Bernhard Sick

Address Wilhelmshöher Allee 73
34121 Kassel
Building: WA-altes Gebäude (WA 73)
Room ohne Raumangabe
Telephone +49 561 804 6020
Telefax +49 561 804 6022
E-mail address bsick@uni-kassel.de
Picture of Prof. Dr. rer. nat. Bernhard  Sick

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

2020 [to top]

  • 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).
     
  • 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).
     

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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • Kottke, D., Schellinger, J., Huseljic, D., Sick, B.: Limitations of Assessing Active Learning Performance at Runtime. CoRR. abs/1901.10338, (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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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.: 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).
     
  • 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).
     
  • 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).
     

2018 [to top]

  • 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).
     
  • 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).
     
  • 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).
     
  • Gruhl, C., Sick, B.: Novelty detection with CANDIES: a holistic technique based on probabilistic models. Int. J. Machine Learning & Cybernetics. 9, 927--945 (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).
     
  • 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).
     
  • 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).
     
  • Sick, B., Oeste-Reiß, S., Schmidt, A., Tomforde, S., Zweig, K.A.: Collaborative Interactive Learning. Informatik Spektrum. 41, 52--55 (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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • Jänicke, M., Sick, B., Tomforde, S.: Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models. Informatics. 5, 38 (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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • Bieshaar, M., Depping, M., Schneegans, J., Sick, B.: Starting Movement Detection of Cyclists Using Smart Devices. DSAA. , Turin, Italy (2018).
     
  • Schreiber, J., Sick, B.: Quantifying the Influences on Probabilistic Wind Power Forecasts. International Conference on Power and Renewable Energy. p. 6 (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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     

2017 [to top]

  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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., Sick, B., Müller-Schloer, C.: Organic Computing in the Spotlight. arXiv:1701.08125. 1--10 (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).
     
  • Henze, J., Kneiske, T., Braun, M., Sick, B.: Identifying Representative Load Time Series for Load Flow Calculations. In: Woon, W.L., Aung, Z., Kramer, O., and Madnick, S. (eds.) Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy. p. 83--93. Springer International Publishing, Cham (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).
     

2016 [to top]

  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • Gruhl, C., Sick, B.: Detecting Novelty with CANDIES -- Improved Awareness Techniques Based on Probabilstic Knowledge Models. International Journal of Machine Learning and Cybernetics. 7, 1--19 (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).
     
  • 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., 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).
     
  • Gruhl, C., Sick, B.: Detecting Novel Processes with CANDIES -- An Holistic Novelty Detection Technique based on Probabilistic Models. arXiv:1605.05628. 1--17 (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).
     
  • 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).
     
  • 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).
     
  • Gruhl, C., Sick, B.: Variational Bayesian Inference for Hidden Markov Models With Multivariate Gaussian Output Distributions. arXiv:1605.08618. 1--6 (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. IEEE Symposium Series on Computational Intelligence. p. 1--9. , Athens, Greece (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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • Gruhl, C., Sick, B.: Novelty detection with CANDIES: a holistic technique based on probabilistic models. International Journal of Machine Learning and Cybernetics. 9, 1--19 (2016).
     

2015 [to top]

  • 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).
     
  • 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., 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).
     
  • Calma, A., Reitmaier, T., Sick, B., Lukowicz, P., Embrechts, M.: A New Vision of Collaborative Active Learning. arXiv arXiv:1504.00284v2. (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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • Stone, T.C., Huber, A., Siwy, R., Sick, B.: Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen. Elektronik automotive. 2, 32--36 (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).
     
  • 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).
     
  • 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).
     

2014 [to top]

  • 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).
     
  • 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).
     
  • 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., Sick, B.: Interwoven Systems. Informatik-Spektrum. 37, 483--487 (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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     

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).
     
  • 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).
     
  • 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).
     

2012 [to top]

  • 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).
     
  • 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.: Techniques for knowledge acquisition in dynamically changing environments. ACM Transactions on Autonomous and Adaptive Systems. 7, 16:1--16:25 (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).
     
  • 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., 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.: 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).