Publikationen

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

2020 [to top]

  • Scharei, K., Heidecker, F., Bieshaar, M.: Knowledge Representations in Technical Systems -- A Taxonomy, (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).
     

2019 [to top]

  • 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).
     
  • 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).
     
  • Barnes, C.M., Bellman, K., Botev, J., Diaconescu, A., Esterle, L., Gruhl, C., Landauer, C., Lewis, P.R., Nelson, P.R., Stein, A., Stewart, C., Tomforde, S.: CHARIOT -- Towards a Continuous High-Level Adaptive Runtime Integration Testbed. 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W). pp. 52-55 (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).
     
  • 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).
     
  • 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).
     
  • Lesch, V., Krupitzer, C., Tomforde, S.: Emerging Self-Integration through Coordination of Autonomous Adaptive Systems. 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W). pp. 6-9 (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).
     
  • Tomforde, S.: From ``Normal'' to ``Abnormal'': A Concept for Determining Expected Self-Adaptation Behaviour. 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W). 126-129 (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).
     
  • 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).
     
  • Draude, C., Lange, M., Sick, B. eds: INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge), 23.-26.9.2019, Kassel, Deutschland. GI (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).
     
  • Kottke, D., Schellinger, J., Huseljic, D., Sick, B.: Limitations of Assessing Active Learning Performance at Runtime. CoRR. abs/1901.10338, (2019).
     
  • Lesch, V., Krupitzer, C., Tomforde, S.: Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems. ARCS Workshop 2019; 32nd International Conference on Architecture of Computing Systems. pp. 1-8 (2019).
     
  • Rudolph, S., Tomforde, S., Hähner, J.: Mutual Influence-aware Runtime Learning of Self-adaptation Behavior. ACM Transactions on Autonomous and Adaptive Systems. 14, 4:1--4:37 (2019).
     
  • D'Angelo, M., Gerasimou, S., Ghahremani, S., Grohmann, J., Nunes, I., Pournaras, E., Tomforde, S.: On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework. 2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). pp. 13-24 (2019).
     
  • Rudolph, S., Tomforde, S., Hähner, J.: On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes. CoRR. abs/1905.04205, (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).
     
  • 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).
     
  • 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).
     
  • Bellman, K.L., Gruhl, C., Landauer, C., Tomforde, S.: Self-Improving System Integration -- On a Definition and Characteristics of the Challenge. 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W). p. 1--3 (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).
     
  • Krupitzer, C., Tomforde, S.: The Organic Computing Doctoral Dissertation Colloquium: Status and Overview in 2019. In: Draude, C., Lange, M., and Sick, B. (eds.) INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge). p. 545--554. Gesellschaft für Informatik e.V., Bonn (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).
     
  • 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.: 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. In: Tomforde, S. and Sick, B. (eds.) Organic Computing -- Doctoral Dissertation Colloquium 2018. p. 75--87. Kassel university press (2019).
     
  • Stein, A., Tomforde, S.: Transfer Learning is a Crucial Capability of Intelligent Systems Self-Integrating at Runtime. 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W). pp. 32-35 (2019).
     
  • Knauer, U., Styp von Rekowski, C., Stecklina, M., Krokotsch, T., Pham Minh, T., Hauffe, V., Kilias, D., Ehrhardt, I., Sagischewski, H., Chmara, S., Seiffert, U.: Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers. Remote Sensing. 11, (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).
     
  • 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]

  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • Sick, B., Oeste-Reiß, S., Schmidt, A., Tomforde, S., Zweig, K.A.: Collaborative Interactive Learning. Informatik Spektrum. 41, 52--55 (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).
     
  • 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).
     
  • 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).
     
  • Deist, S., Bieshaar, M., Schreiber, J., Gensler, A., Sick, B.: Coopetitive Soft Gating Ensemble. Workshop on Self-Improving System Integration (SISSY). , Trento, Italy (2018).
     
  • Calma, A., Dellermann, D.: Decision Support with Hybrid Intelligence. Organic Computing -- Doctoral Dissertation Colloquium 2018. kassel university press, Würzburg, Germany (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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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., Sick, B.: Novelty detection with CANDIES: a holistic technique based on probabilistic models. Int. J. Machine Learning & Cybernetics. 9, 927--945 (2018).
     
  • Krempl, G., Lemaire, V., Kottke, D., Calma, A., Holzinger, A., Polikar, R., Sick, B. eds: Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning (ECML 2018) and Principles and Practice of Knowledge Discovery in Databases (PKDD 2018), Dublin, Ireland, September 10th, 2018. CEUR-WS.org (2018).
     
  • Schreiber, J., Sick, B.: Quantifying the Influences on Probabilistic Wind Power Forecasts. International Conference on Power and Renewable Energy. p. 6 (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).
     
  • 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).
     
  • 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).
     
  • Jänicke, M., Sick, B., Tomforde, S.: Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models. Informatics. 5, 38 (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).
     
  • 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., Depping, M., Schneegans, J., Sick, B.: Starting Movement Detection of Cyclists Using Smart Devices. DSAA. , Turin, Italy (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).
     
  • 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).
     
  • 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).
     
  • Bieshaar, M.: Where is my Device? Detecting the Smart Device's Wearing Position in the Context of Active Safety for Vulnerable Road Users. In: Tomforde, S. and Sick, B. (eds.) Organic Computing: Doctoral Dissertation Colloquium. Kassel University Press (2018).
     

2017 [to top]

  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • Gruhl, C.: Highly Autonomous Learning in Collaborative, Technical Systems. In: Tomforde, S. and Sick, B. (eds.) Organic Computing -- Doctoral Dissertation Colloquium 2017. kassel university press, Kassel, Germany (2017).
     
  • Kantert, J., Tomforde, S., Scharrer, R., Weber, S., Müller-Schloer, C., Edenhofer, S.: Identification and Classification of Agent Behaviour at Runtime in Open, Trust-based Organic Computing Systems. Elsevier Journal of Systems Architecture. 75, 68--78 (2017).
     
  • Stein, A., Rauh, D., Tomforde, S., Hähner, J.: Interpolation in the eXtended Classifier System: An Architectural Perspective. Elsevier Journal of Systems Architecture. 75, 79--94 (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. , Tucson, AZ (2017).
     
  • Beyer, C., Bieshaar, M., Calma, A., Heck, H., Kottke, D., Würtz, R.: Learning Without Ground Truth. Organic Computing -- Doctoral Dissertation Colloquium 2017. , Bochum, Germany (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).
     
  • 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).
     
  • Müller-Schloer, C., Tomforde, S.: Organic Computing -- Techncial Systems for Survival in the Real World. Birkhäuser Verlag (2017).
     
  • Tomforde, S., Sick, B., Müller-Schloer, C.: Organic Computing in the Spotlight. arXiv:1701.08125. 1--10 (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).
     
  • 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).
     
  • 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).
     
  • Stein, A., Rudolph, S., Tomforde, S., Hähner, J.: Self-Learning Smart Cameras – Harnessing the Generalisation Capability of XCS. Proceedings of the 9th International Joint Conference on Computational Intelligence. , Funchal, Portugal (2017).
     
  • Calma, A.: Simulation of Annotators for Active Learning: Uncertain Oracles. Organic Computing -- Doctoral Dissertation Colloquium 2017. kassel university press, Kassel, Germany (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).
     
  • Bellman, K., Botev, J., Hildmann, H., Lewis, P.R., Marsh, S., Pitt, J., Scholtes, I., Tomforde, S.: Socially-Sensitive Systems Design. IEEE Technology & Society Magazine, Special Issue on Social Concepts in Self-Organising Systems. (2017).
     

2016 [to top]

  • 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).
     
  • Kantert, J., Reinbold, C., Tomforde, S., Müller-Schloer, C.: A Comparison of Trust-based Autonomic/Organic Grid Computing Systems for Volunteer-Based Distributed Rendering. International Conference on Autonomic Computing. p. 137--146. IEEE, Würzburg, 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).
     
  • 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).
     
  • von Mammen, S., Tomforde, S., Hähner, J.: An Organic Computing Approach to Self-organising Robot Ensembles. Frontiers in Robotics and AI. 3, 1--67 (2016).
     
  • Tomforde, S., Rudolph, S., Bellman, K., Würtz, R.P.: An Organic Computing Perspective on Self-Improving System Interwearing at Runtime. International Conference on Autonomic Computing. p. 276--284. IEEE, Würzburg, Germany (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).
     
  • Stein, A., Rauh, D., Tomforde, S., Hähner, J.: Augmenting the Algorithmic Structure of XCS by Means of Interpolation. ARCS 2016. p. 348--360. Springer International Publishing, Nuremberg, Germany (2016).
     
  • Hähner, J., Tomforde, S.: Cellular Traffic Offloading through Network-Assisted Ad-Hoc Routing in Cellular Networks. IEEE Symposium on Computers and Communications. p. 469--476. , Messina, Italy (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).
     
  • Kantert, J., Reinhard, F., von Zengen, G., Tomforde, S., Weber, S., Wolf, L., Müller-Schloer, C.: Combining Trust and ETX to Provide Robust Wireless Sensor Networks. In: Varbanescu, A.L. (ed.) ARCS 2016. p. 1--7. VDE Verlag GmbH, Berlin, Offenbach, Germany, Nuremberg, Germany (2016).
     
  • Rudolph, S., Hihn, R., Tomforde, S., Hähner, J.: Comparision of Dependency Measures for the Detection of Mutual Influences in Organic Computing Systems. ARCS 2016. p. 334--347. Springer International Publishing, Nuremberg, Germany (2016).
     
  • Kantert, J., Tomforde, S., Kauder, M., Scharrer, R., Edenhofer, S., Hähner, J., Müller-Schloer, C.: Controlling Negative Emergent Behavior by Graph Analysis at Runtime. ACM Transactions on Autonomous and Adaptive Systems. 11, 1--34 (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).
     
  • 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).
     
  • Kantert, J., Tomforde, S., Weber, S., Müller-Schloer, C.: Coverage-guided Intelligent Test Loop -- A Concept for Applying Instrumented Testing to Self-organising Systems. In: Gusikhin, O., Peaucelle, D., and Madani, K. (eds.) ICINCO 2016. p. 221--226. SCITEPRESS, Lisbon, Portugal (2016).
     
  • Stein, A., Tomforde, S., Rauh, D., Hähner, J.: Dealing with Unforeseen Situations in the Context of Self-Adaptive Urban Traffic Control: How to Bridge the Gap. IEEE International Conference on Autonomic Computing. p. 167--172. IEEE, Würzburg, Germany (2016).
     
  • Edenhofer, S., Tomforde, S., Fischer, D., Hähner, J., Menzel, F., von Mammen, S.: Decentralised Trust-Management Inspired by Ant Pheromones. International Journal of Mobile Network Design and Innovation, Special Issue on Signal Processing, Security and Privacy for Mobile/Wireless and Computer Networks. 7, 46--55 (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. IEEE Symposium Series on Computational Intelligence. p. 1--9. , Athens, Greece (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).
     
  • 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).
     
  • Tomforde, S., Meier, D., Stein, A., von Mammen, S.: Distributed Resource Allocation as Co-Evolution Problem. IEEE Congress on Evolutionary Computation. p. 1815--1822. , Vancouver, BC, Canada (2016).
     
  • Calma, A.: Exploit the Potential of the Group: Putting Humans in the Dedicated Collaborative Interactive Learning Loop. In: Sick, B. and Tomforde, S. (eds.) Organic Computing -- Doctoral Dissertation Colloquium 2016. kassel university press, Kassel, 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).
     
  • 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).
     
  • Kalkowski, E., Sick, B.: Generative Exponential Smoothing and Generative ARMA Models to Forecast Time-Variant Rates or Probabilities. Springer International Publishing, Cham, Switzerland (2016).
     
  • Diaconescu, A., Frey, S., Müller-Schloer, C., Pitt, J., Tomforde, S.: Goal-oriented Holonics for Complex System (Self-)Integration: Concepts and Case Studies. IEEE International Conference on Self-Adaptive and Self-Organizing Systems. p. 100--109. , Augsburg, Germany (2016).
     
  • Stein, A., Eymüller, C., Rauh, D., Tomforde, S., Hähner, J.: Interpolation-based Classifier Generation in XCSF. IEEE Congress on Evolutionary Computation. p. 3990--3998. , Vancouver, BC (2016).
     
  • Lang, D., Kottke, D., Krempl, G., Spiliopoulou, M.: Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning. In: Krempl, G., Lemaire, V., Lughofer, E., and Kottke, D. (eds.) Active Learning: Applications, Foundations and Emerging Trends @iKnow. p. 25--34. , Graz, Austria (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).
     
  • Rudolph, S., Kantert, J., Jänen, U., Tomforde, S., Hähner, J., Müller-Schloer, C.: Measuring Self-Organisation Processes in Smart Camera Networks. In: Varbanescu, A.L. (ed.) ARCS 2016. p. 1--6. VDE Verlag GmbH, Berlin, Offenbach, Germany, Nuremberg, 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).
     
  • Tomforde, S., Sick, B. eds: Organic Computing -- Doctoral Dissertation Colloquium 2016. kassel university press, Kassel, Germany (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).
     
  • Gruhl, C.: Probabilistic Obsoleteness Detection for Gaussian Mixture Models. In: Tomforde, S. and Sick, B. (eds.) Organic Computing -- Doctoral Dissertation Colloquium 2016. p. 45--56. kassel university press, Kassel, Germany (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).
     
  • Kantert, J., Scharrer, R., Tomforde, S., Edenhofer, S., Müller-Schloer, C.: Runtime Clustering of Similarity Behaving Agents in Open Organic Computing Systems. ARCS 2016. p. 321--333. Springer International Publishing, Nuremberg, Germany (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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • Gruhl, C., Sick, B.: Variational Bayesian Inference for Hidden Markov Models With Multivariate Gaussian Output Distributions. arXiv:1605.08618. 1--6 (2016).
     

2015 [to top]

  • Calma, A.: 4DSPro: A New Selection Strategy for Pool-based Active Learning. In: Tomforde, S. and Sick, B. (eds.) Organic Computing -- Doctoral Dissertation Colloquium 2015. p. 121--133. kassel university press, Kassel, 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).
     
  • 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).
     
  • 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).
     
  • Calma, A., Reitmaier, T., Sick, B., Lukowicz, P., Embrechts, M.: A New Vision of Collaborative Active Learning. arXiv arXiv:1504.00284v2. (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).
     
  • Reitmaier, T.: Aktives Lernen für Klassifikationsprobleme unter der Nutzung von Strukturinformation. In: Hölldobler, S., Bernstein, A., Effelsberg, W., Freiling, F., H.-P.Lenhof,, Molitor, P., Neumann, G., Reischuk, R., Schweikardt, N., Spiliopoulou, M., Störrle, H., and Süsstrunk, S. (eds.) Lecture Notes in Informatics Ausgezeichnete Informatikdissertationen. p. 239--248 (2015).
     
  • Reitmaier, T.: Aktives Lernen für Klassifikationsprobleme unter der Nutzung von Strukturinformationen, (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).
     
  • 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.: Anomalies in Generative Trajectory Models -- Discovering Suspicious Traces with Novelty Detection Methods. In: Tomforde, S. and Sick, B. (eds.) Organic Computing -- Doctoral Dissertation Colloquium 2015. p. 95--107. kassel university press, Kassel, Germany (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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • Calma, A., Jänicke, M., Kantert, J., Kopal, N., Siefert, F., Tomforde, S.: Horizontal Integration of Organic Computing and Control Theory Concepts. In: Sick, B. and Tomforde, S. (eds.) Organic Computing -- Doctoral Dissertation Colloquium 2015. p. 157--164. kassel university press, Kassel, Germany (2015).
     
  • Breker, S.: Klassifikation von Niederspannungsnetzen mit Support Vector Machines: Bewertung des Aufnahmevermögens für Dezentrale Erzeugungsanlagen, (2015).
     
  • Heck, H., Edenhofer, S., Gruhl, C., Lund, A., Shuka, R., Hähner, J.: On the Application Possibilities of Organic Computing Principles in Socio-technical Systems. In: Tomforde, S. and Sick, B. (eds.) Organic Computing -- Doctoral Dissertation Colloquium 2015. p. 165--170. kassel university press, Kassel, Germany (2015).
     
  • Tomforde, S., Sick, B. eds: Organic Computing -- Doctoral Dissertation Colloquium 2015. kassel university press, Kassel, 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).
     
  • Jänicke, M.: Self-adapting Multi-Sensor System Using Classifiers Based on Gaussian Mixture Models. In: Tomforde, S. and Sick, B. (eds.) Organic Computing -- Doctoral Dissertation Colloquium 2015. p. 109--120. kassel university press, Kassel, Germany (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).
     
  • 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).
     
  • 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).
     

2014 [to top]

  • 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. p. 150--159. , London, UK (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).
     
  • 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).
     
  • 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).
     
  • Tomforde, S., Hähner, J., Sick, B.: Interwoven Systems. Informatik-Spektrum. 37, 483--487 (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).
     
  • 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).
     
  • 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).
     
  • 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., Sick, B. eds: Organic Computing -- Doctoral Dissertation Colloquium 2014. kassel university press, Kassel, Germany (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).
     
  • 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).
     
  • Reitmaier, T., Calma, A.: Resp-kNN: A Semi-Supervised kNN-Classifier for Sparsely Labeled Data in the Field of Organic Computing. In: Tomforde, S. and Sick, B. (eds.) Organic Computing -- Doctoral Dissertation Colloquium 2014. p. 85--97. kassel university press, Kassel, Germany (2014).
     
  • Gruhl, C.: Self-Adapting Generative Modeling Techniques -- A Basic Building Block for Many Organic Computing Techniques. In: Tomforde, S. and Sick, B. (eds.) Organic Computing -- Doctoral Dissertation Colloquium 2014. p. 99--109. kassel university press, Kassel, 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).
     
  • Kalkowski, E.: Self-Extending Training Sets: Using Ontologies to Improve Machine Learning Performance. In: Tomforde, S. and Sick, B. (eds.) Organic Computing -- Doctoral Dissertation Colloquium 2014. p. 111--125. kassel university press, Kassel, Germany (2014).
     
  • Früchtl, M.: Sicherheit eingebetteter Systeme auf Basis arithmetischer Codierungen, (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).
     
  • 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).
     

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).
     
  • Reitmaier, T.: Active Learning of Generative and Discriminative Classifiers for Organic Computing. In: Tomforde, S. (ed.) First Organic Computing Doctoral Dissertation Colloquium. p. 1--27. , Augsburg, Germany (2013).
     
  • Kubátová, H., Hochberger, C., Danek, M., Sick, B. eds: Architecture of Computing Systems -- ARCS 2013. Springer, Heidelberg, Germany (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).
     
  • Jänicke, M.: Self-Adaption of Multi-Sensor-Systems with Organic Computing Techniques. In: Tomforde, S. (ed.) Organic Computing Doctoral Dissertation Colloquium. p. 20--23. , Augsburg, Germany (2013).
     

2012 [to top]

  • Gruber, T.: Analyse von Zeitreihen unter Verwendung orthogonaler Polynome am Beispiel der Online-Motivsuche und ihrer Anwendungen. In: Hölldobler, S., Bernstein, A., Effelsberg, W., H.-P.Lenhof,, Löhr, K.-P., Molitor, P., Neumann, G., Reischuk, R., Schweikardt, N., Spiliopoulou, M., Störrle, H., and Süsstrunk, S. (eds.) Lecture Notes in Informatics Ausgezeichnete Informatikdissertationen. p. 131--140 (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).
     
  • Herwig, B.: Dynamische interaktive Klassifikation graphometrischer Daten am Beispiel der Händigkeitsanalyse. In: Goltz, U., Magnor, M., Appelrath, H.-J., Matthies, H.K., Balke, W.-T., and Wolf, L. (eds.) INFORMATIK 2012: Was bewegt uns in der/die Zukunft?, LNI Proceedings 42. p. 1850--1856. , Braunschweig, Germany (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).
     
  • 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).
     
  • Fisch, D.: Intelligente technische Systeme mit der Fähigkeit zum kollaborativen Wissenserwerb, (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).
     
  • 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).
     

2011 [to top]

  • 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).
     
  • 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., 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., 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).
     
  • Westmeier, M., Herwig, B., Börcsök, J.: Enhancing a simulation environment for computer architecture to a SystemC based testbench tool for design verification. International Symposium on Information, Communication and Automation Technologies. pp. 1-6. , Sarajevo, Bosnia and Herzegovina (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).
     
  • 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).
     
  • 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., 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).
     
  • 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., Pree, H., Sick, B.: Temporal Data Mining Using Shape Space Representations of Time Series. Neurocomputing. 74, 379--393 (2010).