Abstract
Purpose: The purpose of this paper is to present the results of a research on the importance of Employer Branding (EB) for corporate brand success in the context of small and medium businesses (SME), from the perspective of the employees compared to the founders, considering a set of stable variables. This is an ongoing process through which company employees want to understand "who" and "what" is behind the brand's value proposition. Methodology: A qualitative methodology was used based on interviews with open questions in an exploratory case study, to gather as much information as possible and without constraints. This was properly supported by a literature review, from published works, academic works, as well as papers on employer branding. This approach is part of the scientific paradigm of realism, also known as critical realism, and is therefore an appropriate method in marketing and management research. Findings: The findings of this research demonstrate that EB seems not to be contributing for a successful corporate brand in the SME context. For that purpose, a medium-and long-term marketing and communication plan is needed, particularly regarding a brand plan, duly defined and implemented. The mere market/sector leadership cannot be viewed as a source in the long run for SME. If no one believes or bets on brand equity appreciation, EB won't certainly be potentiated. In this way, there is a risk that this type of organizations would become weak entities with no sense of belonging. Originality/value: Because there is a lack of applied scientific research on this topic in the context of SME, the aim was to understand EB from a specific case study, enabling the acquisition of knowledge on how this concept works and is applied (or not) in practice. Practical implications: This research aimed at contributing to a better strategic alignment of EB, from the institutional level to the operational level, passing through the intermediate level, in SME. It appears that the managers/administrators/directors in SME don't have a holistic perspective of the brand. It also emphasizes the great importance of employees in building the brand in this particular context. Research limitations: This investigation needed a deeper market analysis, namely of direct competitors. It was not possible to obtain enough information to carry out a more reliable analysis. It would be very interesting to understand what led some collaborators to leave the studied company and move to its direct competitors, as well as to realize what makes others leave the competitors or return to the company.

Abstract
This paper reports on recent activities by the Portugal Chapter of IEEE Education Society. It begins with a reflection on the work of a Chapter and on several aspects that may impact its performance. The major activities are also presented, as well as the policies developed by the Portugal Chapter in the last year, concluding with the presentation of the two awards received at the last FIE, namely the Chapter Achievement Award and the Distinguished Chapter Leadership Award.

Abstract
Sign Language Recognition (SLR) has become an appealing topic in modern societies because such technology can ideally be used to bridge the gap between deaf and hearing people. Although important steps have been made towards the development of real-world SLR systems, signer-independent SLR is still one of the bottleneck problems of this research field. In this regard, we propose a deep neural network along with an adversarial training objective, specifically designed to address the signer-independent problem. Concretely speaking, the proposed model consists of an encoder, mapping from input images to latent representations, and two classifiers operating on these underlying representations: (i) the signclassifier, for predicting the class/sign labels, and (ii) the signer-classifier, for predicting their signer identities. During the learning stage, the encoder is simultaneously trained to help the sign-classifier as much as possible while trying to fool the signer-classifier. This adversarial training procedure allows learning signer-invariant latent representations that are in fact highly discriminative for sign recognition. Experimental results demonstrate the effectiveness of the proposed model and its capability of dealing with the large inter-signer variations.

Abstract
The deep inferior epigastric perforator (DIEP) is the most commonly used free flap in mastectomy reconstruction. Preoperative imaging techniques are routinely used to detect location, diameter and course of perforators, with direct intervention from the imaging team, who subsequently draw a chart that will help surgeons choosing the best vascular support for the reconstruction. In this work, the feasibility of using a computer software to support the preoperative planning of 40 patients proposed for breast reconstruction with a DIEP flap is evaluated for the first time. Blood vessel centreline extraction and local characterization algorithms are applied to identify perforators and compared with the manual mapping, aiming to reduce the time spent by the imaging team, as well as the inherent subjectivity to the task. Comparing with the measures taken during surgery, the software calibre estimates were worse for vessels smaller than 1.5 mm (P = 6e-4) but better for the remaining ones (P = 2e-3). Regarding vessel location, the vertical component of the software output was significantly different from the manual measure (P = 0.02), nonetheless that was irrelevant during surgery as errors in the order of 2-3 mm do not have impact in the dissection step. Our trials support that a reduction of the time spent is achievable using the automatic tool (about 2 h/case). The introduction of artificial intelligence in clinical practice intends to simplify the work of health professionals and to provide better outcomes to patients. This pilot study paves the way for a success story. (C) 2020 The Authors. Published by Elsevier Ltd.

Abstract
Breast cancer is a highly mutable and rapidly evolving disease, with a large worldwide incidence. Even though, it is estimated that approximately 90% of the cases are treatable and curable if detected on early staging and given the best treatment. Nowadays, with the existence of breast cancer routine screening habits, better clinical treatment plans and proper management of the disease, it is possible to treat most cancers with conservative approaches, also known as breast cancer conservative treatments (BCCT). With such a treatment methodology, it is possible to focus on the aesthetic results of the surgery and the patient's Quality of Life, which may influence BCCT outcomes. In the past, this assessment would be done through subjective methods, where a panel of experts would be needed to perform the assessment; however, with the development of computer vision techniques, objective methods, such as BAT (c) and BCCT.core, which perform the assessment based on asymmetry measurements, have been used. On the other hand, they still require information given by the user and none of them has been considered the gold standard for this task. Recently, with the advent of deep learning techniques, algorithms capable of improving the performance of traditional methods on the detection of breast fiducial points (required for asymmetry measurements) have been proposed and showed promising results. There is still, however, a large margin for investigation on how to integrate such algorithms in a complete application, capable of performing an end-to-end classification of the BCCT outcomes. Taking this into account, this thesis shows a comparative study between deep convolutional networks for image segmentation and two different quality-driven keypoint detection architectures for the detection of the breast contour. One that uses a deep learning model that has learned to predict the quality (given by the mean squared error) of an array of keypoints, and, based on this quality, applies the backpropagation algorithm, with gradient descent, to improve them; another which uses a deep learning model which was trained with the quality as a regularization method and that used iterative refinement, in each training step, to improve the quality of the keypoints that were fed into the network. Although none of the methods surpasses the current state of the art, they present promising results for the creation of alternative methodologies to address other regression problems in which the learning of the quality metric may be easier. Following the current trend in the field of web development and with the objective of transferring BCCT.core to an online format, a prototype of a web application for the automatic keypoint detection was developed and is presented in this document. Currently, the user may upload an image and automatically detect and/or manipulate its keypoints. This prototype is completely scalable and can be upgraded with new functionalities according to the user's needs.

Abstract
The Breast Cancer overall survival rate has raised impressively in the last 20 years mainly due to improved screening and effectiveness of treatments. This increase in survival paralleled the awareness over the long-lasting impact of the side effects of treatments on patient quality of life, emphasizing the motto "a longer but better life for breast cancer patients". In breast cancer more strikingly than in other cancers, besides the side effects of systemic treatments, there is the visible impact of surgery and radiotherapy on patients' body image. This has sparked interest on the development of tools for the aesthetic evaluation of Breast Cancer locoregional treatments, which evolved from manual, subjective approaches to computerized, automated solutions. However, although studied for almost four decades, past solutions were not mature enough to become a standard. Recent advancements in machine learning have inspired trends toward deep-learning-based medical image analysis, also bringing new promises to the field of aesthetic assessment of locoregional treatments. In this paper, a review and discussion of the previous state-of-the-art methods in the field is conducted and the extracted knowledge is used to understand the evolution and current challenges. The aim of this paper is to delve into the current opportunities as well as motivate and guide future research in the aesthetic assessment of Breast Cancer locoregional treatments. (C) 2019 Elsevier Ltd.