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Dumphart, B., Slijepcevic, D., Kranz, A., Zeppelzauer, M., & Horsak, B. (2023). Is it time to re-think the appropriateness of autocorrelation for gait event detection? Preliminary results of an ongoing study. Gait & Posture, 106, S50–S51.
Dumphart, B., Slijepcevic, D., Zeppelzauer, M., Kranzl, A., Unglaube, F., Baca, A., & Horsak, B. (2023). Robust deep learning-based gait event detection across various pathologies. PLOS ONE, 18(8), e0288555.
Horst, F., Slijepcevic, D., Simak, M., Horsak, B., Schöllhorn, W. I., & Zeppelzauer, M. (2023). Modeling biological individuality using machine learning: A study on human gait. Computational and Structural Biotechnology Journal, 21, 3414–3423.
Kovac, F. (2023, June 3). [Keynote] Standing Still Is Not An Option: Alternative Baselines for Attainable Utility Preservation. Machine Learning Prague (MLPRAGUE) 2023, Prague, Czech Republic.
Slijepcevic, D., Zeppelzauer, M., Unglaube, F., Kranzl, A., Breiteneder, C., & Horsak, B. (2023). Explainable Machine Learning in Human Gait Analysis: A Study on Children With Cerebral Palsy. IEEE Access, 11, 65906–65923.
Slijepcevic, D., Zeppelzauer, M., Unglaube, F., Kranzl, A., Breiteneder, C., & Horsak, B. (2023). Towards more transparency: The utility of Grad-CAM in tracing back deep learning based classification decisions in children with cerebral palsy. Gait & Posture, 100, 32–33.
Hogan, A., Cochez, M., Melo, G. de, & Neumaier, S. (2022). Knowledge graphs. Morgan & Claypool Publishers.
Holzinger, A., Kieseberg, P., Tjoa, A. M., & Weippl, E. (2022). 6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2022 Vienna, August 23–26, 2022 Proceedings. Springer.
Kovac, F., Eigner, O., Adrowitzer, A., Scholnast, H., & Buchelt, A. (2022). Classification of rain events using directional radio data of commercial microwave links. 2022 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), 1–6.
Nurgazina, J., Felberbauer, T., Asprion, B., & Pinnamaraju, P. (2022). Visualization and clustering for rolling forecast quality verification: A case study in the automotive industry. Procedia Computer Science, 200, 1048–1057.
Rind, A., Slijepcevic, D., Zeppelzauer, M., Unglaube, F., Kranzl, A., & Horsak, B. (2022). Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy. Proc. 2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX), 7–15.
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.
Slijepcevic, D., Horst, F., Simak, M., Lapuschkin, S., Raberger, A. M., Samek, W., Breiteneder, C., Schöllhorn, W. I., Zeppelzauer, M., & Horsak, B. (2022). Explaining machine learning models for age classification in human gait analysis. Gait & Posture, 97, S252–S253.
Adensamer, A., Gsenger, R., & Klausner, L. D. (2021). “Computer Says No”: Algorithmic Decision Support and Organisational Responsibility. Journal of Responsible Technology, 7–8.
Adensamer, A., & Klausner, L. D. (2021). “Part Man, Part Machine, All Cop”: Automation in Policing. Frontiers in Artificial Intelligence, 2021(4).
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.
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.
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.
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. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1–37.
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.