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.

Abstract
The segmentation and characterization of the lung lobes are important tasks for Computer Aided Diagnosis (CAD) systems related to pulmonary disease. The detection of the fissures that divide the lung lobes is non-trivial when using classical methods that rely on anatomical information like the localization of the airways and vessels. This work presents a fully automatic and supervised approach to the problem of the segmentation of the five pulmonary lobes from a chest Computer Tomography (CT) scan using a Fully RegularizedV-Net (FRV- Net), a 3D Fully Convolutional Neural Network trained end-to- end. Our network was trained and tested in a custom dataset that we make publicly available. It can correctly separate the lobes even in cases when the fissure is not well delineated, achieving 0.93 in per-lobe Dice Coefficient and 0.85 in the inter-lobar Dice Coefficient in the test set. Both quantitative and qualitative results show that the proposed method can learn to produce correct lobe segmentations even when trained on a reduced dataset.

Abstract
The operation problem of a micro-grid (MG) in grid-connected mode is an optimization one in which the main objective of the MG operator (MGO) is to minimize the operation cost with optimal scheduling of resources and optimal trading energy with the main grid. The MGO can use incentive-based demand response programs (DRPs) to pay an incentive to the consumers to change their demands in the peak hours. Moreover, the MGO forecasts the output power of renewable energy resources (RERs) and models their uncertainties in its problem. In this paper, the operation problem of an MGO is modeled as a risk-based two-stage stochastic optimization problem. To model the uncertainties of RERs, two-stage stochastic programming is considered and conditional value at risk (CVaR) index is used to manage the MGO's risk-level. Moreover, the non-linear economic models of incentive-based DRPs are used by the MGO to change the peak load. The numerical studies are done to investigate the effect of incentive-based DRPs on the operation problem of the MGO. Moreover, to show the effect of the risk-averse parameter on MGO decisions, a sensitivity analysis is carried out.

Abstract
The constant need of operators to ensure a high quality of service requires an adequate estimation of signal level in the service area. Drive Tests (DTs) are used to monitor signal level. Still this is an expensive task, not being able to cover all areas. A methodology to build complete path loss grids for a given site is proposed. Starting from available DTs measurements for certain pixels, path loss is estimated for the remaining ones by tuning a propagation model and extrapolating the path loss for neighboring pixels. This allows to provide a complete path loss estimation for the desired area around the cell.

Abstract
The legislation on the accessibility of the information and communication technologies (ICT) is not new and consequently there have been a number of international and national initiatives, however they are very focus in the Web. We are in the threshold of a more comprehensive approach in the ICT domain. The European Standard EN 301 549 specifies the accessibility requirements for public procurement of ICT products and services in Europe. This study focuses on the analysis of the benefits / impact, for each group of People with Special Needs (PSN), the parameters of the European Norms (EN) for ICT products and services, which support the new legislation in the domain of public acquisition for the countries of the European Union (EU), the EU Public Procurement Directive 2014/24/EU. Questions related to methodologies, techniques and tools to evaluate the parameters of these EN will be studied in the future, as well as the models for the certification of ICT products and services, with the purpose of presenting solutions. In the future, a framework will be also designed and developed with specific functionalities and being capable of assist and facilitate the evaluation of EN parameters, and consequently the certification of the ICT products and services.

Abstract