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

[ 2022 ] [ 2021 ] [ 2020 ] [ 2019 ] [ 2018 ] [ 2017 ] [ 2016 ] [ 2015 ] [ 2014 ] [ 2013 ] [ 2012 ] [ 2011 ] [ 2010 ] [ 2007 ] [ 2006 ]

2022 [ nach oben ]

  • 1.
    Nivarthi, C.P., Vogt, S., Sick, B.: Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting. International Conference on Machine Learning and Applications (ICMLA). IEEE (2022).
     
  • 2.
    Loeser, I., Braun, M., Gruhl, C., Menke, J.-H., Sick, B., Tomforde, S.: The Vision of Self-Management in Cognitive Organic Power Distribution Systems. Energies. 15, 881 (2022).
     
  • 3.
    Krupitzer, C., Gruhl, C., Sick, B., Tomforde, S.: Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario. Information and Software Technology. 145, 106826 (2022).
     
  • 4.
    Decke, J., Engelhardt, A., Rauch, L., Degener, S., Sajjadifar, S., Scharifi, E., Steinhoff, K., Niendorf, T., Sick, B.: Predicting flow stress behavior of an AA7075 alloy using machine learning methods. Crystals. (2022).
     
  • 5.
    Decke, J., Schmeißing, J., Botache, D., Bieshaar, M., Sick, B., Gruhl, C.: NDNET: a Unified Framework for Anomaly and Novelty Detection. International Conference on Architecture of Computing Systems (ARCS) (2022).
     
  • 6.
    Westmeier, T., Botache, D., Bieshaar, M., Sick, B.: Generating Synthetic Time Series for Machine-Learning-Empowered Monitoring of Electric Motor Test Benches. IEEE International Conference on Data Science and Advanced Analytics (DSAA) (2022).
     
  • 7.
    Rauch, L., Huseljic, D., Sick, B.: Enhancing Active Learning with Weak Supervision and Transfer Learning by Leveraging Information and Knowledge Sources. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll. 27–42 (2022).
     
  • 8.
    He, Y., Huang, Z., Sick, B.: Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning. Workshop on Interactive Machine Learning Workshop (IMLW), AAAI (2022).
     
  • 9.
    Zernetsch, S., Reichert, H., Kress, V., Doll, K., Sick, B.: Cyclist Intention Detection: A Probabilistic Approach. IEEE Intelligent Vehicles Symposium (IV). IEEE (2022).
     
  • 10.
    Kottke, D., Sandrock, C., Krempl, G., Sick, B.: A Stopping Criterion for Transductive Active Learning. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD). Springer (2022).
     

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. 37, 8498–8507 (2021).
     
  • 3.
    Huhnstock, R., Reginka, M., Tomita, A., Merkel, M., Dingel, K., Holzinger, D., Sick, B., Vogel, M., Ehresmann, A.: Translatory and rotatory motion of exchange-bias capped Janus particles controlled by dynamic magnetic field landscapes. Scientific Reports. 11, 21794 (2021).
     
  • 4.
    König, I., Bachmann, M., Bieshaar, M., Schindler, S., Lambrecht, F., David, K., Sick, B., Hornung, G., Sommer, C.: Traffic Safety in Future Cities by Using a Safety Approach Based on AI and Wireless Communications. ITG-Symposium on Mobile Communication - Technologies and Applications. bll. 1–6. , Osnabrück, Germany (2021).
     
  • 5.
    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 (ISC2). bll. 1–4. IEEE (2021).
     
  • 6.
    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. IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). bll. 120–130. IEEE, Washington, DC, USA (2021).
     
  • 7.
    Heidecker, F., Hannan, A., Bieshaar, M., Sick, B.: Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks. Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll. 361–374. IEEE, Milan, Italy (2021).
     
  • 8.
    Kottke, D., Herde, M., Sandrock, C., Huseljic, D., Krempl, G., Sick, B.: Toward optimal probabilistic active learning using a Bayesian approach. Machine Learning. 110, 1199–1231 (2021).
     
  • 9.
    He, Y., Huang, Z., Sick, B.: Toward Application of Continuous Power Forecasts in a Regional Flexibility Market. International Joint Conference on Neural Networks (IJCNN). bll. 1–8. IEEE (2021).
     
  • 10.
    Dingel, K., Otto, T., Marder, L., Funke, L., Held, A., Savio, S., Hans, A., Hartmann, G., Meier, D., Viefhaus, J., Sick, B., Ehresmann, A., Ilchen, M., Helml, W.: Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses. arXiv e-prints. arXiv:2108.13979 (2021).
     
  • 11.
    Schreiber, J., Vogt, S., Sick, B.: Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast. European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD): Applied Data Science Track. bll. 118–134. Springer (2021).
     
  • 12.
    Pham, T., Kottke, D., Krempl, G., Sick, B.: Stream-Based Active Learning for Sliding Windows Under Verification Latency. Machine Learning. (2021).
     
  • 13.
    Hetzel, M., Reichert, H., Doll, K., Sick, B.: Smart Infrastructure: A Research Junction. IEEE International Smart Cities Conference (ISC2). IEEE (2021).
     
  • 14.
    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). bll. 9172–9179. IEEE (2021).
     
  • 15.
    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. Workshop From Benchmarking Behavior Prediction to Socially Compatible Behavior Generation in Autonomous Driving, IV (2021).
     
  • 16.
    Kress, V., Zernetsch, S., Doll, K., Sick, B.: Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks. Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll. 57–71. Springer (2021).
     
  • 17.
    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. Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD), CVPR. bll. 1–10 (2021).
     
  • 18.
    Scheiner, N., Kraus, F., Appenrodt, N., Dickmann, J., Sick, B.: Object Detection For Automotive Radar Point Clouds -- A Comparison. AI Perspectives. 3, 6 (2021).
     
  • 19.
    Gruhl, C., Sick, B., Tomforde, S.: Novelty detection in continuously changing environments. Future Generation Computer Systems. 114, 138–154 (2021).
     
  • 20.
    Heidecker, F., Gruhl, C., Sick, B.: Novelty based Driver Identification on RR Intervals from ECG Data. Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll. 407–421. IEEE, Milan, Italy (2021).
     
  • 21.
    Herde, M., Kottke, D., Huseljic, D., Sick, B.: Multi-annotator Probabilistic Active Learning. International Conference on Pattern Recognition (ICPR). bll. 10281–10288. IEEE (2021).
     
  • 22.
    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). bll. 9483–9490. IEEE (2021).
     
  • 23.
    Schneegans, J., Bieshaar, M., Heidecker, F., Sick, B.: Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks. Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll. 375–389. IEEE, Milan, Italy (2021).
     
  • 24.
    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). bll. 2620–2626. IEEE (2021).
     
  • 25.
    Schreiber, J., Sick, B.: Emerging Relation Network and Task Embedding for Multi-Task Regression Problems. International Conference on Pattern Recognition (ICPR). bll. 2663–2670. IEEE (2021).
     
  • 26.
    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. Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV. bll. 1023–1028,. IEEE (2021).
     
  • 27.
    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 (ISC2). bll. 1–7. IEEE (2021).
     
  • 28.
    Bieshaar, M., Zernetsch, S., Riepe, K., Doll, K., Sick, B.: Cyclist Motion State Forecasting -- Going beyond Detection. IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Orlando, FL, USA (2021).
     
  • 29.
    He, Y., Sick, B.: CLeaR: An adaptive continual learning framework for regression tasks. AI Perspectives. 3, 2 (2020).
     
  • 30.
    Hannan, A., Gruhl, C., Sick, B.: Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders. IEEE International Conference on Cyber Security and Resilience (CSR). bll. 1–7. IEEE (2021).
     
  • 31.
    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. IEEE Intelligent Vehicles Symposium (IV). bll. 644–651. IEEE, Nagoya, Japan (2021).
     
  • 32.
    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. bll. 150–153. IEEE (2021).
     
  • 33.
    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).
     
  • 34.
    Reuse, M., Simon, M., Sick, B.: About the Ambiguity of Data Augmentation for 3D Object Detection in Autonomous Driving. Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV. bll. 979–987. IEEE (2021).
     
  • 35.
    Herde, M., Huseljic, D., Sick, B., Calma, A.: A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification. IEEE Access. 9, 166970–166989 (2021).
     
  • 36.
    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. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (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 (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. 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. IEEE (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 International Conference on Information Fusion (FUSION). bll. 1–8. IEEE (2020).
     
  • 9.
    Gruhl, C., Schmeißing, J., Tomforde, S., Sick, B.: Normal-Wishart clustering for novelty detection. Workshop on Self-Improving System Integration (SISSY), ACSOS. 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. International Joint Conference on Neural Networks (IJCNN). IEEE (2020).
     
  • 12.
    He, Y., Henze, J., Sick, B.: Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks. International Joint Conference on Neural Networks (IJCNN). IEEE (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. Elsevier (2020).
     
  • 16.
    Tomforde, S., Gruhl, C., Sick, B.: A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour. Workshop on Self -Aware Computing (SeAC), ACSOS. 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. 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. 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. IEEE Intelligent Vehicles Symposium (IV). bll. 810–815. IEEE (2019).
     
  • 4.
    Heidecker, F., Bieshaar, M., Sick, B.: Towards Corner Case Identification in Cyclists’ Trajectories. ACM Computer Science in Cars Symposium (CSCS). ACM (2019).
     
  • 5.
    Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Start Intention Detection of Cyclists using an LSTM Network. 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. 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). IEEE, Xiamen (2019).
     
  • 8.
    Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Pose Based Start Intention Detection of Cyclists. IEEE International Conference on Intelligent Transportation Systems (ITSC). bll. 2381–2386. IEEE (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. 21, 3035–3045 (2019).
     
  • 11.
    Schreiber, J., Buschin, A., Sick, B.: Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models. 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. International Conference on Artificial Neural Networks and Machine Learning (ICANN): Image Processing. bll. 550–564. Springer, Cham (2019).
     
  • 13.
    Tomforde, S., Gelhausen, P., Gruhl, C., Haering, I., Sick, B.: Explicit Consideration of Resilience in Organic Computing Design Processes. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–6. VDE (2019).
     
  • 14.
    Botache, D., Dandan, L., Bieshaar, M., Sick, B.: Early Pedestrian Movement Detection Using Smart Devices Based on Human Activity Recognition. 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. International Joint Conference on Neural Networks (IJCNN). bll. 1–8. IEEE (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. IEEE (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. 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. IEEE International Conference on Intelligent Transportation Systems (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. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–8. VDE (2018).
     
  • 2.
    Jahn, A., Tomforde, S., Morold, M., David, K., Sick, B.: Towards Cooperative Self-adapting Activity Recognition. International Joint Conference on Pervasive and Embedded Computing and Communication Systems (PECCS). 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 (IJCNN). IEEE, Rio de Janiero, Brazil (2018).
     
  • 4.
    Bieshaar, M., Depping, M., Schneegans, J., Sick, B.: Starting Movement Detection of Cyclists Using Smart Devices. IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, Turin, Italy (2018).
     
  • 5.
    Jänicke, M., Schmidt, V., Sick, B., Tomforde, S., Lukowicz, P., Schmeißing, J.: Smart Device Stealing and CANDIES. International Conference on Agents and Artificial Intelligence (ICAART). bll. 247–273. Springer (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. Workshop on Self-Improving System Integration (SISSY), FAS*W. bll. 176–181. IEEE (2018).
     
  • 9.
    Henze, J., Kutzner, S., Sick, B.: Sampling Strategies for Representative Time Series in Load Flow Calculations. Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD. bll. 27–48. Springer (2018).
     
  • 10.
    Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: Radar-based Feature Design and Multiclass Classification for Road User Recognition. 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 (ICPRE). bll. 1–6 (2018).
     
  • 12.
    Gruhl, C., Sick, B.: Novelty detection with CANDIES: a holistic technique based on probabilistic models. International Journal of Machine Learning and 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. Hawaii International Conference on System Sciences (HICSS) (2018).
     
  • 14.
    Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Human Pose Estimation in Real Traffic Scenes. IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 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. International Conference on Agents and Artificial Intelligence (ICAART) (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 International Conference on Big Data Analysis (ICBDA). IEEE, 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. IEEE Intelligent Vehicles Symposium (IV). bll. 1–6. IEEE (2018).
     
  • 20.
    Schreiber, J., Bieshaar, M., Gensler, A., Sick, B., Deist, S.: Coopetitive Soft Gating Ensemble. Workshop on Self-Improving System Integration (SISSY), FAS*W. IEEE, 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. IEEE International Conference on Intelligent Transportation Systems (ITSC). IEEE, 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, 534–544 (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, 16 (2018).
     
  • 26.
    Gruhl, C., Tomforde, S., Sick, B.: Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime. Workshop on Self-Improving System Integration (SISSY), FAS*W. bll. 198–203. IEEE (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 (IJCNN). IEEE, 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 (IJCNN). IEEE, 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. Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS. bll. 150–159. VDE, London, UK (2018).
     

2017 [ nach oben ]

  • 1.
    Calma, A., Sick, B.: Simulation of Annotators for Active Learning: Uncertain Oracles. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. 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). bll. 39–50. , Porto, Portugal (2017).
     
  • 4.
    Gensler, A., Sick, B.: Probabilistic wind power forecasting: A multi-scheme ensemble technique with gradual coopetitive soft gating. IEEE Symposium Series on Computational Intelligence (SSCI). bll. 1–10. IEEE (2017).
     
  • 5.
    Lang, D., Kottke, D., Krempl, G., Sick, B.: Probabilistic Active Learning with Structure-Sensitive Kernels. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. 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. 21, 543–562 (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. International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH). 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). bll. 96–106. , 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. Workshop on Self-Improving System Integration (SISSY), FAS*W. bll. 109–116. IEEE, 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. Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD. bll. 83–93. Springer, Cham, Switzerland (2017).
     
  • 13.
    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 Computer Science in Cars Symposium (CSCS). ACM, Munich, Germany (2017).
     
  • 14.
    Bieshaar, M., Reitberger, G., Zernetsch, S., Sick, B., Fuchs, E., Doll, K.: Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence. Automatisiertes und vernetztes Fahren Symposium (AAET). bll. 67–87. , Braunschweig, Germany (2017).
     
  • 15.
    Schlegel, B., Sick, B.: Dealing with class imbalance the scalable way: Evaluation of various techniques based on classification grade and computational complexity. Workshop on Data Science and Big Data Analytics (DSBDA), ICDM. bll. 69–78. IEEE (2017).
     
  • 16.
    Bieshaar, M., Zernetsch, S., Depping, M., Sick, B., Doll, K.: Cooperative Starting Intention Detection of Cyclists Based on Smart Devices and Infrastructure. IEEE International Conference on Intelligent Transportation Systems (ITSC). IEEE, Yokohama, Japan (2017).
     
  • 17.
    Kottke, D., Calma, A., Huseljic, D., Krempl, G., Sick, B.: Challenges of Reliable, Realistic and Comparable Active Learning Evaluation. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll. 2–14 (2017).
     
  • 18.
    Gruhl, C., Beer, F., Heck, H., Sick, B., Bühler, U., Wacker, A., Tomforde, S.: A Concept for Intelligent Collaborative Network Intrusion Detection. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. 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. IEEE Intelligent Vehicles Symposium (IV). bll. 833–838. IEEE, Gothenburg, Sweden (2016).
     
  • 2.
    Jänicke, M., Tomforde, S., Sick, B.: Towards Self-Improving Activity Recognition Systems based on Probabilistic, Generative Models. Workshop on Self-Improving System Integration (SISSY), ICAC. bll. 285–291. IEEE, Würzburg, Germany (2016).
     
  • 3.
    Heck, H., Wacker, A., Rudolph, S., Gruhl, C., Sick, B., Tomforde, S.: Towards Autonomous Self-tests at Runtime. IEEE International Workshop on Quality Assurance for Self-Adaptive, Self-Organising Systems (QA4SASO), FAS*W. bll. 98–99. IEEE (2016).
     
  • 4.
    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).
     
  • 5.
    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).
     
  • 6.
    Calma, A., Reitmaier, T., Sick, B.: Resp-kNN: A probabilistic k-nearest neighbor classifier for sparsely labeled data. International Joint Conference on Neural Networks (IJCNN). bll. 4040–4047. IEEE, Vancouver, BC (2016).
     
  • 7.
    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. Workshop on the Future of Interactive Learning Machines, NIPS. bll. 1–9. , Barcelona, Spain (2016).
     
  • 8.
    Heck, H., Gruhl, C., Rudolph, S., Wacker, A., Sick, B., Hähner, J.: Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–8. VDE, Nuremberg, Germany (2016).
     
  • 9.
    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. Workshop on Self-Improving System Integration (SISSY), ICAC. bll. 1–10. IEEE, Würzburg, Germany (2016).
     
  • 10.
    Kalkowski, E., Sick, B.: Generative Exponential Smoothing and Generative ARMA Models to Forecast Time-Variant Rates or Probabilities. International Work-Conference on Time Series (ITISE): Selected Contributions. bll. 75–88. Springer, Cham, Switzerland (2016).
     
  • 11.
    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. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–8. VDE, Nuremberg, Germany (2016).
     
  • 12.
    Gensler, A., Sick, B.: Forecasting Wind Power -- An Ensemble Technique With Gradual Coopetitive Weighting Based on Weather Situation. International Joint Conference on Neural Networks (IJCNN). bll. 4976–4984. IEEE, Vancouver, BC (2016).
     
  • 13.
    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 (SSCI). bll. 1–9. IEEE, Athens, Greece (2016).
     
  • 14.
    Gensler, A., Henze, J., Sick, B., Raabe, N.: Deep Learning for Solar Power Forecasting -- An Approach using Autoencoder and LSTM Neural Networks. IEEE International Conference on Systems, Man and Cybernetics (SMC). bll. 2858–2865. IEEE, Budapest, Hungary (2016).
     
  • 15.
    Kalkowski, E., Sick, B.: Correlation of Ontology-Based Semantic Similarity and Human Judgement for a Domain Specific Fashion Ontology. International Conference on Web Engineering (ICWE). bll. 207–224. Springer (2016).
     
  • 16.
    Kreil, M., Sick, B., Lukowicz, P.: Coping with variability in motion based activity recognition. International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR). bll. 1–8. , Rostock, Germany (2016).
     
  • 17.
    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 (IJCNN). bll. 2164–2173. IEEE, Vancouver, BC (2016).
     
  • 18.
    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 (UbiComp). bll. 353–356. ACM, Heidelberg, Germany (2016).
     
  • 19.
    Gensler, A., Sick, B., Pankraz, V.: An Analog Ensemble-Based Similarity Search Technique for Solar Power Forecasting. IEEE International Conference on Systems, Man and Cybernetics (SMC). bll. 2850–2857. IEEE (2016).
     
  • 20.
    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. IEEE, Athens, Greece (2016).
     
  • 21.
    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. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–8. VDE, 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 (SMC). bll. 1693–1699. IEEE, 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.
    Goldhammer, M., Köhler, S., Doll, K., Sick, B.: Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptions. SAI Intelligent Systems Conference (IntelliSys). bll. 259–279. Springer, Cham, Switzerland (2015).
     
  • 4.
    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).
     
  • 5.
    Hähner, J., Brinkschulte, U., Lukowicz, P., Mostaghim, S., Sick, B., Tomforde, S.: Runtime Self-Integration as Key Challenge for Mastering Interwoven Systems. International Conference on Architecture of Computing Systems (ARCS). bll. 1–8. VDE, Porto, Portugal (2015).
     
  • 6.
    Kalkowski, E., Sick, B.: Generative Exponential Smoothing Models for Rate Forecasting with Uncertainty Estimation. International Work-Conference on Time Series (ITISE). bll. 806–817. , Granada, Spain (2015).
     
  • 7.
    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 (TIME). bll. 29–37. IEEE, Kassel, Germany (2015).
     
  • 8.
    Breker, S., Sick, B.: Effiziente Bewertung des Anschlusspotentials von Niederspannungsnetzen für dezentrale Erzeugungsanlagen: Klassifikation mit Methoden der Computational Intelligence. Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS). bll. 51–56. , Hamburg, Germany (2015).
     
  • 9.
    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).
     
  • 10.
    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).
     
  • 11.
    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 (IntelliSys). bll. 390–399. Springer, London, UK (2015).
     
  • 12.
    Rudolph, J., Breker, S., Sick, B.: Bewertung verschiedener Spannungsregelungskonzepte in einem einspeisegeprägten Mittelspannungsnetz und Ausblick auf neue Konzepte basierend auf Methoden der Computational Intelligence. Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS). bll. 57–63. , Hamburg, Germany (2015).
     
  • 13.
    Stone, T.C., Huber, A., Siwy, R., Sick, B.: Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen. Elektronik automotive. 2, 32–36 (2015).
     
  • 14.
    Rudolph, S., Tomforde, S., Sick, B., Heck, H., Wacker, A., Hähner, J.: An Online Influence Detection Algorithm for Organic Computing Systems. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–8. VDE, Porto, Portugal (2015).
     
  • 15.
    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 (MOCS), VTC. bll. 1–5. , Boston, MA (2015).
     
  • 16.
    Calma, A., Reitmaier, T., Sick, B., Lukowicz, P., Embrechts, M.: A New Vision of Collaborative Active Learning. arXiv e-prints. arXiv:1504.00284 (2015).
     
  • 17.
    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 (SASO). bll. 144–149. IEEE, Cambridge, MA (2015).
     
  • 18.
    Embrechts, M., Sick, B.: A Generalized Hebb (GH) rule based on a cross-entropy error function for deep belief recursive learning. International Conference on Neural Networks - Fuzzy Systems (NN-FS). bll. 21–24. , Vienna, Austria (2015).
     
  • 19.
    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 (iCAST). bll. 194–200. IEEE, 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 and International Conference on Signal and Information Processing (ChinaSIP). bll. 753–757. IEEE, 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. 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. Workshop on Self-Improving System Integration (SISSY), SASO. bll. 128–136. IEEE, 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. Neues Handbuch Hochschullehre. bll. 71–94. Raabe (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 (ICPR). bll. 4110–4115. IEEE, 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. Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML), LWA. 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. INFORMATIK 2014. bll. 2121–2132. Gesellschaft für Informatik e.V (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. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–8. VDE, L"ubeck, Germany (2014).
     
  • 12.
    Kreil, M., Sick, B., Lukowicz, P.: Dealing with human variability in motion based, wearable activity recognition. Symposium on Activity and Context Modeling and Recognition (ACOMORE), PerCom. 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 (ITSC). bll. 1758–1763. IEEE, 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. Workshop on Self-Improving System Integration (SISSY), SASO. IEEE, 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. International Workshop on Spatial and Spatio-Temporal Data Mining (SSTDM), ICDM. bll. 1002–1011. IEEE, 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. Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS. bll. 1–13. VDE, 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 (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. International Joint Conference on Neural Networks (IJCNN). bll. 1–8. IEEE, 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 (iCT). 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: Preface. In: Müller-Schloer, C., Schmeck, H., en Ungerer, T. (reds.) Organic Computing -- A Paradigm Shift for Complex Systems. bll. 235–236. Springer (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 (ICAART). 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 (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 (2011).
     
  • 8.
    Bannach, D., Sick, B., Lukowicz, P.: Automatic Adaptation of Mobile Activity Recognition Systems to New Sensors. Workshop Mobile Sensing: Challenges, Opportunities, and Future Directions, UbiComp. bll. 1–5. ACM, Beijing, China (2011).
     
  • 9.
    Reitmaier, T., Sick, B.: Active classifier training with the 3DS strategy. IEEE Symposium on Computational Intelligence and Data Mining (CIDM). bll. 88–95. IEEE, 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 (SASO). bll. 94–103. IEEE, 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. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), SSCI. bll. 494–501. IEEE (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. IEEE, Logan (2006).