Publications
Filtered:
Ordered by year and author
2020
Holzinger, A., Kieseberg, P., & Müller, H. (2020). KANDINSKY Patterns: A Swiss-Knife for the Study of Explainable AI. ERCIM-News, 120, 41–42. https://phaidra.fhstp.ac.at/o:4336
Holzinger, A., Kieseberg, P., Tjoa, A. M., & Weippl, E. (2020). Machine Learning and Knowledge Extraction: Fourth IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2020. Springer. https://link.springer.com/book/10.1007/978-3-030-57321-8
Horsak, B., Dumphart, B., Slijepcevic, D., & Zeppelzauer, M. (2020). Explainable Artificial Intelligence (XAI) und ihre Anwendung auf Klassifikationsprobleme in der Ganganalyse. Abstractband Des 3. GAMMA Kongress. 3. GAMMA Kongress, München, Deutschland.
Horsak, B., Slijepcevic, D., Raberger, A.-M., Schwab, C., Worisch, M., & Zeppelzauer, M. (2020). GaitRec, a large-scale ground reaction force dataset of healthy and impaired gait. Scientific Data, 7:143(1), 1–8. https://doi.org/10/gh372d
Horst, F., Slijepcevic, D., Zeppelzauer, M., Raberger, A. M., Lapuschkin, S., Samek, W., Schöllhorn, W. I., Breiteneder, C., & Horsak, B. (2020). Explaining automated gender classification of human gait. Gait & Posture, 81, supplement 1, 159–160. https://doi.org/10/ghr9k6
Longo, L., Goebel, R., Lecue, F., Kieseberg, P., & Holzinger, A. (2020, August 27). Explainable Artificial Intelligence: Concepts, Applications, Research Challenges and Visions. International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Virtuell.
Pellegrini, T., & Litschka, M. (2020). Überlegungen zur Governance von Open Data – eine institutionenökonomische Perspektive. In J. Müller-Lietzkow (Ed.), Beyond Digital (Vol. 13, pp. 111–130). Nomos Verlagsgesellschaft mbH & Co. KG. https://doi.org/10.5771/9783748905240-111
Schacht, B., & Kieseberg, P. (2020). An Analysis of 5 Million OpenPGP Keys. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 11(3), 107–140. http://isyou.info/jowua/papers/jowua-v11n3-6.pdf
Slijepcevic, D., Zeppelzauer, M., Schwab, Caterine, Raberger, A.-M., Breitender, C., & Horsak, B. (2020). Input Representations and Classification Strategies for Automated Human Gait Analysis. Gait & Posture, 76, 198–203. https://doi.org/10/ghz24x
2019
Amiri, F., Quirchmayr, G., Kieseberg, P., Bertone, A., & Weippl, E. (2019). Efficiently Vectorized Anonymization in Data Mining using Genetic Algorithms. Proceedings of the 34th International Conference on ICT Systems Security and Privacy Protection-IFIP SEC 2019.
Despotovic, M., Koch, D., Leiber, S., Döller, M., Sakeena, M., & Zeppelzauer, M. (2019). Prediction and analysis of heating energy demand for detached houses by computer vision. Energy & Buildings, 193, 29–35. https://doi.org/10/fsxn
Litschka, M., & Pellegrini, T. (2019). Considerations on the Governance of Open Data – an Institutional Economic Perspective. International Journal of Intellectual Property Management, 9(3/4), 247–263. https://doi.org/10/gnk9k3
Seidl, Markus, & Zeppelzauer, Matthias. (2019). Towards Distinction of Rock Art Pecking Styles with a Hybrid 2D/3D Approach. Proceedings of the International Conference on Content-Based Multimedia Indexing (CBMI), 4.
Slijepcevic, D., Raberger, A.-M., Zeppelzauer, M., Dumphart, B., Breiteneder, C., & Horsak, B. (2019). On the usefullness of statistical parameter mapping for feature selection in automated gait classification. Book of Abstracts of the 25th Conference of the European Society of Biomechanics (ESB), 1.
Slijepcevic, D., Raberger, A.-M., Zeppelzauer, M., Dumphart, B., Breiteneder, C., & Horsak, B. (2019). On the usefulness of statistical parameter mapping for feature selection in automated gait classification. Book of Abstracts of the 25th Conference of the European Society of Biomechanics (ESB), 1.
Zielinski, B., Lipinski, Michal, Juda, M., Zeppelzauer, M., & Dlotko, Pawel. (2019). Persistence Bag-of-Words for Topological Data Analysis. Proceedings of the International Joint Conference on Artificial Intelligence 2019, 6. https://doi.org/10/ghpp7z
2018
Amiri, F., Quirchmayr, G., & Kieseberg, P. (2018). A Machine Learning Approach for Privacy-preservation in E-business Applications: Proceedings of the 15th International Joint Conference on E-Business and Telecommunications, 443–452. https://doi.org/10/gh38cd
Bernard, J., Zeppelzauer, M., Sedlmair, M., & Aigner, W. (2018). VIAL – A Unified Process for Visual-Interactive Labeling. The Visual Computer, 34(1189), 16. https://doi.org/10/gd5hr3
Bernard, J., Zeppelzauer, M., Lehmann, M., Müller, M., & Sedlmair, M. (2018). Towards User-Centered Active Learning Algorithms. Computer Graphics Forum, 37, 121–132. https://doi.org/10/gdw79h
Fensel, A., de Boer, V., Pellegrini, T., Kiesling, E., Haslhofer, B., Hollink, L., & Schindler, A. (2018). Proceedings of the 14th International Conference on Semantic Systems (Vol. 137). Elsevier. https://www.sciencedirect.com/journal/procedia-computer-science/vol/137