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This study investigates whether ICT technology can enhance applied behavior analysis (ABA) rehabilitation therapy for children with autism. A technology-enhanced rehabilitation system to support the daily work of ABA tutors, parents and teachers was created, involving ABA experts and parents of autistic children in the entire design phase, in order to better understand the system’s functional requirements and enhance its usability. Thus, combining the ABA programs with a learning analytics tool, an open-source Web application, ABCD SW, was implemented for teaching and monitoring learning in low-functioning autistic children. In a small pilot study the system was tested on seven children with autism enrolled in an intensive intervention lasting 9 months. The children were assessed before and after the pilot test, using Vineland Adaptive Behavior Scales to measure their personal and social skills. Test participants showed improved communication, especially in the expressive category (p < 0.05). Subjective feedback from the ABA team involved in the user test confirmed the children’s improvement in socialization, communication and behavior. ABCD SW expedites the intervention (thus increasing its efficiency) and makes it more pleasant for the children. Furthermore, ABCD SW enables caregivers to easily conduct and personalize the intervention, reducing its cost. The study seems to suggest that ABCD SW, and ICT technology in general, can enhance ABA rehabilitation therapy for children with autism, encouraging further investigation of this promising research field. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss various reports within the issue on topics including parallel programming, parallel computers, and CPU/GPU multi-core architectures.

Visual sensor networks (VSNs) perform complex scene analysis algorithms that require significant computations and communications. Under this respect, the use of skeletons contributes to reduce the complexity of VSN programming and may ensure an easier and better optimization of the code. In this context, we propose INS, a stencil based skeleton targeted for wireless/visual sensor networks (W/VSNs) and give a preliminary analysis of its benefits using tracking as a case study. INS abstracts a distributed approximation schema in which the estimation of a given metric is organized in a sequence of steps. Each step includes collecting estimates from some neighbor nodes and local computation of a new approximation. In particular, INS takes inspiration from some stencil based skeletons proposed for parallel computation and merges it with the classical event driven model typical of sensor programming. As a result, the execution of each step is triggered by the detection of a relevant event in the environment. Tracking consists in periodically predicting position and velocity of one or more mobile targets. We discuss how INS can be instantiated to a distributed version of Kalman filtering. As energy efficiency is central in W/VSNs, we derive analytic models for energy dissipation of the INS skeleton depending on different concepts of neighborhood for the data exchanged at each step. Then, these models are used to guide the deployment of our tracking application on a real scenario. [ABSTRACT FROM AUTHOR]

The Network of Tasks (NOT) model allows adaptive node programs written in a variety of parallel languages to be connected together in an almost acyclic task graph. The main difference between NOT and other task graphs is that it is designed to make the performance of the graph predictable from knowledge of the performance of the component node programs and the visible structure of the graph. It can therefore be regarded as a coordination language that is transparent about performance. For large-scale computations that are distributed to geographically-distributed compute servers, the NOT model helps programmers to plan, assemble, schedule, and distribute their problems. [ABSTRACT FROM AUTHOR]