Abstract
Crowdsourcing has become an essential source of information for tourists and tourism industry. Every day, large volumes of data are exchanged among stakeholders in the form of searches, posts, shares, reviews or ratings. Specifically, this paper explores inter-guest trust and similarity post-filtering, using crowdsourced ratings collected from the Expedia and TripAdvisor platforms, to improve hotel recommendations generated by incremental collaborative filtering. First, the profiles of hotels and guests are created using multi-criteria ratings and inter-guest trust and similarity. Next, incremental model-based collaborative filtering is adopted to predict unknown hotel ratings based on the multi-criteria ratings and, finally, post-recommendation filtering sorts the generated predictions based on the inter-guest trust and similarity. The proposed method was tested both off-line (post-processing) and on-line (real time processing) for performance comparison. The results highlight: (i) the increase of the quality of recommendations with the inter-guest trust and similarity; and (ii) the decrease of the predictive errors with the on-line incremental collaborative filtering. Thus, this work contributes with a novel method, integrating incremental collaborative filtering and inter-guest trust and similarity post-filtering, for on-line hotel recommendation based on multi-criteria crowdsourced rating streams. © 2019, Springer Nature Switzerland AG.

Abstract
Engineering education addresses the development of professional competencies in undergraduates. In this context, the core set of professional competencies includes critical thinking and problem solving, effective communication, collaboration and team building, and creativity and innovation-also known as the four Cs-as well as socio-professional ethics and sustainable development-referred in this paper as the two Ss. While the four Cs were identified by the associates of the American Management Association based on the needs of the society, professional associations, and businesses; this paper proposes the two S extension to ensure that future engineers contribute to the well-being of individuals and the preservation of life on Earth. It proposes a tangible framework-the 4C2S-and an application method to analyse the contributions made by engineering capstone programmes to the development of these core competencies in future engineering professionals. The method is applied to an engineering capstone programme-the European Project Semester (EPS) offered by the Instituto Superior de Engenharia do Porto (ISEP)-and a specific project case-EPS@ISEP Pet Tracker project developed in 2013, constituting, in addition, a road map for the application of the 4C2S framework to engineering capstone programmes. The results show that EPS@ISEP complies with the 4C2S framework.

Abstract
The design, development and deployment of autonomous sustainable ocean platforms for exploration and monitoring can provide researchers and decision makers with valuable data, trends and insights into the largest ecosystem on Earth. Although these outcomes can be used to prevent, identify and minimise problems, as well as to drive multiple market sectors, the design and development of such platforms remains an open challenge. In particular, energy efficiency, control and robustness are major concerns with implications for autonomy and sustainability. Rigid wingsails allow autonomous boats to navigate with increased autonomy due to lower power consumption and increased robustness as a result of mechanically simpler control compared to traditional sails. These platforms are currently the subject of deep interest, but several important research problems remain open. In order to foster dissemination and identify future trends, this paper presents a survey of the latest developments in the field of rigid wing sailboats, describing the main academic and commercial solutions both in terms of hardware and software.

Abstract
The first international workshop on Data-driven Personalisation of Television aims to highlight the significantly growing importance of data in the support of new television content consumption experiences. This includes automatic video summarization, dynamic insertion of content into media streams and object based media broadcasting, to serve the recommendation of TV content and personalization in media delivery. The workshop has two keynote talks alongside five paper presentations and several related demos.

Abstract
This paper presents an overview on how the European Project Semester capstone programme offered by the Instituto Superior de Engenharia do Porto (EPS@ISEP) fosters learning by challenging engineering, business and product development undergraduates to address sustainability issues afflicting cities and communities nowadays. This will be done by analysing the reports and the learning journey of three multicultural and multidisciplinary EPS@ISEP teams during the design, development and test of a smart billboard, a self-oriented solar mirror and a level monitoring system for waste oil bins. These three projects were conducted within EPS@ISEP, a project-based learning framework dedicated to the development of key engineering skills, namely multidisciplinary teamwork, intercultural communication, ethical and sustainability-oriented problem-solving. The involved students contributed, not only, to make cities more inclusive, safe, resilient and sustainable, one of UNESCO's sustainable development goals, but learnt and practiced together sustainability-driven design, while searching for an innovative solution for a smart city problem. This conclusion is supported by the analysis of the content the three project reports.

Abstract
Nowadays, with the widely usage of on-line stream video platforms, the number of media resources available and the volume of crowd-sourced feedback volunteered by viewers is increasing exponentially. In this scenario, the adoption of recommendation systems allows platforms to match viewers with resources. However, due to the sheer size of the data and the pace of the arriving data, there is the need to adopt stream mining algorithms to build and maintain models of the viewer preferences as well as to make timely personalised recommendations. In this paper, we propose the adoption of optimal individual hyper-parameters to build more accurate dynamic viewer models. First, we use a grid search algorithm to identify the optimal individual hyper-parameters (IHP) and, then, use these hyper-parameters to update incrementally the user model. This technique is based on an 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. © 2019 for this paper by its authors.