Population Diversity and Economic Growth: A Meta‐Regression Analysis

peer reviewedIn this study, we apply meta‐regression techniques to 1537 estimates derived from 83 studies that investigate the effect of population diversity on economic growth. We find a subtle inclination toward publishing results that assert a negative correlation between diversity and economic growth, indicating a mild publication bias. However, the extent and direction of this bias vary according to the specific diversity dimension considered. After adjusting for both publication bias and the methodological quality of the underlying studies, our results indicate that while ethnic and linguistic diversity exhibit a small and statistically insignificant positive effect on economic growth, the remaining diversity dimensions—namely religious, genetic, birthplace, and other forms of diversity—exert a significant positive impact on growth, with effect sizes ranging from moderate to large. Additionally, our findings reveal that the reported estimates are influenced by several factors, including the methodologies employed by researchers in measuring economic growth and diversity, the characteristics of the data and estimation techniques utilized, and the consideration of other growth‐related factors

Cloud-Assisted 360-Degree 3D Perception for Autonomous Vehicles Using V2X Communication and Hybrid Computing

peer reviewedA key challenge for autonomous driving lies in maintaining real-time situational awareness, particularly in complex urban settings. This study introduces an innovative cloud-driven solution for 360-degree perception in autonomous vehicles using vehicle-to-everything (V2X) communications. Our approach utilizes transformer-based models to fuse multi-camera sensor data into a comprehensive bird’s-eye view (BEV) representation, enabling accurate 3D object detection. By offloading computationally intensive tasks to the cloud, the system achieves scalable processing while reducing latency. In addition, techniques such as feature vector clipping, compression, and quantization are applied to optimize data transmission, ensuring real-time performance. Experimental results show an over 79.2% reduction in end-to-end delay compared to onboard-only computing. These experiments validate the integration of AI and V2X technology to enhance autonomous vehicle perception.Infrastructure Assisted Cooperative Driving Strategy For Connected Vehicles (Acdc)9. Industry, innovation and infrastructur

The Science of Desire: Beauty, Masculinity, and Ideology on the Far Right

peer reviewedScores of male right-wing influencers offer advice to young men online on fitness, diet, and bodybuilding. Representations of the “right” kind of man draw attention to rippling muscles, square jaws, and beautifully symmetrical faces as evidence of racial superiority. This contemporary resurgence of “body fascism” in the hypersemiotized online spaces of the far right, however, remains underexamined. In this article, we analyze Man’s World magazine, a digital publication edited by the neofascist lifestyle influencer “Raw Egg Nationalist.” Through gendered semiotic and linguistic anthropological analysis of the text, we argue that hardness, understood in myriad ways, is the moral flavor of a far-right masculinist speech register that combines elements of mental fortitude, muscular strength, sexual potency, and physical beauty at the individual level with racial renewal and national invulnerability at the political level. We show how readiness for violence and the “return” to traditional masculine violence are legitimated through graftings onto scientific and academic registers, and how neofascist influencers ultimately operate within boundaries delimited by neoliberal modernity. We argue that the production of a “dissident” right-wing male subjectivity is intimately interwoven with the dissemination and use of this register.</p

Comprehensive Analysis of CYGNSS GNSS-R data for Enhanced Soil Moisture Retrieval

peer reviewe

The BENELUX, Regional Groupings and the Dynamics of European Integration: Contemporary and Historical Perspectives

Regional (sub-)groupings have played a significant, but comparatively neglected role in the processes of European integration. The BENELUX and the Nordic Council both offer longstanding models of regional cooperation which have, in various ways, often been cited as examples for the wider European integration project. More recently, both the Visegrád and Baltic states have seen the (re-) emergence of forms of regional cooperation in connection with their accession to and later membership of the European Union. Yet, though often cited, these experiences of (sub-)regional cooperation within the wider European project have been the object of relatively little systematic or comparative study. The aim of the edited volume is to address this gap by bringing together specialists on these regional groupings with a view to providing a fuller understanding of both their historical significance and their possible future role relative to a potentially fragmenting European political landscape

Potential of AI for User-Centric Cybersecurity in the Financial Sector

peer reviewedThe use of cybersecurity tools powered by artificial intelligence (AI) continues to gain traction in the financial services industry. On the one hand, they can strengthen an organization’s technical cybersecurity posture. On the other hand, even if cybercriminals also leverage AI to exploit human weaknesses, there are early indications that AI can help equip the workforce against evolving threats. Based on a structured literature review (SLR) and a Delphi study, this article identifies the most promising end-user-focused use cases in which AI can assist financial institutions in combating cybersecurity threats and gearing their workforce up to thwart cyberattacks. For information security executives and researchers alike, this study provides a first set of general directions on which AI-powered and user-centric tools and solutions to focus on in the near future.R-AGR-3728 - PEARL/IS/13342933/DFS - FRIDGEN GilbertU-AGR-7503 - NCER22/IS/16570468/NCER-FT_CryptoReg_UL - FRIDGEN Gilbert4. Quality education9. Industry, innovation and infrastructur

Remote secure object authentication: Secure sketches, fuzzy extractors, and security protocols

peer reviewedCoating objects with microscopic droplets of liquid crystals makes it possible to identify and authenticate objects as if they had biometric-like features: this is extremely valuable as an anti-counterfeiting measure. How to extract features from images has been studied elsewhere, but exchanging data about features is not enough if we wish to build secure cryptographic authentication protocols. What we need are authentication tokens (i.e., bitstrings), strategies to cope with noise, always present when processing images, and solutions to protect the original features so that it is impossible to reproduce them from the tokens. Secure sketches and fuzzy extractors are the cryptographic toolkits that offer these functionalities, but they must be instantiated to work with the peculiar specific features extracted from images of liquid crystals. We show how this can work and how we can obtain uniform, error-tolerant, and random strings, and how they are used to authenticate liquid crystal coated objects. Our protocol reminds an existing biometric-based protocol, but only apparently. Using the original protocol as-it-is would make the process vulnerable to an attack that exploits certain physical peculiarities of our liquid crystal coatings. Instead, our protocol is robust against the attack. We prove all our security claims formally, by modeling and verifying in Proverif, our protocol and its cryptographic schemes. We implement and benchmark our solution, measuring both the performance and the quality of authentication

Adaptation of quizzing in learning psychology concepts

peer reviewedBackground: In the domain of psychology, declarative concepts are a core component of the foundational knowledge that is to be learned. A promising means to enhance retention and comprehension of such concepts is to provide learners with open-ended quiz questions and corrective feedback (i.e., practice quizzing). As adapting quiz question complexity to the individual learners can increase the benefits of practice quizzing, in previous research adaptations based on the real-time process measures of cognitive load ratings and of self-assessed quizzing performance during quizzing have been developed. To date, however, it is unclear whether and, if so, why the two types of adaptation differ in their effectiveness. Aims: The main goal of the present study was to compare the two adaptation mechanisms in learning declarative psychology concepts via practice quizzing. Sample: Participants were N = 177 university students. Methods: After watching an e-lecture on new declarative psychology concepts, the learners were randomly assigned to either note-taking or to responding to quiz questions. The complexity of the quiz questions was increased either according to a preset sequence, or dependent on subjective cognitive load, self-assessed quizzing performance, or both. Results: Cognitive-load-adapted quizzing was most effective. These benefits were mediated via higher levels of knowledge whenever increases in quiz question complexity were suggested by the adaptation mechanism/took place in the preset sequence, which fostered quizzing performance, which, in turn, fostered learning outcomes. Conclusions: This study shows that simple cognitive load ratings are a promising basis for adapting practice quizzing in learning declarative psychology concepts.4. Quality educatio

CAPITALIZING ON TEXTUAL DATA TO PREDICT SME’s MULTIDIMENSIONAL RISK

This thesis examines how various data sources impact the risk profiles of small and medium-sized enterprises (SMEs) in Luxembourg. Effective risk analysis and prediction is crucial for societal development, as it enhances decision-making processes related to the allocation of financial resources. By identifying and managing risks, businesses can make informed investment choices, fostering growth and optimizing returns. This contributes not only to the success of individual enterprises but also to broader economic stability and development. Most current risk prediction methods rely on financial statements, ratios, and other numerical data. Some researchers also analyze short text pieces, such as tweets, news articles, and headlines. Additionally, certain studies focus on specific sections of annual reports that discuss risk evaluation. However, these approaches are often limited to large companies. Financial data is often seen as providing a limited perspective on a company's risk level. Therefore, it is essential that additional variables influencing risk be considered. Many factors within a business can contribute to risk. In this context, based on the data that can be collected, extracted, and generated, potential risk information will be derived from text-based insights found in annual reports, people networks, and geographic location. The proposed multidimensional risk model incorporates diverse information from reliable sources, such as the Luxembourgish Business Registry (LBR). The current work is presented following a data pipeline process. Information is extracted using data provided by the partner company, with textual content obtained from various official company documents through OCR and PDF reading tools. Relationships between companies, audit firms, auditors, and notaries are created using extracted information from textual sources and additional datasources. The proposed Long-Text BERT model is applied to predict the risk of bankruptcy based on the annexes from annual accounts, while also categorizing pages for subsequent information extraction using a fine-tuned GPT-based model. With a proposed autoclustering algorithm, clusters of hidden accountants or consultancy firms were identified and added in the company people's network. Geolocation is performed using addresses found through information extraction and those registered in the company profile within the LBR, from which latitude and longitude coordinates are obtained. This information is integrated into a graph network, where companies relationships are analyzed to identify various risk factors and complement the text-based risk assessment. As a first outcome of this thesis, a dataset containing both financial and non-financial information was created. This existing data was enriched using NLP tools, and a network of companies and individuals was established. Additionally, valuable insights were extracted from the textual information, achieving approximately 80% precision in risk prediction based solely on the textual data from financial annexes. Companies were also clustered based on the hidden accountant concept. Information from various data sources is integrated using graphs to calculate dimensional risk for each company. An initial user interface has been proposed to enable users to navigate and explore some data more effectively. In conclusion, this thesis successfully developed a data pipeline to process information from Luxembourgish SMEs, leveraging publicly available information. The data was enriched using advanced Machine Learning and Deep Learning techniques to assess company risk from multiple dimensions. This approach provides decision-makers with deeper insights, enabling more informed and strategic decisions. The findings suggest that these models can be adapted for application in other countries or scaled to analyze larger enterprises. Furthermore, the analysis could be significantly enhanced by integrating additional datasources, such as social networks, and employing more sophisticated methods like Graph Neural Networks for data integration.U-AGR-7012 - BRIDGES2020/IS/15403349/SCRiPT_Yoba Cont - BRORSSON Mats Hakan8. Decent work and economic growt

Réseaux sociaux : Projet Monnet: vers un Facebook européen

Les réseaux sociaux façonnent nos opinions, nos interactions et même nos démocraties. Pourtant, leur modèle actuel est vivement critiqué pour ses failles: protection insuffisante des données, biais algorithmiques, dépendance aux plateformes américaines et chinoises, diffusion massive de désinformation…Face à ces dérives, un projet européen ambitionne d’offrir une alternative. Monnet, porté par Christos Floros, vise à repenser les réseaux sociaux en intégrant des valeurs européennes de neutralité, de transparence et de protection des utilisateurs. Un tel projet est-il réalisable? Peut-il réellement rivaliser avec les géants du secteur? Pour mieux comprendre les enjeux, nous avons interrogé Stéphanie Lukasik, chercheuse à l’Université du Luxembourg et coordinatrice du projet Medialux, qui apporte un éclairage sur la nécessité d’une telle initiative.Maison Modern