Abstract

Abstract
High Level Synthesis (HLS) tools targeting Field Programmable Gate Arrays (FPGAs) aim to provide a method for programming these devices via high-level abstractions. Initially, HLS support for FPGAs focused on compiling C/C CC to hardware circuits. This raised the issue of determining the programming practices which resulted in the best performing circuits. Recently, to further increase the applicability of HLS approaches, renewed effort was placed on support for HLS of OpenCL code for FPGA, raising the same issues of coding practices and performance portability. This paper explores the performance of OpenCL code compiled for FPGAs for different coding techniques. We evaluate the use of task-kernels versus NDRange kernels, data vectorization, the use of on-chip local memories, and data transfer optimizations by exploiting burst access inference. We present this exploration via a case study of the k-means algorithm, and produce a total of 10 OpenCL implementations of the kernel. To determine the effects of different data set characteristics, and to determine the gains from specialization based on number of attributes, we generated a total of 12 integer data sets. The data sets vary regarding the number of instances, number of attributes (i.e., features), and number of clusters. We also vary the number of processing cores, and present the resulting required resources and operating frequencies. Finally, we execute the same OpenCL code on a 4 GHz Intel i7-6700K CPU, showing that the FPGA achieves speedups up to 1.54 x for four cases, and energy savings up to 80% in all cases.

Abstract
Childhood obesity is associated with unbalanced lifestyle patterns, and new strategies are needed to support parents in the compliance with the guidelines for children's age. Tailored automatic recommendations mimic interpersonal counseling and are promising strategies to be considered for health promotion programs. This study aimed to develop and test a mobile recommendation system for parents of preschool children identified with overweight/obesity at health care centers. Evidence-based recommendations related to children's eating, drinking, moving, and sleeping habits were developed and tested using a questionnaire. A pilot study was conducted in a health care center to test how using an app with those tailored recommendations, in video format, influenced parents' perceptions of the child's weight status and their knowledge about the guidelines, compared to a control group. The chi-squared test was used for categorical variables and the Mann-Whitney U test for continuous variables (p< 0.05). A high proportion of parents were already informed about the guidelines, but their children were not meeting them. After watching the tailored recommendations, there was an increased knowledge of the guideline on water intake, but there was no improvement in the perception of the child's excessive weight. Parents may benefit from a mobile-based tailored recommendation system to improve their knowledge about the guidelines. However, there is a need to work with parents on motivation to manage the child's weight with additional strategies.

Abstract
Although the growing concern to make museums accessible to individuals with visual impairments, their participation in these institutions is still limited, given the multiple barriers they often experience and the lack of assistive technologies to promote access. This research investigates the perspectives of 72 blind and partially sighted individuals on enhancing their visiting experience in museums. A co-created framework to improve visitors' autonomy is proposed, concluding that sensory, intellectual and physical access must be integrated into the pre, on-site and post phases of visiting museums.

Abstract
Background: Internet-based cognitive-behavioral therapy (iCBT) is more effective when it is guided by human support than when it is unguided. This may be attributable to higher adherence rates that result from a positive effect of the accompanying support on motivation and on engagement with the intervention. This protocol presents the design of a pilot randomized controlled trial that aims to start bridging the gap between guided and unguided interventions. It will test an intervention that includes automated support delivered by an embodied conversational agent (ECA) in the form of a virtual coach. Methods/design: The study will employ a pilot two-armed randomized controlled trial design. The primary outcomes of the trial will be (1) the effectiveness of iCBT, as supported by a virtual coach, in terms of improved intervention adherence in comparison with unguided iCBT, and (2) the feasibility of a future, larger-scale trial in terms of recruitment, acceptability, and sample size calculation. Secondary aims will be to assess the virtual coach's effect on motivation, users' perceptions of the virtual coach, and general feasibility of the intervention as supported by a virtual coach. We will recruitN = 70 participants from the general population who wish to learn how they can improve their mood by using Moodbuster Lite, a 4-week cognitive-behavioral therapy course. Candidates with symptoms of moderate to severe depression will be excluded from study participation. Included participants will be randomized in a 1:1 ratio to either (1) Moodbuster Lite with automated support delivered by a virtual coach or (2) Moodbuster Lite without automated support. Assessments will be taken at baseline and post-study 4 weeks later. Discussion: The study will assess the preliminary effectiveness of a virtual coach in improving adherence and will determine the feasibility of a larger-scale RCT. It could represent a significant step in bridging the gap between guided and unguided iCBT interventions.

Abstract
Bayesian Monte Carlo (BMC) is a promising integration technique which considerably broadens the theoretical tools that can be used to maximize and exploit the information produced by sampling, while keeping the fundamental property of data dimension independence of classical Monte Carlo (CMC). Moreover, BMC uses information that is ignored in the CMC method, such as the position of the samples and prior stochastic information about the integrand, which often leads to better integral estimates. Nevertheless, the use of BMC in computer graphics is still in an incipient phase and its application to more evolved and widely used rendering algorithms remains cumbersome. In this article we propose to apply BMC to a two-level adaptive sampling scheme for illumination integrals. We propose an efficient solution for the second level quadrature computation and show that the proposed method outperforms adaptive quasi-Monte Carlo in terms of image error and high frequency noise. © 2016 The Eurographics Association.