Abstract
This paper provides an overview of the development of a selforiented solar mirror (SOSM) project within the European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP). While the main objective of the EPS@ISEP project-based educational framework is to foster teamwork, communication, interpersonal and problem solving skills in an international, multidisciplinary engineering environment, the goal of the SOSM is to track and reflect the Sun radiation onto a pre-defined area. In the spring of 2017 a group of five students chose to develop a proof-of-concept domestic SOSM called SUNO. The students undertook project supportive modules in Ethics, Sustainability, Marketing and Project Management together with project coaching meetings to assist the development of SUNO. The paper details this process, describing the initial project definition, the research of current technologies, the designing, the manufacturing and testing of the SUNO prototype, and discusses what the students gained from this learning experience. © 2017 Association for Computing Machinery.

Abstract
Crowdsourcing has become an essential source of information for tourists and the tourism industry. Every day, large volumes of data are exchanged among stakeholders in the form of searches, posts, shares, reviews or ratings. This paper presents a tourist-centred analysis of crowd-sourced hotel information collected from the Expedia platform. The analysis relies on Data Mining methodologies to predict trends and patterns which are relevant to tourists and businesses. First, we propose an approach to reduce the crowd-sourced data dimensionality, using correlation and Multiple Linear Regression to identify the single most representative rating. Finally, we use this rating to model the hotel customers and predict hotel ratings, using the Alternating Least Squares algorithm. In terms of contributions, this work proposes: (i) a new crowd-sourced hotel data set; (ii) a crowd-sourced rating analysis methodology; and (iii) a model for the prediction of personalised hotel ratings.

Abstract
The IPP4B workshop assembles a group of researchers from academia and industry - BBC R&D, Ericsson and MOG Technologies - to discuss the state of the art and together envisage future directions for in-programme personalisation in broadcasting. The workshop comprises one invited keynote, two invited presentations together with a paper and discussion sessions.

Abstract
This paper describes the collaborative learning environment, aligned with the United Nations Millennium Development Goals, provided by the European Project Semester (EPS). EPS is a one semester capstone project programme offered by eighteen European engineering schools as part of their student exchange programme portfolio. In this international programme, students are organized in teams, grouping individuals from diverse academic backgrounds and nationalities. The teams, after choosing a project proposal, become fully responsible for the conduction of their projects. By default, project proposals refer to open multidisciplinary real problems. The purpose of the project is to expose students to problems of a greater dimension and complexity than those faced throughout the degree programme as well as to put them in contact with the so-called real world, in opposition to the academic world. EPS provides an integrated framework for undertaking capstone projects, which is focused on multicultural and multidisciplinary teamwork, communication, problem-solving, creativity, leadership, entrepreneurship, ethical reasoning and global contextual analysis. Specifically, the design and development of sustainable systems for growing food allow students not only to reach the described objectives, but to foster sustainable development practices. As a result, we recommend the adoption of this category of projects within EPS for the benefit of engineering students and of the society as a whole.

Abstract
Sustainability plays a key role in EPS@ISEP programme - the European Project Semester programme at the School of Engineering of the Polytechnics of Porto. Not just the environmental, but also economical (marketing) and social (ethics) perspectives are explored by multicultural teams during this one semester capstone/internship programme. In 2015, a team of EPS@ISEP students choose to design and develop an insectarium to grow insects for reptile feeding. The team, after exploiting the topic, contemplated growing insects not only for animal feed, but also for human food. Their motivation resulted from the fact that insects, when compared with traditional sources of protein, are more sustainable, i.e., require considerably less resources per kg of protein. This approach, in the current Earth’s population growth scenario, contributes to minimise the resources required for meeting food needs. The main goal of the proposal was to raise the awareness of the participants regarding sustainable development while creating a functional, cost-effective, eco-friendly and attractive prototype. The team, driven by this multidisciplinary problem, performed: (i) a survey of competing products; (ii) a selection of the insect species to grow based on the study and comparison of the life cycle and habitat requirements of different species of insects; (iii) a marketing plan; (iv) a sustainability and an ethic and deontological analysis of the proposed solution; and (v) the design, assembling and testing of the prototype. Furthermore, the students also developed cross-cultural understanding, teamwork and communication skills. The project provided an excellent opportunity to foster the concept of sustainable development amongst the students.

Abstract
The scalable analysis of crowdsourced data repositories and streams has quickly become a critical experimental asset in multiple fields. It enables the systematic aggregation of otherwise disperse data sources and their efficient processing using significant amounts of computational resources. However, the considerable amount of crowdsourced social data and the numerous criteria to observe can limit analytical off-line and on-line processing due to the intrinsic computational complexity. This paper demonstrates the efficient parallelisation of profiling and recommendation algorithms using tourism crowdsourced data repositories and streams. Using the Yelp data set for restaurants, we have explored two different profiling approaches: entity-based and feature-based using ratings, comments, and location. Concerning recommendation, we use a collaborative recommendation filter employing singular value decomposition with stochastic gradient descent (SVD-SGD). To accurately compute the final recommendations, we have applied post-recommendation filters based on venue suitability, value for money, and sentiment. Additionally, we have built a social graph for enrichment. Our master-worker implementation shows super-linear scalability for 10, 20, 30, 40, 50, and 60 concurrent instances.