Abstract
Data is everywhere and in everything we do. Most of the time, usable information is hidden in raw data and because of that, there is an increasing demand for people capable of working creatively with it. To fully understand how we can assist data science workers to become more productive in their jobs, we first need to understand who they are, how they work, what are the skills they hold and lack, and which tools they need. In this paper, we present the results of the analysis of several interviews conducted with data scientists. Our research allowed us to conclude that the heterogeneity between these professionals is still understudied, which makes the development of methodologies and tools more challenging and error prone. The results of this research are particularly useful for both the scientific community and industry to propose adequate solutions for these professionals.

Abstract
In this study machine learning methods were applied to RGB data obtained by an unmanned aerial vehicle (UAV) to assess this effectiveness in vineyard classification. The very high-resolution UAV-based imagery was subjected to a photogrammetric processing allowing the generation of different outcomes: orthophoto mosaic, crop surface model and five vegetation indices. The orthophoto mosaic was used in an object-based image analysis approach to group pixels with similar values into objects. Three machine learning techniques-support vector machine (SVM), random forest (RF) and artificial neural network (ANN)-were applied to classify the data into four classes: grapevine, shadow, soil and other vegetation. The data were divided with 22% (n=240, 60 per class) for training purposes and 78% (n = 850) for testing purposes. The mean value of the objects from each feature were used to create a dataset for prediction. The results demonstrated that both RF and ANN models showed a good performance, yet the RF classifier achieved better results.

Abstract
The learning of programming is traditionally challenging for students. However, this is also one of the most fundamental skills for any computer scientist, and is becoming an important skill in other areas of knowledge. In this paper we analyze the use of game-elements in a challenging long-term programming task, with students of the 3rd year of a Informatics Engineering degree. We conducted a quantitative study using the AMS scale to assess students' motivation. Results show that with the use of game-elements, students are both intrinsically and extrinsically motivated, and that they consider learning/working fun, which contributes positively to their academic performance. 2012 ACM Subject Classification Human-centered computing!Collaborative and social computing theory, concepts and paradigms.

Abstract
Individual identification is a crucial step to answer many questions in evolutionary biology and is mostly performed by marking animals with tags. Such methods are well-established, but often make data collection and analyses time-consuming, or limit the contexts in which data can be collected. Recent computational advances, specifically deep learning, can help overcome the limitations of collecting large-scale data across contexts. However, one of the bottlenecks preventing the application of deep learning for individual identification is the need to collect and identify hundreds to thousands of individually labelled pictures to train convolutional neural networks (CNNs). Here we describe procedures for automating the collection of training data, generating training datasets, and training CNNs to allow identification of individual birds. We apply our procedures to three small bird species, the sociable weaverPhiletairus socius,the great titParus majorand the zebra finchTaeniopygia guttata, representing both wild and captive contexts. We first show how the collection of individually labelled images can be automated, allowing the construction of training datasets consisting of hundreds of images per individual. Second, we describe how to train a CNN to uniquely re-identify each individual in new images. Third, we illustrate the general applicability of CNNs for studies in animal biology by showing that trained CNNs can re-identify individual birds in images collected in contexts that differ from the ones originally used to train the CNNs. Finally, we present a potential solution to solve the issues of new incoming individuals. Overall, our work demonstrates the feasibility of applying state-of-the-art deep learning tools for individual identification of birds, both in the laboratory and in the wild. These techniques are made possible by our approaches that allow efficient collection of training data. The ability to conduct individual recognition of birds without requiring external markers that can be visually identified by human observers represents a major advance over current methods.

Abstract
A evolução das redes de telecomunicações tem promovido o desenvolvimento de novas aplicações para dispositivos móveis. Algumas destas aplicações exigem requisitos computacionais e energéticos que vão para além das capacidades dos dispositivos móveis. Neste contexto, pode ser utilizada a arquitetura Mobile Cloud Computing (MCC), que permite executar as aplicações em datacenters na cloud e aliviar o processamento nos dispositivos móveis. No entanto, algumas aplicações mais exigentes, e.g. interativas e de tempo real, são mais sensíveis ao atraso no processamento e comunicação da informação. Para estas aplicações, a arquitetura Mobile Edge Computing (MEC) pode ser utilizada como uma tecnologia intermédia que disponibiliza recursos computacionais e de armazenamento a partir da periferia da rede. Este artigo apresenta um estudo que avalia o desempenho das arquiteturas MCC e MEC na execução de duas aplicações tomadas como representativas do espectro das aplicações interativas, de tempo real e de processamento intensivo: o Fluid e o FaceSwap. São apresentados resultados que permitem quantificar o desempenho destas arquiteturas em diferentes circunstâncias.;Telecommunication networks evolution is driving the development of new applications for mobile devices. Some of these applications are resource-intensive and push computational and energy demands of mobile devices beyond the mobile hardware capabilities. In this context, Mobile Cloud Computing (MCC) architecture emerges as a solution for offloading mobile devices that allows to execute these applications in cloud datacenters thus reducing the processing demand in mobile devices. However, more demanding applications, e.g. interactive and real-time applications, are sensitive to processing and communications delay. For these applications, Mobile Edge Computing (MEC) can be used as an intermediary technology, providing computing and storage resources in the network edge. This paper presents a study carried out to evaluate the performance of MEC and MCC architectures when executing two applications, Fluid and FaceSwap, representative of real time and computing intensive applications. A set of scenarios were designed to quantify the performance of these architectures in different settings.

Abstract
Entrepreneurship can be perceived as an individual's ability to identify opportunities, and to develop solutions that create innovative solutions for society. It can be a business, a project or even a movement that generates real change and impact on people's daily lives. What defines an entrepreneur is his/her behavior and attitudes and not personality traits or any other innate characteristics. It is not possible to predict which individuals have an entrepreneurial profile, but we can work on developing the skills that are necessary to become an entrepreneur. Therefore, this view assumes that entrepreneurial skills can be developed through education and life experiences. The teaching of entrepreneurship is a theme that has generated considerable interest in higher education institutions. Several programs have been proposed. Currently, it is necessary to evaluate their effectiveness, considering multiple perspectives. Only through the evaluation of the performance of these courses we can ensure that students are properly prepared for the challenges they will encounter in the labor market, whether through the launch of a start-up or in the management of a company or project. This study uses the EntreComp framework to assess the development of entrepreneurial skills and to identify the factors that influence their performance. This study offers both theoretical and practical contributions. In the conceptual component, a framework that has a multidisciplinary view of the entrepreneurial skills has been developed. On a practical level, it can be applied within a course of entrepreneurship, which will enable its replication to various educational institutions.