Abstract
The iMPAcT tool combines the benefits of existing user recurring behaviour (User Interface Patterns) on mobile applications to facilitate the test automation of Android mobile applications. It uses an automatic exploration process combined with reverse engineering to identify the existing user interface patterns on a mobile application and then tests those patterns with generic test strategies (designated Test Patterns). The Test Patterns are defined in a catalogue that can be reused for testing other applications. However the results obtained by the iMPAcT tool depend on the exploration mode and on the order in which the test strategies are applied. This paper describes an experiment conducted to evaluate the impact of using different exploration modes and of changing the order by which UI patterns are searched and subsequently tested on the failures found and on the number of events fired. (C) 2016 The Authors. Published by Elsevier B.V.

Abstract
In this paper we describe the participation of InfoLab in the patient-centred information retrieval task of the CLEF eHealth 2016 lab. We analyse the performance of several query expansion strategies using difierent sources of terms and difierent methods to select the terms to be added to the original query. One of the strategies uses pseudo relevance feedback for term selection. The other strategies use external sources such as Wikipedia articles and definitions from the UMLS Metathesaurus for term selection. In the end, readability metrics such as SMOG, FOG and Flesch-Kincaid were used to re-rank the documents retrieved using the expanded queries. As the relevance and readability assessments are not available we can't make any conclusion regarding the results of our approaches.

Abstract
The health domain is rich in specific vocabulary and information structures. Previous work on this area includes the collection of this information in information systems. However, the language of these can limit their use. To overcome this, we present Health Translations, a web application that uses crowd-sourcing to translate a large vocabulary set that, currently, is only available in English. To increase usage, gamification methods are applied that reward both the quantity of collaboration and the quality of it. When completed, these translations can be made available without costs to the research community. This paper presents the platform as a responsive web application.

Abstract
If it were possible to automatically detect proficiency in languages using data from eye movements, new levels of customizing computer applications could possibly be achieved. An example in case is web searches where suggestions and results could be adjusted to the user's knowledge of the language. The objective of this study is to compare the reading habits of users with high and low English language proficiency, having in mind the possible automatic detection of the English proficiency level through reading. For this purpose, a study was conducted with two types of user, those with a high level of proficiency (Proficient Users), and those with low proficiency (Basic Users) in the English language. An eye-tracker was used to collect users' eye movements while reading a text in English. Results show that users with high proficiency engage in more careful reading. In contrast, low English proficiency users take more time to read, revisit sentences and paragraphs more often, have more and longer fixations and also a higher number of saccades. As expected, these users have more difficulties in understanding the text.

Abstract
To help laypeople surpass the common difficulties they face when searching for health information on the Web, we built Health Suggestions, an extension for Google Chrome to assist users obtaining high-quality search results in the health domain. This is achieved by providing users with suggestions of queries formulated in different terminologies and languages. Translations of health expressions might not be obvious and queries in languages with a strong presence on the Web facilitate the access to high-quality health contents. On the other hand, the use of lay terminology might contribute to increase users' comprehension and medico-scientific terminology to obtain more detailed and technical contents. Results show a good acceptance of the suggestions, confirm the utility of a multilingual and multi-terminology approach and show its usefulness to more successful searches.

Abstract
Nowadays, online communities are becoming an important resource for health consumers who want to retrieve and share information about health subjects. These communities have the potential to influence patients' health behaviors and increase their engagement with therapies. However, the interaction dynamics in this type of media remains poorly understood what might hinder the development of strategies that facilitate and encourage participation. Social Network Analysis is a technique that tries to expose the hidden channels of communication and information flow, leading to a better understanding of how members relate to each other on online social network. In this study we do a systematic review of the literature regarding the apply this technique in the study of online health communities. We show that this type of studies is scarce and that, in this domain, Social Network Analysis is mainly applied to identify influential key members, as well as the most active members in terms of posting or answering questions.