@inproceedings{59483, abstract = {{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 = {{Reschke, Gregor and Brosius, Alexander}}, booktitle = {{Materials Research Proceedings}}, issn = {{2474-395X}}, keywords = {{Joining, Machine Learning, Transient Dynamic Analysis}}, location = {{Paderborn}}, pages = {{293--300}}, publisher = {{Materials Research Forum LLC}}, title = {{{Transient dynamic analysis: Performance evaluation of tactile measurement}}}, doi = {{10.21741/9781644903551-36}}, volume = {{52}}, year = {{2025}}, } @inproceedings{62080, abstract = {{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 = {{Gerritzen, Johannes and Hornig, Andreas and Gude, Maik}}, booktitle = {{Sheet Metal 2025}}, editor = {{Meschut, G. and Bobbert, M. and Duflou, J. and Fratini, L. and Hagenah, H. and Martins, P. and Merklein, M. and Micari, F.}}, isbn = {{978-1-64490-354-4}}, keywords = {{Failure, Fiber Reinforced Plastic, Machine Learning}}, pages = {{260–267}}, publisher = {{Materials Research Forum LLC, Materials Research Foundations}}, 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}}, year = {{2025}}, } @article{58885, abstract = {{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.}}, author = {{Zeller, Jannis and Riese, Josef}}, issn = {{1098-2736}}, journal = {{Journal of Research in Science Teaching}}, keywords = {{computational grounded theory, language analysis, machine learning, pedagogical content knowledge, unsupervised learning}}, 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}}, year = {{2025}}, } @inproceedings{60680, abstract = {{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 different 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 = {{Zapata Gonzalez, David Ricardo and Meyer, Marcel and Müller, Oliver}}, keywords = {{Causal Machine Learning, Causality in Time Series, Causal Discovery, Human-Machine Collaboration}}, location = {{Amman, Jordan}}, title = {{{Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems}}}, year = {{2025}}, } @article{61327, abstract = {{Robot learning from humans has been proposed and researched for several decades as a means to enable robots to learn new skills or adapt existing ones to new situations. Recent advances in artificial intelligence, including learning approaches like reinforcement learning and architectures like transformers and foundation models, combined with access to massive datasets, has created attractive opportunities to apply those data-hungry techniques to this problem. We argue that the focus on massive amounts of pre-collected data, and the resulting learning paradigm, where humans demonstrate and robots learn in isolation, is overshadowing a specialized area of work we term Human-Interactive-Robot-Learning (HIRL). This paradigm, wherein robots and humans interact during the learning process, is at the intersection of multiple fields (artificial intelligence, robotics, human-computer interaction, design and others) and holds unique promise. Using HIRL, robots can achieve greater sample efficiency (as humans can provide task knowledge through interaction), align with human preferences (as humans can guide the robot behavior towards their expectations), and explore more meaningfully and safely (as humans can utilize domain knowledge to guide learning and prevent catastrophic failures). This can result in robotic systems that can more quickly and easily adapt to new tasks in human environments. The objective of this paper is to provide a broad and consistent overview of HIRL research and to guide researchers toward understanding the scope of HIRL, and current open or underexplored challenges related to four themes — namely, human, robot learning, interaction, and broader context. The paper includes concrete use cases to illustrate the interaction between these challenges and inspire further research according to broad recommendations and a call for action for the growing HIRL community}}, 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 Vollmer, Anna-Lisa and Andriella, Antonio and Beierling, Helen and Horter, Tiffany and Kober, Jens and Sheidlower, Isaac and Taylor, Matthew E. and van Waveren, Sanne and Xiao, Xuesu}}, journal = {{Transactions on Human-Robot Interaction}}, keywords = {{Robot learning, Interactive learning systems, Human-robot interaction, Human-in-the-loop machine learning, Teaching and learning}}, title = {{{Human-Interactive Robot Learning: Definition, Challenges, and Recommendations}}}, year = {{2025}}, } @article{63498, author = {{Kirchgässner, Wilhelm and Förster, Nikolas and Piepenbrock, Till and Schweins, Oliver and Wallscheid, Oliver}}, journal = {{IEEE Transactions on Power Electronics}}, keywords = {{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}}, number = {{2}}, pages = {{3326--3335}}, title = {{{HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores}}}, doi = {{10.1109/TPEL.2024.3488174}}, volume = {{40}}, year = {{2025}}, } @inproceedings{54960, abstract = {{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 = {{Zeller, Jannis and Riese, Josef}}, booktitle = {{Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023}}, editor = {{van Vorst, Helena}}, keywords = {{Physikdidaktisches Wissen, Fähigkeitsprofile, Machine Learning}}, location = {{Hamburg}}, pages = {{122--125}}, publisher = {{Gesellschaft für Didaktik der Chemie und Physik}}, title = {{{Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von Machine Learning}}}, year = {{2024}}, } @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{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}}, } @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{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}}, } @inproceedings{34140, abstract = {{In this paper, machine learning techniques will be used to classify different PCB layouts given their electromagnetic frequency spectra. These spectra result from a simulated near-field measurement of electric field strengths at different locations. Measured values consist of real and imaginary parts (amplitude and phase) in X, Y and Z directions. Training data was obtained in the time domain by varying transmission line geometries (size, distance and signaling). It was then transformed into the frequency domain and used as deep neural network input. Principal component analysis was applied to reduce the sample dimension. The results show that classifying different designs is possible with high accuracy based on synthetic data. Future work comprises measurements of real, custom-made PCB with varying parameters to adapt the simulation model and also test the neural network. Finally, the trained model could be used to give hints about the error’s cause when overshooting EMC limits.}}, author = {{Maalouly, Jad and Hemker, Dennis and Hedayat, Christian and Rückert, Christian and Kaufmann, Ivan and Olbrich, Marcel and Lange, Sven and Mathis, Harald}}, booktitle = {{2022 Kleinheubach Conference}}, keywords = {{emc, pcb, electronic system development, machine learning, neural network}}, location = {{Miltenberg, Germany}}, publisher = {{IEEE}}, title = {{{AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development}}}, year = {{2022}}, } @inproceedings{33510, abstract = {{In the manufacture of real wood products, defects can quickly occur during the production process. To quickly sort out these defects, a system is needed that finds damage in the irregularly structured surfaces of the product. The difficulty in this task is that each surface is visually different and no standard defects can be defined. Thus, damage detection using correlation does not work, so this paper will test different machine learning methods. To evaluate different machine learning methods, a data set is needed. For this reason, the available samples were recorded manually using a static fixed camera. Subsequently, the images were divided into sub-images, which resulted in a relatively small data set. Next, a convolutional neural network (CNN) was constructed to classify the images. However, this approach did not lead to a generalized solution, so the dataset was hashed using the a- and pHash. These hash values were then trained with a fully supervised system that will later serve as a reference model, in the semi-supervised learning procedures. To improve the supervised model and not have to label every data point, semi-supervised learning methods are used in the following. For this purpose, the CEAL method (wrapper method) is considered in the first and then the Π-Model (intrinsically semi-supervised).}}, author = {{Sander, Tom and Lange, Sven and Hilleringmann, Ulrich and Geneiß, Volker and Hedayat, Christian and Kuhn, Harald}}, booktitle = {{2022 Smart Systems Integration (SSI)}}, keywords = {{Machine Learning, CNN, Hashing, semi-supervised learning}}, location = {{Grenoble, France}}, publisher = {{IEEE}}, title = {{{Detection of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised Learning Methods}}}, doi = {{10.1109/ssi56489.2022.9901433}}, year = {{2022}}, } @article{48878, abstract = {{Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse\textemdash ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field.}}, author = {{Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, issn = {{2076-3417}}, journal = {{Applied Sciences}}, keywords = {{big data, data mining, data stream analysis, machine learning, stream classification, supervised learning}}, number = {{18}}, pages = {{9094}}, publisher = {{{Multidisciplinary Digital Publishing Institute}}}, title = {{{Process-Oriented Stream Classification Pipeline: A Literature Review}}}, doi = {{10.3390/app12189094}}, volume = {{12}}, year = {{2022}}, } @article{35732, abstract = {{While the Information Systems (IS) discipline has researched digital platforms extensively, the body of knowledge appertaining to platforms still appears fragmented and lacking conceptual consistency. Based on automated text mining and unsupervised machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive research on platforms—comprising 11,049 papers spanning 44 years of research activity. From a cluster analysis concerning platform concepts’ semantically most similar words, we identify six research streams on platforms, each with their own platform terms. Based on interpreting the identified concepts vis-à-vis the extant research and considering a temporal perspective on the concepts’ application, we present a lexicon of platform concepts, to guide further research on platforms in the IS discipline. Researchers and managers can build on our results to position their work appropriately, applying a specific theoretical perspective on platforms in isolation or combining multiple perspectives to study platform phenomena at a more abstract level.}}, author = {{Bartelheimer, Christian and zur Heiden, Philipp and Lüttenberg, Hedda and Beverungen, Daniel}}, issn = {{1019-6781}}, journal = {{Electronic Markets}}, keywords = {{Platform, Text mining, Machine learning, Data communications, Interpretive research, Systems design and implementation}}, pages = {{375--396}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Systematizing the lexicon of platforms in information systems: a data-driven study}}}, doi = {{10.1007/s12525-022-00530-6}}, volume = {{32}}, year = {{2022}}, } @inproceedings{31066, abstract = {{While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model. }}, author = {{Schön, Oliver and Götte, Ricarda-Samantha and Timmermann, Julia}}, booktitle = {{14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)}}, keywords = {{neural networks, physics-guided, data-driven, multi-objective optimization, system identification, machine learning, dynamical systems}}, location = {{Casablanca, Morocco}}, number = {{12}}, pages = {{19--24}}, title = {{{Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}}}, doi = {{https://doi.org/10.1016/j.ifacol.2022.07.282}}, volume = {{55}}, year = {{2022}}, } @article{21004, abstract = {{Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.}}, author = {{Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}}, issn = {{0162-8828}}, journal = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}}, keywords = {{Automated Machine Learning, Multi Label Classification, Hierarchical Planning, Bayesian Optimization}}, pages = {{1--1}}, title = {{{AutoML for Multi-Label Classification: Overview and Empirical Evaluation}}}, doi = {{10.1109/tpami.2021.3051276}}, year = {{2021}}, }