2024W

• Data-oriented Programming Paradigms / 188.995 / VU
• Fundamentals of Information Retrieval / 188.977 / VU
• Introduction to Information Retrieval / 194.166 / VU

• A Reproducibility and Generalizability Study of Large Language Models for Query Generation / Staudinger, M., Kusa, W., Piroi, F., Lipani, A., & Hanbury, A. (2024). A Reproducibility and Generalizability Study of Large Language Models for Query Generation. In SIGIR-AP 2024: Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (pp. 186–196). The Association for Computing Machinery. https://doi.org/10.1145/3673791.3698432
• Reproducible Hybrid Time-Travel Retrieval in Evolving Corpora / Staudinger, M., Piroi, F., & Rauber, A. (2024). Reproducible Hybrid Time-Travel Retrieval in Evolving Corpora. In SIGIR-AP 2024: Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (pp. 203–208). Association for Computing Machinery. https://doi.org/10.1145/3673791.3698421
• Beyond ChatGPT: A Reproducibility and Generalizability Study of Large Language Models for Query Generation / Staudinger, M., Kusa, W., Piroi, F., Lipani, A., & Hanbury, A. (2024, November 9). Beyond ChatGPT: A Reproducibility and Generalizability Study of Large Language Models for Query Generation [Poster Presentation]. ML in PL Conference 2024, Warsaw, Poland.
• Mission Reproducibility: An Investigation on Reproducibility Issues in Machine Learning and Information Retrieval Research / Staudinger, M., Kern, B. M. J., Miksa, T., Arnhold, L., Knees, P., Rauber, A., & Hanbury, A. (2024). Mission Reproducibility: An Investigation on Reproducibility Issues in Machine Learning and Information Retrieval Research. In Proceedings 2024 IEEE 20th International Conference on e-Science (e-Science). IEEE eScience 2024, Osaka, Japan. IEEE. https://doi.org/10.1109/e-Science62913.2024.10678657
  Projects: FAIR-AI (2024–2026) / HumRec (2021–2025)
• Using Cochrane Systematic Literature Reviews to Reduce Contamination in the Evaluation of Large Language Models / Kusa, W., Staudinger, M., Harry Scells, & Hanbury, A. (2024, August 16). Using Cochrane Systematic Literature Reviews to Reduce Contamination in the Evaluation of Large Language Models [Poster Presentation]. The 1st Workshop on Data Contamination (CONDA), Bangkok, Thailand. http://hdl.handle.net/20.500.12708/208243
• Normalised Precision at Fixed Recall for Evaluating TAR / Kusa, W., Peikos, G., Staudinger, M., Lipani, A., & Hanbury, A. (2024). Normalised Precision at Fixed Recall for Evaluating TAR. In ICTIR ’24: Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval (pp. 43–49). Association for Computing Machinery. https://doi.org/10.1145/3664190.3672532
  Download: Normalised Precision at Fixed Recall for Evaluating TAR (1.3 MB)
  Project: DoSSIER (2019–2024)
• Leveraging Cochrane Systematic Literature Reviews for Prospective Evaluation of Large Language Models / Kusa, W., Scells, H., Staudinger, M., & Hanbury, A. (2024, March 28). Leveraging Cochrane Systematic Literature Reviews for Prospective Evaluation of Large Language Models [Conference Presentation]. ALTARS 2024: 3rd Workshop on Augmented Intelligence for Technology-Assisted Reviews Systems: Evaluation Metrics and Protocols for eDiscovery and Systematic Review Systems, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/200670
• AMATU@Simpletext2024: Are LLMs Any Good for Scientific Leaderboard Extraction? : Notebook for the SimpleText Lab at CLEF 2024 / Staudinger, M., El-Ebshihy, A., Ningtyas, A. M., Piroi, F., & Hanbury, A. (2024). AMATU@Simpletext2024: Are LLMs Any Good for Scientific Leaderboard Extraction? : Notebook for the SimpleText Lab at CLEF 2024. In G. Faggioli, N. Ferro, P. Galuščáková, & A. Garcia Seco de Herrera (Eds.), CLEF 2024 Working Notes: Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024) (pp. 3300–3316).
• An Analysis of Tasks and Datasets in Peer Reviewing / Staudinger, M., Kusa, W., Piroi, F., & Hanbury, A. (2024). An Analysis of Tasks and Datasets in Peer Reviewing. In Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024) (pp. 257–268). Association for Computational Linguistics.
• Reproducible Query Processing and Data Citation of in Situ Soil Moisture Data / Staudinger, M., Hajszan, T., Miksa, T., Himmelbauer, I., Aberer, D., Rauber, A., & Dorigo, W. (2023). Reproducible Query Processing and Data Citation of in Situ Soil Moisture Data. In 2023 IEEE 19th International Conference on e-Science (pp. 1–10). IEEE. https://doi.org/10.1109/e-Science58273.2023.10254929
• Reproducible query processing in relational databases with evolving database schemas / Staudinger, M. (2023). Reproducible query processing in relational databases with evolving database schemas [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.101569
  Download: PDF (2.29 MB)
• DBRepo: A Semantic Digital Repository for Relational Databases / Weise, M., Staudinger, M., Michlits, C., Gergely, E., Stytsenko, K., Ganguly, R., & Rauber, A. (2022). DBRepo: A Semantic Digital Repository for Relational Databases. International Journal of Digital Curation, 17(1). https://doi.org/10.2218/ijdc.v17i1.825
• FDA-DBRepo: A Data Preservation Repository Supporting FAIR Principles, Data Versioning and Reproducible Queries / Weise, M., Michlits, C., Staudinger, M., Gergely, E., Stytsenko, K., Ganguly, R., & Rauber, A. (2021). FDA-DBRepo: A Data Preservation Repository Supporting FAIR Principles, Data Versioning and Reproducible Queries. In Proceedings of the 17th International Conference on Digital Preservation (p. 34). https://doi.org/10.17605/OSF.IO/B7NX5

• Automated quality indicators for machine-actionable data management plans / Arnhold, L. (2024). Automated quality indicators for machine-actionable data management plans [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2024.117145
  Download: PDF (3.32 MB)