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Web accessibility, the design of web apps to be usable by users with disabilities, impacts millions of people around the globe. Although accessibility has traditionally been a marginal afterthought that is often ignored in many software products, it is increasingly becoming a legal requirement that must be satisfied. While some web accessibility testing tools exist, most only perform rudimentary syntactical checks that do not assess the more important high-level semantic aspects that users with disabilities rely on. Accordingly, assessing web accessibility has largely remained a laborious manual process requiring human input. In this paper, we propose an approach, called AXERAY, that infers semantic groupings of various regions of a web page and their semantic roles. We evaluate our approach on 30 real-world websites and assess the accuracy of semantic inference as well as the ability to detect accessibility failures. The results show that AXERAY achieves, on average, an F-measure of 87% for inferring semantic groupings, and is able to detect accessibility failures with 85% accuracy.

Diffusion magnetic resonance imaging (dMRI) provides unique capabilities for non-invasive mapping of fiber tracts in the brain. It however suffers from relatively low spatial resolution, often leading to partial volume effects. In this paper, we propose to use a super-resolution approach based on dictionary learning for alleviating this problem. Unlike the majority of existing super-resolution algorithms, our proposed solution does not entail acquiring multiple scans from the same subject which renders it practical in clinical settings and applicable to legacy data. Moreover, this approach can be used in conjunction with any diffusion model. Motivated by how functional connectivity (FC) reflects the underlying structural connectivity (SC), we quantitatively validate our results by investigating the consistency between SC and FC before and after super-resolving the data. Based on this scheme, we show that our method outperforms traditional interpolation strategies and the only existing single image super-resolution method for dMRI that is not dependent on a specific diffusion model. Qualitatively, we illustrate that fiber tracts and tract-density maps reconstructed from super-resolved dMRI data reveal exquisite details beyond what is achievable with the original data.