Access control is a crucial part of a system's security, restricting what actions users can perform on resources. Therefore, access control is a core component when dealing with e-Health data and resources, discriminating which is available for a certain party. We consider that current systems that attempt to assure the share of policies between facilities are prone to system's and network's faults and do not assure the integrity of policies lifecycle. By approaching this problem with the use of a distributed ledger, namely a consortium blockchain, where the operations are stored as transactions, we ensure that the different facilities have knowledge about all the parties that can act over the e-Health resources while maintaining integrity, auditability, authenticity, and scalability.

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In many engineering and science areas, models are developed and validated using high-level programing languages and environments as is the case with MATLAB. In order to target the multicore heterogeneous architectures being used on embedded systems to provide high performance computing with acceptable energy/power envelops, developers manually migrate critical code sections to lower-level languages such as C and OpenCL, a time consuming and error prone process. Thus, automatic source-tosource approaches are highly desirable. We present an approach to compile MATLAB and output equivalent C/OpenCL code to target architectures, such as GPU based hardware accelerators. We evaluate our approach on an existing MATLAB compiler framework named MATISSE. The OpenCL generation relies on the manual insertion of directives to guide the compilation and is also capable of generating C wrapper code to interface and synchronize with the OpenCL code. We evaluated the compiler with a number of benchmarks from different domains and the results are very encouraging.

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Many stochastic search algorithms are designed to optimize a fixed objective function to learn a task, i.e., if the objective function changes slightly, for example, due to a change in the situation or context of the task, relearning is required to adapt to the new context. For instance, if we want to learn a kicking movement for a soccer robot, we have to relearn the movement for different ball locations. Such relearning is undesired as it is highly inefficient and many applications require a fast adaptation to a new context/situation. Therefore, we investigate contextual stochastic search algorithms that can learn multiple, similar tasks simultaneously. Current contextual stochastic search methods are based on policy search algorithms and suffer from premature convergence and the need for parameter tuning. In this paper, we extend the well known CMA-ES algorithm to the contextual setting and illustrate its performance on several contextual tasks. Our new algorithm, called contextual CMA-ES, leverages from contextual learning while it preserves all the features of standard CMA-ES such as stability, avoidance of premature convergence, step size control and a minimal amount of parameter tuning.

This chapter presents the research performed in the context of FC Portugal project in the areas of agent architectures, coordination methodologies, coaching and agent development tools. FC Portugal's research has been integrated in several teams that have participated with considerable success in distinct RoboCup leagues and competitions. The chapter includes a brief description of the main competitions in which FC Portugal has participated with focus in the simulation leagues and related challenges. It also presents some of the developed techniques and results achieved in controlled experiments. These results, together with the impressive record of results achieved by FC Portugal teams in RoboCup competitions show that the techniques developed can significantly improve any soccer robotics team performance.

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FHR rFetal Heart Rate'? signals analysis is an important diagnostic tool in the assessment of the fetus well being. One of the most important FHR features is its baseline. Visual evaluation of FHR baseline reveals however a large inter and intraobserver variability.In this paper a new FHR base line determination method using artificial neural networks (ANN) is presented.Two base line determination methods with multilqer perceptron ANNs (namely base line estimation and base line classijkation) are described and compared based on their practical application results.