TY - JOUR AU - Janicki, Nicole AU - Tenberge, Claudia ID - 39976 JF - Australasian Journal of Technology Education KW - technology education KW - teacher professionalisation KW - Computational Thinking KW - digitalization KW - learning robots TI - Technology education in elementary school using the example of 'learning robots' – development and evaluation of an in-service teacher training concept ER - TY - CONF AU - Hoffmann, Max AU - Biehler, Rolf ED - Trigueros, Marı́a ED - Barquero, Berta ED - Hochmuth, Reinhard ED - Peters, Jana ID - 31849 KW - Teaching and learning of specific topics in university mathematics KW - Transition to KW - across and from university mathematics KW - Student Teachers KW - Geometry KW - Congruence KW - Double Discontinuity. T2 - Proceedings of the Fourth Conference of the International Network for Didactic Research in University Mathematics (INDRUM 2022, 19-22 October 2022) TI - Student Teachers ’ Knowledge of Congruence before a University Course on Geometry ER - TY - JOUR AB - 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. AU - Kucklick, Jan-Peter ID - 45299 JF - Journal of Decision Systems KW - Explainable AI (XAI) KW - machine learning KW - interpretability KW - real estate appraisal KW - framework KW - taxonomy SN - 1246-0125 TI - HIEF: a holistic interpretability and explainability framework ER - TY - CONF AB - 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 AU - KOUAGOU, N'Dah Jean AU - Heindorf, Stefan AU - Demir, Caglar AU - Ngonga Ngomo, Axel-Cyrille ED - Pesquita, Catia ED - Jimenez-Ruiz, Ernesto ED - McCusker, Jamie ED - Faria, Daniel ED - Dragoni, Mauro ED - Dimou, Anastasia ED - Troncy, Raphael ED - Hertling, Sven ID - 33734 KW - Neural network KW - Concept learning KW - Description logics T2 - The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023) TI - Neural Class Expression Synthesis VL - 13870 ER - TY - JOUR AB - 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. AU - Ober-Blöbaum, Sina AU - Offen, Christian ID - 29240 JF - Journal of Computational and Applied Mathematics KW - Lagrangian learning KW - variational backward error analysis KW - modified Lagrangian KW - variational integrators KW - physics informed learning SN - 0377-0427 TI - Variational Learning of Euler–Lagrange Dynamics from Data VL - 421 ER - TY - CONF AB - 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. AU - Halimeh, Haya AU - Caron, Matthew AU - Müller, Oliver ID - 45270 KW - Social Media and Healthcare Technology KW - early depression detection KW - liwc KW - mental health KW - transfer learning KW - transformer architectures T2 - Hawaii International Conference on System Sciences TI - Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features ER - TY - CONF AB - 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. AU - Qudus, Umair AU - Röder, Michael AU - Kirrane, Sabrina AU - Ngomo, Axel-Cyrille Ngonga ED - R. Payne, Terry ED - Presutti, Valentina ED - Qi, Guilin ED - Poveda-Villalón, María ED - Stoilos, Giorgos ED - Hollink, Laura ED - Kaoudi, Zoi ED - Cheng, Gong ED - Li, Juanzi ID - 50479 KW - temporal fact checking · ensemble learning · transfer learning · time-point prediction · temporal knowledge graphs SN - 0302-9743 T2 - The Semantic Web – ISWC 2023 TI - TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs VL - 14265 ER - TY - CONF AB - 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. AU - Gräßler, Iris AU - Hieb, Michael ID - 52816 KW - synthetic training data KW - machine vision quality gates KW - deep learning KW - automated inspection and quality control KW - production control T2 - Lectures TI - Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing ER - TY - JOUR AB - 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. AU - Schuldt, Alessa AU - Palm, Manfred AU - Neumann, Phillip AU - Böhm-Kasper, Oliver AU - Demmer, Christine AU - Lütje-Klose, Birgit ID - 49434 IS - 4 JF - Zeitschrift für Konzepte Und Arbeitsmaterialien für Lehrer*innenbildung Und Unterricht KW - Rollenspiel KW - mulitprofessionelle Kooperation KW - inklusionssensible Lehrerbildung KW - Hochschuldidaktik KW - Blended Learning TI - „Jede*r von uns sieht die Situation eben unterschiedlich – das ist zwar eine Schwierigkeit, aber auch eine Bereicherung“ VL - 5 ER - TY - CONF AB - Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive results. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field. To this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of wireless mobile networks. AU - Schneider, Stefan Balthasar AU - Werner, Stefan AU - Khalili, Ramin AU - Hecker, Artur AU - Karl, Holger ID - 30236 KW - wireless mobile networks KW - network management KW - continuous control KW - cognitive networks KW - autonomous coordination KW - reinforcement learning KW - gym environment KW - simulation KW - open source T2 - IEEE/IFIP Network Operations and Management Symposium (NOMS) TI - mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks ER - TY - BOOK AB - The volume comprises a variety of research approaches that seek to explore and understand employees’ learning and development through and for work. Working life reveals challenges through technological, economic and societal development that can only rudimentarily be addressed by formal education and training. Workplace learning becomes more and more important for employees and enterprises to successfully cope with these challenges. Workplace learning is a steadily growing field of educational research but it lacks so far a scholastic canon – there is rather a diversity of research approaches. This volume reflects this diversity by bringing together researchers from different countries and different theoretical backgrounds, presenting their current research on topics that all are relevant for understanding presages, processes and outcomes of workplace learning. Hence, this volume is of relevance for researchers as well as practitioners in the field and policy makers. ED - Harteis, Christian ED - Gijbels, David ED - Kyndt, Eva ID - 30291 KW - tivesTeam learningTeam climateSocial influences on team learningKnowledge construction in teamsLearning cultureAcknowledgement of competencesTechnology and professional learningCreation of a learning eco-systemDiversity as a challenge for organisationsHigher education as preparation for WPLSocial support in networks and professional learningvocational and professional education SN - 2210-5549 TI - Research Approaches on Workplace Learning ER - TY - CHAP AB - This chapter presents a discussion of the concept of agency. Agency is understood as a multifaceted construct describing the idea that human beings make choices, act on these choices, and thereby exercise influence on their own lives as well as their environment. We argue that the concept is discussed from three different perspectives in the literature—transformational, dispositional, and relational—that are each related to learning and development in work contexts. These perspectives do not reflect incompatible positions but rather different aspects of the same phenomena. The chapter also offers an avenue of insight into empirical studies that employ agency as a central concept as well as discussions about concepts that closely overlap with ideas of human beings as agents of power and influence. AU - Goller, Michael AU - Paloniemi, Susanna ID - 30289 KW - Agency Workplace learning Professional development Proactivity Self-direction SN - 2210-5549 T2 - Research Approaches on Workplace Learning TI - Agency: Taking Stock of Workplace Learning Research ER - TY - CONF AB - 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. AU - Maalouly, Jad AU - Hemker, Dennis AU - Hedayat, Christian AU - Rückert, Christian AU - Kaufmann, Ivan AU - Olbrich, Marcel AU - Lange, Sven AU - Mathis, Harald ID - 34140 KW - emc KW - pcb KW - electronic system development KW - machine learning KW - neural network T2 - 2022 Kleinheubach Conference TI - AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development ER - TY - CONF AB - 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). AU - Sander, Tom AU - Lange, Sven AU - Hilleringmann, Ulrich AU - Geneiß, Volker AU - Hedayat, Christian AU - Kuhn, Harald ID - 33510 KW - Machine Learning KW - CNN KW - Hashing KW - semi-supervised learning T2 - 2022 Smart Systems Integration (SSI) TI - Detection of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised Learning Methods ER - TY - CHAP AU - Janicki, Nicole ED - Binder, Martin ED - Wiesmüller, Christian ED - Finkbeiner, Timo ID - 37902 KW - technology education KW - teacher professionalisation KW - Computational Thinking KW - digitalization KW - learning robots T2 - Technik: Verstehen wir, was wir nutzen!? - Tagungsband zur 23. Jahrestagung der Deutschen Gesellschaft für Technische Bildung in Mannheim vom 24.-25. September 2021. TI - Entwicklung und Evaluation eines Fortbildungskonzepts im Kontext der technischen Bildung an Grundschulen am Beispiel von Lernrobotern ER - TY - CONF AB - Smart home systems contain plenty of features that enhance wellbeing in everyday life through artificial intelligence (AI). However, many users feel insecure because they do not understand the AI’s functionality and do not feel they are in control of it. Combining technical, psychological and philosophical views on AI, we rethink smart homes as interactive systems where users can partake in an intelligent agent’s learning. Parallel to the goals of explainable AI (XAI), we explored the possibility of user involvement in supervised learning of the smart home to have a first approach to improve acceptance, support subjective understanding and increase perceived control. In this work, we conducted two studies: In an online pre-study, we asked participants about their attitude towards teaching AI via a questionnaire. In the main study, we performed a Wizard of Oz laboratory experiment with human participants, where participants spent time in a prototypical smart home and taught activity recognition to the intelligent agent through supervised learning based on the user’s behaviour. We found that involvement in the AI’s learning phase enhanced the users’ feeling of control, perceived understanding and perceived usefulness of AI in general. The participants reported positive attitudes towards training a smart home AI and found the process understandable and controllable. We suggest that involving the user in the learning phase could lead to better personalisation and increased understanding and control by users of intelligent agents for smart home automation. AU - Sieger, Leonie Nora AU - Hermann, Julia AU - Schomäcker, Astrid AU - Heindorf, Stefan AU - Meske, Christian AU - Hey, Celine-Chiara AU - Doğangün, Ayşegül ID - 34674 KW - human-agent interaction KW - smart homes KW - supervised learning KW - participation T2 - International Conference on Human-Agent Interaction TI - User Involvement in Training Smart Home Agents ER - TY - CONF AB - 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. AU - Schön, Oliver AU - Götte, Ricarda-Samantha AU - Timmermann, Julia ID - 31066 IS - 12 KW - neural networks KW - physics-guided KW - data-driven KW - multi-objective optimization KW - system identification KW - machine learning KW - dynamical systems T2 - 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022) TI - Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems VL - 55 ER - TY - JOUR AB - According to the German Rectors’ Conference (HRK), German higher education teaching fails to meet the demand to integrate competence-oriented learning objectives. Despite a wide-ranging debate on the use of learning objectives, empirical research on their effectiveness is scarce. The present study uses the features of digital teaching platforms to investigate the perception and effectiveness of learning objectives applying a randomised controlled experiment followed by a survey in a course for undergraduate economics students (N = 30). Controlling group preconditions and the treatment effect allows to draw conclusions about the different learning outcomes of the student groups. The specification of behaviour-oriented learning objectives in the online course system leads to significantly better performance in the treatment group. A stronger perception of the learning objectives in the treatment group supports this effect that remains significant in a regression analysis. Thus, the study provides an empirical justification to integrate learning objectives in university teaching. AU - Auer, Thorsten Fabian ID - 44529 IS - 1 JF - die hochschullehre KW - learning objectives KW - academic performance KW - perception KW - teaching methods KW - experiment SN - 2199-8825 TI - Die Wirksamkeit von Lernzielen für Studienleistungen – eine experimentelle Studie VL - 8 ER - TY - JOUR AB - 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. AU - Clever, Lena AU - Pohl, Janina Susanne AU - Bossek, Jakob AU - Kerschke, Pascal AU - Trautmann, Heike ID - 48878 IS - 18 JF - Applied Sciences KW - big data KW - data mining KW - data stream analysis KW - machine learning KW - stream classification KW - supervised learning SN - 2076-3417 TI - Process-Oriented Stream Classification Pipeline: A Literature Review VL - 12 ER - TY - GEN AB - Information is one of the most important ingredients for decision-making. While the neoclassical assumption of perfect information is surely an important conceptual benchmark for discussing efficient allocations, it is obviously far from describing a rational choice under real conditions. In reality, optimal choices should be considered choices under imperfect information. Thus, decision-makers' information problem can be solved by two strategies. Either they collect an optimal set of information to make an optimal allocation choice under this imperfect information set or they can apply heuristic reasoning. In this paper, we suggest a formal model framework for the example of a simple consumer decision for the allocation of differentiated goods to explore information acquisition strategies in such a simple standard choice situation. Using the model variation under perfect information as a benchmark, we answer the following questions. First and most importantly, under imperfect information, can a heuristic rule substitute information acquisition as an optimal choice? Second, what is the role of risk aversion in the information acquisition process? Finally, we explore the differences to the benchmark, both ex ante the first purchase decision and ex post when repeated purchases and consumption allows for experiences with the choices made. AU - Burs, Carina AU - Gries, Thomas ID - 49308 KW - information economics KW - imperfect information KW - Bayesian learning KW - risk KW - heuristics KW - differentiated products TI - Decision-making under Imperfect Information with Bayesian Learning or Heuristic Rules VL - No. 149 ER -