@phdthesis{65522, abstract = {{Wie verläuft der Leseprozess im Geschichtsunterricht für Schüler:innen, die neu in das deutsche Bildungssystem einsteigen – und welche Faktoren beeinflussen ihr Verstehen? Diese Studie untersucht an der Schnittstelle von Deutsch als Zweitsprache und Geschichtsdidaktik die Bedingungen des Lesens im Geschichtsunterricht. Im Fokus stehen die Erfahrungen von Seiteneinsteiger:innen sowie die Perspektiven der Lehrkräfte, die im Rahmen einer qualitativ‑explorativen Untersuchung mithilfe von Unterrichtshospitationen, Interviews und Materialanalysen erhoben wurden. Methodisch ist die Arbeit in der Grounded Theory Methodology nach Strauss und Corbin (1996) verankert, anhand derer die komplexen Einflussfaktoren historischer Leseprozesse systematisch herausgearbeitet werden. Die Ergebnisse zeigen drei zentrale Einflussdimensionen: sprachliche, sozial‑affektive und geschichtsspezifische Faktoren. Zudem wird deutlich, dass Unterstützungsmaßnahmen stets zwischen kurzfristiger Hilfe und dem langfristigen Aufbau von Disciplinary Literacy ausbalanciert werden müssen. Ihre Wirksamkeit hängt wesentlich von der bewussten Auswahl und Zugänglichkeit durch die Lehrkraft sowie von den Möglichkeiten der Lernenden ab, Unterstützung gezielt einzufordern. Lernwirksam wird Geschichtsunterricht besonders dann, wenn hohe fachliche Anforderungen mit reflektierter sprachlicher Unterstützung verbunden werden. Die Studie leistet damit einen Beitrag zur Forschung zu sprachbewusstem Fachunterricht und bietet Impulse für eine kritisch‑reflexive Sprachsensibilität in schulischen Bildungsprozessen.}}, author = {{Müller, Jennifer}}, keywords = {{Scaffolding, Sprache und Fach, Grounded Theory Methodology, Lesen in der Sekundarstufe I, Sprachsensibler Geschichtsunterricht, Durchgängige Sprachbildung}}, publisher = {{Springer Spektrum}}, title = {{{Unterstützung von Verstehen im Fachunterricht. Eine Grounded Theory zum sprach- und fachverbindenden Geschichtsunterricht}}}, year = {{2026}}, } @article{59755, abstract = {{Due to the application of Artificial Intelligence (AI) in high-risk domains like law or medicine, trustworthy AI and trust in AI are of increasing scientific and public relevance. A typical conception, for example in the context of medical diagnosis, is that a knowledgeable user receives AIgenerated classification as advice. Research to improve such interactions often aims to foster the user’s trust, which in turn should improve the combined human-AI performance. Given that AI models can err, we argue that the possibility to critically review, thus to distrust, an AI decision is an equally interesting target of research. We created two image classification scenarios in which the participants received mock-up AI advice. The quality of the advice decreases for a phase of the experiment. We studied the task performance, trust and distrust of the participants, and tested whether an instruction to remain skeptical and review each piece of advice led to a better performance compared to a neutral condition. Our results indicate that this instruction does not improve but rather worsens the participants’ performance. Repeated single-item self-report of trust and distrust shows an increase in trust and a decrease in distrust after the drop in the AI’s classification quality, with no difference between the two instructions. Furthermore, via a Bayesian Signal Detection Theory analysis, we provide a procedure to assess appropriate reliance in detail, by quantifying whether the problems of under- and over-reliance have been mitigated. We discuss implications of our results for the usage of disclaimers before interacting with AI, as prominently used in current LLM-based chatbots, and for trust and distrust research.}}, author = {{Peters, Tobias Martin and Scharlau, Ingrid}}, journal = {{Frontiers in Psychology}}, keywords = {{trust in AI, trust, distrust, human-AI interaction, Signal Detection Theory, Bayesian parameter estimation, image classification}}, title = {{{Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?}}}, doi = {{10.3389/fpsyg.2025.1574809}}, volume = {{16}}, year = {{2025}}, } @article{63950, abstract = {{Sodium-ion batteries are at the forefront of new, sustainable energy systems required for the global energy transition. 23Na in situ solid-state nuclear magnetic resonance spectroscopy is capable of unraveling structures in working electrochemical cells during the charging and discharging processes. To evaluate its suitability for long-term studies, local sodium environments in sodium/sodium ion cells based on silicon carbonitride and hard carbon materials are tracked for up to 49 cycles (228.5?h). The formation of dendrites as well as the decay of a secondary metallic sodium species is observed, and local structures are analyzed up to the point of capacity degradation and cell failure. Initial points of cell breakdown are reflected in the NMR data by characteristic changes in signal intensities, whereas the degradation of the cells is reflected by a cease to periodic signal intensity fluctuations. Meanwhile, ex situ 23Na NMR spectra of the deactivated cells reveal a complex range of environments for sodium ions.}}, author = {{Egert, Sonja and Remesh, Renuka and Jusdi, Agatha Clarissa and Sugawara, Yushi and Schutjajew, Konstantin and Oschatz, Martin and Buntkowsky, Gerd and Gutmann, Torsten}}, journal = {{Batteries & Supercaps}}, keywords = {{solid-state nmr, hard carbon, in-situ, SiCN, sodium ion batteries}}, number = {{n/a}}, pages = {{e202500516}}, publisher = {{John Wiley & Sons, Ltd}}, title = {{{Long-Term Cycling Stability of Sodium/Sodium Ion Cells Probed by In Situ Solid-State NMR Spectroscopy}}}, doi = {{10.1002/batt.202500516}}, volume = {{n/a}}, year = {{2025}}, } @inproceedings{64562, abstract = {{Für das Verständnis und die Weiterentwicklung temperaturgestützter mechanischer Fü-geprozesse mit thermoplastischen Faser-Kunststoff-Verbunden (FKV) ist die zerstörungsfreie Analyse der Materialstruktur im Inneren des Fügepunktes während der Entstehung und Belastung erforderlich. Die Kombination aus Prüfung unter Temperatureinfluss und in situ Computertomographie (CT) eröffnet neue Möglichkeiten für die Fügeprozessanalyse. Dazu wurde dazu eine Thermokammer entwickelt und in eine bestehende in situ CT-Anlage integriert. Anwendungsszenarien sind die Herstellung und Prüfung von Fügepunkten unter Temperatur. Die Erwärmung erfolgt über einzeln regelbare Heizzonen, welche eine gezielte Temperaturführung über die gesamte Probengeometrie ermöglichen. Die Temperaturkurve eines Aufheizversuchs, sowie eine Röntgenprojektion einer Probe innerhalb der Thermokammer validie-ren die Konstruktion.}}, author = {{Dargel, Alrik and Köhler, Daniel and Gude, Maik and Kupfer, Robert}}, booktitle = {{Tagungsband 43. Vortrags- und Diskussionstagung Werkstoffprüfung 2025}}, editor = {{Zimmermann, Martina}}, isbn = {{978-3-88355-454-9}}, keywords = {{in situ CT, Thermokammer, Thermoplastische FKV}}, location = {{Dresden}}, pages = {{165--170}}, publisher = {{Deutsche Gesellschaft für Materialkunde e.V. (DGM)}}, title = {{{In situ CT unter Temperatur: Thermokammer für thermoplastische FKV-Fügeprozesse}}}, volume = {{43}}, 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{55400, abstract = {{This study contributes to the evolving field of robot learning in interaction with humans, examining the impact of diverse input modalities on learning outcomes. It introduces the concept of "meta-modalities" which encapsulate additional forms of feedback beyond the traditional preference and scalar feedback mechanisms. Unlike prior research that focused on individual meta-modalities, this work evaluates their combined effect on learning outcomes. Through a study with human participants, we explore user preferences for these modalities and their impact on robot learning performance. Our findings reveal that while individual modalities are perceived differently, their combination significantly improves learning behavior and usability. This research not only provides valuable insights into the optimization of human-robot interactive task learning but also opens new avenues for enhancing the interactive freedom and scaffolding capabilities provided to users in such settings.}}, author = {{Beierling, Helen and Beierling, Robin and Vollmer, Anna-Lisa}}, journal = {{Frontiers in Robotics and AI}}, keywords = {{human-robot interaction, human-in-the-loop learning, reinforcement learning, interactive robot learning, multi-modal feedback, learning from demonstration, preference-based learning, scaffolding in robot learning}}, publisher = {{Frontiers }}, title = {{{The power of combined modalities in interactive robot learning}}}, volume = {{12}}, 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{63072, abstract = {{Titanium alloys are widely employed for biomedical implants due to their high strength, biocompatibility, and corrosion resistance, yet their lack of intrinsic antibacterial activity remains a major limitation. Incorporating copper, an antibacterial and β-stabilising element, offers a promising strategy to enhance implant performance. This study investigates Ti-6Al-7Nb modified with 1–9 wt.% Cu via in situ alloying during metal-based laser powder bed fusion (PBF-LB/M), with the aim of assessing processability, microstructural evolution, and mechanical properties. Highly dense samples (>99.9%) were produced across all Cu levels, though chemical homogeneity strongly depended on processing parameters. Increasing Cu content promoted β-phase stabilisation, Ti2Cu precipitation, and pronounced grain refinement. Hardness and yield strength increased nearly linearly with Cu addition, while ductility decreased sharply at ≥5 wt.% Cu due to intermetallic formation, hot cracking, and brittle fracture. These results illustrate both the opportunities and constraints of rapid alloy screening via PBF-LB/M. Overall, moderate Cu additions of 1–3 wt.% provide the most favourable balance between mechanical performance, manufacturability, and potential antibacterial functionality. These findings provide a clear guideline for the design of Cu-functionalised titanium implants and demonstrate the efficiency of in situ alloy screening for accelerated materials development.}}, author = {{Steinmeier, Paul and Hoyer, Kay-Peter and Lopes Dias, Nelson Filipe and Zielke, Reiner and Tillmann, Wolfgang and Schaper, Mirko}}, issn = {{2073-4352}}, journal = {{Crystals}}, keywords = {{Biomaterial, In Situ Alloying, Titanium, Additive Manufacturing}}, number = {{12}}, publisher = {{MDPI AG}}, title = {{{In Situ Alloying of Ti-6Al-7Nb with Copper Using Laser Powder Bed Fusion}}}, doi = {{10.3390/cryst15121053}}, volume = {{15}}, year = {{2025}}, } @misc{51133, abstract = {{In order to standardize spray flame synthesis (SFS) studies, intensive work has been done in recent years on the design of burner types. Thus, in 2019, the so-called SpraySyn1 burner was introduced (SS1), which was subsequently characterized in numerical and experimental studies. Based on this research, a modification of the nozzle design was proposed, which has now been considered in the successor model, SpraySyn2 (SS2). As little is known about the effect of the nozzle adaptation on the particle formation, we operated both burners under identical operating conditions to produce maghemite. The final powder comparison showed that SS2 yielded considerable higher specific surface areas (associated with smaller primary particle sizes), lower polydispersity, and higher phase purity. To obtain further information on the size distributions of aggregates and agglomerates generated by SS2, aerosol samples were extracted by hole in a tube (HIAT) sampling and characterized by scanning mobility particle sizing (SMPS). Samples were extracted along the centerline at different heights above the burner (HAB) above the visible flame tip (>7 cm), and quenching experiments were performed to extract the aerosol samples at different dilution rates. Thereby, it was demonstrated that performing detailed quenching experiments is crucial for obtaining representative HIAT-SMPS data. In particular, agglomerates/aggregate sizes were overestimated by up to ~70 % if samples were not sufficiently diluted. If sufficient dilution was applied, distribution widths and mean particle mobility diameters were determined with high accuracy (sample standard derivation <5 %). Our data suggested the evolution of primary particle sizes was mostly completed <7 cm HAB and it was shown aggregates/agglomerates present above the visible flame were compact in structure (non- fractal). The mean diameter of the particle ensemble grew along the centerline from 6.9 nm (7 cm) to 11.4 nm (15 cm), while distribution widths grew from 1.42 to 1.52.}}, booktitle = {{Applications in Energy and Combustion Science}}, editor = {{Tischendorf, Ricardo and Massopo, Orlando and Schmid, Hans-Joachim and Pyrmak, Olek and Dupont, Sophie and Fröde, Fabian and Pitsch, Heinz and Kneer, Reinhold}}, keywords = {{Flame Spray Pyrolysis, SpraySyn2, Spray flame synthesis, Maghemite nanoparticles, Gas to particle-conversion, Hole in a tube sampling}}, publisher = {{Elsevier}}, title = {{{Maghemite nanoparticles synthesis via spray flame synthesis and particle characterization by hole in a tube sampling and scanning mobility particle sizing (HIAT-SMPS)}}}, doi = {{https://doi.org/10.1016/j.jaecs.2023.100235}}, year = {{2024}}, } @article{56935, abstract = {{Der Neologismus ‚MINTfluencer:in‘ beschreibt reichweitenstarke Profile in sozialen Netzwerken von Frauen in technischen und naturwissenschaftlichen Berufen. Die qualitative Inhaltsanalyse untersucht die unterrepräsentierten Gruppen ‚Scientist Mom‘ und ‚Trans* Scientist‘ auf Instagram aus gendermedialer Perspektive. Es werden Erkenntnisse über das Verhältnis von Wissenschaftskommunikation, Berufsrepräsentation sowie Geschlecht generiert und mit dekonstruktivistischen, intersektionalen Theorien und Heteronormativitätskritik verknüpft. Die Betrachtung der Selbstinszenierung berufstätiger Mütter und transsexueller Wissenschaftler:innen als MINTfluencer:innen erforscht, inwiefern ihr Auftreten als feministische Transformation im Kontext weiblicher Berufsausübung und Sichtbarkeit in männlich dominierten Berufen gesehen werden kann. }}, author = {{Pätz, Ricarda}}, journal = {{Gender(ed) thoughts}}, keywords = {{MINTfluencerin, Frauen in MINT, Wissenschaftskommunikation, Instagram, qualitative Inhaltsanalyse, Gender Media Studies, Gender Science Studies}}, publisher = {{University Goettingen}}, title = {{{MINTfluencerinnen auf Instagram. Sozialmediale Sichtbarkeit junger Frauen in MINT-Berufen}}}, year = {{2024}}, } @article{58066, abstract = {{Der Beitrag untersucht den Einsatz von Fachkräften für das Multiprofessionelle Team im Gemeinsamen Lernen (MPT). Diese meist sozialpädagogisch ausgebildeten Fachkräfte sind dezidiert für die Unterstützung der Inklusion an Grund-, Sekundar-und Gesamtschulen angestellt. Erste Befunde (Grüter et al.,2022) und ministeriale Aufgabenbeschreibung (Ministerium für Schule und Bildung des Landes Nordrhein-Westfalen [MSB NRW], 2022) deuten auf ein offenes, wenig definiertes Tätigkeitsspektrum hin. Mögliche Aufgabengebiete der MPT überschneiden sich mit denen der (sonderpädagogischen) Lehrkräfte, der Schulsozialarbeiter*innen und –im Hinblick auf die Unterstützung im Unterricht ohne Unterrichtsverantwortung –mit denen der Schulbegleitungen (Lübeck, 2019).Die Überlappungen legen nahe, dass auch für die MPT das „Zuständigkeitsdiffusitätsproblem“ (Kunze 2016, S.265) gilt und die multiprofessionelle Teamarbeit entsprechend beeinflusst. Da es bislang keine Daten darüber gibt, wie sich die Berufsrolle von MPT-Kräften in der Breite ausgestaltet, eruiert der Beitrag in einer quantitativen Studie ebendiese Frage. Zusätzlich werden die Fragenfokussiert, über welche Qualifikationen und Vorerfahrungen MPT-Kräfte verfügen und mit welchen Akteur*innen MPT-Kräfte in welcher Form kooperieren. Die Untersuchung bezieht sich auf den Einsatz von MPT an Gesamt-und Sekundarschulen in NRW. Insgesamt wurden 109 MPT-Kräfte in Form eines Online-Fragebogens befragt. Anhand der Ergebnisse konnte ein breites Einsatzgebiet mit vereinzelten Aufgabenschwerpunkten (v.a. im Bereich der sonderpädagogisch orientierten Kleingruppenförderung) der MPT-Kräfteidentifiziert werden. Schnittmengen zu Aufgabenschwerpunkten anderer Berufsgruppen werden unter Berücksichtigung der multiprofessionellen Kooperation hinsichtlich der Frage diskutiert, ob der Einsatz von MPT-Kräften inklusive Zielstellungen eher befördert oder im Sinne einer Deprofessionalisierung sonderpädagogischer Förderung eher entgegensteht.}}, author = {{Meusel, Sarah and Müller, Lukas and Jesuthasan, Jonitta}}, journal = {{Zeitschrift für Inklusion}}, keywords = {{Deprofessionalisierung, Kooperation, Fachkräfte für das Multiprofessionelle Team(MPT), Aufgabenprofile in der inklusiven Schule}}, number = {{5}}, title = {{{Bestimmt unbestimmt? - Eine quantitativ-explorative Studie zu Aufgabenprofilen von Fachkräften für das Multiprofessionelle Team (MPT) an inklusiven Gesamt-und Sekundarschulen in NRW.}}}, 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{58324, author = {{Müller, Inez}}, booktitle = {{Von neuen Blicken auf die frühe Nachkriegszeit}}, editor = {{Karlsson Hammarfelt, Linda and Platen, Edgar and Platen, Petra}}, isbn = {{978-3-86205-770-2}}, keywords = {{postmemory, unrelieable telling, war crimes in Austria in the context of the Second World War, Eva Menasse, Raphaela Edelbauer}}, location = {{Universität Göteborg / Schweden}}, pages = {{112--126}}, publisher = {{iudicium}}, title = {{{Zu den Unwägbarkeiten des Erbes - Unzuverlässiges Postmemory-Erzählen in den Gesellschaftsromanen 'Das flüssige Land' von Raphaela Edelbauer und in 'Dunkelblum' von Eva Menasse}}}, 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{49109, abstract = {{We propose a diarization system, that estimates “who spoke when” based on spatial information, to be used as a front-end of a meeting transcription system running on the signals gathered from an acoustic sensor network (ASN). Although the spatial distribution of the microphones is advantageous, exploiting the spatial diversity for diarization and signal enhancement is challenging, because the microphones’ positions are typically unknown, and the recorded signals are initially unsynchronized in general. Here, we approach these issues by first blindly synchronizing the signals and then estimating time differences of arrival (TDOAs). The TDOA information is exploited to estimate the speakers’ activity, even in the presence of multiple speakers being simultaneously active. This speaker activity information serves as a guide for a spatial mixture model, on which basis the individual speaker’s signals are extracted via beamforming. Finally, the extracted signals are forwarded to a speech recognizer. Additionally, a novel initialization scheme for spatial mixture models based on the TDOA estimates is proposed. Experiments conducted on real recordings from the LibriWASN data set have shown that our proposed system is advantageous compared to a system using a spatial mixture model, which does not make use of external diarization information.}}, author = {{Gburrek, Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. Asilomar Conference on Signals, Systems, and Computers}}, keywords = {{Diarization, time difference of arrival, ad-hoc acoustic sensor network, meeting transcription}}, title = {{{Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks}}}, year = {{2023}}, } @inproceedings{37312, abstract = {{Optimal decision making requires appropriate evaluation of advice. Recent literature reports that algorithm aversion reduces the effectiveness of predictive algorithms. However, it remains unclear how people recover from bad advice given by an otherwise good advisor. Previous work has focused on algorithm aversion at a single time point. We extend this work by examining successive decisions in a time series forecasting task using an online between-subjects experiment (N = 87). Our empirical results do not confirm algorithm aversion immediately after bad advice. The estimated effect suggests an increasing algorithm appreciation over time. Our work extends the current knowledge on algorithm aversion with insights into how weight on advice is adjusted over consecutive tasks. Since most forecasting tasks are not one-off decisions, this also has implications for practitioners.}}, author = {{Leffrang, Dirk and Bösch, Kevin and Müller, Oliver}}, booktitle = {{Hawaii International Conference on System Sciences}}, keywords = {{Algorithm aversion, Time series, Decision making, Advice taking, Forecasting}}, title = {{{Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time}}}, 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{33490, abstract = {{Algorithmic fairness in Information Systems (IS) is a concept that aims to mitigate systematic discrimination and bias in automated decision-making. However, previous research argued that different fairness criteria are often incompatible. In hiring, AI is used to assess and rank applicants according to their fit for vacant positions. However, various types of bias also exist for AI-based algorithms (e.g., using biased historical data). To reduce AI’s bias and thereby unfair treatment, we conducted a systematic literature review to identify suitable strategies for the context of hiring. We identified nine fundamental articles in this context and extracted four types of approaches to address unfairness in AI, namely pre-process, in-process, post-process, and feature selection. Based on our findings, we (a) derived a research agenda for future studies and (b) proposed strategies for practitioners who design and develop AIs for hiring purposes.}}, author = {{Rieskamp, Jonas and Hofeditz, Lennart and Mirbabaie, Milad and Stieglitz, Stefan}}, booktitle = {{Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS)}}, keywords = {{fairness in AI, SLR, hiring, AI implementation, AI-based algorithms}}, title = {{{Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research}}}, 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{44092, abstract = {{We study how competition between physicians affects the provision of medical care. In our theoretical model, physicians are faced with a heterogeneous patient population, in which patients systematically vary with regard to both their responsiveness to the provided quality of care and their state of health. We test the behavioral predictions derived from this model in a controlled laboratory experiment. In line with the model, we observe that competition significantly improves patient benefits as long as patients are able to respond to the quality provided. For those patients, who are not able to choose a physician, competition even decreases the patient benefit compared to a situation without competition. This decrease is in contrast to our theoretical prediction implying no change in benefits for passive patients. Deviations from patient-optimal treatment are highest for passive patients in need of a low quantity of medical services. With repetition, both, the positive effects of competition for active patients as well as the negative effects of competition for passive patients become more pronounced. Our results imply that competition can not only improve but also worsen patient outcome and that patients’ responsiveness to quality is decisive.}}, author = {{Brosig-Koch, Jeannette and Hehenkamp, Burkhard and Kokot, Johanna}}, journal = {{Health Economics}}, keywords = {{physician competition, patient characteristics, heterogeneity in quality responses, fee-for-service, laboratory experiment}}, title = {{{Who benefits from quality competition in health care? A theory and a laboratory experiment on the relevance of patient characteristics}}}, doi = {{10.1002/hec.4689}}, year = {{2023}}, }