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2022
Boigner, P., & Luh, R. (2022). WSL2 Forensics: Detection, Analysis & Revirtualization. The 17th International Conference on Availability, Reliability and Security. https://doi.org/https://doi.org/10.1145/3538969.3544439
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. https://doi.org/https://doi.org/10.1016/j.procs.2022.01.304
2021
Adensamer, A., Gsenger, R., & Klausner, L. D. (2021). “Computer Says No”: Algorithmic Decision Support and Organisational Responsibility. Journal of Responsible Technology, 7–8. https://doi.org/10/gm6t7q
Adensamer, A., & Klausner, L. D. (2021). “Part Man, Part Machine, All Cop”: Automation in Policing. Frontiers in Artificial Intelligence, 2021(4). https://doi.org/10/gk3q27
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
Galhuber, M., & Luh, R. (2021). Time for Truth: Forensic Analysis of NTFS Timestamps. The 16th International Conference on Availability, Reliability and Security. https://doi.org/10/gnhmbb
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
Neumaier, S., Havur, G., & Pellegrini, T. (2021). Towards an Architecture for Policy-Aware Decentral Dataset Exchange. SEMAPRO 2021, The Fifteenth International Conference on Advances in Semantic Processing. SEMAPRO 2021, The Fifteenth International Conference on Advances in Semantic Processing, Barcelona. https://www.thinkmind.org/index.php?view=article&articleid=semapro_2021_1_40_30020
Pirker, M., & Piller, E. (2021, August 17). Obstacles and Challenges in Transforming Applications for Distributed Data Ledger Integration. Proceedings of the 16th International Workshop on Frontiers in Availability, Reli- Ability and Security (FARES). ARES 2021: The 16th International Conference on Availability, Reliability and Security. https://doi.org/10/gn622r
Priebe, T., Neumaier, S., & Markus, S. (2021). Finding Your Way Through the Jungle of Big Data Architectures. 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA. https://doi.org/10/gn7mtm
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/10.1016/j.cose.2021.102488
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
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., & 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
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., Blomqvist, E., Groth, P., de Boer, V., Alam, M., Käfer, T., Kieseberg, P., Kirrane, S., Meroño-Peñuela, A., & Pandit, H. J. (Eds.). (2020). Semantic Systems. In the Era of Knowledge Graphs: 16th International Conference on Semantic Systems, SEMANTiCS 2020, Amsterdam, The Netherlands, September 7–10, 2020, Proceedings (Vol. 12378). Springer International Publishing. https://doi.org/10.1007/978-3-030-59833-4
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