Abstract
Featured Timed Automata (FTA) is a formalism that enables the verification of an entire Software Product Line (SPL), by capturing its behavior in a single model instead of product-by-product. However, it disregards compositional aspects inherent to SPL development. This paper introduces Interface FTA (IFTA), which extends FTA with variable interfaces that restrict the way automata can be composed, and with support for transitions with atomic multiple actions, simplifying the design. To support modular composition, a set of Reo connectors are modelled as IFTA. This separation of concerns increases reusability of functionality across products, and simplifies modelling, maintainability, and extension of SPLs. We show how IFTA can be easily translated into FTA and into networks of Timed Automata supported by UPPAAL. We illustrate this with a case study from the electronic government domain.

Abstract
Internationalization is a broad concept encompassing multiple facets. Higher Education Institutions (HEI) collaborators are key actors of the internationalization process and outcomes, being essential for education managers to promote internationalization initiatives, namely among their teachers. Nevertheless, the literature indicates that the number of teachers involved in those initiatives is still modest. It is therefore of utmost importance to understand how teachers initiate and maintain mobility assignments, so that HEI can implement effective strategies to promote this facet of internationalization. In this paper, we explore the teacher mobility topic through the lens of the Theory of Reasoned Action and the Theory of Planned Behavior. These two theories have been widely used to explain how people initiate and maintain behaviors, and are particularly relevant to understand teacher mobility. The paper includes results from phenomenological interviews with teachers that had one or more mobility experience under the Erasmus programme. The narratives of the participants in the study evidence the importance of prior intentions, attitudes and subjective norms in the resolution to undertake international assignments. Self-efficacy was also shown as determinant in the decision process of first and subsequent mobility initiatives. This paper also provides cues on how HEI can facilitate and encourage mobility experiences amongst their teaching staff.

Abstract
The project management area uses several techniques and tools to identify, select, monitor and manage the projects portfolio of the organizations. However, these techniques still present problems and difficulties which, sometimes, inhibit their use. Project Portfolio Management (PPM) is considered a problem of considerable difficulty and complexity, which requires its constant monitoring by the organization, according to the strategic guidelines chosen. Thus, this paper presents an innovative approach for selecting the portfolio of projects by using DEA, and the development of a set of generic indicators, to support the decision makers in considering multiple projects. Each decision maker can use all or some of the indicators proposed, thus each one's knowledge, sensitivity and intuition are taken into account, namely for indicators such as risk perception, level of innovation, market clock speed, project's complexity.

Abstract
The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is “mediation” by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are “cis-mediators” of trans-eQTLs, including those “cis-hubs” involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.

Abstract
Access to information has been made easier in different domains that range from multimedia content, books, music, news, etc. To deal with the huge amount of alternatives, recommendation systems have been often used as a solution to filter the options and provide suggestions of items that might be of interest to an user. The news domain introduces additional challenges due not only to the large amount of new items produced daily but also due to their ephemeral timelife. In this paper, a news recommendation system which combines content-based and georeferenced techniques in a mobility scenario, is proposed. Taking into account the volatility of the information, short-term and long-term user profiles are considered and implicitly built. Besides tracking users' clicks, the system infers different levels of interest an article has by tracking and weighting each action in the system and in social networks. Impact of the different fields that make up a news is also taken into account by following the inverted pyramid model that assumes different levels of importance to each paragraph of the article. The solution was tested with a population of volunteers and results indicate that the quality of the recommendation approach is acknowledged by the users.

Abstract
Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society. A diverse set of techniques from epidemiology, statistics, and computer science are being proposed and studied for ADE discovery from observational health data (e.g., EHR and claims data), social network data (e.g., Google and Twitter posts), and other information sources. Methodologies are needed for evaluating, quantitatively measuring and comparing the ability of these various approaches to accurately discover ADEs. This work is motivated by the observation that text sources such as the Medline/Medinfo library provide a wealth of information on human health. Unfortunately, ADEs often result from unexpected interactions, and the connection between conditions and drugs is not explicit in these sources. Thus, in this work, we address the question of whether we can quantitatively estimate relationships between drugs and conditions from the medical literature. This paper proposes and studies a state-of-the-art NLP-based extraction of ADEs from text.