Abstract
High-level Synthesis (HLS) is of paramount importance to leverage the use of FPGA-based accelerators by software developers. To achieve efficient FPGA implementations, code restructuring and source code annotating with HLS directives are necessary. However, this is still a manual process conducted by experienced developers. This paper proposes a step on a framework to automatically optimize C code via directives, using a source-to-source compiler on a stage before HLS. This optimization is primarily applied by strategies that select, configure, and insert directives on the code input to the Vivado HLS tool to synthesize more latency-efficient FPGA hardware. Those strategies rely on very simple but effective heuristics, which use a small set of properties extracted from the control/dataflow graphs generated from the input source code. We evaluate the framework using a variety of source codes. The experiments show that it can achieve efficient results while maintaining a low resource usage in most cases. Our experiments also compare the framework results to code optimized manually with directives, and they show that for most benchmarks used, it achieves similar results.

Abstract
This article presents Clava, a Clang-based source-to-source compiler, that accepts scripts written in LARA, a JavaScript-based DSL with special constructs for code queries, analysis and transformations. Clava improves Clang's source-to-source capabilities by providing a more convenient and flexible way to analyze, transform and generate C/C++ code, and provides support for building strategies that capture run-time behavior. We present the Clava framework, its main capabilities, and how it can been used. Furthermore, we show that Clava is sufficiently robust to analyze, instrument and test a set of large C/C++ application codes, such as GCC. (C) 2020 The Authors. Published by Elsevier B.V.

Abstract
In order to take advantage of the processing power of current computing platforms, programmers typically need to develop software versions for different target devices. This task is time-consuming and requires significant programming and computer architecture expertise. A possible and more convenient alternative is to start with a single high-level description of a program with minimum implementation details, and generate custom implementations according to the target platform. In this paper, we use MATLAB as a high-level programming language and propose a compiler that targets CPU/GPU computing platforms by generating customized implementations in C and OpenCL. We propose a number of compiler techniques to automatically generate efficient C and OpenCL code from MATLAB programs. One of such compiler techniques relies on heuristics to decide when and how to use Shared Virtual Memory (SVM). The experimental results show that our approach is able to generate code that provides significant speedups (eg, geometric mean speedup of 11x for a set of simple benchmarks) using a discrete GPU over equivalent sequential C code executing on a CPU. With more complex benchmarks, for which only some code regions can be parallelized, and are thus offloaded, the generated code achieved speedups of up to 2.2x. We also show the impact of using SVM, specifically fine-grained buffers, and the results show that the compiler is able to achieve significant speedups, both over the versions without SVM and with naive aggressive SVM use, across three CPU/GPU platforms.

Abstract
Healthcare centers are ideal settings to identify and motivate parents to adhere to healthier lifestyles in order to reverse their child's excessive weight. However, such promotions are time- and resource-consuming, and primary health care needs new approaches to engage parents. In this article, we present an app for that purpose, with tailored recommendations for parents regarding young children's lifestyles (eating, drinking, moving, and sleeping habits) with gamification mechanics for parents, and a serious game for their children, aged 3-6 years. Healthcare center based questionnaires were used to assess parent's acceptance of the app. We also determined their enrollment and retention rates with a pilot study, in order to test the implementation of a randomized controlled trial (RCT) in the future (pilot trial registration: ClinicalTrials.gov NCT03881280). In the pilot study, we tested their engagement with the app during a four-week period through a monitoring website. In the acceptance test (n = 13), parents rated the app a median score of seven on a ten-point scale. In the pilot study (n = 21), all parents in the intervention group used the app. The retention rate was 71.4%. This study indicated some areas of improvement related to gaming mechanics in order to increase participation. Healthcare center's professionals and parents of children with overweight/obesity accepted this innovative approach. In addition, it is feasible to test its impact on children's lifestyle by conducting an RCT in the future.

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
Object detection in the traffic domain has faced growing relevance through the years in developing autonomous driving mechanisms. As with vehicles, pedestrians face a very dynamic context, and identifying relevant objects from a pedestrian perspective presents many challenges. Improving the detection of some objects, such as crosswalks, is very relevant in this regard. This paper presents a technique that applies a computer vision approach to automatically generate datasets for training YOLO-based deep learning algorithms. An initial precision of 0.82 achieved with the generated dataset, which is increased to 0.84 after manually removing incorrect annotations. Results show that our approach leverages the dataset building process by reducing the manual workload needed. The approach could be used for training other object detection models used in traffic scenarios. © 2020, Springer Nature Switzerland AG.