Prof. Dr. rer. nat. Bernhard Sick

Fachgebietsleiter; Teamleiter: Collaborative Interactive Learning (CIL); Teamleiter: AI for Computationally Intelligent Systems (AI4CIS)

Sick, Bernhard
Telefon
+49 561 804-6020
Fax
+49 561 804-6022
E-Mail
Standort
Wilhelmshöher Allee 73
34121 Kassel
Raum
WA-altes Gebäude (WA 73), ohne Raumangabe

Publikationen

2024[ to top ]
  • 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.
  • 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). IEEE, 2024.
  • 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.
  • 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). IEEE, 2024.
  • 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). Springer, 2024.
  • 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). 2024.
  • 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.
  • 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.
  • 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). Springer, 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.
  • 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), bll 1444–1450. 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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), bll 1–6. ACM, 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.
  • 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), bll 1611–1618. IEEE, 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), bll 1522–1529. IEEE, 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 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 ]
  • 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.
  • 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.
  • 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), bll 2011–2036. Springer, 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 ]
  • 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.
  • 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.
  • 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.
  • 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.
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.
  • 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-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 -- 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.
  • 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.
  • 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.
  • 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.
  • 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.
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.
  • 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.
  • 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), bll 4040–4047. IEEE, Vancouver, BC, 2016.
  • Probabilistic Obsoleteness Detection for Gaussian Mixture Models. Gruhl, C. In Organic Computing -- Doctoral Dissertation Colloquium 2016, S. Tomforde, B. Sick (reds.), bll 45–56. kassel university press, Kassel, Germany, 2016.
  • Pals: Interactive Pool-based Active Learning System with Uncertain Oracles. Calma, A. In Organic Computing -- Doctoral Dissertation Colloquium 2016, B. Sick, S. Tomforde (reds.), bll 35–44. kassel university press, Kassel, Germany, 2016.
  • 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, bll 1–8. VDE, Nuremberg, Germany, 2016.
  • 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, bll 75–88. Springer, Cham, Switzerland, 2016.
  • 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), bll 4976–4984. IEEE, Vancouver, BC, 2016.
  • 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.
  • 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), bll 1–9. IEEE, Athens, Greece, 2016.
  • 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), bll 2858–2865. IEEE, Budapest, Hungary, 2016.
  • 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), bll 207–224. Springer, 2016.
  • 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), bll 1–8. Rostock, Germany, 2016.
  • 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), bll 2164–2173. IEEE, Vancouver, BC, 2016.
  • 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), bll 2850–2857. IEEE, 2016.
  • 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), bll 1–9. IEEE, Athens, Greece, 2016.
2015[ to top ]
  • 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), bll 1693–1699. IEEE, Hongkong, China, 2015.
  • 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, bll 275–298. Elsevier, 2015.
  • 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, bll 259–279. Springer, Cham, Switzerland, 2015.
  • The Responsibility Weighted Mahalanobis Kernel for Semi-Supervised Training of Support Vector Machines for Classification. Reitmaier, T.; Sick, B. In Information Sciences, 323, bll 179–198. Elsevier, 2015.
  • 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.), bll 109–120. kassel university press, Kassel, Germany, 2015.
  • 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), bll 1–8. VDE, Porto, Portugal, 2015.
  • Organic Computing -- Doctoral Dissertation Colloquium 2015. Tomforde, S.; Sick, B. In Vol. 7Intelligent Embedded Systems. kassel university press, 2015.
  • 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.), bll 165–170. kassel university press, Kassel, Germany, 2015.
  • 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.), bll 157–164. kassel university press, Kassel, Germany, 2015.
  • Generative Exponential Smoothing Models for Rate Forecasting with Uncertainty Estimation. Kalkowski, E.; Sick, B. In International Work-Conference on Time Series (ITISE), bll 806–817. Granada, Spain, 2015.
  • 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), bll 29–37. IEEE, Kassel, Germany, 2015.
  • 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), bll 51–56. Hamburg, Germany, 2015.
  • 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), bll 53–64. IARIA, 2015.
  • 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), bll 689–700. IEEE, 2015.
  • 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), bll 390–399. Springer, London, UK, 2015.
  • 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), bll 57–63. Hamburg, Germany, 2015.
  • 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.), bll 95–107. kassel university press, Kassel, Germany, 2015.
  • Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen. Stone, T. C.; Huber, A.; Siwy, R.; Sick, B. In Elektronik automotive, 2(2), bll 32–36. WEKA Fachmedien, Landshut, Germany, 2015.
  • 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, bll 1–8. VDE, Porto, Portugal, 2015.
  • 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, bll 1–5. Boston, MA, 2015.
  • 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.
  • 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), bll 144–149. IEEE, Cambridge, MA, 2015.
  • 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), bll 21–24. Vienna, Austria, 2015.
  • 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), bll 194–200. IEEE, Qinhuangdao, China, 2015.
  • 4DSPro: A New Selection Strategy for Pool-based Active Learning. Calma, A. In Organic Computing -- Doctoral Dissertation Colloquium 2015, S. Tomforde, B. Sick (reds.), bll 121–133. kassel university press, Kassel, Germany, 2015.
2014[ to top ]
  • 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), bll 753–757. IEEE, Xian, China, 2014.
  • 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.), bll 111–125. kassel university press, Kassel, Germany, 2014.
  • 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, bll 128–136. IEEE, London, UK, 2014.
  • 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.), bll 99–109. kassel university press, Kassel, Germany, 2014.
  • 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.), bll 85–97. kassel university press, Kassel, Germany, 2014.
  • 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, bll 71–94. Raabe, 2014.
  • 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), bll 4110–4115. IEEE, Stockholm, Sweden, 2014.
  • Organic Computing -- Doctoral Dissertation Colloquium 2014. Tomforde, S.; Sick, B. In Vol. 4Intelligent Embedded Systems. kassel university press, 2014.
  • 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, bll 478–495. Elsevier, 2014.
  • 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, bll 192–204. Aachen, Germany, 2014.
  • 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, bll 2121–2132. Gesellschaft für Informatik e.V., 2014.
  • 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), bll 652–666. IEEE, 2014.
  • Interwoven Systems. Tomforde, S.; Hähner, J.; Sick, B. In Informatik Spektrum, 37(5), bll 483–487. Springer, 2014.
  • 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, bll 36–40. IEEE, Budapest, Hungary, 2014.
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, bll 106–131. Elsevier, 2013.
  • 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), bll 46–63. IEEE, 2013.
  • 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, bll 1002–1011. IEEE, Dallas, TX, 2013.
2012[ to top ]
  • 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.
  • Learning from others: Exchange of classification rules in intelligent distributed systems. Fisch, D.; Jänicke, M.; Kalkowski, E.; Sick, B. In Artificial Intelligence, 187--188, bll 90–114. Elsevier, 2012.
  • 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), bll 1390–1398. Elsevier, 2012.
  • 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), bll 1–8. IEEE, Brisbane, Australia, 2012.
  • 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), bll 221–228. Wels, Austria, 2012.
2011[ to top ]
  • 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), bll 774–787. IEEE, 2011.
  • On-Line Intrusion Alert Aggregation With Generative Data Stream Modeling. Hofmann, A.; Sick, B. In IEEE Transactions on Dependable and Secure Computing, 8(2), bll 282–294. IEEE, 2011.
  • Learning: Preface. Sick, B. In Organic Computing -- A Paradigm Shift for Complex Systems, C. Müller-Schloer, H. Schmeck, T. Ungerer (reds.), bll 235–236. Springer, 2011.
  • 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), bll 414–423. Rome, Italy, 2011.
  • 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.), bll 53–66. Springer, 2011.
  • 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.), bll 267–280. Springer, 2011.
  • 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, bll 1–5. ACM, Beijing, China, 2011.
  • Active classifier training with the 3DS strategy. Reitmaier, T.; Sick, B. In IEEE Symposium on Computational Intelligence and Data Mining (CIDM), bll 88–95. IEEE, Paris, France, 2011.
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), bll 379–393. Elsevier, 2010.
  • 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), bll 94–103. IEEE, Budapest, Hungary, 2010.
  • 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), bll 1088–1100. IEEE, 2010.
  • 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), bll 2232–2245. IEEE, 2010.
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, bll 36–41. IEEE, Logan, 2006.