Optisch basierter Digital-Analog-Umsetzer - Kruse, Stephan; Silberhorn, Christine; Brecht, Benjamin; Schwabe, Tobias

Unpacking Hostile Attitudes towards Islam and Muslims: A Differentiated Approach to Understanding Perceptions of Religion and People - Diekmann, Isabell (Patterns of Prejudice)
Anti-Islam and anti-Muslim attitudes are frequently perceived without sufficient differentiation. The absence of a clear distinction between attitudes towards individuals and attitudes towards the religion itself is evident in definitions that conflate (attitudes towards) Islam and Muslims, as well as in the operationalization process where surveys on overtly anti-Muslim attitudes are labelled as measuring anti-Islam attitudes. To explore whether the interchangeable usage of these terms is valid or whether there is empirical evidence supporting the need to differentiate between anti-Islam and anti-Muslim attitudes, an online survey was conducted (n = 497). The results indicate that anti-Islam and anti- Muslim attitudes are statistically distinct constructs that are nevertheless correlated. Furthermore, the study investigates the extent to which variations in the degree of hostile attitudes towards Islam and Muslims can be observed. The findings reveal that Islam is evaluated significantly more negatively than Muslims. In addition, an analysis of free associations related to the terms Islam and Muslims highlights notable differences. Although the respondents oftentimes see Muslims in the context of migration and integration, associations with Muslims tend to be more positive and based on personal experiences and interactions than associations with Islam. Associations with Islam, on the other hand, are characterized by threat and conflict and externalization to foreign countries. These findings have important implications for the theoretical conceptualization of anti- Islam and anti-Muslim attitudes. By differentiating between anti-Islam and anti- Muslim attitudes, it becomes possible to deconstruct these phenomena into their constituent elements and to understand their various dimensions. It is only through such disentanglement that the interconnected nature of anti-Islam and anti-Muslim attitudes can be comprehensively examined and effectively addressed.

Mathematik (Primarstufe) in der Lehrerinnen- und Lehrerbildung. Qualifizierung für das Lehren von Mathematik in der Grundschule - Häsel-Weide, Uta; Nührenbörger, Marcus (Handbuch Lehrerinnen- und Lehrerbildung)

Critical Edtech Studies. Defining the field; envisioning the future. 2025 Position statement after the inaugural edition of the European Conference on Critical Edtech Studies (ECCES). - Decuypere, Mathias; Hartong, Sigrid; Joecks, Lucas; Knox, Jeremy; Ortegón, Carlos; Schmidt, Rebecca; Williamson, Ben
kein Abstract vorhanden

Online-Lernen und gleichzeitig fühlen? Das Verständnis von Emotionen in Theorie und Praxis - Kirschtein, Claudia (merzWissenschaft)

Potentials of Product Lifecycle Management to Enhance Circular Economy - Gräßler, Iris; Pottebaum, Jens; Rarbach, Sven (The 11th World Sustainability Forum (WSF11))

D3.1 – White Paper: The Futures of Digital Democracy - Fuchs, Christian; Museba, Joel; Friesch, Kevin

Räume in der Internationale Geschichte

A Fully Zero-Shot Approach to Obtaining Specialized and Compact Audio Tagging Models - Werning, Alexander; Häb-Umbach, Reinhold (Proceedings of the 16th ITG Conference on Speech Communication)
Zero-shot classifiers based on Contrastive Language-Audio Pretraining (CLAP) models enable classification of given audio into classes defined at test time using text. These models are costly to run with respect to computation and memory requirements. In this work, we propose to build a specialized low-resource classifier for classes pre-defined using text, using a two-stage procedure consisting of zero-shot data set pruning and model compression. First, relevant in-domain data is selected from a source dataset using class label embeddings obtained from a pre-trained CLAP model. This data is then used to distill the audio encoder of a CLAP model. The proposed compression method produces compact audio encoders with slightly reduced accuracy. Note that neither labeled nor unlabeled in-domain audio data is required for its development. We verify by cross-dataset tests that the resulting classifiers are indeed specialized to their task.

Distilling Efficient Audio Models using Data Pruning with CLAP - Werning, Alexander; Häb-Umbach, Reinhold (Proceedings of DAS|DAGA 2025)
Running state-of-the-art large-scale audio models on edge devices is often infeasible due to their limited storage and computing resources. It is therefore necessary to compress and tune the models for the specific target task and hardware. This is commonly achieved by distilling the audio model, the teacher, to a small target model, the student. However, this approach can be improved by prepending a dataset pruning stage and training the teacher on the pruned data set only, which contains examples relevant to the target task. Recently, CLAP models have emerged that embed audio and text examples in a common embedding space. We use the audio embeddings of the CLAP model for the above pruning stage, which is realized using a domain classifier. After knowledge distillation, the student is eventually fine-tuned on some data from the target domain. The CLAP architecture combines text and audio embedding spaces, which allows to search for data given only a textual description, such as a class label. We show how this can help data pruning.

Tree-Based OWL Class Expression Learner over Large Graphs - Demir, Caglar; Yekini, Moshood; Röder, Michael; Mahmood, Yasir; Ngonga Ngomo, Axel-Cyrille (Lecture Notes in Computer Science)
Learning continuous vector representations for knowledge graphs has signiﬁcantly improved state-of-the-art performances in many challenging tasks. Yet, deep-learning-based models are only post-hoc and locally explainable. In contrast, learning Web Ontology Language (OWL) class expressions in Description Logics (DLs) is ante-hoc and globally explainable. However, state-of-the-art learners have two well-known lim-itations: scaling to large knowledge graphs and handling missing infor-mation. Here, we present a decision-tree-based learner (tDL) to learn Web Ontology Languages (OWLs) class expressions over large knowl-edge graphs, while imputing missing triples. Given positive and negative example individuals, tDL ﬁrstly constructs unique OWL expressions in .SHOIN from concise bounded descriptions of individuals. Each OWL class expression is used as a feature in a binary classiﬁcation problem to represent input individuals. Thereafter, tDL ﬁts a CART decision tree to learn Boolean decision rules distinguishing positive examples from nega-tive examples. A ﬁnal OWL expression in.SHOIN is built by traversing the built CART decision tree from the root node to leaf nodes for each positive example. By this, tDL can learn OWL class expressions without exploration, i.e., the number of queries to a knowledge graph is bounded by the number of input individuals. Our empirical results show that tDL outperforms the current state-of-the-art models across datasets. Impor-tantly, our experiments over a large knowledge graph (DBpedia with 1.1 billion triples) show that tDL can eﬀectively learn accurate OWL class expressions, while the state-of-the-art models fail to return any results. Finally, expressions learned by tDL can be seamlessly translated into natural language explanations using a pre-trained large language model and a DL verbalizer.
Keywords: Decision Tree; OWL Class Expression Learning; Description Logic; Knowledge Graph; Large Language Model; Verbalizer

Assessing Natural Language Explanations of Relational Graph Neural Networks - Heindorf, Stefan; Neib, Daniel (Proceedings of the 34th ACM International Conference on Information and Knowledge Management)

Online-Selbstlernkurs zu digitalen Medien im Physikunterricht - Weiler, David Christoph; Burde, Jan-Philipp; Costan, Kasim; Gieshoff, Rike Isabel; Kulgemeyer, Christoph; Lässer, Armin; Plicht, Katja; Riese, Josef; Schubatzky, Thomas (PhyDid B - Didaktik Der Physik - Beiträge Zur DPG-Frühjahrstagung)
Die fortschreitende Digitalisierung bringt große Herausforderungen für die Aus- und Weiterbildung von Lehrkräften mit sich. Während angehende Lehrkräfte bereits von neuen Entwicklungen in der Ausbildung profitieren, sind viele aktive Lehrkräfte bislang nicht ausreichend auf die digitalen Möglichkeiten im Unterricht vorbereitet. Der Kompetenzverbund lernen:digital setzt genau hier an und unterstützt gezielt Lehrkräfte beim Erwerb digitalisierungsbezogener Kompetenzen. Im Rahmen des Verbundprojekts ComeMINT wurde unter anderem ein Online-Selbstlernkurs entwickelt, der sich mit dem Einsatz digitaler Medien im Physikunterricht befasst. Grundlage des Kurses bilden zum einen eine Bedürfnisanalyse unter praktizierenden Physiklehrkräften und zum anderen bereits bestehende Materialien aus der Lehrkräfteausbildung. Der Kurs vermittelt grundlegende Kompetenzen im Umgang mit physikspezifischen digitalen Medien, wie zum Beispiel digitaler Messwerterfassung, Augmented Reality oder Simulationen. In diesem Beitrag werden die Entwicklung und die Inhalte des Selbstlernkurses skizziert, sowie die nachhaltige Aufbereitung der Materialien zur Nachnutzung dargestellt.

Benchmarking Knowledge Editing using Logical Rules - Moteu Ngoli, Tatiana; Kouagou, N'Dah Jean; Zahera, Hamada Mohamed Abdelsamee; Ngonga Ngomo, Axel-Cyrille (Proceedings of the 24th International Semantic Web Conference (ISWC 2025))
Large Language Models (LLMs) are increasingly deployed in real-world applications that require access to up-to-date knowledge. However, retraining LLMs is computationally expensive. Therefore, knowledge editing techniques are crucial for maintaining current information and correcting erroneous assertions within pre-trained models. Current benchmarks for knowledge editing primarily focus on recalling edited facts, often neglecting their logical consequences. To address this limitation, we introduce a new benchmark designed to evaluate how knowledge editing methods handle the logical consequences of a single fact edit. Our benchmark extracts relevant logical rules from a knowledge graph for a given edit. Then, it generates multi-hop questions based on these rules to assess the impact on logical consequences. Our findings indicate that while existing knowledge editing approaches can accurately insert direct assertions into LLMs, they frequently fail to inject entailed knowledge. Specifically, experiments with popular methods like ROME and FT reveal a substantial performance gap, up to 24%, between evaluations on directly edited knowledge and on entailed knowledge. This highlights the critical need for semantics-aware evaluation frameworks in knowledge editing.
Keywords: dice sailproject moteu kouagou zahera ngonga

Do Energy-Saving Nudges Deliver During High-Price Periods? Field Experimental Evidence From the European Energy Crisis - Tinnefeld, Vicky; Kesternich, Martin; Werthschulte, Madeline
Urged by the European Energy Crisis and the threatening consequences of severe natural gas shortages, energy providers launched gas-saving initiatives incor- porating financial incentives to reduce residential natural gas consumption. In collaboration with one of Germany’s largest energy providers, we conducted a natural field experiment (N = 2,598) to evaluate the effectiveness of a behaviorally-guided co-design of such a gas-saving initiative by implementing two established behavioral instruments – reminders of gas saving intentions and descriptive norm feedback. Our findings show limited effectiveness of the behav- ioral instruments during the high-price period. The feedback risks a “boomerang effect” among households with above-average initial savings, who reduce their conservation efforts in response. The reminder does not significantly enhance sav- ings in our main specifications, yet, realizes 1 percentage point savings in alternate models refining for outliers. Potential mechanisms include a significant intention- action gap and misperceived effectiveness of energy-saving actions, which are not alleviated by the reminder.
Keywords: Residential energy savings; energy crisis; behavioral interventions; survey data; field experiment

Bildauswertendes Verfahren zur Evaluierung der Mikrostruktur von AlSi-Systemen - Neuser, Moritz; Cichon, Gerrit; Hoyer, Kay-Peter; Schaper, Mirko (Bildauswertendes Verfahren zur Evaluierung der Mikrostruktur von AlSi-Systemen)
Aluminium-Silizium-Legierungen (AlSi) werden insbesondere bei der gießtechnischen Herstellung von Leichtbaukomponenten für Fahrzeuge verwendet. Dieses Legierungssystem hat hervorragende Gießeigenschaften bei gleichzeitig akzeptablen mechanischen Eigenschaften. Aufgrund des hohen Silizium-(Si)-Gehaltes, wodurch die Volumenkontraktion im Phasenübergang von flüssig-fest nahezu unterbunden wird, neigen AlSi-Legierungen dazu, feinere oder gröbere Si-Platten bei unterschiedlichen Erstarrungsgeschwindigkeiten zu bilden. Um die mechanischen Eigenschaften zu verbessern, werden dem Legierungssystem in der Schmelzphase entweder Natrium (Na) oder Strontium (Sr) zugesetzt. Dies hat zur Folge, dass sich eine fein lamellare Si-Morphologie bei der Erstarrung ausbildet; dies kann ebenfalls durch hohe Erstarrungsgeschwindigkeiten erreicht werden. Ein nachfolgendes Lösungsglühen bewirkt eine Sphäroidisierung der Si-Partikel und dient der Steigerung der Duktilität. Aktuell fehlen fundierte Erkenntnisse zur Ausprägung der Si-Morphologie in Abhängigkeit der Erstarrungsgeschwindigkeit oder infolge einer Wärmebehandlung. Vor diesem Hintergrund werden in dieser Studie verschiedene Behandlungsparameter in Bezug auf das Einformverhalten der Si-Partikel mit einem bildauswertenden Verfahren evaluiert sowie unter Bezug auf verschiedene chemische Zusammensetzungen miteinander korreliert.
Keywords: Bildauswertendes Verfahren; Mikrostrukturanalyse; AlSi-System; Si-Morphologie

Integrated and demand-responsive energy futures in the context of sustainable development of energy systems - Meschede, Henning; Knorr, Lukas; Piacentino, Antonio; Markovska, Natasa; Duic, Neven (Energy)

The Consequences of Abandoning the Quarterly Reporting Mandate in the Prime Market Segment - Bornemann, Tobias; Moosmann, Anna-Lena; Novotny-Farkas, Zoltán (European Accounting Review)

Does Fair Value Taxation Affect Banks' Investment Portfolio and Risk-Taking? - Bornemann, Tobias; Auer, Sylvia; Eberhartinger, Eva (Journal of the American Taxation Association)

Poster: Computer Security Researchers' Experiences with Vulnerability Disclosures - Sri Ramulu, Harshini; Rotthaler, Anna Lena; Rossel, Jost; Gonzalez Rodriguez, Rachel; Wermke, Dominik; Fahl, Sascha; Kohno, Tadayoshi; Somorovsky, Juraj; Acar, Yasemin (Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security)
Vulnerability disclosures are necessary to improve the security of our digital ecosystem. However, they can also be challenging for researchers: it may be hard to find out who the affected parties even are, or how to contact them. Researchers may be ignored or face adversity when disclosing vulnerabilities. We investigate researchers' experiences with vulnerability disclosures, extract best practices, and make recommendations for researchers, institutions that employ them, industry, and regulators to enable effective vulnerability disclosures.
Keywords: software vulnerabilities; vulnerability disclosure; security research