Publications

Oliver Kirsten, Martin Bogdan, und Sophie Adama. Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient. 2023 7th International Conference on Imaging, Signal Processing and Communications (ICISPC), 37-41, 2023. [PUMA: Complexity Computational Consciousness Information Learning Locked-In Machine Measures Modeling Neuroscience Prediction Predictive Processing Signal Syndrome Theory Training Zno algorithms and data learning modeling models processing]

Andre de Carvalho, Robson Bonidia, Jude Dzevela Kong, Mariana Dauhajre, Claudio Struchiner, Guilherme Goedert, Peter F. Stadler, Maria Emilia Walter, Danilo Sanches, Troy Day, Marcia Castro, John Edmunds, Manuel Colome-Hidalgo, Demian Arturo Herrera Morban, Edian F. Franco, Cesar Ugarte-Gil, Patricia Espinoza-Lopez, Gabriel Carrasco-Escobar, und Ulisses Rocha. Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries. arXiv, 2024. [PUMA: Artificial Computer FOS Intelligence Zno and information sciences] URL

Lucas Lange, Maurice-Maximilian Heykeroth, und Erhard Rahm. Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning. arXiv preprint arXiv:2409.01329, arXiv, September 2024. [PUMA: (cs.CR), (cs.CV), (cs.LG), Computer Cryptography FOS: Learning Machine Pattern Recognition Security Vision and area_bigdata area_responsibleai ep information sciences xack yaff]

Patrick Ebel, Florian Brokhausen, und Andreas Vogelsang. The Role and Potentials of Field User Interaction Data in the Automotive UX Development Lifecycle: An Industry Perspective. 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 141–150, Association for Computing Machinery, New York, NY, USA, 2020. [PUMA: experience in-vehicle information interview study systems user zno] URL

Patrick Ebel, Kim Julian Gülle, Christoph Lingenfelder, und Andreas Vogelsang. ICEBOAT: An Interactive User Behavior Analysis Tool for Automotive User Interfaces. Adjunct Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, Association for Computing Machinery, New York, NY, USA, 2022. [PUMA: Data Design Driving Human-Computer In-Vehicle Information Interaction Naturalistic System Tools Visualization topic_visualcomputing zno] URL

Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini, und Christian L. Müller. Stability selection enables robust learning of partial differential equations from limited noisy data. arXiv, 2019. [PUMA: (cs.LG), (math.NA), (physics.data-an), Analysis Analysis, Computer Data FOS: Learning Machine Mathematics, Numerical Physical Probability Statistics and information sciences sciences, xack] URL

Kim Breitwieser, Allison Lahnala, Charles Welch, Lucie Flek, und Martin Potthast. Modeling Proficiency with Implicit User Representations. arXiv, 2021. [PUMA: (cs.CL), Computation Computer FOS: Language and information sciences sciences, zno] URL

Matti Wiegmann, Jennifer Rakete, Magdalena Wolska, Benno Stein, und Martin Potthast. If there's a Trigger Warning, then where's the Trigger? Investigating Trigger Warnings at the Passage Level. arXiv, 2024. [PUMA: topic_language (cs.CL), (cs.CY), Computation Computer Computers FOS: Language Society and information sciences sciences,] URL

Stefan Heindorf, Yan Scholten, Henning Wachsmuth, Axel-Cyrille Ngonga Ngomo, und Martin Potthast. CauseNet: Towards a Causality Graph Extracted from the Web. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 3023–3030, Association for Computing Machinery, New York, NY, USA, 2020. [PUMA: causality extraction, graph, information knowledge] URL

Lukas Gienapp, Maik Fröbe, Matthias Hagen, und Martin Potthast. The Impact of Negative Relevance Judgments on NDCG. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2037–2040, Association for Computing Machinery, New York, NY, USA, 2020. [PUMA: cumulated discounted evaluation, gain, information judgements, normalized relevance reliability, retrieval, stability] URL

Lukas Gienapp, Benno Stein, Matthias Hagen, und Martin Potthast. Estimating Topic Difficulty Using Normalized Discounted Cumulated Gain. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2033–2036, Association for Computing Machinery, New York, NY, USA, 2020. [PUMA: cumulative difficulty discounted evaluation, gain, information normalized retrieval, topic] URL

Ines Zelch, Matthias Hagen, und Martin Potthast. A User Study on the Acceptance of Native Advertising in Generative IR. Proceedings of the 2024 Conference on Human Information Interaction and Retrieval, 142–152, Association for Computing Machinery, New York, NY, USA, 2024. [PUMA: Advertising Generative LLMs Search information retrieval topic_language yaff] URL