People with autism spectrum disorder (ASD) tend to enjoy themselves and be engaged when interacting with computers, as these interactions occur in a safe and trustworthy environment. In this paper, we present a systematic literature review on the state of the research on the use of technology to teach people with ASD. We reviewed 94 studies that show how the use of technology in educational contexts helps people with ASD develop several skills, how these approaches consider aspects of user experience, usability and accessibility, and how game elements are used to enrich learning environments. This systematic literature review shows that the development and evaluation of systems and applications for users with ASD is very promising. The use of technological advancements such as virtual agents, artificial intelligence, virtual reality, and augmented reality undoubtedly provides a comfortable environment that promotes constant learning for people with ASD.

The Impact of Technology on People with Autism Spectrum Disorder: A Systematic Literature Review.

Background Children with developmental disabilities may need support with motor skills such as balance improvement, cognitive skills such as vocabulary learning, or social skills such as adequate interpretation of emotional expressions. Digital interactive games could support the standard treatments. We aimed to review clinical studies which investigated the application of serious games in children with developmental disabilities. Methods We searched MEDLINE and Scopus on 05 May 2019 limited to the English language. We included people between two and 24 years of age who were affected by neurodevelopmental disorders and who received digital serious game-based medical interventions such as any computer- based or video-based games. We considered any study design reporting primary data. We used title, abstract, and full-text of journal articles to build diagnostic groups, and we described some selected specific game applications. Results The majority of the 145 relevant studies reported on autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), developmental coordination disorder (DCD), and disabilities affecting intellectual abilities (DAIA). 30 of the 145 studies reported a randomized design. We detailed six specific applications aimed at improving abilities in children with ASD, ADHD, cerebral palsy, and Down syndrome. We visualized the diagnostic groups by bibliographic mapping, and limited the text to the title and abstract of journal articles. Conclusion We identified promising results regarding anxiety reduction, stress regulation, emotion recognition, and rehabilitation. Currently, there appears to be a lack of clinical evidence that children with neurodevelopmental disorders can benefit from the application of serious games.

Serious Game-based Intervention for Children with Developmental Disabilities

The recent paradigm shift to contextual word embeddings has seen tremendous success across a wide range of down-stream tasks. However, little is known on how the emergent relation of context and semantics manifests geometrically. We investigate polysemous words as one particularly prominent instance of semantic organization. Our rigorous quantitative analysis of linear separability and cluster organization in embedding vectors produced by BERT shows that semantics do not surface as isolated clusters but form seamless structures, tightly coupled with sentiment and syntax.

/pdf/how-does-bert-capture-semantics-a-closer-look-at-polysemous-1bbg47iid5.pdf

How does BERT capture semantics? A closer look at polysemous words.

Progress in Artificial Intelligence

Autism spectrum disorder (ASD) affects many people; the Center for Disease Control and Prevention estimates that 1 in 59 children are currently identified with ASD in the United States. Although it is difficult to generalize about people with ASD due to their heterogeneity, many share an affinity for technologies; as such, numerous academic endeavors and commercial products have focused on the creation of interactive technologies for ASD. In this article, we present findings from 19 interviews and 230 surveys with parents, teachers, and therapists who had children with ASD in their care and had considered or used interactive technologies with those children. We aimed to understand how interactive technologies were used, perceived, desired, and discovered. Findings of use and perception included the following: participants had tried a wide range of commercially available technologies but had very few reported products in common, products were limited to commercial mobile-based apps, and apps were typically perceived positively. In regard to desires, participants hoped for future technologies on diverse platforms (e.g., robots, virtual reality) with more consideration given to personalization, customization, and incorporation of audio and video. Findings about discovery included the following: participants chose technologies in an information-poor environment, and although there are many academic projects aimed at participants’ desires, no participants reported any experience working with researchers. Implications of this study include the need for a recommendation and information sharing system to help people choose and discover appropriate and effective interactive technologies that are a good fit for their child. This work also pointed to a need for such a system to include findings from lab (experimental and usability) studies of commercially available interactive technologies to provide measures of efficacy and usability. Our envisioned system could also potentially help academic researchers with outreach to wider audiences.

Interactive Technologies Designed for Children with Autism: Reports of Use and Desires from Parents, Teachers, and Therapists

In this work, we present a platform designed for children with Autism Spectrum Disorder to develop language and generalization skills, in response to the lack of applications tailored for the unique abilities, symptoms, and challenges of the autistic children. This platform allows caregivers to build customized multiple choice exercises while taking into account specific needs/characteristics of each child. We also propose a module for the automatic generation of exercises, aiming to ease the task of exercise creation for caregivers.

/pdf/vithea-kids-a-platform-for-improving-language-skills-of-2g5nyly1dj.pdf

VITHEA-Kids: a Platform for Improving Language Skills of Children with Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) often comprises difficulties in the acquisition of communication and language skills. Several researchers and companies have developed software to help individuals with ASD developing those skills; however, there is a lack of applications in Portuguese that are tailored to the individual needs of each child. In this context, we present VITHEA-Kids, a platform where caregivers can create exercises and customize the interaction between each child and the platform. We also developed a module for the automatic generation of multiple choice exercises, meant to be integrated in VITHEA-Kids. We evaluated this work with caregivers (which provided promising indicators), with a child (ongoing upon thesis delivery) and we also evaluated the generation of incorrect answers in multiple choice exercises (achieving acceptance rates between 61.11 % and 92.22 %).

Extending VITHEA in Order to Improve Children's Linguistic Skills

In Machine Translation, assessing the quality of a large amount of automatic translations can be challenging. Automatic metrics are not reliable when it comes to high performing systems. In addition, resorting to human evaluators can be expensive, especially when evaluating multiple systems. To overcome the latter challenge, we propose a novel application of online learning that, given an ensemble of Machine Translation systems, dynamically converges to the best systems, by taking advantage of the human feedback available. Our experiments on WMT’19 datasets show that our online approach quickly converges to the top-3 ranked systems for the language pairs considered, despite the lack of human feedback for many translations.

/pdf/online-learning-meets-machine-translation-evaluation-finding-4iv415w4cz.pdf

Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort

In this work, we improve the performance of a dialogue engine, Say Something Smart, using online learning Given a request by a user, this engine selects an answer from a corpus of movie subtitles, weighting the quality of each candidate answer according to several criteria and selecting the one that is chosen by the most representative criteria We contribute with an online approach, using sequential learning, that adjusts the weights of the different criteria using a reference corpus of actual dialogues as input to simulate user feedback This approach effectively allowed Say Something Smart to improve its performance at each interaction, as shown in an experiment performed in a test corpus

A Conversational Agent Powered by Online Learning

One of the main challenges in CBIR systems is to choose discriminative and compact features, among dozens, to represent the images under comparison. Over the years, a great effort has been made to combine multiple features, mainly using early, late, and hierarchical fusion techniques. Unveiling the perfect combination of features is highly domain-specific and dependent on the type of image. Thus, the process of designing a CBIR system for new datasets or domains involves a huge experimentation overhead, leading to multiple fine-tuned CBIR systems. It would be desirable to dynamically find the best combination of CBIR systems without needing to go through such extensive experimentation and without requiring previous domain knowledge. In this paper, we propose ExpertosLF, a model-agnostic interpretable late fusion technique based on online learning with expert advice, which dynamically combines CBIR systems without knowing a priori which ones are the best for a given domain. At each query, ExpertosLF takes advantage of user’s feedback to determine each CBIR contribution in the ensemble for the following queries. ExpertosLF produces an interpretable ensemble that is independent of the dataset and domain. Moreover, ExpertosLF is designed to be modular, and scalable. Experiments on 13 benchmark datasets from the Biomedical, Real, and Sketch domains revealed that: (i) ExpertosLF surpasses the performance of state of the art late-fusion techniques; (ii) it successfully and quickly converges to the performance of the best CBIR sets across domains without any previous domain knowledge (in most cases, fewer than 25 queries need to receive human feedback). 

/pdf/expertoslf-dynamic-late-fusion-of-cbir-systems-using-online-1bw1pt44.pdf

ExpertosLF: dynamic late fusion of CBIR systems using online learning with relevance feedback

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