Publikationen

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

2020 [to top]

  • Scharei, K., Heidecker, F., Bieshaar, M.: Knowledge Representations in Technical Systems -- A Taxonomy, (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).
     
  • 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).
     
  • 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).
     
  • 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).
     
  • Kottke, D.: Enhanced Probabilistic Active Learning: Cost-sensitive, Unbalanced, Time-variant, Self-optimising, and Parameter-free. Organic Computing: Doctoral Dissertation Colloquium 2017. pp. 67-78. kassel university press GmbH (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).
     
  • 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).
     
  • 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).
     
  • Heppelmann, T., Steiner, A., Vogt, S.: Application of numerical weather prediction in wind power forecasting: Assessment of the diurnal cycle. Meteorologische Zeitschrift. 26, 319--331 (2017).
     
  • Calma, A.: Case Study on Pool-based Active Learning with Human Oracles. Organic Computing -- Doctoral Dissertation Colloquium 2017. kassel university press, Kassel, Germany (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).
     
  • Kurt, M.: Development of an Offshore Specific Wind Power Forecasting System, (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).
     
  • 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).
     
  • 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).
     
  • 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. p. 109--116. , 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).
     
  • 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).
     
  • 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., 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. 36, 72--80 (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).
     
  • Krempl, G., Lemaire, V., Lughofer, E., Kottke, D. eds: Active Learning: Applications, Foundations and Emerging Trends. CEUR-WS.org, Graz, Austria (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. Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. p. 1--9. IEEE (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. 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).
     
  • 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).
     
  • 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).
     
  • Diaconescu, A., Frey, S., Müller-Schloer, C., Pitt, J., Tomforde, S.: On the Benefits of 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).
     
  • 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).
     
  • Lewis, P., Bellman, K., Botev, J., Hildmann, H., Marsh, S., Pitt, J., Scholtes, I., Tomforde, S.: Socially-Sensitive Systems Design. , Dagstuhl, Germany (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. , Braunschweig, 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).
     
  • Haas, S., Wiesner, K., Stone, T.C.: Car Ride Classification for Drive Context Recognition. MOBILITY 2014 : The Fourth International Conference on Mobile Services, Resources, and Users (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).
     
  • Gruber, T.: Analyse von Zeitreihen unter Verwendung orthogonaler Polynome am Beispiel der Online-Motivsuche und ihrer Anwendungen, (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).
     

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).