Contributions in Merlin

Comprehending communication is dependent on analyzing the different modalities of conversation, including audio, visual, and others. This is a natural process for humans, but in digital libraries, where preservation and dissemination of digital information are crucial, it is a complex task. A rich conversational model, encompassing all modalities and their co-occurrences, is required to effectively analyze and interact with digital information. Currently, the analysis of co-speech gestures in videos is done through manual annotation by linguistic experts based on textual searches. However, this approach is limited and does not fully utilize the visual modality of gestures. This paper proposes a visual gesture retrieval method using a deep learning architecture to extend current research in this area. The method is based on body keypoints and uses an attention mechanism to focus on specific groups. Experiments were conducted on a subset of the NewsScape dataset, which presents challenges such as multiple people, camera perspective changes, and occlusions. A user study was conducted to assess the usability of the results, establishing a baseline for future gesture retrieval methods in real-world video collections. The results of the experiment demonstrate the high potential of the proposed method in multimodal communication research and highlight the significance of visual gesture retrieval in enhancing interaction with video content. The integration of visual similarity search for gestures in the open-source multimedia retrieval stack, vitrivr, can greatly contribute to the field of computational linguistics. This research advances the understanding of the role of the visual modality in co-speech gestures and highlights the need for further development in this area.

The aim of our study was twofold: (1) to explore the role of value preferences on peer relations in school classes (selection effect) and (2) to explore the role of peers’ values on adolescents’ values (influence or socialization effect) in three types of networks (friendship, advice, and trust). To answer these questions, we used a longitudinal social network approach in a study of N = 903 adolescents (57% girls) from 34 secondary school classes in Poland. Pupils began participating in the study when they joined their secondary school and were followed over two and a half years. Panel data were collected at six measurement time points during this period. Values were conceptualized according to the values theory proposed by Schwartz and measured by the Portrait Value Questionnaire. The collection of network data followed a roster design. Pupils were asked to evaluate the strength of their friendships, as well as the frequency with which they approached peers to ask for advice about school or homework or to talk about things that are important to them in the last 2 weeks. We found empirical support for both selection and socialization effects, especially for protection values (Conservation and Self-enhancement). The selection effect was most evident in advice and trust networks and the socialization effect was particularly prevalent in friendship and trust networks.

Despite the considerable magnitude of negative brand personalities in the marketplace, prior research has disproportionately focused on positive personality traits. To address this research gap, the authors present a conceptual and empirical approach that draws from the Dark Triad of psychology and applies it to the branding domain. They conceptualize and validate the Dark Triad of brand personality which comprises brand narcissism, Machiavellianism, and psychopathy dimensions. Through a multiphase scale development process, a reliable and valid 12‐item brief version of the Dark Triad of brand personality is created, enabling its assessment in both research and management contexts. Examining the Dark Triad in branding is crucial as it provides a unique lens to understand the rise of negative, dark brand personalities. It accounts for brand personality aspects not yet captured by existing scales, including manipulation, exploitation, grandiosity, and lack of empathy. This introduction of the Dark Triad brand personality opens new avenues for research into brand transgressions and ethics in brand management. In terms of managerial implications, insights from this research can inform strategic brand management, enabling companies to better manage their brand's image.

Due to the explosive growth of social media technology worldwide, consumers are exposed to abundant stimuli across cultures that affect their internalization of societal ideal of beauty and the formation of self‐concept. In response to this, the beauty industry is facing challenges to personalize their offerings to an array of diverse consumers who are seeking brands that resonate with their values and foster a true emotional bond. Consumers' personal value with respect to beauty is an important antecedent of the internalization of societal ideal of beauty, which eventually control their appearance‐conscious emotions and behaviors, thereby play an important role for understanding the psychological mechanisms that underlie diverse beautification procedures. However, a systematic scale to measure personal beauty values of consumers across cultures has yet to be established. In this article, we attempt to bridge this gap by developing, measuring, and validating a new Personal Beauty Values Scale through a series of studies using independent samples from the United States (n = 348, n = 1039), the United Kingdom (n = 401, n = 396), Japan (n = 1011), and Denmark (n = 981). Subsequently, we investigate influences of personal beauty values on one of the critical beautification procedures invasive to the human body, that is, cosmetic surgery. Specifically, the nomological validation using the U.S. sample (n = 1039) demonstrated that the distinct characteristics of the five personal beauty values dimensions differently affected appearance‐conscious emotions such as shame and hubristic pride, thereby unveiling the psychological mechanism behind consideration of cosmetic surgery.

In this paper, we present the Informfully Dataset with Enhanced Attributes (IDEA) for news article recommendations. The dataset consists of an open-source collection of user profiles, news articles with a high topic and outlet diversity, item recommendations, and rich user-item interactions from a field study on behavioral changes in news consumption. The records include both quantitative data from real-time session tracking as well as self-reported data from user surveys on satisfaction with news, knowledge acquisition, and personal background information. This paper outlines the data collection procedure and potential use cases of the dataset for designing normative recommender systems. It provides the documentation of all data collections together with insights into the data quality.

This paper presents Informfully, a research platform for content distribution and user studies. Informfully allows to push algorithmically curated text, image, audio, and video content to users and automatically generates a detailed log of their consumption history. As such, it serves as an open-source platform for conducting user experiments to investigate the impact of item recommendations on users' consumption behavior. The platform was designed to accommodate different experiment types through versatility, ease of use, and scalability. It features three core components: 1) a front end for displaying and interacting with recommended items, 2) a back end for researchers to create and maintain user experiments, and 3) a simple JSON-based exchange format for ranked item recommendations to interface with third-party frameworks. We provide a system overview and outline the three core components of the platform. A sample workflow is shown for conducting field studies incorporating multiple user groups, personalizing recommendations, and measuring the effect of algorithms on user engagement. We present evidence for the versatility, ease of use, and scalability of Informfully by showcasing previous studies that used our platform.

Security management of Internet-of-Things (IoT) infrastructures encompassing the entire lifecycle of products and their continuous certification are fundamental functions to guarantee a high level of security. This paper demonstrates a DLT-based platform enabling secure inventorying and management of IoT devices and sensors distributed across multiple stakeholders. This work makes use of the Hyperledger framework, Decentralized Identifiers (DID), and Manufacturer Usage Description (MUD) files for IoT device inventorying and the identification of critical software updates.

We study the market’s reaction to the disclosure of EU-wide stress test results across four periods (2014, 2016, 2018, and 2021). Our novel approach contributes to the literature by studying how stress test disclosures influence both the mean and variance (first and second moments) of bank stock performance, extending beyond previous studies focused mainly on the first moment of equity returns. Using one-factor market and structural Engle–Siriwardane type GARCH models, we find that the publication of stress tests provides new information to the markets: Banks with weaker performance in the tests experience, on average, a reduction in stock returns and an increase in volatility, while the reverse holds for banks performing well. Our findings confirm the important role of stress tests in enhancing transparency and market discipline, thereby supporting investors in assessing the resilience of the banking sector more effectively, particularly during periods of higher uncertainty.

In einem jährlich auf www.swissauditmonitor.ch veröffentlichten Report werden die Revisionshonorare, die zusätzlichen Honorare, die Mandatsdauer der Revisionsgesellschaften und die verwendeten Rechnungslegungsstandards von Unternehmen des Swiss Market Index (SMI) und des Swiss Performance Index (SPI) erhoben und ausgewertet. Der 8. Ausgabe des Swiss Audit Monitor liegt das Geschäftsjahr 2023 zugrunde. Dieser Beitrag fasst die wichtigsten Erkenntnisse zusammen.

Is deficit finance free when real borrowing rates are routinely lower than growth rates? Specifically, can the government make all generations better off by perpetually taking from the young and giving to the old? We study this in stochastic closed- and open-economy OLG models. Unfortunately, Pareto gains are predicted only for implausible calibrations. Even then, the gains reflect improved inter-generational risk sharing, improved international risk sharing, and beggaring thy neighbor-not intergenerational redistribution, per se. As we show, theoretically and quantitatively, low government borrowing rates suggest state-contingent bilateral transfers between generations-not unconditional, unilateral redistribution from future to current generations.