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.
Page segmentation is a web page analysis process that divides a page into cohesive segments, such as sidebars, headers, and footers. Current page segmentation approaches use either the DOM, textual content, or rendering style information of the page. However, these approaches have a number of drawbacks, such as a large number of parameters and rigid assumptions about the page, which negatively impact their segmentation accuracy. We propose a novel page segmentation approach based on visual analysis of localized adjacency regions. It combines DOM attributes and visual analysis to build features of a given page and guide an unsupervised clustering. We evaluate our approach, implemented in a tool called Cortex, on 35 real-world web pages, and examine the effectiveness and efficiency of segmentation. The results show that, compared with state-ofthe-art, Cortex achieves an average of 156% increase in precision and 249% improvement in F-measure.
A wide range of analysis and testing techniques targeting modern web apps rely on the automated exploration of their state space by firing events that mimic user interactions. However, finding out which elements are actionable in web apps is not a trivial task. To improve the efficacy of exploring the event space of web apps, we propose a browser-independent, instrumentation-free approach based on structural and visual stylistic cues. Our approach, implemented in a tool called StyleX, employs machine learning models, trained on 700,000 web elements from 1,000 real-world websites, to predict actionable elements on a webpage a priori. In addition, our approach uses stylistic cues for ranking these actionable elements while exploring the app. Our actionable predictor models achieve 90.14% precision and 87.76% recall when considering the click event listener, and on average, 75.42% precision and 77.76% recall when considering the five most-frequent event types. Our evaluations show that StyleX can improve the JavaScript code coverage achieved by a general-purpose crawler by up to 23%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.