@inproceedings{BayerlBrasserBuschetal.2019, author = {Bayerl, Sebastian P. and Brasser, Ferdinand and Busch, Christoph and Frassetto, Tommaso and Jauernig, Patrick and Kolberg, Jascha and Nautsch, Andreas and Riedhammer, Korbinian and Sadeghi, Ahmad-Reza and Schneider, Thomas and Stapf, Emmanuel and Treiber, Amos and Weinert, Christian}, title = {Privacy-preserving speech processing via STPC and TEEs (Poster)}, year = {2019}, language = {en} } @inproceedings{VoitSchneiderKriegbaum2020, author = {Voit, Thomas and Schneider, Alexander and Kriegbaum, Mathias}, title = {Towards an Empirically Based Gamification Pattern Language using Machine Learning Techniques}, publisher = {IEEE}, issn = {2377-570X}, doi = {10.1109/cseet49119.2020.9206223}, pages = {4}, year = {2020}, abstract = {The ineffectiveness of many gamification projects can be attributed to wrong decisions made during the conceptual design phase, especially in the selection of game design elements. This paper introduces a data driven method of creating a gamification pattern language similar to software design patterns to help gamification designers select such elements. Thanks to modern machine learning technologies such a pattern language can be based on a comprehensive empirical analysis to assess the actual use of game design elements in games. This paper is the first report on an ongoing research project that has been carried out since the beginning of 2017 in cooperation with the German Games Archive to extract game design elements from more than 30,000 board games using machine learning techniques. Initial tests based on support vector classification and 4,000 games show that game design elements can be reliably identified with accuracy rates between 80 and 90\%.}, language = {en} } @article{CornelGhanemGraebschetal.2023, author = {Cornel, Heinz and Ghanem, Christian and Graebsch, Christine and K{\"o}nig, Andrej and Lindenberg, Michael and Lutz, Tilman and Schneider, Sabine and Trenczek, Thomas}, title = {Punitive Einstellungen von Studierenden der Sozialen Arbeit}, series = {Neue Kriminalpolitik}, volume = {35}, journal = {Neue Kriminalpolitik}, number = {4}, publisher = {Nomos Verlag}, issn = {0934-9200}, doi = {10.5771/0934-9200-2023-4-507}, pages = {507 -- 527}, year = {2023}, abstract = {Der Beitrag berichtet von einer Befragung an sieben Bachelor- Studieng{\"a}ngen der Sozialen Arbeit zu punitiven Einstellungen unter den Studierenden zu Beginn und zum Ende ihres Studiums. Die Erhebungen wurden vom Arbeitskreis Hochschullehrer*innen der Kriminologie und Straff{\"a}lligenhilfe in der Sozialen Arbeit (AKKrimSoz) an ihren Hochschulen durchgef{\"u}hrt. Die Ergebnisse zeigen, dass punitive Einstellungen zu Beginn nicht selten sind, w{\"a}hrend des Studiums jedoch abnehmen. So werden Detaileffekte des Studierens hinsichtlich strafrechtlicher, kriminologischer und kriminalpolitischer Inhalte auf die punitiven Einstellungen sichtbar: Restaurative und spezialpr{\"a}ventive Haltungen nehmen zu, zugleich beharren viele Studierende auf Eingangs{\"u}berzeugungen bez{\"u}glich der Ursachen von Delinquenz. Bei der Kriminalit{\"a}t der M{\"a}chtigen nehmen die Strafbed{\"u}rfnisse sogar leicht zu.}, language = {de} } @inproceedings{RanzenbergerFreierReinoldetal.2024, author = {Ranzenberger, Thomas and Freier, Carolin and Reinold, Luca and Riedhammer, Korbinian and Schneider, Fabian and Simic, Christopher and Simon, Claudia and Freisinger, Steffen and Georges, Munir and Bocklet, Tobias}, title = {A Multidisciplinary Approach to AI-based self-motivated Learning and Teaching with Large Language Models}, series = {Proceedings of DELFI Workshops 2024}, booktitle = {Proceedings of DELFI Workshops 2024}, publisher = {Gesellschaft f{\"u}r Informatik e.V.}, doi = {10.18420/delfi2024_11}, year = {2024}, abstract = {We present a learning experience platform that uses machine learning methods to support students and lecturers in self-motivated online learning and teaching processes. The platform is being developed as an agile open-source collaborative project supported by multiple universities and partners. The development is guided didactically, reviewed, and scientifically evaluated in several cycles. Transparency, data protection and the copyright compliant use of the system is a central part of the project. The system further employs large language models (LLMs). Due to privacy concerns, we utilize locally hosted LLM instances and explicitly do not rely on available cloud products. Students and lecturers can interact with an LLM-based chatbot in the current prototype. The AI-generated outputs contain cross-references to the current educational video's context, indicating if sections are based on the lectures context or world knowledge. We present the prototype and results of our qualitative evaluation from the perspective of lecturers and students.}, language = {en} } @inproceedings{RanzenbergerBockletFreisingeretal.2024, author = {Ranzenberger, Thomas and Bocklet, Tobias and Freisinger, Steffen and Georges, Munir and Glockner, Kevin and Herygers, Aaricia and Riedhammer, Korbinian and Schneider, Fabian and Simic, Christopher and Zakaria, Khabbab}, title = {EXTENDING HANS: LARGE LANGUAGE MODELS FOR QUESTION ANSWERING, SUMMARIZATION, AND TOPIC SEGMENTATION IN AN ML-BASED LEARNING EXPERIENCE PLATFORM}, series = {Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. M{\"a}rz 2024}, booktitle = {Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. M{\"a}rz 2024}, publisher = {TUPress}, address = {Dresden}, isbn = {978-3-95908-325-6}, pages = {219-224}, year = {2024}, abstract = {Abstract: The use of chatbots based on large language models (LLMs) and their impact on society are influencing our learning experience platform Hochschul Assistenz-System (HAnS). HAnS uses machine learning (ML) methods to support students and lecturers in the online learning and teaching processes [1]. This paper introduces LLM-based features available in HAnS which are using the transcript of our improved Automatic Speech Recognition (ASR) pipeline with an average transcription duration of 45 seconds and an average word error rate (WER) of 6.66\% on over 8 hours of audio data of 7 lecture videos. A LLM-based chatbot could be used to answer questions on the lecture content as the ASR transcript is provided as context. The summarization and topic segmentation uses the LLM to improve our learning experience platform. We generate multiple choice questions using the LLM and the ASR transcript as context during playback in a period of 3 minutes and display them in the HAnS frontend}, language = {en} } @inproceedings{RanzenbergerFreierReinoldetal.2024, author = {Ranzenberger, Thomas and Freier, Carolin and Reinold, Luca and Riedhammer, Korbinian and Schneider, Fabian and Simic, Christopher and Simon, Claudia and Freisinger, Steffen and Georges, Munir and Bocklet, Tobias}, title = {A Multidisciplinary Approach to AI-based self-motivated Learning and Teaching with Large Language Models}, series = {Proceedings of DELFI 2024}, booktitle = {Proceedings of DELFI 2024}, publisher = {Gesellschaft f{\"u}r Informatik e.V.}, address = {Bonn}, issn = {2944-7682}, doi = {10.18420/delfi2024_11}, pages = {133-140}, year = {2024}, abstract = {We present a learning experience platform that uses machine learning methods to support students and lecturers in self-motivated online learning and teaching processes. The platform is being developed as an agile open-source collaborative project supported by multiple universities and partners. The development is guided didactically, reviewed, and scientifically evaluated in several cycles. Transparency, data protection and the copyright compliant use of the system is a central part of the project. The system further employs large language models (LLMs). Due to privacy concerns, we utilize locally hosted LLM instances and explicitly do not rely on available cloud products. Students and lecturers can interact with an LLM-based chatbot in the current prototype. The AI-generated outputs contain cross-references to the current educational video's context, indicating if sections are based on the lectures context or world knowledge. We present the prototype and results of our qualitative evaluation from the perspective of lecturers and students.}, language = {en} }