Bernhard Sick

Team leader: Collaborative Interactive Learning (CIL); AI for Computationally Intelligent Systems (AI4CIS)
Anschrift University of Kassel
Intelligent Embedded Systems
Wilhelmshöher Allee 73
34121 Kassel
Germany
Raum
Telefon +49 561 804 6020
Telefax +49 561 804 6022
E-Mail-Adresse bsick@uni-kassel.de
Bild von Prof. Dr. rer. nat. Bernhard  Sick

[2021] [2020] [2019] [2018] [2017] [2016] [2015] [2014] [2013] [2012] [2011] [2010] [2007] [2006]

2021 [to top]

  • Gruhl, C., Sick, B., Tomforde, S.: Novelty detection in continuously changing environments. Future Generation Computer Systems. 114, 138 - 154 (2021).
     
  • Dingel, K., Huhnstock, R., Knie, A., Ehresmann, A., Sick, B.: AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems. Computer Physics Communications. 262, 107859 (2021).
     

2020 [to top]

  • Heidecker, F., Hannan, A., Bieshaar, M., Sick, B.: Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks. ICPR Workshop on Integrated Artificial Intelligence in Data Science. IEEE, Milan, Italy (2020).
     
  • Kress, V., Schreck, S., Zernetsch, S., Doll, K., Sick, B.: Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks. IEEE Symposium Series on Computational Intelligence (SSCI). pp. 2723-2730 (2020).
     
  • Haase-Schütz, C., Stal, R., Hertlein, H., Sick, B.: Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning. International Conference on Pattern Recognition (ICPR) (2020).
     
  • Zernetsch, S., Schreck, S., Kress, V., Doll, K., Sick, B.: Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution. International Conference on Pattern Recognition (ICPR) (2020).
     
  • He, Y., Henze, J., Sick, B.: Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks. 2020 International Joint Conference on Neural Networks (IJCNN) (2020).
     
  • Gruhl, C., Schmeißing, J., Tomforde, S., Sick, B.: Normal-wishart clustering for novelty detection. Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). p. 64--69. IEEE (2020).
     
  • Henze, J., Schreiber, J., Sick, B.: Representation Learning in Power Time Series Forecasting. In: Pedrycz, W. and Chen, S.-M. (eds.) Deep Learning: Algorithms and Applications. p. 67--101. Springer International Publishing (2020).
     
  • Huseljic, D., Sick, B., Herde, M., Kottke, D.: Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks. International Conference on Pattern Recognition (ICPR) (2020).
     
  • Yujiang He, Janosch Henze, B.S.: Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets. IFAC 2020 (2020).
     
  • Henze, J., Siefert, M., Bremicker-Trübelhorn, S., Asanalieva, N., Sick, B.: Probabilistic upscaling and aggregation of wind power forecasts. Energy, Sustainability and Society. 10, 15 (2020).
     
  • Scheiner, N., Kraus, F., Wei, F., Phan, B., Mannan, F., Appenrodt, N., Ritter, W., Dickmann, J., Dietmayer, K., Sick, B., Heide, F.: Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020).
     
  • Schneegans, J., Bieshaar, M., Heidecker, F., Sick, B.: Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks. ICPR Workshop on Integrated Artificial Intelligence in Data Science. IEEE, Milan, Italy (2020).
     
  • Schreiber, J., Sick, B.: Emerging Relation Network and Task Embedding for Multi-Task Regression Problems. International Conference on Pattern Recognition (ICPR) (2020).
     
  • Kress, V., Zernetsch, S., Doll, K., Sick, B.: Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks. ICPR Workshop on Integrated Artificial Intelligence in Data Science. IEEE, Milan, Italy (2020).
     
  • Herde, M., Kottke, D., Huseljic, D., Sick, B.: Multi-annotator Probabilistic Active Learning. International Conference on Pattern Recognition (ICPR) (2020).
     
  • Pham Minh, T., Kottke, D., Tsarenko, A., Gruhl, C., Sick, B.: Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning. 2020 International Joint Conference on Neural Networks (IJCNN) (2020).
     
  • Tomforde, S., Gruhl, C., Sick, B.: A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour. Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). p. 166--169. IEEE (2020).
     
  • Meier, D., Hartmann, G., Völker, J., Viefhaus, J., Sick, B.: Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm. In: Chubar, O. and Sawhney, K. (eds.) Advances in Computational Methods for X-Ray Optics V. p. 71 -- 77. SPIE (2020).
     
  • Scheiner, N., Schumann, O., Kraus, F., Appenrodt, N., Dickmann, J., Sick, B.: Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification? IEEE 23rd International Conference on Information Fusion, FUSION 2020, Rustenburg, South Africa, July 6-9, 2020. p. 1--8. IEEE (2020).
     
  • Heidecker, F., Gruhl, C., Sick, B.: Novelty based Driver Identification on RR Intervals from ECG Data. ICPR Workshop on Integrated Artificial Intelligence in Data Science. IEEE, Milan, Italy (2020).
     

2019 [to top]

  • Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS. IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM). p. 5--9 (2019).
     
  • Tomforde, S., Gelhausen, P., Gruhl, C., Haering, I., Sick, B.: Explicit Consideration of Resilience in Organic Computing Design Processes. ARCS Workshop 2019; 32nd International Conference on Architecture of Computing Systems. p. 1--6 (2019).
     
  • König, I., Heilmann, E., Henze, J., David, K., Wetzel, H., Sick, B.: Using grid supporting flexibility in electricity distribution networks. In: David, K., Geihs, K., Lange, M., and Stumme, G. (eds.) INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft. p. 531--544. Gesellschaft für Informatik e.V., Bonn (2019).
     
  • Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles. 2019 IEEE Intelligent Vehicles Symposium (IV). p. 642--649. IEEE, Paris, France (2019).
     
  • Kottke, D., Schellinger, J., Huseljic, D., Sick, B.: Limitations of Assessing Active Learning Performance at Runtime. CoRR. abs/1901.10338, (2019).
     
  • Heidecker, F., Bieshaar, M., Sick, B.: Towards Corner Case Identification in Cyclists’ Trajectories. Proceedings of CSCS ’19: 3rd ACM Symposium on Computer Science in Cars (CSCS ’19) (2019).
     
  • Scheiner, N., Haag, S., Appenrodt, N., Duraisamy, B., Dickmann, J., Fritzsche, M., Sick, B.: Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices. 2019 20th International Radar Symposium (IRS). p. 1--10. , Ulm, Germany (2019).
     
  • Schreiber, J., Buschin, A., Sick, B.: Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models. In: David, K., Geihs, K., Lange, M., and Stumme, G. (eds.) INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft. p. 585--598. Gesellschaft für Informatik e.V., Bonn (2019).
     
  • Botache, D., Dandan, L., Bieshaar, M., Sick, B.: Early Pedestrian Movement Detection Using Smart Devices Based on Human Activity Recognition. In: Draude, C., Lange, M., and Sick, B. (eds.) INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge). p. 229--238. Gesellschaft für Informatik e.V., Bonn (2019).
     
  • Kress, V., Zernetsch, S., Doll, K., Sick, B.: Pose Based Trajectory Forecast of Vulnerable Road Users. IEEE Symposium Series on Computational Intelligence (SSCI). , Xiamen (2019).
     
  • Schreiber, J., Jessulat, M., Sick, B.: Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic. In: Tetko, I.V., Kurková, V., Karpov, P., and Theis, F. (eds.) Artificial Neural Networks and Machine Learning -- ICANN 2019: Image Processing. p. 550--564. Springer International Publishing, Cham (2019).
     
  • Zernetsch, S., Reichert, H., Kress, V., Doll, K., Sick, B.: Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks. 2019 IEEE Intelligent Vehicles Symposium (IV). p. 810--815 (2019).
     
  • Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Start Intention Detection of Cyclists using an LSTM Network. In: Draude, C., Lange, M., and Sick, B. (eds.) INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge). p. 219--228. Gesellschaft für Informatik e.V., Bonn (2019).
     
  • Sandrock, C., Herde, M., Calma, A., Kottke, D., Sick, B.: Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality. 2019 International Joint Conference on Neural Networks (IJCNN). pp. 1-8 (2019).
     
  • Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: A Multi-Stage Clustering Framework for Automotive Radar Data. 2019 IEEE 22nd Intelligent Transportation Systems Conference (ITSC). p. 2060--2067. IEEE (2019).
     
  • Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Pose Based Start Intention Detection of Cyclists. 2019 IEEE Intelligent Transportation Systems Conference (ITSC). pp. 2381-2386 (2019).
     
  • Hanika, T., Herde, M., Kuhn, J., Leimeister, J.M., Lukowicz, P., Oeste-Reiß, S., Schmidt, A., Sick, B., Stumme, G., Tomforde, S., Zweig, K.A.: Collaborative Interactive Learning - A clarification of terms and a differentiation from other research fields. CoRR. abs/1905.07264, (2019).
     
  • Goldhammer, M., Köhler, S., Zernetsch, S., Doll, K., Sick, B., Dietmayer, K.: Intentions of Vulnerable Road Users -- Detection and Forecasting by Means of Machine Learning. IEEE Transactions on Intelligent Transportation Systems. 1--11 (2019).
     
  • Vogt, S., Braun, A., Dobschinski, J., Sick, B.: Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning. In: Betancourt, U. and Ackermann, T. (eds.) Digital Proceedings of the 18th Wind Integration Workshop. , Dublin, Ireland (2019).
     

2018 [to top]

  • Schreiber, J., Sick, B.: Quantifying the Influences on Probabilistic Wind Power Forecasts. International Conference on Power and Renewable Energy. p. 6 (2018).
     
  • Bieshaar, M., Depping, M., Schneegans, J., Sick, B.: Starting Movement Detection of Cyclists Using Smart Devices. DSAA. , Turin, Italy (2018).
     
  • Gensler, A., Sick, B.: A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation. ArXiv e-prints. (2018).
     
  • Jänicke, M., Schmidt, V., Sick, B., Tomforde, S., Lukowicz, P., Schmeißing, J.: Smart Device Stealing and CANDIES. Agents and Artificial Intelligence - 10th International Conference, ICAART 2018, Funchal, Madeira, Portugal, January 16-18, 2018, Revised Selected Papers. p. 247--273 (2018).
     
  • Bieshaar, M., Zernetsch, S., Hubert, A., Sick, B., Doll, K.: Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble. IEEE Transactions on Intelligent Vehicles. 3, (2018).
     
  • Scharei, K., Herde, M., Bieshaar, M., Calma, A., Kottke, D., Sick, B.: Automated Active Learning with a Robot. Archives of Data Science, Series A (Online First). 5, A16, 15 S. online (2018).
     
  • Sick, B., Oeste-Reiß, S., Schmidt, A., Tomforde, S., Zweig, K.A.: Collaborative Interactive Learning. Informatik Spektrum. 41, 52--55 (2018).
     
  • Calma, A., Stolz, M., Kottke, D., Tomforde, S., Sick, B.: Active Learning with Realistic Data -- A Case Study. International Joint Conference on Neural Networks. , Rio de Janiero, Brazil (2018).
     
  • Calma, A., Reitmaier, T., Sick, B.: Semi-supervised active learning for support vector machines: A novel approach that exploits structure information in data. Information Sciences. 456, 13--33 (2018).
     
  • Deist, S., Bieshaar, M., Schreiber, J., Gensler, A., Sick, B.: Coopetitive Soft Gating Ensemble. Workshop on Self-Improving System Integration (SISSY). , Trento, Italy (2018).
     
  • Schlegel, B., Mrowca, A., Wolf, P., Sick, B., Steinhorst, S.: Generalizing Application Agnostic Remaining Useful Life Estimation Using Data-Driven Open Source Algorithms. IEEE 3rd International Conference on Big Data Analysis. , Shanghai, China (2018).
     
  • Jahn, A., Tomforde, S., Morold, M., David, K., Sick, B.: Towards Cooperative Self-adapting Activity Recognition. Proceedings of the 8th International Joint Conference on Pervasive and Embedded Computing and Communication Systems, PECCS 2018, Porto, Portugal, July 29-30, 2018. p. 215--222 (2018).
     
  • Breker, S., Rentmeister, J., Sick, B., Braun, M.: Hosting capacity of low-voltage grids for distributed generation: Classification by means of machine learning techniques. Appl. Soft Comput. 70, 195--207 (2018).
     
  • Gruhl, C., Sick, B.: Novelty detection with CANDIES: a holistic technique based on probabilistic models. Int. J. Machine Learning & Cybernetics. 9, 927--945 (2018).
     
  • Jänicke, M., Schmidt, V., Sick, B., Tomforde, S., Lukowicz, P.: Hijacked Smart Devices -- Methodical Foundations for Autonomous Theft Awareness based on Activity Recognition and Novelty Detection. Proceedings of the 10th International Conference on Agents and Artificial Intelligence (2018).
     
  • Jänicke, M., Sick, B., Tomforde, S.: Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models. Informatics. 5, 38 (2018).
     
  • Kromat, T., Dehling, T., Haux, R., Peters, C., Sick, B., Tomforde, S., Wolf, K.-H., Sunyaev, A.: Gestaltungsraum für proactive Smart Homes zur Gesundheitsförderung. Multikonferenz Wirtschaftsinformatik. , Lüneburg, Germany (2018).
     
  • Gruhl, C., Tomforde, S., Sick, B.: Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime. 2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W), Trento, Italy, September 3-7, 2018. p. 198--203 (2018).
     
  • Kottke, D., Calma, A., Huseljic, D., Sandrock, C., Kachergis, G., Sick, B.: The Other Human in The Loop -- A Pilot Study to Find Selection Strategies for Active Learning. International Joint Conference on Neural Networks. , Rio de Janiero, Brazil (2018).
     
  • Zernetsch, S., Kress, V., Sick, B., Doll, K.: Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network. 2018 IEEE Intelligent Vehicles Symposium, IV 2018, Changshu, Suzhou, China, June 26-30, 2018. p. 1--6 (2018).
     
  • Calma, A., Kuhn, J., Leimeister, J.M., Lukowicz, P., Oeste-Reiß, S., Schmidt, A., Sick, B., Stumme, G., Tomforde, S., Zweig, A.K.: A Concept for Productivity Tracking based on Collaborative Interactive Learning Techniques. IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops. p. 150--159. , London, UK (2018).
     
  • Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: Radar-based Feature Design and Multiclass Classification for Road User Recognition. 2018 IEEE Intelligent Vehicles Symposium (IV). p. 779--786. IEEE, Changshu, China (2018).
     
  • Heck, H., Sick, B., Tomforde, S.: Security Issues in Self-Improving System Integration - Challenges and Solution Strategies. 2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W), Trento, Italy, September 3-7, 2018. p. 176--181 (2018).
     
  • Henze, J., Kutzner, S., Sick, B.: Sampling Strategies for Representative Time Series in Load Flow Calculations. Data Analytics for Renewable Energy Integration. Technologies, Systems and Society - 6th ECML PKDD Workshop, DARE 2018, Dublin, Ireland, September 10, 2018, Revised Selected Papers. p. 27--48 (2018).
     
  • Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Human Pose Estimation in Real Traffic Scenes. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). , Bangalore, India (2018).
     
  • Reitberger, G., Zernetsch, S., Bieshaar, M., Sick, B., Doll, K., Fuchs, E.: Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure. ITSC. , Maui, HI (2018).
     
  • Calma, A., Oeste-Reiß, S., Sick, B., Leimeister, J.M.: Leveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration. Proceedings of the 51st Hawaii International Conference on System Sciences (2018).
     
  • Herde, M., Kottke, D., Calma, A., Bieshaar, M., Deist, S., Sick, B.: Active Sorting -- An Efficient Training of a Sorting Robot with Active Learning Techniques. International Joint Conference on Neural Networks. , Rio de Janiero, Brazil (2018).
     
  • Tomforde, S., Dehling, T., Haux, R., Huseljic, D., Kottke, D., Scheerbaum, J., Sick, B., Sunyaev, A., Wolf, K.-H.: Towards Proactive Health-enabling Living Environments : Simulation-based Study and Research Challenges. ARCS Workshop 2018, 31th International Conference on Architecture of Computing Systems. p. 1--8. VDE (2018).
     
  • Tomforde, S., Kantert, J., Müller-Schloer, C., Bödelt, S., Sick, B.: Comparing the Effects of Disturbances in Self-adaptive Systems - A Generalised Approach for the Quantification of Robustness. Trans. Computational Collective Intelligence. 28, 193--220 (2018).
     
  • Würtz, R.P., Tomforde, S., Calma, A., Kottke, D., Sick, B.: Interactive Learning Without Ground Truth. Organic Computing: Doctoral Dissertation Colloquium 2017. pp. 1-3. kassel university press GmbH (2018).
     

2017 [to top]

  • Gensler, A., Sick, B.: Probabilistic wind power forecasting: A multi-scheme ensemble technique with gradual coopetitive soft gating. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). pp. 1-10 (2017).
     
  • Bieshaar, M., Zernetsch, S., Depping, M., Sick, B., Doll, K.: Cooperative Starting Intention Detection of Cyclists Based on Smart Devices and Infrastructure. 2017 IEEE 20th International Conference on Intelligent Transportation Systems. , Yokohama, Japan (2017).
     
  • Gruhl, C., Beer, F., Heck, H., Sick, B., Bühler, U., Wacker, A., Tomforde, S.: A Concept for Intelligent Collaborative Network Intrusion Detection. Self-Optimisation in Autonomic & Organic Computing Systems, ARCS Workshops. VDE (2017).
     
  • Tomforde, S., Kantert, J., Sick, B.: Measuring Self Organisation at Runtime -- A Quantification Method based on Divergence Measures. International Conference on Agents and Artificial Intelligence (ICAART 2017). p. 96--106. SCITEPRESS, Porto, Portugal (2017).
     
  • Calma, A., Sick, B.: Simulation of Annotators for Active Learning: Uncertain Oracles. Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning @ ECMLPKDD 2017. pp. 2-14 (2017).
     
  • Calma, A., Kottke, D., Sick, B., Tomforde, S.: Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms. SISSY 2017: 4th International Workshop on Self-Improving System Integration. p. 109--116. , Tucson, AZ (2017).
     
  • Tomforde, S., Sick, B., Müller-Schloer, C.: Organic Computing in the Spotlight. arXiv:1701.08125. 1--10 (2017).
     
  • Bieshaar, M., Reitberger, G., Kreß, V., Zernetsch, S., Doll, K., Fuchs, E., Sick, B.: Highly Automated Learning for Improved Active Safety of Vulnerable Road Users. ACM Chapters Computer Science in Cars Symposium (CSCS-17). , Munich, Germany (2017).
     
  • Bannach, D., Jänicke, M., Fortes Rey, V., Tomforde, S., Sick, B., Lukowicz, P.: Self-Adaptation of Activity Recognition Systems to New Sensors. arXiv:1701.08528. 1--26 (2017).
     
  • Henze, J., Kneiske, T., Braun, M., Sick, B.: Identifying Representative Load Time Series for Load Flow Calculations. Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy. p. 83--93. Springer International Publishing, Cham, Switzerland (2017).
     
  • Kantert, J., Tomforde, S., Müller-Schloer, C., Edenhofer, S., Sick, B.: Quantitative Robustness -- A Generalised Approach to Compare the Impact of Disturbances in Self-organising Systems. International Conference on Agents and Artificial Intelligence (ICAART 2017). p. 39--50. SCITEPRESS, Porto, Portugal (2017).
     
  • Bieshaar, M., Reitberger, G., Zernetsch, S., Sick, B., Fuchs, E., Doll, K.: Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence. AAET -- Automatisiertes und vernetztes Fahren -- Beiträge zum gleichnamigen 18. Braunschweiger Symposium vom 8. und 9. Februar 2017. p. 67--87. , Braunschweig, Germany (2017).
     
  • Tomforde, S., Kantert, J., Müller-Schloer, C., Bödelt, S., Sick, B.: Comparing the Effects of Disturbances in Self-Adaptive Systems -- A Generalised Approach for the Quantification of Robustness. Springer Transactions on Computational Collective Intelligence. (2017).
     
  • Lang, D., Kottke, D., Krempl, G., Sick, B.: Probabilistic Active Learning with Structure-Sensitive Kernels. Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning. p. 37--48. CEUR Workshop Proceedings (2017).
     
  • Gensler, A., Sick, B.: Performing event detection in time series with SwiftEvent: an algorithm with supervised learning of detection criteria. Pattern Analysis and Applications. 1--20 (2017).
     
  • Schlegel, B., Sick, B.: Dealing with class imbalance the scalable way: Evaluation of various techniques based on classification grade and computational complexity. 2017 IEEE International Conference on Data Mining Workshops. p. 69--78. IEEE (2017).
     
  • Wolf, J.-H., Dehling, T., Haux, R., Sick, B., Sunyaev, A., Tomforde, S.: On Methodological and Technological Challenges for Proactive Health Management in Smart Homes. In: Mantas, J., Hasman, A., Gallos, P., and Househ, M.S. (eds.) Informatics Empowers Healthcare Transformation -- Proceedings of the 15th International Conference on Informatics, Management, and Technology in Health Care. p. 209--212. , Athens, Greece (2017).
     
  • Kottke, D., Calma, A., Huseljic, D., Krempl, G., Sick, B.: Challenges of Reliable, Realistic and Comparable Active Learning Evaluation. Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning @ ECMLPKDD 2017. pp. 2-14 (2017).
     

2016 [to top]

  • Heck, H., Wacker, A., Rudolph, S., Gruhl, C., Sick, B., Tomforde, S.: Towards Autonomous Self-tests at Runtime. 2016 IEEE International Workshops on Foundations and Applications of Self* Systems. p. 98--99 (2016).
     
  • Gensler, A., Henze, J., Sick, B., Raabe, N.: Deep Learning for Solar Power Forecasting -- An Approach using Autoencoder and LSTM Neural Networks. Systems, Man and Cybernetics (SMC), 2016 IEEE International Conference on. p. 2858--2865. IEEE, Budapest, Hungary (2016).
     
  • Breker, S., Sick, B.: Combinations of uncertain ordinal expert statements: The combination rule EIDMR and its application to low-voltage grid classification with SVM. International Joint Conference on Neural Networks. p. 2164--2173. , Vancouver, BC (2016).
     
  • Schlegel, B., Sick, B.: Design and optimization of an autonomous feature selection pipeline for high dimensional, heterogeneous feature spaces. Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. p. 1--9. IEEE (2016).
     
  • Kalkowski, E., Sick, B.: Generative Exponential Smoothing and Generative ARMA Models to Forecast Time-Variant Rates or Probabilities. In: Rojas, I. and Pomares, H. (eds.) Time Series Analysis and Forecasting: Selected Contributions from the ITISE Conference. p. 75--88. Springer International Publishing, Cham, Switzerland (2016).
     
  • Calma, A., Leimeister, J.M., Lukowicz, P., Oeste-Reiß, S., Reitmaier, T., Schmidt, A., Sick, B., Stumme, G., Zweig, K.A.: From Active Learning to Dedicated Collaborative Interactive Learning. ARCS 2016. p. 1--8. , Nuremberg, Germany (2016).
     
  • Jänicke, M., Tomforde, S., Sick, B.: Towards Self-Improving Activity Recognition Systems based on Probabilistic, Generative Models. International Conference on Autonomic Computing. p. 285--291. , Würzburg, Germany (2016).
     
  • Hähner, J., von Mammen, S., Timpf, S., Tomforde, S., Sick, B., Geihs, K., Goeble, T., Hornung, G., Stumme, G.: ``Know thyselves'' -- Computational Self-Reflection in Collective Technical Systems. ARCS 2016. p. 1--8. VDE Verlag GmbH, Nuremberg, Germany (2016).
     
  • Kalkowski, E., Sick, B.: Correlation of Ontology-Based Semantic Similarity and Crowdsourced Human Judgement for a Domain Specific Fashion Ontology. In: Bozzon, A., Cudre-Maroux, P., and Pautasso, C. (eds.) Web Engineering. p. 207--224. Springer International Publishing, Cham, Switzerland (2016).
     
  • Kottke, D., Krempl, G., Stecklina, M., Styp von Rekowski, C., Sabsch, T., Pham Minh, T., Deliano, M., Spiliopoulou, M., Sick, B.: Probabilistic Active Learning for Active Class Selection. In: Mathewson, K., Subramanian, K., and Loftin, R. (eds.) NIPS Workshop on the Future of Interactive Learning Machines. p. 1--9. , Barcelona, Spain (2016).
     
  • Gensler, A., Sick, B.: Forecasting Wind Power -- An Ensemble Technique With Gradual Weighting Based on Weather Situation. International Joint Conference on Neural Networks. p. 4976--4984. , Vancouver, BC (2016).
     
  • Gensler, A., Sick, B., Pankraz, V.: An Analogue-Based Similarity Search Technique for Solar Power Forecasting. IEEE International Conference on Systems, Man, and Cybernetics. p. 2850--2857. , Budapest, Hungary (2016).
     
  • Fisch, D., Gruhl, C., Kalkowski, E., Sick, B., Ovaska, S.J.: Towards Automation of Knowledge Understanding: An Approach for Probabilistic Generative Classifiers. Information Sciences. 370--371, 476--496 (2016).
     
  • Gensler, A., Sick, B., Vogt, S.: A Review of Deterministic Error Scores and Normalization Techniques for Power Forecasting Algorithms. IEEE Symposium Series on Computational Intelligence (SSCI). p. 1--9. , Athens, Greece (2016).
     
  • Calma, A., Reitmaier, T., Sick, B.: Resp-kNN: A probabilistic k-nearest neighbor classifier for sparsely labeled data. International Joint Conference on Neural Networks. p. 4040--4047. , Vancouver, BC (2016).
     
  • Goldhammer, M., Köhler, S., Doll, K., Sick, B.: Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptions. In: Bi, Y., Kapoor, S., and Bhatia, R. (eds.) Intelligent Systems and Applications: Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2015. p. 259--279. Springer International Publishing, Cham, Switzerland (2016).
     
  • Reitmaier, T., Calma, A., Sick, B.: Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data. arXiv:1610.03995. 1--35 (2016).
     
  • Kreil, M., Sick, B., Lukowicz, P.: Coping with variability in motion based activity recognition. International Workshop on Sensor-based Activity Recognition and Interaction. p. 1--8. , Rostock, Germany (2016).
     
  • Zernetsch, S., Kohnen, S., Goldhammer, M., Doll, K., Sick, B.: Trajectory Prediction of Cyclists Using a Physical Model and an Artificial Neural Network. Conference on Intelligent Vehicles Symposium (IV). p. 833--838. , Gothenburg, Sweden (2016).
     
  • Pirkl, G., Hevesi, P., Lukowicz, P., Klein, P., Heisel, C., Gröber, S., Kuhn, J., Sick, B.: Any Problems? A wearable sensor-based platform for representational learning-analytics. ACM International Joint Conference on Pervasive and Ubiquitous Computing. p. 353--356. , Heidelberg, Germany (2016).
     
  • Heck, H., Gruhl, C., Rudolph, S., Wacker, A., Sick, B., Hähner, J.: Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems. ARCS 2016. p. 1--8. VDE-Verlag, Nuremberg, Germany (2016).
     
  • Schlegel, B., Sick, B.: Design and optimization of an autonomous feature selection pipeline for high dimensional, heterogeneous feature spaces. IEEE Symposium Series on Computational Intelligence. p. 1--9. , Athens, Greece (2016).
     
  • Bahle, G., Calma, A., Leimeister, J.M., Lukowicz, P., Oeste-Reiß, S., Reitmaier, T., Schmidt, A., Sick, B., Stumme, G., Zweig, K.A.: Lifelong Learning and Collaboration of Smart Technical Systems in Open-Ended Environments -- Opportunistic Collaborative Interactive Learning. International Conference on Autonomic Computing, Workshop on Self-Improving System Integration. p. 1--10. , Würzburg, Germany (2016).
     

2015 [to top]

  • Kalkowski, E., Sick, B.: Using Ontology-Based Similarity Measures to Find Training Data for Problems with Sparse Data. IEEE International Conference on Systems, Man and Cybernetics. p. 1693--1699. , Hongkong, China (2015).
     
  • Kalkowski, E., Sick, B.: Generative Exponential Smoothing Models for Rate Forecasting with Uncertainty Estimation. International Work-Conference on Time Series. p. 806--817. , Granada, Spain (2015).
     
  • Reitmaier, T., Sick, B.: The Responsibility Weighted Mahalanobis Kernel for Semi-Supervised Training of Support Vector Machines for Classification. Information Sciences. 323, 179--198 (2015).
     
  • Embrechts, M., Sick, B.: A Generalized Hebb (GH) rule based on a cross-entropy error function for deep belief recursive learning. New Developments in Computational Intelligence and Computer Science. p. 21--24. , Vienna, Austria (2015).
     
  • Reitmaier, T., Calma, A., Sick, B.: Transductive active learning -- A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data. Information Sciences. 293, 275--298 (2015).
     
  • Rudolph, S., Tomforde, S., Sick, B., Hähner, J.: A Mutual Influence Detection Algorithm for Systems with Local Performance Measurement. IEEE International Conference on Self-Adaptive and Self-Organizing Systems. p. 144--149. , Cambridge, MA (2015).
     
  • Rudolph, S., Tomforde, S., Sick, B., Heck, H., Wacker, A., Hähner, J.: An Online Influence Detection Algorithm for Organic Computing Systems. ARCS 2015. p. 1--8. VDE Verlag GmbH, Porto, Portugal (2015).
     
  • Calma, A., Reitmaier, T., Sick, B., Lukowicz, P., Embrechts, M.: A New Vision of Collaborative Active Learning. arXiv arXiv:1504.00284v2. (2015).
     
  • Goldhammer, M., Köhler, S., Doll, K., Sick, B.: Camera Based Pedestrian Path Prediction by Means of Polynominal Least-squares Approximation and Multilayer Perceptron Neural Networks. SAI Intelligent Systems Conference. p. 390--399. , London, UK (2015).
     
  • Stone, T.C., Haas, S., Breitenstein, S., Wiesner, K., Sick, B.: Car Drive Classification and Context Recognition for Personalized Entertainment Preference Learning. International Journal on Advances in Software. 8, 53--64 (2015).
     
  • Hähner, J., Brinkschulte, U., Lukowicz, P., Mostaghim, S., Sick, B., Tomforde, S.: Runtime Self-Integration as Key Challenge for Mastering Interwoven Systems Workshops. International Conference on Architecture of Computing Systems. p. 1--8. , Porto, Portugal (2015).
     
  • Gensler, A., Gruber, T., Sick, B.: Fast Feature Extraction for Time Series Analysis Using Least-squares Approximations with Orthogonal Basis Functions. International Symposium on Temporal Representation and Reasoning. p. 29--37. , Kassel, Germany (2015).
     
  • Breker, S., Claudi, A., Sick, B.: Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Stochastic Simulations. IEEE Transactions on Power Systems. 30, 689--700 (2015).
     
  • Jahn, A., Lau, S.L., David, K., Sick, B.: A Tool Chain for Context Detection Automating the Investigation of a Multitude of Parameter Sets. International Workshop on Mobile and Context Aware Services. p. 1--5. , Boston, MA (2015).
     
  • Breker, S., Sick, B.: Effiziente Bewertung des Anschlußpotentials von Niederspannungsnetzen für dezentrale Erzeugungsanlagen: Klassifikation mit Methoden der Computational Intelligence. Nachhaltige Energieversorgung und Integration von Speichern. p. 51--56. , Hamburg, Germany (2015).
     
  • Gruhl, C., Sick, B., Wacker, A., Tomforde, S., Hähner, J.: A building block for awareness in technical systems: Online novelty detection and reaction with an application in intrusion detection. IEEE International Conference on Awareness Science and Technology. p. 194--200. , Qinhuangdao, China (2015).
     
  • Rudolph, J., Breker, S., Sick, B.: Bewertung verschiedener Spannungsregelungskonzepte in einem einspeisegeprägten Mittelspannungsnetz und Ausblick auf neue Konzepte basierend auf Methoden der Computational Intelligence. Tagungsband zur Konferenz Nachhaltige Energieversorgung und Integration von Speichern. p. 57--63. , Hamburg, Germany (2015).
     
  • Stone, T.C., Huber, A., Siwy, R., Sick, B.: Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen. Elektronik automotive. 2, 32--36 (2015).
     

2014 [to top]

  • Pree, H., Herwig, B., Gruber, T., Sick, B., David, K., Lukowicz, P.: On General Purpose Time Series Similarity Measures and Their Use as Kernel Functions in Support Vector Machines. Information Sciences. 281, 478--495 (2014).
     
  • Tomforde, S., Hähner, J., von Mammen, S., Gruhl, C., Sick, B., Geihs, K.: ``Know thyself'' -- Computational Self-Reflection in Intelligent Technical Systems. IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops. , Braunschweig, Germany (2014).
     
  • Stone, T., Birth, O., Gensler, A., Huber, A., Jänicke, M., Sick, B.: Location based learning of user behavior for proactive recommender systems in car comfort functions. In: Plödereder, E., Grunske, L., Schneider, E., and Ull, D. (eds.) Informatik 2014 -- Big Data -- Komplexität meistern. p. 2121--2132. Köllen Druck+Verlag GmbH, Bonn, Germany (2014).
     
  • Al-Falouji, G., Prestel, D., Scharfenberg, G., Mandl, R., Deinzer, A., Halang, W., Margraf-Stiksrud, J., Sick, B., Deinzer, R.: SMART-iBrush -- Individuelle Unterstützung der Zahnreinigung durch Messung von Bewegung und Druck mit einer intelligenten Zahnbürste. In: Weidner, R. and Redlich, T. (eds.) Erste Transdisziplinäre Konferenz zum Thema ``Technische Unterstützungssysteme, die die Menschen wirklich wollen''. p. 315--327 (2014).
     
  • Fisch, D., Kalkowski, E., Sick, B.: Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining Applications. IEEE Transactions on Knowledge and Data Engineering. 26, 652--666 (2014).
     
  • Goldhammer, M., Doll, K., Brunsmann, U., Gensler, A., Sick, B.: Pedestrian's Trajectory Forecast in Public Traffic with Artificial Neural Networks. International Conference on Pattern Recognition. p. 4110--4115. , Stockholm, Sweden (2014).
     
  • Tomforde, S., Hähner, J., Sick, B.: Interwoven Systems. Informatik-Spektrum. 37, 483--487 (2014).
     
  • Goldhammer, M., Hubert, A., Köhler, S., Zindler, K., Brunsmann, U., Doll, K., Sick, B.: Analysis on termination of pedestrians' gait at urban intersections. IEEE International Conference on Intelligent Transportation Systems. p. 1758--1763. , Qingdao, China (2014).
     
  • Tomforde, S., Hähner, J., Seebach, H., Reif, W., Sick, B., Wacker, A., Scholtes, I.: Engineering and Mastering Interwoven Systems. ARCS 2015. p. 1--8. , Lübeck, Germany (2014).
     
  • Gensler, A., Sick, B., Pankraz, V.: Novel Criteria to Measure Performance of Time Series Segmentation Techniques. LWA 2014 Workshops: KDML, IR, FGWM. p. 192--204. , Aachen, Germany (2014).
     
  • Herwig, B., Frommann, U., Gruber, T., Sick, B.: Programmierkompetenz prüfen … am Beispiel der Vorlesung "`Einführung in C"' an der Universität Kassel. In: Berendt, B., Fleischmann, A., Schaper, N., Szczyrba, B., and Wildt, J. (eds.) Neues Handbuch Hochschullehre. Lehren und Lernen effizient gestalten. p. 71--94. Raabe, Berlin, Germany (2014).
     
  • Jänicke, M., Sick, B., Lukowicz, P., Bannach, D.: Self-Adapting Multi-sensor Systems: A Concept for Self-Improvement and Self-Healing Techniques. IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops. p. 128--136. , London, UK (2014).
     
  • Gensler, A., Sick, B., Willkomm, J.: Temporal data analytics based on eigenmotif and shape space representations of time series. IEEE China Summit & International Conference on Signal and Information Processing. p. 753--757. , Xian, China (2014).
     
  • Kreil, M., Sick, B., Lukowicz, P.: Dealing with human variability in motion based, wearable activity recognition. International Conference on Pervasive Computing and Communications Workshops. p. 36--40. IEEE, Budapest, Hungary (2014).
     

2013 [to top]

  • Hähner, J., Rudolph, S., Tomforde, S., Fisch, D., Sick, B., Kopal, N., Wacker, A.: A Concept for Securing Cyber-Physical Systems with Organic Computing Techniques. In: Berekovic, M. and Danek, M. (eds.) International Conference on Architecture of Computing Systems Workshops. p. 1--13. VDE-Verlag, Prague, Czech Republic (2013).
     
  • Kaufmann, P., Glette, K., Gruber, T., Platzner, M., Torresen, J., Sick, B.: Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers. IEEE Transactions on Evolutionary Computation. 17, 46--63 (2013).
     
  • Reitmaier, T., Sick, B.: Let us know your decision: Pool-based active training of a generative classifier with the selection strategy 4DS. Information Sciences. 230, 106--131 (2013).
     
  • Gensler, A., Gruber, T., Sick, B.: Blazing Fast Time Series Segmentation Based on Update Techniques for Polynomial Approximations. IEEE International Conference on Data Mining Workshops. p. 1002--1011. , Dallas, TX (2013).
     

2012 [to top]

  • Fisch, D., Jänicke, M., Kalkowski, E., Sick, B.: Techniques for knowledge acquisition in dynamically changing environments. ACM Transactions on Autonomous and Adaptive Systems. 7, 16:1--16:25 (2012).
     
  • Gruber, T., Meixner, B., Prosser, J., Sick, B.: Handedness Tests for Preschool Children: A Novel Approach Based on Graphics Tablets and Support Vector Machines. Applied Soft Computing. 12, 1390--1398 (2012).
     
  • Embrechts, M.J., Gatti, C.J., Linton, J.D., Gruber, T., Sick, B.: Forecasting exchange rates with ensemble neural networks and ensemble K-PLS: A case study for the US Dollar per Indian Rupee. Proceedings of the International Joint Conference on Neural Networks. p. 1--8. , Brisbane, Australia (2012).
     
  • Fisch, D., Jänicke, M., Kalkowski, E., Sick, B.: Learning from others: Exchange of classification rules in intelligent distributed systems. Artificial Intelligence. 187--188, 90--114 (2012).
     
  • Giedl-Wagner, R., Miller, T., Sick, B.: Determination of Optimal CT Scan Parameters Using Radial Basis Function Neural Networks. Conference on Industrial Computed Tomography. p. 221--228. , Wels, Austria (2012).
     

2011 [to top]

  • Sick, B.: Learning. In: Müller-Schloer, C., Schmeck, H., and Ungerer, T. (eds.) Organic Computing -- A Paradigm Shift for Complex Systems. p. 235--236. Springer Basel, Basel, Switzerland (2011).
     
  • Fisch, D., Jänicke, M., Müller-Schloer, C., Sick, B.: Divergence Measures as a Generalised Approach to Quantitative Emergence. In: Müller-Schloer, C., Schmeck, H., and Ungerer, T. (eds.) Organic Computing --- A Paradigm Shift for Complex Systems. p. 53--66. Springer Basel, Basel, Switzerland (2011).
     
  • Hofmann, A., Sick, B.: On-Line Intrusion Alert Aggregation With Generative Data Stream Modeling. IEEE Transactions on Dependable and Secure Computing. 8, 282--294 (2011).
     
  • Fisch, D., Kalkowski, E., Sick, B.: Collaborative Learning by Knowledge Exchange. In: Müller-Schloer, C., Schmeck, H., and Ungerer, T. (eds.) Organic Computing -- A Paradigm Shift for Complex Systems. p. 267--280. Springer Basel, Basel, Switzerland (2011).
     
  • Fisch, D., Gruber, T., Sick, B.: SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis. IEEE Transactions on Knowledge and Data Engineering. 23, 774--787 (2011).
     
  • Fisch, D., Kalkowski, E., Sick, B., Ovaska, S.: In your interest: Objective interestingness measures for a generative classifier. International Conference on Agents and Artificial Intelligence. p. 414--423. , Rome, Italy (2011).
     
  • Bannach, D., Sick, B., Lukowicz, P.: Automatic Adaptation of Mobile Activity Recognition Systems to New Sensors. ACM International Conference on Ubiquitous Computing, Workshop. p. 1--5. , Beijing, China (2011).
     
  • Reitmaier, T., Sick, B.: Active classifier training with the 3DS strategy. IEEE Symposium on Computational Intelligence and Data Mining. p. 88--95. , Paris, France (2011).
     
  • Gottfried, T., Fliege, R., Frömberg, J., Heckmann, G., Sick, B., Triller, U., Wunsch, M.: Wissenschaftspropädeutisches Arbeiten im W-Seminar: Grundlagen -- Chancen -- Herausforderungen, (2011).
     

2010 [to top]

  • Fuchs, E., Gruber, T., Pree, H., Sick, B.: Temporal Data Mining Using Shape Space Representations of Time Series. Neurocomputing. 74, 379--393 (2010).
     
  • Gruber, C., Gruber, T., Krinninger, S., Sick, B.: Online Signature Verification With Support Vector Machines Based on LCSS Kernel Functions. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 40, 1088--1100 (2010).
     
  • Fisch, D., Jänicke, M., Sick, B., Müller-Schloer, C.: Quantitative Emergence -- A Refined Approach Based on Divergence Measures. IEEE International Conference on Self-Adaptive and Self-Organizing Systems. p. 94--103. , Budapest, Hungary (2010).
     
  • Fuchs, E., Gruber, T., Nitschke, J., Sick, B.: Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32, 2232--2245 (2010).
     

2007 [to top]

  • Dose, M., Gruber, C., Grunz, A., Hook, C., Kempf, J., Scharfenberg, G., Sick, B.: Towards an Automated Analysis of Neuroleptics' Impact on Human Hand Motor Skills. Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2007, Honolulu, Hawaii, USA, April 1-5, 2007, Part of the IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2007). p. 494--501 (2007).
     

2006 [to top]

  • Hofer, J., Gruber, C., Sick, B.: Biometric Analysis of Handwriting Dynamics Using a Script Generator Model. IEEE Mountain Workshop on Adaptive and Learning Systems. p. 36--41. , Logan (2006).