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• AutoChemplete - Making Chemical Structural Formulas Accessible

  Merlin Knaeble, Gabriel Sailer, Zihan Chen, Thorsten Schwarz, Kailun Yang, Mario Nadj, Rainer Stiefelhagen, Alexander Maedche · 2023 · Proceedings of the 20th International Web for All Conference (W4A)

  This paper presents AutoChemplete, an interactive labeling tool that makes chemical structural formulas accessible to blind and low-vision (BLV) students. Chemical structural formulas — visual diagrams showing atoms, bonds, and molecular geometry — are fundamental to studying…

  STEM accessibility · blind and low vision · chemistry · machine learning · interactive labeling

• AutoChemplete - Making Chemical Structural Formulas Accessible (Extended Abstract)

  Merlin Knaeble, Gabriel Sailer, Zihan Chen, Thorsten Schwarz, Kailun Yang, Mario Nadj, Rainer Stiefelhagen, Alexander Maedche · 2023 · Proceedings of the 20th International Web for All Conference (W4A)

  This extended abstract presents AutoChemplete, an interactive labeling tool designed to make chemical structural formulas accessible to blind and low vision (BLV) students. The paper highlights a stark gap in STEM education: while 69% of US BLV students express interest in STEM…

  STEM accessibility · chemistry accessibility · blind and low vision · interactive labeling · machine learning

• OPTIMAL-EM: Optimised Population Sourcing for Web Accessibility Evaluation

  Alexander Hambley, Yeliz Yesilada, Markel Vigo, Simon Harper · 2023 · Proceedings of the 20th International Web for All Conference (W4A '23)

  This extended abstract presents OPTIMAL-EM, a prototypical tool and framework designed to optimise the web accessibility evaluation process by systematically selecting representative pages from a website for auditing. Currently, accessibility evaluations supported by…

  automated accessibility testing · web accessibility · accessibility evaluation · web crawling · machine learning

• Towards Optimised Population Sourcing for Web Accessibility Evaluation

  Alexander Hambley · 2023 · Proceedings of the 20th International Web for All Conference (W4A '23)

  This doctoral consortium extended abstract presents a PhD project proposing a novel framework and prototypical tool for optimising web accessibility evaluation through statistically representative page sampling. The work is the single-author companion to the multi-author…

  automated accessibility testing · web accessibility · accessibility evaluation · web crawling · machine learning

• The Sem-Lex Benchmark: Modeling ASL Signs and their Phonemes

  Lee Kezar, Jesse Thomason, Naomi Caselli, Zed Sehyr, Elana Pontecorvo · 2023 · ASSETS '23: Proceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper introduces the Sem-Lex Benchmark, the largest curated dataset of its kind for American Sign Language (ASL) isolated sign recognition, containing over 84,000 videos of isolated sign productions from 41 deaf ASL signers. The dataset addresses two critical barriers in…

  sign language recognition · American Sign Language · machine learning · phonology · dataset

• AdaptiveSound: An Interactive Feedback-Loop System to Improve Sound Recognition for Deaf and Hard of Hearing Users

  Hang Do, Quan Dang, Jeremy Zhengqi Huang, Dhruv Jain · 2023 · ASSETS '23: Proceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper introduces AdaptiveSound, the first feedback-loop sound recognition system designed for deaf and hard of hearing (DHH) users. Sound recognition tools help DHH people become aware of environmental sounds ranging from safety-critical alerts like fire alarms and sirens…

  deaf and hard of hearing · sound recognition · human-in-the-loop · machine learning · incremental learning

• Reimagining Machine Learning's Role in Assistive Technology by Co-Designing Exergames with Children Using a Participatory Machine Learning Design Probe

  Jared Duval, Laia Turmo Vidal, Elena Márquez Segura, Yinchu Li, Annika Waern · 2023 · Proceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2023)

  This paper fundamentally reframes the role of machine learning in assistive technology, arguing that ML models do not need to be accurate to be valuable — they can serve as sources of play and motivation rather than diagnostic tools. The researchers developed Cirkus, a…

  machine learning · participatory design · exergames · children · sensory processing

• "Not There Yet": Feasibility and Challenges of Mobile Sound Recognition to Support Deaf and Hard-of-Hearing People

  Jeremy Zhengqi Huang, Hriday Chhabria, Dhruv Jain · 2023 · Proceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS)

  This paper presents the first longitudinal field study of a mobile sound recognition system used by Deaf and hard of hearing (DHH) people in their daily lives. The researchers deployed SoundWatch, a smartwatch-based app that uses a deep learning model (Google YAMNet…

  deaf and hard of hearing · sound recognition · wearable technology · smartwatch · assistive technology

• Machine Generation of Audio Description for Blind and Visually Impaired People

  Virgínia P. Campos, Tiago M. U. de Araújo, Guido L. de Souza Filho, Luiz M. G. Gonçalves · 2023 · ACM Transactions on Accessible Computing

  This paper presents an extension to CineAD, a system for automatically generating audio descriptions (AD) for videos. The authors address a critical accessibility gap: most videos, films, and cultural programming lack audio descriptions, leaving blind and visually impaired (BVI)…

  audio description · blind and visually impaired · computer vision · machine learning · video accessibility

• Never-ending Learning of User Interfaces

  Jason Wu, Rebecca Krosnick, Eldon Schoop, Amanda Swearngin, Jeffrey P. Bigham, Jeffrey Nichols · 2023 · Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST)

  This paper introduces the Never-ending UI Learner, an automated system that continuously crawls real mobile applications to learn semantic properties of user interfaces. The system addresses a fundamental limitation of current approaches to UI understanding: most machine…

  machine learning · mobile accessibility · UI semantics · automated crawling · tappability

• WebUI: A Dataset for Enhancing Visual UI Understanding with Web Semantics

  Jason Wu, Siyan Wang, Siman Shen, Yi-Hao Peng, Jeffrey Nichols, Jeffrey P. Bigham · 2023 · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

  This paper introduces WebUI, a large-scale dataset of approximately 400,000 web pages automatically crawled and paired with visual, semantic, and stylistic metadata extracted from the browser engine. The dataset addresses a critical bottleneck in UI understanding research:…

  machine learning · computer vision · UI modeling · web semantics · transfer learning

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