Abstract
The Internet of Things can be seen has a growing number of things that inter-operate using an Internet-based infrastructure and that has evolved during the last years with little concern for the privacy of its users, especially regarding how the collected data is stored. Technological measures ensuring users privacy must be established. In this paper we will present a technological framework for the secure storage of data. Things can then interact with the framework's API much in the same way they now interact with its current servers, after which, the framework will performthe required operations in order to secure the data before storing it. Themethods adopted for the secure storage will maintain the sharing ability, conveniently allowing authorized access to other users, the initial user's terms (e.g. data anonymity) and the ability to revoke assigned privileges at all times. © Springer International Publishing Switzerland 2015.

Abstract
Population aging brings increased social problems. Solutions for this new reality must be devised. Providing care services at home may benefit patients, health service providers, and social security systems and needs to be seen as a possible solution for those social problems. By maintaining the patient at home, in his or her own environment, care services costs can be diminished and, at the same time, the comfort and well-being of the person in need are significantly increased. To pursue this goal, we explore the advantages that ambient assisted living can bring to people in a home environment, focusing on the problems of health care services at home. Specifically, in this chapter, we present a framework focused on the monitoring and assistance of the elderly that are living alone, focusing on those elderly with memory disabilities. We believe that this approach will enable the challenges that the current trend of population aging poses to be tackled. © 2015 by Taylor & Francis Group, LLC.

Abstract
In this paper, we present the tools of the MediaViz project, a work-in progress platform that aims to provide researchers, academics and professionals from the media field with a set of analytical and exploratory resources to answer high level and complex questions about the online media panorama, in an eficient, visual and interactive way. Our approach consists of aggregating and processing news data from multiple online sources, and provide programatic access to it through an Application Programming Interface (API). The visualization tools leverage the data provided by the API, allowing users to interact, explore and interrogate that information. Through the use of data visualization techniques, we aim to characterize the publication patterns of multiple online news sources by analyzing and comparing distinct dimensions. Dimensions of interest include the frequency and flow of publications and social shares throughout time, and the geographic coverage of online news outlets. We present some of the developed visualization tools and describe how they can offer meaningful insights by providing a bird's-eye view of distinct characteristics of the online mediascape. © 2015 ACM.

Abstract
In this article we survey the historical background and development of information and data visualization, and an overview of the intersection of data visualization with storytelling applied to the field of data journalism, where it finds its most widespread use in narrative visualizations. We start by explaining why the mere act of visualization can be highly useful to readers, helping them discover patterns and comprehend information. Backed by historical references, we will describe how some of the first data visualizations were used to explain facts, understand certain events, and determine courses of action. We will then outline how storytelling and narrative techniques are being currently used with data visualization to leverage the power of visual expression. Our goal is to characterize storytelling with data as a vibrant and interesting field that current journalism practices employ to help readers understand and form opinions on complex facts. By presenting concepts like storytelling with data and data stories, we aim to spark interest in further research in the applications of data visualization and narrative. © 2015 ACM.

Abstract

Abstract
This article presents a reconfigurable hardware/software architecture for binary acceleration of embedded applications. A Reconfigurable Processing Unit (RPU) is used as a coprocessor of the General Purpose Processor (GPP) to accelerate the execution of repetitive instruction sequences called Megablocks. A toolchain detects Megablocks from instruction traces and generates customized RPU implementations. The implementation of Megablocks with memory accesses uses a memory-sharing mechanism to support concurrent accesses to the entire address space of the GPP's data memory. The scheduling of load/store operations and memory access handling have been optimized to minimize the latency introduced by memory accesses. The system is able to dynamically switch the execution between the GPP and the RPU when executing the original binaries of the input application. Our proof-of-concept prototype achieved geometric mean speedups of 1.60x and 1.18x for, respectively, a set of 37 benchmarks and a subset considering the 9 most complex benchmarks. With respect to a previous version of our approach, we achieved geometric mean speedup improvements from 1.22 to 1.53 for the 10 benchmarks previously used.