In the context of enterprise social platforms, user modeling is essential for improving communication strategies, tailoring services, and enhancing engagement. However, traditional profiling approaches often raise concerns related to data privacy, transparency, and regulatory compliance. This study proposes a privacy-compliant user characterization framework that shifts from individual profiling to behavioral characterization, leveraging fuzzy logic. The framework was applied to a real-world case study involving a retail-sector customer of Beekeeper AG, a private enterprise social network, using a dataset of approximately 39,000 users.Using a combination of data anonymization, feature extension, and fuzzy clustering, users were segmented into interpretable behavioral groups based on app interaction patterns. The results demonstrate that the framework enables effective user modeling---capturing relevant usage typologies and behavioral trends---while ensuring alignment with GDPR principles, such as data minimization and user consent. Comparative evaluation against profiling-based baselines revealed that the characterization approach achieved similar levels of confidence in behavioral inference without relying on sensitive or identifiable attributes. This work highlights the potential of privacy-aware fuzzy methodologies to support ethical and effective personalization in enterprise platforms.

Open Government Data (OGD) taxonomies are critical classification artifacts that structure knowledge to facilitate the understanding, analysis, and comparison of complex data and their impacts, yet their evaluation often lacks a consistent methodological foundation. This paper addresses this gap in two ways. First, it introduces a comprehensive framework for evaluating OGD taxonomies, synthesized from foundational and state-of-the-art literature in Design Science Research and Information Systems (IS). This framework provides a structured lens to assess design process rigor, intrinsic artifact quality, and utility. Second, the paper applies this framework in a detailed, systematic evaluation of three prominent and diverse OGD taxonomies. The results of this analysis reveal a clear trend towards more rigorous development methods over time. However, they also expose a common, critical limitation rooted in the classical definition of a taxonomy: their reliance on crisp, mutually exclusive categories struggles to represent the nuanced, overlapping, and ambiguous nature of real-world OGD phenomena. This “problem of crispness” hinders their practical utility and explanatory power. As a solution, we argue for a fundamental shift in perspective. Specifically, we propose adopting fuzzy logic principles, including linguistic variables and fuzzy modifiers, to introduce flexibility and cognitive plausibility into taxonomy design. Finally, we provide a detailed blueprint outlining how such fuzzy-enhanced taxonomies can be methodically designed, implemented, and applied, thereby advancing both theoretical foundations and practical applications in the OGD field. To our knowledge, this is among the first systematic evaluations of OGD taxonomies employing a rigorously derived evaluation framework.

This paper investigates the enhancement of cancer-related information retrieval using transformer models optimized with specialized angular loss functions. We compare BERT, RoBERTa, and GPT-2, trained with ArcFace and our novel FuzzyArcLoss [1], which dynamically adjusts margins based on confidence levels. Experiments conducted on NVIDIA DGX A100 GPUs reveal that FuzzyArcLoss significantly enhances retrieval performance for BERT (MRR: 1.0000, GPT-4 Score: 8.6 at tau = 0.1) and GPT-2 (MRR: 0.5000, GPT-4 Score: 7.0 at tau = 0.5), outperforming ArcFace in these contexts. RoBERTa with ArcFace achieves the highest overall performance (MRR: 1.0000, GPT-4 Score: 7.0), though FuzzyArcLoss remains competitive. We recommend FuzzyArcLoss as the primary algorithm for BERT and GPT-2 due to its adaptability in ranking relevant ontology classes, with ArcFace optimal for RoBERTa. The study discusses the strengths of FuzzyArcLoss in handling variable data and proposes strategies to address GPT-2's limitations using newer autoregressive models. Practical steps for clinical implementation and experimental methodologies for future research are outlined.

The rapidly growing popularity of digital services requires robust frameworks to identify and address associated ethical concerns. This paper presents a structured framework for assessing ethical concerns of digital services, offering a scalable and adaptable tool to assess concerns, including data misuse, cybersecurity, transparency, inequality, and sustainability. The framework employs a customized Delphi method to gather diverse expert insights, translating them into quantifiable metrics through a mathematical model. These metrics inform structured surveys, generating actionable outputs, including visual summaries, static recommendations, and AI-driven insights. To illustrate the framework’s application, we detail its implementation in the context of electronic voting (e-voting). By addressing key ethical challenges, mainly privacy, transparency, and inclusivity, this use case demonstrates the framework’s utility in analyzing complex digital services. The study highlights the importance of balancing technological innovation with ethical accountability, providing a practical approach to ensuring transparency and trust in public digital services.

Ein Paradigmenwechsel ist eine Änderung elementarer Konzepte und experimenteller Praktiken einer wissenschaftlichen Disziplin. Die von Berkeley-Professor Lotfi A. Zadeh vor 60 Jahren eingeführte Erweiterung der Logik kann als solcher Paradigmenwechsel bezeichnet werden.

Public planning decisions affect the living conditions of diverse categories of people differently. Therefore, voters should express their support and/or rejection to each alternative. Since their opinions tend to be subjective, intensities of support and rejection should be collected and processed (rather than binary yes , no voting) to reveal whether an agreement is in favour or against each alternative. However, inconsistent responses (simultaneous high levels of support and rejection) for the same alternative represent a challenge. The next challenge is the different influence of alternatives to diverse citizens subgroups. To address these issues, this work proposes strengthening the consistent answers and weakening the contradictory responses by the convex combination of t-norm and t-conorm function. Next, the impact of coalitions (agreement) among subgroups is formalised by fuzzy measures and Choquet integral, because the impact is different when two of the most affected subgroups or two lightly affected subgroups agree on a specific alternative. In real-life problems through the Traffic Strategy Case Study in the Street of Unterdorf in Geuensee, Lucerne (Switzerland), 13 alternatives were evaluated by four subgroups of voters. Fuzzy measures or weights are assigned to each subgroup and their possible coalitions considering their features. In addition, the sensitivity analysis is performed by Monte Carlo simulation. Finally, topics for future work are outlined.

Despite the advancements in conversational AI, most chatbots fail to adapt dynamically to users' emotions and personalities. This paper presents the Fuzzy Conversational Character Computing (FCCC) framework, which integrates Fuzzy Logic, Computing with Words and Perceptions, and Character Computing to enable chatbots to deliver adaptive, sentiment-sensitive responses. By leveraging Large Language Models (LLMs) and real-time sentiment analysis, FCCC fosters more empathetic and personalized interactions. Through an experimental evaluation in the healthcare domain, we demonstrate that FCCC-enhanced chatbots positively influence user sentiment and satisfaction, outperforming traditional bots in perceived empathy and adaptability. These findings establish FCCC as a break-through in conversational AI, with broad potential for applications in healthcare, customer service, and beyond. Future research will focus on scaling the framework and exploring its integration with advanced AI technologies.

Digital ethics has become increasingly important in our society due to technology’s profound impact on our lives. As we integrate digital technologies into all aspects of life, it’s crucial to understand their ethical implications. This paper reviews studies on digital ethics to identify ethical concerns in public services and introduces a theoretical digital framework to address these issues. An automated algorithm simplifies the review process, and the literature review and a workshop on digital ethics were instrumental in shaping the framework. This paper evaluates digital ethics concerns in the context of electronic voting (e-voting) provided by Swiss Post, which is owned by the Swiss Confederation and offers various public services such as e-voting, digital health, e-government, and banking. The workshop brought together experts to identify ethical concerns related to the e-voting use case, with the results analyzed numerically. Insights from this workshop contributed to the development of the inaugural Swiss Digital Ethics Compass. The results of this work will update the academic community on recent developments and emphasize the importance of digital ethics, particularly in sensitive public services like e-voting. This paper aims to foster further explorations and discussions, emphasizing the profound implications of ethical considerations in our technologically driven world. Future work will include analyzing the impact of digital ethics on other public services.

This academic paper delves into the captivating intersection of life engineering and algorithms, artificial intelligence (AI), social media, and quantitative metrics on human life, through a comprehensive review of three thought-provoking books. In each critical review, the authors add their own thoughts and impressions, as Computer Science graduates and scholars, illustrating the impact that these eye-opening books have on them. The first book, “Weapons of Math Destruction” by Cathy O’Neil, delves into the hidden dangers of algorithmic decision-making. O’Neil uncovers how algorithms can perpetuate discrimination, biases, and unfairness in domains such as education, advertising, criminal justice, employment, and finance, and emphasizes the need for ethical considerations, transparency, and human judgment in algorithmic systems. The second book, “Atlas of AI” by Kate Crawford, takes a multidimensional approach to AI beyond mere algorithms and deep learning. Crawford addresses issues such as labor exploitation, surveillance technologies, classification systems, wealth concentration, and environmental consequences due to AI. The book calls for responsible and ethical considerations in the development and usage of AI. Shoshana Zuboff’s “The Age of Surveillance Capitalism” is the third book, focusing on the pervasive influence of tech giants like Google and Facebook. Zuboff exposes the dynamics of surveillance capitalism, wherein personal data is extracted and exploited for economic gains. The book illuminates how this form of capitalism erodes privacy, reshapes societal structures, and challenges democratic norms. Illustrating the essence of these disruptive narratives and the tense dialogue taking place between ethicians or scholars and technology developers, this research examines the profound social, economic, and environmental implications brought forth by these transformative technologies. Ultimately, the paper advocates for the embrace of responsible and ethical technology development that not only safeguards the well-being of individuals but also fosters a harmonious coexistence between humans and machines amidst the winds of disruption.