Publikationen

  • Gensler, A., Sick, B., and Vogt, S. (i2016) A Review of Deterministic Error Scores and Normalization Techniques for Power Forecasting Algorithms. In IEEE Symposium Series on Computational Intelligence (SSCI), p 1--9, Athens, Greece.
     
  • Calma, A., Oeste-Reiß, S., Sick, B., and Leimeister, J. M. (2018) Leveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration. In Proceedings of the 51st Hawaii International Conference on System Sciences.
     
  • Bannach, D., Jänicke, M., Fortes Rey, V., Tomforde, S., Sick, B., and Lukowicz, P. (2017) Self-Adaptation of Activity Recognition Systems to New Sensors, arXiv:1701.08528 1--26.
     
  • Kantert, J., Tomforde, S., Müller-Schloer, C., Edenhofer, S., and Sick, B. (2017) Quantitative Robustness -- A Generalised Approach to Compare the Impact of Disturbances in Self-organising Systems. In International Conference on Agents and Artificial Intelligence (ICAART 2017), p 39--50, SCITEPRESS, Porto, Portugal.
     
  • Bellman, K., Botev, J., Hildmann, H., Lewis, P. R., Marsh, S., Pitt, J., Scholtes, I., and Tomforde, S. (2017) Socially-Sensitive Systems Design, IEEE Technology & Society Magazine, Special Issue on Social Concepts in Self-Organising Systems.
     
  • Tomforde, S., Kantert, J., and Sick, B. (2017) Measuring Self Organisation at Runtime -- A Quantification Method based on Divergence Measures. In International Conference on Agents and Artificial Intelligence (ICAART 2017), p 96--106, SCITEPRESS, Porto, Portugal.
     
  • Calma, A. (2017) Simulation of Annotators for Active Learning: Uncertain Oracles. In Organic Computing -- Doctoral Dissertation Colloquium 2017, kassel university press, Kassel, Germany.
     
  • Tomforde, S., Sick, B., and Müller-Schloer, C. (2017) Organic Computing in the Spotlight, arXiv:1701.08125 1--10.
     
  • Calma, A., Kottke, D., Sick, B., and Tomforde, S. (2017) Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms. In SISSY 2017: 4th International Workshop on Self-Improving System Integration, Tucson, AZ.
     
  • Tomforde, S., Kantert, J., Müller-Schloer, C., Bödelt, S., and Sick, B. (2017) Comparing the Effects of Disturbances in Self-Adaptive Systems -- A Generalised Approach for the Quantification of Robustness, Springer Transactions on Computational Collective Intelligence.
     
  • Bieshaar, M., Zernetsch, S., Depping, M., Sick, B., and Doll, K. (2017) Cooperative Starting Intention Detection of Cyclists Based on Smart Devices and Infrastructure. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems, Yokohama, Japan.
     
  • Kantert, J., Tomforde, S., Scharrer, R., Weber, S., Müller-Schloer, C., and Edenhofer, S. (2017) Identification and Classification of Agent Behaviour at Runtime in Open, Trust-based Organic Computing Systems, Elsevier Journal of Systems Architecture 75, 68--78.
     
  • Kottke, D., Calma, A., Huseljic, D., Krempl, G., and Sick, B. (2017) Challenges of Reliable, Realistic and Comparable Active Learning Evaluation. In Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning @ ECMLPKDD 2017, pp 2-14.
     
  • Gruhl, C., Beer, F., Heck, H., Sick, B., Bühler, U., Wacker, A., and Tomforde, S. (2017) A Concept for Intelligent Collaborative Network Intrusion Detection. In Self-Optimisation in Autonomic & Organic Computing Systems, ARCS Workshops, VDE.
     
  • Calma, A., and Sick, B. (2017) Simulation of Annotators for Active Learning: Uncertain Oracles. In Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning @ ECMLPKDD 2017, pp 2-14.
     
  • Bieshaar, M., Reitberger, G., Kreß, V., Zernetsch, S., Doll, K., Fuchs, E., and Sick, B. (2017) Highly Automated Learning for Improved Active Safety of Vulnerable Road Users. In ACM Chapters Computer Science in Cars Symposium (CSCS-17), Munich, Germany.
     
  • Stein, A., Rudolph, S., Tomforde, S., and Hähner, J. (2017) Self-Learning Smart Cameras – Harnessing the Generalisation Capability of XCS. In Proceedings of the 9th International Joint Conference on Computational Intelligence, Funchal, Portugal.
     
  • Müller-Schloer, C., and Tomforde, S. (2017) Organic Computing -- Techncial Systems for Survival in the Real World, Birkhäuser Verlag.
     
  • Stein, A., Rauh, D., Tomforde, S., and Hähner, J. (2017) Interpolation in the eXtended Classifier System: An Architectural Perspective, Elsevier Journal of Systems Architecture 75, 79--94.
     
  • Wolf, J. -H., Dehling, T., Haux, R., Sick, B., Sunyaev, A., and Tomforde, S. (2017) On Methodological and Technological Challenges for Proactive Health Management in Smart Homes. In Informatics Empowers Healthcare Transformation -- Proceedings of the 15th International Conference on Informatics, Management, and Technology in Health Care (Mantas, J., Hasman, A., Gallos, P., and Househ, M. S., Eds.), p 209--212, Athens, Greece.
     
  • Beyer, C., Bieshaar, M., Calma, A., Heck, H., Kottke, D., and Würtz, R. (2017) Learning Without Ground Truth. In Organic Computing -- Doctoral Dissertation Colloquium 2017, Bochum, Germany.
     
  • Bieshaar, M., Reitberger, G., Zernetsch, S., Sick, B., Fuchs, E., and Doll, K. (2017) Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence. In AAET -- Automatisiertes und vernetztes Fahren -- Beiträge zum gleichnamigen 18. Braunschweiger Symposium vom 8. und 9. Februar 2017, p 67--87, Braunschweig, Germany.
     
  • Gruhl, C. (2017) Highly Autonomous Learning in Collaborative, Technical Systems. In Organic Computing -- Doctoral Dissertation Colloquium 2017 (Tomforde, S., and Sick, B., Eds.), kassel university press, Kassel, Germany.
     
  • Diaconescu, A., Frey, S., Müller-Schloer, C., Pitt, J., and Tomforde, S. (2016) Goal-oriented Holonics for Complex System (Self-)Integration: Concepts and Case Studies. In IEEE International Conference on Self-Adaptive and Self-Organizing Systems, p 100--109, Augsburg, Germany.
     
  • Kalkowski, E., and Sick, B. (2016) Generative Exponential Smoothing and Generative ARMA Models to Forecast Time-Variant Rates or Probabilities, Time Series Analysis and Forecasting: Selected Contributions from the ITISE Conference (Rojas, I., and Pomares, H., Eds.), p 75--88, Springer International Publishing, Cham, Switzerland.
     
  • Kantert, J., Reinbold, C., Tomforde, S., and Müller-Schloer, C. (2016) A Comparison of Trust-based Autonomic/Organic Grid Computing Systems for Volunteer-Based Distributed Rendering. In International Conference on Autonomic Computing, p 137--146, IEEE, Würzburg, Germany.
     
  • Tomforde, S., Rudolph, S., Bellman, K., and Würtz, R. P. (2016) An Organic Computing Perspective on Self-Improving System Interwearing at Runtime. In International Conference on Autonomic Computing, p 276--284, IEEE, Würzburg, Germany.
     
  • Edenhofer, S., Tomforde, S., Fischer, D., Hähner, J., Menzel, F., and von Mammen, S. (2016) 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, Inderscience 7, 46--55.
     
  • Stein, A., Eymüller, C., Rauh, D., Tomforde, S., and Hähner, J. (2016) Interpolation-based Classifier Generation in XCSF. In IEEE Congress on Evolutionary Computation, p 3990--3998, Vancouver, BC.
     
  • Lang, D., Kottke, D., Krempl, G., and Spiliopoulou, M. (2016) Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning. In Active Learning: Applications, Foundations and Emerging Trends @iKnow (Krempl, G., Lemaire, V., Lughofer, E., and Kottke, D., Eds.), p 25--34, Graz, Austria.
     
  • Gruhl, C., and Sick, B. (2016) Detecting Novelty with CANDIES -- Improved Awareness Techniques Based on Probabilstic Knowledge Models, International Journal of Machine Learning and Cybernetics 7, 1--19.
     
  • Stein, A., Rauh, D., Tomforde, S., and Hähner, J. (2016) Augmenting the Algorithmic Structure of XCS by Means of Interpolation. In ARCS 2016, p 348--360, Springer International Publishing, Nuremberg, Germany.
     
  • Kreil, M., Sick, B., and Lukowicz, P. (2016) Coping with variability in motion based activity recognition. In International Workshop on Sensor-based Activity Recognition and Interaction, p 1--8, Rostock, Germany.
     
  • Heck, H., Gruhl, C., Rudolph, S., Wacker, A., Sick, B., and Hähner, J. (2016) Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems. In ARCS 2016, p 1--8, VDE-Verlag, Nuremberg, Germany.
     
  • von Mammen, S., Tomforde, S., and Hähner, J. (2016) An Organic Computing Approach to Self-organising Robot Ensembles, Frontiers in Robotics and AI 3, 1--67.
     
  • Kantert, J., Scharrer, R., Tomforde, S., Edenhofer, S., and Müller-Schloer, C. (2016) Runtime Clustering of Similarity Behaving Agents in Open Organic Computing Systems. In ARCS 2016, p 321--333, Springer International Publishing, Nuremberg, Germany.
     
  • Gruhl, C. (2016) Probabilistic Obsoleteness Detection for Gaussian Mixture Models. In Organic Computing -- Doctoral Dissertation Colloquium 2016 (Tomforde, S., and Sick, B., Eds.), p 45--56, kassel university press, Kassel, Germany.
     
  • Stein, A., Tomforde, S., Rauh, D., and Hähner, J. (2016) Dealing with Unforeseen Situations in the Context of Self-Adaptive Urban Traffic Control: How to Bridge the Gap. In IEEE International Conference on Autonomic Computing, p 167--172, IEEE, Würzburg, Germany.
     
  • Gensler, A., and Sick, B. (2016) Forecasting Wind Power -- An Ensemble Technique With Gradual Weighting Based on Weather Situation. In International Joint Conference on Neural Networks, p 4976--4984, Vancouver, BC.
     
  • Tomforde, S., Meier, D., Stein, A., and von Mammen, S. (2016) Distributed Resource Allocation as Co-Evolution Problem. In IEEE Congress on Evolutionary Computation, p 1815--1822, Vancouver, BC, Canada.
     
  • Gensler, A., Sick, B., and Pankraz, V. (2016) An Analogue-Based Similarity Search Technique for Solar Power Forecasting. In IEEE International Conference on Systems, Man, and Cybernetics, p 2850--2857, Budapest, Hungary.
     
  • Breker, S., and Sick, B. (2016) Combinations of uncertain ordinal expert statements: The combination rule EIDMR and its application to low-voltage grid classification with SVM. In International Joint Conference on Neural Networks, p 2164--2173, Vancouver, BC.
     
  • Kantert, J., Tomforde, S., Weber, S., and Müller-Schloer, C. (2016) Coverage-guided Intelligent Test Loop -- A Concept for Applying Instrumented Testing to Self-organising Systems. In ICINCO 2016 (Gusikhin, O., Peaucelle, D., and Madani, K., Eds.), p 221--226, SCITEPRESS, Lisbon, Portugal.
     
  • Reitmaier, T., Calma, A., and Sick, B. (2016) Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data, arXiv:1610.03995 1--35.
     
  • Calma, A. (2016) Exploit the Potential of the Group: Putting Humans in the Dedicated Collaborative Interactive Learning Loop. In Organic Computing -- Doctoral Dissertation Colloquium 2016 (Sick, B., and Tomforde, S., Eds.), kassel university press, Kassel, Germany.
     
  • Kantert, J., Reinhard, F., von Zengen, G., Tomforde, S., Weber, S., Wolf, L., and Müller-Schloer, C. (2016) Combining Trust and ETX to Provide Robust Wireless Sensor Networks. In ARCS 2016 (Varbanescu, A. L., Ed.), p 1--7, VDE Verlag GmbH, Berlin, Offenbach, Germany, Nuremberg, Germany.
     
  • Zernetsch, S., Kohnen, S., Goldhammer, M., Doll, K., and Sick, B. (2016) Trajectory Prediction of Cyclists Using a Physical Model and an Artificial Neural Network. In Conference on Intelligent Vehicles Symposium (IV), p 833--838, Gothenburg, Sweden.
     
  • Kantert, J., Tomforde, S., Kauder, M., Scharrer, R., Edenhofer, S., Hähner, J., and Müller-Schloer, C. (2016) Controlling Negative Emergent Behavior by Graph Analysis at Runtime, ACM Transactions on Autonomous and Adaptive Systems 11, 1--34.
     
  • Hähner, J., and Tomforde, S. (2016) Cellular Traffic Offloading through Network-Assisted Ad-Hoc Routing in Cellular Networks. In IEEE Symposium on Computers and Communications, p 469--476, Messina, Italy.
     
  • Bahle, G., Calma, A., Leimeister, J. M., Lukowicz, P., Oeste-Reiß, S., Reitmaier, T., Schmidt, A., Sick, B., Stumme, G., and Zweig, K. A. (2016) Lifelong Learning and Collaboration of Smart Technical Systems in Open-Ended Environments -- Opportunistic Collaborative Interactive Learning. In International Conference on Autonomic Computing, Workshop on Self-Improving System Integration, p 1--10, Würzburg, Germany.
     
  • Gruhl, C., and Sick, B. (2016) Detecting Novel Processes with CANDIES -- An Holistic Novelty Detection Technique based on Probabilistic Models, arXiv:1605.05628 1--17.
     
  • Gensler, A., Henze, J., Sick, B., and Raabe, N. (2016) Deep Learning for Solar Power Forecasting -- An Approach using Autoencoder and LSTM Neural Networks. In Systems, Man and Cybernetics (SMC), 2016 IEEE International Conference on, p 2858--2865, IEEE, Budapest, Hungary.
     
  • Fisch, D., Gruhl, C., Kalkowski, E., Sick, B., and Ovaska, S. J. (2016) Towards Automation of Knowledge Understanding: An Approach for Probabilistic Generative Classifiers, Information Sciences 370--371, 476--496.
     
  • Gruhl, C., and Sick, B. (2016) Variational Bayesian Inference for Hidden Markov Models With Multivariate Gaussian Output Distributions, arXiv:1605.08618 1--6.
     
  • Hähner, J., von Mammen, S., Timpf, S., Tomforde, S., Sick, B., Geihs, K., Goeble, T., Hornung, G., and Stumme, G. (2016) ``Know thyselves'' -- Computational Self-Reflection in Collective Technical Systems. In ARCS 2016, p 1--8, VDE Verlag GmbH, Nuremberg, Germany.
     
  • Schlegel, B., and Sick, B. (2016) Design and optimization of an autonomous feature selection pipeline for high dimensional, heterogeneous feature spaces. In IEEE Symposium Series on Computational Intelligence, p 1--9, Athens, Greece.
     
  • Kottke, D., Krempl, G., Stecklina, M., Styp von Rekowski, C., Sabsch, T., Pham Minh, T., Deliano, M., Spiliopoulou, M., and Sick, B. (2016) Probabilistic Active Learning for Active Class Selection. In NIPS Workshop on the Future of Interactive Learning Machines (Mathewson, K., Subramanian, K., and Loftin, R., Eds.), p 1--9, Barcelona, Spain.
     
  • Calma, A., Reitmaier, T., and Sick, B. (2016) Resp-kNN: A probabilistic k-nearest neighbor classifier for sparsely labeled data. In International Joint Conference on Neural Networks, p 4040--4047, Vancouver, BC.
     
  • Jänicke, M., Tomforde, S., and Sick, B. (2016) Towards Self-Improving Activity Recognition Systems based on Probabilistic, Generative Models. In International Conference on Autonomic Computing, p 285--291, Würzburg, Germany.
     
  • Goldhammer, M., Köhler, S., Doll, K., and Sick, B. (2016) Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptions. In Intelligent Systems and Applications: Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2015 (Bi, Y., Kapoor, S., and Bhatia, R., Eds.), p 259--279, Springer International Publishing, Cham, Switzerland.
     
  • Rudolph, S., Hihn, R., Tomforde, S., and Hähner, J. (2016) Comparision of Dependency Measures for the Detection of Mutual Influences in Organic Computing Systems. In ARCS 2016, p 334--347, Springer International Publishing, Nuremberg, Germany.
     
  • Tomforde, S., and Sick, B. (Eds.). (2016) Organic Computing -- Doctoral Dissertation Colloquium 2016, kassel university press, Kassel, Germany.
     
  • Rudolph, S., Kantert, J., Jänen, U., Tomforde, S., Hähner, J., and Müller-Schloer, C. (2016) Measuring Self-Organisation Processes in Smart Camera Networks. In ARCS 2016 (Varbanescu, A. L., Ed.), p 1--6, VDE Verlag GmbH, Berlin, Offenbach, Germany, Nuremberg, Germany.
     
  • Pirkl, G., Hevesi, P., Lukowicz, P., Klein, P., Heisel, C., Gröber, S., Kuhn, J., and Sick, B. (2016) Any Problems? A wearable sensor-based platform for representational learning-analytics. In ACM International Joint Conference on Pervasive and Ubiquitous Computing, p 353--356, Heidelberg, Germany.
     
  • Calma, A., Leimeister, J. M., Lukowicz, P., Oeste-Reiß, S., Reitmaier, T., Schmidt, A., Sick, B., Stumme, G., and Zweig, K. A. (2016) From Active Learning to Dedicated Collaborative Interactive Learning. In ARCS 2016, p 1--8, Nuremberg, Germany.
     
  • Kalkowski, E., and Sick, B. (2016) Correlation of Ontology-Based Semantic Similarity and Crowdsourced Human Judgement for a Domain Specific Fashion Ontology. In Web Engineering (Bozzon, A., Cudre-Maroux, P., and Pautasso, C., Eds.), p 207--224, Springer International Publishing, Cham, Switzerland.
     
  • Heck, H., Wacker, A., Rudolph, S., Gruhl, C., Sick, B., and Tomforde, S. (2016) Towards Autonomous Self-tests at Runtime. In 2016 IEEE International Workshops on Foundations and Applications of Self* Systems, p 98--99.
     
  • Stone, T. C., Haas, S., Breitenstein, S., Wiesner, K., and Sick, B. (2015) Car Drive Classification and Context Recognition for Personalized Entertainment Preference Learning, International Journal on Advances in Software 8, 53--64.
     
  • Reitmaier, T. (2015) Aktives Lernen für Klassifikationsprobleme unter der Nutzung von Strukturinformationen, kassel university press, Kassel, Germany.
     
  • Rudolph, S., Tomforde, S., Sick, B., Heck, H., Wacker, A., and Hähner, J. (2015) An Online Influence Detection Algorithm for Organic Computing Systems. In ARCS 2015, p 1--8, VDE Verlag GmbH, Porto, Portugal.
     
  • Goldhammer, M., Köhler, S., Doll, K., and Sick, B. (2015) Camera Based Pedestrian Path Prediction by Means of Polynominal Least-squares Approximation and Multilayer Perceptron Neural Networks. In SAI Intelligent Systems Conference, p 390--399, London, UK.
     
  • Jahn, A., Lau, S. L., David, K., and Sick, B. (2015) A Tool Chain for Context Detection Automating the Investigation of a Multitude of Parameter Sets. In International Workshop on Mobile and Context Aware Services, p 1--5, Boston, MA.
     
  • Breker, S. (2015) Klassifikation von Niederspannungsnetzen mit Support Vector Machines: Bewertung des Aufnahmevermögens für Dezentrale Erzeugungsanlagen, kassel university press, Kassel, Germany.
     
  • Gruhl, C. (2015) Anomalies in Generative Trajectory Models -- Discovering Suspicious Traces with Novelty Detection Methods. In Organic Computing -- Doctoral Dissertation Colloquium 2015 (Tomforde, S., and Sick, B., Eds.), p 95--107, kassel university press, Kassel, Germany.
     
  • Heck, H., Edenhofer, S., Gruhl, C., Lund, A., Shuka, R., and Hähner, J. (2015) On the Application Possibilities of Organic Computing Principles in Socio-technical Systems. In Organic Computing -- Doctoral Dissertation Colloquium 2015 (Tomforde, S., and Sick, B., Eds.), p 165--170, kassel university press, Kassel, Germany.
     
  • Reitmaier, T., and Sick, B. (2015) The Responsibility Weighted Mahalanobis Kernel for Semi-Supervised Training of Support Vector Machines for Classification, Information Sciences 323, 179--198.
     
  • Tomforde, S., and Sick, B. (Eds.). (2015) Organic Computing -- Doctoral Dissertation Colloquium 2015, kassel university press, Kassel, Germany.
     
  • Embrechts, M., and Sick, B. (2015) A Generalized Hebb (GH) rule based on a cross-entropy error function for deep belief recursive learning. In New Developments in Computational Intelligence and Computer Science, p 21--24, Vienna, Austria.
     
  • Calma, A., Jänicke, M., Kantert, J., Kopal, N., Siefert, F., and Tomforde, S. (2015) Horizontal Integration of Organic Computing and Control Theory Concepts. In Organic Computing -- Doctoral Dissertation Colloquium 2015 (Sick, B., and Tomforde, S., Eds.), p 157--164, kassel university press, Kassel, Germany.
     
  • Stone, T. C., Huber, A., Siwy, R., and Sick, B. (2015) Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen, Elektronik automotive, Landshut, Germany 2, 32--36.
     
  • Reitmaier, T., Calma, A., and Sick, B. (2015) 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.
     
  • Rudolph, J., Breker, S., and Sick, B. (2015) Bewertung verschiedener Spannungsregelungskonzepte in einem einspeisegeprägten Mittelspannungsnetz und Ausblick auf neue Konzepte basierend auf Methoden der Computational Intelligence. In Tagungsband zur Konferenz Nachhaltige Energieversorgung und Integration von Speichern, p 57--63, Hamburg, Germany.
     
  • Rudolph, S., Tomforde, S., Sick, B., and Hähner, J. (2015) A Mutual Influence Detection Algorithm for Systems with Local Performance Measurement. In IEEE International Conference on Self-Adaptive and Self-Organizing Systems, p 144--149, Cambridge, MA.
     
  • Kalkowski, E., and Sick, B. (2015) Generative Exponential Smoothing Models for Rate Forecasting with Uncertainty Estimation. In International Work-Conference on Time Series, p 806--817, Granada, Spain.
     
  • Kalkowski, E., and Sick, B. (2015) Using Ontology-Based Similarity Measures to Find Training Data for Problems with Sparse Data. In IEEE International Conference on Systems, Man and Cybernetics, p 1693--1699, Hongkong, China.
     
  • Calma, A. (2015) 4DSPro: A New Selection Strategy for Pool-based Active Learning. In Organic Computing -- Doctoral Dissertation Colloquium 2015 (Tomforde, S., and Sick, B., Eds.), p 121--133, kassel university press, Kassel, Germany.
     
  • Reitmaier, T. (2015) Aktives Lernen für Klassifikationsprobleme unter der Nutzung von Strukturinformation. In Lecture Notes in Informatics Ausgezeichnete Informatikdissertationen (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.), p 239--248.
     
  • Gensler, A., Gruber, T., and Sick, B. (2015) Fast Feature Extraction for Time Series Analysis Using Least-squares Approximations with Orthogonal Basis Functions. In International Symposium on Temporal Representation and Reasoning, p 29--37, Kassel, Germany.
     
  • Calma, A., Reitmaier, T., Sick, B., Lukowicz, P., and Embrechts, M. (2015) A New Vision of Collaborative Active Learning, arXiv arXiv:1504.00284v2.
     
  • Hähner, J., Brinkschulte, U., Lukowicz, P., Mostaghim, S., Sick, B., and Tomforde, S. (2015) Runtime Self-Integration as Key Challenge for Mastering Interwoven Systems Workshops. In International Conference on Architecture of Computing Systems, p 1--8, Porto, Portugal.
     
  • Breker, S., Claudi, A., and Sick, B. (2015) Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Stochastic Simulations, IEEE Transactions on Power Systems, IEEE 30, 689--700.
     
  • Breker, S., and Sick, B. (2015) Effiziente Bewertung des Anschlußpotentials von Niederspannungsnetzen für dezentrale Erzeugungsanlagen: Klassifikation mit Methoden der Computational Intelligence. In Nachhaltige Energieversorgung und Integration von Speichern, p 51--56, Hamburg, Germany.
     
  • Jänicke, M. (2015) Self-adapting Multi-Sensor System Using Classifiers Based on Gaussian Mixture Models. In Organic Computing -- Doctoral Dissertation Colloquium 2015 (Tomforde, S., and Sick, B., Eds.), p 109--120, kassel university press, Kassel, Germany.
     
  • Gruhl, C., Sick, B., Wacker, A., Tomforde, S., and Hähner, J. (2015) A building block for awareness in technical systems: Online novelty detection and reaction with an application in intrusion detection. In IEEE International Conference on Awareness Science and Technology, p 194--200, Qinhuangdao, China.
     
  • Goldhammer, M., Hubert, A., Köhler, S., Zindler, K., Brunsmann, U., Doll, K., and Sick, B. (2014) Analysis on termination of pedestrians' gait at urban intersections. In IEEE International Conference on Intelligent Transportation Systems, p 1758--1763, Qingdao, China.
     
  • Kalkowski, E. (2014) Self-Extending Training Sets: Using Ontologies to Improve Machine Learning Performance. In Organic Computing -- Doctoral Dissertation Colloquium 2014 (Tomforde, S., and Sick, B., Eds.), p 111--125, kassel university press, Kassel, Germany.
     
  • Kreil, M., Sick, B., and Lukowicz, P. (2014) Dealing with human variability in motion based, wearable activity recognition. In International Conference on Pervasive Computing and Communications Workshops, p 36--40, IEEE, Budapest, Hungary.
     
  • Tomforde, S., and Sick, B. (Eds.). (2014) Organic Computing -- Doctoral Dissertation Colloquium 2014, kassel university press, Kassel, Germany.
     
  • Goldhammer, M., Doll, K., Brunsmann, U., Gensler, A., and Sick, B. (2014) Pedestrian's Trajectory Forecast in Public Traffic with Artificial Neural Networks. In International Conference on Pattern Recognition, p 4110--4115, Stockholm, Sweden.
     
  • Tomforde, S., Hähner, J., von Mammen, S., Gruhl, C., Sick, B., and Geihs, K. (2014) ``Know thyself'' -- Computational Self-Reflection in Intelligent Technical Systems. In IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops, p 150--159, London, UK.
     
  • Fisch, D., Kalkowski, E., and Sick, B. (2014) Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining Applications, IEEE Transactions on Knowledge and Data Engineering 26, 652--666.
     
  • Reitmaier, T., and Calma, A. (2014) Resp-kNN: A Semi-Supervised kNN-Classifier for Sparsely Labeled Data in the Field of Organic Computing. In Organic Computing -- Doctoral Dissertation Colloquium 2014 (Tomforde, S., and Sick, B., Eds.), p 85--97, kassel university press, Kassel, Germany.
     
  • Früchtl, M. (2014) Sicherheit eingebetteter Systeme auf Basis arithmetischer Codierungen, kassel university press, Kassel, Germany.
     
  • Gensler, A., Sick, B., and Willkomm, J. (2014) Temporal data analytics based on eigenmotif and shape space representations of time series. In IEEE China Summit & International Conference on Signal and Information Processing, p 753--757, Xian, China.
     
  • Stone, T., Birth, O., Gensler, A., Huber, A., Jänicke, M., and Sick, B. (2014) Location based learning of user behavior for proactive recommender systems in car comfort functions. In Informatik 2014 -- Big Data -- Komplexität meistern (Plödereder, E., Grunske, L., Schneider, E., and Ull, D., Eds.), p 2121--2132, Köllen Druck+Verlag GmbH, Bonn, Germany.
     
  • Jänicke, M., Sick, B., Lukowicz, P., and Bannach, D. (2014) Self-Adapting Multi-sensor Systems: A Concept for Self-Improvement and Self-Healing Techniques. In IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops, p 128--136, London, UK.
     
  • Pree, H., Herwig, B., Gruber, T., Sick, B., David, K., and Lukowicz, P. (2014) On General Purpose Time Series Similarity Measures and Their Use as Kernel Functions in Support Vector Machines, Information Sciences 281, 478--495.
     
  • Herwig, B., Frommann, U., Gruber, T., and Sick, B. (2014) Programmierkompetenz prüfen … am Beispiel der Vorlesung "`Einführung in C"' an der Universität Kassel. In Neues Handbuch Hochschullehre. Lehren und Lernen effizient gestalten (Berendt, B., Fleischmann, A., Schaper, N., Szczyrba, B., and Wildt, J., Eds.), p 71--94, Raabe, Berlin, Germany.
     
  • Tomforde, S., Hähner, J., Seebach, H., Reif, W., Sick, B., Wacker, A., and Scholtes, I. (2014) Engineering and Mastering Interwoven Systems. In ARCS 2015, p 1--8, Lübeck, Germany.
     
  • Gensler, A., Sick, B., and Pankraz, V. (2014) Novel Criteria to Measure Performance of Time Series Segmentation Techniques. In LWA 2014 Workshops: KDML, IR, FGWM, p 192--204, Aachen, Germany.
     
  • Tomforde, S., Hähner, J., and Sick, B. (2014) Interwoven Systems, Informatik-Spektrum 37, 483--487.
     
  • Al-Falouji, G., Prestel, D., Scharfenberg, G., Mandl, R., Deinzer, A., Halang, W., Margraf-Stiksrud, J., Sick, B., and Deinzer, R. (2014) SMART-iBrush -- Individuelle Unterstützung der Zahnreinigung durch Messung von Bewegung und Druck mit einer intelligenten Zahnbürste. In Erste Transdisziplinäre Konferenz zum Thema ``Technische Unterstützungssysteme, die die Menschen wirklich wollen'' (Weidner, R., and Redlich, T., Eds.), p 315--327.
     
  • Gruhl, C. (2014) Self-Adapting Generative Modeling Techniques -- A Basic Building Block for Many Organic Computing Techniques. In Organic Computing -- Doctoral Dissertation Colloquium 2014 (Tomforde, S., and Sick, B., Eds.), p 99--109, kassel university press, Kassel, Germany.
     
  • Kaufmann, P., Glette, K., Gruber, T., Platzner, M., Torresen, J., and Sick, B. (2013) Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers, IEEE Transactions on Evolutionary Computation, IEEE 17, 46--63.
     
  • Hähner, J., Rudolph, S., Tomforde, S., Fisch, D., Sick, B., Kopal, N., and Wacker, A. (2013) A Concept for Securing Cyber-Physical Systems with Organic Computing Techniques. In International Conference on Architecture of Computing Systems Workshops (Berekovic, M., and Danek, M., Eds.), p 1--13, VDE-Verlag, Prague, Czech Republic.
     
  • Reitmaier, T., and Sick, B. (2013) Let us know your decision: Pool-based active training of a generative classifier with the selection strategy 4DS, Information Sciences 230, 106--131.
     
  • Kubátová, H., Hochberger, C., Danek, M., and Sick, B. (Eds.). (2013) Architecture of Computing Systems -- ARCS 2013, Springer, Heidelberg, Germany.
     
  • Gensler, A., Gruber, T., and Sick, B. (2013) Blazing Fast Time Series Segmentation Based on Update Techniques for Polynomial Approximations. In IEEE International Conference on Data Mining Workshops, p 1002--1011, Dallas, TX.
     
  • Reitmaier, T. (2013) Active Learning of Generative and Discriminative Classifiers for Organic Computing. In First Organic Computing Doctoral Dissertation Colloquium (Tomforde, S., Ed.), p 1--27, Augsburg, Germany.
     
  • Jänicke, M. (2013) Self-Adaption of Multi-Sensor-Systems with Organic Computing Techniques. In Organic Computing Doctoral Dissertation Colloquium (Tomforde, S., Ed.), p 20--23, Augsburg, Germany.
     
  • Fisch, D. (2012) Intelligente technische Systeme mit der Fähigkeit zum kollaborativen Wissenserwerb, kassel university press, Kassel, Germany.
     
  • Herwig, B. (2012) Dynamische interaktive Klassifikation graphometrischer Daten am Beispiel der Händigkeitsanalyse. In INFORMATIK 2012: Was bewegt uns in der/die Zukunft?, LNI Proceedings 42 (Goltz, U., Magnor, M., Appelrath, H. -J., Matthies, H. K., Balke, W. -T., and Wolf, L., Eds.), p 1850--1856, Braunschweig, Germany.
     
  • Giedl-Wagner, R., Miller, T., and Sick, B. (2012) Determination of Optimal CT Scan Parameters Using Radial Basis Function Neural Networks. In Conference on Industrial Computed Tomography, p 221--228, Wels, Austria.
     
  • Gruber, T., Meixner, B., Prosser, J., and Sick, B. (2012) Handedness Tests for Preschool Children: A Novel Approach Based on Graphics Tablets and Support Vector Machines, Applied Soft Computing 12, 1390--1398.
     
  • Fisch, D., Jänicke, M., Kalkowski, E., and Sick, B. (2012) Learning from others: Exchange of classification rules in intelligent distributed systems, Artificial Intelligence 187--188, 90--114.
     
  • Fisch, D., Jänicke, M., Kalkowski, E., and Sick, B. (2012) Techniques for knowledge acquisition in dynamically changing environments, ACM Transactions on Autonomous and Adaptive Systems 7, 16:1--16:25.
     
  • Embrechts, M. J., Gatti, C. J., Linton, J. D., Gruber, T., and Sick, B. (2012) Forecasting exchange rates with ensemble neural networks and ensemble K-PLS: A case study for the US Dollar per Indian Rupee. In Proceedings of the International Joint Conference on Neural Networks, p 1--8, Brisbane, Australia.
     
  • Gruber, T. (2012) Analyse von Zeitreihen unter Verwendung orthogonaler Polynome am Beispiel der Online-Motivsuche und ihrer Anwendungen. In Lecture Notes in Informatics Ausgezeichnete Informatikdissertationen (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.), p 131--140.
     
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  • Fisch, D., Jänicke, M., Müller-Schloer, C., and Sick, B. (2011) Divergence Measures as a Generalised Approach to Quantitative Emergence. In Organic Computing --- A Paradigm Shift for Complex Systems (Müller-Schloer, C., Schmeck, H., and Ungerer, T., Eds.), p 53--66, Springer Basel, Basel, Switzerland.
     
  • Westmeier, M., Herwig, B., and Börcsök, J. (2011) Enhancing a simulation environment for computer architecture to a SystemC based testbench tool for design verification. In International Symposium on Information, Communication and Automation Technologies, pp 1-6, Sarajevo, Bosnia and Herzegovina.
     
  • Fisch, D., Gruber, T., and Sick, B. (2011) SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis, IEEE Transactions on Knowledge and Data Engineering 23, 774--787.
     
  • Sick, B. (2011) Learning. In Organic Computing -- A Paradigm Shift for Complex Systems (Müller-Schloer, C., Schmeck, H., and Ungerer, T., Eds.), p 235--236, Springer Basel, Basel, Switzerland.
     
  • Fisch, D., Kalkowski, E., and Sick, B. (2011) Collaborative Learning by Knowledge Exchange. In Organic Computing -- A Paradigm Shift for Complex Systems (Müller-Schloer, C., Schmeck, H., and Ungerer, T., Eds.), p 267--280, Springer Basel, Basel, Switzerland.
     
  • Hofmann, A., and Sick, B. (2011) On-Line Intrusion Alert Aggregation With Generative Data Stream Modeling, IEEE Transactions on Dependable and Secure Computing 8, 282--294.
     
  • Fisch, D., Kalkowski, E., Sick, B., and Ovaska, S. (2011) In your interest: Objective interestingness measures for a generative classifier. In International Conference on Agents and Artificial Intelligence, p 414--423, Rome, Italy.
     
  • Reitmaier, T., and Sick, B. (2011) Active classifier training with the 3DS strategy. In IEEE Symposium on Computational Intelligence and Data Mining, p 88--95, Paris, France.
     
  • Gottfried, T., Fliege, R., Frömberg, J., Heckmann, G., Sick, B., Triller, U., and Wunsch, M. (2011) Wissenschaftspropädeutisches Arbeiten im W-Seminar: Grundlagen -- Chancen -- Herausforderungen, Munich, Germany.
     
  • Bannach, D., Sick, B., and Lukowicz, P. (2011) Automatic Adaptation of Mobile Activity Recognition Systems to New Sensors. In ACM International Conference on Ubiquitous Computing, Workshop, p 1--5, Beijing, China.
     
  • Fisch, D., Jänicke, M., Sick, B., and Müller-Schloer, C. (2010) Quantitative Emergence -- A Refined Approach Based on Divergence Measures. In IEEE International Conference on Self-Adaptive and Self-Organizing Systems, p 94--103, Budapest, Hungary.
     
  • Fuchs, E., Gruber, T., Pree, H., and Sick, B. (2010) Temporal Data Mining Using Shape Space Representations of Time Series, Neurocomputing 74, 379--393.
     
  • Fuchs, E., Gruber, T., Nitschke, J., and Sick, B. (2010) Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations, IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 2232--2245.
     
  • Gruber, C., Gruber, T., Krinninger, S., and Sick, B. (2010) 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.