Prof. Dr. rer. nat. Bernhard Sick

Group Leader; Team Leader: Collaborative Interactive Learning (CIL); Team Leader: AI for Computationally Intelligent Systems (AI4CIS)

Sick, Bernhard
Telephone
+49 561 804-6020
Fax
+49 561 804-6022
Email
Site
Wilhelmshöher Allee 73
34121 Kassel
Room
WA-altes Gebäude (WA 73), ohne Raumangabe

Publications

2023[ to top ]
  • Magnussen, B.M., Stern, C., Sick, B.: Utilizing Continuous Kernels for Processing Irregularly and Inconsistently Sampled Data With Position-Dependent Features In: International Conference on Autonomic and Autonomous Systems (ICAS). bll 49–53. ThinkMind (2023).
  • Hetzel, M., Reichert, H., Reitberger, G., Doll, K., Sick, B., Fuchs, E.: The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset In: IEEE Intelligent Vehicles Symposium (IV). IEEE (2023).
  • Reichert, H., Hetzel, M., Schreck, S., Doll, K., Sick, B.: Sensor Equivariance by LiDAR Projection Images In: IEEE Intelligent Vehicles Symposium (IV). IEEE (2023).
  • Heidecker, F., El-Khateeb, A., Sick, B.: Sampling-based Uncertainty Estimation for an Instance Segmentation Network arXiv e-prints. arXiv:2305.14977 (2023).
  • Meier, D., Ragunathan, R., Degener, S., Liehr, A., Vollmer, M., Niendorf, T., Sick, B.: Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning Scientific Reports. 13, 5410 (2023). https://doi.org/10.1038/s41598-023-31580-1.
  • Eider, M., Sick, B., Berl, A.: Context-aware recommendations for extended electric vehicle battery lifetime Sustainable Computing: Informatics and Systems (SUSCOM). 37, 100845 (2023). https://doi.org/10.1016/j.suscom.2022.100845.
2022[ to top ]
  • Nivarthi, C.P., Vogt, S., Sick, B.: Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting In: International Conference on Machine Learning and Applications (ICMLA). bll 1530–1536. IEEE (2022). https://doi.org/10.1109/ICMLA55696.2022.00240.
  • Huhnstock, R., Reginka, M., Sonntag, C., Merkel, M., Dingel, K., Sick, B., Vogel, M., Ehresmann, A.: Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes Scientific Reports. 12, 1–10 (2022). https://doi.org/10.1038/s41598-022-25391-z.
  • 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). https://doi.org/https://doi.org/10.3390/en15030881.
  • Rösch, K., Heidecker, F., Truetsch, J., Kowol, K., Schicktanz, C., Bieshaar, M., Sick, B., Stiller, C.: Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving In: IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (IEEE CIVTS), IEEE SSCI. bll 86–93. IEEE (2022). https://doi.org/10.1109/SSCI51031.2022.10022241.
  • Draude, C., Gruhl, C., Hornung, G., Kropf, J., Lamla, J., Leimeister, J.M., Sick, B., Stumme, G.: Social Machines Informatik Spektrum. 45, 38–42 (2022). https://doi.org/https://doi.org/10.1007/s00287-021-01421-4.
  • Gruhl, C., Tomforde, S., Sick, B.: Self-Aware Microsystems In: Workshop on Self-Improving System Integration (SISSY), ACSOS. bll 126–127. IEEE (2022). https://doi.org/10.1109/ACSOSC56246.2022.00045.
  • 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). https://doi.org/10.1016/j.infsof.2022.106826.
  • Kress, V., Jeske, F., Zernetsch, S., Doll, K., Sick, B.: Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users Trajectories IEEE Transactions on Intelligent Vehicles. 8, 2592–2603 (2022). https://doi.org/10.1109/TIV.2022.3149624.
  • Meier, D., Ramirez, L.V., Völker, J., Viefhaus, J., Sick, B., Hartmann, G.: Optimizing a superconducting radio-frequency gun using deep reinforcement learning Physical Review Accelerators and Beams. 25, 104604 (2022). https://doi.org/10.1103/PhysRevAccelBeams.25.104604.
  • Decke, J., Schmeißing, J., Botache, D., Bieshaar, M., Sick, B., Gruhl, C.: NDNET: A Unified Framework for Anomaly and Novelty Detection In: International Conference on Architecture of Computing Systems (ARCS). bll 197–210. Springer (2022). https://doi.org/10.1007/978-3-031-21867-5_13.
  • Westmeier, T., Botache, D., Bieshaar, M., Sick, B.: Generating Synthetic Time Series for Machine-Learning-Empowered Monitoring of Electric Motor Test Benches In: IEEE International Conference on Data Science and Advanced Analytics (DSAA). bll 513–522. IEEE (2022). https://doi.org/10.1109/DSAA54385.2022.10032385.
  • Rauch, L., Huseljic, D., Sick, B.: Enhancing Active Learning with Weak Supervision and Transfer Learning by Leveraging Information and Knowledge Sources In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll 27–42 (2022).
  • Englhardt, A., Trittenbach, H., Kottke, D., Sick, B., Böhm, K.: Efficient SVDD sampling with approximation guarantees for the decision boundary Machine Learning. 111, 1349–1375 (2022). https://doi.org/10.1007/s10994-022-06149-0.
  • 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.: Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses Scientific Reports. 12, 1–14 (2022). https://doi.org/10.1038/s41598-022-21646-x.
  • Kottke, D., Sandrock, C., Krempl, G., Sick, B.: A Stopping Criterion for Transductive Active Learning In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD). bll 468–484. Springer (2022). https://doi.org/10.1007/978-3-031-26412-2_29.
  • Schneegans, J., Bieshaar, M., Sick, B.: A Practical Evaluation of Active Learning Approaches for Object Detection In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll 49–67 (2022).
  • Herde, M., Huseljic, D., Mitrovic, J., Granitzer, M., Sick, B.: A Concept for Automated Polarized Web Content Annotation based on Multimodal Active Learning In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll 1–6 (2022).
2021[ to top ]
  • Huang, Z., He, Y., Vogt, S., Sick, B.: Uncertainty and Utility Sampling with Pre-Clustering In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD (2021).
  • 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). https://doi.org/10.1021/acs.langmuir.1c00900.
  • 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). https://doi.org/10.1038/s41598-021-01351-x.
  • 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 In: ITG-Symposium on Mobile Communication - Technologies and Applications. bll 1–6. , Osnabrück, Germany (2021).
  • 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 In: IEEE International Smart Cities Conference (ISC2). bll 1–4. IEEE (2021). https://doi.org/10.1109/isc253183.2021.9562912.
  • 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 In: IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). bll 120–130. IEEE, Washington, DC, USA (2021). https://doi.org/10.1109/acsos52086.2021.00031.
  • Heidecker, F., Hannan, A., Bieshaar, M., Sick, B.: Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks In: Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll 361–374. IEEE, Milan, Italy (2021). https://doi.org/10.1007/978-3-030-68799-1_26.
  • 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). https://doi.org/10.1007/s10994-021-05986-9.
  • He, Y., Huang, Z., Sick, B.: Toward Application of Continuous Power Forecasts in a Regional Flexibility Market In: International Joint Conference on Neural Networks (IJCNN). bll 1–8. IEEE (2021). https://doi.org/10.1109/ijcnn52387.2021.9533626.
  • 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).
  • Schreiber, J., Vogt, S., Sick, B.: Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast In: European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD): Applied Data Science Track. bll 118–134. Springer (2021). https://doi.org/10.1007/978-3-030-86514-6_8.
  • Pham, T., Kottke, D., Krempl, G., Sick, B.: Stream-Based Active Learning for Sliding Windows Under Verification Latency Machine Learning. (2021). https://doi.org/10.1007/s10994-021-06099-z.
  • Hetzel, M., Reichert, H., Doll, K., Sick, B.: Smart Infrastructure: A Research Junction In: IEEE International Smart Cities Conference (ISC2). IEEE (2021). https://doi.org/10.1109/isc253183.2021.9562809.
  • Huseljic, D., Sick, B., Herde, M., Kottke, D.: Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks In: International Conference on Pattern Recognition (ICPR). bll 9172–9179. IEEE (2021). https://doi.org/10.1109/icpr48806.2021.9412616.
  • Kottke, D., Herde, M., Minh, T.P., Benz, A., Mergard, P., Roghman, A., Sandrock, C., Sick, B.: scikit-activeml: A Library and Toolbox for Active Learning Algorithms Preprints. 2021030194 (2021).
  • 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 In: Workshop From Benchmarking Behavior Prediction to Socially Compatible Behavior Generation in Autonomous Driving, IV (2021).
  • Kress, V., Zernetsch, S., Doll, K., Sick, B.: Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks In: Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll 57–71. Springer (2021). https://doi.org/10.1007/978-3-030-68763-2_5.
  • 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 In: Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD), CVPR. bll 1–10 (2021).
  • Scheiner, N., Kraus, F., Appenrodt, N., Dickmann, J., Sick, B.: Object Detection For Automotive Radar Point Clouds -- A Comparison AI Perspectives. 3, 6 (2021). https://doi.org/10.1186/s42467-021-00012-z.
  • Gruhl, C., Sick, B., Tomforde, S.: Novelty detection in continuously changing environments Future Generation Computer Systems. 114, 138–154 (2021). https://doi.org/10.1016/j.future.2020.07.037.
  • Heidecker, F., Gruhl, C., Sick, B.: Novelty based Driver Identification on RR Intervals from ECG Data In: Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll 407–421. IEEE, Milan, Italy (2021). https://doi.org/10.1007/978-3-030-68799-1_29.
  • Herde, M., Kottke, D., Huseljic, D., Sick, B.: Multi-annotator Probabilistic Active Learning In: International Conference on Pattern Recognition (ICPR). bll 10281–10288. IEEE (2021). https://doi.org/10.1109/icpr48806.2021.9412298.
  • Haase-Schütz, C., Stal, R., Hertlein, H., Sick, B.: Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning In: International Conference on Pattern Recognition (ICPR). bll 9483–9490. IEEE (2021). https://doi.org/10.1109/icpr48806.2021.9411918.
  • Schneegans, J., Bieshaar, M., Heidecker, F., Sick, B.: Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks In: Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll 375–389. IEEE, Milan, Italy (2021). https://doi.org/10.1007/978-3-030-68799-1_27.
  • Zernetsch, S., Schreck, S., Kress, V., Doll, K., Sick, B.: Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution In: International Conference on Pattern Recognition (ICPR). bll 2620–2626. IEEE (2021). https://doi.org/10.1109/icpr48806.2021.9413233.
  • Schreiber, J., Sick, B.: Emerging Relation Network and Task Embedding for Multi-Task Regression Problems In: International Conference on Pattern Recognition (ICPR). bll 2663–2670. IEEE (2021). https://doi.org/10.1109/icpr48806.2021.9412476.
  • 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 In: Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV. bll 1023–1028,. IEEE (2021). https://doi.org/10.1109/iccvw54120.2021.00119.
  • Zernetsch, S., Trupp, O., Kress, V., Doll, K., Sick, B.: Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information In: IEEE International Smart Cities Conference (ISC2). bll 1–7. IEEE (2021). https://doi.org/10.1109/isc253183.2021.9562857.
  • Bieshaar, M., Zernetsch, S., Riepe, K., Doll, K., Sick, B.: Cyclist Motion State Forecasting -- Going beyond Detection In: IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Orlando, FL, USA (2021).
  • He, Y., Sick, B.: CLeaR: An adaptive continual learning framework for regression tasks AI Perspectives. 3, 2 (2020). https://doi.org/10.1186/s42467-021-00009-8.
  • Hannan, A., Gruhl, C., Sick, B.: Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders In: IEEE International Conference on Cyber Security and Resilience (CSR). bll 1–7. IEEE (2021). https://doi.org/10.1109/csr51186.2021.9527980.
  • 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 In: IEEE Intelligent Vehicles Symposium (IV). bll 644–651. IEEE, Nagoya, Japan (2021). https://doi.org/10.1109/iv48863.2021.9575933.
  • 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 In: Workshop on Self-Improving System Integration (SISSY), ACSOS. bll 150–153. IEEE (2021). https://doi.org/10.1109/ACSOS-C52956.2021.00048.
  • 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). https://doi.org/10.1016/j.cpc.2021.107859.
  • Reuse, M., Simon, M., Sick, B.: About the Ambiguity of Data Augmentation for 3D Object Detection in Autonomous Driving In: Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV. bll 979–987. IEEE (2021). https://doi.org/10.1109/ICCVW54120.2021.00114.
  • 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). https://doi.org/10.1109/ACCESS.2021.3135514.
  • 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 In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD (2021).
2020[ to top ]
  • 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).
  • 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 In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2020). https://doi.org/10.1109/cvpr42600.2020.00214.
  • 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). https://doi.org/10.1007/978-3-030-31760-7_3.
  • 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: Advances in Computational Methods for X-Ray Optics V. bll 71–77. SPIE (2020). https://doi.org/10.1117/12.2568001.
  • 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).
  • 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). https://doi.org/10.1186/s13705-020-00247-4.
  • Kress, V., Schreck, S., Zernetsch, S., Doll, K., Sick, B.: Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks In: IEEE Symposium Series on Computational Intelligence (SSCI). bll 2723–2730. IEEE (2020). https://doi.org/10.1109/ssci47803.2020.9308462.
  • 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? In: IEEE International Conference on Information Fusion (FUSION). bll 1–8. IEEE (2020). https://doi.org/10.23919/fusion45008.2020.9190338.
  • Gruhl, C., Schmeißing, J., Tomforde, S., Sick, B.: Normal-Wishart clustering for novelty detection In: Workshop on Self-Improving System Integration (SISSY), ACSOS. bll 64–69. IEEE (2020). https://doi.org/10.1109/acsos-c51401.2020.00032.
  • 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).
  • Pham Minh, T., Kottke, D., Tsarenko, A., Gruhl, C., Sick, B.: Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning In: International Joint Conference on Neural Networks (IJCNN). IEEE (2020). https://doi.org/10.1109/ijcnn48605.2020.9206873.
  • He, Y., Henze, J., Sick, B.: Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks In: International Joint Conference on Neural Networks (IJCNN). IEEE (2020). https://doi.org/10.1109/ijcnn48605.2020.9207536.
  • Deist, S., Schreiber, J., Bieshaar, M., Sick, B.: Extended Coopetitive Soft Gating Ensemble arXiv e-prints. arXiv:2004.14026 (2020).
  • 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).
  • He, Y., Henze, J., Sick, B.: Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets In: International Federation of Automatic Control (IFAC) World Congress. bll 12175–12182. Elsevier (2020). https://doi.org/10.1016/j.ifacol.2020.12.1017.
  • Tomforde, S., Gruhl, C., Sick, B.: A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour In: Workshop on Self -Aware Computing (SeAC), ACSOS. bll 166–169. IEEE (2020). https://doi.org/10.1109/acsos-c51401.2020.00050.
2019[ to top ]
  • Vogt, S., Braun, A., Dobschinski, J., Sick, B.: Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning In: Wind Integration Workshop. , Dublin, Ireland (2019).
  • König, I., Heilmann, E., Henze, J., David, K., Wetzel, H., Sick, B.: Using grid supporting flexibility in electricity distribution networks In: INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft. bll 531–544. Gesellschaft für Informatik e.V., Bonn (2019). https://doi.org/10.18420/inf2019_70.
  • Zernetsch, S., Reichert, H., Kress, V., Doll, K., Sick, B.: Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks In: IEEE Intelligent Vehicles Symposium (IV). bll 810–815. IEEE (2019). https://doi.org/10.1109/IVS.2019.8814258.
  • Heidecker, F., Bieshaar, M., Sick, B.: Towards Corner Case Identification in Cyclists’ Trajectories In: ACM Computer Science in Cars Symposium (CSCS). ACM (2019).
  • Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Start Intention Detection of Cyclists using an LSTM Network In: INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge). bll 219–228. Gesellschaft für Informatik e.V., Bonn (2019). https://doi.org/10.18420/inf2019_ws25.
  • Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles In: IEEE Intelligent Vehicles Symposium (IV). bll 642–649. IEEE, Paris, France (2019). https://doi.org/10.1109/ivs.2019.8813773.
  • Kress, V., Zernetsch, S., Doll, K., Sick, B.: Pose Based Trajectory Forecast of Vulnerable Road Users In: IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Xiamen (2019). https://doi.org/10.1109/ssci44817.2019.9003023.
  • Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Pose Based Start Intention Detection of Cyclists In: IEEE International Conference on Intelligent Transportation Systems (ITSC). bll 2381–2386. IEEE (2019). https://doi.org/10.1109/ITSC.2019.8917215.
  • Kottke, D., Schellinger, J., Huseljic, D., Sick, B.: Limitations of Assessing Active Learning Performance at Runtime arXiv e-prints. arXiv:1901.10338 (2019).
  • Goldhammer, M., Köhler, S., Zernetsch, S., Doll, K., Sick, B., Dietmayer, K.: Intentions of Vulnerable Road Users -- Detection and Forecasting by Means of Machine Learning IEEE Transactions on Intelligent Transportation Systems. 21, 3035–3045 (2019). https://doi.org/10.1109/TITS.2019.2923319.
  • Schreiber, J., Buschin, A., Sick, B.: Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models In: INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft. bll 585–598. Gesellschaft für Informatik e.V., Bonn (2019). https://doi.org/10.18420/inf2019_74.
  • Schreiber, J., Jessulat, M., Sick, B.: Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic In: International Conference on Artificial Neural Networks and Machine Learning (ICANN): Image Processing. bll 550–564. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30508-6_44.
  • Tomforde, S., Gelhausen, P., Gruhl, C., Haering, I., Sick, B.: Explicit Consideration of Resilience in Organic Computing Design Processes In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–6. VDE (2019).
  • Botache, D., Dandan, L., Bieshaar, M., Sick, B.: Early Pedestrian Movement Detection Using Smart Devices Based on Human Activity Recognition In: INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft (Workshop-Beiträge). bll 229–238. Gesellschaft für Informatik e.V., Bonn (2019). https://doi.org/10.18420/inf2019_ws26.
  • Sandrock, C., Herde, M., Calma, A., Kottke, D., Sick, B.: Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality In: International Joint Conference on Neural Networks (IJCNN). bll 1–8. IEEE (2019). https://doi.org/10.1109/IJCNN.2019.8852456.
  • 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).
  • Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS In: IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM). bll 5–9. IEEE (2019). https://doi.org/10.1109/ICMIM.2019.8726801.
  • 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 In: International Radar Symposium (IRS). bll 1–10. , Ulm, Germany (2019). https://doi.org/10.23919/irs.2019.8768169.
  • Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: A Multi-Stage Clustering Framework for Automotive Radar Data In: IEEE International Conference on Intelligent Transportation Systems (ITSC). bll 2060–2067. IEEE (2019). https://doi.org/10.1109/itsc.2019.8916873.
2018[ to top ]
  • 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 In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–8. VDE (2018).
  • Jahn, A., Tomforde, S., Morold, M., David, K., Sick, B.: Towards Cooperative Self-adapting Activity Recognition In: International Joint Conference on Pervasive and Embedded Computing and Communication Systems (PECCS). bll 215–222 (2018). https://doi.org/10.5220/0006856102150222.
  • 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 In: International Joint Conference on Neural Networks (IJCNN). IEEE, Rio de Janiero, Brazil (2018). https://doi.org/10.1109/ijcnn.2018.8489637.
  • Bieshaar, M., Depping, M., Schneegans, J., Sick, B.: Starting Movement Detection of Cyclists Using Smart Devices In: IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, Turin, Italy (2018). https://doi.org/10.1109/dsaa.2018.00042.
  • Jänicke, M., Schmidt, V., Sick, B., Tomforde, S., Lukowicz, P., Schmeißing, J.: Smart Device Stealing and CANDIES In: International Conference on Agents and Artificial Intelligence (ICAART). bll 247–273. Springer (2018). https://doi.org/10.1007/978-3-030-05453-3_12.
  • 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). https://doi.org/10.1016/j.ins.2018.04.063.
  • Jänicke, M., Sick, B., Tomforde, S.: Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models Informatics. 5, 38 (2018). https://doi.org/10.3390/informatics5030038.
  • Heck, H., Sick, B., Tomforde, S.: Security Issues in Self-Improving System Integration - Challenges and Solution Strategies In: Workshop on Self-Improving System Integration (SISSY), FAS*W. bll 176–181. IEEE (2018). https://doi.org/10.1109/fas-w.2018.00044.
  • Henze, J., Kutzner, S., Sick, B.: Sampling Strategies for Representative Time Series in Load Flow Calculations In: Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD. bll 27–48. Springer (2018). https://doi.org/10.1007/978-3-030-04303-2_3.
  • Scheiner, N., Appenrodt, N., Dickmann, J., Sick, B.: Radar-based Feature Design and Multiclass Classification for Road User Recognition In: IEEE Intelligent Vehicles Symposium (IV). bll 779–786. IEEE, Changshu, China (2018). https://doi.org/10.1109/ivs.2018.8500607.
  • Schreiber, J., Sick, B.: Quantifying the Influences on Probabilistic Wind Power Forecasts In: International Conference on Power and Renewable Energy (ICPRE). bll 1–6 (2018). https://doi.org/10.1051/e3sconf/20186406002.
  • 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). https://doi.org/10.1007/s13042-016-0618-8.
  • 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 In: Hawaii International Conference on System Sciences (HICSS) (2018). https://doi.org/10.24251/hicss.2018.120.
  • Kress, V., Jung, J., Zernetsch, S., Doll, K., Sick, B.: Human Pose Estimation in Real Traffic Scenes In: IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Bangalore, India (2018). https://doi.org/10.1109/ssci.2018.8628660.
  • 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). https://doi.org/10.1016/j.asoc.2018.05.007.
  • 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 In: International Conference on Agents and Artificial Intelligence (ICAART) (2018). https://doi.org/10.5220/0006594901310142.
  • Kromat, T., Dehling, T., Haux, R., Peters, C., Sick, B., Tomforde, S., Wolf, K.-H., Sunyaev, A.: Gestaltungsraum für proactive Smart Homes zur Gesundheitsförderung In: Multikonferenz Wirtschaftsinformatik. , Lüneburg, Germany (2018).
  • Schlegel, B., Mrowca, A., Wolf, P., Sick, B., Steinhorst, S.: Generalizing Application Agnostic Remaining Useful Life Estimation Using Data-Driven Open Source Algorithms In: IEEE International Conference on Big Data Analysis (ICBDA). IEEE, Shanghai, China (2018). https://doi.org/10.1109/icbda.2018.8367659.
  • Zernetsch, S., Kress, V., Sick, B., Doll, K.: Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network In: IEEE Intelligent Vehicles Symposium (IV). bll 1–6. IEEE (2018). https://doi.org/10.1109/ivs.2018.8500428.
  • Schreiber, J., Bieshaar, M., Gensler, A., Sick, B., Deist, S.: Coopetitive Soft Gating Ensemble In: Workshop on Self-Improving System Integration (SISSY), FAS*W. IEEE, Trento, Italy (2018). https://doi.org/10.1109/fas-w.2018.00046.
  • Reitberger, G., Zernetsch, S., Bieshaar, M., Sick, B., Doll, K., Fuchs, E.: Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure In: IEEE International Conference on Intelligent Transportation Systems (ITSC). IEEE, Maui, HI (2018). https://doi.org/10.1109/itsc.2018.8569267.
  • 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). https://doi.org/10.1109/tiv.2018.2873900.
  • 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). https://doi.org/10.1007/978-3-319-78301-7_9.
  • Sick, B., Oeste-Reiß, S., Schmidt, A., Tomforde, S., Zweig, K.A.: Collaborative Interactive Learning Informatik Spektrum. 41, 52–55 (2018). https://doi.org/10.1007/s00287-017-1082-x.
  • 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). https://doi.org/10.5445/KSP/1000087327/16.
  • Gruhl, C., Tomforde, S., Sick, B.: Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime In: Workshop on Self-Improving System Integration (SISSY), FAS*W. bll 198–203. IEEE (2018). https://doi.org/10.1109/fas-w.2018.00047.
  • 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 In: International Joint Conference on Neural Networks (IJCNN). IEEE, Rio de Janiero, Brazil (2018). https://doi.org/10.1109/ijcnn.2018.8489161.
  • Calma, A., Stolz, M., Kottke, D., Tomforde, S., Sick, B.: Active Learning with Realistic Data -- A Case Study In: International Joint Conference on Neural Networks (IJCNN). IEEE, Rio de Janiero, Brazil (2018). https://doi.org/10.1109/ijcnn.2018.8489394.
  • 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). https://doi.org/10.1016/j.rser.2018.07.042.
  • 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).
  • 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 In: Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS. bll 150–159. VDE, London, UK (2018).
2017[ to top ]
  • Calma, A., Sick, B.: Simulation of Annotators for Active Learning: Uncertain Oracles In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll 49–58 (2017).
  • 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).
  • 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 In: International Conference on Agents and Artificial Intelligence (ICAART). bll 39–50. , Porto, Portugal (2017). https://doi.org/10.5220/0006137300390050.
  • Gensler, A., Sick, B.: Probabilistic wind power forecasting: A multi-scheme ensemble technique with gradual coopetitive soft gating In: IEEE Symposium Series on Computational Intelligence (SSCI). bll 1–10. IEEE (2017). https://doi.org/10.1109/SSCI.2017.8285344.
  • Lang, D., Kottke, D., Krempl, G., Sick, B.: Probabilistic Active Learning with Structure-Sensitive Kernels In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll 37–48 (2017).
  • Gensler, A., Sick, B.: Performing event detection in time series with SwiftEvent: an algorithm with supervised learning of detection criteria Pattern Analysis and Applications. 21, 543–562 (2017). https://doi.org/10.1007/s10044-017-0657-0.
  • Tomforde, S., Sick, B., Müller-Schloer, C.: Organic Computing in the Spotlight arXiv e-prints. arXiv:1701.08125 (2017).
  • Wolf, J.-H., Dehling, T., Haux, R., Sick, B., Sunyaev, A., Tomforde, S.: On Methodological and Technological Challenges for Proactive Health Management in Smart Homes In: International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH). bll 209–212. , Athens, Greece (2017).
  • Tomforde, S., Kantert, J., Sick, B.: Measuring Self Organisation at Runtime -- A Quantification Method based on Divergence Measures In: International Conference on Agents and Artificial Intelligence (ICAART). bll 96–106. , Porto, Portugal (2017). https://doi.org/10.5220/0006240400960106.
  • Calma, A., Kottke, D., Sick, B., Tomforde, S.: Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms In: Workshop on Self-Improving System Integration (SISSY), FAS*W. bll 109–116. IEEE, Tucson, AZ (2017). https://doi.org/10.1109/fas-w.2017.129.
  • 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).
  • Henze, J., Kneiske, T., Braun, M., Sick, B.: Identifying Representative Load Time Series for Load Flow Calculations In: Workshop on Data Analytics for Renewable Energy Integration (DARE), ECML PKDD. bll 83–93. Springer, Cham, Switzerland (2017). https://doi.org/10.1007/978-3-319-71643-5_8.
  • 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 In: ACM Computer Science in Cars Symposium (CSCS). ACM, Munich, Germany (2017).
  • Bieshaar, M., Reitberger, G., Zernetsch, S., Sick, B., Fuchs, E., Doll, K.: Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence In: Automatisiertes und vernetztes Fahren Symposium (AAET). bll 67–87. , Braunschweig, Germany (2017).
  • Schlegel, B., Sick, B.: Dealing with class imbalance the scalable way: Evaluation of various techniques based on classification grade and computational complexity In: Workshop on Data Science and Big Data Analytics (DSBDA), ICDM. bll 69–78. IEEE (2017). https://doi.org/10.1109/icdmw.2017.16.
  • Bieshaar, M., Zernetsch, S., Depping, M., Sick, B., Doll, K.: Cooperative Starting Intention Detection of Cyclists Based on Smart Devices and Infrastructure In: IEEE International Conference on Intelligent Transportation Systems (ITSC). IEEE, Yokohama, Japan (2017). https://doi.org/10.1109/itsc.2017.8317691.
  • Kottke, D., Calma, A., Huseljic, D., Krempl, G., Sick, B.: Challenges of Reliable, Realistic and Comparable Active Learning Evaluation In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll 2–14 (2017).
  • Gruhl, C., Beer, F., Heck, H., Sick, B., Bühler, U., Wacker, A., Tomforde, S.: A Concept for Intelligent Collaborative Network Intrusion Detection In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. VDE (2017).
2016[ to top ]
  • Zernetsch, S., Kohnen, S., Goldhammer, M., Doll, K., Sick, B.: Trajectory Prediction of Cyclists Using a Physical Model and an Artificial Neural Network In: IEEE Intelligent Vehicles Symposium (IV). bll 833–838. IEEE, Gothenburg, Sweden (2016). https://doi.org/10.1109/ivs.2016.7535484.
  • Jänicke, M., Tomforde, S., Sick, B.: Towards Self-Improving Activity Recognition Systems based on Probabilistic, Generative Models In: Workshop on Self-Improving System Integration (SISSY), ICAC. bll 285–291. IEEE, Würzburg, Germany (2016). https://doi.org/10.1109/icac.2016.22.
  • Heck, H., Wacker, A., Rudolph, S., Gruhl, C., Sick, B., Tomforde, S.: Towards Autonomous Self-tests at Runtime In: IEEE International Workshop on Quality Assurance for Self-Adaptive, Self-Organising Systems (QA4SASO), FAS*W. bll 98–99. IEEE (2016). https://doi.org/10.1109/fas-w.2016.32.
  • 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). https://doi.org/10.1016/j.ins.2016.08.016.
  • 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).
  • Calma, A., Reitmaier, T., Sick, B.: Resp-kNN: A probabilistic k-nearest neighbor classifier for sparsely labeled data In: International Joint Conference on Neural Networks (IJCNN). bll 4040–4047. IEEE, Vancouver, BC (2016). https://doi.org/10.1109/ijcnn.2016.7727725.
  • 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: Workshop on the Future of Interactive Learning Machines, NIPS. bll 1–9. , Barcelona, Spain (2016).
  • Heck, H., Gruhl, C., Rudolph, S., Wacker, A., Sick, B., Hähner, J.: Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–8. VDE, Nuremberg, Germany (2016).
  • Bahle, G., Calma, A., Leimeister, J.M., Lukowicz, P., Oeste-Reiss, S., Reitmaier, T., Schmidt, A., Sick, B., Stumme, G., Zweig, K.A.: Lifelong Learning and Collaboration of Smart Technical Systems in Open-Ended Environments -- Opportunistic Collaborative Interactive Learning In: Workshop on Self-Improving System Integration (SISSY), ICAC. bll 1–10. IEEE, Würzburg, Germany (2016). https://doi.org/10.1109/icac.2016.36.
  • Kalkowski, E., Sick, B.: Generative Exponential Smoothing and Generative ARMA Models to Forecast Time-Variant Rates or Probabilities In: International Work-Conference on Time Series (ITISE): Selected Contributions. bll 75–88. Springer, Cham, Switzerland (2016). https://doi.org/10.1007/978-3-319-28725-6_6.
  • 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 In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–8. VDE, Nuremberg, Germany (2016).
  • Gensler, A., Sick, B.: Forecasting Wind Power -- An Ensemble Technique With Gradual Coopetitive Weighting Based on Weather Situation In: International Joint Conference on Neural Networks (IJCNN). bll 4976–4984. IEEE, Vancouver, BC (2016). https://doi.org/10.1109/ijcnn.2016.7727855.
  • Schlegel, B., Sick, B.: Design and optimization of an autonomous feature selection pipeline for high dimensional, heterogeneous feature spaces In: IEEE Symposium Series on Computational Intelligence (SSCI). bll 1–9. IEEE, Athens, Greece (2016). https://doi.org/10.1109/ssci.2016.7850092.
  • Gensler, A., Henze, J., Sick, B., Raabe, N.: Deep Learning for Solar Power Forecasting -- An Approach using Autoencoder and LSTM Neural Networks In: IEEE International Conference on Systems, Man and Cybernetics (SMC). bll 2858–2865. IEEE, Budapest, Hungary (2016). https://doi.org/10.1109/smc.2016.7844673.
  • Kalkowski, E., Sick, B.: Correlation of Ontology-Based Semantic Similarity and Human Judgement for a Domain Specific Fashion Ontology In: International Conference on Web Engineering (ICWE). bll 207–224. Springer (2016). https://doi.org/10.1007/978-3-319-38791-8_12.
  • Kreil, M., Sick, B., Lukowicz, P.: Coping with variability in motion based activity recognition In: International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR). bll 1–8. , Rostock, Germany (2016). https://doi.org/10.1145/2948963.2948967.
  • Breker, S., Sick, B.: Combinations of uncertain ordinal expert statements: The combination rule EIDMR and its application to low-voltage grid classification with SVM In: International Joint Conference on Neural Networks (IJCNN). bll 2164–2173. IEEE, Vancouver, BC (2016). https://doi.org/10.1109/ijcnn.2016.7727467.
  • 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 In: ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp). bll 353–356. ACM, Heidelberg, Germany (2016). https://doi.org/10.1145/2968219.2971383.
  • Gensler, A., Sick, B., Pankraz, V.: An Analog Ensemble-Based Similarity Search Technique for Solar Power Forecasting In: IEEE International Conference on Systems, Man and Cybernetics (SMC). bll 2850–2857. IEEE (2016). https://doi.org/10.1109/SMC.2016.7844672.
  • Gensler, A., Sick, B., Vogt, S.: A Review of Deterministic Error Scores and Normalization Techniques for Power Forecasting Algorithms In: IEEE Symposium Series on Computational Intelligence (SSCI). bll 1–9. IEEE, Athens, Greece (2016). https://doi.org/10.1109/ssci.2016.7849848.
  • 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 In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–8. VDE, Nuremberg, Germany (2016).
2015[ to top ]
  • Kalkowski, E., Sick, B.: Using Ontology-Based Similarity Measures to Find Training Data for Problems with Sparse Data In: IEEE International Conference on Systems, Man and Cybernetics (SMC). bll 1693–1699. IEEE, Hongkong, China (2015). https://doi.org/10.1109/smc.2015.298.
  • 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). https://doi.org/10.1016/j.ins.2014.09.009.
  • Goldhammer, M., Köhler, S., Doll, K., Sick, B.: Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptions In: SAI Intelligent Systems Conference (IntelliSys). bll 259–279. Springer, Cham, Switzerland (2015). https://doi.org/10.1007/978-3-319-33386-1_13.
  • 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). https://doi.org/10.1016/j.ins.2015.06.027.
  • Hähner, J., Brinkschulte, U., Lukowicz, P., Mostaghim, S., Sick, B., Tomforde, S.: Runtime Self-Integration as Key Challenge for Mastering Interwoven Systems In: International Conference on Architecture of Computing Systems (ARCS). bll 1–8. VDE, Porto, Portugal (2015).
  • Kalkowski, E., Sick, B.: Generative Exponential Smoothing Models for Rate Forecasting with Uncertainty Estimation In: International Work-Conference on Time Series (ITISE). bll 806–817. , Granada, Spain (2015).
  • Gensler, A., Gruber, T., Sick, B.: Fast Feature Extraction for Time Series Analysis Using Least-squares Approximations with Orthogonal Basis Functions In: International Symposium on Temporal Representation and Reasoning (TIME). bll 29–37. IEEE, Kassel, Germany (2015). https://doi.org/10.1109/time.2015.21.
  • Breker, S., Sick, B.: Effiziente Bewertung des Anschlu\ss{}potentials von Niederspannungsnetzen für dezentrale Erzeugungsanlagen: Klassifikation mit Methoden der Computational Intelligence In: Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS). bll 51–56. , Hamburg, Germany (2015). https://doi.org/10.1007/978-3-658-10958-5_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).
  • 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). https://doi.org/10.1109/tpwrs.2014.2332361.
  • 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 In: SAI Intelligent Systems Conference (IntelliSys). bll 390–399. Springer, London, UK (2015). https://doi.org/10.1109/IntelliSys.2015.7361171.
  • Rudolph, J., Breker, S., Sick, B.: Bewertung verschiedener Spannungsregelungskonzepte in einem einspeisegeprägten Mittelspannungsnetz und Ausblick auf neue Konzepte basierend auf Methoden der Computational Intelligence In: Tagung Nachhaltige Energieversorgung und Integration von Speichern (NEIS). bll 57–63. , Hamburg, Germany (2015). https://doi.org/10.1007/978-3-658-10958-5_9.
  • Stone, T.C., Huber, A., Siwy, R., Sick, B.: Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen Elektronik automotive. 2, 32–36 (2015).
  • Rudolph, S., Tomforde, S., Sick, B., Heck, H., Wacker, A., Hähner, J.: An Online Influence Detection Algorithm for Organic Computing Systems In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–8. VDE, Porto, Portugal (2015).
  • Jahn, A., Lau, S.L., David, K., Sick, B.: A Tool Chain for Context Detection Automating the Investigation of a Multitude of Parameter Sets In: International Workshop on Mobile and Context Aware Services (MOCS), VTC. bll 1–5. , Boston, MA (2015).
  • Calma, A., Reitmaier, T., Sick, B., Lukowicz, P., Embrechts, M.: A New Vision of Collaborative Active Learning arXiv e-prints. arXiv:1504.00284 (2015).
  • Rudolph, S., Tomforde, S., Sick, B., Hähner, J.: A Mutual Influence Detection Algorithm for Systems with Local Performance Measurement In: IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO). bll 144–149. IEEE, Cambridge, MA (2015). https://doi.org/10.1109/saso.2015.23.
  • Embrechts, M., Sick, B.: A Generalized Hebb (GH) rule based on a cross-entropy error function for deep belief recursive learning In: International Conference on Neural Networks - Fuzzy Systems (NN-FS). bll 21–24. , Vienna, Austria (2015).
  • Gruhl, C., Sick, B., Wacker, A., Tomforde, S., Hähner, J.: A building block for awareness in technical systems: Online novelty detection and reaction with an application in intrusion detection In: IEEE International Conference on Awareness Science and Technology (iCAST). bll 194–200. IEEE, Qinhuangdao, China (2015). https://doi.org/10.1109/icawst.2015.7314046.
2014[ to top ]
  • Gensler, A., Sick, B., Willkomm, J.: Temporal data analytics based on eigenmotif and shape space representations of time series In: IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP). bll 753–757. IEEE, Xian, China (2014). https://doi.org/10.1109/chinasip.2014.6889345.
  • 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: Transdisziplinäre Konferenz zum Thema "Technische Unterstützungssysteme, die die Menschen wirklich wollen". bll 315–327 (2014).
  • Jänicke, M., Sick, B., Lukowicz, P., Bannach, D.: Self-Adapting Multi-sensor Systems: A Concept for Self-Improvement and Self-Healing Techniques In: Workshop on Self-Improving System Integration (SISSY), SASO. bll 128–136. IEEE, London, UK (2014). https://doi.org/10.1109/sasow.2014.22.
  • Herwig, B., Frommann, U., Gruber, T., Sick, B.: Programmierkompetenz prüfen … am Beispiel der Vorlesung "Einführung in C" an der Universität Kassel In: Neues Handbuch Hochschullehre. bll 71–94. Raabe (2014).
  • Goldhammer, M., Doll, K., Brunsmann, U., Gensler, A., Sick, B.: Pedestrian’s Trajectory Forecast in Public Traffic with Artificial Neural Networks In: International Conference on Pattern Recognition (ICPR). bll 4110–4115. IEEE, Stockholm, Sweden (2014). https://doi.org/10.1109/icpr.2014.704.
  • 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). https://doi.org/10.1016/j.ins.2014.05.025.
  • Gensler, A., Sick, B., Pankraz, V.: Novel Criteria to Measure Performance of Time Series Segmentation Techniques In: Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML), LWA. bll 192–204. , Aachen, Germany (2014).
  • Stone, T., Birth, O., Gensler, A., Huber, A., Jänicke, M., Sick, B.: Location based learning of user behavior for proactive recommender systems in car comfort functions In: INFORMATIK 2014. bll 2121–2132. Gesellschaft für Informatik e.V (2014).
  • Fisch, D., Kalkowski, E., Sick, B.: Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining Applications IEEE Transactions on Knowledge and Data Engineering. 26, 652–666 (2014). https://doi.org/10.1109/tkde.2013.20.
  • Tomforde, S., Hähner, J., Sick, B.: Interwoven Systems Informatik Spektrum. 37, 483–487 (2014). https://doi.org/10.1007/s00287-014-0827-z.
  • Tomforde, S., Hähner, J., Seebach, H., Reif, W., Sick, B., Wacker, A., Scholtes, I.: Engineering and Mastering Interwoven Systems In: International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll 1–8. VDE, L"ubeck, Germany (2014).
  • Kreil, M., Sick, B., Lukowicz, P.: Dealing with human variability in motion based, wearable activity recognition In: Symposium on Activity and Context Modeling and Recognition (ACOMORE), PerCom. bll 36–40. IEEE, Budapest, Hungary (2014). https://doi.org/10.1109/percomw.2014.6815161.
  • Goldhammer, M., Hubert, A., Köhler, S., Zindler, K., Brunsmann, U., Doll, K., Sick, B.: Analysis on termination of pedestrians’ gait at urban intersections In: IEEE International Conference on Intelligent Transportation Systems (ITSC). bll 1758–1763. IEEE, Qingdao, China (2014). https://doi.org/10.1109/itsc.2014.6957947.
  • Tomforde, S., Hähner, J., von Mammen, S., Gruhl, C., Sick, B., Geihs, K.: "Know thyself" -- Computational Self-Reflection in Intelligent Technical Systems In: Workshop on Self-Improving System Integration (SISSY), SASO. IEEE, Braunschweig, Germany (2014). https://doi.org/10.1109/SASOW.2014.25.
2013[ to top ]
  • 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). https://doi.org/10.1016/j.ins.2012.11.015.
  • 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). https://doi.org/10.1109/tevc.2012.2185845.
  • Gensler, A., Gruber, T., Sick, B.: Blazing Fast Time Series Segmentation Based on Update Techniques for Polynomial Approximations In: International Workshop on Spatial and Spatio-Temporal Data Mining (SSTDM), ICDM. bll 1002–1011. IEEE, Dallas, TX (2013). https://doi.org/10.1109/icdmw.2013.90.
  • 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: Workshop on Self-Optimisation in Autonomic and Organic Computing Systems (SAOS), ARCS. bll 1–13. VDE, Prague, Czech Republic (2013).
2012[ to top ]
  • 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). https://doi.org/10.1145/2168260.2168276.
  • 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). https://doi.org/10.1016/j.artint.2012.04.002.
  • 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). https://doi.org/10.1016/j.asoc.2011.11.022.
  • 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 In: International Joint Conference on Neural Networks (IJCNN). bll 1–8. IEEE, Brisbane, Australia (2012). https://doi.org/10.1109/ijcnn.2012.6252739.
  • Giedl-Wagner, R., Miller, T., Sick, B.: Determination of Optimal CT Scan Parameters Using Radial Basis Function Neural Networks In: Conference on Industrial Computed Tomography (iCT). bll 221–228. , Wels, Austria (2012).
2011[ to top ]
  • Gottfried, T., Fliege, R., Frömberg, J., Heckmann, G., Sick, B., Triller, U., Wunsch, M.: Wissenschaftspropädeutisches Arbeiten im W-Seminar: Grundlagen -- Chancen -- Herausforderungen, (2011).
  • Fisch, D., Gruber, T., Sick, B.: SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis IEEE Transactions on Knowledge and Data Engineering. 23, 774–787 (2011). https://doi.org/10.1109/tkde.2010.161.
  • 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). https://doi.org/10.1109/tdsc.2009.36.
  • 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). https://doi.org/10.1007/978-3-0348-0130-0.
  • Fisch, D., Kalkowski, E., Sick, B., Ovaska, S.: In your interest: Objective interestingness measures for a generative classifier In: International Conference on Agents and Artificial Intelligence (ICAART). bll 414–423. , Rome, Italy (2011). https://doi.org/10.5220/0003186404140423.
  • 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). https://doi.org/10.1007/978-3-0348-0130-0_3.
  • 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). https://doi.org/10.1007/978-3-0348-0130-0_17.
  • Bannach, D., Sick, B., Lukowicz, P.: Automatic Adaptation of Mobile Activity Recognition Systems to New Sensors In: Workshop Mobile Sensing: Challenges, Opportunities, and Future Directions, UbiComp. bll 1–5. ACM, Beijing, China (2011).
  • Reitmaier, T., Sick, B.: Active classifier training with the 3DS strategy In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM). bll 88–95. IEEE, Paris, France (2011). https://doi.org/10.1109/cidm.2011.5949421.
2010[ to top ]
  • Fuchs, E., Gruber, T., Pree, H., Sick, B.: Temporal Data Mining Using Shape Space Representations of Time Series Neurocomputing. 74, 379–393 (2010). https://doi.org/10.1016/j.neucom.2010.03.022.
  • Fisch, D., Jänicke, M., Sick, B., Müller-Schloer, C.: Quantitative Emergence -- A Refined Approach Based on Divergence Measures In: IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO). bll 94–103. IEEE, Budapest, Hungary (2010). https://doi.org/10.1109/saso.2010.31.
  • 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). https://doi.org/10.1109/tsmcb.2009.2034382.
  • 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). https://doi.org/10.1109/tpami.2010.44.
2007[ to top ]
  • Dose, M., Gruber, C., Grunz, A., Hook, C., Kempf, J., Scharfenberg, G., Sick, B.: Towards an Automated Analysis of Neuroleptics’ Impact on Human Hand Motor Skills In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), SSCI. bll 494–501. IEEE (2007).
2006[ to top ]
  • Hofer, J., Gruber, C., Sick, B.: Biometric Analysis of Handwriting Dynamics Using a Script Generator Model In: IEEE Mountain Workshop on Adaptive and Learning Systems. bll 36–41. IEEE, Logan (2006). https://doi.org/10.1109/smcals.2006.250689.