Social factors of procrastination: group work can reduce procrastination among students - Koppenborg, Markus; Klingsieck, Katrin B. (Social Psychology of Education)
Abstract Research on procrastination covers a variety of individual factors (e.g., conscientiousness) and this focus is reflected in interventions against procrastination. Less emphasis is put on situational and social factors that may help students reduce procrastination, such as social interdependence. Therefore, this study investigates the relationship between interdependence with academic procrastination and affective variables. Two vignette studies with student samples ( N 1 = 320, N 2 = 193) were conducted and data was analyzed with regression analyses and analyses of covariance. Results of both studies show lower state procrastination in group work with interdependence compared to individual work, especially in participants with high trait procrastination. This difference is more pronounced when interdependence is accompanied by an active commitment to finish the task on time. Further, interdependent group work is related to increased positive affect and decreased negative affect. The results demonstrate the relevance of situational and social factors for academic procrastination, and point toward new approaches for intervention.
Keywords: Sociology and Political Science; Developmental and Educational Psychology; Social Psychology

Group work and student procrastination - Koppenborg, Markus; Klingsieck, Katrin B. (Learning and Individual Differences)
Keywords: Developmental and Educational Psychology; Education; Social Psychology

MMS-MSG: A Multi-purpose Multi-Speaker Mixture Signal Generator - Cord-Landwehr, Tobias; von Neumann, Thilo; Boeddeker, Christoph; Haeb-Umbach, Reinhold (2022 International Workshop on Acoustic Signal Enhancement (IWAENC))
The scope of speech enhancement has changed from a monolithic view of single, independent tasks, to a joint processing of complex conversational speech recordings. Training and evaluation of these single tasks requires synthetic data with access to intermediate signals that is as close as possible to the evaluation scenario. As such data often is not available, many works instead use specialized databases for the training of each system component, e.g WSJ0-mix for source separation. We present a Multi-purpose Multi-Speaker Mixture Signal Generator (MMS-MSG) for generating a variety of speech mixture signals based on any speech corpus, ranging from classical anechoic mixtures (e.g., WSJ0-mix) over reverberant mixtures (e.g., SMS-WSJ) to meeting-style data. Its highly modular and flexible structure allows for the simulation of diverse environments and dynamic mixing, while simultaneously enabling an easy extension and modification to generate new scenarios and mixture types. These meetings can be used for prototyping, evaluation, or training purposes. We provide example evaluation data and baseline results for meetings based on the WSJ corpus. Further, we demonstrate the usefulness for realistic scenarios by using MMS-MSG to provide training data for the LibriCSS database.

Couleur Locale - Schulze, Max (Die Wirklichkeit ist sowieso da)

Single Club - Schulze, Max (Düsseldorf und Fotografie)

Rezension von: Viola Schrader: Historisches Denken und sprachliches Handeln. Eine qualitativ-empirische Untersuchung von Schülertexten. Münster et al.: LIT 2021 - Hartung, Olaf (sehepunkte)

On Synchronization of Wireless Acoustic Sensor Networks in the Presence of Time-Varying Sampling Rate Offsets and Speaker Changes - Gburrek, Tobias; Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold (ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall - Letmathe, Sebastian; Feng, Yuanhua; Uhde, André (Journal of Risk)
In this paper new semiparametric generalized autoregressive conditional heteroscedasticity (GARCH) models with long memory are introduced. A multiplicative decomposition of the volatility into a conditional component and an unconditional component is assumed. The estimation of the latter is carried out by means of a data-driven local polynomial smoother. According to the revised recommendations by the Basel Committee on Banking Supervision to measure market risk in the banks’ trading books, these new semiparametric GARCH models are applied to obtain rolling one-step ahead forecasts for the value-at-risk and expected shortfall (ES) for market risk assets. Standard regulatory traffic-light tests and a newly introduced traffic-light test for the ES are carried out for all models. In addition, model performance is assessed via a recently introduced model selection criterion. The practical relevance of our proposal is demonstrated by a comparative study. Our results indicate that semiparametric long-memory GARCH models are a meaningful substitute for their conventional, parametric counterparts.
Keywords: long memory; generalized autoregressive conditional heteroscedasticity (GARCH) models; value-at-risk (VaR); expected shortfall (ES); traffic-light test; backtesting

Warping of Radar Data Into Camera Image for Cross-Modal Supervision in Automotive Applications - Grimm, Christopher; Fei, Tai; Warsitz, Ernst; Farhoud, Ridha; Breddermann, Tobias; Haeb-Umbach, Reinhold (IEEE Transactions on Vehicular Technology)
We present an approach to automatically generate semantic labels for real recordings of automotive range-Doppler (RD) radar spectra. Such labels are required when training a neural network for object recognition from radar data. The automatic labeling approach rests on the simultaneous recording of camera and lidar data in addition to the radar spectrum. By warping radar spectra into the camera image, state-of-the-art object recognition algorithms can be applied to label relevant objects, such as cars, in the camera image. The warping operation is designed to be fully differentiable, which allows backpropagating the gradient computed on the camera image through the warping operation to the neural network operating on the radar data. As the warping operation relies on accurate scene flow estimation, we further propose a novel scene flow estimation algorithm which exploits information from camera, lidar and radar sensors. The proposed scene flow estimation approach is compared against a state-of-the-art scene flow algorithm, and it outperforms it by approximately 30% w.r.t. mean average error. The feasibility of the overall framework for automatic label generation for RD spectra is verified by evaluating the performance of neural networks trained with the proposed framework for Direction-of-Arrival estimation.

Erfolgreiches People Management in KMU und Start-ups - Krebs, Benjamin; Kabst, Rüdiger (PERSONALquartely)

Methodenverliebt am Markt vorbei? - Rebert, Tobias; Tomin, Slawa; Wach, Bernhard; Kabst, Rüdiger (Intrapreneurship)

Technically enabled explaining of voice characteristics - Wiechmann, Jana; Glarner, Thomas; Rautenberg, Frederik; Wagner, Petra; Haeb-Umbach, Reinhold (18. Phonetik und Phonologie im deutschsprachigen Raum (P&P))

Long‐term effects of institutional conditions on perceived corruption – A study on organizational imprinting in post‐communist countries - Auer, Thorsten Fabian; Knorr, Karin; Thommes, Kirsten (Business Ethics, the Environment & Responsibility)
In this paper, we apply imprinting theory to examine how institutional transformation substantially influences perceptions of corruption that we argue to be incorporated to a varying extent in organizations founded in that period. For this purpose, we compare the effect of a sudden shock (dissolution of the Soviet Union) on the managers' present perceptions to that of a steady transition (EU accession). We consult the 5th round of the Business Environment and Enterprise Performance Survey from 2012 to 2014 analyzing 4715 interviews with managers of organizations from twelve Central and Eastern European countries. Our results reveal striking differences in the present perception of corruption for organizations founded immediately before and immediately after these institutional transitions. The study's results contribute to the research on imprinting theory regarding the relationship between organizations and institutional conditions that constitutes a lasting effect on organizational structures. Thus, applying an institutional perspective that considers less stable periods for organizations yields a promising avenue in research on organizational behavior.
Keywords: Central and Eastern Europe; corruption; imprinting theory; institutional transformation; shock-imprinting

Die Wirksamkeit von Lernzielen für Studienleistungen – eine experimentelle Studie - Auer, Thorsten Fabian (die hochschullehre)
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.
Keywords: learning objectives; academic performance; perception; teaching methods; experiment

AI-Based Assistance System for Manufacturing - Deppe, Sahar; Brandt, Lukas; Brünninghaus, Marc; Papenkordt, Jörg; Heindorf, Stefan; Tschirner-Vinke, Gudrun
Manufacturing companies are challenged to make the increasingly complex work processes equally manageable for all employees to prevent an impending loss of competence. In this contribution, an intelligent assistance system is proposed enabling employees to help themselves in the workplace and provide them with competence-related support. This results in increasing the short- and long-term efficiency of problem solving in companies.
Keywords: Assistance system; Knowledge graph; Information retrieval; Neural networks; AR

AI-Based Assistance System for Manufacturing - Deppe, Sahar; Brandt, Lukas; Brunninghaus, Marc; Papenkordt, Jörg; Heindorf, Stefan; Tschirner-Vinke, Gudrun (2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA))

Determinants of Trust in Smart Technologies - Papenkordt, Jörg; Thommes, Kirsten (Human-Technology Interaction)

Warum werde ich nicht satt? Begehren und Begehrt-Werden bei den Ärzt*innen der Utopie. Grey’s Anatomy aus einer inklusiven religionspädagogischen Perspektive - Neumann, Anna (Lust und Abgrund. Theologische und kulturwissenschaftliche Zugänge zum Begehren)

Rationalität in der Islamischen Theologie II: Die Moderne

Ich habe nur die eine Hälfte genommen – Verstehensprozesse zur Achsensymmetrie - Wallner, Melina (Beiträge zum Mathematikunterricht 2022. 56. Jahrestagung der Gesellschaft für Didaktik der Mathematik)