Abstract
Recommendation systems are usually evaluated through accuracy and classification metrics. However, when these systems are supported by crowdsourced data, such metrics are unable to estimate data authenticity, leading to potential unreliability. Consequently, it is essential to ensure data authenticity and processing transparency in large crowdsourced recommendation systems. In this work, processing transparency is achieved by explaining recommendations and data authenticity is ensured via blockchain smart contracts. The proposed method models the pairwise trust and system-wide reputation of crowd contributors; stores the contributor models as smart contracts in a private Ethereum network; and implements a recommendation and explanation engine based on the stored contributor trust and reputation smart contracts. In terms of contributions, this paper explores trust and reputation smart contracts for explainable recommendations. The experiments, which were performed with a crowdsourced data set from Expedia, showed that the proposed method provides cost-free processing transparency and data authenticity at the cost of latency. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The European Project Semester (EPS) project-based learning framework is a multicultural and multidisciplinary one semester engineering capstone programme provided by a network of European Higher Education institutions. Its aim is to prepare 3rd-year undergraduate students to their future professional life, enhancing hard and soft skills and following ethical and sustainable design and development practices. At the School of Engineering of Porto Polytechnic (ISEP) the focus of the EPS programme (EPS@ISEP) is on solving multidisciplinary problems through teamwork, involving engineering, design and business students [1]. The students work in teams of 5 to 6 students, assembled according to the identified Belbin team roles, and also maximizing student cultural and scientific diversity. On the first week each team chooses to solve one of the open-ended multidisciplinary problems on offer. Those projects involve typically some type of automation and control[2]. One of the obstacles these eclectic teams face is the lack of hardware/software skills required to design, assemble and test a microcontroller based systems. To help overcome this situation, the programme syllabus includes an 8-hour intensive "Arduino & Electronics Crash Course" at the beginning of the semester due to its market penetration, low-cost, availability of documentation and support, and soft learning curve. This course has effectively contributed to provide students with the necessary knowledge to design and implement simple control systems, leading to the adoption in multiple EPS@ISEP past projects of the Arduino platform/ecosystem. However, the crescent sophistication of the projects, namely the integration with Internet of Things (IoT) platforms, requires the definition of a new strategy, considering the available hardware/software alternatives. This paper analyses the experience of the EPS@ISEP students regarding the use of microcontroller based platforms in the development of engineering capstone projects, and proposes possible future hardware/software alternatives, both from the technical and pedagogical perspectives.

Abstract

Abstract
This paper provides an overview of the development of a Sail Car within the European Project Semester (EPS), the international multidisciplinary engineering capstone programme offered by the Instituto Superior de Engenharia do Porto (ISEP). The main goal of EPS@ISEP is to offer a project-based educational experience to develop teamwork, communication, interpersonal and problem-solving skills in an international and multidisciplinary set up. The Sail Car team consisted of six Erasmus students, who participated in EPS@ISEP during the spring of 2019. The objective of the project was to design and develop a wind-powered, easy to drive land sailing vehicle. First, the team researched existing commercial solutions and considered the marketing, ethics and sustainability dimensions of the project. Next, based on these studies, specified the full set of requirements, designed the Sailo solution and procured the components and materials required to build a real size proof-of-concept prototype. Finally, the team assembled and tested successfully the prototype. At the end of the semester, the team considered EPS@ISEP a mind-opening opportunity.

Abstract
This paper analyses the impact of trust and reputation modelling on CloudAnchor, a business-to-business brokerage platform for the transaction of single and federated resources on behalf of Small and Medium Sized Enterprises (SME). In CloudAnchor, businesses act as providers or consumers of Infrastructure as a Service (IaaS) resources. The platform adopts a multi-layered multi-agent architecture, where providers, consumers and virtual providers, representing provider coalitions, engage in trust & reputation-based provider look-up, invitation, acceptance and resource negotiations. The goal of this work is to assess the relevance of the distributed trust model and centralised fuzzified reputation service in the number of resources successfully transacted, the global turnover, brokerage fees, losses, expenses and time response. The results show that trust and reputation based brokerage has a positive impact on the CloudAnchor performance by reducing losses and the execution time for the provision of both single and federated resources and increasing considerably the number of federated resources provided. © 2020, Springer Nature Switzerland AG.

Abstract
Nowadays, with the exponential growth of data stream sources (e.g., Internet of Things [IoT], social networks, crowdsourcing platforms, and personal mobile devices), data stream processing has become indispensable for online classification, recommendation, and evaluation. Its main goal is to maintain dynamic models updated, holding the captured patterns, to make accurate predictions. The foundations of data streams algorithms are incremental processing, in order to reduce the computational resources required to process large quantities of data, and relevance model updating. This article addresses data stream knowledge processing, covering classification, recommendation, and evaluation; describing existing algorithms/techniques; and identifying open challenges.