Prof. Dr. rer. nat. Bernhard Sick

Fachgebietsleiter; Teamleiter: Collaborative Interactive Learning (CIL); Teamleiter: AI for Computationally Intelligent Systems (AI4CIS)

Sick, Bernhard
Telefon
+49 561 804-6020
Fax
+49 561 804-6022
E-Mail
Standort
Wilhelmshöher Allee 73
34121 Kassel
Raum
WA-altes Gebäude (WA 73), ohne Raumangabe

Pu­bli­ka­tio­nen

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

2021 [ nach oben ]

  • 1.
    Huang, Z., He, Y., Vogt, S., Sick, B.: Uncertainty and Utility Sampling with Pre-Clustering. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD (2021).
     
  • 2.
    Reginka, M., Hoang, H., Efendi, Özge, Merkel, M., Huhnstock, R., Holzinger, D., Dingel, K., Sick, B., Bertinetti, D., Herberg, F., Ehresmann, A.: Transport efficiency of biofunctionalized magnetic particles tailored by surfactant concentration. Langmuir. (2021).
     
  • 3.
    Reichert, H., Lang, L., Rösch, K., Bogdoll, D., Doll, K., Sick, B., Reuss, H.-C., Stiller, C., Zöllner, J.M.: Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems. IEEE International Smart Cities Conference (2021).
     
  • 4.
    Botache, D., Bethke, F., Hardieck, M., Bieshaar, M., Brabetz, L., Ayeb, M., Zipf, P., Sick, B.: Towards Highly Automated Machine-Learning-Empowered Monitoring of Motor Test Stands. International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). , Washington, DC, USA (2021).
     
  • 5.
    Heidecker, F., Hannan, A., Bieshaar, M., Sick, B.: Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks. ICPR Workshop on Integrated Artificial Intelligence in Data Science. bll. 361–374. IEEE, Milan, Italy (2021).
     
  • 6.
    Kottke, D., Herde, M., Sandrock, C., Huseljic, D., Krempl, G., Sick, B.: Toward optimal probabilistic active learning using a Bayesian approach. Machine Learning. (2021).
     
  • 7.
    He, Y., Huang, Z., Sick, B.: Toward Application of Continuous Power Forecasts in a Regional Flexibility Market. 2021 International Joint Conference on Neural Networks (IJCNN) (2021).
     
  • 8.
    Schreiber, J., Vogt, S., Sick, B.: Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast. ECML PKDD 2021 (2021).
     
  • 9.
    Hetzel, M., Reichert, H., Doll, K., Sick, B.: Smart Infrastructure: A Research Junction. IEEE International Smart Cities Conference (2021).
     
  • 10.
    Huseljic, D., Sick, B., Herde, M., Kottke, D.: Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks. International Conference on Pattern Recognition (ICPR) (2021).
     
  • 11.
    Schneegans, J., Eilbrecht, J., Zernetsch, S., Bieshaar, M., Doll, K., Stursberg, O., Sick, B.: Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning -- A Case Study: Overtaking Cyclists. IV 2021 Workshop From Benchmarking Behavior Prediction to Socially Compatible Behavior Generation in Autonomous Driving (2021).
     
  • 12.
    Kress, V., Zernetsch, S., Doll, K., Sick, B.: Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks. ICPR Workshop on Integrated Artificial Intelligence in Data Science. bll. 2723–2730. IEEE, Milan, Italy (2021).
     
  • 13.
    Möller, F., Botache, D., Huseljic, D., Heidecker, F., Bieshaar, M., Sick, B.: Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders. Proc. of CVPR SAIAD Workshop (2021).
     
  • 14.
    Scheiner, N., Kraus, F., Appenrodt, N., Dickmann, J., Sick, B.: Object Detection For Automotive Radar Point Clouds -- A Comparison. AI Perspectives. (2021).
     
  • 15.
    Gruhl, C., Sick, B., Tomforde, S.: Novelty detection in continuously changing environments. Future Generation Computer Systems. 114, 138–154 (2021).
     
  • 16.
    Heidecker, F., Gruhl, C., Sick, B.: Novelty based Driver Identification on RR Intervals from ECG Data. ICPR Workshop on Integrated Artificial Intelligence in Data Science. bll. 407–421. IEEE, Milan, Italy (2021).
     
  • 17.
    Herde, M., Kottke, D., Huseljic, D., Sick, B.: Multi-annotator Probabilistic Active Learning. International Conference on Pattern Recognition (ICPR) (2021).
     
  • 18.
    Haase-Schütz, C., Stal, R., Hertlein, H., Sick, B.: Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning. International Conference on Pattern Recognition (ICPR) (2021).
     
  • 19.
    Schneegans, J., Bieshaar, M., Heidecker, F., Sick, B.: Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks. ICPR Workshop on Integrated Artificial Intelligence in Data Science. bll. 375–389. IEEE, Milan, Italy (2021).
     
  • 20.
    Zernetsch, S., Schreck, S., Kress, V., Doll, K., Sick, B.: Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution. International Conference on Pattern Recognition (ICPR) (2021).
     
  • 21.
    Schreiber, J., Sick, B.: Emerging Relation Network and Task Embedding for Multi-Task Regression Problems. International Conference on Pattern Recognition (ICPR) (2021).
     
  • 22.
    Bogdoll, D., Breitenstein, J., Heidecker, F., Bieshaar, M., Sick, B., Fingscheidt, T., Zöllner, J.M.: Description of Corner Cases in Automated Driving: Goals and Challenges. ICCV - Embedded and Real-World Computer Vision in Autonomous Driving (ICCV-ERCVAD) (2021).
     
  • 23.
    Zernetsch, S., Trupp, O., Kress, V., Doll, K., Sick, B.: Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information. IEEE International Smart Cities Conference (2021).
     
  • 24.
    He, Y., Sick, B.: CLeaR: An adaptive continual learning framework for regression tasks. AI Perspectives. 3, 2 (2021).
     
  • 25.
    Hannan, A., Gruhl, C., Sick, B.: Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders. IEEE International Conference on Cyber Security and Resilience 2021 (IEEE CSR) (2021).
     
  • 26.
    Heidecker, F., Breitenstein, J., Rösch, K., Löhdefink, J., Bieshaar, M., Stiller, C., Fingscheidt, T., Sick, B.: An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving. 2021 IEEE Intelligent Vehicles Symposium (IV). , Nagoya, Japan (2021).
     
  • 27.
    Dingel, K., Liehr, A., Vogel, M., Degener, S., Meier, D., Niendorf, T., Ehresmann, A., Sick, B.: AI-Based On The Fly Design of Experiments in Physics and Engineering. Workshop on Self-Improving System Integration (SISSY), ACSOS (2021).
     
  • 28.
    Dingel, K., Huhnstock, R., Knie, A., Ehresmann, A., Sick, B.: AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems. Computer Physics Communications. 262, 107859 (2021).
     
  • 29.
    Reuse, M., Simon, M., Sick, B.: About the Ambiguity of Augmentation for 3D Object Detection in Autonomous Driving. ICCV - Embedded and Real-World Computer Vision in Autonomous Driving (ICCV-ERCVAD) (2021).
     
  • 30.
    Bieshaar, M., Herde, M., Huselijc, D., Sick, B.: A Concept for Highly Automated Pre-Labeling via Cross-Domain Label Transfer for Perception in Autonomous Driving. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD (2021).
     

2020 [ nach oben ]

  • 1.
    Kottke, D., Herde, M., Sandrock, C., Huseljic, D., Krempl, G., Sick, B.: Toward Optimal Probabilistic Active Learning Using a Bayesian Approach. arXiv e-prints. arXiv:2006.01732 (2020).
     
  • 2.
    Scheiner, N., Kraus, F., Wei, F., Phan, B., Mannan, F., Appenrodt, N., Ritter, W., Dickmann, J., Dietmayer, K., Sick, B., Heide, F.: Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020).
     
  • 3.
    Henze, J., Schreiber, J., Sick, B.: Representation Learning in Power Time Series Forecasting. In: Pedrycz, W. en Chen, S.-M. (reds.) Deep Learning: Algorithms and Applications. bll. 67–101. Springer International Publishing (2020).
     
  • 4.
    Meier, D., Hartmann, G., Völker, J., Viefhaus, J., Sick, B.: Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm. In: Chubar, O. en Sawhney, K. (reds.) Advances in Computational Methods for X-Ray Optics V. bll. 71–77. SPIE (2020).
     
  • 5.
    Bieshaar, M., Schreiber, J., Vogt, S., Gensler, A., Sick, B.: Quantile Surfaces -- Generalizing Quantile Regression to Multivariate Targets. arXiv e-prints. arXiv:2010.05898 (2020).
     
  • 6.
    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).
     
  • 7.
    Kress, V., Schreck, S., Zernetsch, S., Doll, K., Sick, B.: Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks. IEEE Symposium Series on Computational Intelligence (SSCI). bll. 2723–2730 (2020).
     
  • 8.
    Scheiner, N., Schumann, O., Kraus, F., Appenrodt, N., Dickmann, J., Sick, B.: Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification?. IEEE 23rd International Conference on Information Fusion, FUSION 2020, Rustenburg, South Africa, July 6-9, 2020. bll. 1–8. IEEE (2020).
     
  • 9.
    Gruhl, C., Schmeißing, J., Tomforde, S., Sick, B.: Normal-Wishart clustering for novelty detection. Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). bll. 64–69. IEEE (2020).
     
  • 10.
    Haase-Schütz, C., Stal, R., Hertlein, H., Sick, B.: Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning. arXiv e-prints. arXiv:2002.02705 (2020).
     
  • 11.
    Pham Minh, T., Kottke, D., Tsarenko, A., Gruhl, C., Sick, B.: Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning. 2020 International Joint Conference on Neural Networks (IJCNN) (2020).
     
  • 12.
    He, Y., Henze, J., Sick, B.: Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks. 2020 International Joint Conference on Neural Networks (IJCNN) (2020).
     
  • 13.
    Deist, S., Schreiber, J., Bieshaar, M., Sick, B.: Extended Coopetitive Soft Gating Ensemble. arXiv e-prints. arXiv:2004.14026 (2020).
     
  • 14.
    Englhardt, A., Trittenbach, H., Kottke, D., Sick, B., Böhm, K.: Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary. arXiv e-prints. arXiv:2009.13853 (2020).
     
  • 15.
    He, Y., Henze, J., Sick, B.: Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets. International Federation of Automatic Control (IFAC) World Congress. bll. 12175–12182 (2020).
     
  • 16.
    Tomforde, S., Gruhl, C., Sick, B.: A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour. Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). bll. 166–169. IEEE (2020).
     

2019 [ nach oben ]

  • 1.
    Vogt, S., Braun, A., Dobschinski, J., Sick, B.: Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning. In: Betancourt, U. en Ackermann, T. (reds.) Digital Proceedings of the 18th Wind Integration Workshop. , Dublin, Ireland (2019).
     
  • 2.
    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., en Stumme, G. (reds.) INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft. bll. 531–544. Gesellschaft für Informatik e.V., Bonn (2019).
     
  • 3.
    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). bll. 810–815 (2019).
     
  • 4.
    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).
     
  • 5.
    Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Start Intention Detection of Cyclists using an LSTM Network. In: Draude, C., Lange, M., en Sick, B. (reds.) INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge). bll. 219–228. Gesellschaft für Informatik e.V., Bonn (2019).
     
  • 6.
    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). bll. 642–649. IEEE, Paris, France (2019).
     
  • 7.
    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).
     
  • 8.
    Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Pose Based Start Intention Detection of Cyclists. 2019 IEEE Intelligent Transportation Systems Conference (ITSC). bll. 2381–2386 (2019).
     
  • 9.
    Kottke, D., Schellinger, J., Huseljic, D., Sick, B.: Limitations of Assessing Active Learning Performance at Runtime. arXiv e-prints. arXiv:1901.10338 (2019).
     
  • 10.
    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).
     
  • 11.
    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., en Stumme, G. (reds.) INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft. bll. 585–598. Gesellschaft für Informatik e.V., Bonn (2019).
     
  • 12.
    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., Krurková, V., Karpov, P., en Theis, F. (reds.) Artificial Neural Networks and Machine Learning -- ICANN 2019: Image Processing. bll. 550–564. Springer International Publishing, Cham (2019).
     
  • 13.
    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. bll. 1–6 (2019).
     
  • 14.
    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., en Sick, B. (reds.) INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge). bll. 229–238. Gesellschaft für Informatik e.V., Bonn (2019).
     
  • 15.
    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). bll. 1–8 (2019).
     
  • 16.
    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. arXiv e-prints. arXiv:1905.07264 (2019).
     
  • 17.
    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). bll. 5–9 (2019).
     
  • 18.
    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). bll. 1–10. , Ulm, Germany (2019).
     
  • 19.
    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). bll. 2060–2067. IEEE (2019).
     

2018 [ nach oben ]

  • 1.
    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. bll. 1–8. VDE (2018).
     
  • 2.
    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. bll. 215–222 (2018).
     
  • 3.
    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).
     
  • 4.
    Bieshaar, M., Depping, M., Schneegans, J., Sick, B.: Starting Movement Detection of Cyclists Using Smart Devices. DSAA. , Turin, Italy (2018).
     
  • 5.
    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. bll. 247–273 (2018).
     
  • 6.
    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).
     
  • 7.
    Jänicke, M., Sick, B., Tomforde, S.: Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models. Informatics. 5, 38 (2018).
     
  • 8.
    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. bll. 176–181 (2018).
     
  • 9.
    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. bll. 27–48 (2018).
     
  • 10.
    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). bll. 779–786. IEEE, Changshu, China (2018).
     
  • 11.
    Schreiber, J., Sick, B.: Quantifying the Influences on Probabilistic Wind Power Forecasts. International Conference on Power and Renewable Energy. bl. 6 (2018).
     
  • 12.
    Gruhl, C., Sick, B.: Novelty detection with CANDIES: a holistic technique based on probabilistic models. International Journal of Machine Learning & Cybernetics. 9, 927–945 (2018).
     
  • 13.
    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).
     
  • 14.
    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).
     
  • 15.
    Breker, S., Rentmeister, J., Sick, B., Braun, M.: Hosting capacity of low-voltage grids for distributed generation: Classification by means of machine learning techniques. Applied Soft Computing. 70, 195–207 (2018).
     
  • 16.
    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).
     
  • 17.
    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).
     
  • 18.
    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).
     
  • 19.
    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. bll. 1–6 (2018).
     
  • 20.
    Deist, S., Bieshaar, M., Schreiber, J., Gensler, A., Sick, B.: Coopetitive Soft Gating Ensemble. Workshop on Self-Improving System Integration (SISSY). , Trento, Italy (2018).
     
  • 21.
    Reitberger, G., Zernetsch, S., Bieshaar, M., Sick, B., Doll, K., Fuchs, E.: Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure. 21st International Conference on Intelligent Transportation Systems (ITSC). , Maui, HI (2018).
     
  • 22.
    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).
     
  • 23.
    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. Transactions on Computational Collective Intelligence. 28, 193–220 (2018).
     
  • 24.
    Sick, B., Oeste-Reiß, S., Schmidt, A., Tomforde, S., Zweig, K.A.: Collaborative Interactive Learning. Informatik Spektrum. 41, 52–55 (2018).
     
  • 25.
    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).
     
  • 26.
    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. bll. 198–203 (2018).
     
  • 27.
    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).
     
  • 28.
    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).
     
  • 29.
    Gensler, A., Sick, B., Vogt, S.: A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies. Renewable and Sustainable Energy Reviews. 96, 352–379 (2018).
     
  • 30.
    Gensler, A., Sick, B.: A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation. arXiv e-prints. arXiv:1803.06344 (2018).
     
  • 31.
    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. bll. 150–159. , London, UK (2018).
     

2017 [ nach oben ]

  • 1.
    Calma, A., Sick, B.: Simulation of Annotators for Active Learning: Uncertain Oracles. Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning @ ECMLPKDD 2017. bll. 49–58 (2017).
     
  • 2.
    Bannach, D., Jänicke, M., Rey, V.F., Tomforde, S., Sick, B., Lukowicz, P.: Self-Adaptation of Activity Recognition Systems to New Sensors. arXiv e-prints. arXiv:1701.08528 (2017).
     
  • 3.
    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). bll. 39–50. SCITEPRESS, Porto, Portugal (2017).
     
  • 4.
    Gensler, A., Sick, B.: Probabilistic wind power forecasting: A multi-scheme ensemble technique with gradual coopetitive soft gating. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). bll. 1–10 (2017).
     
  • 5.
    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. bll. 37–48 (2017).
     
  • 6.
    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).
     
  • 7.
    Tomforde, S., Sick, B., Müller-Schloer, C.: Organic Computing in the Spotlight. arXiv e-prints. arXiv:1701.08125 (2017).
     
  • 8.
    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., en Househ, M.S. (reds.) Informatics Empowers Healthcare Transformation -- Proceedings of the 15th International Conference on Informatics, Management, and Technology in Health Care. bll. 209–212. , Athens, Greece (2017).
     
  • 9.
    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). bll. 96–106. SCITEPRESS, Porto, Portugal (2017).
     
  • 10.
    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. bll. 109–116. , Tucson, AZ (2017).
     
  • 11.
    Würtz, R.P., Tomforde, S., Calma, A., Kottke, D., Sick, B.: Interactive Learning Without Ground Truth. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2017. bll. 1–4. kassel university press, Kassel, Germany (2017).
     
  • 12.
    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. bll. 83–93. Springer International Publishing, Cham, Switzerland (2017).
     
  • 13.
    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., en Madnick, S. (reds.) Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy. bll. 83–93. Springer International Publishing, Cham (2017).
     
  • 14.
    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).
     
  • 15.
    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. bll. 67–87. , Braunschweig, Germany (2017).
     
  • 16.
    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. bll. 69–78. IEEE (2017).
     
  • 17.
    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).
     
  • 18.
    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. bll. 2–14 (2017).
     
  • 19.
    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).
     

2016 [ nach oben ]

  • 1.
    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). bll. 833–838. , Gothenburg, Sweden (2016).
     
  • 2.
    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., en Bhatia, R. (reds.) Intelligent Systems and Applications: Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2015. bll. 259–279. Springer International Publishing, Cham, Switzerland (2016).
     
  • 3.
    Jänicke, M., Tomforde, S., Sick, B.: Towards Self-Improving Activity Recognition Systems based on Probabilistic, Generative Models. International Conference on Autonomic Computing. bll. 285–291. , Würzburg, Germany (2016).
     
  • 4.
    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. bll. 98–99 (2016).
     
  • 5.
    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).
     
  • 6.
    Reitmaier, T., Calma, A., Sick, B.: Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data. arXiv e-prints. arXiv:1610.03995 (2016).
     
  • 7.
    Calma, A., Reitmaier, T., Sick, B.: Resp-kNN: A probabilistic k-nearest neighbor classifier for sparsely labeled data. International Joint Conference on Neural Networks. bll. 4040–4047. , Vancouver, BC (2016).
     
  • 8.
    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., en Loftin, R. (reds.) NIPS Workshop on the Future of Interactive Learning Machines. bll. 1–9. , Barcelona, Spain (2016).
     
  • 9.
    Heck, H., Gruhl, C., Rudolph, S., Wacker, A., Sick, B., Hähner, J.: Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems. ARCS 2016. bll. 1–8. VDE-Verlag, Nuremberg, Germany (2016).
     
  • 10.
    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. bll. 1–10. , Würzburg, Germany (2016).
     
  • 11.
    Kalkowski, E., Sick, B.: Generative Exponential Smoothing and Generative ARMA Models to Forecast Time-Variant Rates or Probabilities. In: Rojas, I. en Pomares, H. (reds.) Time Series Analysis and Forecasting: Selected Contributions from the ITISE Conference. bll. 75–88. Springer International Publishing, Cham, Switzerland (2016).
     
  • 12.
    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. bll. 1–8. , Nuremberg, Germany (2016).
     
  • 13.
    Gensler, A., Sick, B.: Forecasting Wind Power -- An Ensemble Technique With Gradual Weighting Based on Weather Situation. International Joint Conference on Neural Networks. bll. 4976–4984. , Vancouver, BC (2016).
     
  • 14.
    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. bll. 1–9. , Athens, Greece (2016).
     
  • 15.
    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. bll. 2858–2865. IEEE, Budapest, Hungary (2016).
     
  • 16.
    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., en Pautasso, C. (reds.) Web Engineering. bll. 207–224. Springer International Publishing, Cham, Switzerland (2016).
     
  • 17.
    Kreil, M., Sick, B., Lukowicz, P.: Coping with variability in motion based activity recognition. International Workshop on Sensor-based Activity Recognition and Interaction. bll. 1–8. , Rostock, Germany (2016).
     
  • 18.
    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. bll. 2164–2173. , Vancouver, BC (2016).
     
  • 19.
    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. bll. 353–356. , Heidelberg, Germany (2016).
     
  • 20.
    Gensler, A., Sick, B., Pankraz, V.: An Analogue-Based Similarity Search Technique for Solar Power Forecasting. IEEE International Conference on Systems, Man, and Cybernetics. bll. 2850–2857. , Budapest, Hungary (2016).
     
  • 21.
    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). bll. 1–9. , Athens, Greece (2016).
     
  • 22.
    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. bll. 1–8. VDE Verlag GmbH, Nuremberg, Germany (2016).
     

2015 [ nach oben ]

  • 1.
    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. bll. 1693–1699. , Hongkong, China (2015).
     
  • 2.
    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).
     
  • 3.
    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).
     
  • 4.
    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. bll. 1–8. , Porto, Portugal (2015).
     
  • 5.
    Kalkowski, E., Sick, B.: Generative Exponential Smoothing Models for Rate Forecasting with Uncertainty Estimation. International Work-Conference on Time Series. bll. 806–817. , Granada, Spain (2015).
     
  • 6.
    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. bll. 29–37. , Kassel, Germany (2015).
     
  • 7.
    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. bll. 51–56. , Hamburg, Germany (2015).
     
  • 8.
    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).
     
  • 9.
    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).
     
  • 10.
    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. bll. 390–399. , London, UK (2015).
     
  • 11.
    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. bll. 57–63. , Hamburg, Germany (2015).
     
  • 12.
    Stone, T.C., Huber, A., Siwy, R., Sick, B.: Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen. Elektronik automotive. 2, 32–36 (2015).
     
  • 13.
    Rudolph, S., Tomforde, S., Sick, B., Heck, H., Wacker, A., Hähner, J.: An Online Influence Detection Algorithm for Organic Computing Systems. ARCS 2015. bll. 1–8. VDE Verlag GmbH, Porto, Portugal (2015).
     
  • 14.
    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. bll. 1–5. , Boston, MA (2015).
     
  • 15.
    Calma, A., Reitmaier, T., Sick, B., Lukowicz, P., Embrechts, M.: A New Vision of Collaborative Active Learning. arXiv e-prints. arXiv:1504.00284 (2015).
     
  • 16.
    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. bll. 144–149. , Cambridge, MA (2015).
     
  • 17.
    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. bll. 21–24. , Vienna, Austria (2015).
     
  • 18.
    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. bll. 194–200. , Qinhuangdao, China (2015).
     

2014 [ nach oben ]

  • 1.
    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. bll. 753–757. , Xian, China (2014).
     
  • 2.
    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. en Redlich, T. (reds.) Erste Transdisziplinäre Konferenz zum Thema ``Technische Unterstützungssysteme, die die Menschen wirklich wollen’’. bll. 315–327 (2014).
     
  • 3.
    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. bll. 128–136. , London, UK (2014).
     
  • 4.
    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., en Wildt, J. (reds.) Neues Handbuch Hochschullehre. Lehren und Lernen effizient gestalten. bll. 71–94. Raabe, Berlin, Germany (2014).
     
  • 5.
    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. bll. 4110–4115. , Stockholm, Sweden (2014).
     
  • 6.
    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).
     
  • 7.
    Gensler, A., Sick, B., Pankraz, V.: Novel Criteria to Measure Performance of Time Series Segmentation Techniques. LWA 2014 Workshops: KDML, IR, FGWM. bll. 192–204. , Aachen, Germany (2014).
     
  • 8.
    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., en Ull, D. (reds.) Informatik 2014 -- Big Data -- Komplexität meistern. bll. 2121–2132. Köllen Druck+Verlag GmbH, Bonn, Germany (2014).
     
  • 9.
    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).
     
  • 10.
    Tomforde, S., Hähner, J., Sick, B.: Interwoven Systems. Informatik-Spektrum. 37, 483–487 (2014).
     
  • 11.
    Tomforde, S., Hähner, J., Seebach, H., Reif, W., Sick, B., Wacker, A., Scholtes, I.: Engineering and Mastering Interwoven Systems. ARCS 2015. bll. 1–8. , L"ubeck, Germany (2014).
     
  • 12.
    Kreil, M., Sick, B., Lukowicz, P.: Dealing with human variability in motion based, wearable activity recognition. International Conference on Pervasive Computing and Communications Workshops. bll. 36–40. IEEE, Budapest, Hungary (2014).
     
  • 13.
    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. bll. 1758–1763. , Qingdao, China (2014).
     
  • 14.
    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).
     

2013 [ nach oben ]

  • 1.
    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).
     
  • 2.
    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).
     
  • 3.
    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. bll. 1002–1011. , Dallas, TX (2013).
     
  • 4.
    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. en Danek, M. (reds.) International Conference on Architecture of Computing Systems Workshops. bll. 1–13. VDE-Verlag, Prague, Czech Republic (2013).
     

2012 [ nach oben ]

  • 1.
    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).
     
  • 2.
    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).
     
  • 3.
    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).
     
  • 4.
    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. bll. 1–8. , Brisbane, Australia (2012).
     
  • 5.
    Giedl-Wagner, R., Miller, T., Sick, B.: Determination of Optimal CT Scan Parameters Using Radial Basis Function Neural Networks. Conference on Industrial Computed Tomography. bll. 221–228. , Wels, Austria (2012).
     

2011 [ nach oben ]

  • 1.
    Gottfried, T., Fliege, R., Frömberg, J., Heckmann, G., Sick, B., Triller, U., Wunsch, M.: Wissenschaftspropädeutisches Arbeiten im W-Seminar: Grundlagen -- Chancen -- Herausforderungen, (2011).
     
  • 2.
    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).
     
  • 3.
    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).
     
  • 4.
    Sick, B.: Learning. In: Müller-Schloer, C., Schmeck, H., en Ungerer, T. (reds.) Organic Computing -- A Paradigm Shift for Complex Systems. bll. 235–236. Springer Basel, Basel, Switzerland (2011).
     
  • 5.
    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. bll. 414–423. , Rome, Italy (2011).
     
  • 6.
    Fisch, D., Jänicke, M., Müller-Schloer, C., Sick, B.: Divergence Measures as a Generalised Approach to Quantitative Emergence. In: Müller-Schloer, C., Schmeck, H., en Ungerer, T. (reds.) Organic Computing --- A Paradigm Shift for Complex Systems. bll. 53–66. Springer Basel, Basel, Switzerland (2011).
     
  • 7.
    Fisch, D., Kalkowski, E., Sick, B.: Collaborative Learning by Knowledge Exchange. In: Müller-Schloer, C., Schmeck, H., en Ungerer, T. (reds.) Organic Computing -- A Paradigm Shift for Complex Systems. bll. 267–280. Springer Basel, Basel, Switzerland (2011).
     
  • 8.
    Bannach, D., Sick, B., Lukowicz, P.: Automatic Adaptation of Mobile Activity Recognition Systems to New Sensors. ACM International Conference on Ubiquitous Computing, Workshop. bll. 1–5. , Beijing, China (2011).
     
  • 9.
    Reitmaier, T., Sick, B.: Active classifier training with the 3DS strategy. IEEE Symposium on Computational Intelligence and Data Mining. bll. 88–95. , Paris, France (2011).
     

2010 [ nach oben ]

  • 1.
    Fuchs, E., Gruber, T., Pree, H., Sick, B.: Temporal Data Mining Using Shape Space Representations of Time Series. Neurocomputing. 74, 379–393 (2010).
     
  • 2.
    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. bll. 94–103. , Budapest, Hungary (2010).
     
  • 3.
    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).
     
  • 4.
    Fuchs, E., Gruber, T., Nitschke, J., Sick, B.: Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32, 2232–2245 (2010).
     

2007 [ nach oben ]

  • 1.
    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). bll. 494–501 (2007).
     

2006 [ nach oben ]

  • 1.
    Hofer, J., Gruber, C., Sick, B.: Biometric Analysis of Handwriting Dynamics Using a Script Generator Model. IEEE Mountain Workshop on Adaptive and Learning Systems. bll. 36–41. , Logan (2006).