Abstract
Robots are being increasingly used in safety-critical contexts, such as transportation and health. The need for flexible behavior in these contexts, due to human interaction factors or unstructured operating environments, led to a transition from hardware-to software-based safety mechanisms in robotic systems, whose reliability and quality is imperative to guarantee. Source code static analysis is a key component in formal software verification. It consists on inspecting code, often using automated tools, to determine a set of relevant properties that are known to influence the occurrence of defects in the final product. This paper presents HAROS, a generic, plug-in-driven, framework to evaluate code quality, through static analysis, in the context of the Robot Operating System (ROS), one of the most widely used robotic middleware. This tool (equipped with plug-ins for computing metrics and conformance to coding standards) was applied to several publicly available ROS repositories, whose results are also reported in the paper, thus providing a first overview of the internal quality of the software being developed in this community.

Abstract

Abstract
In order to attain the promises of the Cloud Computing paradigm, systems need to be able to transparently adapt to environment changes. Such behavior benefits from the ability to predict those changes in order to handle them seamlessly. In this paper, we present a mechanism to accurately predict the resource usage of distributed key-value datastores. Our mechanism requires offline training but, in contrast with other approaches, it is sufficient to run it only once per hardware configuration and subsequently use it for online prediction of database performance under any circumstance. The mechanism accurately estimates the database resource usage for any request distribution with an average accuracy of 94 %, only by knowing two parameters: (i) cache hit ratio; and (ii) incoming throughput. Both input values can be observed in real time or synthesized for request allocation decisions. This novel approach is sufficiently simple and generic, while simultaneously being suitable for other practical applications.

Abstract
Searching for health information is one of the most popular activities on the Web. In this domain, users frequently encounter difficulties in query formulation, either because they lack knowledge of the proper medical terms or because they misspell them. To overcome these difficulties and attempt to retrieve higher-quality content, we developed a query suggestion system that provides alternative queries combining the users' native language and English language with lay and medico-scientific terminology. To assess how the language and terminology impact the use of suggestions, we conducted a user study with 40 subjects considering their English proficiency, health literacy and topic familiarity. Results show that suggestions are used most often at the beginning of search sessions. English suggestions tend to be preferred to the ones formulated in the users' native language, at all levels of English proficiency. Medico-scientific suggestions tend to be preferred to lay suggestions at higher levels of health literacy.

Abstract
Mobile devices are now widespread and multimedia consumption on these devices has increased significantly in recent years. More and more high dynamic range (HDR) content is being produced and its imminent adoption by the broadcast community means that there will soon be a demand to visualize HDR content on mobile devices. Mobile devices, however, have certain differences compared to traditional viewing devices. In particular, they are usually used on-the-go, making the context variables such as ambient lighting levels, or reflections important variables that need to be considered. Furthermore, despite their evolution so far, mobile devices usually have additional hardware limitations such as power supply, display features, or local storage availability. This chapter provides an overview of the work that has been conducted so far in addressing HDR video for mobile devices in order to ensure an optimal experience.

Abstract
Modern search engines are evolving beyond ad hoc document retrieval. Nowadays, the information needs of the users can be directly satisfied through entity-oriented search, by ranking the entities or attributes that better relate to the query, as opposed to the documents that contain the best matching terms. One of the challenges in entity-oriented search is efficient query interpretation. In particular, the task of semantic tagging, for the identification of entity types in query parts, is central to understanding user intent. We compare two approaches for semantic tagging, within a single domain, one based on a Sesame triple store and another one based on a Lucene index. This provides a segmentation and annotation of the query based on the most probable entity types, leading to query classification and its subsequent interpretation. We evaluate the run time performance for the two strategies and find that there is a statistically significant speedup, of at least four times, for the index-based strategy over the triple store strategy.