Abstract
Embedded Computing for High Performance: Design Exploration and Customization Using High-level Compilation and Synthesis Tools provides a set of real-life example implementations that migrate traditional desktop systems to embedded systems. Working with popular hardware, including Xilinx and ARM, the book offers a comprehensive description of techniques for mapping computations expressed in programming languages such as C or MATLAB to high-performance embedded architectures consisting of multiple CPUs, GPUs, and reconfigurable hardware (FPGAs). The authors demonstrate a domain-specific language (LARA) that facilitates retargeting to multiple computing systems using the same source code. In this way, users can decouple original application code from transformed code and enhance productivity and program portability. After reading this book, engineers will understand the processes, methodologies, and best practices needed for the development of applications for high-performance embedded computing systems. Focuses on maximizing performance while managing energy consumption in embedded systems Explains how to retarget code for heterogeneous systems with GPUs and FPGAs Demonstrates a domain-specific language that facilitates migrating and retargeting existing applications to modern systems Includes downloadable slides, tools, and tutorials.

Abstract
Featured Timed Automata (FTA) is a formalism that enables the verification of an entire Software Product Line (SPL), by capturing its behavior in a single model instead of product-by-product. However, it disregards compositional aspects inherent to SPL development. This paper introduces Interface FTA (IFTA), which extends FTA with variable interfaces that restrict the way automata can be composed, and with support for transitions with atomic multiple actions, simplifying the design. To support modular composition, a set of Reo connectors are modelled as IFTA. This separation of concerns increases reusability of functionality across products, and simplifies modelling, maintainability, and extension of SPLs. We show how IFTA can be easily translated into FTA and into networks of Timed Automata supported by UPPAAL. We illustrate this with a case study from the electronic government domain.

Abstract
Access to information has been made easier in different domains that range from multimedia content, books, music, news, etc. To deal with the huge amount of alternatives, recommendation systems have been often used as a solution to filter the options and provide suggestions of items that might be of interest to an user. The news domain introduces additional challenges due not only to the large amount of new items produced daily but also due to their ephemeral timelife. In this paper, a news recommendation system which combines content-based and georeferenced techniques in a mobility scenario, is proposed. Taking into account the volatility of the information, short-term and long-term user profiles are considered and implicitly built. Besides tracking users' clicks, the system infers different levels of interest an article has by tracking and weighting each action in the system and in social networks. Impact of the different fields that make up a news is also taken into account by following the inverted pyramid model that assumes different levels of importance to each paragraph of the article. The solution was tested with a population of volunteers and results indicate that the quality of the recommendation approach is acknowledged by the users.

Abstract
The concept of the water, energy, food nexus is extremely relevant to Kazakhstan as the country faces population growth, economic progress and environmental challenges such as water scarcity, desertification, and climate change. Furthermore, poor sectoral coordination and inadequate infrastructure have caused unsustainable resource use and threaten the long-term water, energy and food security in Kazakhstan. This study presents the key elements required to implement a nexus-based resource management approach in Kazakhstan, by identifying linkages between water resources, energy production and agriculture. A case study illustrates how this methodology can be applied to quantify linkages between the water and energy sectors.

Abstract
In Virtual Reality (VR), the action of selecting virtual objects outside arms-reach still poses significant challenges. In this work, after classifying, with a new taxonomy, and analyzing existing solutions, we propose a novel technique to perform out-of-reach selections in VR. It uses natural pointing gestures, a modifiable cone as selection volume, and an iterative progressive refinement strategy. This can be considered a VR implementation of a discrete zoom approach, although we modify users' position instead of the field-of-view. When the cone intersects several objects, users can either activate the refinement process, or trigger a multiple object selection. We compared our technique against two techniques from literature. Our results show that, although not being the fastest, it is a versatile approach due to the lack of errors and uniform completion times. © 2017 Copyright held by the owner/author(s).

Abstract
Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society. A diverse set of techniques from epidemiology, statistics, and computer science are being proposed and studied for ADE discovery from observational health data (e.g., EHR and claims data), social network data (e.g., Google and Twitter posts), and other information sources. Methodologies are needed for evaluating, quantitatively measuring and comparing the ability of these various approaches to accurately discover ADEs. This work is motivated by the observation that text sources such as the Medline/Medinfo library provide a wealth of information on human health. Unfortunately, ADEs often result from unexpected interactions, and the connection between conditions and drugs is not explicit in these sources. Thus, in this work, we address the question of whether we can quantitatively estimate relationships between drugs and conditions from the medical literature. This paper proposes and studies a state-of-the-art NLP-based extraction of ADEs from text.