Abstract
The rapid expansion of Asian hornets poses a high threat for the honey bee survival, as these invaders pray on them. Furthermore, they also pose a threat to people who are allergic, whose sting can lead to death. This study proposes a Decision Support System that uses Computer Vision techniques to automatically detect signs of Vespa velutina through images from GPS equipped camera. The goal of the system is to provide timely information about the presence of these invaders, allowing park managers and beekeepers to act quickly in removing the Vespidae. The proposed methodology obtained an 85% accuracy in the detection of V. velutina using the Mask RCNN architecture, enabling the system to perform detection at 3 FPS. © 2020 MIR Labs.

Abstract
The star S2 orbiting the compact radio source Sgr A* is a precision probe of the gravitational field around the closest massive black hole (candidate). Over the last 2.7 decades we have monitored the star's radial velocity and motion on the sky, mainly with the SINFONI and NACO adaptive optics (AO) instruments on the ESO VLT, and since 2017, with the four-telescope interferometric beam combiner instrument GRAVITY. In this Letter we report the first detection of the General Relativity (GR) Schwarzschild Precession (SP) in S2's orbit. Owing to its highly elliptical orbit (e=0.88), S2's SP is mainly a kink between the pre-and post-pericentre directions of motion approximate to +/- 1 year around pericentre passage, relative to the corresponding Kepler orbit. The superb 2017-2019 astrometry of GRAVITY defines the pericentre passage and outgoing direction. The incoming direction is anchored by 118 NACO-AO measurements of S2's position in the infrared reference frame, with an additional 75 direct measurements of the S2-Sgr A* separation during bright states ("flares") of Sgr A*. Our 14-parameter model fits for the distance, central mass, the position and motion of the reference frame of the AO astrometry relative to the mass, the six parameters of the orbit, as well as a dimensionless parameter f(SP) for the SP (f(SP)=0 for Newton and 1 for GR). From data up to the end of 2019 we robustly detect the SP of S2, delta phi approximate to 12 ' per orbital period. From posterior fitting and MCMC Bayesian analysis with different weighting schemes and bootstrapping we find f(SP)=1.10 +/- 0.19. The S2 data are fully consistent with GR. Any extended mass inside S2's orbit cannot exceed approximate to 0.1% of the central mass. Any compact third mass inside the central arcsecond must be less than about 1000 M-circle dot.

Abstract
This paper describes the design and development of a Smart Companion Pillow, named bGuard, designed by a multinational and multidisciplinary team enrolled in the European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP) in the spring of 2019. Nowadays, parents spend most of the day at work and become naturally worried about the well-being of their young children, specially babies. The aim of bGuard is to provide a 24-hour remotely accessible baby monitoring service, contributing to reduce parenting stress. The team, based on the survey of related products, as well as on marketing, sustainability, ethics and deontology analyses, developed a remotely interactive Smart Companion Pillow to monitor the baby's health and room air quality. The collected data, once it is saved on an Internet of Things (IoT) platform, becomes remotely accessible. The bGuard pillow, thanks to its shape, reduces the risk of the baby rolling from back to tummy, lowering the risk of Sudden Infant Death Syndrome (SIDS).

Abstract
The case study is a widely used method in qualitative research. Although defining the case study can be simple, it is complex to develop its strategy. Furthermore, it is still often not considered to be a sufficiently robust research strategy in the education field because it does not offer well-defined and use well-structured protocols. One of the most frequent criticisms associated with the case study approach is its low validity and reliability. In this sense, this study aims to concisely explore the main difficulties inherent to the process of developing a case study, also attempting to suggest some practices that can increase its reliability, construct validity, internal and external validity.

Abstract
In this paper, we consider the problem of classifying skin lesions into multiple classes using both dermoscopic and clinical images. Different convolutional neural network architectures are considered for this task and a novel ensemble scheme is proposed, which makes use of a progressive transfer learning strategy. The proposed approach is tested over a dataset of 4000 images containing both dermoscopic and clinical examples and it is shown to achieve an average specificity of 93.3% and an average sensitivity of 79.9% in discriminating skin lesions belonging to four different classes.

Abstract
Social networks are strongly present in the daily life of modern society. Most people use these social networks to share information about their lives, their opinions, places they visit and their state of mind. Generally, these posts are composed of various information, being the location of the users location part of the data. The purpose of this work is to obtain the location of the posts and observe the users mobility pattern in the city of Porto, Portugal. This paper discusses the technologies available for obtaining the data, the social networks currently worth studying and their respective restrictions. It also explores new approaches to collect the data from the desired social networks, respecting all restrictions currently applied. The different software solutions developed for the social networks interactions are explored and described in depth. Subsequently, the necessary software for social networks is reviewed, the possible algorithms for data mining are discussed and its implementation is presented. Finally, the results obtained are interpreted and studied according to the characteristics of the city, tourism promotions and transport routes. © 2020 The Authors. Published by ELSEVIER B.V.