Abstract
Alto Douro Wine Region is located in the Northeast of Portugal and is classified by UNESCO as a World Heritage Site. Snaked by the Douro River, the region has been producing wines for over 2000 years, with the world-famous Porto wine standing out. The vineyards, in that region, are built in a territory marked by steep slopes and the almost inexistence of flat land and water. The vineyards that cover the great slopes rise from the Douro River and form an immense terraced staircase. All these ingredients combined make the right key for exploring precision agriculture techniques. In this study, a preliminary approach allowing to perform terrace vineyards identification is presented. This is a key-enabling task towards the achievement of important goals such as production estimation and multi-temporal crop evaluation. The proposed methodology consists in the use of Convolutional Neural Networks (CNNs) to classify and segment the terrace vineyards, considering a high-resolution dataset acquired with remote sensing sensors mounted in unmanned aerial vehicles (UAVs).

Abstract

Abstract
Peripheral artery disease (PAD) main symptom is intermittent claudication, causing pain and limiting the walking abilities of patients, forcing individuals to temporarily stop walking. One treatment advised to counteract the effects of this disease is the practice of physical exercise with monitoring. Currently the monitored exercise programs are applied at the hospital, so some patients have to travel long distances three times a week, with high costs and low adherence of the patients. This paper presents the cocreation process of a mobile application for quantified supervised home-based exercise therapy on PAD patients. The study aimed to design a solution adapted to users' needs, which collects the necessary information for the therapy supervision by health professionals. The users' behaviour with the application allowed the assessment to a set of limitations and potential sources of noise in the supervision data that suggest the evolution to a pervasive solution, by minimizing, or even eliminating, the interaction with the users. The developed tool is a first step towards the creation of a technological ecosystem for the prescription of supervised therapeutic physical exercise, which leverages self-care and allows access to this type of therapy to the entire population. Cardiovascular disease represents a considerable economic burden to society, therefore effective preventive measures are necessary.

Abstract
Mobile handheld devices, such as smartphones and tablets, have become some of the most prominent ubiquitous terminals within the information and communication technology landscape. Their transformative power within the digital music domain changed the music ecosystem from production to distribution and consumption. Of interest here is the ever-expanding number of mobile music applications. Despite their growing popularity, their design in terms of interaction perception and control is highly arbitrary. It remains poorly addressed in related literature and lacks a clear, systematized approach. In this context, our paper aims to provide the first steps towards defining guidelines for optimal sonic interaction design practices in mobile music applications. Our design approach is informed by user data in appropriating mobile handheld devices. We conducted an experiment to learn links between control gestures and musical parameters, such as pitch, duration, and amplitude. A twofold action-reflection protocol and tool-set for evaluating the aforementioned links-are also proposed. The results collected from the experiment show statistically significant trends in pitch and duration control gesture mappings. On the other hand, amplitude appears to elicit a more diverse mapping approach, showing no definitive trend in this experiment.

Abstract
Probabilistic inductive logic programming (PILP) is a statistical relational learning technique which extends inductive logic programming by considering probabilistic data. The ability to use probabilities to represent uncertainty comes at the cost of an exponential evaluation time when composing theories to model the given problem. For this reason, PILP systems rely on various pruning strategies in order to reduce the search space. However, to the best of the authors' knowledge, there has been no systematic analysis of the different pruning strategies, how they impact the search space and how they interact with one another. This work presents a unified representation for PILP pruning strategies which enables end-users to understand how these strategies work both individually and combined and to make an informed decision on which pruning strategies to select so as to best achieve their goals. The performance of pruning strategies is evaluated both time and quality-wise in two state-of-the-art PILP systems with datasets from three different domains. Besides analysing the performance of the pruning strategies, we also illustrate the utility of PILP in one of the application domains, which is a real-world application.

Abstract
The Malware Information Sharing Platform (MISP) enables the sharing of cyberthreat information within a community, company or organisation. However, this platform presents limitations if its information is deemed as classified or shared only for a given period of time. This implies that this information should to be handled only in encrypted form. One solution is to use MISP with searchable encryption techniques to impose greater control over the sharing of information. In this paper, we propose a controlled information sharing functionality that features a synchronisation procedure that enables classified data exchange between MISP instances, based on policies and ensuring the required confidentiality and integrity of the shared data. Sequence charts are presented validating the configuration, the data synchronisation, and the data searching between multiple entities.