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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.

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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.

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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.

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Data crowdsourcing is a data acquisition process where groups of voluntary contributors feed platforms with highly relevant data ranging from news, comments, and media to knowledge and classifications. It typically processes user-generated data streams to provide and refine popular services such as wikis, collaborative maps, e-commerce sites, and social networks. Nevertheless, this modus operandi raises severe concerns regarding ill-intentioned data manipulation in adver-sarial environments. This paper presents a simulation, modelling, and classification approach to automatically identify human and non-human (bots) as well as benign and malign contributors by using data fabrication to balance classes within experimental data sets, data stream modelling to build and update contributor profiles and, finally, autonomic data stream classification. By employing WikiVoyage - a free worldwide wiki travel guide open to contribution from the general public - as a testbed, our approach proves to significantly boost the confidence and quality of the classifier by using a class-balanced data stream, comprising both real and synthetic data. Our empirical results show that the proposed method distinguishes between benign and malign bots as well as human contributors with a classification accuracy of up to 92%.

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