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Adequate means for easily viewing, browsing and searching knowledge graphs (KGs) are a crucial, still limiting factor. Therefore, this paper aims to present virtual properties as valuable user interface (UI) concept for ontologies and KGs able to improve these issues. Virtual properties provide shortcuts on a KG that can enrich the scope of a class with other information beyond its direct neighborhood.

Virtual properties can be defined as enhancements of shapes constraint language (SHACL) property shapes. Their values are computed on demand via protocol and RDF query language (SPARQL) queries. An approach is demonstrated that can help to identify suitable virtual property candidates. Virtual properties can be realized as integral functionality of generic, frame-based UIs, which can automatically provide views and masks for viewing and searching a KG.

The virtual property approach has been implemented at Bosch and is usable by more than 100,000 Bosch employees in a productive deployment, which proves the maturity and relevance of the approach for Bosch. It has successfully been demonstrated that virtual properties can significantly improve KG UIs by enriching the scope of a class with information beyond its direct neighborhood.

SHACL-defined virtual properties and their automatic identification are a novel concept. To the best of the author’s knowledge, no such approach has been established nor standardized so far.

Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.

This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).

In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.

(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.

Gender and geographical imbalance in production and impact levels is a pressing issue in global knowledge production. Within Health Sciences, while some studies found stark gender and geographical biases and inequalities, others found little empirical evidence of this marginalization. The purpose of the study is to clear the ambiguity concerning the topic.

Based on a comprehensive and systematic analysis of Health Sciences research data downloaded from the Scival (Scopus/Scimago) database from 2017 to 2020 (n = 7,990), this study first compares gender representation in research productivity, as well as differences in terms of citation per document, citations per document view and view per document scores according to geographical location. Additionally, the study clarifies whether there is a geographic bias in productivity and impact measures (i.e. citation per document, citations per document view and view per document) moderated by gender.

Results indicate that gender inequalities in productivity are systematic at the overall disciplinary, as well as the subfield levels. Findings also suggest statistically significant geographical differences in citation per document, citations per document view, and view per document scores, and interaction effect of gender over the relation between geography and (1) the number of citations per view and (2) the number of views per document.

This study contributes to scientometric studies in health sciences by providing insightful findings about the geographical and gender bias in productivity and impact across world regions.

Multi-view fuzzy clustering algorithms are not widely used in image segmentation, and many of these algorithms are lacking in robustness. The purpose of this paper is to investigate a new algorithm that can segment the image better and retain as much detailed information about the image as possible when segmenting noisy images.

The authors present a novel multi-view fuzzy c-means (FCM) clustering algorithm that includes an automatic view-weight learning mechanism. Firstly, this algorithm introduces a view-weight factor that can automatically adjust the weight of different views, thereby allowing each view to obtain the best possible weight. Secondly, the algorithm incorporates a weighted fuzzy factor, which serves to obtain local spatial information and local grayscale information to preserve image details as much as possible. Finally, in order to weaken the effects of noise and outliers in image segmentation, this algorithm employs the kernel distance measure instead of the Euclidean distance.

The authors added different kinds of noise to images and conducted a large number of experimental tests. The results show that the proposed algorithm performs better and is more accurate than previous multi-view fuzzy clustering algorithms in solving the problem of noisy image segmentation.

Most of the existing multi-view clustering algorithms are for multi-view datasets, and the multi-view fuzzy clustering algorithms are unable to eliminate noise points and outliers when dealing with noisy images. The algorithm proposed in this paper has stronger noise immunity and can better preserve the details of the original image.

Chen, Mathieu and Bliese (this volume) propose a useful framework for conceptualizing, testing, and validating multi-level constructs. Their framework focuses on the differences in constructs that occur between individuals and groups. One key question arises with their approach: What happens if the validity of constructs is viewed as potentially varying not only between individuals and between groups but also within individuals and within groups? The focus on within-individuals and within-groups variations is called “frog-pond effects.” Based on such frog-pond effects, this chapter reconsiders the approach of Chen et al. and discusses some of the implications of adding this perspective to multi-level research.

Individuals are often stigmatized by virtue of their status on various dimensions and as a consequence, they typically evoke negative cognitions, affect, and emotions among observers. In addition, they are often the targets of both access and treatment discrimination in organizations. Thus, we present a model of the cognitive, affective, and cultural influences on stigmatization in organizations, detail how stigmatization affects human resource management processes and practices, and consider strategies that can be used to reduce the problems faced by stigmatized individuals in organizations.

The provision of value, as a marketing issue, is receiving increasing attention from managers and scholars. This attention, in combination with strong calls for better quantification and stronger measures in marketing, has lead to increased interest in the assessment, quantified where possible, of the provision of value through buyer–seller relationships. This paper identifies dimensions of value provision through relationships in business markets with specific emphasis on the intangible aspects of value, which are important to long-term competitive advantage. The provision of value to the seller is the prime focus in this paper. The paper discusses the meaning of both tangible and intangible relationship value and the interplay between them and notes the importance of assessing the intangible part of the value, particularly the part which derives from the human aspects of the relationship. Despite their importance, the human aspects of relationships and their contribution to value is a sparse topic among researchers. The paper compares and evaluates potentially useful relationship and value conceptualizations. The paper discusses studies of relationship value and then outlines the results of a recent line of empirical research into the provision of value by a buyer to a seller that utilizes a framework synthesized from the intellectual capital literature. This recent research conceptualizes the potential for a seller's relationship with a buyer to provide intangible value to the seller in terms of, first, the resources available in the buyer and second, the capabilities of the buyer's boundary personnel to aid in facilitating the flow of those resources to the seller. The paper also includes the softer human aspects in the dimensions of value. These latter aspects are important to a full assessment of value. The paper concludes with a discussion of aspects of intangible relationship value that need further elucidation and will thus provide opportunities for future research.

The essential investments in new product development (NPD) made by industrial companies entail effective management of NPD activities. In this context, performance measurement is one of the means that can be employed in the pursuit of effectiveness.