Abstract
Dams require continuous security and monitoring programs, integrated with visual inspection and testing in dam surveillance programs. New approaches for dam monitoring focus on multi-sensor integration, taking into account emerging technologies such as GNSS, optic fiber, TLS, InSAR techniques, GBInSAR, GPR, that can be used as complementary data in dam monitoring, eliciting causes of dam deformation that cannot be assessed with traditional techniques. This paper presents a Multi-temporal InSAR (MT-InSAR) monitoring of La Viñuela dam (Málaga, Spain), a 96 m height earth-fill dam built from 1982 to 1989. The presented MT-InSAR monitoring system comprises three C-band radar (~5,7 cm wavelength) datasets from the European satellites ERS-1/2 (1992-2000), Envisat (2003-2008), and Sentinel-1A/B (2014-2018). ERS-1/2 and Envisat datasets were processed using StaMPS. In the case of Sentinel-1A/B, two different algorithms were applied, SARPROZ and ISCE-SALSIT, allowing the comparison of the estimated LOS velocity pattern. The obtained results confirm that LaViñuela dam is deforming since its construction, as an earth-fill dam. Maximum deformation rates were measured in the initial period (1992-2000), being around -7 mm/yr (LOS direction) on the coronation of the dam. In the period covered by the Envisat dataset (2003-2008), the average deforming pattern was lower, of the order of -4 mm/yr. Sentinel-1A/B monitoring confirms that the deformation is still active in the period 2014-2018 in the central-upper part of the dam, with maximums of velocity reaching -6 mm/yr. SARPROZ and ISCE-SALSIT algorithms provide similar results. It was concluded that MT-InSAR techniques can support the development of new and more effective means of monitoring and analyzing the health of dams complementing actual dam surveillance systems. © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.

Abstract
Learning through practice is crucial to acquire a complex skill. Nevertheless, learning is only effective if students have at their disposal a wide range of exercises that cover all the course syllabus and if their solutions are promptly evaluated and given the appropriate feedback. Currently the teaching-learning process in complex domains, such as computer programming, is characterized by an extensive curricula and a high enrolment of students. This poses a great workload for faculty and teaching assistants responsible for the creation, delivering and assessment of student exercises. In order to address these issues, we created an e-learning framework - called Ensemble - as a conceptual tool to organize and facilitate technical interoperability among systems and services in domains that use complex evaluation. These domains need a diversity of tools, from the environments where exercises are solved, to automatic evaluators providing feedback on the attempts of students, not forgetting the authoring, management and sequencing of exercises. This paper presents and analyzes the use of Ensemble for managing the teaching-learning process in an introductory programming course at ESEIG - a school of the Polytechnic of Porto. An experiment was conducted to validate a set of hypotheses regarding the expected gains: increase in number of solved exercises, increase class attendance, improve final grades. They support the conclusion that the use of this e-learning framework for the practice-based learning has a positive impact on the acquisition of complex skills, such as computer programming. © Springer International Publishing AG, part of Springer Nature 2018.

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This documents introduces \Energyware" as a software engineering discipline aiming at defining, analyzing and optimizing the energy consumption by software systems. In this paper we present energyware analysis in the context of programming languages, software data structures and program's source code. For each of these areas we describe the research work done in the context of the Green Software Laboratory at Minho University: we describe energyaware techniques, tools, libraries, and repositories. © 2018 by the paper's authors.

Abstract
Software developers are more and more eager to understand their code's energy performance. However, even with such knowledge it is difficult to know how to improve the code. Indeed, little tool support exists to understand the energy consumption profile of a software system and to eventually (automatically) improve its code. In this paper we present a tool termed jStanley which automatically finds collections in Java programs that can be replaced by others with a positive impact on the energy consumption as well as on the execution time. In seconds, developers obtain information about energy-eager collection usage. jStanley will further suggest alternative collections to improve the code, making it use less time, energy, or a combination of both. The preliminary evaluation we ran using jStanley shows energy gains between 2% and 17%, and a reduction in execution time between 2% and 13%. A video can be seen at https://greensoftwarelab.github.io/jStanley. © 2018 Association for Computing Machinery.