Contributions in Merlin

In this paper we examine middle managers’ struggle over their subject position as strategists in the context of participative strategy processes. Based on a longitudinal case study of a company undertaking an Open Strategy process, we show how the wider inclusion of front-line employees in developing new strategy undermines the traditional subject position of middle managers. Based on these findings, we develop a process model depicting the recursive dynamics of middle managers’ struggles to maintain their subject positions in the face of employee participation. With these findings we contribute to the literature on middle managers by advancing our understanding of the implications of employee participation for middle managers’ subject position as strategists and their different ways of reclaiming their subject position. We also contribute to the literature on Open Strategy by revealing the implications for traditional strategy actors as well as by explaining the processual dynamics of participation over time.

The internal ratings-based (IRB) approach maps bank risk profiles more adequately than the standardized approach. After switching to IRB, banks’ risk-weighted asset (RWA) densities are thus expected to diverge, especially across countries with different supervisory strictness and risk levels. However, when examining 52 listed banks headquartered in 14 European countries that adopted the IRB approach, we observe a convergence of their RWA densities over time. We test whether this convergence can be entirely explained by differences in the size of the banks, loss levels, country risk, and/or time of IRB implementation. Our findings indicate that this is not the case. Whereas banks in high-risk countries with lax regulation, reduce their RWA densities, banks elsewhere increase theirs. Especially for banks in high-risk countries, RWA densities underestimate banks’ economic risk. Hence, the IRB approach enables regulatory arbitrage, whereby authorities only enforce strict supervision on capital requirements if they do not jeopardize bank viability.

The use of drones (or Unmanned Aerial Vehicles) in urban areas has emerged rapidly in the last decade, and continues to expand at an accelerating pace. Alongside the emergent uses of high-impact technology in both public and private sectors, political debates about the potential risks and challenges have arisen, encompassing diverse perspectives and attitudes about the ethical, legal, social, and regulatory implications of introducing and integrating new technology in society. This scoping review offers an assessment of the societal acceptance factors of urban drones discussed in the current academic literature. We used a hybrid approach including quantitative landscape mapping and qualitative content analysis of the selected articles to inductively develop a typology of acceptance factors associated with urban use of drones. This review illuminates areas that have been the focus of attention within the current body of knowledge (e.g., visual and noise pollution of drones), sketches the evolution of the relevant discussions over time (e.g., a focus on the safety of the drone technology toward safety of the cargo it carries and security of the data it collects), and points to areas that have received less considerations (e.g., media appropriation and social group influence). It can, thus, help situate the topic of societal acceptance of urban drones in specific contexts, and orient future research on promoting value sensitive innovation in society more broadly.

In Industry 4.0, security begins with the workers’ authentication, which can be done individually or in groups. Recently, group authentication is gaining momentum, allowing users to authenticate as group members without the need to specify the particular individual. Continuous authentication and federated learning are promising techniques that might help group authentication by providing privacy, by its own design, and extra security compared to traditional methods based on passwords, tokens, or biometrics. However, these techniques have not previously been combined or evaluated for authenticating workers in Industry 4.0. Thus, this paper proposes a novel continuous group authentication privacy-preserving (CGAPP)platform that is suitable for the industry. The CGAPP platform incorporates statistical data from workers’ smartphones and employs federated learning-based outlier detection for group worker authentication while ensuring the privacy of personal data vectors. A series of experiments were performed to measure the framework’s suitability and address the following research questions: (i) What is the cost of using FL compared to full data access in industrial scenarios? (ii) How robust is federated learning against adversarial attacks, specifically, how much malicious data is required to deceive the model? and (iii) How much noise is required to disrupt the authentication system? The results demonstrate the effectiveness of the CGAPP platform in the industry since it provides factory safety while preserving privacy. This platform achieves an accuracy of 92%, comparable to the 96% obtained by traditional approaches in the literature that do not address privacy concerns. The platform’s robustness is tested against attacks in the second and third experiments, and various countermeasures are evaluated. While the CGAPP platform exhibits certain vulnerabilities to data injection attacks, straightforward countermeasures can alleviate them. Nevertheless, the system’s performance experiences a notable impact in the event of a data perturbation attack, and the countermeasures investigated are ineffective in addressing this issue.

With a rising frequency and scale, Distributed Denial-of-Service (DDoS) attacks persist as a critical cybersecurity issue. While shared attack fingerprints aid many intrusion detection systems in identifying threats, their application for DDoS attacks remains limited due to their distinct nature. However, fingerprints observed from multiple locations can provide valuable insights. This paper presents Reassembler, a novel platform for achieving a global DDoS attack analysis using attack fingerprints recorded from various locations. Reassembler consolidates these fingerprints into a unified view allowing to obtain a global overview of DDoS attacks. The evaluation, conducted on four simulated scenarios, demonstrates Reassembler's ability to extract novel properties, such as the count of intermediate nodes and the estimated percentage of spoofed IPs.

As researchers strive to narrow the gap between machine intelligence and human through the development of artificial intelligence multimedia technologies, it is imperative that we recognize the critical importance of trustworthiness in open-world, which has become ubiquitous in all aspects of daily life for everyone. However, several challenges may create a crisis of trust in current open-world artificial multimedia systems that need to be bridged: 1) Insufficient explanation of predictive results; 2) Inadequate generalization for learning models; 3) Poor adaptability to uncertain environments. Consequently, we explore a neural program to bridge trustworthiness and open-world learning, extending from single-modal to multi-modal scenarios for readers.1) To enhance design-level interpretability, we first customize trustworthy networks with specific physical meanings; 2) We then design environmental well-being task-interfaces via flexible learning regularizers for improving the generalization of trustworthy learning; 3) We propose to increase the robustness of trustworthy learning by integrating open-world recognition losses with agent mechanisms. Eventually, we enhance various trustworthy properties through the establishment of design-level explainability, environmental well-being task-interfaces and open-world recognition programs. As a result, these designed open-world protocols are applicable across a wide range of surroundings, under open-world multimedia recognition scenarios with significant performance improvements observed.

Decentralized finance (DeFi) promises a revolution in financial accessibility, transparency, and automation. Yet, its very novelty exposes participants to a number of additional risks and challenges. This study aims to address the risks associated with DeFi, while also conducting a comparative analysis to those of classical/traditional finance (TradFi). After introducing DeFi and its defining characteristics, such as the use of smart contracts, blockchain technology, and decentralized governance, the paper outlines the principal risks associated with DeFi. Drawing insights from an extensive literature review of 200 recent articles, of which 50 were thoroughly analyzed, the study compares risks of DeFi and TradFi, categorizing these into systematic and unsystematic risks. Furthermore, we introduce the ‘risk wheel’, an innovative tool tailored to understand and navigate the subtleties of DeFi risks, finding potential applications in risk assessment, management, and even education. This paper’s primary objective is to provide a detailed and impartial examination of the risks associated with DeFi and their comparison to traditional finance in order to assist stakeholders in making informed decisions and mitigating possible losses.

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