Publications
223 Übereinstimmungen gefunden / 1-15 16-30 31-45 46-60 61-75 76-90 91-105 106-120 121-135 136-150 151-165 166-180 181-195 196-210 211-223
Gotta catch 'Em All... Or Not?: How LLMs bypass traditional checks & mimic human response behavior in web surveys.Shahania, S., Spiliopoulou, M., & Broneske, D. (2025).Gotta catch 'Em All... Or Not?: How LLMs bypass traditional checks & mimic human response behavior in web surveys. In Association for Computing Machinery (Hrsg.), CI '25: Proceedings of the ACM Collective Intelligence Conference (S. 113-128). New York: ACM. https://doi.org/10.1145/3715928.3737491 |
Neue berufliche Rollen? Kompetenz- und Aufgabenprofile in der IT-gestützten Forschungsberichterstattung.Thiedig, C., Schelske, S., Petersohn, S., & Euler, T. (2025).Neue berufliche Rollen? Kompetenz- und Aufgabenprofile in der IT-gestützten Forschungsberichterstattung. Daten- und Methodenbericht zur quantitativen Erhebung des BMBF-geförderten Projektes BERTI. Hannover: DZHW. |
SBC-SHAP: Increasing the accessibility and interpretability of machine learning algorithms for sepsis prediction.Walke, D., Steinbach, D., Kaiser, T., Schönhuth, A., Saake, G., Broneske, D., & Heyer, R. (2025).SBC-SHAP: Increasing the accessibility and interpretability of machine learning algorithms for sepsis prediction. The Journal of Applied Laboratory Medicine. https://doi.org/10.1093/jalm/jfaf091 |
DZHW-Studienberechtigtenpanel 2012. Daten- und Methodenbericht zur 3. Befragungswelle des Studienberechtigtenjahrgangs 2012.Jahn, V., Spangenberg, H., Ohlendorf, D., Föste-Eggers, D., Niebuhr, J., Vietgen, S., & Euler, T. (2025).DZHW-Studienberechtigtenpanel 2012. Daten- und Methodenbericht zur 3. Befragungswelle des Studienberechtigtenjahrgangs 2012. Hannover: DZHW. Abstract
The DZHW-Panel Study of School Leavers 2012 is part of the DZHW-Panel Study of School Leavers survey series, in which standardized multiple surveys are used to collect information on the post-school careers of school leavers with a (school) higher education entrance qualification. As a rule, several survey waves are conducted at different times before and after the acquisition of the higher education entrance qualification for each year group of persons with a university entrance qualification. Accordingly, this is a combined cohort-panel design. The panel 2012 is the 19th cohort of the study series with currently three waves. Full abstract: https://doi.org/10.21249/DZHW:gsl2012:3.0.0 |
SurveyBot: A new era of web survey pretesting.Shahania, S., Spiliopoulou, M., & Broneske, D. (2025).SurveyBot: A new era of web survey pretesting. In I. Maglogiannis, L. Iliadis, A. Andreou, & A. Papaleonidas (Hrsg.), Artificial Intelligence Applications and Innovations. AIAI 2025. IFIP Advances in Information and Communication Technology. Cham: Springer. https://doi.org/10.1007/978-3-031-96235-6_29 |
Towards automatic bias analysis in multimedia journalism.Hinrichs, R., Steffen, H., Avetisyan, H., Broneske, D., & Ostermann, J. (2025).Towards automatic bias analysis in multimedia journalism. Discover Artificial Intelligence, 5(1), 1-28. https://doi.org/10.1007/s44163-025-00362-1 |
Embracing NVM: Optimizing $B^𝜖$-tree structures and data compression in storage engines.Karim, S., Wünsche, F., Broneske, D., Kuhn, M., & Saake, G. (2025).Embracing NVM: Optimizing $B^𝜖$-tree structures and data compression in storage engines. In Binnig, C. et al. (Hrsg.), Datenbanksysteme für Business, Technologie und Web - Workshopband (BTW 2025) (S. 329-333). Bonn: Gesellschaft für Informatik. https://doi.org/10.18420/BTW2025-137 |
Publishing fine-grained standardized metadata – Lessons learned from three research data centers.Wenzig, K., Daniel, A., Hansen, D., Koberg, T., & Tudose, M. (2025).Publishing fine-grained standardized metadata – Lessons learned from three research data centers. (Working Paper 12 I 2025). Berlin: Konsortium für die Sozial-, Verhaltens-, Bildungs- und Wirtschaftswissenschaften (KonsortSWD). |
A multi-objective evolutionary algorithm for detecting protein complexes in PPI networks using gene ontology.Abbas, M. N., Broneske, D., & Saake, G. (2025).A multi-objective evolutionary algorithm for detecting protein complexes in PPI networks using gene ontology. Scientific Reports, 15. https://doi.org/10.1038/s41598-025-01667-y |
Studienabbruch als Ausdruck problematischer Passungsverhältnisse im universitären Informatikstudium.Schneider, H. (2025).Studienabbruch als Ausdruck problematischer Passungsverhältnisse im universitären Informatikstudium. In H. Bremer & A. Lange-Vester (Hrsg.), Soziale Milieus und Habitus im Feld der Bildung (S. 107-122). Weinheim: Beltz Juventa. |
Datenschutzrechtliche Anforderungen bei Online-Umfragen.Buck, D., Herrenbrück, R., Jacob, K., Lukowski, F., Schneider, J., Thaut, A., & Verbund Forschungsdaten Bildung (2025).Datenschutzrechtliche Anforderungen bei Online-Umfragen. Frankfurt/Main: DIPF, Leibniz-Institut für Bildungsforschung und Bildungsinformation. https://doi.org/10.25656/01:33518 |
AutoML meets hugging face: Domain-aware pretrained model selection for text classification.Safikhani, P., & Broneske, D. (2025).AutoML meets hugging face: Domain-aware pretrained model selection for text classification. In A. Ebrahimi, S. Haider, E. Liu, M. L. Pacheco, & S. Wein (Hrsg.), Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop). Albuquerque, USA: Association for Computational Linguistics. Abstract
The effectiveness of embedding methods is crucial for optimizing text classification performance in Automated Machine Learning (AutoML). However, selecting the most suitable pre-trained model for a given task remains challenging. This study introduces the Corpus-Driven Domain Mapping (CDDM) pipeline, which utilizes a domain-annotated corpus of pre-fine-tuned models from the Hugging Face Model Hub to improve model selection. Integrating these models into AutoML systems significantly boosts classification performance across multiple datasets compared to baseline methods. Despite some domain recognition inaccuracies, results demonstrate CDDM’s potential to enhance model selection, streamline AutoML workflows, and reduce computational costs. |
NVM in data storage: A post-optane future.Karim, S., Wünsche, J., Kuhn, M., Saake, G., & Broneske, D. (2025).NVM in data storage: A post-optane future. ACM Digital Library, ACM Transaction on Storage, 21(3), 1-85. https://doi.org/10.1145/3731454 |
Following political science students through their methods training: Statistics anxiety, student satisfaction, and final grades in the COVID year 2021/22.Vierus, P., Elis, J., Ziller, C., Goerres, A., & Höhne, J. K. (2025).Following political science students through their methods training: Statistics anxiety, student satisfaction, and final grades in the COVID year 2021/22. Politische Vierteljahresschrift (online first). https://doi.org/10.1007/s11615-025-00613-x |
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