Publications

Johannes Kiesel, Fabienne Hubricht, Benno Stein, und Martin Potthast. A Dataset for Content Error Detection in Web Archives. In Maria Bonn, Stephen J. Downie, Alain Martaus, und Dan Wu (Hrsg.), 18th ACM/IEEE Joint Conference on Digital Libraries (JCDL 2019), 349--350, ACM, Juni 2019. [PUMA: Archives Content Dataset Detection Error Web Zno]

Eva Zangerle, Michael Tschuggnall, Günther Specht, Benno Stein, und Martin Potthast. Overview of the Style Change Detection Task at PAN 2019. In Linda Cappellato, Nicola Ferro, David E. Losada, und Henning Müller (Hrsg.), Working Notes Papers of the CLEF 2019 Evaluation Labs, (2380)September 2019. [PUMA: 2019 Change Detection Overview PAN Style Task Zno] URL

Miriam Louise Carnot, Lorenz Heinemann, Jan Braker, Tobias Schreieder, Johannes Kiesel, Maik Fröbe, Martin Potthast, und Benno Stein. On Stance Detection in Image Retrieval for Argumentation. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2562–2571, Association for Computing Machinery, New York, NY, USA, 2023. [PUMA: Xack argumentation detection image retrieval stance] URL

Victor Jüttner, Martin Grimmer, und Erik Buchmann. ChatIDS: Explainable Cybersecurity Using Generative AI. 2023. [PUMA: ChatGPT Detection Intrusion Networks Yaff]

Maksim Kukushkin, Matthias Enders, Reinhard Kaschuba, Martin Bogdan, und Thomas Schmid. Canola seed or not? Autoencoder-based Anomaly Detection in AgriculturalSeedProduction. INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 1645--1652, Gesellschaft für Informatik e.V., Bonn, 2023. [PUMA: Agricultural Anomaly Autoencoder Detection Production Seed Zno]

Siavash Ghiasvand, und Florina M. Ciorba. Anomaly Detection in High Performance Computers: A Vicinity Perspective. 2019 18th International Symposium on Parallel and Distributed Computing (ISPDC), 112--120, Amsterdam, Netherlands, Juni 2019. [PUMA: Anomaly Bridges, Computer Correlation, Graphics HPC Hardware, Resource analysis, anomaly anonymized approach approach, architecture, components, computerised computers, computing computing, detection detection, exascale failure hardware, high hpc, instrumentation, management, mechanism, myOwn node performance prediction, privacy, processing sensors, statistical system system, units, vicinity, vicinity-based,]

Masoud Taghikhah, Nishant Kumar, Sinisa Segvić, Abouzar Eslami, und Stefan Gumhold. Quantile-based maximum likelihood training for outlier detection. Proc. Conf. AAAI Artif. Intell., (38)19:21610--21618, Association for the Advancement of Artificial Intelligence (AAAI), März 2024. [PUMA: topic_visualcomputing Quantile-based detection likelihood maximum outlier training]

Nishant Kumar, Siniša Šegvić, Abouzar Eslami, und Stefan Gumhold. Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection. 2023. [PUMA: topic_visualcomputing Detection Feature Flow Normalizing Object Outlier-Aware Synthesis] URL