Abstract
Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.

Abstract
Image quality assessment has been a topic of intense research over the last decades. Although its application to other disciplines is growing tremendously, its use in retinal imaging is still immature and some fundamental challenges remain unsolved. Thus, we present a research methodology for the objective assessment of the quality in retinal images. The methodology can be used as a preliminary step in any computer-aided system, and is composed of four main steps: the location of the region-of-interest, the extraction of relevant image properties and their analysis by feature selection, and the final binary classification into two classes (good and poor quality). The experimental results demonstrate the adequacy of the proposed methodology in this context, being able to objectively assess the quality of retinal images with an accuracy over 99%.

Abstract
Located in the northern part of continental Portugal, the city of Porto is characterized by low-to-moderate levels of seismicity, thus one of reasons for the lack of studies regarding the influence of site effects in this area. In this study, the HVSR technique (horizontal-to-vertical spectral ratio) was used in order to obtain information about the fundamental frequencies of the ground in several parts of the city which was later compared to the geotechnical information presented in the Geotechnical Map of Porto, with the aim of establishing a possible correlation between frequency values and sub-soil geotechnical characteristics. This type of research plays an important role in understanding how local geological characteristics may influence the amplification of seismic waves. For this purpose, two types of studies were conducted in the city of Porto – two linear test campaigns along two major streets in the city, Boavista Avenue and Constituição Street; and one dispersed test campaign within the hydrographic basin of Frio River, an underground river flowing in the areas of Carregal Garden, Santo António Hospital, Cordoaria Garden and Virtudes Garden. The three campaigns represented a total of 53 recordings of ambient ground noise using a broad band seismometer and the resulted data was used to produce HVSR graphics. From these graphics it was possible to determine the fundamental frequencies (f0) of several points within the city. Resorting to the geotechnical map of the city and having identified the geotechnical units existing in each test place as well as the parameters used to their classification, it was possible to establish a connection between frequency values and substrate competence. The results suggest a strong relation between these parameters, low frequencies relate to softer grounds and as an opposite high frequencies to harder bedrock.

Abstract
This article addresses the developments ongoing in SENSIBLE, an H2020 funded project focused on energy storage and energy management, which demonstration occurs in Évora-Portugal, Nottingham, UK and Nuremberg, Germany. Currently, the presented study focuses on the concepts and developments necessary in order to make possible that residential clients can participate in a market environment with their electrical flexibility, also considering distribution system operator (DSO) needs when gird is under stress caused by any technical constraint. Moreover, than the concept behind it is necessary to consider several developments: (i) a low layer where residential assets will live in customer’s houses; (ii) a high-level layers where market tools and DSO management tools will live; (iii) an intermediate layer, which bridge the gap between the low layer and high layer. These developments are a result of the ongoing works under one of SENSIBLE use cases which demonstrations occurs in a small village in Évora district in Portugal. © 2017 Institution of Engineering and Technology. All rights reserved.

Abstract
Nowadays, it is common for higher education institutions to use computer-based exams, partly or integrally, in their evaluation processes. These exams, much like their paper-based counterparts, are one of the most significant sources of stress in the life of students. However, the fact that exams are undertaken in a computer allows for new features to be acquired that may provide more reliable insights into the behaviour and state of the student during the exam. In this article we analyse these novel behavioural features and explore, to which extent, they can point out previously unknown phenomena. Specifically, we show that the time a student takes to complete an exam is correlated with mouse dynamics features. In practical terms, we are able to predict the duration of each individual exam with a satisfying error based on the interaction patterns of the student.

Abstract