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2022
Slijepcevic, D., Horst, F., Lapuschkin, S., Horsak, B., Raberger, A.-M., Kranzl, A., Samek, W., Breitender, C., Schöllhorn, W., & Zeppelzauer, M. (2022). Explaining Machine Learning Models for Clinical Gait Analysis. ACM Transactions on Computing for Healthcare, 3(2), 14:1–14:27. https://doi.org/10/gnt2s9
2021
Bernard, Jürgen, Hutter, M., Sedlmair, M., Zeppelzauer, Matthias, & Munzner, Tamara. (2021). A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling. ACM Transactions on Interactive Intelligent Systems (TiiS), 11(3–4), 1–42. https://doi.org/10/gnt2wf
Dumphart, B., Slijepčević, D., Unglaube, F., Kranzl, A., Baca, A., Zeppelzauer, M., & Horsak, B. (2021). An automated deep learning-based gait event detection algorithm for various pathologies. Gait & Posture, 90, 50–51. https://doi.org/https://doi.org/10.1016/j.gaitpost.2021.09.026
Eigner, O., Eresheim, S., Kieseberg, P., Klausner, L. D., Pirker, M., Priebe, T., Tjoa, S., Marulli, F., Mercaldo, F., & Priebe, T. (2021). Towards Resilient Artificial Intelligence: Survey and Research Issues. Proceedings of the 2021 IEEE International Conference on Cyber Security and Resilience, 536–542. https://doi.org/10.1109/CSR51186.2021.9527986
Holzinger, A., Weippl, E., Tjoa, A. M., & Kieseberg, P. (2021). Digital Transformation for Sustainable Development Goals (SDGs) - A Security, Safety and Privacy Perspective on AI. In A. Holzinger, P. Kieseberg, A. M. Tjoa, & E. Weippl (Eds.), Machine Learning and Knowledge Extraction (pp. 1–20). Springer International Publishing.
Holzinger, A., Kieseberg, P., Tjoa, A. M., & Weippl, E. (2021). 5th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2021 Virtual Event, August 17–20, 2021 Proceedings. Springer. https://link.springer.com/book/10.1007/978-3-030-84060-0?utm_medium=referral&utm_source=google_books&utm_campaign=3_pier05_buy_print&utm_content=en_08082017
Kieseberg, P., Schrittwieser, S., & Weippl, E. (2021). Secure Internal Data Markets. Future Internet, 13(8). https://doi.org/https://doi.org/10.3390/fi13080208
Krondorfer, P., Slijepčević, D., Unglaube, F., Kranzl, A., Breiteneder, C., Zeppelzauer, M., & Horsak, B. (2021). Deep learning-based similarity retrieval in clinical 3D gait analysis. Gait & Posture, 90, 127–128. https://doi.org/https://doi.org/10.1016/j.gaitpost.2021.09.066
Nurgazina, J., Felberbauer, T., Asprion, B., & Pinnamaraju, P. (2021). Visualization and clustering for rolling forecast quality verification: A case study in the automotive industry. 3rd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2021), Linz.
Pellegrini, T., Beraha, D., Gladyshev, M., de Grosbois, J., Hakopov, Z., Jehadeesan, R., Markov, A., Marmonti, E., & Nenadic, G. (2021). Exploring Semantic Technologies and Their Application to Nuclear Knowledge Management. INTERNATIONAL ATOMIC ENERGY AGENCY. https://www.iaea.org/publications/13469/exploring-semantic-technologies-and-their-application-to-nuclear-knowledge-management
Slijepčević, D., Henzl, M., Klausner, L. D., Dam, T., Kieseberg, P., & Zeppelzauer, M. (2021). k‑Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers. Computers & Security, 111, 19. https://doi.org/10/gnt2wd
Stöger, K., Schneeberger, D., Kieseberg, P., & Holzinger, A. (2021). Legal aspects of data cleansing in medical AI. Computer Law & Security Review, 42. https://doi.org/https://doi.org/10.1016/j.clsr.2021.105587
Zielinski, B., Lipinski, M., Juda, M., Zeppelzauer, Matthias, & Dlotko, Pawel. (2021). Persistence Codebooks for Topological Data Analysis. Journal of Artificial Intelligence Review, 54, 1969–2009. https://doi.org/https://doi.org/10.1007/s10462-020-09897-4
2020
Hogan, A., Blomqvist, E., Cochez, M., d"Amato, C., de Melo, G., Gutierrez, C., Gayo, J. E. L., Kirrane, S., Neumaier, S., Polleres, A., Navigli, R., Ngomo, A.-C. N., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2020). Knowledge Graphs. ArXiv:2003.02320 [Cs]. http://arxiv.org/abs/2003.02320
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
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
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
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.
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