The Quest for a Good Life in Times of Crisis: Hopeful Happiness in the New Testament and Beyond

Intet resum

On the Fit in Fitness Apps: Studying the Interaction of Motivational Affordances and Users’ Goal Orientations in Affecting the Benefits Gained

Lacking regular physical activity is a pertaining problem in most western societies. Fitness apps are positioned to address this issue by offering motivational affordances to the user, which aim to enhance motivation and increase physical activity: self-monitoring, rewards, and social comparison. Yet research provides inconclusive results about their effectiveness. For clarification, this paper draws upon Achievement Goal Theory and theorizes how and why motivational affordances vary in dependence of users’ motivation-relevant goals in supporting motivation and physical activity. Empirical validation among 283 fitness app users generally supports that motivational affordances need to be congruent with users’ underlying goal orientations to achieve the benefits. As such, this paper contributes to fitness app research by resolving prior inconsistencies, offers a theorizing on motivational affordances and individual motivation-relevant differences, and aids practice in designing fitness apps

Why individuals switch to using mobile payment: A migration-theoretic, empirical study

With mobile payment, individuals can buy goods and services through the use of a mobile device and wireless technology. Still, although the usage of mobile payment provides several advantages, such as a more convenient and faster paying-process, it is hardly used. Individuals rather stick with their current payment method, such as cash, EC card or credit card. In this study, we therefore try to find out, what factors would bring individuals to switch from their current payment method to mobile payment. We rely on the pull-push-mooring framework to depict the migration process from the current payment method to mobile payment. The results prove that dissatisfaction with the current payment method has a rather low influence on the intention to switch to mobile payment in comparison with other factors such as perceived usefulness or alternative attractiveness. Furthermore, switching costs have a negative influence on the intention to switch to mobile payment

Gamification: Explaining Brand Loyalty in Mobile Applications

Gamification is one specific way to increase mobile app users’ brand loyalty. We propose that the frequency with which one uses immersion-, achievement- and social-related features relates to brand loyalty. To provide empirical evidence for this proposal, we obtained quantitative data from surveying 243 users on the mobile application Duolingo and conducted a fuzzy-set qualitative comparative analysis (fsQCA). We found that users need to frequently use immersion- and achievement-related features to result in high brand loyalty. On the contrary, we found users who infrequently use at least two gamification features have low brand loyalty. These findings extend the gamification literature by revealing an interaction between multiple gamification features and extend mobile application research by showing how gamification features relate to high and low brand loyalty. We also guide practitioners on how to identify users at risk to discontinue and reduce customer churn

A Hybrid Approach to Assignment of Library of Congress Subject Headings

Library of Congress Subject Headings (LCSH) are popular for indexing library records. We studied the possibility of assigning LCSH automatically by training classifiers for terms used frequently in a large collection of abstracts of the literature on hand and by extracting headings from those abstracts. The resulting classifiers reach an acceptable level of precision, but fail in terms of recall partly because we could only train classifiers for a small number of LCSH. Extraction, i.e., the matching of headings in the text, produces better recall but extremely low precision. We found that combining both methods leads to a significant improvement of recall and a slight improvement of F1 score with only a small decrease in precision

When the Past Is Still in Mind: Using Nostalgia to Create Adoption for Online Games

Inspired by the recent hype about Pokémon Go, a popular augmented reality-based game for smartphones, we aim to reveal that individuals use this game for the most part due to nostalgic reasons. In regard to previous research focusing nostalgia, we assume direct effects of nostalgia factors, such as attitudes about the past, yearning for the past and evoked nostalgia, on the intention to use and to continuously use online games. With this research, we expect to contribute to existing literature as the use of games might be also grounded – next to well-known perceptions studied previously – in nostalgic reasons. This might also explain the phenomenon that so many individuals started using this game immediately after becoming available on mobile app stores /Smartphones

Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning

Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and an reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for a highly accurate OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, specificity 90%).Comment: 12 pages, 5 figure

A 3-D Projection Model for X-ray Dark-field Imaging

Talbot-Lau X-ray phase-contrast imaging is a novel imaging modality, which provides not only an X-ray absorption image, but also additionally a differential phase image and a dark-field image. The dark-field image is related to small angle scattering and has an interesting property when canning oriented structures: the recorded signal depends on the relative orientation of the structure in the imaging system. Exactly this property allows to draw conclusions about the orientation and to reconstruct the structure. However, the reconstruction is a complex, non-trivial challenge. A lot of research was conducted towards this goal in the last years and several reconstruction algorithms were proposed. A key step of the reconstruction algorithm is the inversion of a forward projection model. Up until now, only 2-D projection models are available, with effectively limit the scanning trajectory to a 2-D plane. To obtain true 3-D information, this limitation requires to combine several 2-D scans, which leads to quite complex, impractical acquisitions schemes. Furthermore, it is not possible with these models to use 3-D trajectories that might allow simpler protocols, like for example a helical trajectory. To address these limitations, we propose in this work a very general 3-D projection model. Our projection model defines the dark-field signal dependent on an arbitrarily chosen ray and sensitivity direction. We derive the projection model under the assumption that the observed scatter distribution has a Gaussian shape. We theoretically show the consistency of our model with more constrained existing 2-D models. Furthermore, we experimentally show the compatibility of our model with dark-field measurements of two matchsticks. We believe that this 3-D projection model is an important step towards more flexible trajectories and imaging protocols that are much better applicable in practice.Comment: Shiyang Hu and Lina Felsner contributed equally to this wor