(How) Does Religiousness Impact on Job Satisfaction? Results for Germany

2022 ◽  
Vol 19 (1) ◽  
pp. 21-44
Author(s):  
Dorothea Alewell ◽  
Karla Brinck ◽  
Tobias Moll
Keyword(s):  
Job Satisfaction ◽  
Large Scale ◽  
Workplace Spirituality ◽  
Black Box ◽  
Research Field ◽  
Need Satisfaction ◽  
Mediation Model ◽  
Large Scale Dataset ◽  
Open Question ◽  
Satisfaction Relationship

Although research has established a positive link between spirituality or religiousness and job satisfaction, this influence’s pathways remain a ‘black box’. Whether it is an effect of a trait- relationship or of a need-satisfaction-relationship remains an open question. Additionally, data and results for West European countries are largely missing. Following King and Williamson (2005), and with a large-scale dataset for Germany (N = 2,551), we empirically assess the link between religiousness and job satisfaction, considering individual employees’ desire to express religiousness and actual expression at work in a serial mediation model, scrutinizing also the influences of discrimination experiences and perceived employers’ stances on religiousness at work. Results strongly support the needs-satisfaction perspective, implying high relevance of workplace spirituality for human resource management (HRM) but also of the research field of management, spirituality and religion in general. Contrary to our expectations, experiences of religious-based discrimination and the perception of a negative employer stance influence the desire to express religiousness at work and de facto expressions positively.

scholarly journals End-to-End Transition-Based Online Dialogue Disentanglement

2020 ◽  
Author(s):  
Hui Liu ◽  
Zhan Shi ◽  
Jia-Chen Gu ◽  
Quan Liu ◽  
Si Wei ◽  
...  
Keyword(s):  
Large Scale ◽  
Online Algorithm ◽  
Research Field ◽  
Semantic Coherence ◽  
Large Scale Dataset ◽  
End To End ◽  
Sequential Information ◽  
Almost All ◽  
Classification And Clustering ◽  
Sequential Semantics

Dialogue disentanglement aims to separate intermingled messages into detached sessions. The existing research focuses on two-step architectures, in which a model first retrieves the relationships between two messages and then divides the message stream into separate clusters. Almost all existing work puts significant efforts on selecting features for message-pair classification and clustering, while ignoring the semantic coherence within each session. In this paper, we introduce the first end-to- end transition-based model for online dialogue disentanglement. Our model captures the sequential information of each session as the online algorithm proceeds on processing a dialogue. The coherence in a session is hence modeled when messages are sequentially added into their best-matching sessions. Meanwhile, the research field still lacks data for studying end-to-end dialogue disentanglement, so we construct a large-scale dataset by extracting coherent dialogues from online movie scripts. We evaluate our model on both the dataset we developed and the publicly available Ubuntu IRC dataset [Kummerfeld et al., 2019]. The results show that our model significantly outperforms the existing algorithms. Further experiments demonstrate that our model better captures the sequential semantics and obtains more coherent disentangled sessions.

scholarly journals One does not simply track students: the relationship between teachers’ perceived public track regard and their job satisfaction in a context of rigid tracking

2021 ◽  
Author(s):  
Lorenz Dekeyser ◽  
Mieke Van Houtte ◽  
Charlotte Maene ◽  
Peter A.J. Stevens
Keyword(s):  
Job Satisfaction ◽  
General Education ◽  
Large Scale ◽  
Social Categorization ◽  
The Self ◽  
Categorization Theory ◽  
School Tracking ◽  
Large Scale Dataset ◽  
Self Reports ◽  
The Relationship

AbstractAlthough there is a wealth of research on the educational and broader outcomes of tracking in education, there is virtually no research that investigates teachers’ track identities on such outcomes. Building on research that focuses on the determinants of teachers’ job satisfaction, tracking outcomes and social categorization theory, this study tests the relationship between the perceived public regard of a teachers’ track and their job satisfaction, in a Belgian context of within- (vocational, technical and general education tracks) and between-school tracking (multilateral versus categorical schools). Data of the Belgian SIS (School, Identity and Society)-survey, a large-scale dataset gathered in 2017, containing the self-reports of 324 teachers, clustered in 43 secondary schools is used to test particular hypotheses regarding this relationship. The results of a multilevel analysis show that the relationship between teachers’ public track regard and their job satisfaction varies according to the track they teach and whether they work in a categorical or multilateral school. The findings highlight the importance of carrying out further research on tracked identities in education.

Exploring personality variables as boundary conditions of the justice-job satisfaction relationship

2009 ◽  
Author(s):  
Sabrina Volpone ◽  
Cristina Rubino ◽  
Ari A. Malka ◽  
Christiane Spitzmueller ◽  
Lindsay Brown
Keyword(s):  
Job Satisfaction ◽  
Boundary Conditions ◽  
Personality Variables ◽  
Satisfaction Relationship

scholarly journals Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

2021 ◽  
Author(s):  
Jin Zhou ◽  
Qing Zhang ◽  
Jian-Hao Fan ◽  
Wei Sun ◽  
Wei-Shi Zheng
Keyword(s):  
Large Scale ◽  
Assessment Model ◽  
Generic Model ◽  
Small Subset ◽  
Deep Convolutional Neural Networks ◽  
Personal Taste ◽  
Hinge Loss ◽  
Novel Approach ◽  
Large Scale Dataset ◽  
Image Pairs

AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.

scholarly journals VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

2021 ◽  
Vol 7 (3) ◽  
pp. 50
Author(s):  
Anselmo Ferreira ◽  
Ehsan Nowroozi ◽  
Mauro Barni
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Child Pornography ◽  
Forensic Analysis ◽  
Synthetic Image ◽  
Image Detection ◽  
Face Images ◽  
Large Scale Dataset ◽  
Scanned Images ◽  
Analysis Of The Images

The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.

scholarly journals ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

2021 ◽  
Author(s):  
Anil S. Baslamisli ◽  
Partha Das ◽  
Hoang-An Le ◽  
Sezer Karaoglu ◽  
Theo Gevers
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
State Of The Art ◽  
Image Decomposition ◽  
Natural Environments ◽  
Decomposition Algorithms ◽  
Ambient Light ◽  
Fine Grained ◽  
Large Scale Dataset ◽  
Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

scholarly journals Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets

2021 ◽  
Vol 13 (5) ◽  
pp. 905
Author(s):  
Chuyi Wu ◽  
Feng Zhang ◽  
Junshi Xia ◽  
Yichen Xu ◽  
Guoqing Li ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Large Scale ◽  
Binary Classification ◽  
Open Data ◽  
Building Damage ◽  
Attention Mechanism ◽  
Large Scale Dataset ◽  
Data Program ◽  
The Impact ◽  
Post Disaster

The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.

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