@article{48294, abstract = {{Clinical NLP tasks such as mental health assessment from text, must take social constraints into account - the performance maximization must be constrained by the utmost importance of guaranteeing privacy of user data. Consumer protection regulations, such as GDPR, generally handle privacy by restricting data availability, such as requiring to limit user data to 'what is necessary' for a given purpose. In this work, we reason that providing stricter formal privacy guarantees, while increasing the volume of user data in the model, in most cases increases benefit for all parties involved, especially for the user. We demonstrate our arguments on two existing suicide risk assessment datasets of Twitter and Reddit posts. We present the first analysis juxtaposing user history length and differential privacy budgets and elaborate how modeling additional user context enables utility preservation while maintaining acceptable user privacy guarantees.}}, author = {{Sawhney, Ramit and Neerkaje, Atula and Habernal, Ivan and Flek, Lucie}}, issn = {{2334-0770}}, journal = {{Proceedings of the International AAAI Conference on Web and Social Media}}, pages = {{766--776}}, publisher = {{Association for the Advancement of Artificial Intelligence (AAAI)}}, title = {{{How Much User Context Do We Need? Privacy by Design in Mental Health NLP Applications}}}, doi = {{10.1609/icwsm.v17i1.22186}}, volume = {{17}}, year = {{2023}}, } @inproceedings{48297, author = {{Senge, Manuel and Igamberdiev, Timour and Habernal, Ivan}}, booktitle = {{Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}}, publisher = {{Association for Computational Linguistics}}, title = {{{One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks}}}, doi = {{10.18653/v1/2022.emnlp-main.496}}, year = {{2023}}, } @inproceedings{48292, author = {{Igamberdiev, Timour and Habernal, Ivan}}, booktitle = {{Findings of the Association for Computational Linguistics: ACL 2023}}, publisher = {{Association for Computational Linguistics}}, title = {{{DP-BART for Privatized Text Rewriting under Local Differential Privacy}}}, doi = {{10.18653/v1/2023.findings-acl.874}}, year = {{2023}}, } @inproceedings{48295, author = {{Bongard, Leonard and Held, Lena and Habernal, Ivan}}, booktitle = {{Proceedings of the Natural Legal Language Processing Workshop 2022}}, publisher = {{Association for Computational Linguistics}}, title = {{{The Legal Argument Reasoning Task in Civil Procedure}}}, doi = {{10.18653/v1/2022.nllp-1.17}}, year = {{2023}}, } @article{48290, abstract = {{AbstractIdentifying, classifying, and analyzing arguments in legal discourse has been a prominent area of research since the inception of the argument mining field. However, there has been a major discrepancy between the way natural language processing (NLP) researchers model and annotate arguments in court decisions and the way legal experts understand and analyze legal argumentation. While computational approaches typically simplify arguments into generic premises and claims, arguments in legal research usually exhibit a rich typology that is important for gaining insights into the particular case and applications of law in general. We address this problem and make several substantial contributions to move the field forward. First, we design a new annotation scheme for legal arguments in proceedings of the European Court of Human Rights (ECHR) that is deeply rooted in the theory and practice of legal argumentation research. Second, we compile and annotate a large corpus of 373 court decisions (2.3M tokens and 15k annotated argument spans). Finally, we train an argument mining model that outperforms state-of-the-art models in the legal NLP domain and provide a thorough expert-based evaluation. All datasets and source codes are available under open lincenses at https://github.com/trusthlt/mining-legal-arguments.}}, author = {{Habernal, Ivan and Faber, Daniel and Recchia, Nicola and Bretthauer, Sebastian and Gurevych, Iryna and Spiecker genannt Döhmann, Indra and Burchard, Christoph}}, issn = {{0924-8463}}, journal = {{Artificial Intelligence and Law}}, keywords = {{Law, Artificial Intelligence}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Mining legal arguments in court decisions}}}, doi = {{10.1007/s10506-023-09361-y}}, year = {{2023}}, } @book{48365, abstract = {{

With the focus topic “The Human Factor”, the Jahrbuch Technikphilosophie 2023 aims to investigate on the one hand the manifold arrangements of the deficiency in the technical field (and especially “new” technologies): How do machine worlds, user interfaces, implementation strategies, or even entire large-scale technological ecosystems model, compensate, and even parody “the” human being – that is, “their” version of ourselves? How does technology discriminate? How does it educate? To what extent can it “reduce” the human? On the other hand, it is necessary to take a new look at what “human” actually means in precisely this context, and to reexamine anthropology as part of theories of technology and discourses on technology. Criticism of technology must therefore also be criticism of man. With contributions by Fabian Anicker, Petra Gehring, Axel Gelfert, Martina Heßler, Andreas Kaminski, Ruth Karl, Katerina Krtilova, Joachim Landkammer, Kevin Liggieri, Felix Maschewski, Nicola Mößner, Anna-Verena Nosthoff, Felix Reda, Jean Paul Sartre, Björn Schembera, Stefan Schöberlein, Marcel Siegler, Florian Sprenger and Martin Warnke.

}}, editor = {{Alpsancar, Suzana and Friedrich, Alexander and Gehring, Petra and Hubig, Christoph and Kaminski, Andreas and Nordmann, Alfred}}, isbn = {{9783748941767}}, publisher = {{Nomos}}, title = {{{Faktor Mensch. Jahrbuch Technikphilosophie 2023}}}, doi = {{10.5771/9783748941767}}, year = {{2023}}, } @article{48374, abstract = {{Abstract Purpose Protein-rich foods show heterogeneous associations with the risk of type 2 diabetes (T2D) and it remains unclear whether habitual protein intake is related to T2D risk. We carried out an umbrella review of systematic reviews (SR) of randomised trials and/or cohort studies on protein intake in relation to risks of T2D. Methods Following a pre-specified protocol (PROSPERO: CRD42018082395), we retrieved SRs on protein intake and T2D risk published between July 1st 2009 and May 22nd 2022, and assessed the methodological quality and outcome-specific certainty of the evidence using a modified version of AMSTAR 2 and NutriGrade, respectively. The overall certainty of evidence was rated according to predefined criteria. Results Eight SRs were identified of which six contained meta-analyses. The majority of SRs on total protein intake had moderate or high methodological quality and moderate outcome-specific certainty of evidence according to NutriGrade, however, the latter was low for the majority of SRs on animal and plant protein. Six of the eight SRs reported risk increases with both total and animal protein. According to one SR, total protein intake in studies was ~ 21 energy percentage (%E) in the highest intake category and 15%E in the lowest intake category. Relative Risks comparing high versus low intake in most recent SRs ranged from 1.09 (two SRs, 95% CIs 1.02–1.15 and 1.06–1.13) to 1.11 (1.05–1.16) for total protein (between 8 and 12 cohort studies included) and from 1.13 (1.08–1.19) to 1.19 (two SRs, 1.11–1.28 and 1.11–1.28) (8–9 cohort studies) for animal protein. However, SRs on RCTs examining major glycaemic traits (HbA1c, fasting glucose, fasting insulin) do not support a clear biological link with T2D risk. For plant protein, some recent SRs pointed towards risk decreases and non-linear associations, however, the majority did not support an association with T2D risk. Conclusion Higher total protein intake was possibly associated with higher T2D risk, while there is insufficient evidence for a risk increase with higher intakes of animal protein and a risk decrease with plant protein intake. Given that most SRs on plant protein did not indicate an association, there is possibly a lack of an effect. }}, author = {{Schulze, Matthias B. and Haardt, Julia and Amini, Anna M. and Kalotai, Nicole and Lehmann, Andreas and Schmidt, Annemarie and Buyken, Anette and Egert, Sarah and Ellinger, Sabine and Kroke, Anja and Kühn, Tilman and Louis, Sandrine and Nimptsch, Katharina and Schwingshackl, Lukas and Siener, Roswitha and Zittermann, Armin and Watzl, Bernhard and Lorkowski, Stefan}}, issn = {{1436-6207}}, journal = {{European Journal of Nutrition}}, keywords = {{Nutrition and Dietetics, Medicine (miscellaneous)}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Protein intake and type 2 diabetes mellitus: an umbrella review of systematic reviews for the evidence-based guideline for protein intake of the German Nutrition Society}}}, doi = {{10.1007/s00394-023-03234-5}}, year = {{2023}}, } @article{48373, abstract = {{Abstract Purpose This umbrella review aimed to assess whether dietary protein intake with regard to quantitative (higher vs. lower dietary protein intake) and qualitative considerations (total, plant-based or animal-based protein intake) affects body weight (BW), fat mass (FM) and waist circumference (WC). Methods A systematic literature search was conducted in PubMed, Embase and Cochrane Database of Systematic Reviews for systematic reviews (SRs) with and without meta-analyses of prospective studies published between 04 October 2007 and 04 January 2022. Methodological quality and outcome-specific certainty of evidence of the retrieved SRs were assessed by using AMSTAR 2 and NutriGrade, respectively, in order to rate the overall certainty of evidence using predefined criteria. Results Thirty-three SRs were included in this umbrella review; 29 were based on randomised controlled trials, a few included cohort studies. In studies without energy restriction, a high-protein diet did not modulate BW, FM and WC in adults in general (all “possible” evidence); for older adults, overall certainty of evidence was “insufficient” for all parameters. Under hypoenergetic diets, a high-protein diet mostly decreased BW and FM, but evidence was “insufficient” due to low methodological quality. Evidence regarding an influence of the protein type on BW, FM and WC was “insufficient”. Conclusion “Possible” evidence exists that the amount of protein does not affect BW, FM and WC in adults under isoenergetic conditions. Its impact on the reduction in BW and FM under hypoenergetic conditions remains unclear; evidence for an influence of protein type on BW, FM and WC is “insufficient”. }}, author = {{Ellinger, Sabine and Amini, Anna M. and Haardt, Julia and Lehmann, Andreas and Schmidt, Annemarie and Bischoff-Ferrari, Heike A. and Buyken, Anette and Kroke, Anja and Kühn, Tilman and Louis, Sandrine and Lorkowski, Stefan and Nimptsch, Katharina and Schulze, Matthias B. and Schwingshackl, Lukas and Siener, Roswitha and Stangl, Gabriele I. and Volkert, Dorothee and Zittermann, Armin and Watzl, Bernhard and Egert, Sarah}}, issn = {{1436-6207}}, journal = {{European Journal of Nutrition}}, keywords = {{Nutrition and Dietetics, Medicine (miscellaneous)}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Protein intake and body weight, fat mass and waist circumference: an umbrella review of systematic reviews for the evidence-based guideline on protein intake of the German Nutrition Society}}}, doi = {{10.1007/s00394-023-03220-x}}, year = {{2023}}, } @inproceedings{48391, author = {{Aralikatti, Rohith and Boeddeker, Christoph and Wichern, Gordon and Subramanian, Aswin and Le Roux, Jonathan}}, booktitle = {{ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}}, publisher = {{IEEE}}, title = {{{Reverberation as Supervision For Speech Separation}}}, doi = {{10.1109/icassp49357.2023.10095022}}, year = {{2023}}, } @inproceedings{48422, author = {{Humpert, Lynn and Tihlarik, Amelie and Wäschle, Moritz and Anacker, Harald and Dumitrescu, Roman and Albers, Albert and Röbenack, Silke and Pfeifer, Sabine}}, booktitle = {{IEEE International Conference on Technology Management, Operations and Decisions (IEEE ICTMOD)}}, location = {{Rabat, Marokko}}, title = {{{Investigating the potential of artificial intelligence for the employee from the perspective of AI-experts}}}, year = {{2023}}, } @inproceedings{48421, author = {{Humpert, Lynn and Zagatta, Kristin and Anacker, Harald and Dumitrescu, Roman}}, booktitle = {{IEEE International Conference on Technology Management, Operations and Decisions (IEEE ICTMOD)}}, location = {{Rabat, Marokko}}, title = {{{Identification of fields of action for validation in Systems Engineering}}}, year = {{2023}}, } @inproceedings{48427, author = {{Gabriel, Stefan and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Procedia CIRP}}, location = {{Dublin, Ireland}}, title = {{{Strategic planning of the collaboration between humans and artificial intelligence in production}}}, year = {{2023}}, } @inproceedings{48425, author = {{Mundt, Enrik and Wilke, Daria and Anacker, Harald and Dumitrescu, Roman}}, location = {{Maui, Hawaii}}, title = {{{Principles for the effective application of Systems Engineering: A systematic literature review and application use case}}}, year = {{2023}}, } @inproceedings{48426, author = {{Tekaat, Julian and Wilke, Daria and Anacker, Harald and Dumitrescu, Roman}}, location = {{Würzburg}}, title = {{{Integration von Design Thinking in Systems Engineering mit Hilfe des Systemdenkens}}}, year = {{2023}}, } @misc{48433, author = {{Böttger, Lydia}}, booktitle = {{www.daz-portal.de}}, title = {{{Lydia Böttger (Universität Paderborn) rezensiert: Burwitz-Melzer, Eva; Riemer, Claudia; Schmelter, Lars (Hrsg.) (2022): Feedback beim Lehren und Lernen von Fremd- und Zweitsprachen. Arbeitspapiere der 42. Frühjahrs- konferenz zur Erforschung des Fremdsprachenunterrichts. [Giessener Beiträge zur Fremdsprachendidaktik]. Tübingen: Narr Francke Attempto, 245 Seiten. ISBN 978-3- 8233-8569-1}}}, year = {{2023}}, } @article{48456, abstract = {{Abstract Purpose Our aim was to assess alignment in timing of ‘highest caloric intake’ with individual chronotype and its association with body composition in adolescents. Methods We used repeatedly collected data from n = 196 adolescents (age 9–16 years, providing N = 401 yearly questionnaires) of the DONALD open cohort study. Chronotype was assessed by the Munich Chronotype Questionnaire from which midpoint of sleep (MSFsc) was derived. A sex- and age-specific diet-chrono-alignment score (DCAS) was calculated as the difference in hours between the chronotype-specific median timing of highest caloric intake of the studied population and the individual timing of ‘highest caloric intake’ or vice versa. Repeated-measures regression models were applied to study cross-sectional and longitudinal associations between the DCAS and body composition, e.g., Fat Mass Index (FMI) or Fat Free Mass Index (FFMI). Results DCAS ranged from −6:42 h to + 8:01 h and was not associated with body composition. Among adolescents with a later chronotype (N = 201) a 1 h increase in DCAS (later consumption of ‘highest caloric intake’ in comparison to the median intake of that group), increased FFMI by 1.92 kg/m2 (95% CI: 0.15, 3.69, p value = 0.04) over a median follow-up of 0.94 year. Conclusion Alignment of energy intake with individual chronotype appears beneficial for FFMI among those with a late chronotype. }}, author = {{Jankovic, Nicole and Schmitting, Sarah and Stutz, Bianca and Krüger, Bettina and Buyken, Anette and Alexy, Ute}}, issn = {{1436-6207}}, journal = {{European Journal of Nutrition}}, keywords = {{Nutrition and Dietetics, Medicine (miscellaneous)}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Alignment between timing of ‘highest caloric intake’ and chronotype in relation to body composition during adolescence: the DONALD Study}}}, doi = {{10.1007/s00394-023-03259-w}}, year = {{2023}}, } @inproceedings{46069, author = {{Seebauer, Fritz and Kuhlmann, Michael and Haeb-Umbach, Reinhold and Wagner, Petra}}, booktitle = {{12th Speech Synthesis Workshop (SSW) 2023}}, title = {{{Re-examining the quality dimensions of synthetic speech}}}, year = {{2023}}, } @inproceedings{48475, author = {{Görel, Gamze and Finke, Pauline and Hellmich, Frank}}, location = {{Münster}}, publisher = {{Universität Münster}}, title = {{{Professionelle Unterrichtswahrnehmung von Lehramtsstudierenden – das Projekt iDEAL. Vortrag auf der Herbsttagung 2023 der Arbeitsgruppe Empirische Sonderpädagogische Forschung (AESF)}}}, year = {{2023}}, } @inproceedings{48232, author = {{Mirbabaie, Milad and Rieskamp, Jonas and Hofeditz, Lennart and Stieglitz, Stefan}}, title = {{{Breaking Down Barriers: How Conversational Agents Facilitate Open Science and Data Sharing}}}, year = {{2023}}, } @inproceedings{48468, author = {{Rieskamp, Jonas and Mirbabaie, Milad and Langer, Marie and Kocur, Alexander}}, title = {{{From Virality to Veracity: Examining False Information on Telegram vs. Twitter}}}, year = {{2023}}, }