Publications

2024[ to top ]
  • 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.
  • 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). IEEE, 2024.
  • 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). IEEE, 2024.
2023[ to top ]
  • 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, bll 14–18. 2023.
  • 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, bll 3991–4000. 2023.
  • 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), bll 49–53. ThinkMind, 2023.
  • 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.
  • 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). IEEE, 2023.
  • 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, bll 65–73. 2023.
  • 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), bll 19:1–19:3. 2023.
  • 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), bll 1–7. IEEE, 2023.
  • Targeted Adversarial Attacks on Wind Power Forecasts. Heinrich, René; Scholz, Christoph; Vogt, Stephan; Lehna, Malte. In Machine Learning, 113(2), bll 863–889. Springer, 2023.
  • Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction. Huang, Zhixin; He, Yujiang; Sick, Bernhard. In Computational Science and Computational Intelligence (CSCI). IEEE, 2023.
  • Sensor Equivariance by LiDAR Projection Images. Reichert, Hannes; Hetzel, Manuel; Schreck, Steven; Doll, Konrad; Sick, Bernhard. In IEEE Intelligent Vehicles Symposium (IV), bll 1–6. IEEE, 2023.
  • Self-Integration and Agent Compatibility. Gruhl, Christian; Sick, Bernhard. In Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), bll 71–73. IEEE, 2023.
  • 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), bll 1–6. IEEE, 2023.
  • 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.
  • Role of Hyperparameters in Deep Active Learning. Huseljic, Denis; Herde, Marek; Hahn, Paul; Sick, Bernhard. In Workshop on Interactive Adaptive Learning (IAL), ECML PKDD, bll 19–24. 2023.
  • 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.
  • 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.
  • Organic Computing -- Doctoral Dissertation Colloquium 2022. Tomforde, Sven; Krupitzer, Christian. In Vol. 24Intelligent Embedded Systems. kassel university press, 2023.
  • 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.
  • 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), bll 1214–1233. MDPI, 2023.
  • Multi-annotator Deep Learning: A Probabilistic Framework for Classification. Herde, Marek; Huseljic, Denis; Sick, Bernhard. In Transactions on Machine Learning Research. 2023.
  • 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.
  • 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.
  • 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). ACM, 2023.
  • 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.
  • 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), bll 171–176. IEEE, 2023.
  • Graph Pooling Provably Improves Expressivity. Lachi, Veronica; Moallemy-Oureh, Alice; Roth, Andreas; Welke, Pascal. In Workshop on New Frontiers in Graph Learning, NeurIPS. 2023.
  • 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.
  • 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), bll 265–276. Springer, 2023.
  • 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.
  • 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.
  • 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). 2023.
  • Corner Cases in Machine Learning Processes. Heidecker, Florian; Bieshaar, Maarten; Sick, Bernhard. In AI Perspectives & Advances, 6(1), bll 1–17. 2023.
  • 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), bll 43–50. ThinkMind, 2023.
  • 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.
  • 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). IEEE, 2023.
  • An Examination of Organic Computing Strategies in Design Optimization. Decke, Jens. In Organic Computing -- Doctoral Dissertation Colloquium 2023, S. Tomforde, C. Krupitzer (reds.). kassel university press, 2023.
  • 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), bll 55–74. Springer, 2023.
  • 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, bll 28–45. 2023.
  • 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.
  • 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, bll 1–6. 2023.
2022[ to top ]
  • 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.
  • 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), bll 1530–1536. IEEE, 2022.
  • 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.), bll 47–59. kassel university press, 2022.
  • 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), bll 1–10. Springer Nature, 2022.
  • 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.
  • 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), bll 604–609. ACM, 2022.
  • Student Research Abstract: Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs. Moallemy-Oureh, Alice. In ACM/SIGAPP Symposium on Applied Computing (SAC), bll 600–603. ACM, 2022.
  • Stream-Based Active Learning in Changing Environments under Verification Latency. Pham, Tuan. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), bll 152–164. kassel university press, 2022.
  • 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, bll 86–93. IEEE, 2022.
  • Social Machines. Draude, Claude; Gruhl, Christian; Hornung, Gerrit; Kropf, Jonathan; Lamla, Jörn; Leimeister, Jan Marco; Sick, Bernhard; Stumme, Gerd. In Informatik Spektrum, 45(1), bll 38–42. Springer, 2022.
  • Self-Aware Microsystems. Gruhl, Christian; Tomforde, Sven; Sick, Bernhard. In Workshop on Self-Improving System Integration (SISSY), ACSOS, bll 126–127. IEEE, 2022.
  • 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.), bll 1–16. kassel university press, 2022.
  • 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.
  • 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), bll 1–19. MDPI, 2022.
  • 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), bll 2592–2603. IEEE, 2022.
  • Organic Computing -- Doctoral Dissertation Colloquium 2021. Tomforde, Sven; Krupitzer, Christian. In Vol. 20Intelligent Embedded Systems. kassel university press, 2022.
  • 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.
  • 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, bll 1–13. 2022.
  • 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), bll 197–210. Springer, 2022.
  • 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.
  • 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), bll 57–60. IEEE, 2022.
  • 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.
  • 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), bll 513–522. IEEE, 2022.
  • 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.
  • 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.
  • 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, bll 27–42. 2022.
  • 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), bll 1349–1375. Springer, 2022.
  • Detecting Corner Case in the Context of Highly Automated Driving. Heidecker, Florian. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), bll 60–73. kassel university press, 2022.
  • 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, bll 1–6. 2022.
  • Deep Adaptive Knowledge-Aware Learning for E-Mobility. Ali, Mohammad Wazed. In Organic Computing - Doctoral Dissertation Colloquium 2022, S. Tomforde, C. Krupitzer (reds.), bll 139–155. kassel university press, 2022.
  • 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), bll 1–14. Nature Publishing Group, 2022.
  • 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.), bll 125–140. kassel university press, 2022.
  • 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.), bll 86–98. kassel university press, 2022.
  • Active Learning in Multivariate Time Series Anomaly Detection. Huang, Zhixin. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), bll 113–124. kassel university press, 2022.
  • 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), bll 468–484. Springer, 2022.
  • 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.
  • 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, bll 49–67. 2022.
  • 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), bll 265–272. IEEE, 2022.
  • 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, bll 1–6. 2022.
2021[ to top ]
  • 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.
  • 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), bll 8498–8507. ACS, 2021.
  • 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.
  • 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, bll 1–6. Osnabrück, Germany, 2021.
  • 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), bll 1–4. IEEE, 2021.
  • 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), bll 120–130. IEEE, Washington, DC, USA, 2021.
  • 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, bll 361–374. IEEE, Milan, Italy, 2021.
  • 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), bll 1199–1231. Springer, 2021.
  • 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), bll 1–8. IEEE, 2021.
  • 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.
  • 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, bll 204–209. IEEE, 2021.
  • 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, bll 118–134. Springer, 2021.
  • Stream-Based Active Learning for Sliding Windows Under Verification Latency. Pham, Tuan; Kottke, Daniel; Krempl, Georg; Sick, Bernhard. In Machine Learning. Springer, 2021.
  • Statistical Analysis of Pairwise Connectivity. Krempl, Georg; Kottke, Daniel; Pham, Tuan. In International Conference on Discovery Science (DS), Lecture Notes in Computer Science, bll 138–148. Springer, 2021.
  • Smart Infrastructure: A Research Junction. Hetzel, Manuel; Reichert, Hannes; Doll, Konrad; Sick, Bernhard. In IEEE International Smart Cities Conference (ISC2). IEEE, 2021.
  • 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), bll 9172–9179. IEEE, 2021.
  • 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, bll 29–46. Elsevier, 2021.
  • 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.
  • 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.
  • 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, bll 57–71. Springer, 2021.
  • 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, bll 1–10. 2021.
  • OHODIN -- Online Anomaly Detection for Data Streams. Gruhl, Christian; Tomforde, Sven. In Workshop on Self-Improving System Integration (SISSY), ACSOS, bll 193–197. IEEE, 2021.
  • 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.
  • Novelty detection in continuously changing environments. Gruhl, Christian; Sick, Bernhard; Tomforde, Sven. In Future Generation Computer Systems, 114, bll 138–154. Elsevier, 2021.
  • 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, bll 407–421. IEEE, Milan, Italy, 2021.
  • Multi-annotator Probabilistic Active Learning. Herde, Marek; Kottke, Daniel; Huseljic, Denis; Sick, Bernhard. In International Conference on Pattern Recognition (ICPR), bll 10281–10288. IEEE, 2021.
  • 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), bll 9483–9490. IEEE, 2021.
  • 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, bll 375–389. IEEE, Milan, Italy, 2021.
  • 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), bll 2620–2626. IEEE, 2021.
  • Emerging Relation Network and Task Embedding for Multi-Task Regression Problems. Schreiber, Jens; Sick, Bernhard. In International Conference on Pattern Recognition (ICPR), bll 2663–2670. IEEE, 2021.
  • 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, bll 166–171. IEEE, 2021.
  • 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, bll 1023–1028. IEEE, 2021.
  • 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), bll 1–7. IEEE, 2021.
  • 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.
  • Cooperative intention detection using machine learning. Advanced cyclist protection in the context of automated driving. Bieshaar, Maarten. kassel university press, 2021.
  • CLeaR: An adaptive continual learning framework for regression tasks. He, Yujiang; Sick, Bernhard. In AI Perspectives, 3(1), bl 2. Springer, 2020.
  • 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), bll 1–7. IEEE, 2021.
  • 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), bll 644–651. IEEE, Nagoya, Japan, 2021.
  • 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, bll 150–153. IEEE, 2021.
  • 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.
  • 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, bll 979–987. IEEE, 2021.
  • 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, bll 166970–166989. IEEE, 2021.
  • 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.
  • A Holistic, Decision-Theoretic Framework for Pool-Based Active Learning. Kottke, Daniel. kassel university press, 2021.
  • 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.
2020[ to top ]
  • 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.
  • 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.
  • 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.), bll 67–101. Springer, 2020.
  • 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, bll 71–77. SPIE, 2020.
  • 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.
  • 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.
  • 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), bll 2723–2730. IEEE, 2020.
  • 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), bll 1–8. IEEE, 2020.
  • Normal-Wishart clustering for novelty detection. Gruhl, Christian; Schmeißing, Jörn; Tomforde, Sven; Sick, Bernhard. In Workshop on Self-Improving System Integration (SISSY), ACSOS, bll 64–69. IEEE, 2020.
  • Knowledge Representations in Technical Systems -- A Taxonomy. Scharei, Kristina; Heidecker, Florian; Bieshaar, Maarten. In arXiv e-prints, bl arXiv:2001.04835. 2020.
  • 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.
  • 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.
  • 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.
  • 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, bll 54–59. IEEE, 2020.
  • Extended Coopetitive Soft Gating Ensemble. Deist, Stephan; Schreiber, Jens; Bieshaar, Maarten; Sick, Bernhard. In arXiv e-prints, bl arXiv:2004.14026. 2020.
  • 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.
  • 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, bll 12175–12182. Elsevier, 2020.
  • Active Learning with Uncertain Annotators. Calma, Adrian. kassel university press, 2020.
  • 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, bll 166–169. IEEE, 2020.
2019[ to top ]
  • 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.
  • Wind Power Ensemble Forecasting. Gensler, André. kassel university press, 2019.
  • 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, bll 531–544. Gesellschaft für Informatik e.V., Bonn, 2019.
  • 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.
  • 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, bll 32–35. IEEE, 2019.
  • 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.), bll 75–87. kassel university press, Kassel, Germany, 2019.
  • 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), bll 810–815. IEEE, 2019.
  • Towards Corner Case Identification in Cyclists’ Trajectories. Heidecker, F.; Bieshaar, M.; Sick, B. In ACM Computer Science in Cars Symposium (CSCS). ACM, 2019.
  • 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), bll 545–554. Gesellschaft für Informatik e.V., Bonn, 2019.
  • 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), bll 219–228. Gesellschaft für Informatik e.V., Bonn, 2019.
  • 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.), bll 45–60. kassel university press, Kassel, Germany, 2019.
  • 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, bll 1–3. IEEE, 2019.
  • 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), bll 642–649. IEEE, Paris, France, 2019.
  • 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.
  • 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), bll 2381–2386. IEEE, 2019.
  • Organic Computing -- Doctoral Dissertation Colloquium 2018. Tomforde, S.; Sick, B. In Vol. 13Intelligent Embedded Systems. kassel university press, 2019.
  • 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.
  • 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), bll 13–24. IEEE/ACM, 2019.
  • Off-Board Car Diagnostics Based on Heterogeneous, Highly Imbalanced and High-Dimensional Data Using Machine Learning Techniques. Schlegel, Bernhard. kassel university press, 2019.
  • 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.
  • 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, bll 1–8. VDE, 2019.
  • 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.
  • 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), bll 3035–3045. IEEE, 2019.
  • 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.
  • 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, bll 585–598. Gesellschaft für Informatik e.V., Bonn, 2019.
  • 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, bll 550–564. Springer, Cham, 2019.
  • 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, bll 126–129. IEEE, 2019.
  • 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, bll 1–6. VDE, 2019.
  • 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, bll 6–9. IEEE, 2019.
  • 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), bll 229–238. Gesellschaft für Informatik e.V., Bonn, 2019.
  • Decision Support with Hybrid Intelligence. Calma, Adrian; Dellermann, Dominik. In Organic Computing -- Doctoral Dissertation Colloquium 2018, S. Tomforde, B. Sick (reds.), bll 143–153. kassel university press, Kassel, Germany, 2019.
  • 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), bll 1–8. IEEE, 2019.
  • 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.
  • 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, bll 52–55. IEEE, 2019.
  • 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), bll 5–9. IEEE, 2019.
  • 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), bll 1–10. Ulm, Germany, 2019.
  • 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), bll 2060–2067. IEEE, 2019.
2018[ to top ]
  • 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.
  • 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, bll 1–8. VDE, 2018.
  • 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), bll 215–222. 2018.
  • 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.
  • 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.
  • 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), bll 247–273. Springer, 2018.
  • 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, bll 13–33. Elsevier, 2018.
  • 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.
  • 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, bll 176–181. IEEE, 2018.
  • 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, bll 27–48. Springer, 2018.
  • 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), bll 779–786. IEEE, Changshu, China, 2018.
  • Quantifying the Influences on Probabilistic Wind Power Forecasts. Schreiber, Jens; Sick, Bernhard. In International Conference on Power and Renewable Energy (ICPRE), bll 1–6. 2018.
  • Organic Computing -- Doctoral Dissertation Colloquium 2017. Tomforde, S.; Sick, B. In Vol. 11Intelligent Embedded Systems. kassel university press, 2018.
  • 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), bll 927–945. Springer, 2018.
  • 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.
  • 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.
  • 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, bll 195–207. Elsevier, 2018.
  • 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.
  • 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.
  • 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.
  • 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), bll 1–6. IEEE, 2018.
  • 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.
  • 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.
  • 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), bll 534–544. IEEE, 2018.
  • 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, bll 193–220. Springer, 2018.
  • Collaborative Interactive Learning. Sick, Bernhard; Oeste-Reiß, Sarah; Schmidt, Albrecht; Tomforde, Sven; Zweig, Katharina Anna. In Informatik Spektrum, 41(1), bll 52–55. Springer, 2018.
  • 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.
  • 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, bll 198–203. IEEE, 2018.
  • 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.
  • 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.
  • 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, bll 352–379. Elsevier, 2018.
  • 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.
  • 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, bll 150–159. VDE, London, UK, 2018.
2017[ to top ]
  • 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.), bll 27–37. kassel university press, Kassel, Germany, 2017.
  • 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), bll 72–80. IEEE, 2017.
  • Simulation of Annotators for Active Learning: Uncertain Oracles. Calma, Adrian; Sick, Bernhard. In Workshop on Interactive Adaptive Learning (IAL), ECML PKDD, CEUR Workshop Proceedings, bll 49–58. 2017.
  • 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.
  • 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.
  • 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), bll 39–50. Porto, Portugal, 2017.
  • 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), bll 1–10. IEEE, 2017.
  • 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, bll 37–48. 2017.
  • 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), bll 543–562. Springer, 2017.
  • Organic Computing in the Spotlight. Tomforde, Sven; Sick, Bernhard; Müller-Schloer, Christian. In arXiv e-prints, bl arXiv:1701.08125. 2017.
  • Organic Computing -- Techncial Systems for Survival in the Real World. Müller-Schloer, Christian; Tomforde, Sven. In Autonomic Systems. Birkhäuser Verlag, 2017.
  • Organic Computing -- Doctoral Dissertation Colloquium 2016. Tomforde, S.; Sick, B. In Vol. 10Intelligent Embedded Systems. kassel university press, 2017.
  • 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), bll 209–212. Athens, Greece, 2017.
  • 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), bll 96–106. Porto, Portugal, 2017.
  • 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.
  • 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, bll 109–116. IEEE, Tucson, AZ, 2017.
  • Interpolation in the eXtended Classifier System: An Architectural Perspective. Stein, A.; Rauh, D.; Tomforde, S.; Hähner, J. In Journal of Systems Architecture, 75, bll 79–94. Elsevier, 2017.
  • 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.), bll 1–4. kassel university press, Kassel, Germany, 2017.
  • 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, bll 83–93. Springer, Cham, Switzerland, 2017.
  • 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, bll 68–78. Elsevier, 2017.
  • 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.
  • 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.
  • 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.), bll 67–78. kassel university press, Kassel, Germany, 2017.
  • Development of an Offshore Specific Wind Power Forecasting System. Kurt, Melih. kassel university press, Kassel, Germany, 2017.
  • 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), bll 67–87. Braunschweig, Germany, 2017.
  • 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, bll 69–78. IEEE, 2017.
  • 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.
  • 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, bll 2–14. 2017.
  • Case Study on Pool-based Active Learning with Human Oracles. Calma, A. In Organic Computing -- Doctoral Dissertation Colloquium 2017, S. Tomforde, B. Sick (reds.), bll 39–49. kassel university press, Kassel, Germany, 2017.
  • 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.
2016[ to top ]
  • 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), bll 833–838. IEEE, Gothenburg, Sweden, 2016.
  • 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, bll 285–291. IEEE, Würzburg, Germany, 2016.
  • 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, bll 98–99. IEEE, 2016.
  • 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, bll 476–496. Elsevier, 2016.
  • Socially-Sensitive Systems Design. Lewis, Peter; Bellman, Kirstie; Botev, Jean; Hildmann, Hanno; Marsh, Stephen; Pitt, Jeremy; Scholtes, Ingo; Tomforde, Sven. bll 142–147. Dagstuhl, Germany, 2016.