Hochschule Bonn-Rhein-Sieg
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Masterprüfungsordnung 2022 in der Fassung der 1. Änderungsordnung für den konsekutiven Studiengang Wirtschaftspsychologie (M.Sc.) am Campus Rheinbach der Hochschule Bonn-Rhein-Sieg vom 19.05.2022
No full textDer Faktor Vernunft
No full textUrteil des Landessozialgerichts Essen vom 20.07.2021 – L 14 U 594/20 –, juri
Crafting work and leisure for recovery and optimal functioning across life domains
No full textIntroduction: Recovery experiences have thus far been portrayed as experiences that simply “happen” to people. However, recovery can also be understood from a crafting perspective; that is, individuals may proactively shape their work and non-work activities to recover from stress, satisfy their psychological needs, and achieve optimal functioning.
Materials and Methods: In my talk, I will present the theoretical basis of needs-based crafting based on a conceptual review of the literature. Moreover, I will present empirical findings on the validation of a newly developed off-job crafting scale.
Results: In five sub studies, we found that off-job crafting was related to optimal functioning over time. Moreover, the newly developed off-job crafting scale had good convergent and discriminant validity, internal consistency, and test-retest reliability.
Conclusions: Theoretical and empirical evidence suggests that needs-based crafting can enhance optimal functioning in different life domains and support people in performing their work duties sustainably. Proactive attempts to achieve better recovery through needs satisfaction may be beneficial in an intensified and continually changing and challenging working life. Our line of research provides important avenues for organizational research and practices regarding recovery and needs satisfaction occurring at work and outside work
Forschungsdatenmanagement entlang des Datenlebenszyklus
No full textForschungsdatenmanagement (FDM) nimmt im Forschungsalltag der Hochschulen für angewandte Wissenschaften (HAW) eine zunehmende größere Rolle ein und stellt an viele Forschende bisher unbekannte Anforderungen. So gilt es ein FAIRes und nachhaltiges Datenmanagement im Sinne des Kodex „Leitlinien zur Sicherung guter wissenschaftlicher Praxis“ der DFG zu betreiben – für sich selbst, für die eigene Forschungsgruppe und für die Forschungsgemeinschaft. In dieser Veranstaltung anlässlich des Tags der Forschungsdaten in NRW am 15.11.2022 wurden die wesentlichen Grundzüge des Forschungsdatenmanagements anhand des Forschungsdatenlebenszyklus nähergebracht und Praxisbeispiele und nützliche Links vorgestellt
DExT: Detector Explanation Toolkit
No full textState-of-the-art object detectors are treated as black boxes due to their highly non-linear internal computations. Even with unprecedented advancements in detector performance, the inability to explain how their outputs are generated limits their use in safety-critical applications. Previous work fails to produce explanations for both bounding box and classification decisions, and generally make individual explanations for various detectors. In this paper, we propose an open-source Detector Explanation Toolkit (DExT) which implements the proposed approach to generate a holistic explanation for all detector decisions using certain gradient-based explanation methods. We suggests various multi-object visualization methods to merge the explanations of multiple objects detected in an image as well as the corresponding detections in a single image. The quantitative evaluation show that the Single Shot MultiBox Detector (SSD) is more faithfully explained compared to other detectors regardless of the explanation methods. Both quantitative and human-centric evaluations identify that SmoothGrad with Guided Backpropagation (GBP) provides more trustworthy explanations among selected methods across all detectors. We expect that DExT will motivate practitioners to evaluate object detectors from the interpretability perspective by explaining both bounding box and classification decisions.
21 pages, with supplementar
Buch-Aisthesis: Philologie und Gestaltungsdiskurs
No full textLiteratur kann auch als Verbund von Medien betrachtet werden, die in Kooperations- und Konkurrenzverhältnissen auftreten. Dies wird umso deutlicher, wenn aus literatur- und designwissenschaftlicher Perspektive auf die Beobachtung der Differenz von typographischen und anderen, grundsätzlich nonverbalen visuellen Daten abgestellt wird. Die Beiträger*innen des Bandes leiten daraus ein Verhältnis von Literatur- und Kunstwissenschaft zu ihren Gegenständen ab, das nicht zuletzt zu einer neuen Aufmerksamkeit für die skripturale und typographische Materialität und Medialität der Literatur führt. Dabei geht es um die Theorie der Reflexion und die Praxis der Erzeugung einer je spezifischen Buch-Ästhetik
A Study on the Ambiguity in Human Annotation of German Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis
No full textFor research in audiovisual interview archives often it is not only of interest what is said but also how. Sentiment analysis and emotion recognition can help capture, categorize and make these different facets searchable. In particular, for oral history archives, such indexing technologies can be of great interest. These technologies can help understand the role of emotions in historical remembering. However, humans often perceive sentiments and emotions ambiguously and subjectively. Moreover, oral history interviews have multi-layered levels of complex, sometimes contradictory, sometimes very subtle facets of emotions. Therefore, the question arises of the chance machines and humans have capturing and assigning these into predefined categories. This paper investigates the ambiguity in human perception of emotions and sentiment in German oral history interviews and the impact on machine learning systems. Our experiments reveal substantial differences in human perception for different emotions. Furthermore, we report from ongoing machine learning experiments with different modalities. We show that the human perceptual ambiguity and other challenges, such as class imbalance and lack of training data, currently limit the opportunities of these technologies for oral history archives. Nonetheless, our work uncovers promising observations and possibilities for further research
Are You “Nudgeable”? Factors Affecting the Acceptance of Healthy Eating Nudges in a Cafeteria Setting
No full textResearch has identified nudging as a promising and effective tool to improve healthy eating behavior in a cafeteria setting. However, it remains unclear who is and who is not “nudgeable” (susceptible to nudges). An important influencing factor at the individual level is nudge acceptance. While some progress has been made in determining influences on the acceptance of healthy eating nudges, research on how personal characteristics (such as the perception of social norms) affect nudge acceptance remains scarce. We conducted a survey on 1032 university students to assess the acceptance of nine different types of healthy eating nudges in a cafeteria setting with four influential factors (social norms, health-promoting collaboration, responsibility to promote healthy eating, and procrastination). These factors are likely to play a role within a university and a cafeteria setting. The present study showed that key influential factors of nudge acceptance were the perceived responsibility to promote healthy eating and health-promoting collaboration. We also identified three different student clusters with respect to nudge acceptance, demonstrating that not all nudges were accepted equally. In particular, default, salience, and priming nudges were at least moderately accepted regardless of the degree of nudgeability. Our findings provide useful policy implications for nudge development by university, cafeteria, and public health officials. Recommendations are formulated for strengthening the theoretical background of nudge acceptance and the susceptibility to nudges
ProtSTonKGs: A Sophisticated Transformer Trained on Protein Sequences, Text, and Knowledge Graphs
No full textWhile most approaches individually exploit unstructured data from the biomedical literature or structured data from biomedical knowledge graphs, their union can better exploit the advantages of such approaches, ultimately improving representations of biology. Using multimodal transformers for such purposes can improve performance on context dependent classication tasks, as demonstrated by our previous model, the Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs (STonKGs). In this work, we introduce ProtSTonKGs, a transformer aimed at learning all-encompassing representations of protein-protein interactions. ProtSTonKGs presents an extension to our previous work by adding textual protein descriptions and amino acid sequences (i.e., structural information) to the text- and knowledge graph-based input sequence used in STonKGs. We benchmark ProtSTonKGs against STonKGs, resulting in improved F1 scores by up to 0.066 (i.e., from 0.204 to 0.270) in several tasks such as predicting protein interactions in several contexts. Our work demonstrates how multimodal transformers can be used to integrate heterogeneous sources of information, paving the foundation for future approaches that use multiple modalities for biomedical applications