--- _id: '59483' abstract: - lang: eng text: Abstract. The assessment of mechanically joined connections, such as clinched connections, is usually conducted destructively. Applicable non-destructive testing methods like computed tomography are time-consuming and costly, or, like electrical resistance measurement, provide only a limited amount of information. A fast, non-destructive evaluation of the joints condition shall be made possible by using transient dynamic analysis (TDA). It is based on the introduction of sound waves and the evaluation of the response behavior after passing through the structure. This study focuses the application of TDA to clinched shear connections to evaluate the performance of the tactile measuring setup. Twenty-one series were investigated, covering variations in joining task, manufacturing and defect. The evaluation was carried out using machine learning to determine for which series characteristic signals may be detected. It was shown that a classification of the investigated specimens is possible, whereby the classification accuracy depends on the examined variation. Furthermore, the accuracy was evaluated as a function of frequency and results were concluded to identify the limits of the used measuring setup. author: - first_name: Gregor full_name: Reschke, Gregor last_name: Reschke - first_name: Alexander full_name: Brosius, Alexander last_name: Brosius citation: ama: 'Reschke G, Brosius A. Transient dynamic analysis: Performance evaluation of tactile measurement. In: Materials Research Proceedings. Vol 52. Materials Research Forum LLC; 2025:293-300. doi:10.21741/9781644903551-36' apa: 'Reschke, G., & Brosius, A. (2025). Transient dynamic analysis: Performance evaluation of tactile measurement. Materials Research Proceedings, 52, 293–300. https://doi.org/10.21741/9781644903551-36' bibtex: '@inproceedings{Reschke_Brosius_2025, title={Transient dynamic analysis: Performance evaluation of tactile measurement}, volume={52}, DOI={10.21741/9781644903551-36}, booktitle={Materials Research Proceedings}, publisher={Materials Research Forum LLC}, author={Reschke, Gregor and Brosius, Alexander}, year={2025}, pages={293–300} }' chicago: 'Reschke, Gregor, and Alexander Brosius. “Transient Dynamic Analysis: Performance Evaluation of Tactile Measurement.” In Materials Research Proceedings, 52:293–300. Materials Research Forum LLC, 2025. https://doi.org/10.21741/9781644903551-36.' ieee: 'G. Reschke and A. Brosius, “Transient dynamic analysis: Performance evaluation of tactile measurement,” in Materials Research Proceedings, Paderborn, 2025, vol. 52, pp. 293–300, doi: 10.21741/9781644903551-36.' mla: 'Reschke, Gregor, and Alexander Brosius. “Transient Dynamic Analysis: Performance Evaluation of Tactile Measurement.” Materials Research Proceedings, vol. 52, Materials Research Forum LLC, 2025, pp. 293–300, doi:10.21741/9781644903551-36.' short: 'G. Reschke, A. Brosius, in: Materials Research Proceedings, Materials Research Forum LLC, 2025, pp. 293–300.' conference: end_date: 2025-04-03 location: Paderborn name: 21st SheMet Conference start_date: 2025-04-01 date_created: 2025-04-10T11:27:20Z date_updated: 2025-04-10T11:33:28Z department: - _id: '43' - _id: '157' doi: 10.21741/9781644903551-36 intvolume: ' 52' keyword: - Joining - Machine Learning - Transient Dynamic Analysis language: - iso: eng page: 293-300 project: - _id: '130' grant_number: '418701707' name: 'TRR 285: TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten' - _id: '133' name: 'TRR 285 - C: TRR 285 - Project Area C' - _id: '148' name: 'TRR 285 – C04: TRR 285 - Subproject C04' publication: Materials Research Proceedings publication_identifier: issn: - 2474-395X publication_status: published publisher: Materials Research Forum LLC quality_controlled: '1' status: public title: 'Transient dynamic analysis: Performance evaluation of tactile measurement' type: conference user_id: '98812' volume: 52 year: '2025' ... --- _id: '62080' abstract: - lang: eng text: The failure behavior of fiber reinforced polymers (FRP) is strongly influenced by their microstructure, i.e. fiber arrangement or local fiber volume content. However, this information cannot be directly used for structural analyses, since it requires a discretization on micrometer level. Therefore, current failure theories do not directly account for such effects, but describe the behavior averaged over an entire specimen. This foundation in experimentally accessible loading conditions leads to purely theory based extension to more complex stress states without direct validation possibilities. This work aims at leveraging micro-scale simulations to obtain failure information under arbitrary loading conditions. The results are propagated to the meso-scale, enabling efficient structural analyses, by means of machine learning (ML). It is shown that the ML model is capable of correctly assessing previously unseen stress states and therefore poses an efficient tool of exploiting information from the micro-scale in larger simulations. author: - first_name: Johannes full_name: Gerritzen, Johannes id: '105344' last_name: Gerritzen orcid: 0000-0002-0169-8602 - first_name: Andreas full_name: Hornig, Andreas last_name: Hornig - first_name: Maik full_name: Gude, Maik last_name: Gude citation: ama: 'Gerritzen J, Hornig A, Gude M. Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning. In: Meschut G, Bobbert M, Duflou J, et al., eds. Sheet Metal 2025. Materials Research Proceedings. Materials Research Forum LLC, Materials Research Foundations; 2025:260–267. doi:10.21741/9781644903551-32' apa: 'Gerritzen, J., Hornig, A., & Gude, M. (2025). Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning. In G. Meschut, M. Bobbert, J. Duflou, L. Fratini, H. Hagenah, P. Martins, M. Merklein, & F. Micari (Eds.), Sheet Metal 2025 (pp. 260–267). Materials Research Forum LLC, Materials Research Foundations. https://doi.org/10.21741/9781644903551-32' bibtex: '@inproceedings{Gerritzen_Hornig_Gude_2025, series={Materials Research Proceedings}, title={Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning}, DOI={10.21741/9781644903551-32}, booktitle={Sheet Metal 2025}, publisher={Materials Research Forum LLC, Materials Research Foundations}, author={Gerritzen, Johannes and Hornig, Andreas and Gude, Maik}, editor={Meschut, G. and Bobbert, M. and Duflou, J. and Fratini, L. and Hagenah, H. and Martins, P. and Merklein, M. and Micari, F.}, year={2025}, pages={260–267}, collection={Materials Research Proceedings} }' chicago: 'Gerritzen, Johannes, Andreas Hornig, and Maik Gude. “Efficient Failure Information Propagation under Complex Stress States in Fiber Reinforced Polymers: From Micro- to Meso-Scale Using Machine Learning.” In Sheet Metal 2025, edited by G. Meschut, M. Bobbert, J. Duflou, L. Fratini, H. Hagenah, P. Martins, M. Merklein, and F. Micari, 260–267. Materials Research Proceedings. Materials Research Forum LLC, Materials Research Foundations, 2025. https://doi.org/10.21741/9781644903551-32.' ieee: 'J. Gerritzen, A. Hornig, and M. Gude, “Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning,” in Sheet Metal 2025, 2025, pp. 260–267, doi: 10.21741/9781644903551-32.' mla: 'Gerritzen, Johannes, et al. “Efficient Failure Information Propagation under Complex Stress States in Fiber Reinforced Polymers: From Micro- to Meso-Scale Using Machine Learning.” Sheet Metal 2025, edited by G. Meschut et al., Materials Research Forum LLC, Materials Research Foundations, 2025, pp. 260–267, doi:10.21741/9781644903551-32.' short: 'J. Gerritzen, A. Hornig, M. Gude, in: G. Meschut, M. Bobbert, J. Duflou, L. Fratini, H. Hagenah, P. Martins, M. Merklein, F. Micari (Eds.), Sheet Metal 2025, Materials Research Forum LLC, Materials Research Foundations, 2025, pp. 260–267.' date_created: 2025-11-04T12:48:37Z date_updated: 2026-02-27T06:43:37Z doi: 10.21741/9781644903551-32 editor: - first_name: G. full_name: Meschut, G. last_name: Meschut - first_name: M. full_name: Bobbert, M. last_name: Bobbert - first_name: J. full_name: Duflou, J. last_name: Duflou - first_name: L. full_name: Fratini, L. last_name: Fratini - first_name: H. full_name: Hagenah, H. last_name: Hagenah - first_name: P. full_name: Martins, P. last_name: Martins - first_name: M. full_name: Merklein, M. last_name: Merklein - first_name: F. full_name: Micari, F. last_name: Micari keyword: - Failure - Fiber Reinforced Plastic - Machine Learning language: - iso: eng page: 260–267 project: - _id: '130' name: 'TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten' - _id: '137' name: TRR 285 - Subproject A03 - _id: '131' name: TRR 285 - Project Area A publication: Sheet Metal 2025 publication_identifier: isbn: - 978-1-64490-354-4 publisher: Materials Research Forum LLC, Materials Research Foundations series_title: Materials Research Proceedings status: public title: 'Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning' type: conference user_id: '105344' year: '2025' ... --- _id: '58885' abstract: - lang: eng text: 'There have been several attempts to conceptualize and operationalize pedagogical content knowledge (PCK) in the context of teachers'' professional competencies. A recent and popular model is the Refined Consensus Model (RCM), which proposes a framework of dispositional competencies (personal PCK—pPCK) that influence more action-related competencies (enacted PCK—ePCK) and vice versa. However, descriptions of the internal structure of pPCK and possible knowledge domains that might develop independently are still limited, being either primarily theoretically motivated or strictly hierarchical and therefore of limited use, for example, for formative feedback and further development of the RCM. Meanwhile, a non-hierarchical differentiation for the ePCK regarding the plan-teach-reflect cycle has emerged. In this study, we present an exploratory computational approach to investigate pre-service teachers'' pPCK for a similar non-hierarchical structure using a large dataset of responses to a pPCK questionnaire (N=846). We drew on theoretical foundations and previous empirical findings to achieve interpretability by integrating this external knowledge into our analyses using the Computational Grounded Theory (CGT) framework. The results of a cluster analysis of the pPCK scores indicate the emergence of prototypical groups, which we refer to as competency profiles: (1) a group with low performance, (2) a group with relatively advanced competency in using pPCK to create instructional elements, (3) a group with relatively advanced competency in using pPCK to assess and analyze described instructional elements, and (4) a group with high performance. These groups show tendencies for certain language usage, which we analyze using a structural topic model in a CGT-inspired pattern refinement step. We verify these patterns by demonstrating the ability of a machine learning model to predict the competency profile assignments. Finally, we discuss some implications of the results for the further development of the RCM and their potential usability for an automated formative assessment.' article_type: original author: - first_name: Jannis full_name: Zeller, Jannis id: '99022' last_name: Zeller orcid: 0000-0002-1834-5520 - first_name: Josef full_name: Riese, Josef id: '429' last_name: Riese orcid: 0000-0003-2927-2619 citation: ama: 'Zeller J, Riese J. Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses? Journal of Research in Science Teaching. Published online 2025. doi:10.1002/tea.70001' apa: 'Zeller, J., & Riese, J. (2025). Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses? Journal of Research in Science Teaching. https://doi.org/10.1002/tea.70001' bibtex: '@article{Zeller_Riese_2025, title={Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses?}, DOI={10.1002/tea.70001}, journal={Journal of Research in Science Teaching}, author={Zeller, Jannis and Riese, Josef}, year={2025} }' chicago: 'Zeller, Jannis, and Josef Riese. “Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of PPCK Emerge From Non-Hierarchical Analyses?” Journal of Research in Science Teaching, 2025. https://doi.org/10.1002/tea.70001.' ieee: 'J. Zeller and J. Riese, “Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses?,” Journal of Research in Science Teaching, 2025, doi: 10.1002/tea.70001.' mla: 'Zeller, Jannis, and Josef Riese. “Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of PPCK Emerge From Non-Hierarchical Analyses?” Journal of Research in Science Teaching, 2025, doi:10.1002/tea.70001.' short: J. Zeller, J. Riese, Journal of Research in Science Teaching (2025). date_created: 2025-03-04T08:08:37Z date_updated: 2025-03-04T08:08:42Z department: - _id: '299' doi: 10.1002/tea.70001 keyword: - computational grounded theory - language analysis - machine learning - pedagogical content knowledge - unsupervised learning language: - iso: eng main_file_link: - open_access: '1' url: https://onlinelibrary.wiley.com/doi/epdf/10.1002/tea.70001 oa: '1' publication: Journal of Research in Science Teaching publication_identifier: eissn: - 1098-2736 issn: - 0022-4308 publication_status: published status: public title: 'Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses?' type: journal_article user_id: '99022' year: '2025' ... --- _id: '60680' abstract: - lang: eng text: "Classical machine learning techniques often struggle with overfitting and unreliable predictions when exposed to novel conditions. Introducing causality into the modelling process offers a promising way to mitigate these challenges by enhancing predictive robustness. However, constructing an initial causal graph manually using domain knowledge is time-consuming, particularly in complex time series with numerous variables. To address this, causal discovery algorithms can provide a preliminary causal structure that domain experts can refine. This study investigates causal feature selection with domain knowledge using a data center system as an example. We use simulated time-series data to compare \r\ndifferent causal feature selection with traditional machine-learning feature selection methods. Our results show that predictions based on causal features are more robust compared to those derived from traditional methods. These findings underscore the potential of combining causal discovery algorithms with human expertise to improve machine learning applications." author: - first_name: David Ricardo full_name: Zapata Gonzalez, David Ricardo id: '105506' last_name: Zapata Gonzalez - first_name: Marcel full_name: Meyer, Marcel id: '105120' last_name: Meyer - first_name: Oliver full_name: Müller, Oliver id: '72849' last_name: Müller citation: ama: 'Zapata Gonzalez DR, Meyer M, Müller O. Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems. In: ; 2025.' apa: Zapata Gonzalez, D. R., Meyer, M., & Müller, O. (2025). Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems. European Conference on Information Systems, Amman, Jordan. bibtex: '@inproceedings{Zapata Gonzalez_Meyer_Müller_2025, title={Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems}, author={Zapata Gonzalez, David Ricardo and Meyer, Marcel and Müller, Oliver}, year={2025} }' chicago: Zapata Gonzalez, David Ricardo, Marcel Meyer, and Oliver Müller. “Bridging the Gap between Data-Driven and Theory-Driven Modelling – Leveraging Causal Machine Learning for Integrative Modelling of Dynamical Systems,” 2025. ieee: D. R. Zapata Gonzalez, M. Meyer, and O. Müller, “Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems,” presented at the European Conference on Information Systems, Amman, Jordan, 2025. mla: Zapata Gonzalez, David Ricardo, et al. Bridging the Gap between Data-Driven and Theory-Driven Modelling – Leveraging Causal Machine Learning for Integrative Modelling of Dynamical Systems. 2025. short: 'D.R. Zapata Gonzalez, M. Meyer, O. Müller, in: 2025.' conference: end_date: 18.06.2025 location: Amman, Jordan name: European Conference on Information Systems start_date: 16.06.2025 date_created: 2025-07-21T07:52:03Z date_updated: 2025-07-22T06:30:37Z department: - _id: '196' keyword: - Causal Machine Learning - Causality in Time Series - Causal Discovery - Human-Machine Collaboration language: - iso: eng main_file_link: - url: https://aisel.aisnet.org/ecis2025/bus_analytics/bus_analytics/2/ status: public title: Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems type: conference user_id: '72849' year: '2025' ... --- _id: '55400' abstract: - lang: eng text: "This study contributes to the evolving field of robot learning in interaction\r\nwith humans, examining the impact of diverse input modalities on learning\r\noutcomes. It introduces the concept of \"meta-modalities\" which encapsulate\r\nadditional forms of feedback beyond the traditional preference and scalar\r\nfeedback mechanisms. Unlike prior research that focused on individual\r\nmeta-modalities, this work evaluates their combined effect on learning\r\noutcomes. Through a study with human participants, we explore user preferences\r\nfor these modalities and their impact on robot learning performance. Our\r\nfindings reveal that while individual modalities are perceived differently,\r\ntheir combination significantly improves learning behavior and usability. This\r\nresearch not only provides valuable insights into the optimization of\r\nhuman-robot interactive task learning but also opens new avenues for enhancing\r\nthe interactive freedom and scaffolding capabilities provided to users in such\r\nsettings." article_type: original author: - first_name: Helen full_name: Beierling, Helen last_name: Beierling - first_name: 'Robin ' full_name: 'Beierling, Robin ' last_name: Beierling - first_name: Anna-Lisa full_name: Vollmer, Anna-Lisa last_name: Vollmer citation: ama: Beierling H, Beierling R, Vollmer A-L. The power of combined modalities in interactive robot learning. Frontiers in Robotics and AI. 2025;12. apa: Beierling, H., Beierling, R., & Vollmer, A.-L. (2025). The power of combined modalities in interactive robot learning. Frontiers in Robotics and AI, 12. bibtex: '@article{Beierling_Beierling_Vollmer_2025, title={The power of combined modalities in interactive robot learning}, volume={12}, journal={Frontiers in Robotics and AI}, publisher={Frontiers }, author={Beierling, Helen and Beierling, Robin and Vollmer, Anna-Lisa}, year={2025} }' chicago: Beierling, Helen, Robin Beierling, and Anna-Lisa Vollmer. “The Power of Combined Modalities in Interactive Robot Learning.” Frontiers in Robotics and AI 12 (2025). ieee: H. Beierling, R. Beierling, and A.-L. Vollmer, “The power of combined modalities in interactive robot learning,” Frontiers in Robotics and AI, vol. 12, 2025. mla: Beierling, Helen, et al. “The Power of Combined Modalities in Interactive Robot Learning.” Frontiers in Robotics and AI, vol. 12, Frontiers , 2025. short: H. Beierling, R. Beierling, A.-L. Vollmer, Frontiers in Robotics and AI 12 (2025). date_created: 2024-07-26T08:35:24Z date_updated: 2025-09-17T13:38:18Z ddc: - '004' extern: '1' file: - access_level: closed content_type: application/pdf creator: helebeen date_created: 2025-09-17T13:36:09Z date_updated: 2025-09-17T13:36:09Z file_id: '61331' file_name: frobt-12-1598968.pdf file_size: 36978223 relation: main_file success: 1 file_date_updated: 2025-09-17T13:36:09Z funded_apc: '1' has_accepted_license: '1' intvolume: ' 12' keyword: - human-robot interaction - human-in-the-loop learning - reinforcement learning - interactive robot learning - multi-modal feedback - learning from demonstration - preference-based learning - scaffolding in robot learning language: - iso: eng main_file_link: - open_access: '1' url: https://pmc.ncbi.nlm.nih.gov/articles/PMC12312635/ oa: '1' project: - _id: '123' name: 'TRR 318 - B5: TRR 318 - Subproject B5' publication: Frontiers in Robotics and AI publication_status: published publisher: 'Frontiers ' status: public title: The power of combined modalities in interactive robot learning type: journal_article user_id: '50995' volume: 12 year: '2025' ... --- _id: '61327' abstract: - lang: eng text: "Robot learning from humans has been proposed and researched for several decades as a means to enable robots to learn new skills or\r\nadapt existing ones to new situations. Recent advances in artificial intelligence, including learning approaches like reinforcement\r\nlearning and architectures like transformers and foundation models, combined with access to massive datasets, has created attractive\r\nopportunities to apply those data-hungry techniques to this problem. We argue that the focus on massive amounts of pre-collected\r\ndata, and the resulting learning paradigm, where humans demonstrate and robots learn in isolation, is overshadowing a specialized\r\narea of work we term Human-Interactive-Robot-Learning (HIRL). This paradigm, wherein robots and humans interact during the\r\nlearning process, is at the intersection of multiple fields (artificial intelligence, robotics, human-computer interaction, design and others)\r\nand holds unique promise. Using HIRL, robots can achieve greater sample efficiency (as humans can provide task knowledge through\r\ninteraction), align with human preferences (as humans can guide the robot behavior towards their expectations), and explore more\r\nmeaningfully and safely (as humans can utilize domain knowledge to guide learning and prevent catastrophic failures). This can result\r\nin robotic systems that can more quickly and easily adapt to new tasks in human environments. The objective of this paper is to\r\nprovide a broad and consistent overview of HIRL research and to guide researchers toward understanding the scope of HIRL, and\r\ncurrent open or underexplored challenges related to four themes — namely, human, robot learning, interaction, and broader context.\r\nThe paper includes concrete use cases to illustrate the interaction between these challenges and inspire further research according to\r\nbroad recommendations and a call for action for the growing HIRL community" article_type: original author: - first_name: 'Kim ' full_name: 'Baraka, Kim ' last_name: Baraka - first_name: Ifrah full_name: Idrees, Ifrah last_name: Idrees - first_name: Taylor Kessler full_name: Faulkner, Taylor Kessler last_name: Faulkner - first_name: Erdem full_name: Biyik, Erdem last_name: Biyik - first_name: Serena full_name: Booth, Serena last_name: Booth - first_name: Mohamed full_name: Chetouani, Mohamed last_name: Chetouani - first_name: Daniel H. full_name: Grollman, Daniel H. last_name: Grollman - first_name: Akanksha full_name: Saran, Akanksha last_name: Saran - first_name: Emmanuel full_name: Senft, Emmanuel last_name: Senft - first_name: Silvia full_name: Tulli, Silvia last_name: Tulli - first_name: Anna-Lisa full_name: Vollmer, Anna-Lisa last_name: Vollmer - first_name: Antonio full_name: Andriella, Antonio last_name: Andriella - first_name: Helen full_name: Beierling, Helen last_name: Beierling - first_name: Tiffany full_name: Horter, Tiffany last_name: Horter - first_name: Jens full_name: Kober, Jens last_name: Kober - first_name: Isaac full_name: Sheidlower, Isaac last_name: Sheidlower - first_name: Matthew E. full_name: Taylor, Matthew E. last_name: Taylor - first_name: Sanne full_name: van Waveren, Sanne last_name: van Waveren - first_name: Xuesu full_name: Xiao, Xuesu last_name: Xiao citation: ama: 'Baraka K, Idrees I, Faulkner TK, et al. Human-Interactive Robot Learning: Definition, Challenges, and Recommendations. Transactions on Human-Robot Interaction.' apa: 'Baraka, K., Idrees, I., Faulkner, T. K., Biyik, E., Booth, S., Chetouani, M., Grollman, D. H., Saran, A., Senft, E., Tulli, S., Vollmer, A.-L., Andriella, A., Beierling, H., Horter, T., Kober, J., Sheidlower, I., Taylor, M. E., van Waveren, S., & Xiao, X. (n.d.). Human-Interactive Robot Learning: Definition, Challenges, and Recommendations. Transactions on Human-Robot Interaction.' bibtex: '@article{Baraka_Idrees_Faulkner_Biyik_Booth_Chetouani_Grollman_Saran_Senft_Tulli_et al., title={Human-Interactive Robot Learning: Definition, Challenges, and Recommendations}, journal={Transactions on Human-Robot Interaction}, author={Baraka, Kim and Idrees, Ifrah and Faulkner, Taylor Kessler and Biyik, Erdem and Booth, Serena and Chetouani, Mohamed and Grollman, Daniel H. and Saran, Akanksha and Senft, Emmanuel and Tulli, Silvia and et al.} }' chicago: 'Baraka, Kim , Ifrah Idrees, Taylor Kessler Faulkner, Erdem Biyik, Serena Booth, Mohamed Chetouani, Daniel H. Grollman, et al. “Human-Interactive Robot Learning: Definition, Challenges, and Recommendations.” Transactions on Human-Robot Interaction, n.d.' ieee: 'K. Baraka et al., “Human-Interactive Robot Learning: Definition, Challenges, and Recommendations,” Transactions on Human-Robot Interaction.' mla: 'Baraka, Kim, et al. “Human-Interactive Robot Learning: Definition, Challenges, and Recommendations.” Transactions on Human-Robot Interaction.' short: K. Baraka, I. Idrees, T.K. Faulkner, E. Biyik, S. Booth, M. Chetouani, D.H. Grollman, A. Saran, E. Senft, S. Tulli, A.-L. Vollmer, A. Andriella, H. Beierling, T. Horter, J. Kober, I. Sheidlower, M.E. Taylor, S. van Waveren, X. Xiao, Transactions on Human-Robot Interaction (n.d.). date_created: 2025-09-17T12:42:45Z date_updated: 2025-09-17T13:40:16Z keyword: - Robot learning - Interactive learning systems - Human-robot interaction - Human-in-the-loop machine learning - Teaching and learning language: - iso: eng project: - _id: '123' name: TRR 318 - Subproject B5 publication: Transactions on Human-Robot Interaction publication_status: submitted status: public title: 'Human-Interactive Robot Learning: Definition, Challenges, and Recommendations' type: journal_article user_id: '50995' year: '2025' ... --- _id: '62701' abstract: - lang: eng text: 'Learning continuous vector representations for knowledge graphs has significantly improved state-of-the-art performances in many challenging tasks. Yet, deep-learning-based models are only post-hoc and locally explainable. In contrast, learning Web Ontology Language (OWL) class expressions in Description Logics (DLs) is ante-hoc and globally explainable. However, state-of-the-art learners have two well-known lim-itations: scaling to large knowledge graphs and handling missing infor-mation. Here, we present a decision-tree-based learner (tDL) to learn Web Ontology Languages (OWLs) class expressions over large knowl-edge graphs, while imputing missing triples. Given positive and negative example individuals, tDL firstly constructs unique OWL expressions in .SHOIN from concise bounded descriptions of individuals. Each OWL class expression is used as a feature in a binary classification problem to represent input individuals. Thereafter, tDL fits a CART decision tree to learn Boolean decision rules distinguishing positive examples from nega-tive examples. A final OWL expression in.SHOIN is built by traversing the built CART decision tree from the root node to leaf nodes for each positive example. By this, tDL can learn OWL class expressions without exploration, i.e., the number of queries to a knowledge graph is bounded by the number of input individuals. Our empirical results show that tDL outperforms the current state-of-the-art models across datasets. Impor-tantly, our experiments over a large knowledge graph (DBpedia with 1.1 billion triples) show that tDL can effectively learn accurate OWL class expressions, while the state-of-the-art models fail to return any results. Finally, expressions learned by tDL can be seamlessly translated into natural language explanations using a pre-trained large language model and a DL verbalizer.' author: - first_name: Caglar full_name: Demir, Caglar last_name: Demir - first_name: Moshood full_name: Yekini, Moshood last_name: Yekini - first_name: Michael full_name: Röder, Michael last_name: Röder - first_name: Yasir full_name: Mahmood, Yasir last_name: Mahmood - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille last_name: Ngonga Ngomo citation: ama: 'Demir C, Yekini M, Röder M, Mahmood Y, Ngonga Ngomo A-C. Tree-Based OWL Class Expression Learner over Large Graphs. In: Lecture Notes in Computer Science. Springer Nature Switzerland; 2025. doi:10.1007/978-3-032-06066-2_29' apa: Demir, C., Yekini, M., Röder, M., Mahmood, Y., & Ngonga Ngomo, A.-C. (2025). Tree-Based OWL Class Expression Learner over Large Graphs. In Lecture Notes in Computer Science. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD, Porto, Portugal. Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-06066-2_29 bibtex: '@inbook{Demir_Yekini_Röder_Mahmood_Ngonga Ngomo_2025, place={Cham}, title={Tree-Based OWL Class Expression Learner over Large Graphs}, DOI={10.1007/978-3-032-06066-2_29}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Nature Switzerland}, author={Demir, Caglar and Yekini, Moshood and Röder, Michael and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille}, year={2025} }' chicago: 'Demir, Caglar, Moshood Yekini, Michael Röder, Yasir Mahmood, and Axel-Cyrille Ngonga Ngomo. “Tree-Based OWL Class Expression Learner over Large Graphs.” In Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-032-06066-2_29.' ieee: 'C. Demir, M. Yekini, M. Röder, Y. Mahmood, and A.-C. Ngonga Ngomo, “Tree-Based OWL Class Expression Learner over Large Graphs,” in Lecture Notes in Computer Science, Cham: Springer Nature Switzerland, 2025.' mla: Demir, Caglar, et al. “Tree-Based OWL Class Expression Learner over Large Graphs.” Lecture Notes in Computer Science, Springer Nature Switzerland, 2025, doi:10.1007/978-3-032-06066-2_29. short: 'C. Demir, M. Yekini, M. Röder, Y. Mahmood, A.-C. Ngonga Ngomo, in: Lecture Notes in Computer Science, Springer Nature Switzerland, Cham, 2025.' conference: end_date: 2025-09-19 location: Porto, Portugal name: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD start_date: 2025-09-15 date_created: 2025-11-28T14:09:17Z date_updated: 2025-11-28T14:57:39Z department: - _id: '34' - _id: '574' doi: 10.1007/978-3-032-06066-2_29 keyword: - Decision Tree - OWL Class Expression Learning - Description Logic - Knowledge Graph - Large Language Model - Verbalizer language: - iso: eng place: Cham project: - _id: '285' name: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen publication: Lecture Notes in Computer Science publication_identifier: isbn: - '9783032060655' - '9783032060662' issn: - 0302-9743 - 1611-3349 publication_status: published publisher: Springer Nature Switzerland status: public title: Tree-Based OWL Class Expression Learner over Large Graphs type: book_chapter user_id: '114533' year: '2025' ... --- _id: '62007' abstract: - lang: eng text: "Ensemble methods are widely employed to improve generalization in machine learning. This has also prompted the adoption of ensemble learning for the knowledge graph embedding (KGE) models in performing link prediction. Typical approaches to this end train multiple models as part of the ensemble, and the diverse predictions are then averaged. However, this approach has some significant drawbacks. For instance, the computational overhead of training multiple models increases latency and memory overhead. In contrast, model merging approaches offer a promising alternative that does not require training multiple models. In this work, we introduce model merging, specifically weighted averaging, in\r\nKGE models. Herein, a running average of model parameters from a training epoch onward is maintained and used for predictions. To address this, we additionally propose an approach that selectively updates the running average of the ensemble model parameters only when the generalization performance improves on a validation dataset. We evaluate these two different weighted averaging approaches on link prediction tasks, comparing the state-of-the-art benchmark ensemble approach. Additionally, we evaluate the weighted averaging approach considering literal-augmented KGE models and multi-hop query answering tasks as well. The results demonstrate that the proposed weighted averaging approach consistently improves performance across diverse evaluation settings." author: - first_name: Rupesh full_name: Sapkota, Rupesh id: '89326' last_name: Sapkota - first_name: Caglar full_name: Demir, Caglar last_name: Demir - first_name: Arnab full_name: Sharma, Arnab last_name: Sharma - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille last_name: Ngonga Ngomo citation: ama: 'Sapkota R, Demir C, Sharma A, Ngonga Ngomo A-C. Parameter Averaging in Link Prediction. In: Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025). ACM; 2025. doi:https://doi.org/10.1145/3731443.3771365' apa: Sapkota, R., Demir, C., Sharma, A., & Ngonga Ngomo, A.-C. (2025). Parameter Averaging in Link Prediction. Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025). Knowledge Capture Conference 2025, Dayton, OH, USA. https://doi.org/10.1145/3731443.3771365 bibtex: '@inproceedings{Sapkota_Demir_Sharma_Ngonga Ngomo_2025, place={Dayton, OH, USA}, title={Parameter Averaging in Link Prediction}, DOI={https://doi.org/10.1145/3731443.3771365}, booktitle={Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025)}, publisher={ACM}, author={Sapkota, Rupesh and Demir, Caglar and Sharma, Arnab and Ngonga Ngomo, Axel-Cyrille}, year={2025} }' chicago: 'Sapkota, Rupesh, Caglar Demir, Arnab Sharma, and Axel-Cyrille Ngonga Ngomo. “Parameter Averaging in Link Prediction.” In Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025). Dayton, OH, USA: ACM, 2025. https://doi.org/10.1145/3731443.3771365.' ieee: 'R. Sapkota, C. Demir, A. Sharma, and A.-C. Ngonga Ngomo, “Parameter Averaging in Link Prediction,” presented at the Knowledge Capture Conference 2025, Dayton, OH, USA, 2025, doi: https://doi.org/10.1145/3731443.3771365.' mla: Sapkota, Rupesh, et al. “Parameter Averaging in Link Prediction.” Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025), ACM, 2025, doi:https://doi.org/10.1145/3731443.3771365. short: 'R. Sapkota, C. Demir, A. Sharma, A.-C. Ngonga Ngomo, in: Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025), ACM, Dayton, OH, USA, 2025.' conference: end_date: 2025-12-10 location: Dayton, OH, USA name: Knowledge Capture Conference 2025 start_date: 2025-12-10 date_created: 2025-10-28T10:02:40Z date_updated: 2025-12-04T09:15:07Z ddc: - '000' department: - _id: '574' doi: https://doi.org/10.1145/3731443.3771365 file: - access_level: open_access content_type: application/pdf creator: rupezzz date_created: 2025-10-28T10:02:13Z date_updated: 2025-10-28T10:02:13Z file_id: '62008' file_name: public.pdf file_size: 837462 relation: main_file file_date_updated: 2025-10-28T10:02:13Z has_accepted_license: '1' keyword: - Knowledge Graphs - Embeddings - Ensemble Learning language: - iso: eng main_file_link: - url: https://papers.dice-research.org/2025/KCAP_ASWA/public.pdf oa: '1' place: Dayton, OH, USA project: - _id: '285' name: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen publication: Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025) publisher: ACM status: public title: Parameter Averaging in Link Prediction type: conference user_id: '89326' year: '2025' ... --- _id: '58874' author: - first_name: Manuel full_name: Fahrbach, Manuel last_name: Fahrbach - first_name: Tobias full_name: Jenert, Tobias id: '71994' last_name: Jenert orcid: ' https://orcid.org/0000-0001-9262-5646' - first_name: Alexander full_name: Fust, Alexander last_name: Fust - first_name: Noah full_name: Bellwald, Noah last_name: Bellwald - first_name: Christoph full_name: Winkler, Christoph last_name: Winkler citation: ama: 'Fahrbach M, Jenert T, Fust A, Bellwald N, Winkler C. Fostering self-regulated entrepreneurial learning in entrepreneurship education. In: Annals of Entrepreneurship Education and Pedagogy - 2025. Edward Elgar Publishing; 2025:249–265. doi:10.4337/9781035325795.00021' apa: Fahrbach, M., Jenert, T., Fust, A., Bellwald, N., & Winkler, C. (2025). Fostering self-regulated entrepreneurial learning in entrepreneurship education. In Annals of Entrepreneurship Education and Pedagogy - 2025 (pp. 249–265). Edward Elgar Publishing. https://doi.org/10.4337/9781035325795.00021 bibtex: '@inbook{Fahrbach_Jenert_Fust_Bellwald_Winkler_2025, title={Fostering self-regulated entrepreneurial learning in entrepreneurship education}, DOI={10.4337/9781035325795.00021}, booktitle={Annals of Entrepreneurship Education and Pedagogy - 2025}, publisher={Edward Elgar Publishing}, author={Fahrbach, Manuel and Jenert, Tobias and Fust, Alexander and Bellwald, Noah and Winkler, Christoph}, year={2025}, pages={249–265} }' chicago: Fahrbach, Manuel, Tobias Jenert, Alexander Fust, Noah Bellwald, and Christoph Winkler. “Fostering Self-Regulated Entrepreneurial Learning in Entrepreneurship Education.” In Annals of Entrepreneurship Education and Pedagogy - 2025, 249–265. Edward Elgar Publishing, 2025. https://doi.org/10.4337/9781035325795.00021. ieee: M. Fahrbach, T. Jenert, A. Fust, N. Bellwald, and C. Winkler, “Fostering self-regulated entrepreneurial learning in entrepreneurship education,” in Annals of Entrepreneurship Education and Pedagogy - 2025, Edward Elgar Publishing, 2025, pp. 249–265. mla: Fahrbach, Manuel, et al. “Fostering Self-Regulated Entrepreneurial Learning in Entrepreneurship Education.” Annals of Entrepreneurship Education and Pedagogy - 2025, Edward Elgar Publishing, 2025, pp. 249–265, doi:10.4337/9781035325795.00021. short: 'M. Fahrbach, T. Jenert, A. Fust, N. Bellwald, C. Winkler, in: Annals of Entrepreneurship Education and Pedagogy - 2025, Edward Elgar Publishing, 2025, pp. 249–265.' date_created: 2025-02-28T14:42:29Z date_updated: 2025-12-08T10:57:18Z department: - _id: '208' - _id: '640' doi: 10.4337/9781035325795.00021 keyword: - Self-Regulated Learning - Entrepreneurship Education - Entrepreneurship Research language: - iso: eng page: 249–265 project: - _id: '618' name: Self-Regulated Learning for Entrepreneurs – Förderung der Selbstregulationsfähigkeit angehender Unternehmer*innen publication: Annals of Entrepreneurship Education and Pedagogy - 2025 publication_identifier: isbn: - '9781035325795' - '9781035325788' - '9781035325795' publication_status: published publisher: Edward Elgar Publishing quality_controlled: '1' status: public title: Fostering self-regulated entrepreneurial learning in entrepreneurship education type: book_chapter user_id: '71994' year: '2025' ... --- _id: '63498' author: - first_name: Wilhelm full_name: Kirchgässner, Wilhelm last_name: Kirchgässner - first_name: Nikolas full_name: Förster, Nikolas last_name: Förster - first_name: Till full_name: Piepenbrock, Till last_name: Piepenbrock - first_name: Oliver full_name: Schweins, Oliver last_name: Schweins - first_name: Oliver full_name: Wallscheid, Oliver last_name: Wallscheid citation: ama: 'Kirchgässner W, Förster N, Piepenbrock T, Schweins O, Wallscheid O. HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores. IEEE Transactions on Power Electronics. 2025;40(2):3326-3335. doi:10.1109/TPEL.2024.3488174' apa: 'Kirchgässner, W., Förster, N., Piepenbrock, T., Schweins, O., & Wallscheid, O. (2025). HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores. IEEE Transactions on Power Electronics, 40(2), 3326–3335. https://doi.org/10.1109/TPEL.2024.3488174' bibtex: '@article{Kirchgässner_Förster_Piepenbrock_Schweins_Wallscheid_2025, title={HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores}, volume={40}, DOI={10.1109/TPEL.2024.3488174}, number={2}, journal={IEEE Transactions on Power Electronics}, author={Kirchgässner, Wilhelm and Förster, Nikolas and Piepenbrock, Till and Schweins, Oliver and Wallscheid, Oliver}, year={2025}, pages={3326–3335} }' chicago: 'Kirchgässner, Wilhelm, Nikolas Förster, Till Piepenbrock, Oliver Schweins, and Oliver Wallscheid. “HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores.” IEEE Transactions on Power Electronics 40, no. 2 (2025): 3326–35. https://doi.org/10.1109/TPEL.2024.3488174.' ieee: 'W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, and O. Wallscheid, “HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores,” IEEE Transactions on Power Electronics, vol. 40, no. 2, pp. 3326–3335, 2025, doi: 10.1109/TPEL.2024.3488174.' mla: 'Kirchgässner, Wilhelm, et al. “HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores.” IEEE Transactions on Power Electronics, vol. 40, no. 2, 2025, pp. 3326–35, doi:10.1109/TPEL.2024.3488174.' short: W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, O. Wallscheid, IEEE Transactions on Power Electronics 40 (2025) 3326–3335. date_created: 2026-01-06T08:07:13Z date_updated: 2026-01-06T08:08:01Z department: - _id: '52' doi: 10.1109/TPEL.2024.3488174 intvolume: ' 40' issue: '2' keyword: - Mathematical models - Estimation - Data models - Convolutional neural networks - Accuracy - Magnetic hysteresis - Magnetic cores - Temperature measurement - Magnetic domains - Temperature distribution - Convolutional neural network (CNN) - machine learning (ML) - magnetics page: 3326-3335 publication: IEEE Transactions on Power Electronics status: public title: 'HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores' type: journal_article user_id: '83383' volume: 40 year: '2025' ... --- _id: '53793' abstract: - lang: eng text: We utilize extreme learning machines for the prediction of partial differential equations (PDEs). Our method splits the state space into multiple windows that are predicted individually using a single model. Despite requiring only few data points (in some cases, our method can learn from a single full-state snapshot), it still achieves high accuracy and can predict the flow of PDEs over long time horizons. Moreover, we show how additional symmetries can be exploited to increase sample efficiency and to enforce equivariance. author: - first_name: Hans full_name: Harder, Hans id: '98879' last_name: Harder - first_name: Sebastian full_name: Peitz, Sebastian id: '47427' last_name: Peitz orcid: 0000-0002-3389-793X citation: ama: Harder H, Peitz S. Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines. apa: Harder, H., & Peitz, S. (n.d.). Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines. bibtex: '@article{Harder_Peitz, title={Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines}, author={Harder, Hans and Peitz, Sebastian} }' chicago: Harder, Hans, and Sebastian Peitz. “Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines,” n.d. ieee: H. Harder and S. Peitz, “Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines.” . mla: Harder, Hans, and Sebastian Peitz. Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines. short: H. Harder, S. Peitz, (n.d.). date_created: 2024-04-30T08:43:14Z date_updated: 2024-04-30T08:45:24Z keyword: - extreme learning machines - partial differential equations - data-driven prediction - high-dimensional systems language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2404.18530 oa: '1' publication_status: unpublished status: public title: Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines type: preprint user_id: '98879' year: '2024' ... --- _id: '54960' abstract: - lang: ger text: Das Fachdidaktische Wissen (FDW) wird als zentrale Komponente des Professionswissens von Lehrkräften bereits lange intensiv untersucht. Bislang liegen Ergebnisse zu Zusammenhängen des FDW mit anderen Professionswissensbereichen, zur Performanz in prototypischen Handlungssituationen und erste datengestützte inhaltlich-hierarchische Analysen auf Basis von Item Response Modellen (IRT-Modellen) vor. Im Zusammenhang mit einem projektübergreifend durchgeführten Vergleich entsprechender IRT-Modelle haben sich jedoch Limitationen bei der Vereinbarkeit und der inhaltlichen Reichhaltigkeit entsprechender Ergebnisse gezeigt, wie im Beitrag vorgestellt wird . Daher werden Analysemethoden aus dem Bereich des Machine Learning (unsupervised) vorgeschlagen, welche im Gegensatz zu IRT-Modellen auch nicht-hierarchische inhaltliche Strukturen aufdecken können. Es werden Ergebnisse entsprechender Clusteranalysen sowie Analysepläne zur Unterstützung dieser auf Basis der authentischen Sprachproduktionen von Proband:innen mithilfe von Natural Language Processing vorgestellt. author: - first_name: Jannis full_name: Zeller, Jannis id: '99022' last_name: Zeller orcid: 0000-0002-1834-5520 - first_name: Josef full_name: Riese, Josef id: '429' last_name: Riese orcid: 0000-0003-2927-2619 citation: ama: 'Zeller J, Riese J. Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von Machine Learning. In: van Vorst H, ed. Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023. Gesellschaft für Didaktik der Chemie und Physik; 2024:122-125.' apa: Zeller, J., & Riese, J. (2024). Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von Machine Learning. In H. van Vorst (Ed.), Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023 (pp. 122–125). Gesellschaft für Didaktik der Chemie und Physik. bibtex: '@inproceedings{Zeller_Riese_2024, place={Hamburg}, title={Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von Machine Learning}, booktitle={Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023}, publisher={Gesellschaft für Didaktik der Chemie und Physik}, author={Zeller, Jannis and Riese, Josef}, editor={van Vorst, Helena}, year={2024}, pages={122–125} }' chicago: 'Zeller, Jannis, and Josef Riese. “Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von Machine Learning.” In Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023, edited by Helena van Vorst, 122–25. Hamburg: Gesellschaft für Didaktik der Chemie und Physik, 2024.' ieee: J. Zeller and J. Riese, “Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von Machine Learning,” in Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023, Hamburg, 2024, pp. 122–125. mla: Zeller, Jannis, and Josef Riese. “Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von Machine Learning.” Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023, edited by Helena van Vorst, Gesellschaft für Didaktik der Chemie und Physik, 2024, pp. 122–25. short: 'J. Zeller, J. Riese, in: H. van Vorst (Ed.), Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023, Gesellschaft für Didaktik der Chemie und Physik, Hamburg, 2024, pp. 122–125.' conference: location: Hamburg name: GDCP Jahrestagung 2023 date_created: 2024-07-01T14:33:40Z date_updated: 2024-07-03T08:47:31Z ddc: - '370' department: - _id: '15' - _id: '299' editor: - first_name: Helena full_name: van Vorst, Helena last_name: van Vorst file: - access_level: closed content_type: application/pdf creator: jzeller date_created: 2024-07-01T14:27:20Z date_updated: 2024-07-01T14:27:20Z file_id: '54961' file_name: Zeller, Riese (2024) Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von ML.pdf file_size: 389778 relation: main_file success: 1 file_date_updated: 2024-07-01T14:27:20Z has_accepted_license: '1' keyword: - Physikdidaktisches Wissen - Fähigkeitsprofile - Machine Learning language: - iso: ger page: 122-125 place: Hamburg publication: Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023 publication_status: published publisher: Gesellschaft für Didaktik der Chemie und Physik status: public title: Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von Machine Learning type: conference user_id: '99022' year: '2024' ... --- _id: '55159' abstract: - lang: eng text: "We introduce a method based on Gaussian process regression to identify discrete variational principles from observed solutions of a field theory. The method is based on the data-based identification of a discrete Lagrangian density. It is a geometric machine learning technique in the sense that the variational structure of the true field theory is reflected in the data-driven model by design. We provide a rigorous convergence statement of the method. The proof circumvents challenges posed by the ambiguity of discrete Lagrangian densities in the inverse problem of variational calculus.\r\nMoreover, our method can be used to quantify model uncertainty in the equations of motions and any linear observable of the discrete field theory. This is illustrated on the example of the discrete wave equation and Schrödinger equation.\r\nThe article constitutes an extension of our previous article arXiv:2404.19626 for the data-driven identification of (discrete) Lagrangians for variational dynamics from an ode setting to the setting of discrete pdes." author: - first_name: Christian full_name: Offen, Christian id: '85279' last_name: Offen orcid: 0000-0002-5940-8057 citation: ama: Offen C. Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification. apa: Offen, C. (n.d.). Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification. bibtex: '@article{Offen, title={Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification}, author={Offen, Christian} }' chicago: Offen, Christian. “Machine Learning of Discrete Field Theories with Guaranteed Convergence and Uncertainty Quantification,” n.d. ieee: C. Offen, “Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification.” . mla: Offen, Christian. Machine Learning of Discrete Field Theories with Guaranteed Convergence and Uncertainty Quantification. short: C. Offen, (n.d.). date_created: 2024-07-10T13:43:50Z date_updated: 2024-08-12T13:43:32Z ddc: - '510' department: - _id: '636' external_id: arxiv: - '2407.07642' file: - access_level: open_access content_type: application/pdf creator: coffen date_created: 2024-07-10T13:39:32Z date_updated: 2024-07-10T13:39:32Z description: |- We introduce a method based on Gaussian process regression to identify discrete variational principles from observed solutions of a field theory. The method is based on the data-based identification of a discrete Lagrangian density. It is a geometric machine learning technique in the sense that the variational structure of the true field theory is reflected in the data-driven model by design. We provide a rigorous convergence statement of the method. The proof circumvents challenges posed by the ambiguity of discrete Lagrangian densities in the inverse problem of variational calculus. Moreover, our method can be used to quantify model uncertainty in the equations of motions and any linear observable of the discrete field theory. This is illustrated on the example of the discrete wave equation and Schrödinger equation. The article constitutes an extension of our previous article for the data-driven identification of (discrete) Lagrangians for variational dynamics from an ode setting to the setting of discrete pdes. file_id: '55160' file_name: L_Collocation.pdf file_size: 4569314 relation: main_file title: Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification file_date_updated: 2024-07-10T13:39:32Z has_accepted_license: '1' keyword: - System identification - inverse problem of variational calculus - Gaussian process - Lagrangian learning - physics informed machine learning - geometry aware learning language: - iso: eng oa: '1' page: '28' project: - _id: '52' name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing' publication_status: submitted related_material: link: - description: GitHub relation: software url: https://github.com/Christian-Offen/Lagrangian_GP_PDE status: public title: Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification type: preprint user_id: '85279' year: '2024' ... --- _id: '56948' abstract: - lang: ger text: Das Fachdidaktische Wissen (FDW) steht als zentrale Komponente des Professionswissens angehender Lehrkräfte bereits länger im Fokus der fachdidaktischen Forschung. Bisherige Ergebnisse zu möglichen Entwicklungsstufen oder prototypischen Ausprägungen des FDW ermöglichen eine differenzierte Einordnung von Lernenden auf Basis der Bearbeitung erprobter, validierter Testinstrumente. Diese Testinstrumente sind häufig mit offenen Antwortformaten gestaltet und die nachträgliche Schließung solcher Testinstrumente hat sich als nicht unproblematisch in Hinblick auf Validität und Authentizität erwiesen. Um ein automatisiertes reichhaltiges Assessment-System auf Basis der bisherigen Forschungsergebnisse zu entwickeln, können alternativ erprobte offene Testinstrumente in Kombination mit Machine-Learning basierten Auswertungsverfahren genutzt werden. Im Vortrag werden Ergebnisse einer entsprechenden Analyse auf Basis eines vergleichsweise großen (844 Bearbeitungen) Datensatzes präsentiert. Dabei wird ein zweistufiger Assessment Prozess, in dem zunächst die offenen Aufgaben mithilfe eines Sprachmodells bepunktet werden und anschließend aus den Bepunktungen inhaltlich reichhaltiges Feedback erstellt wird, genutzt. author: - first_name: Jannis full_name: Zeller, Jannis id: '99022' last_name: Zeller orcid: 0000-0002-1834-5520 - first_name: Josef full_name: Riese, Josef id: '429' last_name: Riese orcid: 0000-0003-2927-2619 citation: ama: 'Zeller J, Riese J. Assessment des physikdidaktischen Wissens mithilfe von Machine Learning. In: Entdecken, lehren und forschen im Schülerlabor. GDCP Jahrestagung 2024.' apa: Zeller, J., & Riese, J. (n.d.). Assessment des physikdidaktischen Wissens mithilfe von Machine Learning. Entdecken, lehren und forschen im Schülerlabor. GDCP Jahrestagung 2024. Entdecken, lehren und forschen im Schülerlabor. GDCP Jahrestagung 2024, Bochum. bibtex: '@inproceedings{Zeller_Riese, title={Assessment des physikdidaktischen Wissens mithilfe von Machine Learning}, booktitle={Entdecken, lehren und forschen im Schülerlabor. GDCP Jahrestagung 2024}, author={Zeller, Jannis and Riese, Josef} }' chicago: Zeller, Jannis, and Josef Riese. “Assessment des physikdidaktischen Wissens mithilfe von Machine Learning.” In Entdecken, lehren und forschen im Schülerlabor. GDCP Jahrestagung 2024, n.d. ieee: J. Zeller and J. Riese, “Assessment des physikdidaktischen Wissens mithilfe von Machine Learning,” presented at the Entdecken, lehren und forschen im Schülerlabor. GDCP Jahrestagung 2024, Bochum. mla: Zeller, Jannis, and Josef Riese. “Assessment des physikdidaktischen Wissens mithilfe von Machine Learning.” Entdecken, lehren und forschen im Schülerlabor. GDCP Jahrestagung 2024. short: 'J. Zeller, J. Riese, in: Entdecken, lehren und forschen im Schülerlabor. GDCP Jahrestagung 2024, n.d.' conference: end_date: 2024-09-12 location: Bochum name: Entdecken, lehren und forschen im Schülerlabor. GDCP Jahrestagung 2024 start_date: 2024-09-09 date_created: 2024-11-08T07:39:57Z date_updated: 2024-11-08T07:40:44Z ddc: - '370' department: - _id: '15' - _id: '299' file: - access_level: closed content_type: application/pdf creator: jzeller date_created: 2024-11-08T07:39:37Z date_updated: 2024-11-08T07:39:37Z file_id: '56949' file_name: Tagungsbandbeitrag-Jannis-Zeller-GDCP2024_preprint.pdf file_size: 622347 relation: main_file success: 1 file_date_updated: 2024-11-08T07:39:37Z keyword: - Physikdidaktisches Wissen - Assessment - Machine Learning language: - iso: ger publication: Entdecken, lehren und forschen im Schülerlabor. GDCP Jahrestagung 2024 publication_status: inpress status: public title: Assessment des physikdidaktischen Wissens mithilfe von Machine Learning type: conference user_id: '99022' year: '2024' ... --- _id: '62078' abstract: - lang: eng text: 'Fiber reinforced plastics (FRP) exhibit strongly non-linear deformation behavior. To capture this in simulations, intricate models with a variety of parameters are typically used. The identification of values for such parameters is highly challenging and requires in depth understanding of the model itself. Machine learning (ML) is a promising approach for alleviating this challenge by directly predicting parameters based on experimental results. So far, this works mostly for purely artificial data. In this work, two approaches to generalize to experimental data are investigated: a sequential approach, leveraging understanding of the constitutive model and a direct, purely data driven approach. This is exemplary carried out for a highly non-linear strain rate dependent constitutive model for the shear behavior of FRP.The sequential model is found to work better on both artificial and experimental data. It is capable of extracting well suited parameters from the artificial data under realistic conditions. For the experimental data, the model performance depends on the composition of the experimental curves, varying between excellently suiting and reasonable predictions. Taking the expert knowledge into account for ML-model training led to far better results than the purely data driven approach. Robustifying the model predictions on experimental data promises further improvement. ' author: - first_name: Johannes full_name: Gerritzen, Johannes id: '105344' last_name: Gerritzen orcid: 0000-0002-0169-8602 - first_name: Andreas full_name: Hornig, Andreas last_name: Hornig - first_name: Peter full_name: Winkler, Peter last_name: Winkler - first_name: Maik full_name: Gude, Maik last_name: Gude citation: ama: 'Gerritzen J, Hornig A, Winkler P, Gude M. Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning. In: ECCM21 - Proceedings of the 21st European Conference on Composite Materials. Vol 3. European Society for Composite Materials (ESCM); 2024:1252–1259. doi:10.60691/yj56-np80' apa: Gerritzen, J., Hornig, A., Winkler, P., & Gude, M. (2024). Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning. ECCM21 - Proceedings of the 21st European Conference on Composite Materials, 3, 1252–1259. https://doi.org/10.60691/yj56-np80 bibtex: '@inproceedings{Gerritzen_Hornig_Winkler_Gude_2024, title={Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning}, volume={3}, DOI={10.60691/yj56-np80}, booktitle={ECCM21 - Proceedings of the 21st European Conference on Composite Materials}, publisher={European Society for Composite Materials (ESCM)}, author={Gerritzen, Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik}, year={2024}, pages={1252–1259} }' chicago: Gerritzen, Johannes, Andreas Hornig, Peter Winkler, and Maik Gude. “Direct Parameter Identification for Highly Nonlinear Strain Rate Dependent Constitutive Models Using Machine Learning.” In ECCM21 - Proceedings of the 21st European Conference on Composite Materials, 3:1252–1259. European Society for Composite Materials (ESCM), 2024. https://doi.org/10.60691/yj56-np80. ieee: 'J. Gerritzen, A. Hornig, P. Winkler, and M. Gude, “Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning,” in ECCM21 - Proceedings of the 21st European Conference on Composite Materials, 2024, vol. 3, pp. 1252–1259, doi: 10.60691/yj56-np80.' mla: Gerritzen, Johannes, et al. “Direct Parameter Identification for Highly Nonlinear Strain Rate Dependent Constitutive Models Using Machine Learning.” ECCM21 - Proceedings of the 21st European Conference on Composite Materials, vol. 3, European Society for Composite Materials (ESCM), 2024, pp. 1252–1259, doi:10.60691/yj56-np80. short: 'J. Gerritzen, A. Hornig, P. Winkler, M. Gude, in: ECCM21 - Proceedings of the 21st European Conference on Composite Materials, European Society for Composite Materials (ESCM), 2024, pp. 1252–1259.' date_created: 2025-11-04T12:47:06Z date_updated: 2026-02-27T06:46:21Z doi: 10.60691/yj56-np80 intvolume: ' 3' keyword: - Direct parameter identification - Machine learning - Convolutional neural networks - Strain rate dependency - Fiber reinforced plastics - woven composites - segmentation - synthetic training data - x-ray computed tomography language: - iso: eng page: 1252–1259 project: - _id: '130' name: 'TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten' - _id: '137' name: TRR 285 - Subproject A03 - _id: '131' name: TRR 285 - Project Area A publication: ECCM21 - Proceedings of the 21st European Conference on Composite Materials publication_identifier: isbn: - 978-2-912985-01-9 publisher: European Society for Composite Materials (ESCM) status: public title: Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning type: conference user_id: '105344' volume: 3 year: '2024' ... --- _id: '57895' abstract: - lang: eng text: "In our paper, we present a study in which we investigate which strategies pre-service teachers (PSTs) use to find and, if necessary, reject possible candidates for congruence theorems for quadrilaterals. This study was conducted before the PTSs attended a university geometry course. In this way, statements about learning prerequisites can be made. For the study, we analyzed group discussions of PSTs to identify typical approaches and evaluate them from a mathematical perspective. The results can be considered for the further development of courses for PSTs and generate hypotheses\r\nfor further research." author: - first_name: Max full_name: Hoffmann, Max id: '32202' last_name: Hoffmann orcid: 0000-0002-6964-7123 - first_name: Sarah full_name: Schlüter, Sarah last_name: Schlüter citation: ama: 'Hoffmann M, Schlüter S. How Do Advanced Pre-Service Teachers Develop Congruence Theorems for Quadrilaterals? In: González-Martín AS, Gueudet G, Florensa I, Lombard N, eds. Proceedings of the Fifth Conference of the International Network for Didactic Research in University Mathematics (INDRUM 2024, 10-14 June 2024). Escola Univerist`aria Salesiana de Sarri`a – Univ. Aut`onoma de Barcelona and INDRUM; 2024.' apa: Hoffmann, M., & Schlüter, S. (2024). How Do Advanced Pre-Service Teachers Develop Congruence Theorems for Quadrilaterals? In A. S. González-Martín, G. Gueudet, I. Florensa, & N. Lombard (Eds.), Proceedings of the Fifth Conference of the International Network for Didactic Research in University Mathematics (INDRUM 2024, 10-14 June 2024). Escola Univerist`aria Salesiana de Sarri`a – Univ. Aut`onoma de Barcelona and INDRUM. bibtex: '@inproceedings{Hoffmann_Schlüter_2024, place={Barcelona}, title={How Do Advanced Pre-Service Teachers Develop Congruence Theorems for Quadrilaterals?}, booktitle={Proceedings of the Fifth Conference of the International Network for Didactic Research in University Mathematics (INDRUM 2024, 10-14 June 2024)}, publisher={Escola Univerist`aria Salesiana de Sarri`a – Univ. Aut`onoma de Barcelona and INDRUM}, author={Hoffmann, Max and Schlüter, Sarah}, editor={González-Martín, Alejandro S. and Gueudet, Ghislaine and Florensa, Ignasi and Lombard, Nathan}, year={2024} }' chicago: 'Hoffmann, Max, and Sarah Schlüter. “How Do Advanced Pre-Service Teachers Develop Congruence Theorems for Quadrilaterals?” In Proceedings of the Fifth Conference of the International Network for Didactic Research in University Mathematics (INDRUM 2024, 10-14 June 2024), edited by Alejandro S. González-Martín, Ghislaine Gueudet, Ignasi Florensa, and Nathan Lombard. Barcelona: Escola Univerist`aria Salesiana de Sarri`a – Univ. Aut`onoma de Barcelona and INDRUM, 2024.' ieee: M. Hoffmann and S. Schlüter, “How Do Advanced Pre-Service Teachers Develop Congruence Theorems for Quadrilaterals?,” in Proceedings of the Fifth Conference of the International Network for Didactic Research in University Mathematics (INDRUM 2024, 10-14 June 2024), 2024. mla: Hoffmann, Max, and Sarah Schlüter. “How Do Advanced Pre-Service Teachers Develop Congruence Theorems for Quadrilaterals?” Proceedings of the Fifth Conference of the International Network for Didactic Research in University Mathematics (INDRUM 2024, 10-14 June 2024), edited by Alejandro S. González-Martín et al., Escola Univerist`aria Salesiana de Sarri`a – Univ. Aut`onoma de Barcelona and INDRUM, 2024. short: 'M. Hoffmann, S. Schlüter, in: A.S. González-Martín, G. Gueudet, I. Florensa, N. Lombard (Eds.), Proceedings of the Fifth Conference of the International Network for Didactic Research in University Mathematics (INDRUM 2024, 10-14 June 2024), Escola Univerist`aria Salesiana de Sarri`a – Univ. Aut`onoma de Barcelona and INDRUM, Barcelona, 2024.' date_created: 2025-01-02T10:45:53Z date_updated: 2025-01-02T10:45:59Z ddc: - '370' - '510' department: - _id: '97' - _id: '643' editor: - first_name: Alejandro S. full_name: González-Martín, Alejandro S. last_name: González-Martín - first_name: Ghislaine full_name: Gueudet, Ghislaine last_name: Gueudet - first_name: Ignasi full_name: Florensa, Ignasi last_name: Florensa - first_name: Nathan full_name: Lombard, Nathan last_name: Lombard file: - access_level: closed content_type: application/pdf creator: maxh date_created: 2025-01-02T10:42:21Z date_updated: 2025-01-02T10:42:21Z file_id: '57896' file_name: 2024_Hoffmann_Schlueter_CongruenceQuadrilaterals.pdf file_size: 315111 relation: main_file success: 1 file_date_updated: 2025-01-02T10:42:21Z has_accepted_license: '1' keyword: - Teachers’ and students’ practices at university level - Transition to - across and from university mathematics - Teaching and learning of specific topics in university mathematics - Congruence - Quadrilaterals language: - iso: eng main_file_link: - open_access: '1' url: https://indrum2024.sciencesconf.org/data/pages/Proceedings_INDRUM2024.pdf oa: '1' place: Barcelona publication: Proceedings of the Fifth Conference of the International Network for Didactic Research in University Mathematics (INDRUM 2024, 10-14 June 2024) publication_status: published publisher: Escola Univerist`aria Salesiana de Sarri`a – Univ. Aut`onoma de Barcelona and INDRUM quality_controlled: '1' status: public title: How Do Advanced Pre-Service Teachers Develop Congruence Theorems for Quadrilaterals? type: conference user_id: '32202' year: '2024' ... --- _id: '56983' abstract: - lang: eng text: Detecting the veracity of a statement automatically is a challenge the world is grappling with due to the vast amount of data spread across the web. Verifying a given claim typically entails validating it within the framework of supporting evidence like a retrieved piece of text. Classifying the stance of the text with respect to the claim is called stance classification. Despite advancements in automated fact-checking, most systems still rely on a substantial quantity of labeled training data, which can be costly. In this work, we avoid the costly training or fine-tuning of models by reusing pre-trained large language models together with few-shot in-context learning. Since we do not train any model, our approach ExPrompt is lightweight, demands fewer resources than other stance classification methods and can serve as a modern baseline for future developments. At the same time, our evaluation shows that our approach is able to outperform former state-of-the-art stance classification approaches regarding accuracy by at least 2 percent. Our scripts and data used in this paper are available at https://github.com/dice-group/ExPrompt. author: - first_name: Umair full_name: Qudus, Umair id: '83392' last_name: Qudus orcid: 0000-0001-6714-8729 - first_name: Michael full_name: Röder, Michael id: '67199' last_name: Röder orcid: https://orcid.org/0000-0002-8609-8277 - first_name: Daniel full_name: Vollmers, Daniel last_name: Vollmers - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille id: '65716' last_name: Ngonga Ngomo citation: ama: 'Qudus U, Röder M, Vollmers D, Ngonga Ngomo A-C. ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification. In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. Vol 9. ACM; 2024:3994-3999. doi:10.1145/3627673.3679923' apa: 'Qudus, U., Röder, M., Vollmers, D., & Ngonga Ngomo, A.-C. (2024). ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 9, 3994–3999. https://doi.org/10.1145/3627673.3679923' bibtex: '@inproceedings{Qudus_Röder_Vollmers_Ngonga Ngomo_2024, title={ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification}, volume={9}, DOI={10.1145/3627673.3679923}, booktitle={Proceedings of the 33rd ACM International Conference on Information and Knowledge Management}, publisher={ACM}, author={Qudus, Umair and Röder, Michael and Vollmers, Daniel and Ngonga Ngomo, Axel-Cyrille}, year={2024}, pages={3994–3999} }' chicago: 'Qudus, Umair, Michael Röder, Daniel Vollmers, and Axel-Cyrille Ngonga Ngomo. “ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification.” In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 9:3994–99. ACM, 2024. https://doi.org/10.1145/3627673.3679923.' ieee: 'U. Qudus, M. Röder, D. Vollmers, and A.-C. Ngonga Ngomo, “ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification,” in Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, Boise, ID, USA, 2024, vol. 9, pp. 3994–3999, doi: 10.1145/3627673.3679923.' mla: 'Qudus, Umair, et al. “ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification.” Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, vol. 9, ACM, 2024, pp. 3994–99, doi:10.1145/3627673.3679923.' short: 'U. Qudus, M. Röder, D. Vollmers, A.-C. Ngonga Ngomo, in: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, ACM, 2024, pp. 3994–3999.' conference: end_date: 2024-10-25 location: Boise, ID, USA name: 'CIKM ''24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management' start_date: 2024-10-21 date_created: 2024-11-11T13:15:25Z date_updated: 2025-09-11T09:49:07Z ddc: - '006' doi: 10.1145/3627673.3679923 file: - access_level: closed content_type: application/pdf creator: uqudus date_created: 2024-11-11T13:24:19Z date_updated: 2024-11-11T13:24:19Z file_id: '56984' file_name: public.pdf file_size: 531579 relation: main_file success: 1 file_date_updated: 2024-11-11T13:24:19Z has_accepted_license: '1' intvolume: ' 9' keyword: - Stance Classification - Few-shot in-context learning - Pre-trained large language models language: - iso: eng main_file_link: - url: https://dl.acm.org/doi/10.1145/3627673.3679923 page: 3994 - 3999 popular_science: '1' project: - _id: '412' name: 'NEBULA: Nutzerzentrierte KI-basierte Erkennung von Fake-News und Fehlinformationen' publication: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management publication_identifier: isbn: - 79-8-4007-0436-9/24/10 publication_status: published publisher: ACM quality_controlled: '1' status: public title: 'ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification' type: conference user_id: '83392' volume: 9 year: '2024' ... --- _id: '57240' abstract: - lang: eng text: Validating assertions before adding them to a knowledge graph is an essential part of its creation and maintenance. Due to the sheer size of knowledge graphs, automatic fact-checking approaches have been developed. These approaches rely on reference knowledge to decide whether a given assertion is correct. Recent hybrid approaches achieve good results by including several knowledge sources. However, it is often impractical to provide a sheer quantity of textual knowledge or generate embedding models to leverage these hybrid approaches. We present FaVEL, an approach that uses algorithm selection and ensemble learning to amalgamate several existing fact-checking approaches that rely solely on a reference knowledge graph and, hence, use fewer resources than current hybrid approaches. For our evaluation, we create updated versions of two existing datasets and a new dataset dubbed FaVEL-DS. Our evaluation compares our approach to 15 fact-checking approaches—including the state-of-the-art approach HybridFC—on 3 datasets. Our results demonstrate that FaVEL outperforms all other approaches significantly by at least 0.04 in terms of the area under the ROC curve. Our source code, datasets, and evaluation results are open-source and can be found at https://github.com/dice-group/favel. author: - first_name: Umair full_name: Qudus, Umair id: '83392' last_name: Qudus orcid: 0000-0001-6714-8729 - first_name: Michael full_name: Röder, Michael id: '67199' last_name: Röder orcid: https://orcid.org/0000-0002-8609-8277 - first_name: Franck Lionel full_name: Tatkeu Pekarou, Franck Lionel last_name: Tatkeu Pekarou - first_name: Ana Alexandra full_name: Morim da Silva, Ana Alexandra id: '72108' last_name: Morim da Silva - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille id: '65716' last_name: Ngonga Ngomo citation: ama: 'Qudus U, Röder M, Tatkeu Pekarou FL, Morim da Silva AA, Ngonga Ngomo A-C. FaVEL: Fact Validation Ensemble Learning. In: Rospocher M, Mehwish Alam, eds. EKAW 2024. ; 2024.' apa: 'Qudus, U., Röder, M., Tatkeu Pekarou, F. L., Morim da Silva, A. A., & Ngonga Ngomo, A.-C. (2024). FaVEL: Fact Validation Ensemble Learning. In M. Rospocher & Mehwish Alam (Eds.), EKAW 2024.' bibtex: '@inproceedings{Qudus_Röder_Tatkeu Pekarou_Morim da Silva_Ngonga Ngomo_2024, title={FaVEL: Fact Validation Ensemble Learning}, booktitle={EKAW 2024}, author={Qudus, Umair and Röder, Michael and Tatkeu Pekarou, Franck Lionel and Morim da Silva, Ana Alexandra and Ngonga Ngomo, Axel-Cyrille}, editor={Rospocher, Marco and Mehwish Alam}, year={2024} }' chicago: 'Qudus, Umair, Michael Röder, Franck Lionel Tatkeu Pekarou, Ana Alexandra Morim da Silva, and Axel-Cyrille Ngonga Ngomo. “FaVEL: Fact Validation Ensemble Learning.” In EKAW 2024, edited by Marco Rospocher and Mehwish Alam, 2024.' ieee: 'U. Qudus, M. Röder, F. L. Tatkeu Pekarou, A. A. Morim da Silva, and A.-C. Ngonga Ngomo, “FaVEL: Fact Validation Ensemble Learning,” in EKAW 2024, Amsterdam, Netherlands, 2024.' mla: 'Qudus, Umair, et al. “FaVEL: Fact Validation Ensemble Learning.” EKAW 2024, edited by Marco Rospocher and Mehwish Alam, 2024.' short: 'U. Qudus, M. Röder, F.L. Tatkeu Pekarou, A.A. Morim da Silva, A.-C. Ngonga Ngomo, in: M. Rospocher, Mehwish Alam (Eds.), EKAW 2024, 2024.' conference: end_date: 2024-11-28 location: Amsterdam, Netherlands name: 24th International Conference on Knowledge Engineering and Knowledge Management start_date: 2024-11-26 corporate_editor: - Mehwish Alam date_created: 2024-11-19T14:12:49Z date_updated: 2025-09-11T09:48:12Z ddc: - '600' department: - _id: '34' editor: - first_name: Marco full_name: Rospocher, Marco last_name: Rospocher file: - access_level: closed content_type: application/pdf creator: uqudus date_created: 2024-11-19T14:14:14Z date_updated: 2024-11-19T14:14:14Z file_id: '57241' file_name: favel.pdf file_size: 190661 relation: main_file success: 1 file_date_updated: 2024-11-19T14:14:14Z has_accepted_license: '1' keyword: - fact checking - ensemble learning - transfer learning - knowledge management. language: - iso: eng popular_science: '1' project: - _id: '412' name: 'NEBULA: Nutzerzentrierte KI-basierte Erkennung von Fake-News und Fehlinformationen' - _id: '285' name: 'SAIL: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen' - _id: '410' name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale' publication: EKAW 2024 quality_controlled: '1' status: public title: 'FaVEL: Fact Validation Ensemble Learning' type: conference user_id: '83392' year: '2024' ... --- _id: '61273' abstract: - lang: eng text: "In human-machine explanation interactions, such as tutoring systems or customer support chatbots, it is important for the machine explainer to infer the human user's understanding. Nonverbal signals play an important role for expressing mental states like understanding and confusion in these interactions. However, an individual's expressions may vary depending on other factors. In cases where these factors are unknown, machine learning methods that infer understanding from nonverbal cues become unreliable. Stress for example has been shown to affect human expression, but it is not clear from the current research how stress affects the expression of understanding.\r\nTo address this gap, we design a paradigm that induces understanding and confusion through game rule explanations. During the explanations, self-perceived understanding and confusion are annotated by the participants. A stress condition is also introduced to enable the investigation of changes in the expression of social signals under stress.\r\nWe conducted a study to validate the stress induction and participants reported a statistically significant increase in stress during the stress condition compared to the neutral control condition. \r\nAdditionally, feedback from participants shows that the paradigm is effective in inducing understanding and confusion. \r\nThis paradigm paves the way for further studies investigating social signals of understanding to improve human-machine explanation interactions for varying contexts." author: - first_name: Jonas full_name: Paletschek, Jonas id: '98941' last_name: Paletschek citation: ama: 'Paletschek J. A Paradigm to Investigate Social Signals of Understanding and Their Susceptibility to Stress. In: 12th International Conference on  Affective Computing & Intelligent Interaction. IEEE; 2024. doi:10.1109/ACII63134.2024.00040' apa: Paletschek, J. (2024). A Paradigm to Investigate Social Signals of Understanding and Their Susceptibility to Stress. 12th International Conference on  Affective Computing & Intelligent Interaction. 12th International Conference on  Affective Computing & Intelligent Interaction, Glasgow. https://doi.org/10.1109/ACII63134.2024.00040 bibtex: '@inproceedings{Paletschek_2024, title={A Paradigm to Investigate Social Signals of Understanding and Their Susceptibility to Stress}, DOI={10.1109/ACII63134.2024.00040}, booktitle={12th International Conference on  Affective Computing & Intelligent Interaction}, publisher={IEEE}, author={Paletschek, Jonas}, year={2024} }' chicago: Paletschek, Jonas. “A Paradigm to Investigate Social Signals of Understanding and Their Susceptibility to Stress.” In 12th International Conference on  Affective Computing & Intelligent Interaction. IEEE, 2024. https://doi.org/10.1109/ACII63134.2024.00040. ieee: 'J. Paletschek, “A Paradigm to Investigate Social Signals of Understanding and Their Susceptibility to Stress,” presented at the 12th International Conference on  Affective Computing & Intelligent Interaction, Glasgow, 2024, doi: 10.1109/ACII63134.2024.00040.' mla: Paletschek, Jonas. “A Paradigm to Investigate Social Signals of Understanding and Their Susceptibility to Stress.” 12th International Conference on  Affective Computing & Intelligent Interaction, IEEE, 2024, doi:10.1109/ACII63134.2024.00040. short: 'J. Paletschek, in: 12th International Conference on  Affective Computing & Intelligent Interaction, IEEE, 2024.' conference: end_date: 2024-09-18 location: Glasgow name: 12th International Conference on Affective Computing & Intelligent Interaction start_date: 2024-09-15 date_created: 2025-09-15T11:24:56Z date_updated: 2025-09-16T07:57:53Z ddc: - '150' department: - _id: '660' doi: 10.1109/ACII63134.2024.00040 file: - access_level: closed content_type: application/pdf creator: paletsch date_created: 2025-09-15T11:18:01Z date_updated: 2025-09-15T11:18:01Z file_id: '61274' file_name: ACII2024_Camera_Ready.pdf file_size: 8807478 relation: main_file success: 1 file_date_updated: 2025-09-15T11:18:01Z has_accepted_license: '1' keyword: - Understanding - Nonverbal Social Signals - Stress Induction - Explanation - Machine Learning Bias language: - iso: eng project: - _id: '1200' name: TRR 318 - Teilprojekt A6 - Inklusive Ko-Konstruktion sozialer Signale des Verstehens publication: 12th International Conference on Affective Computing & Intelligent Interaction publication_status: published publisher: IEEE status: public title: A Paradigm to Investigate Social Signals of Understanding and Their Susceptibility to Stress type: conference user_id: '98941' year: '2024' ... --- _id: '55999' abstract: - lang: eng text: Clean hydrogen is a key aspect of carbon neutrality, necessitating robust methods for monitoring hydrogen concentration in critical infrastructures like pipelines or power plants. While semiconducting metal oxides such as In2O3 can monitor gas concentrations down to the ppm range, they often exhibit cross-sensitivity to other gases like H2O. In this study, we investigated whether cyclic optical illumination of a gas-sensitive In2O3 layer creates identifiable changes in a gas sensor´s electronic resistance that can be linked to H2 and H2O concentrations via machine learning. We exposed nanostructured In2O3 with a large surface area of 95 m2 g-1 to H2 concentrations (0-800 ppm) and relative humidity (0-70%) under cyclic activation utilizing blue light. The sensors were tested for 20 classes of gas combinations. A support vector machine achieved classification rates up to 92.0%, with reliable reproducibility (88.2 ± 2.7%) across five individual sensors using 10-fold cross-validation. Our findings suggest that cyclic optical activation can be used as a tool to classify H2 and H2O concentrations. article_type: original author: - first_name: 'Dominik ' full_name: 'Baier, Dominik ' last_name: Baier - first_name: 'Alexander ' full_name: 'Krüger, Alexander ' last_name: Krüger - first_name: 'Thorsten ' full_name: 'Wagner, Thorsten ' last_name: Wagner - first_name: Michael full_name: Tiemann, Michael id: '23547' last_name: Tiemann orcid: 0000-0003-1711-2722 - first_name: Christian full_name: Weinberger, Christian id: '11848' last_name: Weinberger citation: ama: 'Baier D, Krüger A, Wagner T, Tiemann M, Weinberger C. Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O. Chemosensors. 2024;12(9):178. doi:10.3390/chemosensors12090178' apa: 'Baier, D., Krüger, A., Wagner, T., Tiemann, M., & Weinberger, C. (2024). Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O. Chemosensors, 12(9), 178. https://doi.org/10.3390/chemosensors12090178' bibtex: '@article{Baier_Krüger_Wagner_Tiemann_Weinberger_2024, title={Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O}, volume={12}, DOI={10.3390/chemosensors12090178}, number={9}, journal={Chemosensors}, publisher={MDPI}, author={Baier, Dominik and Krüger, Alexander and Wagner, Thorsten and Tiemann, Michael and Weinberger, Christian}, year={2024}, pages={178} }' chicago: 'Baier, Dominik , Alexander Krüger, Thorsten Wagner, Michael Tiemann, and Christian Weinberger. “Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O.” Chemosensors 12, no. 9 (2024): 178. https://doi.org/10.3390/chemosensors12090178.' ieee: 'D. Baier, A. Krüger, T. Wagner, M. Tiemann, and C. Weinberger, “Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O,” Chemosensors, vol. 12, no. 9, p. 178, 2024, doi: 10.3390/chemosensors12090178.' mla: 'Baier, Dominik, et al. “Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O.” Chemosensors, vol. 12, no. 9, MDPI, 2024, p. 178, doi:10.3390/chemosensors12090178.' short: D. Baier, A. Krüger, T. Wagner, M. Tiemann, C. Weinberger, Chemosensors 12 (2024) 178. date_created: 2024-09-03T13:49:42Z date_updated: 2025-11-26T12:14:21Z ddc: - '540' department: - _id: '2' - _id: '307' doi: 10.3390/chemosensors12090178 file: - access_level: closed content_type: application/pdf creator: cweinber date_created: 2024-09-03T13:58:18Z date_updated: 2024-09-03T13:58:18Z file_id: '56000' file_name: chemosensors-12-00178.pdf file_size: 3275869 relation: main_file success: 1 file_date_updated: 2024-09-03T13:58:18Z has_accepted_license: '1' intvolume: ' 12' issue: '9' keyword: - resistive gas sensor - chemiresistor - semiconductor - metal oxide - In2O3 - mesoporous - hydrogen - humidtiy - machine learning - sustainable language: - iso: eng main_file_link: - open_access: '1' url: https://www.mdpi.com/2227-9040/12/9/178 oa: '1' page: '178' publication: Chemosensors publication_identifier: issn: - 2227-9040 publication_status: published publisher: MDPI quality_controlled: '1' status: public title: 'Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O' type: journal_article user_id: '11848' volume: 12 year: '2024' ...