Abstract
Tone mapping operators (TMOs) allow the visual appearance of High Dynamic Range (HDR) video to be reproduced on Low Dynamic Range (LDR) displays. While several studies have been made to compare the performance of different TMOs on standard displays, there is only one preliminary study that takes into account the characteristics of Small Screen Devices (SSDs) which are common on mobile platforms that are growing rapidly in popularity. Due to the unique characteristics of mobile devices, the variety of viewing conditions where they are used, and the fact that they are becoming so widespread, it is important to identify what is the best method to deliver HDR video content to these devices. This paper presents an evaluation of HDR video on mobile devices using an HDR display as reference. Six state-of-the-art HDR video tone mappers are evaluated by conducting a psychophysical experiment where participants were asked to rank the tone mappers applied to different HDR video footage. A comparison was made between tone mapped HDR video footage shown on a tablet and an LCD display compared with the same HDR video footage shown simultaneously on an HDR display. This study shows there is a subtle but statistically significant difference in the preference of TMOs between SSD and LDR even though the overall ordering of TMOs is the same across both displays.

Abstract
Domain-Specific Languages (DSLs) developers aim to narrow the gap between the level of abstraction used by domain users and the one provided by the DSL, in order to help taming the increased complexity of computer systems and real-world problems. The quality in use of a DSL is essential for its successful adoption. We illustrate how a usability evaluation process can be weaved into the development process of a concrete DSL - FlowSL - used for specifying humanitarian campaign processes lead by an international Non-Governmental Organization. FlowSL is being developed following an agile process using Model-Driven Development (MDD) tools, to cope with vague and poorly understood requirements in the beginning of the development process.

Abstract
The description of data is a central task in research data management. Describing datasets requires deep knowledge of both the data and the data creation process to ensure adequate capture of their meaning and context. Metadata schemas are usually followed in resource description to enforce comprehensiveness and interoperability, but they can be hard to understand and adopt by researchers. We propose to address data description using ontologies, which can evolve easily, express semantics at different granularity levels and be directly used in system development. Considering that existing ontologies are often hard to use in a cross domain research data management environment, we present an approach for creating lightweight ontologies to describe research data. We illustrate our process with two ontologies, and then use them as configuration parameters for Dendro, a software platform for research data management currently being developed at the University of Porto.

Abstract
Teaching and learning computer programming is as challenging as it is difficult. Assessing the work of students and providing individualised feedback is time-consuming and error prone for teachers and frequently involves a time delay. The existent tools prove to be insufficient in domains where there is a greater need to practice. At the same time, Massive Open Online Courses (MOOC) are appearing, revealing a new way of learning. However, this paradigm raises serious questions regarding the monitoring of student progress and its timely feedback. This chapter provides a conceptual design model for a computer programming learning environment. It uses the portal interface design model, gathering information from a network of services such as repositories, program evaluators, and learning management systems, a central piece in the MOOC realm. This model is not limited to the domain of computer programming and can be adapted to any area that requires evaluation with immediate feedback. © 2015, IGI Global.

Abstract

Abstract
Prime numbers play a pivotal role in current encryption algorithms and given the rise of cloud computing, the need for larger primes has never been so high. This increase in available computation power can be used to either try to break the encryption or to strength it by finding larger prime numbers. With this in mind, this paper provides an analysis of different sieve implementations that can be used to generate primes to near 2(64). It starts by analyzing cache friendly sequential sieves with wheel factorization, then expands to multi-core architectures and ends with a cache friendly segmented hybrid implementation of a distributed prime sieve, designed to efficiently use all the available computation resources of heterogeneous computer clusters with variable workload and to scale very well in both the shared and distributed memory versions.