Abstract
Engineering aims to improve the quality of daily life on a planet with limited resources. This chapter describes how the European Project Semester offered by the School of Engineering of the Porto Polytechnic contributes to make engineering undergraduates conscious of their environmental and societal impact. The program is a project-based active learning framework where problem-solving is supported by intercultural communication and creativity, ethical and sustainable reasoning, and global contextual analysis. This analysis shows that it fosters scientific, technical, and interpersonal competencies, emphasizing ethical and sustainable design as recommended by several reference institutions through the testimonials left on the project reports and the evolution perceived by the participants during the semester. These findings are supported by a sample of 45 projects conducted by 228 students from 23 countries and 65 degrees. All projects aimed to have a lasting impact on the future engineering practice of participants and, somehow, transform the world positively.

Abstract
Ethics promotes collective respect, well-being, and development. Its values drive individual, social, and environmental conduct and apply to all human activities. Professional ethics or deontology refers to the moral obligations binding professionals to society in the work sphere. These moral obligations are fundamental in professions that, like engineering, are behind technological innovation, given its current daily and worldwide impact. As such, it is essential to train engineers for holistic problem-solving, that is, driven by ethical, sustainability, technical, and economic constraints. This chapter analyses engineering ethics and how it is addressed within the European Project Semester, an undergraduate one-semester international exchange program, focusing on this aspect of engineering education. European Project Semester relies on multicultural teamwork, project-based learning, ethics, and sustainability-oriented design to motivate and prepare global engineers committed to protecting and improving life on the planet.

Abstract
This chapter aims to introduce the European Project Semester concept and the network of providers together with historical and current data on participants and providers. EPS is a one-semester international exchange program designed for engineering, product designers, and business undergraduates. It embraces student-centered project-based learning and multicultural and transdisciplinary teamwork to help students develop the competencies and skills required for the 21st century. It was created in 1995 in Denmark to prepare future engineers for global challenges, and it combines scientific and technical education, ethical and sustainability-driven problem-solving, intercultural communication, and teamwork. EPS has since been adopted by 19 European higher education institutions, establishing a network of EPS providers across Europe. The network offers international students a wide range of European study locations and cultural insights and, above all, the opportunity to enjoy the EPS learning experience. The number of alumni shows the program's attractiveness, range, and vitality.

Abstract

Abstract
Human interaction with intelligent systems, services, and devices generates large volumes of user-related data. This multi-source information can be used to build richer user profiles and improve personalization. Our goal is to combine multi-source data to create user profiles by assigning dynamic individual weights. This paper describes a multi-source user profiling methodology and illustrates its application with a film recommendation system. The contemplated data sources include (i) personal history, (ii) explicit preferences (ratings), and (iii) social activities (likes, comments, or shares). The MovieLens dataset was selected and adapted to assess our approach by comparing the standard and the proposed methodologies. In the standard approach, we calculate the best global weights to apply to the different profile sources and generate all user profiles accordingly. In the proposed approach, we determine, for each user, individual weights for the different profile sources. The approach proved to be an efficient solution to a complex problem by continuously updating the individual data source weights and improving the accuracy of the generated personalised multimedia recommendations. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Collaborative filtering is a widely used recommendation technique, which often relies on rating information shared by users, i.e., crowdsourced data. These filters rely on predictive algorithms, such as, memory or model based predictors, to build direct or latent user and item profiles from crowdsourced data. To predict unknown ratings, memory-based approaches rely on the similarity between users or items, whereas model-based mechanisms explore user and item latent profiles. However, many of these filters are opaque by design, leaving users with unexplained recommendations. To overcome this drawback, this paper introduces Explug, a local model-agnostic plug-in that works alongside stream-based collaborative filters to reorder and explain recommendations. The explanations are based on incremental user Trust & Reputation profiling and co-rater relationships. Experiments performed with crowdsourced data from TripAdvisor show that Explug explains and improves the quality of stream-based collaborative filter recommendations.