Abstract
People in modern societies have increasingly sedentary lifestyles. They usually do not have time to take part in physical activity on a regular basis. Additionally, due to time constraints, people are consuming more processed and junk foods. This behavior may lead to health issues, such as obesity or cardiovascular disease. On the other hand people are becoming more aware and more interested in doing physical activities, which has resulted in an increase of memberships at gymnasiums. People usually obtain better results in training by having a personal trainer, especially in the beginning, because personal trainers can recommend safer and more effective exercises, as well as provide motivation. Moreover, personal trainers can also play the roles of life coaches or nutritionists. Despite the benefits, having a personal trainer can be difficult. Due to time constraints, it might not be simple to combine both schedules of the personal trainer and the client. In this paper we present a novel health training platform to maximize the personal trainer and client relationship and, therefore, increase the client's well-being. The health training platform allows clients to have sensors connected to their smartphones and send their exercise data to their personal trainer. It also allows personal trainers to observe their clients' evolution and provide feedback. The health training platform has an architecture that allows multiple configurations involving personal trainers, clients and gymnasiums. We have built and tested a prototype of a health training platform.

Abstract
Dealing with increasing amounts of data creates the need to deal with redundant, inconsistent and/or complementary repositories which may be different in their data models and/or in their schema. Current data cleaning techniques developed to tackle data quality problems are just suitable for scenarios were all repositories share the same model and schema. Recently, an ontology-based methodology was proposed to overcome this limitation. In this paper, this methodology is briefly described and applied to a real scenario in the health domain with data quality problems. © 2016 ACM.

Abstract
Modelling dietary intake of older adults can prevent nutritional deficiencies and diet-related diseases, improving their quality of life. Towards such direction, a Fuzzy Cognitive Map (FCM)-based modelling approach that models the interdependencies between the factors that affect the Quality of Nutrition (QoN) is presented here. The proposed FCM-QoN model uses a FCM with seven input-one output concepts, i.e., five food groups of the UK Eatwell Plate, Water (H2O), and older adult's Emotional State (EmoS), outputting the QoN. The weights incorporated in the FCM structure were drawn from an experts' panel, via a Fuzzy Logic-based knowledge representation process. Using various levels of analysis (causalities, static/feedback cycles), the role of EmoS and H2O in the QoN was identified, along with the one of Fruits/Vegetables and Protein affecting the sustainability of effective food combinations. In general, the FCM-QoN approach has the potential to explore different dietary scenarios, helping health professionals to promote healthy ageing and providing prognostic simulations for diseases effect (such as Parkinson's) on dietary habits, as used in the H2020 i-Prognosis project (www.i-prognosis.eu).

Abstract
In this document we outline a Ph. D. research plan and a summary of preliminary results on test automation for distributed and heterogeneous systems.

Abstract
The requirements of a website are constantly changing and evolving. In this context, managing the changes related to the baseline and reviewing the requirements' prioritization is critical. Nowadays, web analytics tools are used to analyze the behaviour of users on a website in order to improve its usability and user experience. The analysis of the usage of websites may also help software requirements maintenance which can be a contribution to the overall quality of the service provided. This paper presents an approach through a recommender system that collects the information about the usage of a website using a web analytics tools and generate recommendations reports that may help the requirements maintenance and increase the quality of the software requirements specification of the website. This paper presents also an experimental evaluation of a case study based on an online newspaper website. The results of the proposed approach provide more readable reports in a language more closer to the business and suggest requirements priority change, identify new requirements and functionalities that may be removed.

Abstract
The use of the World Wide Web has had a huge growth and there is a greater variety of web applications with an increasing importance in society and in supporting the development to all kinds of business. Often, most of websites are providing support services that must be maintained and improved over time. This maintenance and upgrade can be difficult because frequently the requirements are no longer actual and/or often not even exist documented. Websites are increasingly monitoring usage data, and this type of information is increasingly abundant. Analyzing the usage of the websites can help identify improvements and help to maintain the website and its software requirements. This paper presents REQAnalytics, a recommender system that collects the information about the usage of a website, processes it and generates recommendations to the requirements specification of the website. This research work also presents an experimental evaluation of a case study based on an online newspaper website. The results showed that REQAnalytics can produce reports in a language closer to the business, identify most and less used finictionalities, requirements that can be split in two or more requirements and give support to the maintenance of requirements of the website being analyzed. (C) 2016 The Authors. Published by Elsevier B.V.