Data Science Chair

About Us

The Data Science Chair (computer science institute JMU) is a highly interdisciplinary group of researchers led by Prof. Andreas Hotho and member of CAIDAS. We are active in data science and machine learning research with focus on deep learning and applications in multiple domains.

Address

We have moved! We're now located at:

University of Würzburg
Data Science Chair (Computer Science X)
Emil-Fischer-Straße 50
97074 Würzburg

Phone.: +49 931 31-89675
sekretariat.lsx@uni-wuerzburg.de

We are part of:

Latest Publications

Enhancing Sequential Next-Item Prediction through Modelling Non-Item Pages. Fischer, Elisabeth; Schlör, Daniel; Zehe, Albin; Hotho, Andreas. 2023
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Evaluating feature relevance XAI in network intrusion detection. Tritscher, Julian; Wolf, Maximilian; Hotho, Andreas; Schlör, Daniel. In The World Conference on eXplainable Artificial Intelligence (xAI 2023) - to appear. 2023
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Feature relevance XAI in anomaly detection: Reviewing approaches and challenges. Tritscher, Julian; Krause, Anna; Hotho, Andreas. In Frontiers in Artificial Intelligence, Vol. 6. 2023
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Long-Term Effects of Perceived Friendship with Intelligent Voice Assistants on Usage Behavior, User Experience, and Social Perceptions. Wienrich, Carolin; Carolus, Astrid; Markus, André; Augustin, Yannik; Pfister, Jan; Hotho, Andreas. In Computers, Vol. 12, Issue 4. 2023
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Towards a Computational Analysis of Suspense: Detecting Dangerous Situations. Zehe, Albin; Schröter, Julian; Hotho, Andreas. 2023
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Zero-Shot Clickbait Spoiling by Rephrasing Titles as Questions. Wangsadirdja, Dirk; Pfister, Jan; Kobs, Konstantin; Hotho, Andreas. In Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), pp. 1090–1095. Association for Computational Linguistics, Toronto, Canada, 2023
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Optimizing Medical Service Request Processes through Language Modeling and Semantic Search. Schl{ö}r, Daniel; Pfister, Jan; Hotho, Andreas. In 2023 the 7th International Conference on Medical and Health Informatics (ICMHI), of ICMHI 2023, pp. 136–141. Association for Computing Machinery, Kyoto, Japan, 2023
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Swarming Detection in Smart Beehives Using Auto Encoders for Audio Data. Janetzky, Pascal; Schaller, Melanie; Krause, Anna; Hotho, Andreas. In 2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 1–5. 2023
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CapsKG: Enabling Continual Knowledge Integration in Language Models for Automatic Knowledge Graph Completion. Omeliyanenko, Janna; Zehe, Albin; Hotho, Andreas; Schlör, Daniel. In International Semantic Web Conference ISWC 2023, to appear. 2023
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Occupational Fraud Detection through Agent-based Data Generation. Tritscher, Julian; Roos, Alexander; Schlör, Daniel; Hotho, Andreas; Krause, Anna. In The 8th Workshop on MIning DAta for financial applicationS MIDAS 2023 - to appear. 2023
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ConvMOS: climate model output statistics with deep learning. Steininger, Michael; Abel, Daniel; Ziegler, Katrin; Krause, Anna; Paeth, Heiko; Hotho, Andreas. In Data Mining and Knowledge Discovery, Vol. 37, Issue 1, pp. 136–166. 2023
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Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks. Schaller, Melanie; Steininger, Michael; Dulny, Andrzej; Schlör, Daniel; Hotho, Andreas. In LWDA’23: Lernen, Wissen, Daten, Analysen. October 09--11, 2023, Marburg, Germany, M. Leyer (ed.). 2023
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Higher-Order DeepTrails: Unified Approach to *Trails. Koopmann, Tobias; Pfister, Jan; Markus, André; Carolus, Astrid; Wienrich, Carolin; Hotho, Andreas. In Proceedings of the {LWDA} 2023 Workshops: FGWM, FGKD, and FGDB, Marburg (Germany), Oktober 9-11th, 2023, of {CEUR} Workshop Proceedings, M. Leyer, J. Wichmann (eds.). CEUR-WS.org, 2023
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Automatic Speech Detection on a Smart Beehive’s Raspberry Pi. Janetzky, Pascal; Lissmann, Philipp; Hotho, Andreas; Krause, Anna. In LWDA 2023 - Learning, Knowledge, Data, and Analysis. 2023
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DynaBench: A Benchmark Dataset for Learning Dynamical Systems from Low-Resolution Data. Dulny, Andrzej; Hotho, Andreas; Krause, Anna. In Machine Learning and Knowledge Discovery in Databases: Research Track, D. Koutra, C. Plant, M. Gomez Rodriguez, E. Baralis, F. Bonchi (eds.), pp. 438–455. Springer Nature Switzerland, Cham, 2023
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TaylorPDENet: Learning PDEs from non-grid Data. Heinisch, Paul; Dulny, Andrzej; Krause, Anna; Hotho, Andreas. 2023
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Can Neural Networks Distinguish High-school Level Mathematical Concepts?. Wankerl, Sebastian; Dulny, Andrzej; Goetz, Gerhard; Hotho, Andreas. In Accepted at ICDM. 2023
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Can Neural Networks Distinguish High-school Level Mathematical Concepts?. Wankerl, Sebastian; Dulny, Andrzej; Götz, Gerhard; Hotho, Andreas. In Accepted at ICDM. 2023
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On Background Bias in Deep Metric Learning. Kobs, Konstantin; Hotho, Andreas. In CoRR, Vol. abs/2210.01615. 2022
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Do Different Deep Metric Learning Losses Lead to Similar Learned Features?. Kobs, Konstantin; Steininger, Michael; Dulny, Andrzej; Hotho, Andreas. In CoRR, Vol. abs/2205.02698. 2022
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The Impact of Different System Call Representations on Intrusion Detection. Wunderlich, Sarah; Ring, Markus; Landes, Dieter; Hotho, Andreas. In Log. J. {IGPL}, Vol. 30, Issue 2, pp. 239–251. 2022
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SenPoi at SemEval-2022 Task 10: Point me to your Opinion, SenPoi. Pfister, Jan; Wankerl, Sebastian; Hotho, Andreas. In Proceedings of the 16th International Workshop on Semantic Evaluation, SemEval@NAACL 2022, Seattle, Washington, United States, July 14-15, 2022, G. Emerson, N. Schluter, G. Stanovsky, R. Kumar, A. Palmer, N. Schneider, S. Singh, S. Ratan (eds.), pp. 1313–1323. Association for Computational Linguistics, 2022
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On Learning Hierarchical Embeddings from Encrypted Network Traffic. Wehner, Nikolas; Ring, Markus; Sch{{ü}}ler, Joshua; Hotho, Andreas; Hoßfeld, Tobias; Seufert, Michael. In 2022 {IEEE/IFIP} Network Operations and Management Symposium, {NOMS} 2022, Budapest, Hungary, April 25-29, 2022, pp. 1–7. {IEEE}, 2022
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A sensor-driven approach for analyzing a beehive’s state. Davidson, Padraig; Hotho, Andreas; Krause, Anna. 2022
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CLIP knows image aesthetics. Hentschel, Simon; Kobs, Konstantin; Hotho, Andreas. In Frontiers in artificial intelligence, Vol. 5, p. 976235. 2022
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Towards Explainable Occupational Fraud Detection. Tritscher, Julian; Schlör, Daniel; Gwinner, Fabian; Krause, Anna; Hotho, Andreas. In Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022, Vol. Communications in Computer and Information Science, Issue 1753, pp. 79–96. 2022
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Comparison of Data Representations and Machine Learning Architectures for User Identification on Arbitrary Motion Sequences. Schell, Christian; Hotho, Andreas; Latoschik, Marc Erich. In CoRR, Vol. abs/2210.00527. 2022
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Anomaly Detection in Beehives: An Algorithm Comparison. Davidson, Padraig; Steininger, Michael; Lautenschlager, Florian; Krause, Anna; Hotho, Andreas. In International Conference on Sensor Networks, Vol. 1674 of Communications in Computer and Information Science, A. Ahrens, R. Prasad, C. Benavente-Peces, N. Ansari (eds.), pp. 1–20. Springer, 2021
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A financial game with opportunities for fraud. Tritscher, Julian; Krause, Anna; Schl{ö}r, Daniel; Gwinner, Fabian; von Mammen, Sebastian; Hotho, Andreas. In IEE COG 2021, Vol. 2021. 2021
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Classification of text-types in german novels. Schl{ö}r, D; Sch{ö}ch, C; Hotho, A. In Digital Humanities 2019: Conference Abstracts. 2019
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News

With our paper "Enhancing Sequential Next-Item Prediction through Modelling Non-Item Pages" Elisabeth Fischer won the Best Paper Award at the ICDM NeuRec Workshop 2023

At the 4th international workshop on advanced neural algorithms and theories for recommender systems Elisabeth Fischer won the award for the best Paper with "Enhancing Sequential Next-Item Prediction through Modelling Non-Item Pages".

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The Erlangen National High Performance Computing Center accepted our application for additional compute resources for the MAGNET-Project

The MAGNET4Cardiac7T Project has been granted 40.000 GPU-hours in the Erlangen National High Performance Computing Center for training several deep learning models for simulating electromagnetic fields.

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