Abstract

Abstract
Cloud computing promises high dynamism, flexibility, and elasticity of applications at lower infrastructure costs. However, resource management, portability, and interoperability remain a challenge for cloud application users, since the current major cloud application providers have not converged to a standard interface, and the deployment supporting tools are highly heterogeneous. Besides, by their very nature, cloud applications bring serious traceability, security and privacy issues. This position paper describes a research thread on an extensible Domain Specific Language (DSL), a platform for the automated deployment, and a generic architecture of an ops application manager for self-adaptive distributed applications on hybrid cloud infrastructures. The idea is to overcome the cited limitations by empowering the cloud applications with self-configuration, self-healing, and self-protection capabilities. Such autonomous governance can be achieved by letting cloud users define their policies concerning security, data protection, dependability and functional compliance behavior using the proposed DSL. Real world trials in different application domains are discussed.

Abstract
Este trabalho teve por objetivo avaliar a eficiência técnica dos cursos de Administração no Brasil, por meio da Análise Envoltória de Dados (DEA), uma técnica não paramétrica introduzida por Charnes, Cooper e Rhodes (1978). Foram utilizados como variáveis do modelo os oito indicadores que compõem o Conceito Preliminar de Cursos (CPC). A amostra foi composta de 1229 cursos de Administração, com base nos resultados do Exame Nacional de Desempenho dos Estudantes (ENADE) de 2009. Como resultado, constatou-se que apenas 1,2% destes cursos podem ser considerados eficientes. Após esta aferição, uma etapa de recomendações foi realizada a fim de propor algumas metas reparatórias aos cursos ineficientes, trazendo-se assim à tona a possibilidade de alcançarem melhores resultados, dados os insumos já disponíveis pelos cursos de Administração.

Abstract
Wine authenticity methods are in increasing demand mainly in Denomination of Origin designations. The DNA-based methodologies are a reliable means of tracking food/wine varietal composition. The main aim of this work was the study of High Resolution Melting (HRM) application as a screening method for must and wine authenticity. Three sample types (leaf, must and wine) were used to validate the three developed HRM assays (Vv1-705 bp; Vv2-375 bp; and. Vv3-119 bp). The Vv1 HRM assay was only successful when applied to leaf and must samples. The Vv2 HRM assay successfully amplified all sample types, allowing genotype discrimination based on melting temperature values. The smallest amplicon, Vv3, produced a coincident melting curve shape in all sample types (leaf and wine) with corresponding genotypes. This study presents sensitive, rapid and efficient HRM assays applied for the first time to wine samples suitable for wine authenticity purposes.

Abstract
Deep Learning (DL) has been proven as a powerful recognition method as evidenced by its success in recent computer vision competitions. The most accurate results have been obtained by ensembles of DL models that pool their results. However, such ensembles are computationally costly, making them inapplicable to real-time applications. In this paper, we apply model compression techniques to the problem of semantic segmentation, which is one of the most challenging problems in computer vision. Our results suggest that compressed models can approach the accuracy of full ensembles on this task, combining the diverse strengths of networks of very different architectures, while maintaining real-time performance. (C) 2017 Published by Elsevier Inc.

Abstract
130In the majority of European countries, the evaluation of schools is at the heart of the educational system as a means to guarantee the quality of education. Every year, in most countries around the world, students perform national exams. Their results are analyzed by several stakeholders, including governmental agencies, the media, and researchers on educational issues. At present, advances in information and communication technology (ICT) and data analysis techniques allow schools to make use of massive amounts of data in their daily management. This chapter focuses in particular on the use of students’? data to benchmark schools. It illustrates the potential contribution of the information gathered and analyzed through data analytics to promote the continuous improvement of schools’? educational processes. © 2018 by Taylor & Francis Group, LLC.