Abstract
Internet is becoming one of the most adopted technologies of all times and one of its particular uses concerns the public health issues. The search for health related information and the exchange of experiences and opinions on symptoms and treatments is one of the main activities associated with eHealth websites, hence the need for these websites to be accessible to everybody, including those with some sort of disability. Nevertheless, when assessing the level of the WCAG 2.0 compliance of Iberian eHealth websites, the results achieved during a two stage, one year apart, evaluation indicated that these websites were definitely not accessible. By adding this finding to other similar results achieved by means of similar researches we believe that a new full scope Web accessibility and usability evaluation procedure was needed and is now presented. The referred proposal aims at creating a basis for both organisations and Web developers to understand how to perform an adequate assessment of their websites. © 2016 Elsevier Ltd.

Abstract
In the framework of the Horizon 2020 project UPGRID, the Portuguese demo is focused on promoting the exchange of smart metering data between the DSO and different stakeholders, guaranteeing neutrality, efficiency and transparency. The platform described in this study, named the Market Hub Platform, has two main objectives: (i) to guarantee neutral data access to all market agents and (ii) to operate as a market hub for the home energy management systems flexibility, in terms of consumption shift under dynamic retailing tariffs and contracted power limitation requests in response to technical problems. The validation results are presented and discussed in terms of scalability, availability and reliability.

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Abstract
In the last years researchers in the field of intelligent transportation systems have made several efforts to extract valuable information from social media streams. However, collecting domain-specific data from any social media is a challenging task demanding appropriate and robust classification methods. In this work we focus on exploring geolocated tweets in order to create a travel-related tweet classifier using a combination of bag-of-words and word embeddings. The resulting classification makes possible the identification of interesting spatio-temporal relations in Sao Paulo and Rio de Janeiro.

Abstract
A large amount of information is continuously generated in intensive health care. An analysis of these data streams can supply valuable insights to improve the monitoring of the patients. The volume, frequency and complexity of data, which come unlabeled, make their analysis a challenging task. Machine learning (ML) techniques have been successfully employed for mining data streams to extract useful knowledge for health care monitoring. It includes the detection of changes in the behavior of sensors, failures on machines or systems, and data anomalies. Anomaly (or outlier) detection is a ML task that aims to find exceptions or abnormalities in a dataset. These exceptions, in a medical context, can represent a new disease pattern, an event to be further investigated, behavior changes or potential health complications. Despite of its analysis in data streams is a challenging task, temporal abstractions techniques should help due to they deal with the management and abstraction of time based data, offering high level of visualization of each data object in its context. The aim of this paper is to review recent research in anomaly detection and temporal abstractions and discuss the application of their combination to intensive care data streams.