Abstract
Information security plays a key role in enterprises management, as it deals with the confidentiality, privacy, integrity, and availability of one of their most valuable resources: data and information. Small and Medium-sized enterprises (SME) are seen as a blind spot in information security and cybersecurity management, which is mainly due to their size, regional and familiar scope, and financial resources. This paper presents an information security and cybersecurity management project, in which a methodology based on the well-known ISO-27001:2013 standard was designed and implemented in fifty SMEs that were located in the center region of Portugal. The project was conducted by a business association located at the center of Portugal and mainly participated by SMEs. The Polytechnic of Leiria and an IT auditing/consulting team were the other two entities that participated on the project. The characterisation of the participating enterprises, the ISO-27001:2013 based methodology developed and implemented in SMEs, as well as the results obtained in this case study, are depicted and analysed in the paper. The attained results show a clear benefit to the audited and intervened SMEs, being mainly attested by the increasing of their information security management robustness and collaborators’ cyberawareness.

Abstract
The scarcity of balanced and annotated datasets has been a recurring problem in medical image analysis. Several researchers have tried to fill this gap employing dataset synthesis with adversarial networks (GANs). Breast magnetic resonance imaging (MRI) provides complex, texture-rich medical images, with the same annotation shortage issues, for which, to the best of our knowledge, no previous work tried synthesizing data. Within this context, our work addresses the problem of synthesizing breast MRI images from corresponding annotations and evaluate the impact of this data augmentation strategy on a semantic segmentation task. We explored variations of image-to-image translation using conditional GANs, namely fitting the generator's architecture with residual blocks and experimenting with cycle consistency approaches. We studied the impact of these changes on visual verisimilarity and how an U-Net segmentation model is affected by the usage of synthetic data. We achieved sufficiently realistic-looking breast MRI images and maintained a stable segmentation score even when completely replacing the dataset with the synthetic set. Our results were promising, especially when concerning to Pix2PixHD and Residual CycleGAN architectures.

Abstract
Optimal dispatch of modern power systems often entails efficiently solving large-scale optimization problems, especially when generators have to respond to the fast fluctuation of renewable generation. This paper develops a method to learn the optimal strategy from a mixed-integer quadratic program with time-varying parameters, which can model many power system operation problems such as unit commitment and optimal power flow. Different from existing machine learning methods that learn a map from the parameter to the optimal action, the proposed method learns the map from the parameter to the optimal integer solution and the optimal basis, forming a discrete pattern. Such a framework naturally gives rise to a classification problem: the parameter set is partitioned into polyhedral regions; in each region, the optimal 0-1 variable and the set of active constraints remain unchanged, and the optimal continuous variables are affine functions in the parameter. The outcome of classification is compared with analytical results derived from multi-parametric programming theory, showing interesting connections between traditional mathematical programming theory and the interpretability of the learning-based method. Tests on a small-scale problem demonstrate the partition of the parameter set learned from data meets the theoretical outcome. More tests on the IEEE 57-bus system and a real-world 1881-bus system validate the performance of the proposed method with a high-dimensional parameter for which the analytical method is intractable.

Abstract
The papers in this special section focus on compiler optimization for FPGA-based systems. Reconfigurable computing (RC) is growing in importance in many computing domains and systems, from embedded, mobile to cloud, and high-performance computing. We have witnessed important advancements regarding the programming of RC-based systems, but further improvements are needed, especially regarding efficient techniques for automatic mapping of computations described in high-level languages to the RC resources. The resources of high-end FPGAs allow these devices to implement complex Systemson-a-Chip (SoCs) and substantial computational components of software applications, e.g., when used as hardware accelerators and/or as more energy-efficient computing platforms. This, however, increases the continuous need for efficient compilers targeting FPGAs, and other RC platforms, from high-level programming languages.

Abstract
Biometric recognition and presentation attack detection (PAD) methods strongly rely on deep learning algorithms. Though often more accurate, these models operate as complex black boxes. Interpretability tools are now being used to delve deeper into the operation of these methods, which is why this work advocates their integration in the PAD scenario. Building upon previous work, a face PAD model based on convolutional neural networks was implemented and evaluated both through traditional PAD metrics and with interpretability tools. An evaluation on the stability of the explanations obtained from testing models with attacks known and unknown in the learning step is made. To overcome the limitations of direct comparison, a suitable representation of the explanations is constructed to quantify how much two explanations differ from each other. From the point of view of interpretability, the results obtained in intra and inter class comparisons led to the conclusion that the presence of more attacks during training has a positive effect in the generalisation and robustness of the models. This is an exploratory study that confirms the urge to establish new approaches in biometrics that incorporate interpretability tools. Moreover, there is a need for methodologies to assess and compare the quality of explanations.

Abstract
Due to the pervasiveness of Interconnected devices, large amounts of heterogeneous data types are being continuously collected. Regardless of the benefits that come from sharing data, exposing sensitive and private information arises serious privacy concerns. To prevent unwanted disclosures and, hence, to protect users' privacy, several privacy-preserving mechanisms have been proposed. However, the data heterogeneity and the inherent correlations among the different data types have been disregarded when developing such mechanisms. Our goal is to develop privacy-preserving mechanisms that are suitable for data heterogeneity and data correlation. These aspects will also be considered to develop mechanisms to achieve private learning. © 2021 Owner/Author.