Autism Spectrum Disorder (ASD) prevalence rates are increasing and serious games have shown a valuable potential to aid the treatment of autistic individuals. Hence, a Systematic Literature Review was conducted aiming at categorizing serious games for ASD children regarding which skills they aim to develop, how their activities were operationalized, and which customization options they provide to users. Our results showed that a large number of serious games aimed at developing distinct skills in ASD children have been proposed, with their main focus being on social and socio-emotional skills. Nonetheless, for each skill we characterize the existing games according to their features, that is the platform they are developed for, I/O devices used, required users’ action, audiovisual elements, and number of players. We also identify strategies adopted in the games regarding specific features and skills that emerged from the analysis. Finally, our results highlight that offering broader customization options in the games could expand their applicability and utility to their users. This work provides a thorough examination of research on games for ASD children, contributing both to researchers interested in the topic, by identifying existing contributions and open issues for research, and to professionals interested in developing serious games for this public.

Translational research projects target a wide variety of diseases, test many different kinds of biomedical hypotheses, and employ a large assortment of experimental methodologies. Diverse data, complex execution environments, and demanding security and reliability requirements make the implementation of these projects extremely challenging and require novel e-Science technologies.

Frequent itemset mining (FIM) is a core operation for several data mining applications as association rules computation, correlations, document classification, and many others, which has been extensively studied over the last decades. Moreover, databases are becoming increasingly larger, thus requiring a higher computing power to mine them in reasonable time. At the same time, the advances in high performance computing platforms are transforming them into hierarchical parallel environments equipped with multi-core processors and many-core accelerators, such as GPUs. Thus, fully exploiting these systems to perform FIM tasks poses as a challenging and critical problem that we address in this paper. We present efficient multi-core and GPU accelerated parallelizations of the Tree Projection, one of the most competitive FIM algorithms. The experimental results show that our Tree Projection implementation scales almost linearly in a CPU shared-memory environment after careful optimizations, while the GPU versions are up to 173 times faster than standard the CPU version.