Abstract
Annotated data is one of the most important components for supervised learning tasks. To ensure the reliability of the models, this data is usually labeled by several human annotators through volunteering or using Crowdsourcing platforms. However, such approaches are unfeasible (regarding time and cost) in datasets with an enormous number of entries, which in the specific case of journalistic relevance detection in social media posts, is necessary due to the wide scope of topics that can be considered relevant. Therefore, with the goal of building a relevance detection model, we propose an architecture to build a large scale annotated dataset regarding the journalistic relevance of Twitter posts (i.e. tweets). This methodology is based on the predictability of the content in Twitter accounts. Next, we used the retrieved dataset and build relevance detection models, combining text, entities, and sentiment features. Finally, we validated the best model through a smaller manually annotated dataset with posts from Facebook and Twitter. The F1-measure achieved in the validation dataset was 63% which is still far from excellent. However, given the characteristics of the validation data, these results are encouraging since 1) our model is not affected by content from other social networks and 2) our validation dataset was restrained to a specific time interval and specific keywords (which can affect the performance of the model). © 2017 IEEE.

Abstract
Social networks as Facebook and Twitter gained a remarkable attention in the last decade. A huge amount of data is emerging and posted everyday by users that are becoming more interested in and relying on social network for information, news and opinions. Real time posting came to rise and turned easier to report news and events. However, due to its dimensions, in this work we focus on building a system architecture capable of detecting journalistic relevance of posts automatically on this 'haystack' full of data. More specifically, users will have the change to interact with a 'friendly user interface' which will provide several tools to analyze data. © 2017.

Abstract
Purpose - The purpose of this paper is to examine the influence of structural empowerment, psychological empowerment and intrinsic motivation on perceived customer satisfaction in contact centers. Design/methodology/approach - A questionnaire was conducted among 703 employees of a contact center. Data analysis was based on structural equation modeling. Findings - Structural empowerment results in higher levels of perceived customer satisfaction through psychological empowerment and intrinsic motivation. Furthermore, structural empowerment effect on psychological empowerment is mediated by intrinsic motivation. Practical implications - Previous predictions regarding counterproductive impact of empowerment in a low-service heterogeneity sector, such as contact center are challenged and a transformative message is disclosed in what concerns human resource management (HRM) in contact centers. Originality/value - The research provides valuable insights for both scholars and practitioners regarding the process through which employees' psychological empowerment and intrinsic motivation improves customer satisfaction in the context of contact centers.

Abstract
Motivate students is one of the biggest challenges that teachers have to face, in general and in particular in programming courses. In this article two techniques, aimed at supporting the teaching of programming, are discussed: program animation, and automatic evaluation of programs. Based on the combination of these techniques and their currently available tools, we will describe two possible approaches to increase motivation and improve the success. The conclusions of a first experiment conducted in the classroom will be presented. PEP, a Web-based tool that implements one of the approaches proposed, will be introduced.

Abstract

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.