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 Backed by historical references, we will describe how some of the first data visualizations were used to explain facts, understand certain events, and determine courses of action.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
We describe GPU implementations of the matrix recommender algorithms CCD++ and ALS. We compare the processing time and predictive ability of the GPU implementations with existing multi- core versions of the same algorithms. Results on the GPU are better than the results of the multi- core versions (maximum speedup of 14.8).

Abstract
Since high dynamic range (HDR) displays are not yet widely available, there is still a need to perform a dynamic range reduction of HDR content to reproduce it properly on standard dynamic range (SDR) displays. The most common techniques for performing this reduction are termed tone-mapping operators (TMOs). Although mobile devices are becoming widespread, methods for displaying HDR content on these SDR screens are still very much in their infancy. While several studies have been conducted to evaluate TMOs, few have been done with a goal of testing small screen displays (SSDs), common on mobile devices. This paper presents an evaluation of six state-of-the-art HDR video TMOs. The experiments considered three different levels of ambient luminance under which 180 participants were asked to rank the TMOs for seven tone-mapped HDR video sequences. A comparison was conducted between tone-mapped HDR video footage shown on an SSD and on a large screen SDR display using an HDR display as reference. The results show that there are differences between the performance of the TMOs under different ambient lighting levels and the TMOs that perform well on traditional large screen displays also perform well on SSDs at the same given luminance level.

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.

Abstract
Model finders are very popular for exploring scenarios, helping users validate specifications by navigating through conforming model instances. To be practical, the semantics of such scenario exploration operations should be formally defined and, ideally, controlled by the users, so that they are able to quickly reach interesting scenarios. This paper explores the landscape of scenario exploration operations, by formalizing them with a relational model finder. Several scenario exploration operations provided by existing tools are formalized, and new ones are proposed, namely to allow the user to easily explore very similar (or different) scenarios, by attaching preferences to model elements. As a proof-of-concept, such operations were implemented in the popular Alloy Analyzer, further increasing its usefulness for (user-guided) scenario exploration.

Abstract
Search engines typically estimate relevance using features of the documents. We believe that several features from the user and task can also contribute to this process. In the health domain there are specific characteristics of web documents that can also add value to this estimation. In the present work, using a dataset composed by set of annotated web pages and their assessment by a set of users regarding their relevance and comprehension, we analyse what characteristics affect documents' relevance and what characteristics influence how well users comprehend them. We have conducted a bivariate analysis using characteristics of the above data collection. The strongest relations we have found are linked to the task features, suggesting a direct association between tasks' clarity and easiness and both the relevance and the comprehension of the content. The language of the document, its medical certification, the update status, the content in pathology definitions, the content in prevention, prognosis and treatment information, are other characteristics valued by consumers in terms of relevance. Users' previous experience on health searches and, particularly, on the topic being searched, their gender, the language and terminology of their queries were shown to be related to their success in the search tasks. We have also found that lay terminology, knowledge about the medico-scientific terms and the language of the documents are good indicators of comprehension. Documents containing links and testimonies, and the ones recently updated were observed to be better understood by users, as well as blog posts and comments. (C) 2015 The Authors. Published by Elsevier B.V.