Jordan Hayes

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Email client software is widely used for personal task management, a purpose for which it was not designed and is poorly suited. Past attempts to remedy the problem have focused on adding task management features to the client UI. RADAR uses an alternative approach modeled on a trusted human assistant who reads mail, identifies task-relevant message(More)
The RADAR project involves a collection of machine learning research thrusts that are integrated into a cognitive personal assistant. Progress is examined with a test developed to measure the impact of learning when used by a human user. Three conditions (conventional tools, Radar without learning, and Radar with learning) are evaluated in a large-scale,(More)
Performance of a cognitive personal assistant, RADAR, consisting of multiple machine learning components, natural language processing, and optimization was examined with a test explicitly developed to measure the impact of integrated machine learning when used by a human user in a real world setting. Three conditions (conventional tools, Radar without(More)
Current authentication mechanisms pose significant challenges for people with visual impairments. This paper presents results from a contextual inquiry study that investigated the experiences this population encounters when logging into their computers, smart phones, and websites that they use. By triangulating results from observation, contextual inquiry(More)
Visually impaired users face various challenges in web authentication. We designed UniPass, an accessible password manager for visually impaired users based on a smart device. To evaluate UniPass, we tested and compared UniPass with two commercial password managers: LastPass, a popular password manager and StrongPass, a smart device-based password manager.(More)
Authentication is a mundane yet often integral part of people's experiences with computing devices and Internet services. Since most authentication mechanisms were designed without explicitly considering people with disabilities, these mechanisms may pose significant challenges for these users. In this paper, we report results from a contextual inquiry(More)
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