e-learning can be seen as service creation process between students and supervisors. In order to automate and enhance thesis and seminar processes at our university, we aim to increase transparency, traceability, communication, and success for our students and lecturers. Therefore, we suggest standardized process templates for students’ theses and their implementation with an IT support system....

Drawing on resource drain theory, we introduce self-regulatory resource (ego) depletion stemming from family–work conflict (FWC) as an alternative theoretical perspective on why supervisors behave abusively toward subordinates. Our two-study examination of a cross-domain antecedent of abusive supervision stands in contrast to prior research, which has focused primarily on work-related factors t...

Semi-supervised learning has largely alleviated the strong demand for large amount of annotations in deep learning. However, most methods have adopted a common assumption that there is always labeled data from same class unlabeled data, which impractical and restricted real-world applications. In this research work, our focus on semi-supervised when categories are disjoint each other. The main ...

Deep detection approaches are powerful in controlled conditions, but appear brittle and fail when source models used off-the-shelf on unseen domains. Most of the existing works domain adaptation simplify setting access jointly both a large dataset sizable amount target samples. However this scenario is unrealistic many practical cases as monitoring image feeds from social media: only pretrained...

Background: This study explored the relationship of perceived self-efficacy to demographic and fieldwork variables for occupational therapy (OT) students. Self-efficacy is related to student and employee success and competency. Methods: OT students (n = 306) from 42 OT programs in the United States completed the Student Confidence Questionnaire and the Demographic Questionnaire and Survey addre...

Reinforcement learning optimizes policies for expected cumulative reward. Need the supervision be so narrow? Reward is delayed and sparse for many tasks, making it a difficult and impoverished signal for end-to-end optimization. To augment reward, we consider a range of selfsupervised tasks that incorporate states, actions, and successors to provide auxiliary losses. These losses offer ubiquito...