@unpublished{55159, abstract = {{We introduce a method based on Gaussian process regression to identify discrete variational principles from observed solutions of a field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 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 = {{Offen, Christian}}, keywords = {{System identification, inverse problem of variational calculus, Gaussian process, Lagrangian learning, physics informed machine learning, geometry aware learning}}, pages = {{28}}, title = {{{Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification}}}, year = {{2024}}, } @inproceedings{56948, abstract = {{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 = {{Zeller, Jannis and Riese, Josef}}, booktitle = {{Entdecken, lehren und forschen im Schülerlabor. GDCP Jahrestagung 2024}}, keywords = {{Physikdidaktisches Wissen, Assessment, Machine Learning}}, location = {{Bochum}}, title = {{{Assessment des physikdidaktischen Wissens mithilfe von Machine Learning}}}, year = {{2024}}, } @inproceedings{62078, abstract = {{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 = {{Gerritzen, Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik}}, booktitle = {{ECCM21 - Proceedings of the 21st European Conference on Composite Materials}}, isbn = {{978-2-912985-01-9}}, keywords = {{Direct parameter identification, Machine learning, Convolutional neural networks, Strain rate dependency, Fiber reinforced plastics, woven composites, segmentation, synthetic training data, x-ray computed tomography}}, pages = {{1252–1259}}, publisher = {{European Society for Composite Materials (ESCM)}}, title = {{{Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning}}}, doi = {{10.60691/yj56-np80}}, volume = {{3}}, year = {{2024}}, } @inproceedings{57895, abstract = {{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 for further research.}}, author = {{Hoffmann, Max and Schlüter, Sarah}}, booktitle = {{Proceedings of the Fifth Conference of the International Network for Didactic Research in University Mathematics (INDRUM 2024, 10-14 June 2024)}}, editor = {{González-Martín, Alejandro S. and Gueudet, Ghislaine and Florensa, Ignasi and Lombard, Nathan}}, keywords = {{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}}, publisher = {{Escola Univerist`aria Salesiana de Sarri`a – Univ. Aut`onoma de Barcelona and INDRUM}}, title = {{{How Do Advanced Pre-Service Teachers Develop Congruence Theorems for Quadrilaterals?}}}, year = {{2024}}, } @inproceedings{56983, abstract = {{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 = {{Qudus, Umair and Röder, Michael and Vollmers, Daniel and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management}}, isbn = {{79-8-4007-0436-9/24/10}}, keywords = {{Stance Classification, Few-shot in-context learning, Pre-trained large language models}}, location = {{Boise, ID, USA}}, pages = {{3994 -- 3999}}, publisher = {{ACM}}, title = {{{ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification}}}, doi = {{10.1145/3627673.3679923}}, volume = {{9}}, year = {{2024}}, } @inproceedings{57240, abstract = {{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 = {{Qudus, Umair and Röder, Michael and Tatkeu Pekarou, Franck Lionel and Morim da Silva, Ana Alexandra and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{EKAW 2024}}, editor = {{Rospocher, Marco}}, keywords = {{fact checking, ensemble learning, transfer learning, knowledge management.}}, location = {{Amsterdam, Netherlands}}, title = {{{FaVEL: Fact Validation Ensemble Learning}}}, year = {{2024}}, } @inproceedings{61273, abstract = {{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. To 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. We 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. Additionally, feedback from participants shows that the paradigm is effective in inducing understanding and confusion. This paradigm paves the way for further studies investigating social signals of understanding to improve human-machine explanation interactions for varying contexts.}}, author = {{Paletschek, Jonas}}, booktitle = {{12th International Conference on Affective Computing & Intelligent Interaction}}, keywords = {{Understanding, Nonverbal Social Signals, Stress Induction, Explanation, Machine Learning Bias}}, location = {{Glasgow}}, publisher = {{IEEE}}, title = {{{A Paradigm to Investigate Social Signals of Understanding and Their Susceptibility to Stress}}}, doi = {{10.1109/ACII63134.2024.00040}}, year = {{2024}}, } @article{55999, abstract = {{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.}}, author = {{Baier, Dominik and Krüger, Alexander and Wagner, Thorsten and Tiemann, Michael and Weinberger, Christian}}, issn = {{2227-9040}}, journal = {{Chemosensors}}, keywords = {{resistive gas sensor, chemiresistor, semiconductor, metal oxide, In2O3, mesoporous, hydrogen, humidtiy, machine learning, sustainable}}, number = {{9}}, pages = {{178}}, publisher = {{MDPI}}, title = {{{Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O}}}, doi = {{10.3390/chemosensors12090178}}, volume = {{12}}, year = {{2024}}, } @inbook{63109, abstract = {{Game-based Learning (GBL) und Gamification (GF) gewinnen im schulischen Umfeld zunehmend an Bedeutung. Ihr Einsatz bietet die Möglichkeit, eigenverantwortliches Lernen im Unterricht zu fördern, bringt aber auch Herausforderungen für Lehr- und Lernprozesse mit sich. Daher ist es von großer Bedeutung, angehenden Lehrkräften die notwendigen Kompetenzen zu vermitteln, um GBL und GF effektiv in den Unterricht zu integrieren. Vor diesem Hintergrund wurde ein Lehrkonzept für die Hochschullehre entwickelt und hinsichtlich der Zielerreichung evaluiert. Der vorliegende Beitrag gibt einen Einblick in die Gestaltungsaspekte des Seminars sowie erste Erkenntnisse und Erfahrungen der Studierenden.}}, author = {{Truong, Ha My}}, booktitle = {{Lehrkräftebildung in der digitalen Welt. Zukunftsorientierte Forschungs- und Praxisperspektiven}}, editor = {{Herzig, Bardo and Eickelmann, Birgit and Schwabl, Franziska and Schulze, Johanna and Niemann, Jan}}, isbn = {{9783830948377}}, keywords = {{Game-based learning, Gamification, Hochschullehre, Lehrkräftebildung}}, pages = {{179--189}}, publisher = {{Waxmann Verlag GmbH}}, title = {{{Level Up! Gamification in der Lehrkräfteausbildung - Konzeption und Erfahrung eines gamifizierten Seminars in der Hochschullehre für Lehramtsstudierende}}}, doi = {{10.31244/9783830998372}}, year = {{2024}}, } @article{65163, abstract = {{Dieser Beitrag untersucht aktuelle pädagogische und hermeneutische Ansätze der jüdischen, christlichen und muslimischen Religionspädagogik in Kindertora, Kinderbibel und Kinderkoran. Er betont die Notwendigkeit für Lehrkräfte, sich mit den spezifischen pädagogischen und hermeneutischen Ansätzen der drei monotheistischen Religionen vertraut zu machen, um die didaktischen Heiligen Schriften im Unterricht angemessen nutzen zu können. Beispiele aus dem aktuellen Religionsunterricht zeigen Missverständnisse und Überraschungen auf, die durch unzureichendes Wissen entstehen. Der Artikel hebt die Bedeutung einer jüdischen Identitätsbildung, einer christlichen diversitätssensiblen Perspektive und von muslimischen normativen Diskursen in den verschiedenen Religionspädagogiken hervor und diskutiert die Herausforderungen und Chancen, die mit der Nutzung didaktisierter Heiliger Schriften verbunden sind. }}, author = {{Keuchen, Marion}}, journal = {{TheoWeb. Zeitschrift für Religionspädagogik}}, keywords = {{Heilige Schriften, interreligiöses Lernen, Schrifthermeneutik, Identität, diversitätssensible Religionspädagogik, jüdische Religionspädagogik, muslimische Religionspädagogik, christliche Religionspädagogik, Holy scriptures, interreligious learning, hermeneutics of scripture, identity, diversity-sensitive religious education, Jewish religious education, Muslim religious education, Christian religious education}}, pages = {{224--237}}, title = {{{Aktuelle pädagogische und hermeneutische Ansätze aus Judentum, Christentum und Islam in Kindertora, Kinderbibel und Kinderkoran: Identitätsbildung, diversitätssensible Religionspädagogik und normative Diskurse}}}, doi = {{10.23770/tw0360}}, volume = {{2}}, year = {{2024}}, } @inproceedings{45270, abstract = {{Clinical depression is a serious mental disorder that poses challenges for both personal and public health. Millions of people struggle with depression each year, but for many, the disorder goes undiagnosed or untreated. Over the last decade, early depression detection on social media emerged as an interdisciplinary research field. However, there is still a gap in detecting hesitant, depression-susceptible individuals with minimal direct depressive signals at an early stage. We, therefore, take up this open point and leverage posts from Reddit to fill the addressed gap. Our results demonstrate the potential of contemporary Transformer architectures in yielding promising predictive capabilities for mental health research. Furthermore, we investigate the model’s interpretability using a surrogate and a topic modeling approach. Based on our findings, we consider this work as a further step towards developing a better understanding of mental eHealth and hope that our results can support the development of future technologies.}}, author = {{Halimeh, Haya and Caron, Matthew and Müller, Oliver}}, booktitle = {{Hawaii International Conference on System Sciences}}, keywords = {{Social Media and Healthcare Technology, early depression detection, liwc, mental health, transfer learning, transformer architectures}}, title = {{{Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features}}}, year = {{2023}}, } @inproceedings{50479, abstract = {{Verifying assertions is an essential part of creating and maintaining knowledge graphs. Most often, this task cannot be carried out manually due to the sheer size of modern knowledge graphs. Hence, automatic fact-checking approaches have been proposed over the last decade. These approaches aim to compute automatically whether a given assertion is correct or incorrect. However, most fact-checking approaches are binary classifiers that fail to consider the volatility of some assertions, i.e., the fact that such assertions are only valid at certain times or for specific time intervals. Moreover, the few approaches able to predict when an assertion was valid (i.e., time-point prediction approaches) rely on manual feature engineering. This paper presents TEMPORALFC, a temporal fact-checking approach that uses multiple sources of background knowledge to assess the veracity and temporal validity of a given assertion. We evaluate TEMPORALFC on two datasets and compare it to the state of the art in fact-checking and time-point prediction. Our results suggest that TEMPORALFC outperforms the state of the art on the fact-checking task by 0.13 to 0.15 in terms of Area Under the Receiver Operating Characteristic curve and on the time-point prediction task by 0.25 to 0.27 in terms of Mean Reciprocal Rank. Our code is open-source and can be found at https://github.com/dice-group/TemporalFC.}}, author = {{Qudus, Umair and Röder, Michael and Kirrane, Sabrina and Ngomo, Axel-Cyrille Ngonga}}, booktitle = {{The Semantic Web – ISWC 2023}}, editor = {{R. Payne, Terry and Presutti, Valentina and Qi, Guilin and Poveda-Villalón, María and Stoilos, Giorgos and Hollink, Laura and Kaoudi, Zoi and Cheng, Gong and Li, Juanzi}}, isbn = {{9783031472398}}, issn = {{0302-9743}}, keywords = {{temporal fact checking · ensemble learning · transfer learning · time-point prediction · temporal knowledge graphs}}, location = {{Athens, Greece}}, pages = {{465–483}}, publisher = {{Springer, Cham}}, title = {{{TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs}}}, doi = {{10.1007/978-3-031-47240-4_25}}, volume = {{14265}}, year = {{2023}}, } @inproceedings{52816, abstract = {{Manufacturing companies face the challenge of reaching required quality standards. Using optical sensors and deep learning might help. However, training deep learning algorithms require large amounts of visual training data. Using domain randomization to generate synthetic image data can alleviate this bottleneck. This paper presents the application of synthetic image training data for optical quality inspections using visual sensor technology. The results show synthetically generated training data are appropriate for visual quality inspections.}}, author = {{Gräßler, Iris and Hieb, Michael}}, booktitle = {{Lectures}}, keywords = {{synthetic training data, machine vision quality gates, deep learning, automated inspection and quality control, production control}}, location = {{Nuremberg}}, pages = {{253--524}}, publisher = {{AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany}}, title = {{{Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing}}}, doi = {{10.5162/smsi2023/d7.4}}, year = {{2023}}, } @article{49434, abstract = {{Die Rollenspiel-Methode ist ein handlungs- und anwendungsbezogenes Instrument, um Studierende bereits während der universitären Ausbildung für unterschiedliche professionelle Sicht- und Handlungsweisen zu sensibilisieren. In diesem Sinne stellt der folgende Beitrag ein Rollenspiel vor, welches als hochschuldidaktisches Material für die inklusionssensible Lehrer*innenbildung genutzt werden kann und Studierende auf zukünftige multiprofessionelle Kooperationshandlungen in der schulischen Praxis vorbereiten soll. Dieses bietet einen geeigneten Anlass, um die professionsübergreifende Zusammenarbeit „gefahrlos“ im Rahmen einer fiktiven kollegialen Fallkonferenz zu erproben sowie unterschiedliche pädagogische Professionsverständnisse aufzudecken und zu reflektieren. Darüber hinaus werden erste Durchführungserfahrungen und Evaluationsergebnisse diskutiert, die im Zuge der wissenschaftlichen Begleitforschung der Teilmaßnahme „Multiprofessionelle Kooperation in inklusiven Ganztagsschulen“ des Bielefelder QLB-Projekts BiProfessional erhoben wurden.}}, author = {{Schuldt, Alessa and Palm, Manfred and Neumann, Phillip and Böhm-Kasper, Oliver and Demmer, Christine and Lütje-Klose, Birgit}}, journal = {{Zeitschrift für Konzepte Und Arbeitsmaterialien für Lehrer*innenbildung Und Unterricht}}, keywords = {{Rollenspiel, mulitprofessionelle Kooperation, inklusionssensible Lehrerbildung, Hochschuldidaktik, Blended Learning}}, number = {{4}}, title = {{{„Jede*r von uns sieht die Situation eben unterschiedlich – das ist zwar eine Schwierigkeit, aber auch eine Bereicherung“}}}, doi = {{10.11576/DIMAWE-6699}}, volume = {{5}}, year = {{2023}}, } @article{39976, abstract = {{The context of the study is the increasing digitalisation of the living environment of primary school students, which is to be introduced into primary schools according to theoretical and educational policy guidelines. In this regard, further teacher training on digital media in classrooms are particularly relevant, on the one hand to promote teachers’ digital-related pedagogical knowledge and content knowledge (DPaCK). On the other hand, studies also reveal positive correlations among teacher training, teaching activities, and students’ learning outcomes. In-service teacher training courses with adaptive support by a trainer in particular have proven to be effective. Against the background of various research studies on professional development of teachers, a corresponding model of tripartite learning outcomes has been established and serves as a broad theoretical framework. However, the specific relationship between in-service teacher training with adaptive support, DPaCK, and computational thinking of primary school students in the context of the German primary school subject Sachunterricht has not been sufficiently studied. Therefore, the following research questions can be derived: (1) To what extent does training with adaptive support on the topic of learning robots contribute to the development of teachers’ DPaCK? (2) Which effects can be ascertained on the students’ computational thinking in technology-related Sachunterricht? To investigate this relationship, an intervention study in a pre-post design with an experimental group, a control group, and a baseline is appropriate. As results are not yet available at this point, the present paper focuses on the presentation of the theoretical background and empirical approaches.}}, author = {{Janicki, Nicole and Tenberge, Claudia}}, journal = {{Australasian Journal of Technology Education}}, keywords = {{technology education, teacher professionalisation, Computational Thinking, digitalization, learning robots}}, title = {{{Technology education in elementary school using the example of 'learning robots' – development and evaluation of an in-service teacher training concept}}}, doi = {{https://doi.org/10.15663/ajte.v9.i0.103}}, volume = {{9}}, year = {{2023}}, } @inproceedings{31849, author = {{Hoffmann, Max and Biehler, Rolf}}, booktitle = {{Proceedings of the Fourth Conference of the International Network for Didactic Research in University Mathematics (INDRUM 2022, 19-22 October 2022)}}, editor = {{Trigueros, Marı́a and Barquero, Berta and Hochmuth, Reinhard and Peters, Jana}}, keywords = {{Teaching and learning of specific topics in university mathematics, Transition to, across and from university mathematics, Student Teachers, Geometry, Congruence, Double Discontinuity.}}, publisher = {{University of Hannover and INDRUM.}}, title = {{{Student Teachers ’ Knowledge of Congruence before a University Course on Geometry}}}, year = {{2023}}, } @article{45299, abstract = {{Many applications are driven by Machine Learning (ML) today. While complex ML models lead to an accurate prediction, their inner decision-making is obfuscated. However, especially for high-stakes decisions, interpretability and explainability of the model are necessary. Therefore, we develop a holistic interpretability and explainability framework (HIEF) to objectively describe and evaluate an intelligent system’s explainable AI (XAI) capacities. This guides data scientists to create more transparent models. To evaluate our framework, we analyse 50 real estate appraisal papers to ensure the robustness of HIEF. Additionally, we identify six typical types of intelligent systems, so-called archetypes, which range from explanatory to predictive, and demonstrate how researchers can use the framework to identify blind-spot topics in their domain. Finally, regarding comprehensiveness, we used a random sample of six intelligent systems and conducted an applicability check to provide external validity.}}, author = {{Kucklick, Jan-Peter}}, issn = {{1246-0125}}, journal = {{Journal of Decision Systems}}, keywords = {{Explainable AI (XAI), machine learning, interpretability, real estate appraisal, framework, taxonomy}}, pages = {{1--41}}, publisher = {{Taylor & Francis}}, title = {{{HIEF: a holistic interpretability and explainability framework}}}, doi = {{10.1080/12460125.2023.2207268}}, year = {{2023}}, } @inproceedings{33734, abstract = {{Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis}}, author = {{KOUAGOU, N'Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)}}, editor = {{Pesquita, Catia and Jimenez-Ruiz, Ernesto and McCusker, Jamie and Faria, Daniel and Dragoni, Mauro and Dimou, Anastasia and Troncy, Raphael and Hertling, Sven}}, keywords = {{Neural network, Concept learning, Description logics}}, location = {{Hersonissos, Crete, Greece}}, pages = {{209 -- 226}}, publisher = {{Springer International Publishing}}, title = {{{Neural Class Expression Synthesis}}}, doi = {{https://doi.org/10.1007/978-3-031-33455-9_13}}, volume = {{13870}}, year = {{2023}}, } @article{29240, abstract = {{The principle of least action is one of the most fundamental physical principle. It says that among all possible motions connecting two points in a phase space, the system will exhibit those motions which extremise an action functional. Many qualitative features of dynamical systems, such as the presence of conservation laws and energy balance equations, are related to the existence of an action functional. Incorporating variational structure into learning algorithms for dynamical systems is, therefore, crucial in order to make sure that the learned model shares important features with the exact physical system. In this paper we show how to incorporate variational principles into trajectory predictions of learned dynamical systems. The novelty of this work is that (1) our technique relies only on discrete position data of observed trajectories. Velocities or conjugate momenta do not need to be observed or approximated and no prior knowledge about the form of the variational principle is assumed. Instead, they are recovered using backward error analysis. (2) Moreover, our technique compensates discretisation errors when trajectories are computed from the learned system. This is important when moderate to large step-sizes are used and high accuracy is required. For this, we introduce and rigorously analyse the concept of inverse modified Lagrangians by developing an inverse version of variational backward error analysis. (3) Finally, we introduce a method to perform system identification from position observations only, based on variational backward error analysis.}}, author = {{Ober-Blöbaum, Sina and Offen, Christian}}, issn = {{0377-0427}}, journal = {{Journal of Computational and Applied Mathematics}}, keywords = {{Lagrangian learning, variational backward error analysis, modified Lagrangian, variational integrators, physics informed learning}}, pages = {{114780}}, publisher = {{Elsevier}}, title = {{{Variational Learning of Euler–Lagrange Dynamics from Data}}}, doi = {{10.1016/j.cam.2022.114780}}, volume = {{421}}, year = {{2023}}, } @inproceedings{60304, abstract = {{The focus towards multi-material and lightweight assemblies, driven by legal requirements on reducing emissions and energy consumptions, reveals important drawbacks and disadvantages of established joining processes, such as welding. In this context, mechanical joining technologies, such as clinching, are becoming more and more relevant especially in the automotive industry. However, the availability of only few standards and almost none systematic design methods causes a still very time- and cost-intensive assembly development process considering mainly expert knowledge and a considerable amount of experimental studies. Motivated by this, the presented work introduces a novel approach for the methodical design and dimensioning of mechanically clinched assemblies. Therefore, the utilization of regression models, such as machine learning algorithms, combined with manufacturing knowledge ensures a reliable estimation of individual clinched joint characteristics. In addition, the implementation of an engineering workbench enables the following data-driven and knowledge-based generation of high-quality initial assembly designs already in early product development phases. In a subsequent analysis and adjustment, these designs are being improved while guaranteeing joining safety and loading conformity. The presented results indicate that the methodological approach can pave the way to a more systematic design process of mechanical joining assemblies, which can significantly shorten the required number of iteration loops and therefore the product development time.}}, author = {{Zirngibl, Christoph and Martin, Sven and Steinfelder, Christian and Schleich, Benjamin and Tröster, Thomas and Brosius, Alexander and Wartzack, Sandro}}, booktitle = {{Materials Research Proceedings}}, issn = {{2474-395X}}, keywords = {{Joining, Structural Analysis, Machine Learning}}, location = {{Erlangen-Nürnberg}}, publisher = {{Materials Research Forum LLC}}, title = {{{Methodical approach for the design and dimensioning of mechanical clinched assemblies}}}, doi = {{10.21741/9781644902417-23}}, volume = {{25}}, year = {{2023}}, }