Publications

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A domain specific students' assistance system for the provision of instructional feedback.

Obionwu, C. V., Tiwari, T., Chovatta Valappil, B. B., Raikar, N., Walia, D. S., ... & Saake, G. (2024).
A domain specific students' assistance system for the provision of instructional feedback. In IEEE Institute of Electrical and Electronic Engineers (Hrsg.), 2023 International Conference on Machine Learning and Applications (ICMLA) (S. 2065-2070). Jacksonville, Florida, USA: IEEE. https://doi.org/10.1109/ICMLA58977.2023.00312

Enforcing right to be forgotten in cloud-based data lakes.

Bhardwaj, P., Darrab, S., Broneske, D., Klose, I., & Saake, G. (2024).
Enforcing right to be forgotten in cloud-based data lakes. In Arai, K. (Hrsg.), Advances in Information and Communication (FICC 2024) (S. 220-234). Cham: Springer. https://doi.org/10.1007/978-3-031-53963-3_16

An evolutionary algorithm with heuristic operator for detecting protein complexes in protein interaction networks with negative controls.

Abbas, M. N., Attea, B. A., Broneske, D., & Saake, G. (2024).
An evolutionary algorithm with heuristic operator for detecting protein complexes in protein interaction networks with negative controls. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3367746
Abstract

Computational biology research faces a formidable challenge in the detection of complexes within protein-protein interaction (PPI) networks, critical for unraveling cellular processes, predicting functions of uncharacterized proteins, and diagnosing diseases. While evolutionary algorithms (EAs), particularly state-of-the-art methods, often partition PPI networks based on graph properties or biological semantics, their resilience to noisy or missing interactions remains an underexplored territory. In this paper, we propose a groundbreaking heuristic operator, termed "strong neighbor-node migration", specifically designed to elevate solution quality [...] Full Abstract: https://ieeexplore.ieee.org/document/10440281

Workshopdokumentation: Langzeitarchivierung (LZA) in Forschungsdatenzentren (FDZ).

Hoffstätter, U., & Beck, K. (2024).
Workshopdokumentation: Langzeitarchivierung (LZA) in Forschungsdatenzentren (FDZ). KonsortSWD Working Paper Nr. 8/2024. Konsortium für die Sozial-, Verhaltens-, Bildungs- und Wirtschaftswissenschaften (KonsortSWD). Hannover: DZHW. https://doi.org/10.5281/zenodo.10261654

Framing and BERTology: A data-centric approach to integration of linguistic features into transformer-based pre-trained language models.

Avetisyan, H., Safikhani, P., & Broneske, D. (2024).
Framing and BERTology: A data-centric approach to integration of linguistic features into transformer-based pre-trained language models. In Arai, K. (Hrsg.), Intelligent Systems and Applications (S. 81-90). Cham: Springer. https://doi.org/10.1007/978-3-031-47718-8

Stamp—Standardized data management plan for educational research: A blueprint to improve data management across disciplines.

Netscher, S., Bongartz, E. C., Schwickerath, A. K., Braun, D., Stephan, K., & Mauer, R. (2024).
Stamp—Standardized data management plan for educational research: A blueprint to improve data management across disciplines. Data Science Journal, 23(1). https://doi.org/10.5334/dsj-2024-007

Examining final comment questions with requests for written and oral answers.

Höhne, J. K., & Claaßen, J. (2024).
Examining final comment questions with requests for written and oral answers. International Journal of Market Research (online first). https://doi.org/10.1177/14707853241229329

More or less the same? An exploration of the evolution of the PhD wage premium in a decade of higher education expansion.

Euler, T., & Trennt, F. (2024).
More or less the same? An exploration of the evolution of the PhD wage premium in a decade of higher education expansion. Soziale Welt - Special Edition 26, 55-88. https://doi.org/10.5771/9783748925590-55

Clustering graph data: the roadmap to spectral techniques.

Mondal, R., Ignatova, E., Walke, D., Broneske, D., Saake, G., & Heyer, R. (2024).
Clustering graph data: the roadmap to spectral techniques. Discover Artificial Intelligence, 4(7), 1-22. https://doi.org/10.1007/s44163-024-00102-x
Abstract

Graph data models enable efficient storage, visualization, and analysis of highly interlinked data, by providing the benefits of horizontal scalability and high query performance. Clustering techniques, such as K-means, hierarchical clustering, are highly beneficial tools in data mining and machine learning to find meaningful similarities and differences between data points. Recent developments in graph data models, as well as clustering algorithms for graph data, have shown promising results in image segmentation, gene data analysis, etc. This has been primarily achieved through research and development of algorithms in the field of spectral theory, [...] Full abstract: https://doi.org/10.1007/s44163-024-00102-x

Laughing out loud – Exploring AI-generated and human-generated humor.

Safikhani, P., Avetisyan, H., & Broneske, D. (2023).
Laughing out loud – Exploring AI-generated and human-generated humor. Computer Science & Information Technology (CS & IT), 2023, 59-76.

Mustervertrag Datenaufnahme KonsortSWD (Version 2.0.0).

Schallaböck, J., Kreutzer, T., Hoffstätter, U., & Buck, D. (2023).
Mustervertrag Datenaufnahme KonsortSWD (Version 2.0.0). Hannover: DZHW. https://doi.org/10.5281/zenodo.10406480

Mustervertrag Datennutzung KonsortSWD (Version 3.0.0).

Schallaböck, J., Hoffstätter, U., Buck, D., & Linne, M. (2023).
Mustervertrag Datennutzung KonsortSWD (Version 3.0.0). Hannover: DZHW. https://doi.org/10.5281/zenodo.10409864

Anomaly detection algorithms: Comparative analysis and explainability perspectives.

Darab, S., Allipilli, H., Ghani, S., Changaramkulath, H., Koneru, S., Broneske, D., & Saake, G. (2023).
Anomaly detection algorithms: Comparative analysis and explainability perspectives. In D. Benavides-Prado et al. (Hrsg.), Data Science and Machine Learning, 21st Australasian Conference, AusDM 2023, Auckland, New Zealand, December 11–13, 2023, Proceedings (S. 90-104). Singapore: Springer Nature.

The sound of respondents: predicting respondents’ level of interest in questions with voice data in smartphone surveys.

Höhne, J. K., Kern, C., Gavras, K., & Schlosser, S. (2023).
The sound of respondents: predicting respondents’ level of interest in questions with voice data in smartphone surveys. Quality & Quantity, International Journal of Methodology, 57(6). https://doi.org/10.1007/s11135-023-01776-8
Abstract

Web surveys completed on smartphones open novel ways for measuring respondents’ attitudes, behaviors, and beliefs that are crucial for social science research and many adjacent research fields. In this study, we make use of the built-in microphones of smartphones to record voice answers in a smartphone survey and extract non-verbal cues, such as amplitudes and pitches, from the collected voice data. This allows us to predict respondents’ level of interest (i.e., disinterest, neutral, and high interest) based on their voice answers, which expands the opportunities for researching respondents’ engagement and answer behavior. [...] Full abstract: https://doi.org/10.1007/s11135-023-01776-8

WISHFUL - Website extraction of Institutional Sources with Heterogeneous Factors and User-Driven Linkage.

Shahania, S., Spiliopoulou, M., & Broneske, D. (2023).
WISHFUL - Website extraction of Institutional Sources with Heterogeneous Factors and User-Driven Linkage. In Delir Haghighi, P. et al. (Hrsg.), Information Integration and Web Intelligence (iiWAS 2023) (S. 20-26). Cham: Springer. https://doi.org/10.1007/978-3-031-48316-5_3
Abstract

Extracting information from diverse websites is increasingly important, especially for analyzing vast data sets to detect trends, gain insights. By studying job ads, researchers can monitor employer demand shifts, assisting policymakers in aiding affected workers and industries. However, extraction faces challenges like varied website formats, dynamic content, and duplicate data. This study introduces a method for extracting data from diverse private university websites involving keyword identification, website categorization, and extraction pipelines.

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Karsten Stephan
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