Abstract
This paper reports the collaborative design and development of Helios, a wearable UltraViolet (UV) meter. Helios is intended to help preventing the negative effects of over-exposure to UV radiation, e.g., sun burning, photo ageing, eye damage and skin cancer, as well as of under-exposure to solar radiation, e.g., the risk of developing vitamin D shortage. This project-based learning experience involved five Erasmus students who participated in EPS@ISEP - the European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP) - in the spring of 2017. The Team, motivated by the desire to find a solution to this problem, conducted multiple studies, including scientific, technical, sustainability, marketing, ethics and deontology analyses, and discussions to derive the requirements, design structure, functional system and list of materials and components. The result is Helios, a prototype Wearable UV Meter that can be worn as both a bracelet and a clip-on. The tangible result was the Helios prototype, but more importantly was the learning experience of the Team, as concluded from their closing statements.

Abstract
This paper presents the development of an Escargot Nursery by a multinational and multidisciplinary team of 3rd year undergraduates within the framework of EPS@ISEP - the European Project Semester (EPS) offered by the Instituto Superior de Engenharia do Porto (ISEP). The challenge was to design, develop and test a snail farm compliant with the applicable EU directives and the given budget. The Team, motivated by the desire to solve this multidisciplinary problem, embarked on an active learning journey, involving scientific, technical, marketing, sustainable and ethical development studies, brainstorming and decision-making. Based on this project-based learning approach, the Team identified the lack of innovative domestic snail farm products and, consequently, proposed the development of "EscarGO", a stylish solution for the domestic market. The paper details the proposed design and control system, including materials, components and technologies. This learning experience, which was focussed on the development of multicultural communication, multidisciplinary teamwork, problem-solving and decision-making competencies in students, produced as a tangible evidence the proof of concept prototype of "EscarGO", an Escargot Nursery designed for families to easily grow snails at home.

Abstract
This paper presents a platform for real and simulated autonomous sailing competitions, which can also be used as a research tool to test and assess navigation algorithms. The platform provides back-end services – competition server, boat modelling and data storage – and supports external browsers and software agents as front-end clients. The back-end adopts the Multi-Agent System (MAS) paradigm for the internal modelling of sailing boats and offers a Web Service Application Programming Interface (API) for the external software agents and a Web application for Web browsers. As a whole, the platform offers tracking (real competitions) and simulation (simulated competitions) modes. The testing and assessment of navigation algorithms and boat models correspond to private simulated competitions. In simulation mode, the back-end internal boat agent implements a simplified physical model, including the weight, sail area, angle of the sail and rudder, velocity and direction of the wind and position and velocity of the hull, whereas the front-end external boat agent implements the navigation algorithm on the team side, ensuring the privacy of strategic knowledge. The Web application allows the configuration and launching of competitions, the registration of teams and researchers, the uploading of boat physical features for simulation as well as the live or playback viewing of real and simulated competitions. The simulation mode is illustrated with the help of a case study. The proposed platform, which is open, scalable, modular and distributed, was designed for the research community to prepare, run and gather data from real and simulated autonomous sailing competitions.

Abstract
Nowadays, not only the number of multimedia resources available is increasing exponentially, but also the crowd-sourced feedback volunteered by viewers generates huge volumes of ratings, likes, shares and posts/reviews. Since the data size involved surpasses human filtering and searching capabilities, there is the need to create and maintain the profiles of viewers and resources to develop recommendation systems to match viewers with resources. In this paper, we propose a personalised viewer profiling technique which creates individual viewer models dynamically. This technique is based on a novel incremental learning algorithm designed for stream data. The results show that our approach outperforms previous approaches, reducing substantially the prediction errors and, thus, increasing the accuracy of the recommendations. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
Tourism crowdsourcing platforms have a profound influence on the tourist behaviour particularly in terms of travel planning. Not only they hold the opinions shared by other tourists concerning tourism resources, but, with the help of recommendation engines, are the pillar of personalised resource recommendation. However, since prospective tourists are unaware of the trustworthiness or reputation of crowd publishers, they are in fact taking a leap of faith when then rely on the crowd wisdom. In this paper, we argue that modelling publisher Trust & Reputation improves the quality of the tourism recommendations supported by crowdsourced information. Therefore, we present a tourism recommendation system which integrates: (i) user profiling using the multi-criteria ratings; (ii) k-Nearest Neighbours (k-NN) prediction of the user ratings; (iii) Trust & Reputation modelling; and (iv) incremental model update, i.e., providing near real-time recommendations. In terms of contributions, this paper provides two different Trust & Reputation approaches: (i) general reputation employing the pairwise trust values using all users; and (ii) neighbour-based reputation employing the pairwise trust values of the common neighbours. The proposed method was experimented using crowdsourced datasets from Expedia and TripAdvisor platforms. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
Nowadays tourists rely on technology for inspiration, research, booking, experiencing and sharing. Not only it provides access to endless sources of information, but has become an unbounded source of tourist-related data. In such crowd-sourced data-intensive scenario, we argue that new approaches are required to enrich current and new travelling experiences. This work, which supports the "dreaming stage", proposes the automatic recommendation of personalised destinations based on textual reviews, i.e.,a semantic content-based filter of crowd-sourced information. Our approach relies on Topic Modelling - to extract meaningful information from textual reviews - and Semantic Similarity to identify relevant recommendations. Our main contribution is the processing of crowd-sourced tourism information employing data mining techniques in order to automatically discover untapped destinations on behalf of tourists.