Abstract

Abstract
The growing availability of online data creates new opportunities to improve the timeliness and detail of official statistics, particularly in domains such as price monitoring and inflation measurement. However, leveraging web-scraped data for official use requires alignment with standardized classification frameworks such as the European Classification of Individual Consumption According to Purpose (ECOICOP). We train two natural-language models, a lightweight convolutional neural network (CNN) and a fine-tuned BERTimbau transformer, to classify Portuguese food and beverage items into ECOICOP categories. Using 100,000 product titles scraped from six national supermarket sites and labeled via a human-in-the-loop workflow, the CNN reaches a macro-F1 of 92.19 % with minimal computing cost, while the transformer attains 94.00 %, the first such result for Portuguese. Both models are published on Hugging Face, enabling reproducible inference at scale while the source data remain confidential. The study delivers the first open-source Portuguese ECOICOP classifiers for food and beverage products, a replicable low-resource labeling workflow, and a benchmark of accuracy-speed trade-offs to guide researchers in similar tasks.

Abstract
Abstract Misinformation poses one of the most urgent challenges of our society and raises the question of how to deal with it and manage its rapid spread. To address this problem, a promising approach relies on AI-based misinformation detection. This chapter of the book offers a critical analysis of the ethical implications associated with the design, deployment, and use of misinformation detectors (MDs). Designing and deploying an MD—an AI system that automatically identifies misinformation—is a complex undertaking that requires an interdisciplinary approach, as the challenges faced by MD designers and deployers encompass not only technical aspects, but also linguistic, sociological, political, and especially ethical dimensions. Our analysis is ethics-oriented and follows two main lines of inquiry: (1) Ethics by Design, which focuses on issues related to the design process of an MD, and (2) Ethics of Impact, which addresses the intended and unintended effects of MD deployment and use.

Abstract
Education 5.0 is a new paradigm in education posing many challenges and opportunities. This paper uses qualitative methods to explore students’ and teachers’ experiences with online learning to understand the challenges, benefits, and vision for a successful blended learning model, proposing a dynamic framework for blended learning. Results of in-depth interviews show the three main challenges of blended learning: pedagogical design, technological design, and environment/ setup design. Finally, the study discusses insights into future directions for developing Education 5.0, including the need for ongoing research, collaboration communities, curricula personalization, and innovation in the field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
This study examines the scientific contributions of the Higher Institute of Accounting and Administration at the University of Aveiro (ISCA-UA) from 2019 to 2022, focusing on how these align with Education 5.0 and Society 5.0 goals. Using a case study approach, data were collected from institutional records, analyzing publications by type and thematic focus, emphasizing areas that promote societal well-being, multiliteracy, and educational innovation. The methodology involves a mixed-methods approach: quantitative analysis assesses publication trends, distribution by faculty rank, and output frequency, while qualitative analysis identifies themes relevant to societal and educational advancements. This approach provides insights into how ISCA-UA's research aligns with Education 5.0 objectives, fostering both technical and socio-emotional skills needed for a super-smart society. Findings highlight an increase in publications addressing digital transformation, sustainability, and governance, reflecting the institution's adaptability and responsiveness to societal shifts, particularly noticeable during the COVID-19 pandemic. This emphasis supports Education 5.0s aims of preparing students with versatile skills for modern challenges. The study contributes to the academic literature by showing how higher education institutions can align research outputs with global educational frameworks, promoting interdisciplinary skills and social responsibility. Future research could explore the impact of these themes on curriculum design and student development, further supporting the evolution toward Education 5.0.

Abstract
Manual selection of real estate properties can pose considerable challenges for agents since it needs a careful balance of various factors to satisfy client requirements while also manoeuvring through the complexities of the market. Although automated valuation models are widely used to estimate property market values, they are not designed to support property recommendation tasks. To address this gap, filteringbased recommendation methods have been explored, including collaborative and content-based approaches. However, these methods face several limitations in the real estate domain. This paper proposes a recommendation methodology designed to identify houses that closely resemble a given property, allowing agents to select the best matches based on geographical and physical characteristics. To assess the performance of the proposed methodology, we employ a range of evaluation metrics that measure different aspects of the model's effectiveness in ranking and recommending relevant items. The findings suggest that, while geographic features may slightly influence ranking behaviour, the model is capable of producing diverse and relevant recommendations consistently.