Abstract
Software Defined Networking (SDN) facilitates the orchestration and configuration of network resources in a flexible and scalable form, where policies are managed by controller components that interact with network elements through multiple interfaces. The ubiquitous adoption of SDN leads to the availability of multiple SDN controllers, which have different characteristics in terms of performance and security support. SDN controllers are a common target in network attacks since their compromise leads to the capability of impairing the entire network. Thus, the choice of a SDN controller must be a meticulous process from early phases (design to production). CROCUS, herein proposed, provides a mechanism to enable an objective assessment of the security support of SDN controllers. CROCUS relies on the information provided by the Common Vulnerability Scoring System (CVSS) and considers security features derived from scenarios with stringent security requirements. Considering a vehicular communication scenario supported by multiple technologies, we narrow the selection of SDN controllers to OpenDayLight and ONOS choices. The results put in evidence that both controllers have security features relevant for demanding scenarios with ONOS excelling in some aspects.

Abstract
Retirees have been growing in importance as a consumer segment targeted by the tourism industry, namely because they can minimize the typical seasonality of tourism and increase its sustainability. This study aims to contribute to a more in-depth knowledge of retirees' behaviour and has two objectives: (i) describe tourist behaviour of seniors prior to and after retirement; (ii) identify and analyse retired consumers' current motivations and constraints towards tourism. Qualitative research was conducted comprising interviews with 40 Portuguese retirees. The results indicate a diversity of experiences regarding tourism activities both before and after retirement, evidencing that past experience stands out as a determinant of retirees' tourism behaviour. Moreover, three distinct segments of tourists emerge: the experts, the new tourists, and the non-tourists.

Abstract
In this paper, we address a subtopic of the broad domain of audio enhancement, namely musical audio bandwidth extension. We formulate the bandwidth extension problem using deep neural networks, where a band-limited signal is provided as input to the network, with the goal of reconstructing a full-bandwidth output. Our main contribution centers on the impact of the choice of low-pass filter when training and subsequently testing the network. For two different state-of-the-art deep architectures, ResNet and U-Net, we demonstrate that when the training and testing filters are matched, improvements in signal-to-noise ratio (SNR) of up to 7 dB can be obtained. However, when these filters differ, the improvement falls considerably and under some training conditions results in a lower SNR than the band-limited input. To circumvent this apparent overfitting to filter shape, we propose a data augmentation strategy which utilizes multiple low-pass filters during training and leads to improved generalization to unseen filtering conditions at test time.

Abstract

Abstract
Imbalanced learning is one of the most relevant problems in machine learning. However, it faces two crucial challenges. First, the amount of methods proposed to deal with such problem has grown immensely, making the validation of a large set of methods impractical. Second, it requires specialised knowledge, hindering its use by those without such level of experience. In this paper, we propose the Automated Imbalanced Classification method, ATOMIC. Such a method is the first automated machine learning approach for imbalanced classification tasks. It provides a ranking of solutions most likely to ensure an optimal approximation to a new domain, drastically reducing associated computational complexity and energy consumption. We carry this out by anticipating the loss of a large set of predictive solutions in new imbalanced learning tasks. We compare the predictive performance of ATOMIC against state-of-the-art methods using 101 imbalanced data sets. Results demonstrate that the proposed method provides a relevant approach to imbalanced learning while reducing learning and testing efforts of candidate solutions by approximately 95%.

Abstract
We detail a study of the techniques and sealing materials for optical fiber sensors used in dynamic environments with high pressure (>300 bar) and high temperature (>300 °C). The sealing techniques and materials are the key for the robustness of sensors in harsh dynamic environments, such as large combustion engines. The sealing materials and techniques studied in this work are high-temperature epoxies, metallic polymer, metallic solders, glass solder, cement, brazing and electroless nickel plating. Because obtaining high temperature simultaneously with high pressure is very difficult in the same chamber in the laboratory, we developed a new and simple method to test sealed fibers in these conditions in the laboratory. In addition, some sensors using the materials tested in the laboratory were also field tested in real thermoelectric combustion engines. The study also discusses the methods of fabrication and the cost-benefit ratio of each method.