, Viola; Kilias, David; Ehrhardt, Ina; Sagischewski, Herbert; Chmara, Sergej; Seiffert, Udo. In Remote Sensing, 11(23), bl. 2788. MDPI, 2019.
- [ Abstract ]
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- [ EndNote ]
- [ URL ]
In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).
@article{knauer2019tree, abstract = {In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).}, author = {Knauer, Uwe and Styp von Rekowski, Cornelius and Stecklina, Marianne and Krokotsch, Tilman and Pham Minh, Tuan and Hauffe, Viola and Kilias, David and Ehrhardt, Ina and Sagischewski, Herbert and Chmara, Sergej and Seiffert, Udo}, journal = {Remote Sensing}, keywords = {imported}, number = 23, pages = 2788, publisher = {MDPI}, title = {Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers}, volume = 11, year = 2019 }
%0 Journal Article %1 knauer2019tree %A Knauer, Uwe %A Styp von Rekowski, Cornelius %A Stecklina, Marianne %A Krokotsch, Tilman %A Pham Minh, Tuan %A Hauffe, Viola %A Kilias, David %A Ehrhardt, Ina %A Sagischewski, Herbert %A Chmara, Sergej %A Seiffert, Udo %D 2019 %I MDPI %J Remote Sensing %N 23 %P 2788 %R 10.3390/rs11232788 %T Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers %U https://www.mdpi.com/2072-4292/11/23/2788 %V 11 %X In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).
Off-Board Car Diagnostics Based on Heterogeneous, Highly Imbalanced and High-Dimensional Data Using Machine Learning Techniques. Schlegel, Bernhard. kassel university press, 2019.
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@phdthesis{schlegel2019board, author = {Schlegel, Bernhard}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, title = {Off-Board Car Diagnostics Based on Heterogeneous, Highly Imbalanced and High-Dimensional Data Using Machine Learning Techniques}, year = 2019 }
%0 Thesis %1 schlegel2019board %A Schlegel, Bernhard %D 2019 %I kassel university press %R 10.17170/kobra-202008141582 %T Off-Board Car Diagnostics Based on Heterogeneous, Highly Imbalanced and High-Dimensional Data Using Machine Learning Techniques
INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beitr{{ä}}ge). Draude, Claude; Lange, Martin; Sick, Bernhard. Vol. P295. Gesellschaft für Informatik e.V., 2019.
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@proceedings{draude2019informatik, editor = {Draude, Claude and Lange, Martin and Sick, Bernhard}, keywords = {imported}, publisher = {Gesellschaft für Informatik e.V.}, title = {INFORMATIK 2019: 50 Jahre Gesellschaft f{\"{u}}r Informatik – Informatik f{\"{u}}r Gesellschaft (Workshop-Beitr{\"{a}}ge)}, volume = {{P295}}, year = 2019 }
%0 Conference Proceedings %1 draude2019informatik %D 2019 %E Draude, Claude %E Lange, Martin %E Sick, Bernhard %I Gesellschaft für Informatik e.V. %T INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beitr{{ä}}ge) %U https://dl.gi.de/handle/20.500.12116/25040 %V P295 %@ 978-3-88579-689-3
Mutual Influence-aware Runtime Learning of Self-adaptation Behavior.Rudolph, Stefan; Tomforde, Sven; Hähner, Jörg. In ACM Transactions on Autonomous and Adaptive Systems, 14(1), bl. 4. ACM, New York, NY, USA, 2019.
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@article{rudolph2019mutual, address = {New York, NY, USA}, author = {Rudolph, Stefan and Tomforde, Sven and H\"{a}hner, J\"{o}rg}, journal = {ACM Transactions on Autonomous and Adaptive Systems}, keywords = {imported}, number = 1, pages = 4, publisher = {ACM}, title = {Mutual Influence-aware Runtime Learning of Self-adaptation Behavior}, volume = 14, year = 2019 }
%0 Journal Article %1 rudolph2019mutual %A Rudolph, Stefan %A Tomforde, Sven %A Hähner, Jörg %C New York, NY, USA %D 2019 %I ACM %J ACM Transactions on Autonomous and Adaptive Systems %N 1 %P 4 %R 10.1145/3345319 %T Mutual Influence-aware Runtime Learning of Self-adaptation Behavior %U http://doi.acm.org/10.1145/3345319 %V 14
Emerging Self-Integration through Coordination of Autonomous Adaptive Systems.Lesch, V.; Krupitzer, C.; Tomforde, S. In Workshop on Self-Improving System Integration (SISSY), FAS*W, ble. 6–9. IEEE, 2019.
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@inproceedings{lesch2019emerging, author = {{Lesch}, V. and {Krupitzer}, C. and {Tomforde}, S.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, pages = {6–9}, publisher = {IEEE}, title = {Emerging Self-Integration through Coordination of Autonomous Adaptive Systems}, year = 2019 }
%0 Conference Paper %1 lesch2019emerging %A Lesch, V. %A Krupitzer, C. %A Tomforde, S. %B Workshop on Self-Improving System Integration (SISSY), FAS*W %D 2019 %I IEEE %P 6--9 %R 10.1109/FAS-W.2019.00016 %T Emerging Self-Integration through Coordination of Autonomous Adaptive Systems
Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality.Sandrock, C.; Herde, M.; Calma, A.; Kottke, D.; Sick, B. In International Joint Conference on Neural Networks (IJCNN), ble. 1–8. IEEE, 2019.
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@inproceedings{sandrock2019combining, author = {{Sandrock}, C. and {Herde}, M. and {Calma}, A. and {Kottke}, D. and {Sick}, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {annotators;confidence}, pages = {1–8}, publisher = {IEEE}, title = {Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality}, year = 2019 }
%0 Conference Paper %1 sandrock2019combining %A Sandrock, C. %A Herde, M. %A Calma, A. %A Kottke, D. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %D 2019 %I IEEE %P 1--8 %R 10.1109/IJCNN.2019.8852456 %T Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality
Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems.Lesch, V.; Krupitzer, C.; Tomforde, S. In International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS, ble. 1–8. VDE, 2019.
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- [ URL ]
@inproceedings{lesch2019multi, author = {{Lesch}, V. and {Krupitzer}, C. and {Tomforde}, S.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems}, year = 2019 }
%0 Conference Paper %1 lesch2019multi %A Lesch, V. %A Krupitzer, C. %A Tomforde, S. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %D 2019 %I VDE %P 1--8 %T Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems %U https://ieeexplore.ieee.org/document/8836195
Intentions of Vulnerable Road Users -- Detection and Forecasting by Means of Machine Learning.Goldhammer, M.; Köhler, S.; Zernetsch, S.; Doll, K.; Sick, B.; Dietmayer, K. In IEEE Transactions on Intelligent Transportation Systems, 21(7), ble. 3035–3045. IEEE, 2019.
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@article{goldhammer2019intentions, author = {{Goldhammer}, M. and {Köhler}, S. and {Zernetsch}, S. and {Doll}, K. and {Sick}, B. and {Dietmayer}, K.}, journal = {IEEE Transactions on Intelligent Transportation Systems}, keywords = {imported}, number = 7, pages = {3035–3045}, publisher = {IEEE}, title = {Intentions of Vulnerable Road Users – {D}etection and Forecasting by Means of Machine Learning}, volume = 21, year = 2019 }
%0 Journal Article %1 goldhammer2019intentions %A Goldhammer, M. %A Köhler, S. %A Zernetsch, S. %A Doll, K. %A Sick, B. %A Dietmayer, K. %D 2019 %I IEEE %J IEEE Transactions on Intelligent Transportation Systems %N 7 %P 3035--3045 %R 10.1109/TITS.2019.2923319 %T Intentions of Vulnerable Road Users -- Detection and Forecasting by Means of Machine Learning %V 21
Wind Power Ensemble Forecasting. Gensler, André. kassel university press, 2019.
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@phdthesis{gensler2019wind, author = {Gensler, André}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, title = {Wind Power Ensemble Forecasting}, year = 2019 }
%0 Thesis %1 gensler2019wind %A Gensler, André %D 2019 %I kassel university press %R 10.17170/kobra-202008181589 %T Wind Power Ensemble Forecasting
From "Normal" to "Abnormal": A Concept for Determining Expected Self-Adaptation Behaviour.Tomforde, Sven. In IEEE International Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC-SeAC), FAS*W, ble. 126–129. IEEE, 2019.
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@inproceedings{tomforde2019normal, author = {Tomforde, Sven}, booktitle = {IEEE International Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC-SeAC), FAS*W}, keywords = {imported}, pages = {126–129}, publisher = {IEEE}, title = {From "Normal" to "Abnormal": A Concept for Determining Expected Self-Adaptation Behaviour}, year = 2019 }
%0 Conference Paper %1 tomforde2019normal %A Tomforde, Sven %B IEEE International Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC-SeAC), FAS*W %D 2019 %I IEEE %P 126--129 %R 10.1109/fas-w.2019.00041 %T From "Normal" to "Abnormal": A Concept for Determining Expected Self-Adaptation Behaviour

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A Concept for Productivity Tracking based on Collaborative Interactive Learning Techniques.Calma, Adrian; Kuhn, Jochen; Leimeister, Jan Marco; Lukowicz, Paul; Oeste-Reiss, Sarah; Schmidt, Albrecht; Sick, Bernhard; Stumme, Gerd; Tomforde, Sven; Zweig, Anna Katharina. In Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS, ble. 150–159. VDE, London, UK, 2018.
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@inproceedings{calma2018concept, address = {London, UK}, author = {Calma, Adrian and Kuhn, Jochen and Leimeister, Jan Marco and Lukowicz, Paul and Oeste-Rei\ss, Sarah and Schmidt, Albrecht and Sick, Bernhard and Stumme, Gerd and Tomforde, Sven and Zweig, Anna Katharina}, booktitle = {Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {150–159}, publisher = {VDE}, title = {A Concept for Productivity Tracking based on Collaborative Interactive Learning Techniques}, year = 2018 }
%0 Conference Paper %1 calma2018concept %A Calma, Adrian %A Kuhn, Jochen %A Leimeister, Jan Marco %A Lukowicz, Paul %A Oeste-Reiss, Sarah %A Schmidt, Albrecht %A Sick, Bernhard %A Stumme, Gerd %A Tomforde, Sven %A Zweig, Anna Katharina %B Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS %C London, UK %D 2018 %I VDE %P 150--159 %T A Concept for Productivity Tracking based on Collaborative Interactive Learning Techniques %U https://ieeexplore.ieee.org/document/8385431
Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble.Bieshaar, M.; Zernetsch, S.; Hubert, A.; Sick, B.; Doll, K. In IEEE Transactions on Intelligent Vehicles, 3(4), ble. 534–544. IEEE, 2018.
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@article{bieshaar2018cooperative, author = {Bieshaar, M. and Zernetsch, S. and Hubert, A. and Sick, B. and Doll, K.}, journal = {IEEE Transactions on Intelligent Vehicles}, keywords = {imported}, number = 4, pages = {534–544}, publisher = {IEEE}, title = {Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble}, volume = 3, year = 2018 }
%0 Journal Article %1 bieshaar2018cooperative %A Bieshaar, M. %A Zernetsch, S. %A Hubert, A. %A Sick, B. %A Doll, K. %D 2018 %I IEEE %J IEEE Transactions on Intelligent Vehicles %N 4 %P 534--544 %R 10.1109/tiv.2018.2873900 %T Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble %V 3
Comparing the Effects of Disturbances in Self-adaptive Systems - A Generalised Approach for the Quantification of Robustness.Tomforde, Sven; Kantert, Jan; Müller-Schloer, Christian; Bödelt, Sebastian; Sick, Bernhard. In Transactions on Computational Collective Intelligence, 28, ble. 193–220. Springer, 2018.
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Self-adaptation and self-organisation (SASO) are increasingly used in information and communication technology to master complexity and keep the administrative effort at an acceptable level. However, using SASO mechanisms is not an end in itself -- the primary goal is typically to allow for a higher autonomy of systems in order to react appropriately to disturbances and dynamics in the environmental conditions. We refer to this goal as achieving "robustness". During design-time, engineers have different possibilities to develop SASO mechanisms for an underlying control problem. When deciding which path to follow, an analysis of the inherent robustness of possible solutions is necessary. In this article, we present a novel quantification method for robustness that provides the basis to compare different control strategies in similar conditions.
@article{tomforde2018comparing, abstract = {Self-adaptation and self-organisation (SASO) are increasingly used in information and communication technology to master complexity and keep the administrative effort at an acceptable level. However, using SASO mechanisms is not an end in itself – the primary goal is typically to allow for a higher autonomy of systems in order to react appropriately to disturbances and dynamics in the environmental conditions. We refer to this goal as achieving "robustness". During design-time, engineers have different possibilities to develop SASO mechanisms for an underlying control problem. When deciding which path to follow, an analysis of the inherent robustness of possible solutions is necessary. In this article, we present a novel quantification method for robustness that provides the basis to compare different control strategies in similar conditions.}, author = {Tomforde, Sven and Kantert, Jan and M{\"{u}}ller{-}Schloer, Christian and B{\"{o}}delt, Sebastian and Sick, Bernhard}, journal = {Transactions on Computational Collective Intelligence}, keywords = {imported}, pages = {193–220}, publisher = {Springer}, title = {Comparing the Effects of Disturbances in Self-adaptive Systems - {A} Generalised Approach for the Quantification of Robustness}, volume = 28, year = 2018 }
%0 Journal Article %1 tomforde2018comparing %A Tomforde, Sven %A Kantert, Jan %A Müller-Schloer, Christian %A Bödelt, Sebastian %A Sick, Bernhard %D 2018 %I Springer %J Transactions on Computational Collective Intelligence %P 193--220 %R 10.1007/978-3-319-78301-7_9 %T Comparing the Effects of Disturbances in Self-adaptive Systems - A Generalised Approach for the Quantification of Robustness %V 28 %X Self-adaptation and self-organisation (SASO) are increasingly used in information and communication technology to master complexity and keep the administrative effort at an acceptable level. However, using SASO mechanisms is not an end in itself -- the primary goal is typically to allow for a higher autonomy of systems in order to react appropriately to disturbances and dynamics in the environmental conditions. We refer to this goal as achieving "robustness". During design-time, engineers have different possibilities to develop SASO mechanisms for an underlying control problem. When deciding which path to follow, an analysis of the inherent robustness of possible solutions is necessary. In this article, we present a novel quantification method for robustness that provides the basis to compare different control strategies in similar conditions.
A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation.Gensler, André; Sick, Bernhard. In arXiv e-prints, bl. arXiv:1803.06344. 2018.
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@article{gensler2018multi, author = {Gensler, Andr{\'e} and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:1803.06344}, title = {{A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation}}, year = 2018 }
%0 Journal Article %1 gensler2018multi %A Gensler, André %A Sick, Bernhard %D 2018 %J arXiv e-prints %P arXiv:1803.06344 %T A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation
A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies.Gensler, André; Sick, Bernhard; Vogt, Stephan. In Renewable and Sustainable Energy Reviews, 96, ble. 352–379. Elsevier, 2018.
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@article{gensler2018review, author = {Gensler, Andr{\'e} and Sick, Bernhard and Vogt, Stephan}, journal = {Renewable and Sustainable Energy Reviews}, keywords = {imported}, pages = {352–379}, publisher = {Elsevier}, title = {A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies}, volume = 96, year = 2018 }
%0 Journal Article %1 gensler2018review %A Gensler, André %A Sick, Bernhard %A Vogt, Stephan %D 2018 %I Elsevier %J Renewable and Sustainable Energy Reviews %P 352--379 %R 10.1016/j.rser.2018.07.042 %T A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies %V 96
Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network.Zernetsch, Stefan; Kress, Viktor; Sick, Bernhard; Doll, Konrad. In IEEE Intelligent Vehicles Symposium (IV), ble. 1–6. IEEE, 2018.
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@inproceedings{zernetsch2018early, author = {Zernetsch, Stefan and Kress, Viktor and Sick, Bernhard and Doll, Konrad}, booktitle = {{IEEE} Intelligent Vehicles Symposium (IV)}, keywords = {imported}, pages = {1–6}, publisher = {IEEE}, title = {Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network}, year = 2018 }
%0 Conference Paper %1 zernetsch2018early %A Zernetsch, Stefan %A Kress, Viktor %A Sick, Bernhard %A Doll, Konrad %B IEEE Intelligent Vehicles Symposium (IV) %D 2018 %I IEEE %P 1--6 %R 10.1109/ivs.2018.8500428 %T Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network
Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure.Reitberger, G.; Zernetsch, S.; Bieshaar, M.; Sick, B.; Doll, K.; Fuchs, E. In IEEE International Conference on Intelligent Transportation Systems (ITSC). IEEE, Maui, HI, 2018.
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@inproceedings{reitberger2018cooperative, address = {Maui, HI}, author = {Reitberger, G. and Zernetsch, S. and Bieshaar, M. and Sick, B. and Doll, K. and Fuchs, E.}, booktitle = {IEEE International Conference on Intelligent Transportation Systems (ITSC)}, keywords = {imported}, publisher = {IEEE}, title = {Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure}, year = 2018 }
%0 Conference Paper %1 reitberger2018cooperative %A Reitberger, G. %A Zernetsch, S. %A Bieshaar, M. %A Sick, B. %A Doll, K. %A Fuchs, E. %B IEEE International Conference on Intelligent Transportation Systems (ITSC) %C Maui, HI %D 2018 %I IEEE %R 10.1109/itsc.2018.8569267 %T Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure
Active Learning with Realistic Data -- A Case Study.Calma, Adrian; Stolz, Moritz; Kottke, Daniel; Tomforde, Sven; Sick, Bernhard. In International Joint Conference on Neural Networks (IJCNN). IEEE, Rio de Janiero, Brazil, 2018.
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@inproceedings{calma2018active, address = {Rio de Janiero, Brazil}, author = {Calma, Adrian and Stolz, Moritz and Kottke, Daniel and Tomforde, Sven and Sick, Bernhard}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {imported}, publisher = {IEEE}, title = {Active Learning with Realistic Data – A Case Study}, year = 2018 }
%0 Conference Paper %1 calma2018active %A Calma, Adrian %A Stolz, Moritz %A Kottke, Daniel %A Tomforde, Sven %A Sick, Bernhard %B International Joint Conference on Neural Networks (IJCNN) %C Rio de Janiero, Brazil %D 2018 %I IEEE %R 10.1109/ijcnn.2018.8489394 %T Active Learning with Realistic Data -- A Case Study
Active Sorting -- An Efficient Training of a Sorting Robot with Active Learning Techniques.Herde, Marek; Kottke, Daniel; Calma, Adrian; Bieshaar, Maarten; Deist, Stephan; Sick, Bernhard. In International Joint Conference on Neural Networks (IJCNN). IEEE, Rio de Janiero, Brazil, 2018.
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@inproceedings{herde2018active, address = {Rio de Janiero, Brazil}, author = {Herde, Marek and Kottke, Daniel and Calma, Adrian and Bieshaar, Maarten and Deist, Stephan and Sick, Bernhard}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {imported}, publisher = {IEEE}, title = {Active Sorting – An Efficient Training of a Sorting Robot with Active Learning Techniques}, year = 2018 }
%0 Conference Paper %1 herde2018active %A Herde, Marek %A Kottke, Daniel %A Calma, Adrian %A Bieshaar, Maarten %A Deist, Stephan %A Sick, Bernhard %B International Joint Conference on Neural Networks (IJCNN) %C Rio de Janiero, Brazil %D 2018 %I IEEE %R 10.1109/ijcnn.2018.8489161 %T Active Sorting -- An Efficient Training of a Sorting Robot with Active Learning Techniques
Collaborative Interactive Learning.Sick, Bernhard; Oeste-Reiß, Sarah; Schmidt, Albrecht; Tomforde, Sven; Zweig, Katharina Anna. In Informatik Spektrum, 41(1), ble. 52–55. Springer, 2018.
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@article{sick2018collaborative, author = {Sick, Bernhard and Oeste{-}Rei{\ss}, Sarah and Schmidt, Albrecht and Tomforde, Sven and Zweig, Katharina Anna}, journal = {Informatik Spektrum}, keywords = {imported}, number = 1, pages = {52–55}, publisher = {Springer}, title = {Collaborative Interactive Learning}, volume = 41, year = 2018 }
%0 Journal Article %1 sick2018collaborative %A Sick, Bernhard %A Oeste-Reiß, Sarah %A Schmidt, Albrecht %A Tomforde, Sven %A Zweig, Katharina Anna %D 2018 %I Springer %J Informatik Spektrum %N 1 %P 52--55 %R 10.1007/s00287-017-1082-x %T Collaborative Interactive Learning %V 41
Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime.Gruhl, Christian; Tomforde, Sven; Sick, Bernhard. In Workshop on Self-Improving System Integration (SISSY), FAS*W, ble. 198–203. IEEE, 2018.
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@inproceedings{gruhl2018aspects, author = {Gruhl, Christian and Tomforde, Sven and Sick, Bernhard}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, pages = {198–203}, publisher = {IEEE}, title = {Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime}, year = 2018 }
%0 Conference Paper %1 gruhl2018aspects %A Gruhl, Christian %A Tomforde, Sven %A Sick, Bernhard %B Workshop on Self-Improving System Integration (SISSY), FAS*W %D 2018 %I IEEE %P 198--203 %R 10.1109/fas-w.2018.00047 %T Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime
Automated Active Learning with a Robot.Scharei, Kristina; Herde, Marek; Bieshaar, Maarten; Calma, Adrian; Kottke, Daniel; Sick, Bernhard. In Archives of Data Science, Series A (Online First), 5(1), bl. 16. KIT, 2018.
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@article{scharei2018automated, author = {Scharei, Kristina and Herde, Marek and Bieshaar, Maarten and Calma, Adrian and Kottke, Daniel and Sick, Bernhard}, journal = {Archives of Data Science, Series A (Online First)}, keywords = {imported}, number = 1, pages = 16, publisher = {KIT}, title = {Automated Active Learning with a Robot}, volume = 5, year = 2018 }
%0 Journal Article %1 scharei2018automated %A Scharei, Kristina %A Herde, Marek %A Bieshaar, Maarten %A Calma, Adrian %A Kottke, Daniel %A Sick, Bernhard %D 2018 %I KIT %J Archives of Data Science, Series A (Online First) %N 1 %P 16 %R 10.5445/KSP/1000087327/16 %T Automated Active Learning with a Robot %V 5
Coopetitive Soft Gating Ensemble.Schreiber, J.; Bieshaar, M.; Gensler, A.; Sick, B.; Deist, S. In Workshop on Self-Improving System Integration (SISSY), FAS*W. IEEE, Trento, Italy, 2018.
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@inproceedings{schreiber2018coopetitive, address = {Trento, Italy}, author = {Schreiber, J. and Bieshaar, M. and Gensler, A. and Sick, B. and Deist, S.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, publisher = {IEEE}, title = {Coopetitive Soft Gating Ensemble}, year = 2018 }
%0 Conference Paper %1 schreiber2018coopetitive %A Schreiber, J. %A Bieshaar, M. %A Gensler, A. %A Sick, B. %A Deist, S. %B Workshop on Self-Improving System Integration (SISSY), FAS*W %C Trento, Italy %D 2018 %I IEEE %R 10.1109/fas-w.2018.00046 %T Coopetitive Soft Gating Ensemble
Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. Krempl, Georg; Lemaire, Vincent; Kottke, Daniel; Calma, Adrian; Holzinger, Andreas; Polikar, Robi; Sick, Bernhard. In Vol. 2192CEUR Workshop Proceedings. 2018.
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@proceedings{krempl2018workshop, editor = {Krempl, Georg and Lemaire, Vincent and Kottke, Daniel and Calma, Adrian and Holzinger, Andreas and Polikar, Robi and Sick, Bernhard}, keywords = {imported}, series = {{CEUR} Workshop Proceedings}, title = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, volume = 2192, year = 2018 }
%0 Conference Proceedings %1 krempl2018workshop %B CEUR Workshop Proceedings %D 2018 %E Krempl, Georg %E Lemaire, Vincent %E Kottke, Daniel %E Calma, Adrian %E Holzinger, Andreas %E Polikar, Robi %E Sick, Bernhard %T Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %U http://ceur-ws.org/Vol-2192 %V 2192
Generalizing Application Agnostic Remaining Useful Life Estimation Using Data-Driven Open Source Algorithms.Schlegel, B.; Mrowca, A.; Wolf, P.; Sick, B.; Steinhorst, S. In IEEE International Conference on Big Data Analysis (ICBDA). IEEE, Shanghai, China, 2018.
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@inproceedings{schlegel2018generalizing, address = {Shanghai, China}, author = {Schlegel, B. and Mrowca, A. and Wolf, P. and Sick, B. and Steinhorst, S.}, booktitle = {IEEE International Conference on Big Data Analysis (ICBDA)}, keywords = {imported}, publisher = {IEEE}, title = {Generalizing Application Agnostic Remaining Useful Life Estimation Using Data-Driven Open Source Algorithms}, year = 2018 }
%0 Conference Paper %1 schlegel2018generalizing %A Schlegel, B. %A Mrowca, A. %A Wolf, P. %A Sick, B. %A Steinhorst, S. %B IEEE International Conference on Big Data Analysis (ICBDA) %C Shanghai, China %D 2018 %I IEEE %R 10.1109/icbda.2018.8367659 %T Generalizing Application Agnostic Remaining Useful Life Estimation Using Data-Driven Open Source Algorithms
Hijacked Smart Devices -- Methodical Foundations for Autonomous Theft Awareness based on Activity Recognition and Novelty Detection.Jänicke, Martin; Schmidt, Viktor; Sick, Bernhard; Tomforde, Sven; Lukowicz, Paul. In International Conference on Agents and Artificial Intelligence (ICAART). 2018.
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@inproceedings{jaenicke2018hijacked, author = {J{\"a}nicke, Martin and Schmidt, Viktor and Sick, Bernhard and Tomforde, Sven and Lukowicz, Paul}, booktitle = {International Conference on Agents and Artificial Intelligence (ICAART)}, keywords = {imported}, title = {Hijacked Smart Devices – Methodical Foundations for Autonomous Theft Awareness based on Activity Recognition and Novelty Detection}, year = 2018 }
%0 Conference Paper %1 jaenicke2018hijacked %A Jänicke, Martin %A Schmidt, Viktor %A Sick, Bernhard %A Tomforde, Sven %A Lukowicz, Paul %B International Conference on Agents and Artificial Intelligence (ICAART) %D 2018 %R 10.5220/0006594901310142 %T Hijacked Smart Devices -- Methodical Foundations for Autonomous Theft Awareness based on Activity Recognition and Novelty Detection
Starting Movement Detection of Cyclists Using Smart Devices.Bieshaar, M.; Depping, M.; Schneegans, J.; Sick, B. In IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, Turin, Italy, 2018.
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@inproceedings{bieshaar2018starting, address = {Turin, Italy}, author = {Bieshaar, M. and Depping, M. and Schneegans, J. and Sick, B.}, booktitle = {IEEE International Conference on Data Science and Advanced Analytics (DSAA)}, keywords = {imported}, publisher = {IEEE}, title = {Starting Movement Detection of Cyclists Using Smart Devices}, year = 2018 }
%0 Conference Paper %1 bieshaar2018starting %A Bieshaar, M. %A Depping, M. %A Schneegans, J. %A Sick, B. %B IEEE International Conference on Data Science and Advanced Analytics (DSAA) %C Turin, Italy %D 2018 %I IEEE %R 10.1109/dsaa.2018.00042 %T Starting Movement Detection of Cyclists Using Smart Devices
Sampling Strategies for Representative Time Series in Load Flow Calculations.Henze, Janosch; Kutzner, Stephan; Sick, Bernhard. In Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD, ble. 27–48. Springer, 2018.
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@inproceedings{henze2018sampling, author = {Henze, Janosch and Kutzner, Stephan and Sick, Bernhard}, booktitle = {Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD}, keywords = {imported}, pages = {27–48}, publisher = {Springer}, title = {Sampling Strategies for Representative Time Series in Load Flow Calculations}, year = 2018 }
%0 Conference Paper %1 henze2018sampling %A Henze, Janosch %A Kutzner, Stephan %A Sick, Bernhard %B Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD %D 2018 %I Springer %P 27--48 %R 10.1007/978-3-030-04303-2_3 %T Sampling Strategies for Representative Time Series in Load Flow Calculations
Towards Cooperative Self-adapting Activity Recognition.Jahn, Andreas; Tomforde, Sven; Morold, Michel; David, Klaus; Sick, Bernhard. In International Joint Conference on Pervasive and Embedded Computing and Communication Systems (PECCS), ble. 215–222. 2018.
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@inproceedings{jahn2018towards, author = {Jahn, Andreas and Tomforde, Sven and Morold, Michel and David, Klaus and Sick, Bernhard}, booktitle = {International Joint Conference on Pervasive and Embedded Computing and Communication Systems (PECCS)}, keywords = {imported}, pages = {215–222}, title = {Towards Cooperative Self-adapting Activity Recognition}, year = 2018 }
%0 Conference Paper %1 jahn2018towards %A Jahn, Andreas %A Tomforde, Sven %A Morold, Michel %A David, Klaus %A Sick, Bernhard %B International Joint Conference on Pervasive and Embedded Computing and Communication Systems (PECCS) %D 2018 %P 215--222 %R 10.5220/0006856102150222 %T Towards Cooperative Self-adapting Activity Recognition
Security Issues in Self-Improving System Integration - Challenges and Solution Strategies.Heck, Henner; Sick, Bernhard; Tomforde, Sven. In Workshop on Self-Improving System Integration (SISSY), FAS*W, ble. 176–181. IEEE, 2018.
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@inproceedings{heck2018security, author = {Heck, Henner and Sick, Bernhard and Tomforde, Sven}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, pages = {176–181}, publisher = {IEEE}, title = {Security Issues in Self-Improving System Integration - Challenges and Solution Strategies}, year = 2018 }
%0 Conference Paper %1 heck2018security %A Heck, Henner %A Sick, Bernhard %A Tomforde, Sven %B Workshop on Self-Improving System Integration (SISSY), FAS*W %D 2018 %I IEEE %P 176--181 %R 10.1109/fas-w.2018.00044 %T Security Issues in Self-Improving System Integration - Challenges and Solution Strategies
Organic Computing -- Doctoral Dissertation Colloquium 2017. Tomforde, S.; Sick, B. In Vol. 11Intelligent Embedded Systems. kassel university press, 2018.
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@book{tomforde2018organic, editor = {Tomforde, S. and Sick, B.}, keywords = {itegpub}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Organic Computing – Doctoral Dissertation Colloquium 2017}, volume = 11, year = 2018 }
%0 Book %1 tomforde2018organic %B Intelligent Embedded Systems %D 2018 %E Tomforde, S. %E Sick, B. %I kassel university press %R 10.19211/KUP9783737605151 %T Organic Computing -- Doctoral Dissertation Colloquium 2017 %V 11
Human Pose Estimation in Real Traffic Scenes.Kress, V.; Jung, J.; Zernetsch, S.; Doll, K.; Sick, B. In IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Bangalore, India, 2018.
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@inproceedings{kress2018human, address = {Bangalore, India}, author = {Kress, V. and Jung, J. and Zernetsch, S. and Doll, K. and Sick, B.}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, keywords = {imported}, publisher = {IEEE}, title = {Human Pose Estimation in Real Traffic Scenes}, year = 2018 }
%0 Conference Paper %1 kress2018human %A Kress, V. %A Jung, J. %A Zernetsch, S. %A Doll, K. %A Sick, B. %B IEEE Symposium Series on Computational Intelligence (SSCI) %C Bangalore, India %D 2018 %I IEEE %R 10.1109/ssci.2018.8628660 %T Human Pose Estimation in Real Traffic Scenes
Hosting capacity of low-voltage grids for distributed generation: Classification by means of machine learning techniques.Breker, Sebastian; Rentmeister, Jan; Sick, Bernhard; Braun, Martin. In Applied Soft Computing, 70, ble. 195–207. Elsevier, 2018.
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@article{breker2018hosting, author = {Breker, Sebastian and Rentmeister, Jan and Sick, Bernhard and Braun, Martin}, journal = {Applied Soft Computing}, keywords = {imported}, pages = {195–207}, publisher = {Elsevier}, title = {Hosting capacity of low-voltage grids for distributed generation: Classification by means of machine learning techniques}, volume = 70, year = 2018 }
%0 Journal Article %1 breker2018hosting %A Breker, Sebastian %A Rentmeister, Jan %A Sick, Bernhard %A Braun, Martin %D 2018 %I Elsevier %J Applied Soft Computing %P 195--207 %R 10.1016/j.asoc.2018.05.007 %T Hosting capacity of low-voltage grids for distributed generation: Classification by means of machine learning techniques %V 70
Novelty detection with CANDIES: a holistic technique based on probabilistic models.Gruhl, Christian; Sick, Bernhard. In International Journal of Machine Learning and Cybernetics, 9(6), ble. 927–945. Springer, 2018.
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@article{gruhl2018novelty, author = {Gruhl, Christian and Sick, Bernhard}, journal = {International Journal of Machine Learning and Cybernetics}, keywords = {imported}, number = 6, pages = {927–945}, publisher = {Springer}, title = {Novelty detection with {CANDIES:} a holistic technique based on probabilistic models}, volume = 9, year = 2018 }
%0 Journal Article %1 gruhl2018novelty %A Gruhl, Christian %A Sick, Bernhard %D 2018 %I Springer %J International Journal of Machine Learning and Cybernetics %N 6 %P 927--945 %R 10.1007/s13042-016-0618-8 %T Novelty detection with CANDIES: a holistic technique based on probabilistic models %V 9
Semi-supervised active learning for support vector machines: A novel approach that exploits structure information in data.Calma, A.; Reitmaier, T.; Sick, B. In Information Sciences, 456, ble. 13–33. Elsevier, 2018.
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@article{calma2018semi, author = {Calma, A. and Reitmaier, T. and Sick, B.}, journal = {Information Sciences}, keywords = {imported}, pages = {13–33}, publisher = {Elsevier}, title = {Semi-supervised active learning for support vector machines: {A} novel approach that exploits structure information in data}, volume = 456, year = 2018 }
%0 Journal Article %1 calma2018semi %A Calma, A. %A Reitmaier, T. %A Sick, B. %D 2018 %I Elsevier %J Information Sciences %P 13--33 %R 10.1016/j.ins.2018.04.063 %T Semi-supervised active learning for support vector machines: A novel approach that exploits structure information in data %V 456
Smart Device Stealing and CANDIES.Jänicke, Martin; Schmidt, Viktor; Sick, Bernhard; Tomforde, Sven; Lukowicz, Paul; Schmeißing, Jörn. In International Conference on Agents and Artificial Intelligence (ICAART), ble. 247–273. Springer, 2018.
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- [ EndNote ]
@inproceedings{jaenicke2018smart, author = {J{\"{a}}nicke, Martin and Schmidt, Viktor and Sick, Bernhard and Tomforde, Sven and Lukowicz, Paul and Schmei{\ss}ing, J{\"{o}}rn}, booktitle = {International Conference on Agents and Artificial Intelligence (ICAART)}, keywords = {imported}, pages = {247–273}, publisher = {Springer}, title = {Smart Device Stealing and {CANDIES}}, year = 2018 }
%0 Conference Paper %1 jaenicke2018smart %A Jänicke, Martin %A Schmidt, Viktor %A Sick, Bernhard %A Tomforde, Sven %A Lukowicz, Paul %A Schmeißing, Jörn %B International Conference on Agents and Artificial Intelligence (ICAART) %D 2018 %I Springer %P 247--273 %R 10.1007/978-3-030-05453-3_12 %T Smart Device Stealing and CANDIES
The Other Human in The Loop -- A Pilot Study to Find Selection Strategies for Active Learning.Kottke, Daniel; Calma, Adrian; Huseljic, Denis; Sandrock, Christoph; Kachergis, George; Sick, Bernhard. In International Joint Conference on Neural Networks (IJCNN). IEEE, Rio de Janiero, Brazil, 2018.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{kottke2018other, address = {Rio de Janiero, Brazil}, author = {Kottke, Daniel and Calma, Adrian and Huseljic, Denis and Sandrock, Christoph and Kachergis, George and Sick, Bernhard}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {imported}, publisher = {IEEE}, title = {The Other Human in The Loop – A Pilot Study to Find Selection Strategies for Active Learning}, year = 2018 }
%0 Conference Paper %1 kottke2018other %A Kottke, Daniel %A Calma, Adrian %A Huseljic, Denis %A Sandrock, Christoph %A Kachergis, George %A Sick, Bernhard %B International Joint Conference on Neural Networks (IJCNN) %C Rio de Janiero, Brazil %D 2018 %I IEEE %R 10.1109/ijcnn.2018.8489637 %T The Other Human in The Loop -- A Pilot Study to Find Selection Strategies for Active Learning
Gestaltungsraum für proactive Smart Homes zur Gesundheitsförderung.Kromat, Theresa; Dehling, Tobias; Haux, Reinhold; Peters, Christoph; Sick, Bernhard; Tomforde, Sven; Wolf, Klaus-Hendrik; Sunyaev, Ali. In Multikonferenz Wirtschaftsinformatik. Lüneburg, Germany, 2018.
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- [ EndNote ]
@inproceedings{kromat2018gestaltungsraum, address = {L{\"u}neburg, Germany}, author = {Kromat, Theresa and Dehling, Tobias and Haux, Reinhold and Peters, Christoph and Sick, Bernhard and Tomforde, Sven and Wolf, Klaus-Hendrik and Sunyaev, Ali}, booktitle = {Multikonferenz Wirtschaftsinformatik}, keywords = {imported}, title = {Gestaltungsraum für proactive Smart Homes zur Gesundheitsförderung}, year = 2018 }
%0 Conference Paper %1 kromat2018gestaltungsraum %A Kromat, Theresa %A Dehling, Tobias %A Haux, Reinhold %A Peters, Christoph %A Sick, Bernhard %A Tomforde, Sven %A Wolf, Klaus-Hendrik %A Sunyaev, Ali %B Multikonferenz Wirtschaftsinformatik %C Lüneburg, Germany %D 2018 %T Gestaltungsraum für proactive Smart Homes zur Gesundheitsförderung
Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models.Jänicke, Martin; Sick, Bernhard; Tomforde, Sven. In Informatics, 5(3), bl. 38. MDPI, 2018.
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- [ EndNote ]
@article{jaenicke2018self, author = {J{\"{a}}nicke, Martin and Sick, Bernhard and Tomforde, Sven}, journal = {Informatics}, keywords = {imported}, number = 3, pages = 38, publisher = {MDPI}, title = {Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models}, volume = 5, year = 2018 }
%0 Journal Article %1 jaenicke2018self %A Jänicke, Martin %A Sick, Bernhard %A Tomforde, Sven %D 2018 %I MDPI %J Informatics %N 3 %P 38 %R 10.3390/informatics5030038 %T Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models %V 5
Leveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration.Calma, Adrian; Oeste-Reiß, Sarah; Sick, Bernhard; Leimeister, Jan Marco. In Hawaii International Conference on System Sciences (HICSS). 2018.
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- [ EndNote ]
@inproceedings{calma2018leveraging, author = {Calma, Adrian and Oeste-Rei{\ss}, Sarah and Sick, Bernhard and Leimeister, Jan Marco}, booktitle = {Hawaii International Conference on System Sciences (HICSS)}, keywords = {imported}, title = {Leveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration}, year = 2018 }
%0 Conference Paper %1 calma2018leveraging %A Calma, Adrian %A Oeste-Reiß, Sarah %A Sick, Bernhard %A Leimeister, Jan Marco %B Hawaii International Conference on System Sciences (HICSS) %D 2018 %R 10.24251/hicss.2018.120 %T Leveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration
Radar-based Feature Design and Multiclass Classification for Road User Recognition.Scheiner, Nicolas; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard. In IEEE Intelligent Vehicles Symposium (IV), ble. 779–786. IEEE, Changshu, China, 2018.
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@inproceedings{scheiner2018radar, address = {Changshu, China}, author = {Scheiner, Nicolas and Appenrodt, Nils and Dickmann, J{\"{u}}rgen and Sick, Bernhard}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {imported}, pages = {779–786}, publisher = {IEEE}, title = {Radar-based Feature Design and Multiclass Classification for Road User Recognition}, year = 2018 }
%0 Conference Paper %1 scheiner2018radar %A Scheiner, Nicolas %A Appenrodt, Nils %A Dickmann, Jürgen %A Sick, Bernhard %B IEEE Intelligent Vehicles Symposium (IV) %C Changshu, China %D 2018 %I IEEE %P 779--786 %R 10.1109/ivs.2018.8500607 %T Radar-based Feature Design and Multiclass Classification for Road User Recognition
Towards Proactive Health-enabling Living Environments: Simulation-based Study and Research Challenges.Tomforde, Sven; Dehling, Tobias; Haux, Reinhold; Huseljic, Denis; Kottke, Daniel; Scheerbaum, Jonas; Sick, Bernhard; Sunyaev, Ali; Wolf, Klaus-Hendrik. In International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS, ble. 1–8. VDE, 2018.
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- [ EndNote ]
@inproceedings{tomforde2018towards, author = {Tomforde, Sven and Dehling, Tobias and Haux, Reinhold and Huseljic, Denis and Kottke, Daniel and Scheerbaum, Jonas and Sick, Bernhard and Sunyaev, Ali and Wolf, Klaus-Hendrik}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {Towards Proactive Health-enabling Living Environments: Simulation-based Study and Research Challenges}, year = 2018 }
%0 Conference Paper %1 tomforde2018towards %A Tomforde, Sven %A Dehling, Tobias %A Haux, Reinhold %A Huseljic, Denis %A Kottke, Daniel %A Scheerbaum, Jonas %A Sick, Bernhard %A Sunyaev, Ali %A Wolf, Klaus-Hendrik %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %D 2018 %I VDE %P 1--8 %T Towards Proactive Health-enabling Living Environments: Simulation-based Study and Research Challenges
Quantifying the Influences on Probabilistic Wind Power Forecasts.Schreiber, Jens; Sick, Bernhard. In International Conference on Power and Renewable Energy (ICPRE), ble. 1–6. 2018.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
In recent years, probabilistic forecasts techniques were proposed in research as well as in applications to integrate volatile renewable energy resources into the electrical grid. These techniques allow decision makers to take the uncertainty of the prediction into account and, therefore, to devise optimal decisions, e.g., related to costs and risks in the electrical grid. However, it was yet not studied how the input, such as numerical weather predictions, affects the model output of forecasting models in detail. Therefore, we examine the potential influences with techniques from the field of sensitivity analysis on three different black-box models to obtain insights into differences and similarities of these probabilistic models. The analysis shows a considerable number of potential influences in those models depending on, e.g., the predicted probability and the type of model. These effects motivate the need to take various influences into account when models are tested, analyzed, or compared. Nevertheless, results of the sensitivity analysis will allow us to select a model with advantages in the practical application.
@inproceedings{schreiber2018quantifying, abstract = {In recent years, probabilistic forecasts techniques were proposed in research as well as in applications to integrate volatile renewable energy resources into the electrical grid. These techniques allow decision makers to take the uncertainty of the prediction into account and, therefore, to devise optimal decisions, e.g., related to costs and risks in the electrical grid. However, it was yet not studied how the input, such as numerical weather predictions, affects the model output of forecasting models in detail. Therefore, we examine the potential influences with techniques from the field of sensitivity analysis on three different black-box models to obtain insights into differences and similarities of these probabilistic models. The analysis shows a considerable number of potential influences in those models depending on, e.g., the predicted probability and the type of model. These effects motivate the need to take various influences into account when models are tested, analyzed, or compared. Nevertheless, results of the sensitivity analysis will allow us to select a model with advantages in the practical application.}, author = {Schreiber, Jens and Sick, Bernhard}, booktitle = {International Conference on Power and Renewable Energy (ICPRE)}, keywords = {imported}, pages = {1–6}, title = {Quantifying the Influences on Probabilistic Wind Power Forecasts}, year = 2018 }
%0 Conference Paper %1 schreiber2018quantifying %A Schreiber, Jens %A Sick, Bernhard %B International Conference on Power and Renewable Energy (ICPRE) %D 2018 %P 1--6 %R 10.1051/e3sconf/20186406002 %T Quantifying the Influences on Probabilistic Wind Power Forecasts %X In recent years, probabilistic forecasts techniques were proposed in research as well as in applications to integrate volatile renewable energy resources into the electrical grid. These techniques allow decision makers to take the uncertainty of the prediction into account and, therefore, to devise optimal decisions, e.g., related to costs and risks in the electrical grid. However, it was yet not studied how the input, such as numerical weather predictions, affects the model output of forecasting models in detail. Therefore, we examine the potential influences with techniques from the field of sensitivity analysis on three different black-box models to obtain insights into differences and similarities of these probabilistic models. The analysis shows a considerable number of potential influences in those models depending on, e.g., the predicted probability and the type of model. These effects motivate the need to take various influences into account when models are tested, analyzed, or compared. Nevertheless, results of the sensitivity analysis will allow us to select a model with advantages in the practical application.

2017[ to top ]

Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence.Bieshaar, M.; Reitberger, G.; Zernetsch, S.; Sick, B.; Fuchs, E.; Doll, K. In Automatisiertes und vernetztes Fahren Symposium (AAET), ble. 67–87. Braunschweig, Germany, 2017.
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@inproceedings{bieshaar2017detecting, address = {Braunschweig, Germany}, author = {Bieshaar, M. and Reitberger, G. and Zernetsch, S. and Sick, B. and Fuchs, E. and Doll, K.}, booktitle = {Automatisiertes und vernetztes Fahren Symposium (AAET)}, keywords = {imported}, pages = {67–87}, title = {Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence}, year = 2017 }
%0 Conference Paper %1 bieshaar2017detecting %A Bieshaar, M. %A Reitberger, G. %A Zernetsch, S. %A Sick, B. %A Fuchs, E. %A Doll, K. %B Automatisiertes und vernetztes Fahren Symposium (AAET) %C Braunschweig, Germany %D 2017 %P 67--87 %T Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence
Development of an Offshore Specific Wind Power Forecasting System. Kurt, Melih. kassel university press, Kassel, Germany, 2017.
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@phdthesis{kurt2017development, address = {Kassel, Germany}, author = {Kurt, Melih}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, title = {Development of an Offshore Specific Wind Power Forecasting System}, year = 2017 }
%0 Thesis %1 kurt2017development %A Kurt, Melih %C Kassel, Germany %D 2017 %I kassel university press %T Development of an Offshore Specific Wind Power Forecasting System
Challenges of Reliable, Realistic and Comparable Active Learning Evaluation.Kottke, Daniel; Calma, Adrian; Huseljic, Denis; Krempl, Georg; Sick, Bernhard. In Workshop on Interactive Adaptive Learning (IAL), ECML PKDD, CEUR Workshop Proceedings, ble. 2–14. 2017.
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- [ URL ]
@inproceedings{kottke2017challenges, author = {Kottke, Daniel and Calma, Adrian and Huseljic, Denis and Krempl, Georg and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {imported}, pages = {2–14}, series = {CEUR Workshop Proceedings}, title = {Challenges of Reliable, Realistic and Comparable Active Learning Evaluation}, year = 2017 }
%0 Conference Paper %1 kottke2017challenges %A Kottke, Daniel %A Calma, Adrian %A Huseljic, Denis %A Krempl, Georg %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2017 %P 2--14 %T Challenges of Reliable, Realistic and Comparable Active Learning Evaluation %U http://ceur-ws.org/Vol-1924/ialatecml_paper0.pdf
Probabilistic Active Learning with Structure-Sensitive Kernels.Lang, Dominik; Kottke, Daniel; Krempl, Georg; Sick, Bernhard. In Workshop on Interactive Adaptive Learning (IAL), ECML PKDD, CEUR Workshop Proceedings, ble. 37–48. 2017.
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- [ URL ]
@inproceedings{lang2017probabilistic, author = {Lang, Dominik and Kottke, Daniel and Krempl, Georg and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {imported}, pages = {37–48}, series = {CEUR Workshop Proceedings}, title = {Probabilistic Active Learning with Structure-Sensitive Kernels}, year = 2017 }
%0 Conference Paper %1 lang2017probabilistic %A Lang, Dominik %A Kottke, Daniel %A Krempl, Georg %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2017 %P 37--48 %T Probabilistic Active Learning with Structure-Sensitive Kernels %U http://ceur-ws.org/Vol-1924/ialatecml_paper3.pdf
Measuring Self Organisation at Runtime -- A Quantification Method based on Divergence Measures.Tomforde, S.; Kantert, J.; Sick, B. In International Conference on Agents and Artificial Intelligence (ICAART), ble. 96–106. Porto, Portugal, 2017.
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@inproceedings{tomforde2017measuring, address = {Porto, Portugal}, author = {Tomforde, S. and Kantert, J. and Sick, B.}, booktitle = {International Conference on Agents and Artificial Intelligence (ICAART)}, keywords = {imported}, pages = {96–106}, title = {Measuring Self Organisation at Runtime – A Quantification Method based on Divergence Measures}, year = 2017 }
%0 Conference Paper %1 tomforde2017measuring %A Tomforde, S. %A Kantert, J. %A Sick, B. %B International Conference on Agents and Artificial Intelligence (ICAART) %C Porto, Portugal %D 2017 %P 96--106 %R 10.5220/0006240400960106 %T Measuring Self Organisation at Runtime -- A Quantification Method based on Divergence Measures
Socially-Sensitive Systems Design: Exploring Social Potential.Bellman, K.; Botev, J.; Hildmann, H.; Lewis, P. R.; Marsh, S.; Pitt, J.; Scholtes, I.; Tomforde, S. In IEEE Technology and Society Magazine, 36(3), ble. 72–80. IEEE, 2017.
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@article{bellman2017socially, author = {Bellman, K. and Botev, J. and Hildmann, H. and Lewis, P. R. and Marsh, S. and Pitt, J. and Scholtes, I. and Tomforde, S.}, journal = {IEEE Technology and Society Magazine}, keywords = {imported}, number = 3, pages = {72–80}, publisher = {IEEE}, title = {Socially-Sensitive Systems Design: Exploring Social Potential}, volume = 36, year = 2017 }
%0 Journal Article %1 bellman2017socially %A Bellman, K. %A Botev, J. %A Hildmann, H. %A Lewis, P. R. %A Marsh, S. %A Pitt, J. %A Scholtes, I. %A Tomforde, S. %D 2017 %I IEEE %J IEEE Technology and Society Magazine %N 3 %P 72--80 %R 10.1109/mts.2017.2728727 %T Socially-Sensitive Systems Design: Exploring Social Potential %V 36
Probabilistic wind power forecasting: A multi-scheme ensemble technique with gradual coopetitive soft gating.Gensler, A.; Sick, B. In IEEE Symposium Series on Computational Intelligence (SSCI), ble. 1–10. IEEE, 2017.
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@inproceedings{gensler2017probabilistic, author = {{Gensler}, A. and {Sick}, B.}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, keywords = {itegpub}, pages = {1–10}, publisher = {IEEE}, title = {Probabilistic wind power forecasting: A multi-scheme ensemble technique with gradual coopetitive soft gating}, year = 2017 }
%0 Conference Paper %1 gensler2017probabilistic %A Gensler, A. %A Sick, B. %B IEEE Symposium Series on Computational Intelligence (SSCI) %D 2017 %I IEEE %P 1--10 %R 10.1109/SSCI.2017.8285344 %T Probabilistic wind power forecasting: A multi-scheme ensemble technique with gradual coopetitive soft gating
Dealing with class imbalance the scalable way: Evaluation of various techniques based on classification grade and computational complexity.Schlegel, Bernhard; Sick, Bernhard. In Workshop on Data Science and Big Data Analytics (DSBDA), ICDM, ble. 69–78. IEEE, 2017.
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@inproceedings{schlegel2017dealing, author = {Schlegel, Bernhard and Sick, Bernhard}, booktitle = {Workshop on Data Science and Big Data Analytics (DSBDA), ICDM}, keywords = {imported}, pages = {69–78}, publisher = {IEEE}, title = {Dealing with class imbalance the scalable way: Evaluation of various techniques based on classification grade and computational complexity}, year = 2017 }
%0 Conference Paper %1 schlegel2017dealing %A Schlegel, Bernhard %A Sick, Bernhard %B Workshop on Data Science and Big Data Analytics (DSBDA), ICDM %D 2017 %I IEEE %P 69--78 %R 10.1109/icdmw.2017.16 %T Dealing with class imbalance the scalable way: Evaluation of various techniques based on classification grade and computational complexity
On Methodological and Technological Challenges for Proactive Health Management in Smart Homes.Wolf, J.-H.; Dehling, T.; Haux, R.; Sick, B.; Sunyaev, A.; Tomforde, S. In International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH), ble. 209–212. Athens, Greece, 2017.
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@inproceedings{wolf2017methodological, address = {Athens, Greece}, author = {Wolf, J.-H. and Dehling, T. and Haux, R. and Sick, B. and Sunyaev, A. and Tomforde, S.}, booktitle = {International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH)}, keywords = {imported}, pages = {209–212}, title = {On Methodological and Technological Challenges for Proactive Health Management in Smart Homes}, year = 2017 }
%0 Conference Paper %1 wolf2017methodological %A Wolf, J.-H. %A Dehling, T. %A Haux, R. %A Sick, B. %A Sunyaev, A. %A Tomforde, S. %B International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH) %C Athens, Greece %D 2017 %P 209--212 %T On Methodological and Technological Challenges for Proactive Health Management in Smart Homes
Performing event detection in time series with SwiftEvent: an algorithm with supervised learning of detection criteria.Gensler, A.; Sick, B. In Pattern Analysis and Applications, 21(2), ble. 543–562. Springer, 2017.
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@article{gensler2017performing, author = {Gensler, A. and Sick, B.}, journal = {Pattern Analysis and Applications}, keywords = {imported}, number = 2, pages = {543–562}, publisher = {Springer}, title = {Performing event detection in time series with {SwiftEvent}: an algorithm with supervised learning of detection criteria}, volume = 21, year = 2017 }
%0 Journal Article %1 gensler2017performing %A Gensler, A. %A Sick, B. %D 2017 %I Springer %J Pattern Analysis and Applications %N 2 %P 543--562 %R 10.1007/s10044-017-0657-0 %T Performing event detection in time series with SwiftEvent: an algorithm with supervised learning of detection criteria %V 21
Organic Computing in the Spotlight.Tomforde, Sven; Sick, Bernhard; Müller-Schloer, Christian. In arXiv e-prints, bl. arXiv:1701.08125. 2017.
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@article{tomforde2017organic, author = {{Tomforde}, Sven and {Sick}, Bernhard and {M{\"u}ller-Schloer}, Christian}, journal = {arXiv e-prints}, keywords = {Science}, pages = {arXiv:1701.08125}, title = {{Organic Computing in the Spotlight}}, year = 2017 }
%0 Journal Article %1 tomforde2017organic %A Tomforde, Sven %A Sick, Bernhard %A Müller-Schloer, Christian %D 2017 %J arXiv e-prints %P arXiv:1701.08125 %T Organic Computing in the Spotlight
Organic Computing -- Techncial Systems for Survival in the Real World. Müller-Schloer, Christian; Tomforde, Sven. In Autonomic Systems. Birkhäuser Verlag, 2017.
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@book{muellerschloer2017organic, author = {M\"uller-Schloer, Christian and Tomforde, Sven}, keywords = {imported}, publisher = {Birkh\"auser Verlag}, series = {Autonomic Systems}, title = {Organic Computing – Techncial Systems for Survival in the Real World}, year = 2017 }
%0 Book %1 muellerschloer2017organic %A Müller-Schloer, Christian %A Tomforde, Sven %B Autonomic Systems %D 2017 %I Birkhäuser Verlag %R 10.1007/978-3-319-68477-2 %T Organic Computing -- Techncial Systems for Survival in the Real World
Cooperative Starting Intention Detection of Cyclists Based on Smart Devices and Infrastructure.Bieshaar, M.; Zernetsch, S.; Depping, M.; Sick, B.; Doll, K. In IEEE International Conference on Intelligent Transportation Systems (ITSC). IEEE, Yokohama, Japan, 2017.
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@inproceedings{bieshaar2017cooperative, address = {Yokohama, Japan}, author = {Bieshaar, M. and Zernetsch, S. and Depping, M. and Sick, B. and Doll, K.}, booktitle = {IEEE International Conference on Intelligent Transportation Systems (ITSC)}, keywords = {imported}, publisher = {IEEE}, title = {Cooperative Starting Intention Detection of Cyclists Based on Smart Devices and Infrastructure}, year = 2017 }
%0 Conference Paper %1 bieshaar2017cooperative %A Bieshaar, M. %A Zernetsch, S. %A Depping, M. %A Sick, B. %A Doll, K. %B IEEE International Conference on Intelligent Transportation Systems (ITSC) %C Yokohama, Japan %D 2017 %I IEEE %R 10.1109/itsc.2017.8317691 %T Cooperative Starting Intention Detection of Cyclists Based on Smart Devices and Infrastructure
Case Study on Pool-based Active Learning with Human Oracles.Calma, A. In Organic Computing -- Doctoral Dissertation Colloquium 2017, S. Tomforde, B. Sick (reds.), ble. 39–49. kassel university press, Kassel, Germany, 2017.
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@incollection{calma2017case, address = {Kassel, Germany}, author = {Calma, A.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2017}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {39–49}, publisher = {kassel university press}, title = {Case Study on Pool-based Active Learning with Human Oracles}, year = 2017 }
%0 Book Section %1 calma2017case %A Calma, A. %B Organic Computing -- Doctoral Dissertation Colloquium 2017 %C Kassel, Germany %D 2017 %E Tomforde, S. %E Sick, B. %I kassel university press %P 39--49 %T Case Study on Pool-based Active Learning with Human Oracles
Learning Without Ground Truth.Beyer, C.; Bieshaar, M.; Calma, A.; Heck, H.; Kottke, D.; Würtz, R. In Organic Computing -- Doctoral Dissertation Colloquium 2017, S. Tomforde, B. Sick (reds.). kassel university press, Bochum, Germany, 2017.
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@incollection{beyer2017learning, address = {Bochum, Germany}, author = {Beyer, C. and Bieshaar, M. and Calma, A. and Heck, H. and Kottke, D. and W{\"u}rtz, R.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2017}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, publisher = {kassel university press}, title = {Learning Without Ground Truth}, year = 2017 }
%0 Book Section %1 beyer2017learning %A Beyer, C. %A Bieshaar, M. %A Calma, A. %A Heck, H. %A Kottke, D. %A Würtz, R. %B Organic Computing -- Doctoral Dissertation Colloquium 2017 %C Bochum, Germany %D 2017 %E Tomforde, S. %E Sick, B. %I kassel university press %T Learning Without Ground Truth
Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms.Calma, A.; Kottke, D.; Sick, B.; Tomforde, S. In Workshop on Self-Improving System Integration (SISSY), FAS*W, ble. 109–116. IEEE, Tucson, AZ, 2017.
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@inproceedings{calma2017learning, address = {Tucson, AZ}, author = {Calma, A. and Kottke, D. and Sick, B. and Tomforde, S.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, pages = {109–116}, publisher = {IEEE}, title = {Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms}, year = 2017 }
%0 Conference Paper %1 calma2017learning %A Calma, A. %A Kottke, D. %A Sick, B. %A Tomforde, S. %B Workshop on Self-Improving System Integration (SISSY), FAS*W %C Tucson, AZ %D 2017 %I IEEE %P 109--116 %R 10.1109/fas-w.2017.129 %T Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms
Interpolation in the eXtended Classifier System: An Architectural Perspective.Stein, A.; Rauh, D.; Tomforde, S.; Hähner, J. In Journal of Systems Architecture, 75, ble. 79–94. Elsevier, 2017.
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@article{stein2017interpolation, author = {Stein, A. and Rauh, D. and Tomforde, S. and H{\"a}hner, J.}, journal = {Journal of Systems Architecture}, keywords = {imported}, pages = {79–94}, publisher = {Elsevier}, title = {{Interpolation in the e{X}tended {C}lassifier {S}ystem: An Architectural Perspective}}, volume = 75, year = 2017 }
%0 Journal Article %1 stein2017interpolation %A Stein, A. %A Rauh, D. %A Tomforde, S. %A Hähner, J. %D 2017 %I Elsevier %J Journal of Systems Architecture %P 79--94 %R 10.1016/j.sysarc.2017.01.010 %T Interpolation in the eXtended Classifier System: An Architectural Perspective %V 75
Self-Learning Smart Cameras -- Harnessing the Generalisation Capability of XCS.Stein, A.; Rudolph, S.; Tomforde, S.; Hähner, J. In International Joint Conference on Computational Intelligence (IJCCI). Funchal, Portugal, 2017.
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@inproceedings{stein2017self, address = {Funchal, Portugal}, author = {Stein, A. and Rudolph, S. and Tomforde, S. and Hähner, J}, booktitle = {International Joint Conference on Computational Intelligence (IJCCI)}, keywords = {imported}, title = {Self-Learning Smart Cameras – Harnessing the Generalisation Capability of {XCS}}, year = 2017 }
%0 Conference Paper %1 stein2017self %A Stein, A. %A Rudolph, S. %A Tomforde, S. %A Hähner, J %B International Joint Conference on Computational Intelligence (IJCCI) %C Funchal, Portugal %D 2017 %R 10.5220/0006512101290140 %T Self-Learning Smart Cameras -- Harnessing the Generalisation Capability of XCS
Highly Automated Learning for Improved Active Safety of Vulnerable Road Users.Bieshaar, M.; Reitberger, G.; Kress, V.; Zernetsch, S.; Doll, K.; Fuchs, E.; Sick, B. In ACM Computer Science in Cars Symposium (CSCS). ACM, Munich, Germany, 2017.
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- [ EndNote ]
@inproceedings{bieshaar2017highly, address = {Munich, Germany}, author = {Bieshaar, M. and Reitberger, G. and Kre\ss, V. and Zernetsch, S. and Doll, K. and Fuchs, E. and Sick, B.}, booktitle = {ACM Computer Science in Cars Symposium (CSCS)}, keywords = {imported}, publisher = {ACM}, title = {Highly Automated Learning for Improved Active Safety of Vulnerable Road Users}, year = 2017 }
%0 Conference Paper %1 bieshaar2017highly %A Bieshaar, M. %A Reitberger, G. %A Kress, V. %A Zernetsch, S. %A Doll, K. %A Fuchs, E. %A Sick, B. %B ACM Computer Science in Cars Symposium (CSCS) %C Munich, Germany %D 2017 %I ACM %T Highly Automated Learning for Improved Active Safety of Vulnerable Road Users
Highly Autonomous Learning in Collaborative, Technical Systems.Gruhl, C. In Organic Computing -- Doctoral Dissertation Colloquium 2017, S. Tomforde, B. Sick (reds.). kassel university press, Kassel, Germany, 2017.
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- [ EndNote ]
@incollection{gruhl2017highly, address = {Kassel, Germany}, author = {Gruhl, C.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2017}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Highly Autonomous Learning in Collaborative, Technical Systems}, year = 2017 }
%0 Book Section %1 gruhl2017highly %A Gruhl, C. %B Organic Computing -- Doctoral Dissertation Colloquium 2017 %C Kassel, Germany %D 2017 %E Tomforde, S. %E Sick, B. %I kassel university press %T Highly Autonomous Learning in Collaborative, Technical Systems
Self-Adaptation of Activity Recognition Systems to New Sensors.Bannach, David; Jänicke, Martin; Rey, Vitor F.; Tomforde, Sven; Sick, Bernhard; Lukowicz, Paul. In arXiv e-prints, bl. arXiv:1701.08528. 2017.
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- [ EndNote ]
@article{bannach2017self, author = {{Bannach}, David and {J{\"a}nicke}, Martin and {Rey}, Vitor F. and {Tomforde}, Sven and {Sick}, Bernhard and {Lukowicz}, Paul}, journal = {arXiv e-prints}, keywords = {Recognition,}, pages = {arXiv:1701.08528}, title = {{Self-Adaptation of Activity Recognition Systems to New Sensors}}, year = 2017 }
%0 Journal Article %1 bannach2017self %A Bannach, David %A Jänicke, Martin %A Rey, Vitor F. %A Tomforde, Sven %A Sick, Bernhard %A Lukowicz, Paul %D 2017 %J arXiv e-prints %P arXiv:1701.08528 %T Self-Adaptation of Activity Recognition Systems to New Sensors
Identification and Classification of Agent Behaviour at Runtime in Open, Trust-based Organic Computing Systems.Kantert, J.; Tomforde, S.; Scharrer, R.; Weber, S.; Müller-Schloer, C.; Edenhofer, S. In Journal of Systems Architecture, 75, ble. 68–78. Elsevier, 2017.
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@article{kantert2017identification, author = {Kantert, J. and Tomforde, S. and Scharrer, R. and Weber, S. and M{\"u}ller-Schloer, C. and Edenhofer, S.}, journal = {Journal of Systems Architecture}, keywords = {imported}, pages = {68–78}, publisher = {Elsevier}, title = {Identification and Classification of Agent Behaviour at Runtime in Open, Trust-based Organic Computing Systems}, volume = 75, year = 2017 }
%0 Journal Article %1 kantert2017identification %A Kantert, J. %A Tomforde, S. %A Scharrer, R. %A Weber, S. %A Müller-Schloer, C. %A Edenhofer, S. %D 2017 %I Elsevier %J Journal of Systems Architecture %P 68--78 %R 10.1016/j.sysarc.2017.02.003 %T Identification and Classification of Agent Behaviour at Runtime in Open, Trust-based Organic Computing Systems %V 75
Identifying Representative Load Time Series for Load Flow Calculations.Henze, Janosch; Kneiske, Tanja; Braun, Martin; Sick, Bernhard. In Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD, ble. 83–93. Springer, Cham, Switzerland, 2017.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Power system analyses increasingly use annual time series for temporal and spatial assessment of operational and also planning aspects. These analyses are often limited due to the computational time of the large amount of load flow calculation. By introducing algorithms which are capable of generating shorter and representative time series of measured load or power generation time series, the calculation time for load flow calculations can be reduced. We present a method which is capable of extracting features from the time series and use those features to create a representative time series. Furthermore, we show that our method is capable of maintaining the most important statistical features of the original time series by applying a Fisher-Pitman Permutation test.
@inproceedings{henze2017identifying, abstract = {Power system analyses increasingly use annual time series for temporal and spatial assessment of operational and also planning aspects. These analyses are often limited due to the computational time of the large amount of load flow calculation. By introducing algorithms which are capable of generating shorter and representative time series of measured load or power generation time series, the calculation time for load flow calculations can be reduced. We present a method which is capable of extracting features from the time series and use those features to create a representative time series. Furthermore, we show that our method is capable of maintaining the most important statistical features of the original time series by applying a Fisher-Pitman Permutation test.}, address = {Cham, Switzerland}, author = {Henze, Janosch and Kneiske, Tanja and Braun, Martin and Sick, Bernhard}, booktitle = {Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD}, keywords = {imported}, pages = {83–93}, publisher = {Springer}, title = {Identifying Representative Load Time Series for Load Flow Calculations}, year = 2017 }
%0 Conference Paper %1 henze2017identifying %A Henze, Janosch %A Kneiske, Tanja %A Braun, Martin %A Sick, Bernhard %B Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD %C Cham, Switzerland %D 2017 %I Springer %P 83--93 %R 10.1007/978-3-319-71643-5_8 %T Identifying Representative Load Time Series for Load Flow Calculations %X Power system analyses increasingly use annual time series for temporal and spatial assessment of operational and also planning aspects. These analyses are often limited due to the computational time of the large amount of load flow calculation. By introducing algorithms which are capable of generating shorter and representative time series of measured load or power generation time series, the calculation time for load flow calculations can be reduced. We present a method which is capable of extracting features from the time series and use those features to create a representative time series. Furthermore, we show that our method is capable of maintaining the most important statistical features of the original time series by applying a Fisher-Pitman Permutation test.
Where is my Device? Detecting the Smart Device’s Wearing Position in the Context of Active Safety for Vulnerable Road Users.Bieshaar, M. In Organic Computing -- Doctoral Dissertation Colloquium 2017, S. Tomforde, B. Sick (reds.), ble. 27–37. kassel university press, Kassel, Germany, 2017.
- [ BibTeX ]
- [ EndNote ]
@incollection{bieshaar2017where, address = {Kassel, Germany}, author = {Bieshaar, M.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2017}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {27–37}, publisher = {kassel university press}, title = {Where is my Device? Detecting the Smart Device's Wearing Position in the Context of Active Safety for Vulnerable Road Users}, year = 2017 }
%0 Book Section %1 bieshaar2017where %A Bieshaar, M. %B Organic Computing -- Doctoral Dissertation Colloquium 2017 %C Kassel, Germany %D 2017 %E Tomforde, S. %E Sick, B. %I kassel university press %P 27--37 %T Where is my Device? Detecting the Smart Device's Wearing Position in the Context of Active Safety for Vulnerable Road Users
Interactive Learning Without Ground Truth.Würtz, Rolf P.; Tomforde, Sven; Calma, Adrian; Kottke, Daniel; Sick, Bernhard. In Organic Computing -- Doctoral Dissertation Colloquium 2017, S. Tomforde, B. Sick (reds.), ble. 1–4. kassel university press, Kassel, Germany, 2017.
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- [ EndNote ]
@incollection{wuertz2017interactive, address = {Kassel, Germany}, author = {Würtz, Rolf P. and Tomforde, Sven and Calma, Adrian and Kottke, Daniel and Sick, Bernhard}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2017}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {1–4}, publisher = {kassel university press}, title = {Interactive Learning Without Ground Truth}, year = 2017 }
%0 Book Section %1 wuertz2017interactive %A Würtz, Rolf P. %A Tomforde, Sven %A Calma, Adrian %A Kottke, Daniel %A Sick, Bernhard %B Organic Computing -- Doctoral Dissertation Colloquium 2017 %C Kassel, Germany %D 2017 %E Tomforde, S. %E Sick, B. %I kassel university press %P 1--4 %T Interactive Learning Without Ground Truth
Organic Computing -- Doctoral Dissertation Colloquium 2016. Tomforde, S.; Sick, B. In Vol. 10Intelligent Embedded Systems. kassel university press, 2017.
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@book{tomforde2017organica, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Organic Computing – Doctoral Dissertation Colloquium 2016}, volume = 10, year = 2017 }
%0 Book %1 tomforde2017organica %B Intelligent Embedded Systems %D 2017 %E Tomforde, S. %E Sick, B. %I kassel university press %R 10.19211/KUP9783737603010 %T Organic Computing -- Doctoral Dissertation Colloquium 2016 %V 10
A Concept for Intelligent Collaborative Network Intrusion Detection.Gruhl, C.; Beer, F.; Heck, H.; Sick, B.; Bühler, U.; Wacker, A.; Tomforde, S. In International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. VDE, 2017.
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@inproceedings{gruhl2017concept, author = {Gruhl, C. and Beer, F. and Heck, H. and Sick, B. and B{\"u}hler, U. and Wacker, A. and Tomforde, S.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, publisher = {VDE}, title = {A Concept for Intelligent Collaborative Network Intrusion Detection}, year = 2017 }
%0 Conference Paper %1 gruhl2017concept %A Gruhl, C. %A Beer, F. %A Heck, H. %A Sick, B. %A Bühler, U. %A Wacker, A. %A Tomforde, S. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %D 2017 %I VDE %T A Concept for Intelligent Collaborative Network Intrusion Detection
Quantitative Robustness -- A Generalised Approach to Compare the Impact of Disturbances in Self-organising Systems.Kantert, J.; Tomforde, S.; Müller-Schloer, C.; Edenhofer, S.; Sick, B. In International Conference on Agents and Artificial Intelligence (ICAART), ble. 39–50. Porto, Portugal, 2017.
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@inproceedings{kantert2017quantitative, address = {Porto, Portugal}, author = {Kantert, J. and Tomforde, S. and M{\"u}ller-Schloer, C. and Edenhofer, S. and Sick, B.}, booktitle = {International Conference on Agents and Artificial Intelligence (ICAART)}, keywords = {imported}, pages = {39–50}, title = {Quantitative Robustness – A Generalised Approach to Compare the Impact of Disturbances in Self-organising Systems}, year = 2017 }
%0 Conference Paper %1 kantert2017quantitative %A Kantert, J. %A Tomforde, S. %A Müller-Schloer, C. %A Edenhofer, S. %A Sick, B. %B International Conference on Agents and Artificial Intelligence (ICAART) %C Porto, Portugal %D 2017 %P 39--50 %R 10.5220/0006137300390050 %T Quantitative Robustness -- A Generalised Approach to Compare the Impact of Disturbances in Self-organising Systems
Enhanced Probabilistic Active Learning: Cost-sensitive, Unbalanced, Time-variant, Self-optimising, and Parameter-free.Kottke, Daniel. In Organic Computing -- Doctoral Dissertation Colloquium 2017, S. Tomforde, B. Sick (reds.), ble. 67–78. kassel university press, Kassel, Germany, 2017.
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@incollection{kottke2017enhanced, address = {Kassel, Germany}, author = {Kottke, Daniel}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2017}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {67–78}, publisher = {kassel university press}, title = {Enhanced Probabilistic Active Learning: Cost-sensitive, Unbalanced, Time-variant, Self-optimising, and Parameter-free}, year = 2017 }
%0 Book Section %1 kottke2017enhanced %A Kottke, Daniel %B Organic Computing -- Doctoral Dissertation Colloquium 2017 %C Kassel, Germany %D 2017 %E Tomforde, S. %E Sick, B. %I kassel university press %P 67--78 %T Enhanced Probabilistic Active Learning: Cost-sensitive, Unbalanced, Time-variant, Self-optimising, and Parameter-free
Simulation of Annotators for Active Learning: Uncertain Oracles.Calma, Adrian; Sick, Bernhard. In Workshop on Interactive Adaptive Learning (IAL), ECML PKDD, CEUR Workshop Proceedings, ble. 49–58. 2017.
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- [ URL ]
@inproceedings{calma2017simulation, author = {Calma, Adrian and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {imported}, pages = {49–58}, series = {CEUR Workshop Proceedings}, title = {Simulation of Annotators for Active Learning: Uncertain Oracles}, year = 2017 }
%0 Conference Paper %1 calma2017simulation %A Calma, Adrian %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2017 %P 49--58 %T Simulation of Annotators for Active Learning: Uncertain Oracles %U http://ceur-ws.org/Vol-1924/ialatecml_paper4.pdf

2016[ to top ]

An Organic Computing Approach to Self-organising Robot Ensembles.von Mammen, S.; Tomforde, S.; Hähner, J. In Frontiers in Robotics and AI, 3, bl. 67. Frontiers, 2016.
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@article{mammen2016organic, author = {von Mammen, S. and Tomforde, S. and H{\"a}hner, J.}, journal = {Frontiers in Robotics and AI}, keywords = {imported}, pages = 67, publisher = {Frontiers}, title = {An Organic Computing Approach to Self-organising Robot Ensembles}, volume = 3, year = 2016 }
%0 Journal Article %1 mammen2016organic %A von Mammen, S. %A Tomforde, S. %A Hähner, J. %D 2016 %I Frontiers %J Frontiers in Robotics and AI %P 67 %R 10.3389/frobt.2016.00067 %T An Organic Computing Approach to Self-organising Robot Ensembles %V 3
An Organic Computing Perspective on Self-Improving System Interwearing at Runtime.Tomforde, S.; Rudolph, S.; Bellman, K.; Würtz, R. P. In Workshop on Self-Improving System Integration (SISSY), ICAC, ble. 276–284. IEEE, Würzburg, Germany, 2016.
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- [ EndNote ]
@inproceedings{tomforde2016organic, address = {W{\"u}rzburg, Germany}, author = {Tomforde, S. and Rudolph, S. and Bellman, K. and W{\"u}rtz, R. P.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ICAC}, keywords = {imported}, pages = {276–284}, publisher = {IEEE}, title = {An {Organic Computing} Perspective on Self-Improving System Interwearing at Runtime}, year = 2016 }
%0 Conference Paper %1 tomforde2016organic %A Tomforde, S. %A Rudolph, S. %A Bellman, K. %A Würtz, R. P. %B Workshop on Self-Improving System Integration (SISSY), ICAC %C Würzburg, Germany %D 2016 %I IEEE %P 276--284 %R 10.1109/icac.2016.15 %T An Organic Computing Perspective on Self-Improving System Interwearing at Runtime
Controlling Negative Emergent Behavior by Graph Analysis at Runtime.Kantert, J.; Tomforde, S.; Kauder, M.; Scharrer, R.; Edenhofer, S.; Hähner, J.; Müller-Schloer, C. In ACM Transactions on Autonomous and Adaptive Systems, 11(2), bl. 7. ACM, 2016.
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@article{kantert2016controlling, author = {Kantert, J. and Tomforde, S. and Kauder, M. and Scharrer, R. and Edenhofer, S. and H{\"a}hner, J. and M{\"u}ller-Schloer, C.}, journal = {ACM Transactions on Autonomous and Adaptive Systems}, keywords = {imported}, number = 2, pages = 7, publisher = {ACM}, title = {Controlling Negative Emergent Behavior by Graph Analysis at Runtime}, volume = 11, year = 2016 }
%0 Journal Article %1 kantert2016controlling %A Kantert, J. %A Tomforde, S. %A Kauder, M. %A Scharrer, R. %A Edenhofer, S. %A Hähner, J. %A Müller-Schloer, C. %D 2016 %I ACM %J ACM Transactions on Autonomous and Adaptive Systems %N 2 %P 7 %R 10.1145/2890507 %T Controlling Negative Emergent Behavior by Graph Analysis at Runtime %V 11
An Analog Ensemble-Based Similarity Search Technique for Solar Power Forecasting.Gensler, A.; Sick, B.; Pankraz, V. In IEEE International Conference on Systems, Man and Cybernetics (SMC), ble. 2850–2857. IEEE, 2016.
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@inproceedings{gensler2016analog, author = {Gensler, A. and Sick, B. and Pankraz, V.}, booktitle = {IEEE International Conference on Systems, Man and Cybernetics (SMC)}, keywords = {imported}, pages = {2850–2857}, publisher = {IEEE}, title = {An Analog Ensemble-Based Similarity Search Technique for Solar Power Forecasting}, year = 2016 }
%0 Conference Paper %1 gensler2016analog %A Gensler, A. %A Sick, B. %A Pankraz, V. %B IEEE International Conference on Systems, Man and Cybernetics (SMC) %D 2016 %I IEEE %P 2850--2857 %R 10.1109/SMC.2016.7844672 %T An Analog Ensemble-Based Similarity Search Technique for Solar Power Forecasting
Socially-Sensitive Systems Design. Lewis, Peter; Bellman, Kirstie; Botev, Jean; Hildmann, Hanno; Marsh, Stephen; Pitt, Jeremy; Scholtes, Ingo; Tomforde, Sven. ble. 142–147. Dagstuhl, Germany, Dagstuhl, Germany, 2016.
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@techreport{lewis2016socially, address = {Dagstuhl, Germany}, author = {Lewis, Peter and Bellman, Kirstie and Botev, Jean and Hildmann, Hanno and Marsh, Stephen and Pitt, Jeremy and Scholtes, Ingo and Tomforde, Sven}, institution = {Dagstuhl, Germany}, keywords = {imported}, pages = {142–147}, title = {Socially-Sensitive Systems Design}, year = 2016 }
%0 Report %1 lewis2016socially %A Lewis, Peter %A Bellman, Kirstie %A Botev, Jean %A Hildmann, Hanno %A Marsh, Stephen %A Pitt, Jeremy %A Scholtes, Ingo %A Tomforde, Sven %C Dagstuhl, Germany %D 2016 %P 142--147 %T Socially-Sensitive Systems Design
Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data.Reitmaier, Tobias; Calma, Adrian; Sick, Bernhard. In arXiv e-prints, bl. arXiv:1610.03995. 2016.
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@article{reitmaier2016semi, author = {{Reitmaier}, Tobias and {Calma}, Adrian and {Sick}, Bernhard}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:1610.03995}, title = {{Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data}}, year = 2016 }
%0 Journal Article %1 reitmaier2016semi %A Reitmaier, Tobias %A Calma, Adrian %A Sick, Bernhard %D 2016 %J arXiv e-prints %P arXiv:1610.03995 %T Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data
Pals: Interactive Pool-based Active Learning System with Uncertain Oracles.Calma, A. In Organic Computing -- Doctoral Dissertation Colloquium 2016, B. Sick, S. Tomforde (reds.), ble. 35–44. kassel university press, Kassel, Germany, 2016.
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@incollection{calma2016pals, address = {Kassel, Germany}, author = {Calma, A.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2016}, editor = {Sick, B. and Tomforde, S.}, keywords = {imported}, pages = {35–44}, publisher = {kassel university press}, title = {Pals: Interactive Pool-based Active Learning System with Uncertain Oracles}, year = 2016 }
%0 Book Section %1 calma2016pals %A Calma, A. %B Organic Computing -- Doctoral Dissertation Colloquium 2016 %C Kassel, Germany %D 2016 %E Sick, B. %E Tomforde, S. %I kassel university press %P 35--44 %T Pals: Interactive Pool-based Active Learning System with Uncertain Oracles
Any Problems? A wearable sensor-based platform for representational learning-analytics.Pirkl, G.; Hevesi, P.; Lukowicz, P.; Klein, P.; Heisel, C.; Gröber, S.; Kuhn, J.; Sick, B. In ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), ble. 353–356. ACM, Heidelberg, Germany, 2016.
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@inproceedings{pirkl2016any, address = {Heidelberg, Germany}, author = {Pirkl, G. and Hevesi, P. and Lukowicz, P. and Klein, P. and Heisel, C. and Gr{\"o}ber, S. and Kuhn, J. and Sick, B.}, booktitle = {ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)}, keywords = {imported}, pages = {353–356}, publisher = {ACM}, title = {Any Problems? {A} wearable sensor-based platform for representational learning-analytics}, year = 2016 }
%0 Conference Paper %1 pirkl2016any %A Pirkl, G. %A Hevesi, P. %A Lukowicz, P. %A Klein, P. %A Heisel, C. %A Gröber, S. %A Kuhn, J. %A Sick, B. %B ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) %C Heidelberg, Germany %D 2016 %I ACM %P 353--356 %R 10.1145/2968219.2971383 %T Any Problems? A wearable sensor-based platform for representational learning-analytics
Probabilistic Active Learning for Active Class Selection.Kottke, D.; Krempl, G.; Stecklina, M.; Styp von Rekowski, C.; Sabsch, T.; Pham Minh, T.; Deliano, M.; Spiliopoulou, M.; Sick, B. In Workshop on the Future of Interactive Learning Machines, NIPS, ble. 1–9. Barcelona, Spain, 2016.
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- [ EndNote ]
@inproceedings{kottke2016probabilistic, address = {Barcelona, Spain}, author = {Kottke, D. and Krempl, G. and Stecklina, M. and {Styp von Rekowski}, C. and Sabsch, T. and Pham Minh, T. and Deliano, M. and Spiliopoulou, M. and Sick, B.}, booktitle = {Workshop on the Future of Interactive Learning Machines, NIPS}, keywords = {imported}, pages = {1–9}, title = {Probabilistic Active Learning for Active Class Selection}, year = 2016 }
%0 Conference Paper %1 kottke2016probabilistic %A Kottke, D. %A Krempl, G. %A Stecklina, M. %A Styp von Rekowski, C. %A Sabsch, T. %A Pham Minh, T. %A Deliano, M. %A Spiliopoulou, M. %A Sick, B. %B Workshop on the Future of Interactive Learning Machines, NIPS %C Barcelona, Spain %D 2016 %P 1--9 %T Probabilistic Active Learning for Active Class Selection
Comparision of Dependency Measures for the Detection of Mutual Influences in Organic Computing Systems.Rudolph, S.; Hihn, R.; Tomforde, S.; Hähner, J. In International Conference on Architecture of Computing Systems (ARCS), ble. 334–347. Springer, 2016.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{rudolph2016comparision, author = {Rudolph, S. and Hihn, R. and Tomforde, S. and H{\"a}hner, J.}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {imported}, pages = {334–347}, publisher = {Springer}, title = {Comparision of Dependency Measures for the Detection of Mutual Influences in {Organic Computing Systems}}, year = 2016 }
%0 Conference Paper %1 rudolph2016comparision %A Rudolph, S. %A Hihn, R. %A Tomforde, S. %A Hähner, J. %B International Conference on Architecture of Computing Systems (ARCS) %D 2016 %I Springer %P 334--347 %R 10.1007/978-3-319-30695-7_25 %T Comparision of Dependency Measures for the Detection of Mutual Influences in Organic Computing Systems
From Active Learning to Dedicated Collaborative Interactive Learning.Calma, A.; Leimeister, J. M.; Lukowicz, P.; Oeste-Reiß, S.; Reitmaier, T.; Schmidt, A.; Sick, B.; Stumme, G.; Zweig, K. A. In International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS, ble. 1–8. VDE, Nuremberg, Germany, 2016.
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- [ EndNote ]
@inproceedings{calma2016active, address = {Nuremberg, Germany}, author = {Calma, A. and Leimeister, J. M. and Lukowicz, P. and Oeste-Rei{\ss}, S. and Reitmaier, T. and Schmidt, A. and Sick, B. and Stumme, G. and Zweig, K. A.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {From Active Learning to Dedicated Collaborative Interactive Learning}, year = 2016 }
%0 Conference Paper %1 calma2016active %A Calma, A. %A Leimeister, J. M. %A Lukowicz, P. %A Oeste-Reiß, S. %A Reitmaier, T. %A Schmidt, A. %A Sick, B. %A Stumme, G. %A Zweig, K. A. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C Nuremberg, Germany %D 2016 %I VDE %P 1--8 %T From Active Learning to Dedicated Collaborative Interactive Learning
Cellular Traffic Offloading through Network-Assisted Ad-Hoc Routing in Cellular Networks.Hähner, J.; Tomforde, S. In IEEE Symposium on Computers and Communications (ISCC), ble. 469–476. IEEE, Messina, Italy, 2016.
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@inproceedings{haehner2016cellular, address = {Messina, Italy}, author = {H{\"a}hner, J. and Tomforde, S.}, booktitle = {IEEE Symposium on Computers and Communications (ISCC)}, keywords = {imported}, pages = {469–476}, publisher = {IEEE}, title = {Cellular Traffic Offloading through Network-Assisted Ad-Hoc Routing in Cellular Networks}, year = 2016 }
%0 Conference Paper %1 haehner2016cellular %A Hähner, J. %A Tomforde, S. %B IEEE Symposium on Computers and Communications (ISCC) %C Messina, Italy %D 2016 %I IEEE %P 469--476 %R 10.1109/iscc.2016.7543783 %T Cellular Traffic Offloading through Network-Assisted Ad-Hoc Routing in Cellular Networks
Resp-kNN: A probabilistic k-nearest neighbor classifier for sparsely labeled data.Calma, A.; Reitmaier, T.; Sick, B. In International Joint Conference on Neural Networks (IJCNN), ble. 4040–4047. IEEE, Vancouver, BC, 2016.
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- [ EndNote ]
@inproceedings{calma2016resp, address = {Vancouver, BC}, author = {Calma, A. and Reitmaier, T. and Sick, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {imported}, pages = {4040–4047}, publisher = {IEEE}, title = {{Resp-kNN}: A probabilistic k-nearest neighbor classifier for sparsely labeled data}, year = 2016 }
%0 Conference Paper %1 calma2016resp %A Calma, A. %A Reitmaier, T. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %C Vancouver, BC %D 2016 %I IEEE %P 4040--4047 %R 10.1109/ijcnn.2016.7727725 %T Resp-kNN: A probabilistic k-nearest neighbor classifier for sparsely labeled data
Combining Trust and ETX to Provide Robust Wireless Sensor Networks.Kantert, J.; Reinhard, F.; von Zengen, G.; Tomforde, S.; Weber, S.; Wolf, L.; Müller-Schloer, C. In International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS, ble. 1–7. VDE, Nuremberg, Germany, 2016.
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@inproceedings{kantert2016combining, address = {Nuremberg, Germany}, author = {Kantert, J. and Reinhard, F. and von Zengen, G. and Tomforde, S. and Weber, S. and Wolf, L. and M{\"u}ller-Schloer, C.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, chapter = 16, keywords = {imported}, pages = {1–7}, publisher = {VDE}, title = {Combining Trust and {ETX} to Provide Robust Wireless Sensor Networks}, year = 2016 }
%0 Conference Paper %1 kantert2016combining %A Kantert, J. %A Reinhard, F. %A von Zengen, G. %A Tomforde, S. %A Weber, S. %A Wolf, L. %A Müller-Schloer, C. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C Nuremberg, Germany %D 2016 %I VDE %P 1--7 %T Combining Trust and ETX to Provide Robust Wireless Sensor Networks %& 16
Active Learning: Applications, Foundations and Emerging Trends, iKNOW. Krempl, G.; Lemaire, V.; Lughofer, E.; Kottke, D. In Vol. 1707CEUR Workshop Proceedings. Graz, Austria, 2016.
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@proceedings{krempl2016active, address = {Graz, Austria}, editor = {Krempl, G. and Lemaire, V. and Lughofer, E. and Kottke, D.}, keywords = {imported}, series = {{CEUR} Workshop Proceedings}, title = {Active Learning: Applications, Foundations and Emerging Trends, {iKNOW}}, volume = 1707, year = 2016 }
%0 Conference Proceedings %1 krempl2016active %B CEUR Workshop Proceedings %C Graz, Austria %D 2016 %E Krempl, G. %E Lemaire, V. %E Lughofer, E. %E Kottke, D. %T Active Learning: Applications, Foundations and Emerging Trends, iKNOW %U http://ceur-ws.org/Vol-1707/ %V 1707
Runtime Clustering of Similarity Behaving Agents in Open Organic Computing Systems.Kantert, J.; Scharrer, R.; Tomforde, S.; Edenhofer, S.; Müller-Schloer, C. In International Conference on Architecture of Computing Systems (ARCS), ble. 321–333. Springer, Nuremberg, Germany, 2016.
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@inproceedings{kantert2016runtime, address = {Nuremberg, Germany}, author = {Kantert, J. and Scharrer, R. and Tomforde, S. and Edenhofer, S. and M{\"u}ller-Schloer, C.}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {imported}, pages = {321–333}, publisher = {Springer}, title = {Runtime Clustering of Similarity Behaving Agents in Open {Organic Computing} Systems}, year = 2016 }
%0 Conference Paper %1 kantert2016runtime %A Kantert, J. %A Scharrer, R. %A Tomforde, S. %A Edenhofer, S. %A Müller-Schloer, C. %B International Conference on Architecture of Computing Systems (ARCS) %C Nuremberg, Germany %D 2016 %I Springer %P 321--333 %R 10.1007/978-3-319-30695-7_24 %T Runtime Clustering of Similarity Behaving Agents in Open Organic Computing Systems
Augmenting the Algorithmic Structure of XCS by Means of Interpolation.Stein, A.; Rauh, D.; Tomforde, S.; Hähner, J. In International Conference on Architecture of Computing Systems (ARCS), ble. 348–360. Springer, Nuremberg, Germany, 2016.
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@inproceedings{stein2016augmenting, address = {Nuremberg, Germany}, author = {Stein, A. and Rauh, D. and Tomforde, S. and H{\"a}hner, J.}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {imported}, pages = {348–360}, publisher = {Springer}, title = {Augmenting the Algorithmic Structure of {XCS} by Means of Interpolation}, year = 2016 }
%0 Conference Paper %1 stein2016augmenting %A Stein, A. %A Rauh, D. %A Tomforde, S. %A Hähner, J. %B International Conference on Architecture of Computing Systems (ARCS) %C Nuremberg, Germany %D 2016 %I Springer %P 348--360 %R 10.1007/978-3-319-30695-7_26 %T Augmenting the Algorithmic Structure of XCS by Means of Interpolation
Probabilistic Obsoleteness Detection for Gaussian Mixture Models.Gruhl, C. In Organic Computing -- Doctoral Dissertation Colloquium 2016, S. Tomforde, B. Sick (reds.), ble. 45–56. kassel university press, Kassel, Germany, 2016.
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@incollection{gruhl2016probabilistic, address = {Kassel, Germany}, author = {Gruhl, C.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2016}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {45–56}, publisher = {kassel university press}, title = {Probabilistic Obsoleteness Detection for Gaussian Mixture Models}, year = 2016 }
%0 Book Section %1 gruhl2016probabilistic %A Gruhl, C. %B Organic Computing -- Doctoral Dissertation Colloquium 2016 %C Kassel, Germany %D 2016 %E Tomforde, S. %E Sick, B. %I kassel university press %P 45--56 %T Probabilistic Obsoleteness Detection for Gaussian Mixture Models
Combinations of uncertain ordinal expert statements: The combination rule EIDMR and its application to low-voltage grid classification with SVM.Breker, S.; Sick, B. In International Joint Conference on Neural Networks (IJCNN), ble. 2164–2173. IEEE, Vancouver, BC, 2016.
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@inproceedings{breker2016combinations, address = {Vancouver, BC}, author = {Breker, S. and Sick, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {imported}, pages = {2164–2173}, publisher = {IEEE}, title = {Combinations of uncertain ordinal expert statements: The combination rule {EIDMR} and its application to low-voltage grid classification with {SVM}}, year = 2016 }
%0 Conference Paper %1 breker2016combinations %A Breker, S. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %C Vancouver, BC %D 2016 %I IEEE %P 2164--2173 %R 10.1109/ijcnn.2016.7727467 %T Combinations of uncertain ordinal expert statements: The combination rule EIDMR and its application to low-voltage grid classification with SVM
Coverage-guided Intelligent Test Loop -- A Concept for Applying Instrumented Testing to Self-organising Systems.Kantert, J.; Tomforde, S.; Weber, S.; Müller-Schloer, C. In International Conference on Informatics in Control, Automation and Robotics (ICINCO), ble. 221–226. Lisbon, Portugal, 2016.
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@inproceedings{kantert2016coverage, address = {Lisbon, Portugal}, author = {Kantert, J. and Tomforde, S. and Weber, S. and M{\"u}ller-Schloer, C.}, booktitle = {International Conference on Informatics in Control, Automation and Robotics (ICINCO)}, keywords = {imported}, pages = {221–226}, title = {Coverage-guided Intelligent Test Loop – A Concept for Applying Instrumented Testing to Self-organising Systems}, year = 2016 }
%0 Conference Paper %1 kantert2016coverage %A Kantert, J. %A Tomforde, S. %A Weber, S. %A Müller-Schloer, C. %B International Conference on Informatics in Control, Automation and Robotics (ICINCO) %C Lisbon, Portugal %D 2016 %P 221--226 %R 10.5220/0005992702210226 %T Coverage-guided Intelligent Test Loop -- A Concept for Applying Instrumented Testing to Self-organising Systems
An Evaluation of Two Trust-based Autonomic/Organic Grid Computing Systems for Volunteer-Based Distributed Rendering.Kantert, J.; Reinbold, C.; Tomforde, S.; Müller-Schloer, C. In IEEE International Conference on Autonomic Computing (ICAC), ble. 137–146. IEEE, Würzburg, Germany, 2016.
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@inproceedings{kantert2016evaluation, address = {W{\"u}rzburg, Germany}, author = {Kantert, J. and Reinbold, C. and Tomforde, S. and M{\"u}ller-Schloer, C.}, booktitle = {IEEE International Conference on Autonomic Computing (ICAC)}, keywords = {imported}, pages = {137–146}, publisher = {IEEE}, title = {An Evaluation of Two Trust-based {Autonomic/Organic Grid Computing Systems} for Volunteer-Based Distributed Rendering}, year = 2016 }
%0 Conference Paper %1 kantert2016evaluation %A Kantert, J. %A Reinbold, C. %A Tomforde, S. %A Müller-Schloer, C. %B IEEE International Conference on Autonomic Computing (ICAC) %C Würzburg, Germany %D 2016 %I IEEE %P 137--146 %R 10.1109/icac.2016.55 %T An Evaluation of Two Trust-based Autonomic/Organic Grid Computing Systems for Volunteer-Based Distributed Rendering
Deep Learning for Solar Power Forecasting -- An Approach using Autoencoder and LSTM Neural Networks.Gensler, A.; Henze, J.; Sick, B.; Raabe, N. In IEEE International Conference on Systems, Man and Cybernetics (SMC), ble. 2858–2865. IEEE, Budapest, Hungary, 2016.
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@inproceedings{gensler2016deep, address = {Budapest, Hungary}, author = {Gensler, A. and Henze, J. and Sick, B. and Raabe, N.}, booktitle = {IEEE International Conference on Systems, Man and Cybernetics (SMC)}, keywords = {imported}, pages = {2858–2865}, publisher = {IEEE}, title = {Deep Learning for Solar Power Forecasting – An Approach using Autoencoder and {LSTM} Neural Networks}, year = 2016 }
%0 Conference Paper %1 gensler2016deep %A Gensler, A. %A Henze, J. %A Sick, B. %A Raabe, N. %B IEEE International Conference on Systems, Man and Cybernetics (SMC) %C Budapest, Hungary %D 2016 %I IEEE %P 2858--2865 %R 10.1109/smc.2016.7844673 %T Deep Learning for Solar Power Forecasting -- An Approach using Autoencoder and LSTM Neural Networks
Exploit the Potential of the Group: Putting Humans in the Dedicated Collaborative Interactive Learning Loop.Calma, A. In Organic Computing -- Doctoral Dissertation Colloquium 2016, B. Sick, S. Tomforde (reds.). kassel university press, Kassel, Germany, 2016.
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@incollection{calma2016exploit, address = {Kassel, Germany}, author = {Calma, A.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2016}, editor = {Sick, B. and Tomforde, S.}, keywords = {imported}, publisher = {kassel university press}, title = {Exploit the Potential of the Group: Putting Humans in the Dedicated Collaborative Interactive Learning Loop}, year = 2016 }
%0 Book Section %1 calma2016exploit %A Calma, A. %B Organic Computing -- Doctoral Dissertation Colloquium 2016 %C Kassel, Germany %D 2016 %E Sick, B. %E Tomforde, S. %I kassel university press %T Exploit the Potential of the Group: Putting Humans in the Dedicated Collaborative Interactive Learning Loop
Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning.Lang, D.; Kottke, D.; Krempl, G.; Spiliopoulou, M. In Workshop on Active Learning: Applications, Foundations and Emerging Trends, iKnow, CEUR Workshop Proceedings, ble. 25–34. Graz, Austria, 2016.
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@inproceedings{lang2016investigating, address = {Graz, Austria}, author = {Lang, D. and Kottke, D. and Krempl, G. and Spiliopoulou, M.}, booktitle = {Workshop on Active Learning: Applications, Foundations and Emerging Trends, iKnow}, keywords = {imported}, pages = {25–34}, series = {{CEUR} Workshop Proceedings}, title = {Investigating Exploratory Capabilities of Uncertainty Sampling using {SVMs} in Active Learning}, year = 2016 }
%0 Conference Paper %1 lang2016investigating %A Lang, D. %A Kottke, D. %A Krempl, G. %A Spiliopoulou, M. %B Workshop on Active Learning: Applications, Foundations and Emerging Trends, iKnow %C Graz, Austria %D 2016 %P 25--34 %T Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning %U http://ceur-ws.org/Vol-1707/alatiknow_paper4.pdf
Coping with variability in motion based activity recognition.Kreil, M.; Sick, B.; Lukowicz, P. In International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR), ble. 1–8. Rostock, Germany, 2016.
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@inproceedings{kreil2016coping, address = {Rostock, Germany}, author = {Kreil, M. and Sick, B. and Lukowicz, P.}, booktitle = {International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR)}, keywords = {imported}, pages = {1–8}, title = {Coping with variability in motion based activity recognition}, year = 2016 }
%0 Conference Paper %1 kreil2016coping %A Kreil, M. %A Sick, B. %A Lukowicz, P. %B International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR) %C Rostock, Germany %D 2016 %P 1--8 %R 10.1145/2948963.2948967 %T Coping with variability in motion based activity recognition
Goal-oriented Holonics for Complex System (Self-)Integration: Concepts and Case Studies.Diaconescu, A.; Frey, S.; Müller-Schloer, C.; Pitt, J.; Tomforde, S. In IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), ble. 100–109. IEEE, Augsburg, Germany, 2016.
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@inproceedings{diaconescu2016goal, address = {Augsburg, Germany}, author = {Diaconescu, A. and Frey, S. and M{\"u}ller-Schloer, C. and Pitt, J. and Tomforde, S.}, booktitle = {IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO)}, keywords = {imported}, pages = {100–109}, publisher = {IEEE}, title = {Goal-oriented Holonics for Complex System (Self-)Integration: Concepts and Case Studies}, year = 2016 }
%0 Conference Paper %1 diaconescu2016goal %A Diaconescu, A. %A Frey, S. %A Müller-Schloer, C. %A Pitt, J. %A Tomforde, S. %B IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) %C Augsburg, Germany %D 2016 %I IEEE %P 100--109 %R 10.1109/saso.2016.16 %T Goal-oriented Holonics for Complex System (Self-)Integration: Concepts and Case Studies
"Know thyselves" -- Computational Self-Reflection in Collective Technical Systems.Hähner, J.; von Mammen, S.; Timpf, S.; Tomforde, S.; Sick, B.; Geihs, K.; Goeble, T.; Hornung, G.; Stumme, G. In International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS, ble. 1–8. VDE, Nuremberg, Germany, 2016.
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@inproceedings{haehner2016know, address = {Nuremberg, Germany}, author = {H{\"a}hner, J. and von Mammen, S. and Timpf, S. and Tomforde, S. and Sick, B. and Geihs, K. and Goeble, T. and Hornung, G. and Stumme, G.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {"{K}now thyselves" – Computational Self-Reflection in Collective Technical Systems}, year = 2016 }
%0 Conference Paper %1 haehner2016know %A Hähner, J. %A von Mammen, S. %A Timpf, S. %A Tomforde, S. %A Sick, B. %A Geihs, K. %A Goeble, T. %A Hornung, G. %A Stumme, G. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C Nuremberg, Germany %D 2016 %I VDE %P 1--8 %T "Know thyselves" -- Computational Self-Reflection in Collective Technical Systems %U https://ieeexplore.ieee.org/document/7499243
Trajectory Prediction of Cyclists Using a Physical Model and an Artificial Neural Network.Zernetsch, S.; Kohnen, S.; Goldhammer, M.; Doll, K.; Sick, B. In IEEE Intelligent Vehicles Symposium (IV), ble. 833–838. IEEE, Gothenburg, Sweden, 2016.
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@inproceedings{zernetsch2016trajectory, address = {Gothenburg, Sweden}, author = {Zernetsch, S. and Kohnen, S. and Goldhammer, M. and Doll, K. and Sick, B.}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {imported}, pages = {833–838}, publisher = {IEEE}, title = {Trajectory Prediction of Cyclists Using a Physical Model and an {Artificial Neural Network}}, year = 2016 }
%0 Conference Paper %1 zernetsch2016trajectory %A Zernetsch, S. %A Kohnen, S. %A Goldhammer, M. %A Doll, K. %A Sick, B. %B IEEE Intelligent Vehicles Symposium (IV) %C Gothenburg, Sweden %D 2016 %I IEEE %P 833--838 %R 10.1109/ivs.2016.7535484 %T Trajectory Prediction of Cyclists Using a Physical Model and an Artificial Neural Network
Towards Self-Improving Activity Recognition Systems based on Probabilistic, Generative Models.Jänicke, M.; Tomforde, S.; Sick, B. In Workshop on Self-Improving System Integration (SISSY), ICAC, ble. 285–291. IEEE, Würzburg, Germany, 2016.
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@inproceedings{jaenicke2016towards, address = {W{\"u}rzburg, Germany}, author = {J{\"a}nicke, M. and Tomforde, S. and Sick, B.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ICAC}, keywords = {imported}, pages = {285–291}, publisher = {IEEE}, title = {Towards Self-Improving Activity Recognition Systems based on Probabilistic, Generative Models}, year = 2016 }
%0 Conference Paper %1 jaenicke2016towards %A Jänicke, M. %A Tomforde, S. %A Sick, B. %B Workshop on Self-Improving System Integration (SISSY), ICAC %C Würzburg, Germany %D 2016 %I IEEE %P 285--291 %R 10.1109/icac.2016.22 %T Towards Self-Improving Activity Recognition Systems based on Probabilistic, Generative Models
Distributed Resource Allocation as Co-Evolution Problem.Tomforde, S.; Meier, D.; Stein, A.; von Mammen, S. In IEEE Congress on Evolutionary Computation (CEC), ble. 1815–1822. IEEE, Vancouver, BC, Canada, 2016.
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@inproceedings{tomforde2016distributed, address = {Vancouver, BC, Canada}, author = {Tomforde, S. and Meier, D. and Stein, A. and von Mammen, S.}, booktitle = {IEEE Congress on Evolutionary Computation (CEC)}, keywords = {imported}, pages = {1815–1822}, publisher = {IEEE}, title = {Distributed Resource Allocation as Co-Evolution Problem}, year = 2016 }
%0 Conference Paper %1 tomforde2016distributed %A Tomforde, S. %A Meier, D. %A Stein, A. %A von Mammen, S. %B IEEE Congress on Evolutionary Computation (CEC) %C Vancouver, BC, Canada %D 2016 %I IEEE %P 1815--1822 %R 10.1109/cec.2016.7744009 %T Distributed Resource Allocation as Co-Evolution Problem
Design and optimization of an autonomous feature selection pipeline for high dimensional, heterogeneous feature spaces.Schlegel, B.; Sick, B. In IEEE Symposium Series on Computational Intelligence (SSCI), ble. 1–9. IEEE, Athens, Greece, 2016.
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@inproceedings{schlegel2016design, address = {Athens, Greece}, author = {Schlegel, B. and Sick, B.}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, keywords = {imported}, pages = {1–9}, publisher = {IEEE}, title = {Design and optimization of an autonomous feature selection pipeline for high dimensional, heterogeneous feature spaces}, year = 2016 }
%0 Conference Paper %1 schlegel2016design %A Schlegel, B. %A Sick, B. %B IEEE Symposium Series on Computational Intelligence (SSCI) %C Athens, Greece %D 2016 %I IEEE %P 1--9 %R 10.1109/ssci.2016.7850092 %T Design and optimization of an autonomous feature selection pipeline for high dimensional, heterogeneous feature spaces
Measuring Self-Organisation Processes in Smart Camera Networks.Rudolph, S.; Kantert, J.; Jänen, U.; Tomforde, S.; Hähner, J.; Müller-Schloer, C. In International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS, ble. 1–6. VDE, Nuremberg, Germany, 2016.
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@inproceedings{rudolph2016measuring, address = {Nuremberg, Germany}, author = {Rudolph, S. and Kantert, J. and J{\"a}nen, U. and Tomforde, S. and H{\"a}hner, J. and M{\"u}ller-Schloer, C.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, chapter = 14, keywords = {imported}, pages = {1–6}, publisher = {VDE}, title = {Measuring Self-Organisation Processes in Smart Camera Networks}, year = 2016 }
%0 Conference Paper %1 rudolph2016measuring %A Rudolph, S. %A Kantert, J. %A Jänen, U. %A Tomforde, S. %A Hähner, J. %A Müller-Schloer, C. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C Nuremberg, Germany %D 2016 %I VDE %P 1--6 %T Measuring Self-Organisation Processes in Smart Camera Networks %& 14
Lifelong Learning and Collaboration of Smart Technical Systems in Open-Ended Environments -- Opportunistic Collaborative Interactive Learning.Bahle, G.; Calma, A.; Leimeister, J. M.; Lukowicz, P.; Oeste-Reiss, S.; Reitmaier, T.; Schmidt, A.; Sick, B.; Stumme, G.; Zweig, K. A. In Workshop on Self-Improving System Integration (SISSY), ICAC, ble. 1–10. IEEE, Würzburg, Germany, 2016.
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@inproceedings{bahle2016lifelong, address = {W{\"u}rzburg, Germany}, author = {Bahle, G. and Calma, A. and Leimeister, J. M. and Lukowicz, P. and Oeste-Rei\ss, S. and Reitmaier, T. and Schmidt, A. and Sick, B. and Stumme, G. and Zweig, K. A.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ICAC}, keywords = {imported}, pages = {1–10}, publisher = {IEEE}, title = {Lifelong Learning and Collaboration of Smart Technical Systems in Open-Ended Environments – Opportunistic Collaborative Interactive Learning}, year = 2016 }
%0 Conference Paper %1 bahle2016lifelong %A Bahle, G. %A Calma, A. %A Leimeister, J. M. %A Lukowicz, P. %A Oeste-Reiss, S. %A Reitmaier, T. %A Schmidt, A. %A Sick, B. %A Stumme, G. %A Zweig, K. A. %B Workshop on Self-Improving System Integration (SISSY), ICAC %C Würzburg, Germany %D 2016 %I IEEE %P 1--10 %R 10.1109/icac.2016.36 %T Lifelong Learning and Collaboration of Smart Technical Systems in Open-Ended Environments -- Opportunistic Collaborative Interactive Learning
Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems.Heck, H.; Gruhl, C.; Rudolph, S.; Wacker, A.; Sick, B.; Hähner, J. In International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS, ble. 1–8. VDE, Nuremberg, Germany, 2016.
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@inproceedings{heck2016multi, address = {Nuremberg, Germany}, author = {Heck, H. and Gruhl, C. and Rudolph, S. and Wacker, A. and Sick, B. and H{\"a}hner, J.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems}, year = 2016 }
%0 Conference Paper %1 heck2016multi %A Heck, H. %A Gruhl, C. %A Rudolph, S. %A Wacker, A. %A Sick, B. %A Hähner, J. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C Nuremberg, Germany %D 2016 %I VDE %P 1--8 %T Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems
Towards Automation of Knowledge Understanding: An Approach for Probabilistic Generative Classifiers.Fisch, D.; Gruhl, C.; Kalkowski, E.; Sick, B.; Ovaska, S. J. In Information Sciences, 370--371, ble. 476–496. Elsevier, 2016.
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@article{fisch2016towards, author = {Fisch, D. and Gruhl, C. and Kalkowski, E. and Sick, B. and Ovaska, S. J.}, journal = {Information Sciences}, keywords = {imported}, pages = {476–496}, publisher = {Elsevier}, title = {Towards Automation of Knowledge Understanding: An Approach for Probabilistic Generative Classifiers}, volume = {370–371}, year = 2016 }
%0 Journal Article %1 fisch2016towards %A Fisch, D. %A Gruhl, C. %A Kalkowski, E. %A Sick, B. %A Ovaska, S. J. %D 2016 %I Elsevier %J Information Sciences %P 476--496 %R 10.1016/j.ins.2016.08.016 %T Towards Automation of Knowledge Understanding: An Approach for Probabilistic Generative Classifiers %V 370--371
Correlation of Ontology-Based Semantic Similarity and Human Judgement for a Domain Specific Fashion Ontology.Kalkowski, E.; Sick, B. In International Conference on Web Engineering (ICWE), ble. 207–224. Springer, 2016.
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@inproceedings{kalkowski2016correlation, author = {Kalkowski, E. and Sick, B.}, booktitle = {International Conference on Web Engineering (ICWE)}, keywords = {imported}, pages = {207–224}, publisher = {Springer}, title = {Correlation of Ontology-Based Semantic Similarity and Human Judgement for a Domain Specific Fashion Ontology}, year = 2016 }
%0 Conference Paper %1 kalkowski2016correlation %A Kalkowski, E. %A Sick, B. %B International Conference on Web Engineering (ICWE) %D 2016 %I Springer %P 207--224 %R 10.1007/978-3-319-38791-8_12 %T Correlation of Ontology-Based Semantic Similarity and Human Judgement for a Domain Specific Fashion Ontology
Generative Exponential Smoothing and Generative ARMA Models to Forecast Time-Variant Rates or Probabilities.Kalkowski, E.; Sick, B. In International Work-Conference on Time Series (ITISE): Selected Contributions, ble. 75–88. Springer, Cham, Switzerland, 2016.
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@inproceedings{kalkowski2016generative, address = {Cham, Switzerland}, author = {Kalkowski, E. and Sick, B.}, booktitle = {International Work-Conference on Time Series (ITISE): Selected Contributions}, keywords = {imported}, pages = {75–88}, publisher = {Springer}, title = {Generative Exponential Smoothing and Generative {ARMA} Models to Forecast Time-Variant Rates or Probabilities}, year = 2016 }
%0 Conference Paper %1 kalkowski2016generative %A Kalkowski, E. %A Sick, B. %B International Work-Conference on Time Series (ITISE): Selected Contributions %C Cham, Switzerland %D 2016 %I Springer %P 75--88 %R 10.1007/978-3-319-28725-6_6 %T Generative Exponential Smoothing and Generative ARMA Models to Forecast Time-Variant Rates or Probabilities
Forecasting Wind Power -- An Ensemble Technique With Gradual Coopetitive Weighting Based on Weather Situation.Gensler, A.; Sick, B. In International Joint Conference on Neural Networks (IJCNN), ble. 4976–4984. IEEE, Vancouver, BC, 2016.
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@inproceedings{gensler2016forecasting, address = {Vancouver, BC}, author = {Gensler, A. and Sick, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {imported}, pages = {4976–4984}, publisher = {IEEE}, title = {Forecasting Wind Power – An Ensemble Technique With Gradual Coopetitive Weighting Based on Weather Situation}, year = 2016 }
%0 Conference Paper %1 gensler2016forecasting %A Gensler, A. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %C Vancouver, BC %D 2016 %I IEEE %P 4976--4984 %R 10.1109/ijcnn.2016.7727855 %T Forecasting Wind Power -- An Ensemble Technique With Gradual Coopetitive Weighting Based on Weather Situation
Decentralised Trust-Management Inspired by Ant Pheromones.Edenhofer, S.; Tomforde, S.; Fischer, D.; Hähner, J.; Menzel, F.; von Mammen, S. In International Journal of Mobile Network Design and Innovation, 7(1), ble. 46–55. Inderscience, 2016.
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- [ EndNote ]
@article{edenhofer2016decentralised, author = {Edenhofer, S. and Tomforde, S. and Fischer, D. and H{\"a}hner, J. and Menzel, F. and von Mammen, S.}, journal = {International Journal of Mobile Network Design and Innovation}, keywords = {imported}, number = 1, pages = {46–55}, publisher = {Inderscience}, title = {Decentralised Trust-Management Inspired by Ant Pheromones}, volume = 7, year = 2016 }
%0 Journal Article %1 edenhofer2016decentralised %A Edenhofer, S. %A Tomforde, S. %A Fischer, D. %A Hähner, J. %A Menzel, F. %A von Mammen, S. %D 2016 %I Inderscience %J International Journal of Mobile Network Design and Innovation %N 1 %P 46--55 %R 10.1504/ijmndi.2017.082804 %T Decentralised Trust-Management Inspired by Ant Pheromones %V 7
Interpolation-based Classifier Generation in XCSF.Stein, A.; Eymüller, C.; Rauh, D.; Tomforde, S.; Hähner, J. In IEEE Congress on Evolutionary Computation (CEC), ble. 3990–3998. IEEE, Vancouver, BC, 2016.
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- [ EndNote ]
@inproceedings{stein2016interpolation, address = {Vancouver, BC}, author = {Stein, A. and Eym{\"u}ller, C. and Rauh, D. and Tomforde, S. and H{\"a}hner, J.}, booktitle = {IEEE Congress on Evolutionary Computation (CEC)}, keywords = {imported}, pages = {3990–3998}, publisher = {IEEE}, title = {Interpolation-based Classifier Generation in {XCSF}}, year = 2016 }
%0 Conference Paper %1 stein2016interpolation %A Stein, A. %A Eymüller, C. %A Rauh, D. %A Tomforde, S. %A Hähner, J. %B IEEE Congress on Evolutionary Computation (CEC) %C Vancouver, BC %D 2016 %I IEEE %P 3990--3998 %R 10.1109/cec.2016.7744296 %T Interpolation-based Classifier Generation in XCSF
A Review of Deterministic Error Scores and Normalization Techniques for Power Forecasting Algorithms.Gensler, A.; Sick, B.; Vogt, S. In IEEE Symposium Series on Computational Intelligence (SSCI), ble. 1–9. IEEE, Athens, Greece, 2016.
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- [ EndNote ]
@inproceedings{gensler2016review, address = {Athens, Greece}, author = {Gensler, A. and Sick, B. and Vogt, S.}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, keywords = {imported}, pages = {1–9}, publisher = {IEEE}, title = {A Review of Deterministic Error Scores and Normalization Techniques for Power Forecasting Algorithms}, year = 2016 }
%0 Conference Paper %1 gensler2016review %A Gensler, A. %A Sick, B. %A Vogt, S. %B IEEE Symposium Series on Computational Intelligence (SSCI) %C Athens, Greece %D 2016 %I IEEE %P 1--9 %R 10.1109/ssci.2016.7849848 %T A Review of Deterministic Error Scores and Normalization Techniques for Power Forecasting Algorithms
Towards Autonomous Self-tests at Runtime.Heck, H.; Wacker, A.; Rudolph, S.; Gruhl, C.; Sick, B.; Tomforde, S. In IEEE International Workshop on Quality Assurance for Self-Adaptive, Self-Organising Systems (QA4SASO), FAS*W, ble. 98–99. IEEE, 2016.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{heck2016towards, author = {Heck, H. and Wacker, A. and Rudolph, S. and Gruhl, C. and Sick, B. and Tomforde, S.}, booktitle = {IEEE International Workshop on Quality Assurance for Self-Adaptive, Self-Organising Systems (QA4SASO), FAS*W}, keywords = {imported}, pages = {98–99}, publisher = {IEEE}, title = {Towards Autonomous Self-tests at Runtime}, year = 2016 }
%0 Conference Paper %1 heck2016towards %A Heck, H. %A Wacker, A. %A Rudolph, S. %A Gruhl, C. %A Sick, B. %A Tomforde, S. %B IEEE International Workshop on Quality Assurance for Self-Adaptive, Self-Organising Systems (QA4SASO), FAS*W %D 2016 %I IEEE %P 98--99 %R 10.1109/fas-w.2016.32 %T Towards Autonomous Self-tests at Runtime
Dealing with Unforeseen Situations in the Context of Self-Adaptive Urban Traffic Control: How to Bridge the Gap.Stein, A.; Tomforde, S.; Rauh, D.; Hähner, J. In IEEE International Conference on Autonomic Computing (ICAC), ble. 167–172. IEEE, Würzburg, Germany, 2016.
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- [ EndNote ]
@inproceedings{stein2016dealing, address = {W{\"u}rzburg, Germany}, author = {Stein, A. and Tomforde, S. and Rauh, D. and H{\"a}hner, J.}, booktitle = {IEEE International Conference on Autonomic Computing (ICAC)}, keywords = {imported}, pages = {167–172}, publisher = {IEEE}, title = {Dealing with Unforeseen Situations in the Context of Self-Adaptive Urban Traffic Control: How to Bridge the Gap}, year = 2016 }
%0 Conference Paper %1 stein2016dealing %A Stein, A. %A Tomforde, S. %A Rauh, D. %A Hähner, J. %B IEEE International Conference on Autonomic Computing (ICAC) %C Würzburg, Germany %D 2016 %I IEEE %P 167--172 %R 10.1109/icac.2016.20 %T Dealing with Unforeseen Situations in the Context of Self-Adaptive Urban Traffic Control: How to Bridge the Gap

2015[ to top ]

Aktives Lernen für Klassifikationsprobleme unter der Nutzung von Strukturinformation.Reitmaier, T. In Lecture Notes in Informatics: Ausgezeichnete Informatikdissertationen, ble. 239–248. 2015.
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@inproceedings{reitmaier2015aktives, author = {Reitmaier, T.}, booktitle = {Lecture Notes in Informatics: Ausgezeichnete Informatikdissertationen}, keywords = {imported}, pages = {239–248}, title = {{Aktives Lernen für Klassifikationsprobleme unter der Nutzung von Strukturinformation}}, year = 2015 }
%0 Conference Paper %1 reitmaier2015aktives %A Reitmaier, T. %B Lecture Notes in Informatics: Ausgezeichnete Informatikdissertationen %D 2015 %P 239--248 %T Aktives Lernen für Klassifikationsprobleme unter der Nutzung von Strukturinformation
Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen.Stone, T. C.; Huber, A.; Siwy, R.; Sick, B. In Elektronik automotive, 2(2), ble. 32–36. WEKA Fachmedien, Landshut, Germany, 2015.
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@article{stone2015analyse, address = {Landshut, Germany}, author = {Stone, T. C. and Huber, A. and Siwy, R. and Sick, B.}, journal = {Elektronik automotive}, keywords = {imported}, number = 2, pages = {32–36}, publisher = {WEKA Fachmedien}, title = {{Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen}}, volume = 2, year = 2015 }
%0 Journal Article %1 stone2015analyse %A Stone, T. C. %A Huber, A. %A Siwy, R. %A Sick, B. %C Landshut, Germany %D 2015 %I WEKA Fachmedien %J Elektronik automotive %N 2 %P 32--36 %T Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen %V 2
Aktives Lernen für Klassifikationsprobleme unter der Nutzung von Strukturinformationen. Reitmaier, T. kassel university press, Kassel, Germany, 2015.
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@phdthesis{reitmaier2015aktivesa, address = {Kassel, Germany}, author = {Reitmaier, T.}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, series = {Intelligent Embedded Systems}, title = {{Aktives Lernen f{\"u}r Klassifikationsprobleme unter der Nutzung von Strukturinformationen}}, year = 2015 }
%0 Thesis %1 reitmaier2015aktivesa %A Reitmaier, T. %B Intelligent Embedded Systems %C Kassel, Germany %D 2015 %I kassel university press %T Aktives Lernen für Klassifikationsprobleme unter der Nutzung von Strukturinformationen
Anomalies in Generative Trajectory Models -- Discovering Suspicious Traces with Novelty Detection Methods.Gruhl, C. In Organic Computing -- Doctoral Dissertation Colloquium 2015, S. Tomforde, B. Sick (reds.), ble. 95–107. kassel university press, Kassel, Germany, 2015.
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@incollection{gruhl2015anomalies, address = {Kassel, Germany}, author = {Gruhl, C.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2015}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {95–107}, publisher = {kassel university press}, title = {Anomalies in Generative Trajectory Models – Discovering Suspicious Traces with Novelty Detection Methods}, year = 2015 }
%0 Book Section %1 gruhl2015anomalies %A Gruhl, C. %B Organic Computing -- Doctoral Dissertation Colloquium 2015 %C Kassel, Germany %D 2015 %E Tomforde, S. %E Sick, B. %I kassel university press %P 95--107 %T Anomalies in Generative Trajectory Models -- Discovering Suspicious Traces with Novelty Detection Methods
Using Ontology-Based Similarity Measures to Find Training Data for Problems with Sparse Data.Kalkowski, E.; Sick, B. In IEEE International Conference on Systems, Man and Cybernetics (SMC), ble. 1693–1699. IEEE, Hongkong, China, 2015.
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@inproceedings{kalkowski2015using, address = {Hongkong, China}, author = {Kalkowski, E. and Sick, B.}, booktitle = {IEEE International Conference on Systems, Man and Cybernetics (SMC)}, keywords = {imported}, pages = {1693–1699}, publisher = {IEEE}, title = {Using Ontology-Based Similarity Measures to Find Training Data for Problems with Sparse Data}, year = 2015 }
%0 Conference Paper %1 kalkowski2015using %A Kalkowski, E. %A Sick, B. %B IEEE International Conference on Systems, Man and Cybernetics (SMC) %C Hongkong, China %D 2015 %I IEEE %P 1693--1699 %R 10.1109/smc.2015.298 %T Using Ontology-Based Similarity Measures to Find Training Data for Problems with Sparse Data
Generative Exponential Smoothing Models for Rate Forecasting with Uncertainty Estimation.Kalkowski, E.; Sick, B. In International Work-Conference on Time Series (ITISE), ble. 806–817. Granada, Spain, 2015.
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@inproceedings{kalkowski2015generative, address = {Granada, Spain}, author = {Kalkowski, E. and Sick, B.}, booktitle = {International Work-Conference on Time Series (ITISE)}, keywords = {imported}, pages = {806–817}, title = {Generative Exponential Smoothing Models for Rate Forecasting with Uncertainty Estimation}, year = 2015 }
%0 Conference Paper %1 kalkowski2015generative %A Kalkowski, E. %A Sick, B. %B International Work-Conference on Time Series (ITISE) %C Granada, Spain %D 2015 %P 806--817 %T Generative Exponential Smoothing Models for Rate Forecasting with Uncertainty Estimation
4DSPro: A New Selection Strategy for Pool-based Active Learning.Calma, A. In Organic Computing -- Doctoral Dissertation Colloquium 2015, S. Tomforde, B. Sick (reds.), ble. 121–133. kassel university press, Kassel, Germany, 2015.
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@incollection{calma20154dspro, address = {Kassel, Germany}, author = {Calma, A.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2015}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {121–133}, publisher = {kassel university press}, title = {{4DSPro}: A New Selection Strategy for Pool-based Active Learning}, year = 2015 }
%0 Book Section %1 calma20154dspro %A Calma, A. %B Organic Computing -- Doctoral Dissertation Colloquium 2015 %C Kassel, Germany %D 2015 %E Tomforde, S. %E Sick, B. %I kassel university press %P 121--133 %T 4DSPro: A New Selection Strategy for Pool-based Active Learning
Fast Feature Extraction for Time Series Analysis Using Least-squares Approximations with Orthogonal Basis Functions.Gensler, A.; Gruber, T.; Sick, B. In International Symposium on Temporal Representation and Reasoning (TIME), ble. 29–37. IEEE, Kassel, Germany, 2015.
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@inproceedings{gensler2015fast, address = {Kassel, Germany}, author = {Gensler, A. and Gruber, T. and Sick, B.}, booktitle = {International Symposium on Temporal Representation and Reasoning (TIME)}, keywords = {imported}, pages = {29–37}, publisher = {IEEE}, title = {Fast Feature Extraction for Time Series Analysis Using Least-squares Approximations with Orthogonal Basis Functions}, year = 2015 }
%0 Conference Paper %1 gensler2015fast %A Gensler, A. %A Gruber, T. %A Sick, B. %B International Symposium on Temporal Representation and Reasoning (TIME) %C Kassel, Germany %D 2015 %I IEEE %P 29--37 %R 10.1109/time.2015.21 %T Fast Feature Extraction for Time Series Analysis Using Least-squares Approximations with Orthogonal Basis Functions
Horizontal Integration of Organic Computing and Control Theory Concepts.Calma, A.; Jänicke, M.; Kantert, J.; Kopal, N.; Siefert, F.; Tomforde, S. In Organic Computing -- Doctoral Dissertation Colloquium 2015, S. Tomforde, B. Sick (reds.), ble. 157–164. kassel university press, Kassel, Germany, 2015.
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@incollection{calma2015horizontal, address = {Kassel, Germany}, author = {Calma, A. and J{\"a}nicke, M. and Kantert, J. and Kopal, N. and Siefert, F. and Tomforde, S.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2015}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {157–164}, publisher = {kassel university press}, title = {Horizontal Integration of Organic Computing and Control Theory Concepts}, year = 2015 }
%0 Book Section %1 calma2015horizontal %A Calma, A. %A Jänicke, M. %A Kantert, J. %A Kopal, N. %A Siefert, F. %A Tomforde, S. %B Organic Computing -- Doctoral Dissertation Colloquium 2015 %C Kassel, Germany %D 2015 %E Tomforde, S. %E Sick, B. %I kassel university press %P 157--164 %T Horizontal Integration of Organic Computing and Control Theory Concepts
An Online Influence Detection Algorithm for Organic Computing Systems.Rudolph, S.; Tomforde, S.; Sick, B.; Heck, H.; Wacker, A.; Hähner, J. In International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS, ble. 1–8. VDE, Porto, Portugal, 2015.
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- [ URL ]
@inproceedings{rudolph2015online, address = {Porto, Portugal}, author = {Rudolph, S. and Tomforde, S. and Sick, B. and Heck, H. and Wacker, A. and H{\"a}hner, J.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {An Online Influence Detection Algorithm for Organic Computing Systems}, year = 2015 }
%0 Conference Paper %1 rudolph2015online %A Rudolph, S. %A Tomforde, S. %A Sick, B. %A Heck, H. %A Wacker, A. %A Hähner, J. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C Porto, Portugal %D 2015 %I VDE %P 1--8 %T An Online Influence Detection Algorithm for Organic Computing Systems %U https://www.vde-verlag.de/proceedings-de/563657011.html
A building block for awareness in technical systems: Online novelty detection and reaction with an application in intrusion detection.Gruhl, C.; Sick, B.; Wacker, A.; Tomforde, S.; Hähner, J. In IEEE International Conference on Awareness Science and Technology (iCAST), ble. 194–200. IEEE, Qinhuangdao, China, 2015.
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- [ EndNote ]
@inproceedings{gruhl2015building, address = {Qinhuangdao, China}, author = {Gruhl, C. and Sick, B. and Wacker, A. and Tomforde, S. and H{\"a}hner, J.}, booktitle = {IEEE International Conference on Awareness Science and Technology (iCAST)}, keywords = {imported}, pages = {194–200}, publisher = {IEEE}, title = {A building block for awareness in technical systems: Online novelty detection and reaction with an application in intrusion detection}, year = 2015 }
%0 Conference Paper %1 gruhl2015building %A Gruhl, C. %A Sick, B. %A Wacker, A. %A Tomforde, S. %A Hähner, J. %B IEEE International Conference on Awareness Science and Technology (iCAST) %C Qinhuangdao, China %D 2015 %I IEEE %P 194--200 %R 10.1109/icawst.2015.7314046 %T A building block for awareness in technical systems: Online novelty detection and reaction with an application in intrusion detection
Self-adapting Multi-Sensor System Using Classifiers Based on Gaussian Mixture Models.Jänicke, M. In Organic Computing -- Doctoral Dissertation Colloquium 2015, S. Tomforde, B. Sick (reds.), ble. 109–120. kassel university press, Kassel, Germany, 2015.
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@incollection{jaenicke2015self, address = {Kassel, Germany}, author = {J{\"a}nicke, M.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2015}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {109–120}, publisher = {kassel university press}, title = {Self-adapting Multi-Sensor System Using Classifiers Based on Gaussian Mixture Models}, year = 2015 }
%0 Book Section %1 jaenicke2015self %A Jänicke, M. %B Organic Computing -- Doctoral Dissertation Colloquium 2015 %C Kassel, Germany %D 2015 %E Tomforde, S. %E Sick, B. %I kassel university press %P 109--120 %T Self-adapting Multi-Sensor System Using Classifiers Based on Gaussian Mixture Models
Organic Computing -- Doctoral Dissertation Colloquium 2015. Tomforde, S.; Sick, B. In Vol. 7Intelligent Embedded Systems. kassel university press, 2015.
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@book{tomforde2015organic, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Organic Computing – Doctoral Dissertation Colloquium 2015}, volume = 7, year = 2015 }
%0 Book %1 tomforde2015organic %B Intelligent Embedded Systems %D 2015 %E Tomforde, S. %E Sick, B. %I kassel university press %R 10.19211/KUP9783737600293 %T Organic Computing -- Doctoral Dissertation Colloquium 2015 %V 7
Runtime Self-Integration as Key Challenge for Mastering Interwoven Systems.Hähner, J.; Brinkschulte, U.; Lukowicz, P.; Mostaghim, S.; Sick, B.; Tomforde, S. In International Conference on Architecture of Computing Systems (ARCS), ble. 1–8. VDE, Porto, Portugal, 2015.
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- [ URL ]
@inproceedings{haehner2015runtime, address = {Porto, Portugal}, author = {H{\"a}hner, J. and Brinkschulte, U. and Lukowicz, P. and Mostaghim, S. and Sick, B. and Tomforde, S.}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {Runtime Self-Integration as Key Challenge for Mastering Interwoven Systems}, year = 2015 }
%0 Conference Paper %1 haehner2015runtime %A Hähner, J. %A Brinkschulte, U. %A Lukowicz, P. %A Mostaghim, S. %A Sick, B. %A Tomforde, S. %B International Conference on Architecture of Computing Systems (ARCS) %C Porto, Portugal %D 2015 %I VDE %P 1--8 %T Runtime Self-Integration as Key Challenge for Mastering Interwoven Systems %U https://ieeexplore.ieee.org/document/7107107
Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptions.Goldhammer, M.; Köhler, S.; Doll, K.; Sick, B. In SAI Intelligent Systems Conference (IntelliSys), Studies in Computational Intelligence, ble. 259–279. Springer, Cham, Switzerland, 2015.
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@inproceedings{goldhammer2015track, address = {Cham, Switzerland}, author = {Goldhammer, M. and K{\"o}hler, S. and Doll, K. and Sick, B.}, booktitle = {SAI Intelligent Systems Conference (IntelliSys)}, keywords = {imported}, pages = {259–279}, publisher = {Springer}, series = {Studies in Computational Intelligence}, title = {Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptions}, year = 2015 }
%0 Conference Paper %1 goldhammer2015track %A Goldhammer, M. %A Köhler, S. %A Doll, K. %A Sick, B. %B SAI Intelligent Systems Conference (IntelliSys) %C Cham, Switzerland %D 2015 %I Springer %P 259--279 %R 10.1007/978-3-319-33386-1_13 %T Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptions
A Tool Chain for Context Detection Automating the Investigation of a Multitude of Parameter Sets.Jahn, A.; Lau, S. L.; David, K.; Sick, B. In International Workshop on Mobile and Context Aware Services (MOCS), VTC, ble. 1–5. Boston, MA, 2015.
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@inproceedings{jahn2015tool, address = {Boston, MA}, author = {Jahn, A. and Lau, S. L. and David, K. and Sick, B.}, booktitle = {International Workshop on Mobile and Context Aware Services (MOCS), VTC}, keywords = {imported}, pages = {1–5}, title = {A Tool Chain for Context Detection Automating the Investigation of a Multitude of Parameter Sets}, year = 2015 }
%0 Conference Paper %1 jahn2015tool %A Jahn, A. %A Lau, S. L. %A David, K. %A Sick, B. %B International Workshop on Mobile and Context Aware Services (MOCS), VTC %C Boston, MA %D 2015 %P 1--5 %T A Tool Chain for Context Detection Automating the Investigation of a Multitude of Parameter Sets
The Responsibility Weighted Mahalanobis Kernel for Semi-Supervised Training of Support Vector Machines for Classification.Reitmaier, T.; Sick, B. In Information Sciences, 323, ble. 179–198. Elsevier, 2015.
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@article{reitmaier2015responsibility, author = {Reitmaier, T. and Sick, B.}, journal = {Information Sciences}, keywords = {imported}, pages = {179–198}, publisher = {Elsevier}, title = {The Responsibility Weighted {M}ahalanobis Kernel for Semi-Supervised Training of Support Vector Machines for Classification}, volume = 323, year = 2015 }
%0 Journal Article %1 reitmaier2015responsibility %A Reitmaier, T. %A Sick, B. %D 2015 %I Elsevier %J Information Sciences %P 179--198 %R 10.1016/j.ins.2015.06.027 %T The Responsibility Weighted Mahalanobis Kernel for Semi-Supervised Training of Support Vector Machines for Classification %V 323
On the Application Possibilities of Organic Computing Principles in Socio-technical Systems.Heck, H.; Edenhofer, S.; Gruhl, C.; Lund, A.; Shuka, R.; Hähner, J. In Organic Computing -- Doctoral Dissertation Colloquium 2015, S. Tomforde, B. Sick (reds.), ble. 165–170. kassel university press, Kassel, Germany, 2015.
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@incollection{heck2015application, address = {Kassel, Germany}, author = {Heck, H. and Edenhofer, S. and Gruhl, C. and Lund, A. and Shuka, R. and H{\"a}hner, J.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2015}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {165–170}, publisher = {kassel university press}, title = {On the Application Possibilities of Organic Computing Principles in Socio-technical Systems}, year = 2015 }
%0 Book Section %1 heck2015application %A Heck, H. %A Edenhofer, S. %A Gruhl, C. %A Lund, A. %A Shuka, R. %A Hähner, J. %B Organic Computing -- Doctoral Dissertation Colloquium 2015 %C Kassel, Germany %D 2015 %E Tomforde, S. %E Sick, B. %I kassel university press %P 165--170 %T On the Application Possibilities of Organic Computing Principles in Socio-technical Systems
A New Vision of Collaborative Active Learning.Calma, Adrian; Reitmaier, Tobias; Sick, Bernhard; Lukowicz, Paul; Embrechts, Mark. In arXiv e-prints, bl. arXiv:1504.00284. 2015.
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@article{calma2015new, author = {Calma, Adrian and Reitmaier, Tobias and Sick, Bernhard and Lukowicz, Paul and Embrechts, Mark}, journal = {arXiv e-prints}, keywords = {Machine}, pages = {arXiv:1504.00284}, title = {{A New Vision of Collaborative Active Learning}}, year = 2015 }
%0 Journal Article %1 calma2015new %A Calma, Adrian %A Reitmaier, Tobias %A Sick, Bernhard %A Lukowicz, Paul %A Embrechts, Mark %D 2015 %J arXiv e-prints %P arXiv:1504.00284 %T A New Vision of Collaborative Active Learning
A Mutual Influence Detection Algorithm for Systems with Local Performance Measurement.Rudolph, S.; Tomforde, S.; Sick, B.; Hähner, J. In IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), ble. 144–149. IEEE, Cambridge, MA, 2015.
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@inproceedings{rudolph2015mutual, address = {Cambridge, MA}, author = {Rudolph, S. and Tomforde, S. and Sick, B. and H{\"a}hner, J.}, booktitle = {IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO)}, keywords = {imported}, pages = {144–149}, publisher = {IEEE}, title = {A Mutual Influence Detection Algorithm for Systems with Local Performance Measurement}, year = 2015 }
%0 Conference Paper %1 rudolph2015mutual %A Rudolph, S. %A Tomforde, S. %A Sick, B. %A Hähner, J. %B IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) %C Cambridge, MA %D 2015 %I IEEE %P 144--149 %R 10.1109/saso.2015.23 %T A Mutual Influence Detection Algorithm for Systems with Local Performance Measurement
Car Drive Classification and Context Recognition for Personalized Entertainment Preference Learning.Stone, T. C.; Haas, S.; Breitenstein, S.; Wiesner, K.; Sick, B. In International Journal on Advances in Software, 8(1--2), ble. 53–64. IARIA, 2015.
- [ BibTeX ]
- [ EndNote ]
@article{stone2015car, author = {Stone, T. C. and Haas, S. and Breitenstein, S. and Wiesner, K. and Sick, B.}, journal = {International Journal on Advances in Software}, keywords = {imported}, number = {1–2}, pages = {53–64}, publisher = {IARIA}, title = {Car Drive Classification and Context Recognition for Personalized Entertainment Preference Learning}, volume = 8, year = 2015 }
%0 Journal Article %1 stone2015car %A Stone, T. C. %A Haas, S. %A Breitenstein, S. %A Wiesner, K. %A Sick, B. %D 2015 %I IARIA %J International Journal on Advances in Software %N 1--2 %P 53--64 %T Car Drive Classification and Context Recognition for Personalized Entertainment Preference Learning %V 8
A Generalized Hebb (GH) rule based on a cross-entropy error function for deep belief recursive learning.Embrechts, M.; Sick, B. In International Conference on Neural Networks - Fuzzy Systems (NN-FS), ble. 21–24. Vienna, Austria, 2015.
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- [ EndNote ]
- [ URL ]
@inproceedings{embrechts2015generalized, address = {Vienna, Austria}, author = {Embrechts, M. and Sick, B.}, booktitle = {International Conference on Neural Networks - Fuzzy Systems (NN-FS)}, keywords = {imported}, pages = {21–24}, title = {A {Generalized Hebb (GH)} rule based on a cross-entropy error function for deep belief recursive learning}, year = 2015 }
%0 Conference Paper %1 embrechts2015generalized %A Embrechts, M. %A Sick, B. %B International Conference on Neural Networks - Fuzzy Systems (NN-FS) %C Vienna, Austria %D 2015 %P 21--24 %T A Generalized Hebb (GH) rule based on a cross-entropy error function for deep belief recursive learning %U http://www.inase.org/library/2015/vienna/bypaper/APNE/APNE-02.pdf
Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Stochastic Simulations.Breker, S.; Claudi, A.; Sick, B. In IEEE Transactions on Power Systems, 30(2), ble. 689–700. IEEE, 2015.
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@article{breker2015capacity, author = {Breker, S. and Claudi, A. and Sick, B.}, journal = {IEEE Transactions on Power Systems}, keywords = {imported}, number = 2, pages = {689–700}, publisher = {IEEE}, title = {Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Stochastic Simulations}, volume = 30, year = 2015 }
%0 Journal Article %1 breker2015capacity %A Breker, S. %A Claudi, A. %A Sick, B. %D 2015 %I IEEE %J IEEE Transactions on Power Systems %N 2 %P 689--700 %R 10.1109/tpwrs.2014.2332361 %T Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Stochastic Simulations %V 30
Klassifikation von Niederspannungsnetzen mit Support Vector Machines: Bewertung des Aufnahmevermögens für Dezentrale Erzeugungsanlagen. Technical Report (6), . Breker, S. ble. 1–170. kassel university press, Kassel, Germany, 2015.
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@phdthesis{breker2015klassifikation, address = {Kassel, Germany}, author = {Breker, S.}, keywords = {imported}, number = 6, pages = {1–170}, publisher = {kassel university press}, school = {University of Kassel}, series = {Intelligent Embedded Systems}, title = {Klassifikation von Niederspannungsnetzen mit Support Vector Machines: Bewertung des Aufnahmeverm{\"o}gens f{\"u}r Dezentrale Erzeugungsanlagen}, year = 2015 }
%0 Thesis %1 breker2015klassifikation %A Breker, S. %B Intelligent Embedded Systems %C Kassel, Germany %D 2015 %I kassel university press %N 6 %P 1--170 %T Klassifikation von Niederspannungsnetzen mit Support Vector Machines: Bewertung des Aufnahmevermögens für Dezentrale Erzeugungsanlagen
Effiziente Bewertung des Anschlu\ss{}potentials von Niederspannungsnetzen für dezentrale Erzeugungsanlagen: Klassifikation mit Methoden der Computational Intelligence.Breker, S.; Sick, B. In Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS), ble. 51–56. Hamburg, Germany, 2015.
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- [ EndNote ]
@inproceedings{breker2015effiziente, address = {Hamburg, Germany}, author = {Breker, S. and Sick, B.}, booktitle = {Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS)}, keywords = {imported}, pages = {51–56}, title = {{Effiziente Bewertung des Anschlu\ss{}potentials von Niederspannungsnetzen f{\"u}r dezentrale Erzeugungsanlagen: Klassifikation mit Methoden der Computational Intelligence}}, year = 2015 }
%0 Conference Paper %1 breker2015effiziente %A Breker, S. %A Sick, B. %B Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS) %C Hamburg, Germany %D 2015 %P 51--56 %R 10.1007/978-3-658-10958-5_8 %T Effiziente Bewertung des Anschlu\ss{}potentials von Niederspannungsnetzen für dezentrale Erzeugungsanlagen: Klassifikation mit Methoden der Computational Intelligence
Bewertung verschiedener Spannungsregelungskonzepte in einem einspeisegeprägten Mittelspannungsnetz und Ausblick auf neue Konzepte basierend auf Methoden der Computational Intelligence.Rudolph, J.; Breker, S.; Sick, B. In Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS), ble. 57–63. Hamburg, Germany, 2015.
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- [ EndNote ]
@inproceedings{rudolph2015bewertung, address = {Hamburg, Germany}, author = {Rudolph, J. and Breker, S. and Sick, B.}, booktitle = {Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS)}, keywords = {imported}, pages = {57–63}, title = {Bewertung verschiedener {Spannungsregelungskonzepte} in einem einspeisegepr{\"a}gten {Mittelspannungsnetz und Ausblick auf neue Konzepte basierend auf Methoden der Computational Intelligence}}, year = 2015 }
%0 Conference Paper %1 rudolph2015bewertung %A Rudolph, J. %A Breker, S. %A Sick, B. %B Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS) %C Hamburg, Germany %D 2015 %P 57--63 %R 10.1007/978-3-658-10958-5_9 %T Bewertung verschiedener Spannungsregelungskonzepte in einem einspeisegeprägten Mittelspannungsnetz und Ausblick auf neue Konzepte basierend auf Methoden der Computational Intelligence
Transductive active learning -- A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data.Reitmaier, T.; Calma, A.; Sick, B. In Information Sciences, 293, ble. 275–298. Elsevier, 2015.
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- [ EndNote ]
@article{reitmaier2015transductive, author = {Reitmaier, T. and Calma, A. and Sick, B.}, journal = {Information Sciences}, keywords = {imported}, pages = {275–298}, publisher = {Elsevier}, title = {Transductive active learning – A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data}, volume = 293, year = 2015 }
%0 Journal Article %1 reitmaier2015transductive %A Reitmaier, T. %A Calma, A. %A Sick, B. %D 2015 %I Elsevier %J Information Sciences %P 275--298 %R 10.1016/j.ins.2014.09.009 %T Transductive active learning -- A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data %V 293
Camera Based Pedestrian Path Prediction by Means of Polynominal Least-squares Approximation and Multilayer Perceptron Neural Networks.Goldhammer, M.; Köhler, S.; Doll, K.; Sick, B. In SAI Intelligent Systems Conference (IntelliSys), ble. 390–399. Springer, London, UK, 2015.
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- [ EndNote ]
@inproceedings{goldhammer2015camera, address = {London, UK}, author = {Goldhammer, M. and K\"ohler, S. and Doll, K. and Sick, B.}, booktitle = {SAI Intelligent Systems Conference (IntelliSys)}, keywords = {imported}, pages = {390–399}, publisher = {Springer}, title = {Camera Based Pedestrian Path Prediction by Means of Polynominal Least-squares Approximation and Multilayer Perceptron Neural Networks}, year = 2015 }
%0 Conference Paper %1 goldhammer2015camera %A Goldhammer, M. %A Köhler, S. %A Doll, K. %A Sick, B. %B SAI Intelligent Systems Conference (IntelliSys) %C London, UK %D 2015 %I Springer %P 390--399 %R 10.1109/IntelliSys.2015.7361171 %T Camera Based Pedestrian Path Prediction by Means of Polynominal Least-squares Approximation and Multilayer Perceptron Neural Networks

2014[ to top ]

Sicherheit eingebetteter Systeme auf Basis arithmetischer Codierungen. Früchtl, M. kassel university press, Kassel, Germany, 2014.
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- [ EndNote ]
@phdthesis{fruechtl2014sicherheit, address = {Kassel, Germany}, author = {Fr{\"u}chtl, M.}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, series = {Intelligent Embedded Systems}, title = {Sicherheit eingebetteter Systeme auf Basis arithmetischer Codierungen}, year = 2014 }
%0 Thesis %1 fruechtl2014sicherheit %A Früchtl, M. %B Intelligent Embedded Systems %C Kassel, Germany %D 2014 %I kassel university press %T Sicherheit eingebetteter Systeme auf Basis arithmetischer Codierungen
SMART-iBrush -- Individuelle Unterstützung der Zahnreinigung durch Messung von Bewegung und Druck mit einer intelligenten Zahnbürste.Al-Falouji, G.; Prestel, D.; Scharfenberg, G.; Mandl, R.; Deinzer, A.; Halang, W.; Margraf-Stiksrud, J.; Sick, B.; Deinzer, R. In Transdisziplinäre Konferenz zum Thema "Technische Unterstützungssysteme, die die Menschen wirklich wollen", ble. 315–327. 2014.
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- [ EndNote ]
@inproceedings{alfalouji2014smart, author = {Al-Falouji, G. and Prestel, D. and Scharfenberg, G. and Mandl, R. and Deinzer, A. and Halang, W. and Margraf-Stiksrud, J. and Sick, B. and Deinzer, R.}, booktitle = {Transdisziplin{\"a}re Konferenz zum Thema "Technische Unterst{\"u}tzungssysteme, die die Menschen wirklich wollen"}, keywords = {imported}, pages = {315–327}, title = {{SMART-iBrush – Individuelle Unterst{\"u}tzung der Zahnreinigung durch Messung von Bewegung und Druck mit einer intelligenten Zahnb{\"u}rste}}, year = 2014 }
%0 Conference Paper %1 alfalouji2014smart %A Al-Falouji, G. %A Prestel, D. %A Scharfenberg, G. %A Mandl, R. %A Deinzer, A. %A Halang, W. %A Margraf-Stiksrud, J. %A Sick, B. %A Deinzer, R. %B Transdisziplinäre Konferenz zum Thema "Technische Unterstützungssysteme, die die Menschen wirklich wollen" %D 2014 %P 315--327 %T SMART-iBrush -- Individuelle Unterstützung der Zahnreinigung durch Messung von Bewegung und Druck mit einer intelligenten Zahnbürste
Temporal data analytics based on eigenmotif and shape space representations of time series.Gensler, A.; Sick, B.; Willkomm, J. In IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), ble. 753–757. IEEE, Xian, China, 2014.
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@inproceedings{gensler2014temporal, address = {Xian, China}, author = {Gensler, A. and Sick, B. and Willkomm, J.}, booktitle = {IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP)}, keywords = {imported}, pages = {753–757}, publisher = {IEEE}, title = {Temporal data analytics based on eigenmotif and shape space representations of time series}, year = 2014 }
%0 Conference Paper %1 gensler2014temporal %A Gensler, A. %A Sick, B. %A Willkomm, J. %B IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP) %C Xian, China %D 2014 %I IEEE %P 753--757 %R 10.1109/chinasip.2014.6889345 %T Temporal data analytics based on eigenmotif and shape space representations of time series
Self-Extending Training Sets: Using Ontologies to Improve Machine Learning Performance.Kalkowski, E. In Organic Computing -- Doctoral Dissertation Colloquium 2014, S. Tomforde, B. Sick (reds.), ble. 111–125. kassel university press, Kassel, Germany, 2014.
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@incollection{kalkowski2014self, address = {Kassel, Germany}, author = {Kalkowski, E.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2014}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {111–125}, publisher = {kassel university press}, title = {Self-Extending Training Sets: Using Ontologies to Improve Machine Learning Performance}, year = 2014 }
%0 Book Section %1 kalkowski2014self %A Kalkowski, E. %B Organic Computing -- Doctoral Dissertation Colloquium 2014 %C Kassel, Germany %D 2014 %E Tomforde, S. %E Sick, B. %I kassel university press %P 111--125 %T Self-Extending Training Sets: Using Ontologies to Improve Machine Learning Performance
"Know thyself" -- Computational Self-Reflection in Intelligent Technical Systems.Tomforde, S.; Hähner, J.; von Mammen, S.; Gruhl, C.; Sick, B.; Geihs, K. In Workshop on Self-Improving System Integration (SISSY), SASO. IEEE, Braunschweig, Germany, 2014.
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@inproceedings{tomforde2014know, address = {Braunschweig, Germany}, author = {Tomforde, S. and H{\"a}hner, J. and von Mammen, S. and Gruhl, C. and Sick, B. and Geihs, K.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), SASO}, keywords = {imported}, publisher = {IEEE}, title = {"{K}now thyself" – Computational Self-Reflection in Intelligent Technical Systems}, year = 2014 }
%0 Conference Paper %1 tomforde2014know %A Tomforde, S. %A Hähner, J. %A von Mammen, S. %A Gruhl, C. %A Sick, B. %A Geihs, K. %B Workshop on Self-Improving System Integration (SISSY), SASO %C Braunschweig, Germany %D 2014 %I IEEE %R 10.1109/SASOW.2014.25 %T "Know thyself" -- Computational Self-Reflection in Intelligent Technical Systems
Organic Computing -- Doctoral Dissertation Colloquium 2014. Tomforde, S.; Sick, B. In Vol. 4Intelligent Embedded Systems. kassel university press, 2014.
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- [ URL ]
@book{tomforde2014organic, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Organic Computing – Doctoral Dissertation Colloquium 2014}, volume = 4, year = 2014 }
%0 Book %1 tomforde2014organic %B Intelligent Embedded Systems %D 2014 %E Tomforde, S. %E Sick, B. %I kassel university press %T Organic Computing -- Doctoral Dissertation Colloquium 2014 %U https://www.uni-kassel.de/ub/publizieren/kassel-university-press/verlagsprogramm?h=9783862198320 %V 4
Pedestrian’s Trajectory Forecast in Public Traffic with Artificial Neural Networks.Goldhammer, M.; Doll, K.; Brunsmann, U.; Gensler, A.; Sick, B. In International Conference on Pattern Recognition (ICPR), ble. 4110–4115. IEEE, Stockholm, Sweden, 2014.
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@inproceedings{goldhammer2014pedestrians, address = {Stockholm, Sweden}, author = {Goldhammer, M. and Doll, K. and Brunsmann, U. and Gensler, A. and Sick, B.}, booktitle = {International Conference on Pattern Recognition (ICPR)}, keywords = {imported}, pages = {4110–4115}, publisher = {IEEE}, title = {Pedestrian's Trajectory Forecast in Public Traffic with Artificial Neural Networks}, year = 2014 }
%0 Conference Paper %1 goldhammer2014pedestrians %A Goldhammer, M. %A Doll, K. %A Brunsmann, U. %A Gensler, A. %A Sick, B. %B International Conference on Pattern Recognition (ICPR) %C Stockholm, Sweden %D 2014 %I IEEE %P 4110--4115 %R 10.1109/icpr.2014.704 %T Pedestrian's Trajectory Forecast in Public Traffic with Artificial Neural Networks
On General Purpose Time Series Similarity Measures and Their Use as Kernel Functions in Support Vector Machines.Pree, H.; Herwig, B.; Gruber, T.; Sick, B.; David, K.; Lukowicz, P. In Information Sciences, 281, ble. 478–495. Elsevier, 2014.
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@article{pree2014general, author = {Pree, H. and Herwig, B. and Gruber, T. and Sick, B. and David, K. and Lukowicz, P.}, journal = {Information Sciences}, keywords = {imported}, pages = {478–495}, publisher = {Elsevier}, title = {On General Purpose Time Series Similarity Measures and Their Use as Kernel Functions in Support Vector Machines}, volume = 281, year = 2014 }
%0 Journal Article %1 pree2014general %A Pree, H. %A Herwig, B. %A Gruber, T. %A Sick, B. %A David, K. %A Lukowicz, P. %D 2014 %I Elsevier %J Information Sciences %P 478--495 %R 10.1016/j.ins.2014.05.025 %T On General Purpose Time Series Similarity Measures and Their Use as Kernel Functions in Support Vector Machines %V 281
Dealing with human variability in motion based, wearable activity recognition.Kreil, M.; Sick, B.; Lukowicz, P. In Symposium on Activity and Context Modeling and Recognition (ACOMORE), PerCom, ble. 36–40. IEEE, Budapest, Hungary, 2014.
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@inproceedings{kreil2014dealing, address = {Budapest, Hungary}, author = {Kreil, M. and Sick, B. and Lukowicz, P.}, booktitle = {Symposium on Activity and Context Modeling and Recognition (ACOMORE), PerCom}, keywords = {imported}, pages = {36–40}, publisher = {IEEE}, title = {Dealing with human variability in motion based, wearable activity recognition}, year = 2014 }
%0 Conference Paper %1 kreil2014dealing %A Kreil, M. %A Sick, B. %A Lukowicz, P. %B Symposium on Activity and Context Modeling and Recognition (ACOMORE), PerCom %C Budapest, Hungary %D 2014 %I IEEE %P 36--40 %R 10.1109/percomw.2014.6815161 %T Dealing with human variability in motion based, wearable activity recognition
Novel Criteria to Measure Performance of Time Series Segmentation Techniques.Gensler, A.; Sick, B.; Pankraz, V. In Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML), LWA, ble. 192–204. Aachen, Germany, 2014.
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- [ URL ]
@inproceedings{gensler2014novel, address = {Aachen, Germany}, author = {Gensler, A. and Sick, B. and Pankraz, V.}, booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML), LWA}, keywords = {imported}, pages = {192–204}, title = {Novel Criteria to Measure Performance of Time Series Segmentation Techniques}, year = 2014 }
%0 Conference Paper %1 gensler2014novel %A Gensler, A. %A Sick, B. %A Pankraz, V. %B Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML), LWA %C Aachen, Germany %D 2014 %P 192--204 %T Novel Criteria to Measure Performance of Time Series Segmentation Techniques %U http://ceur-ws.org/Vol-1226/paper31.pdf
Programmierkompetenz prüfen … am Beispiel der Vorlesung "Einführung in C" an der Universität Kassel.Herwig, B.; Frommann, U.; Gruber, T.; Sick, B. In Neues Handbuch Hochschullehre, ble. 71–94. Raabe, 2014.
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@incollection{herwig2014programmierkompetenz, author = {Herwig, B. and Frommann, U. and Gruber, T. and Sick, B.}, booktitle = {Neues Handbuch Hochschullehre}, keywords = {imported}, number = 64, pages = {71–94}, publisher = {Raabe}, title = {Programmierkompetenz pr{\"u}fen … am Beispiel der Vorlesung "Einf{\"u}hrung in C" an der Universit{\"a}t Kassel}, year = 2014 }
%0 Book Section %1 herwig2014programmierkompetenz %A Herwig, B. %A Frommann, U. %A Gruber, T. %A Sick, B. %B Neues Handbuch Hochschullehre %D 2014 %I Raabe %N 64 %P 71--94 %T Programmierkompetenz prüfen … am Beispiel der Vorlesung "Einführung in C" an der Universität Kassel
Location based learning of user behavior for proactive recommender systems in car comfort functions.Stone, T.; Birth, O.; Gensler, A.; Huber, A.; Jänicke, M.; Sick, B. In INFORMATIK 2014, ble. 2121–2132. Gesellschaft für Informatik e.V., 2014.
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@inproceedings{stone2014location, author = {Stone, T. and Birth, O. and Gensler, A. and Huber, A. and J{\"a}nicke, M. and Sick, B.}, booktitle = {INFORMATIK 2014}, keywords = {imported}, pages = {2121–2132}, publisher = {Gesellschaft für Informatik e.V.}, title = {Location based learning of user behavior for proactive recommender systems in car comfort functions}, year = 2014 }
%0 Conference Paper %1 stone2014location %A Stone, T. %A Birth, O. %A Gensler, A. %A Huber, A. %A Jänicke, M. %A Sick, B. %B INFORMATIK 2014 %D 2014 %I Gesellschaft für Informatik e.V. %P 2121--2132 %T Location based learning of user behavior for proactive recommender systems in car comfort functions %U https://dl.gi.de/handle/20.500.12116/2842
Car Ride Classification for Drive Context Recognition.Haas, Stefan; Wiesner, Kevin; Stone, Thomas Christian. In International Conference on Mobile Services, Resources, and Users (MOBILITY). 2014.
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- [ URL ]
@inproceedings{haas2014car, author = {Haas, Stefan and Wiesner, Kevin and Stone, Thomas Christian}, booktitle = {International Conference on Mobile Services, Resources, and Users (MOBILITY)}, keywords = {imported}, title = {Car Ride Classification for Drive Context Recognition}, year = 2014 }
%0 Conference Paper %1 haas2014car %A Haas, Stefan %A Wiesner, Kevin %A Stone, Thomas Christian %B International Conference on Mobile Services, Resources, and Users (MOBILITY) %D 2014 %T Car Ride Classification for Drive Context Recognition %U http://www.thinkmind.org/articles/mobility_2014_3_30_70023.pdf
Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining Applications.Fisch, D.; Kalkowski, E.; Sick, B. In IEEE Transactions on Knowledge and Data Engineering, 26(3), ble. 652–666. IEEE, 2014.
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@article{fisch2014knowledge, author = {Fisch, D. and Kalkowski, E. and Sick, B.}, journal = {IEEE Transactions on Knowledge and Data Engineering}, keywords = {imported}, number = 3, pages = {652–666}, publisher = {IEEE}, title = {Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining Applications}, volume = 26, year = 2014 }
%0 Journal Article %1 fisch2014knowledge %A Fisch, D. %A Kalkowski, E. %A Sick, B. %D 2014 %I IEEE %J IEEE Transactions on Knowledge and Data Engineering %N 3 %P 652--666 %R 10.1109/tkde.2013.20 %T Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining Applications %V 26
Interwoven Systems.Tomforde, S.; Hähner, J.; Sick, B. In Informatik Spektrum, 37(5), ble. 483–487. Springer, 2014.
- [ BibTeX ]
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@article{tomforde2014interwoven, author = {Tomforde, S. and H{\"a}hner, J. and Sick, B.}, journal = {Informatik Spektrum}, keywords = {imported}, number = 5, pages = {483–487}, publisher = {Springer}, title = {Interwoven Systems}, volume = 37, year = 2014 }
%0 Journal Article %1 tomforde2014interwoven %A Tomforde, S. %A Hähner, J. %A Sick, B. %D 2014 %I Springer %J Informatik Spektrum %N 5 %P 483--487 %R 10.1007/s00287-014-0827-z %T Interwoven Systems %V 37
Resp-kNN: A Semi-Supervised kNN-Classifier for Sparsely Labeled Data in the Field of Organic Computing.Reitmaier, T.; Calma, A. In Organic Computing -- Doctoral Dissertation Colloquium 2014, S. Tomforde, B. Sick (reds.), ble. 85–97. kassel university press, Kassel, Germany, 2014.
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@incollection{reitmaier2014resp, address = {Kassel, Germany}, author = {Reitmaier, T. and Calma, A.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2014}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {85–97}, publisher = {kassel university press}, title = {Resp-{kNN}: A Semi-Supervised {kNN}-Classifier for Sparsely Labeled Data in the Field of Organic Computing}, year = 2014 }
%0 Book Section %1 reitmaier2014resp %A Reitmaier, T. %A Calma, A. %B Organic Computing -- Doctoral Dissertation Colloquium 2014 %C Kassel, Germany %D 2014 %E Tomforde, S. %E Sick, B. %I kassel university press %P 85--97 %T Resp-kNN: A Semi-Supervised kNN-Classifier for Sparsely Labeled Data in the Field of Organic Computing
Self-Adapting Generative Modeling Techniques -- A Basic Building Block for Many Organic Computing Techniques.Gruhl, C. In Organic Computing -- Doctoral Dissertation Colloquium 2014, S. Tomforde, B. Sick (reds.), ble. 99–109. kassel university press, Kassel, Germany, 2014.
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- [ EndNote ]
@incollection{gruhl2014self, address = {Kassel, Germany}, author = {Gruhl, C.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2014}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {99–109}, publisher = {kassel university press}, title = {Self-Adapting Generative Modeling Techniques – A Basic Building Block for Many Organic Computing Techniques}, year = 2014 }
%0 Book Section %1 gruhl2014self %A Gruhl, C. %B Organic Computing -- Doctoral Dissertation Colloquium 2014 %C Kassel, Germany %D 2014 %E Tomforde, S. %E Sick, B. %I kassel university press %P 99--109 %T Self-Adapting Generative Modeling Techniques -- A Basic Building Block for Many Organic Computing Techniques
Self-Adapting Multi-sensor Systems: A Concept for Self-Improvement and Self-Healing Techniques.Jänicke, M.; Sick, B.; Lukowicz, P.; Bannach, D. In Workshop on Self-Improving System Integration (SISSY), SASO, ble. 128–136. IEEE, London, UK, 2014.
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- [ EndNote ]
@inproceedings{jaenicke2014self, address = {London, UK}, author = {J{\"a}nicke, M. and Sick, B. and Lukowicz, P. and Bannach, D.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), SASO}, keywords = {imported}, pages = {128–136}, publisher = {IEEE}, title = {Self-Adapting Multi-sensor Systems: A Concept for Self-Improvement and Self-Healing Techniques}, year = 2014 }
%0 Conference Paper %1 jaenicke2014self %A Jänicke, M. %A Sick, B. %A Lukowicz, P. %A Bannach, D. %B Workshop on Self-Improving System Integration (SISSY), SASO %C London, UK %D 2014 %I IEEE %P 128--136 %R 10.1109/sasow.2014.22 %T Self-Adapting Multi-sensor Systems: A Concept for Self-Improvement and Self-Healing Techniques
Analysis on termination of pedestrians’ gait at urban intersections.Goldhammer, M.; Hubert, A.; Köhler, S.; Zindler, K.; Brunsmann, U.; Doll, K.; Sick, B. In IEEE International Conference on Intelligent Transportation Systems (ITSC), ble. 1758–1763. IEEE, Qingdao, China, 2014.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{goldhammer2014analysis, address = {Qingdao, China}, author = {Goldhammer, M. and Hubert, A. and K{\"o}hler, S. and Zindler, K. and Brunsmann, U. and Doll, K. and Sick, B.}, booktitle = {IEEE International Conference on Intelligent Transportation Systems (ITSC)}, keywords = {imported}, pages = {1758–1763}, publisher = {IEEE}, title = {Analysis on termination of pedestrians' gait at urban intersections}, year = 2014 }
%0 Conference Paper %1 goldhammer2014analysis %A Goldhammer, M. %A Hubert, A. %A Köhler, S. %A Zindler, K. %A Brunsmann, U. %A Doll, K. %A Sick, B. %B IEEE International Conference on Intelligent Transportation Systems (ITSC) %C Qingdao, China %D 2014 %I IEEE %P 1758--1763 %R 10.1109/itsc.2014.6957947 %T Analysis on termination of pedestrians' gait at urban intersections
Engineering and Mastering Interwoven Systems.Tomforde, S.; Hähner, J.; Seebach, H.; Reif, W.; Sick, B.; Wacker, A.; Scholtes, I. In International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS, ble. 1–8. VDE, L"ubeck, Germany, 2014.
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- [ EndNote ]
@inproceedings{tomforde2014engineering, address = {L{"u}beck, Germany}, author = {Tomforde, S. and H{\"a}hner, J. and Seebach, H. and Reif, W. and Sick, B. and Wacker, A. and Scholtes, I.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {Engineering and Mastering Interwoven Systems}, year = 2014 }
%0 Conference Paper %1 tomforde2014engineering %A Tomforde, S. %A Hähner, J. %A Seebach, H. %A Reif, W. %A Sick, B. %A Wacker, A. %A Scholtes, I. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C L"ubeck, Germany %D 2014 %I VDE %P 1--8 %T Engineering and Mastering Interwoven Systems

2013[ to top ]

Let us know your decision: Pool-based active training of a generative classifier with the selection strategy 4DS.Reitmaier, T.; Sick, B. In Information Sciences, 230, ble. 106–131. Elsevier, 2013.
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- [ EndNote ]
@article{reitmaier2013let, author = {Reitmaier, T. and Sick, B.}, journal = {Information Sciences}, keywords = {imported}, pages = {106–131}, publisher = {Elsevier}, title = {Let us know your decision: Pool-based active training of a generative classifier with the selection strategy {4DS}}, volume = 230, year = 2013 }
%0 Journal Article %1 reitmaier2013let %A Reitmaier, T. %A Sick, B. %D 2013 %I Elsevier %J Information Sciences %P 106--131 %R 10.1016/j.ins.2012.11.015 %T Let us know your decision: Pool-based active training of a generative classifier with the selection strategy 4DS %V 230
A Concept for Securing Cyber-Physical Systems with Organic Computing Techniques.Hähner, J.; Rudolph, S.; Tomforde, S.; Fisch, D.; Sick, B.; Kopal, N.; Wacker, A. In Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS, ble. 1–13. VDE, Prague, Czech Republic, 2013.
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- [ URL ]
@inproceedings{haehner2013concept, address = {Prague, Czech Republic}, author = {H{\"a}hner, J. and Rudolph, S. and Tomforde, S. and Fisch, D. and Sick, B. and Kopal, N. and Wacker, A.}, booktitle = {Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–13}, publisher = {VDE}, title = {A Concept for Securing Cyber-Physical Systems with Organic Computing Techniques}, year = 2013 }
%0 Conference Paper %1 haehner2013concept %A Hähner, J. %A Rudolph, S. %A Tomforde, S. %A Fisch, D. %A Sick, B. %A Kopal, N. %A Wacker, A. %B Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS %C Prague, Czech Republic %D 2013 %I VDE %P 1--13 %T A Concept for Securing Cyber-Physical Systems with Organic Computing Techniques %U http://www.vde-verlag.de/proceedings-de/563492001.html
Self-Adaption of Multi-Sensor-Systems with Organic Computing Techniques.Jänicke, M. In Organic Computing -- Doctoral Dissertation Colloquium 2013, S. Tomforde (red.), ble. 20–23. kassel university press, Augsburg, Germany, 2013.
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- [ EndNote ]
@incollection{jaenicke2013self, address = {Augsburg, Germany}, author = {J{\"a}nicke, M.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2013}, editor = {Tomforde, S.}, keywords = {imported}, pages = {20–23}, publisher = {kassel university press}, title = {Self-Adaption of Multi-Sensor-Systems with Organic Computing Techniques}, year = 2013 }
%0 Book Section %1 jaenicke2013self %A Jänicke, M. %B Organic Computing -- Doctoral Dissertation Colloquium 2013 %C Augsburg, Germany %D 2013 %E Tomforde, S. %I kassel university press %P 20--23 %T Self-Adaption of Multi-Sensor-Systems with Organic Computing Techniques
Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers.Kaufmann, P.; Glette, K.; Gruber, T.; Platzner, M.; Torresen, J.; Sick, B. In IEEE Transactions on Evolutionary Computation, 17(1), ble. 46–63. IEEE, 2013.
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- [ EndNote ]
@article{kaufmann2013classification, author = {Kaufmann, P. and Glette, K. and Gruber, T. and Platzner, M. and Torresen, J. and Sick, B.}, journal = {IEEE Transactions on Evolutionary Computation}, keywords = {imported}, number = 1, pages = {46–63}, publisher = {IEEE}, title = {Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers}, volume = 17, year = 2013 }
%0 Journal Article %1 kaufmann2013classification %A Kaufmann, P. %A Glette, K. %A Gruber, T. %A Platzner, M. %A Torresen, J. %A Sick, B. %D 2013 %I IEEE %J IEEE Transactions on Evolutionary Computation %N 1 %P 46--63 %R 10.1109/tevc.2012.2185845 %T Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers %V 17
Blazing Fast Time Series Segmentation Based on Update Techniques for Polynomial Approximations.Gensler, A.; Gruber, T.; Sick, B. In International Workshop on Spatial and Spatio-Temporal Data Mining (SSTDM), ICDM, ble. 1002–1011. IEEE, Dallas, TX, 2013.
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@inproceedings{gensler2013blazing, address = {Dallas, TX}, author = {Gensler, A. and Gruber, T. and Sick, B.}, booktitle = {International Workshop on Spatial and Spatio-Temporal Data Mining (SSTDM), ICDM}, keywords = {imported}, pages = {1002–1011}, publisher = {IEEE}, title = {Blazing Fast Time Series Segmentation Based on Update Techniques for Polynomial Approximations}, year = 2013 }
%0 Conference Paper %1 gensler2013blazing %A Gensler, A. %A Gruber, T. %A Sick, B. %B International Workshop on Spatial and Spatio-Temporal Data Mining (SSTDM), ICDM %C Dallas, TX %D 2013 %I IEEE %P 1002--1011 %R 10.1109/icdmw.2013.90 %T Blazing Fast Time Series Segmentation Based on Update Techniques for Polynomial Approximations
Active Learning of Generative and Discriminative Classifiers for Organic Computing.Reitmaier, T. In Organic Computing -- Doctoral Dissertation Colloquium 2013, S. Tomforde (red.), ble. 27–30. kassel university press, Augsburg, Germany, 2013.
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- [ EndNote ]
@incollection{reitmaier2013active, address = {Augsburg, Germany}, author = {Reitmaier, T.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2013}, editor = {Tomforde, S.}, keywords = {imported}, pages = {27–30}, publisher = {kassel university press}, title = {Active Learning of Generative and Discriminative Classifiers for {Organic Computing}}, year = 2013 }
%0 Book Section %1 reitmaier2013active %A Reitmaier, T. %B Organic Computing -- Doctoral Dissertation Colloquium 2013 %C Augsburg, Germany %D 2013 %E Tomforde, S. %I kassel university press %P 27--30 %T Active Learning of Generative and Discriminative Classifiers for Organic Computing
Architecture of Computing Systems -- ARCS 2013. Kubátová, H.; Hochberger, C.; Danue}k, M.; Sick, B. Springer, Heidelberg, Germany, 2013.
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- [ EndNote ]
@book{kubatova2013architecture, address = {Heidelberg, Germany}, editor = {Kub{\'a}tov{\'a}, H. and Hochberger, C. and Dan{\ue}k, M. and Sick, B.}, keywords = {imported}, publisher = {Springer}, title = {Architecture of Computing Systems – ARCS 2013}, year = 2013 }
%0 Book %1 kubatova2013architecture %C Heidelberg, Germany %D 2013 %E Kubátová, H. %E Hochberger, C. %E Danue}k, M. %E Sick, B. %I Springer %R 10.1007/978-3-642-36424-2 %T Architecture of Computing Systems -- ARCS 2013

2012[ to top ]

Forecasting exchange rates with ensemble neural networks and ensemble K-PLS: A case study for the US Dollar per Indian Rupee.Embrechts, M. J.; Gatti, C. J.; Linton, J. D.; Gruber, T.; Sick, B. In International Joint Conference on Neural Networks (IJCNN), ble. 1–8. IEEE, Brisbane, Australia, 2012.
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@inproceedings{embrechts2012forecasting, address = {Brisbane, Australia}, author = {Embrechts, M. J. and Gatti, C. J. and Linton, J. D. and Gruber, T. and Sick, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {imported}, pages = {1–8}, publisher = {IEEE}, title = {Forecasting exchange rates with ensemble neural networks and ensemble {K-PLS}: A case study for the {US Dollar per Indian Rupee}}, year = 2012 }
%0 Conference Paper %1 embrechts2012forecasting %A Embrechts, M. J. %A Gatti, C. J. %A Linton, J. D. %A Gruber, T. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %C Brisbane, Australia %D 2012 %I IEEE %P 1--8 %R 10.1109/ijcnn.2012.6252739 %T Forecasting exchange rates with ensemble neural networks and ensemble K-PLS: A case study for the US Dollar per Indian Rupee
Handedness Tests for Preschool Children: A Novel Approach Based on Graphics Tablets and Support Vector Machines.Gruber, T.; Meixner, B.; Prosser, J.; Sick, B. In Applied Soft Computing, 12(4), ble. 1390–1398. Elsevier, 2012.
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- [ EndNote ]
@article{gruber2012handedness, author = {Gruber, T. and Meixner, B. and Prosser, J. and Sick, B.}, journal = {Applied Soft Computing}, keywords = {imported}, number = 4, pages = {1390–1398}, publisher = {Elsevier}, title = {Handedness Tests for Preschool Children: A Novel Approach Based on Graphics Tablets and Support Vector Machines}, volume = 12, year = 2012 }
%0 Journal Article %1 gruber2012handedness %A Gruber, T. %A Meixner, B. %A Prosser, J. %A Sick, B. %D 2012 %I Elsevier %J Applied Soft Computing %N 4 %P 1390--1398 %R 10.1016/j.asoc.2011.11.022 %T Handedness Tests for Preschool Children: A Novel Approach Based on Graphics Tablets and Support Vector Machines %V 12
Intelligente technische Systeme mit der Fähigkeit zum kollaborativen Wissenserwerb. Technical Report (1), . Fisch, D. ble. 1–254. kassel university press, Kassel, Germany, 2012.
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@phdthesis{fisch2012intelligente, address = {Kassel, Germany}, author = {Fisch, D.}, keywords = {imported}, number = 1, pages = {1–254}, publisher = {kassel university press}, school = {University of Kassel}, series = {Intelligent Embedded Systems}, title = {Intelligente technische Systeme mit der F{\"a}higkeit zum kollaborativen Wissenserwerb}, year = 2012 }
%0 Thesis %1 fisch2012intelligente %A Fisch, D. %B Intelligent Embedded Systems %C Kassel, Germany %D 2012 %I kassel university press %N 1 %P 1--254 %T Intelligente technische Systeme mit der Fähigkeit zum kollaborativen Wissenserwerb
Analyse von Zeitreihen unter Verwendung orthogonaler Polynome am Beispiel der Online-Motivsuche und ihrer Anwendungen.Gruber, T. In Lecture Notes in Informatics: Ausgezeichnete Informatikdissertationen, ble. 131–140. 2012.
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@inproceedings{gruber2012analyse, author = {Gruber, T.}, booktitle = {Lecture Notes in Informatics: Ausgezeichnete Informatikdissertationen}, keywords = {imported}, pages = {131–140}, title = {{Analyse von Zeitreihen unter Verwendung orthogonaler Polynome am Beispiel der Online-Motivsuche und ihrer Anwendungen}}, year = 2012 }
%0 Conference Paper %1 gruber2012analyse %A Gruber, T. %B Lecture Notes in Informatics: Ausgezeichnete Informatikdissertationen %D 2012 %P 131--140 %T Analyse von Zeitreihen unter Verwendung orthogonaler Polynome am Beispiel der Online-Motivsuche und ihrer Anwendungen
Learning from others: Exchange of classification rules in intelligent distributed systems.Fisch, D.; Jänicke, M.; Kalkowski, E.; Sick, B. In Artificial Intelligence, 187--188, ble. 90–114. Elsevier, 2012.
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@article{fisch2012learning, author = {Fisch, D. and J{\"a}nicke, M. and Kalkowski, E. and Sick, B.}, journal = {Artificial Intelligence}, keywords = {imported}, pages = {90–114}, publisher = {Elsevier}, title = {Learning from others: Exchange of classification rules in intelligent distributed systems}, volume = {187–188}, year = 2012 }
%0 Journal Article %1 fisch2012learning %A Fisch, D. %A Jänicke, M. %A Kalkowski, E. %A Sick, B. %D 2012 %I Elsevier %J Artificial Intelligence %P 90--114 %R 10.1016/j.artint.2012.04.002 %T Learning from others: Exchange of classification rules in intelligent distributed systems %V 187--188
Determination of Optimal CT Scan Parameters Using Radial Basis Function Neural Networks.Giedl-Wagner, R.; Miller, T.; Sick, B. In Conference on Industrial Computed Tomography (iCT), ble. 221–228. Wels, Austria, 2012.
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- [ URL ]
@inproceedings{giedlwagner2012determination, address = {Wels, Austria}, author = {Giedl-Wagner, R. and Miller, T. and Sick, B.}, booktitle = {Conference on Industrial Computed Tomography (iCT)}, keywords = {imported}, pages = {221–228}, title = {Determination of Optimal {CT} Scan Parameters Using Radial Basis Function Neural Networks}, year = 2012 }
%0 Conference Paper %1 giedlwagner2012determination %A Giedl-Wagner, R. %A Miller, T. %A Sick, B. %B Conference on Industrial Computed Tomography (iCT) %C Wels, Austria %D 2012 %P 221--228 %T Determination of Optimal CT Scan Parameters Using Radial Basis Function Neural Networks %U https://www.ndt.net/article/ctc2012/papers/235.pdf
Techniques for knowledge acquisition in dynamically changing environments.Fisch, D.; Jänicke, M.; Kalkowski, E.; Sick, B. In ACM Transactions on Autonomous and Adaptive Systems, 7(1), bl. 16. ACM, 2012.
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@article{fisch2012techniques, author = {Fisch, D. and J{\"a}nicke, M. and Kalkowski, E. and Sick, B.}, journal = {{ACM} Transactions on Autonomous and Adaptive Systems}, keywords = {imported}, number = 1, pages = 16, publisher = {ACM}, title = {Techniques for knowledge acquisition in dynamically changing environments}, volume = 7, year = 2012 }
%0 Journal Article %1 fisch2012techniques %A Fisch, D. %A Jänicke, M. %A Kalkowski, E. %A Sick, B. %D 2012 %I ACM %J ACM Transactions on Autonomous and Adaptive Systems %N 1 %P 16 %R 10.1145/2168260.2168276 %T Techniques for knowledge acquisition in dynamically changing environments %V 7
Analyse von Zeitreihen unter Verwendung orthogonaler Polynome am Beispiel der Online-Motivsuche und ihrer Anwendungen. Gruber, T. kassel university press, Kassel, Germany, 2012.
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- [ EndNote ]
@phdthesis{gruber2012analysea, address = {Kassel, Germany}, author = {Gruber, T.}, keywords = {imported}, publisher = {kassel university press}, school = {University of Kassel}, series = {Intelligent Embedded Systems}, title = {{Analyse von Zeitreihen unter Verwendung orthogonaler Polynome am Beispiel der Online-Motivsuche und ihrer Anwendungen}}, year = 2012 }
%0 Thesis %1 gruber2012analysea %A Gruber, T. %B Intelligent Embedded Systems %C Kassel, Germany %D 2012 %I kassel university press %T Analyse von Zeitreihen unter Verwendung orthogonaler Polynome am Beispiel der Online-Motivsuche und ihrer Anwendungen

2011[ to top ]

On-Line Intrusion Alert Aggregation With Generative Data Stream Modeling.Hofmann, A.; Sick, B. In IEEE Transactions on Dependable and Secure Computing, 8(2), ble. 282–294. IEEE, 2011.
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@article{hofmann2011line, author = {Hofmann, A. and Sick, B.}, journal = {IEEE Transactions on Dependable and Secure Computing}, keywords = {imported}, number = 2, pages = {282–294}, publisher = {IEEE}, title = {On-Line Intrusion Alert Aggregation With Generative Data Stream Modeling}, volume = 8, year = 2011 }
%0 Journal Article %1 hofmann2011line %A Hofmann, A. %A Sick, B. %D 2011 %I IEEE %J IEEE Transactions on Dependable and Secure Computing %N 2 %P 282--294 %R 10.1109/tdsc.2009.36 %T On-Line Intrusion Alert Aggregation With Generative Data Stream Modeling %V 8
In your interest: Objective interestingness measures for a generative classifier.Fisch, D.; Kalkowski, E.; Sick, B.; Ovaska, S. In International Conference on Agents and Artificial Intelligence (ICAART), ble. 414–423. Rome, Italy, 2011.
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@inproceedings{fisch2011your, address = {Rome, Italy}, author = {Fisch, D. and Kalkowski, E. and Sick, B. and Ovaska, S.}, booktitle = {International Conference on Agents and Artificial Intelligence (ICAART)}, keywords = {imported}, pages = {414–423}, title = {In your interest: Objective interestingness measures for a generative classifier}, year = 2011 }
%0 Conference Paper %1 fisch2011your %A Fisch, D. %A Kalkowski, E. %A Sick, B. %A Ovaska, S. %B International Conference on Agents and Artificial Intelligence (ICAART) %C Rome, Italy %D 2011 %P 414--423 %R 10.5220/0003186404140423 %T In your interest: Objective interestingness measures for a generative classifier
Divergence Measures as a Generalised Approach to Quantitative Emergence.Fisch, D.; Jänicke, M.; Müller-Schloer, C.; Sick, B. In Organic Computing -- A Paradigm Shift for Complex Systems, C. Müller-Schloer, H. Schmeck, T. Ungerer (reds.), ble. 53–66. Springer, 2011.
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@inbook{fisch2011divergence, author = {Fisch, D. and J{\"a}nicke, M. and M{\"u}ller-Schloer, C. and Sick, B.}, booktitle = {Organic Computing – A Paradigm Shift for Complex Systems}, chapter = 1, editor = {M{\"u}ller-Schloer, C. and Schmeck, H. and Ungerer, T.}, keywords = {imported}, pages = {53–66}, publisher = {Springer}, title = {Divergence Measures as a Generalised Approach to Quantitative Emergence}, year = 2011 }
%0 Book Section %1 fisch2011divergence %A Fisch, D. %A Jänicke, M. %A Müller-Schloer, C. %A Sick, B. %B Organic Computing -- A Paradigm Shift for Complex Systems %D 2011 %E Müller-Schloer, C. %E Schmeck, H. %E Ungerer, T. %I Springer %P 53--66 %R 10.1007/978-3-0348-0130-0_3 %T Divergence Measures as a Generalised Approach to Quantitative Emergence %& 1
Active classifier training with the 3DS strategy.Reitmaier, T.; Sick, B. In IEEE Symposium on Computational Intelligence and Data Mining (CIDM), ble. 88–95. IEEE, Paris, France, 2011.
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- [ EndNote ]
@inproceedings{reitmaier2011active, address = {Paris, France}, author = {Reitmaier, T. and Sick, B.}, booktitle = {IEEE Symposium on Computational Intelligence and Data Mining (CIDM)}, keywords = {imported}, pages = {88–95}, publisher = {IEEE}, title = {Active classifier training with the {3DS} strategy}, year = 2011 }
%0 Conference Paper %1 reitmaier2011active %A Reitmaier, T. %A Sick, B. %B IEEE Symposium on Computational Intelligence and Data Mining (CIDM) %C Paris, France %D 2011 %I IEEE %P 88--95 %R 10.1109/cidm.2011.5949421 %T Active classifier training with the 3DS strategy
Learning: Preface.Sick, B. In Organic Computing -- A Paradigm Shift for Complex Systems, C. Müller-Schloer, H. Schmeck, T. Ungerer (reds.), ble. 235–236. Springer, 2011.
- [ BibTeX ]
- [ EndNote ]
@inbook{sick2011learning, author = {Sick, B.}, booktitle = {Organic Computing – A Paradigm Shift for Complex Systems}, chapter = 3, editor = {M{\"u}ller-Schloer, C. and Schmeck, H. and Ungerer, T.}, keywords = {imported}, pages = {235–236}, publisher = {Springer}, title = {Learning: Preface}, year = 2011 }
%0 Book Section %1 sick2011learning %A Sick, B. %B Organic Computing -- A Paradigm Shift for Complex Systems %D 2011 %E Müller-Schloer, C. %E Schmeck, H. %E Ungerer, T. %I Springer %P 235--236 %R 10.1007/978-3-0348-0130-0 %T Learning: Preface %& 3
SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis.Fisch, D.; Gruber, T.; Sick, B. In IEEE Transactions on Knowledge and Data Engineering, 23(5), ble. 774–787. IEEE, 2011.
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@article{fisch2011swiftrule, author = {Fisch, D. and Gruber, T. and Sick, B.}, journal = {IEEE Transactions on Knowledge and Data Engineering}, keywords = {imported}, number = 5, pages = {774–787}, publisher = {IEEE}, title = {{SwiftRule}: Mining Comprehensible Classification Rules for Time Series Analysis}, volume = 23, year = 2011 }
%0 Journal Article %1 fisch2011swiftrule %A Fisch, D. %A Gruber, T. %A Sick, B. %D 2011 %I IEEE %J IEEE Transactions on Knowledge and Data Engineering %N 5 %P 774--787 %R 10.1109/tkde.2010.161 %T SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis %V 23
Automatic Adaptation of Mobile Activity Recognition Systems to New Sensors.Bannach, D.; Sick, B.; Lukowicz, P. In Workshop Mobile Sensing: Challenges, Opportunities, and Future Directions, UbiComp, ble. 1–5. ACM, Beijing, China, 2011.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{bannach2011automatic, address = {Beijing, China}, author = {Bannach, D. and Sick, B. and Lukowicz, P.}, booktitle = {Workshop Mobile Sensing: Challenges, Opportunities, and Future Directions, UbiComp}, keywords = {imported}, pages = {1–5}, publisher = {ACM}, title = {Automatic Adaptation of Mobile Activity Recognition Systems to New Sensors}, year = 2011 }
%0 Conference Paper %1 bannach2011automatic %A Bannach, D. %A Sick, B. %A Lukowicz, P. %B Workshop Mobile Sensing: Challenges, Opportunities, and Future Directions, UbiComp %C Beijing, China %D 2011 %I ACM %P 1--5 %T Automatic Adaptation of Mobile Activity Recognition Systems to New Sensors
Collaborative Learning by Knowledge Exchange.Fisch, D.; Kalkowski, E.; Sick, B. In Organic Computing -- A Paradigm Shift for Complex Systems, C. Müller-Schloer, H. Schmeck, T. Ungerer (reds.), ble. 267–280. Springer, 2011.
- [ BibTeX ]
- [ EndNote ]
@inbook{fisch2011collaborative, author = {Fisch, D. and Kalkowski, E. and Sick, B.}, booktitle = {Organic Computing – A Paradigm Shift for Complex Systems}, chapter = 3, editor = {M{\"u}ller-Schloer, C. and Schmeck, H. and Ungerer, T.}, keywords = {imported}, pages = {267–280}, publisher = {Springer}, title = {Collaborative Learning by Knowledge Exchange}, year = 2011 }
%0 Book Section %1 fisch2011collaborative %A Fisch, D. %A Kalkowski, E. %A Sick, B. %B Organic Computing -- A Paradigm Shift for Complex Systems %D 2011 %E Müller-Schloer, C. %E Schmeck, H. %E Ungerer, T. %I Springer %P 267--280 %R 10.1007/978-3-0348-0130-0_17 %T Collaborative Learning by Knowledge Exchange %& 3
Enhancing a simulation environment for computer architecture to a SystemC based testbench tool for design verification.Westmeier, M.; Herwig, B.; Börcsök, J. In International Symposium on Information, Communication and Automation Technologies (ICAT), ble. 1–6. IEEE, Sarajevo, Bosnia and Herzegovina, 2011.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{westmeier2011enhancing, address = {Sarajevo, Bosnia and Herzegovina}, author = {Westmeier, M. and Herwig, B. and B{\"o}rcs{\"o}k, J.}, booktitle = {International Symposium on Information, Communication and Automation Technologies (ICAT)}, keywords = {imported}, pages = {1–6}, publisher = {IEEE}, title = {Enhancing a simulation environment for computer architecture to a SystemC based testbench tool for design verification}, year = 2011 }
%0 Conference Paper %1 westmeier2011enhancing %A Westmeier, M. %A Herwig, B. %A Börcsök, J. %B International Symposium on Information, Communication and Automation Technologies (ICAT) %C Sarajevo, Bosnia and Herzegovina %D 2011 %I IEEE %P 1--6 %R 10.1109/icat.2011.6102113 %T Enhancing a simulation environment for computer architecture to a SystemC based testbench tool for design verification
Wissenschaftspropädeutisches Arbeiten im W-Seminar: Grundlagen -- Chancen -- Herausforderungen.Gottfried, T.; Fliege, R.; Frömberg, J.; Heckmann, G.; Sick, B.; Triller, U.; Wunsch, M. Munich, Germany, 2011.
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- [ EndNote ]
@misc{gottfried2011wissenschaftspropadeutisches, address = {Munich, Germany}, author = {Gottfried, T. and Fliege, R. and Fr{\"o}mberg, J. and Heckmann, G. and Sick, B. and Triller, U. and Wunsch, M.}, keywords = {imported}, title = {Wissenschaftsprop{\"a}deutisches {Arbeiten} im {W-Seminar}: Grundlagen – {Chancen} – {Herausforderungen}}, year = 2011 }
%0 Generic %1 gottfried2011wissenschaftspropadeutisches %A Gottfried, T. %A Fliege, R. %A Frömberg, J. %A Heckmann, G. %A Sick, B. %A Triller, U. %A Wunsch, M. %C Munich, Germany %D 2011 %T Wissenschaftspropädeutisches Arbeiten im W-Seminar: Grundlagen -- Chancen -- Herausforderungen

2010[ to top ]

Temporal Data Mining Using Shape Space Representations of Time Series.Fuchs, E.; Gruber, T.; Pree, H.; Sick, B. In Neurocomputing, 74(1--3), ble. 379–393. Elsevier, 2010.
- [ BibTeX ]
- [ EndNote ]
@article{fuchs2010temporal, author = {Fuchs, E. and Gruber, T. and Pree, H. and Sick, B.}, journal = {Neurocomputing}, keywords = {imported}, number = {1–3}, pages = {379–393}, publisher = {Elsevier}, title = {Temporal Data Mining Using Shape Space Representations of Time Series}, volume = 74, year = 2010 }
%0 Journal Article %1 fuchs2010temporal %A Fuchs, E. %A Gruber, T. %A Pree, H. %A Sick, B. %D 2010 %I Elsevier %J Neurocomputing %N 1--3 %P 379--393 %R 10.1016/j.neucom.2010.03.022 %T Temporal Data Mining Using Shape Space Representations of Time Series %V 74
Online Signature Verification With Support Vector Machines Based on LCSS Kernel Functions.Gruber, C.; Gruber, T.; Krinninger, S.; Sick, B. In IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 40(4), ble. 1088–1100. IEEE, 2010.
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@article{gruber2010online, author = {Gruber, C. and Gruber, T. and Krinninger, S. and Sick, B.}, journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)}, keywords = {imported}, number = 4, pages = {1088–1100}, publisher = {IEEE}, title = {Online Signature Verification With Support Vector Machines Based on {LCSS} Kernel Functions}, volume = 40, year = 2010 }
%0 Journal Article %1 gruber2010online %A Gruber, C. %A Gruber, T. %A Krinninger, S. %A Sick, B. %D 2010 %I IEEE %J IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) %N 4 %P 1088--1100 %R 10.1109/tsmcb.2009.2034382 %T Online Signature Verification With Support Vector Machines Based on LCSS Kernel Functions %V 40
Quantitative Emergence -- A Refined Approach Based on Divergence Measures.Fisch, D.; Jänicke, M.; Sick, B.; Müller-Schloer, C. In IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), ble. 94–103. IEEE, Budapest, Hungary, 2010.
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@inproceedings{fisch2010quantitative, address = {Budapest, Hungary}, author = {Fisch, D. and J{\"a}nicke, M. and Sick, B. and M{\"u}ller-Schloer, C.}, booktitle = {IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO)}, keywords = {imported}, pages = {94–103}, publisher = {IEEE}, title = {Quantitative Emergence – A Refined Approach Based on Divergence Measures}, year = 2010 }
%0 Conference Paper %1 fisch2010quantitative %A Fisch, D. %A Jänicke, M. %A Sick, B. %A Müller-Schloer, C. %B IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) %C Budapest, Hungary %D 2010 %I IEEE %P 94--103 %R 10.1109/saso.2010.31 %T Quantitative Emergence -- A Refined Approach Based on Divergence Measures
Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations.Fuchs, E.; Gruber, T.; Nitschke, J.; Sick, B. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(12), ble. 2232–2245. IEEE, 2010.
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- [ EndNote ]
@article{fuchs2010online, author = {Fuchs, E. and Gruber, T. and Nitschke, J. and Sick, B.}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, keywords = {imported}, number = 12, pages = {2232–2245}, publisher = {IEEE}, title = {Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations}, volume = 32, year = 2010 }
%0 Journal Article %1 fuchs2010online %A Fuchs, E. %A Gruber, T. %A Nitschke, J. %A Sick, B. %D 2010 %I IEEE %J IEEE Transactions on Pattern Analysis and Machine Intelligence %N 12 %P 2232--2245 %R 10.1109/tpami.2010.44 %T Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations %V 32

2007[ to top ]

Towards an Automated Analysis of Neuroleptics’ Impact on Human Hand Motor Skills.Dose, Matthias; Gruber, Christian; Grunz, Ariane; Hook, Christian; Kempf, Jürgen; Scharfenberg, Georg; Sick, Bernhard. In IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), SSCI, ble. 494–501. IEEE, 2007.
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- [ EndNote ]
- [ URL ]
@inproceedings{dose2007towards, author = {Dose, Matthias and Gruber, Christian and Grunz, Ariane and Hook, Christian and Kempf, J{\"{u}}rgen and Scharfenberg, Georg and Sick, Bernhard}, booktitle = {IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), SSCI}, keywords = {imported}, pages = {494–501}, publisher = {IEEE}, title = {Towards an Automated Analysis of Neuroleptics' Impact on Human Hand Motor Skills}, year = 2007 }
%0 Conference Paper %1 dose2007towards %A Dose, Matthias %A Gruber, Christian %A Grunz, Ariane %A Hook, Christian %A Kempf, Jürgen %A Scharfenberg, Georg %A Sick, Bernhard %B IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), SSCI %D 2007 %I IEEE %P 494--501 %T Towards an Automated Analysis of Neuroleptics' Impact on Human Hand Motor Skills %U http://ieeexplore.ieee.org/document/4221261/

2006[ to top ]

Biometric Analysis of Handwriting Dynamics Using a Script Generator Model.Hofer, J.; Gruber, C.; Sick, B. In IEEE Mountain Workshop on Adaptive and Learning Systems, ble. 36–41. IEEE, Logan, 2006.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{hofer2006biometric, address = {Logan}, author = {Hofer, J. and Gruber, C. and Sick, B.}, booktitle = {IEEE Mountain Workshop on Adaptive and Learning Systems}, keywords = {imported}, pages = {36–41}, publisher = {IEEE}, title = {Biometric Analysis of Handwriting Dynamics Using a Script Generator Model}, year = 2006 }
%0 Conference Paper %1 hofer2006biometric %A Hofer, J. %A Gruber, C. %A Sick, B. %B IEEE Mountain Workshop on Adaptive and Learning Systems %C Logan %D 2006 %I IEEE %P 36--41 %R 10.1109/smcals.2006.250689 %T Biometric Analysis of Handwriting Dynamics Using a Script Generator Model