Abstract

Abstract
Tabling is a technique that overcomes some limitations of traditional Prolog systems in dealing with redundant sub-computations and recursion. When tabling is combined with multithreading, we have the best of both worlds, since we can exploit the combination of higher declarative semantics with higher procedural control. To support this combination, the Yap Prolog system has, at engine level, multiple designs that vary from a No-Sharing design, where each thread allocates fully private tables, to a Full-Sharing (FS) design, where threads share the complete table space. In this work, we propose an extension to the table space data structures, which we named Private Answer Chaining (PAC), as way to support batched scheduling evaluation with the FS design. Batched scheduling is one of the most successful tabling scheduling strategies, known to be useful when a tabled logic program requires an eager propagation of answers and/or do not requires the complete set of answers to be found. Experimental results show that PAC is a good first approach, since with it the FS design remains quite competitive.

Abstract
This paper presents an evaluation of high-dynamic-range (HDR) video tone mapping on a small screen device (SSD) under reflections. Reflections are common on mobile devices as these devices are predominantly used on the go. With this evaluation, we study the impact of reflections on the screen and how different HDR video tone mapping operators (TMOs) perform under reflective conditions as well as understand if there is a need to develop a new or hybrid TMO that can deal with reflections better. Two well-known HDR video TMOs were evaluated in order to test their performance with and without on-screen reflections. Ninety participants were asked to rank the TMOs for a number of tone-mapped HDR video sequences on an SSD against a reference HDR display. The results show that the greater the area exposed to reflections, the larger the negative impact on a TMO's perceptual accuracy. The results also show that under observed conditions, when reflections are present, the hybrid TMOs do not perform better than the standard TMOs.

Abstract
Virtual environments for driving simulation aimed to scientific purposes require three-dimensional models of realistic road networks. The generation of these networks according to the requirements, if done manually by road design specialists, results in a time consuming task. Procedural generation of road networks comes as a solution to this problem with the creation of complete road networks definition adequate to simulation. This paper proposes a method to automatically generate an optimized definition of very large roads network, in an integrated process, from the selection of nodes in a terrain area, to the network topological definition. The human supervisor can interact with this generation process at any stage, in order to obtain custom road networks definitions. The proposed method reduces the use of specialists for preparing large road networks definitions. These definitions are suitable to integrate into a broader process to create road environments, with different road types, appropriate to conducting scientific experiments in driving simulation.

Abstract
Research data management is acknowledged as an important concern for institutions and several platforms to support data deposits have emerged. In this paper we start by overviewing the current practices in the data management workflow and identifying the stakeholders in this process. We then compare four recently proposed data repository platforms-DSpace, CKAN, Zenodo and Figshare-considering their architecture, support for metadata, API completeness, as well as their search mechanisms and community acceptance. To evaluate these features, we take into consideration the identified stakeholders' requirements. In the end, we argue that, depending on local requirements, different data repositories can meet some of the stakeholders requirements. Nevertheless, there is still room for improvements, mainly regarding the compatibility with the description of data from different research domains, to further improve data reuse.

Abstract
Evolutionary-based algorithms play an important role in finding solutions to many problems that are not solved by classical methods, and particularly so for those cases where solutions lie within extreme non-convex multidimensional spaces. The intrinsic parallel structure of evolutionary algorithms are amenable to the simultaneous testing of multiple solutions; this has proved essential to the circumvention of local optima, and such robustness comes with high computational overhead, though custom digital processor use may reduce this cost. This paper presents a new implementation of an old, and almost forgotten, evolutionary algorithm: the population-based incremental learning method. We show that the structure of this algorithm is well suited to implementation within programmable logic, as compared with contemporary genetic algorithms. Further, the inherent concurrency of our FPGA implementation facilitates the integration and testing of micro-populations. Copyright