In this paper, we investigate the requirements for designing systems to support wayfinding for visually impaired individuals. We report the results of an interview study with 20 individuals with visual impairments, asking about their way-finding tools, techniques, and obstacles. Our findings provide an account of the practices followed when navigating familiar, unfamiliar, and dynamic environments, and common breakdowns encountered during the wayfinding process. The findings from this study suggest ways of implementing a location-based system to assist in the recovery from various obstacles.
Abstract-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 learning, and Radar with learning) were evaluated in a large-scale, between-subjects study. The study revealed that integrated machine learning does produce a positive impact on overall performance. This paper also discusses how specific machine learning components contributed to human-system performance.
Prior research suggests that interruptions by software components are undesirable and detrimental in many work scenarios. However, there are clear instances where interruptions can conceivably provide a net benefit. For example, interruption is appropriate when reminding a user to accomplish an important task instead of working on lower value activities. This paper examines the pros and cons of interruption and how interruption should occur in the context of an integrated intelligent assistant system. Results from a study and future directions are discussed.
Research in CSCW has demonstrated that people use technology in inventive ways, yet little work investigates the adoption and adaptation of collaborative technologies by unanticipated users. In this paper, we present a study investigating an unanticipated user group's appropriation of a leaning management system, CTools. This group of users, staff at a large research university, has adapted the system, which was designed to support student-content-faculty interactions at the University of Michigan. We present the User/Use Technology Appropriation Matrix (UTAM) as a way to frame our understanding of users and their system use. Based on findings from system log data and surveys, we show that staff use the system similarly to students and faculty, though they value the tools and work affordances differently in their varied work contexts. We discuss these findings, how UTAM can be used to frame these findings, and suggestions for future research.
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, betweensubjects study. This paper describes the RADAR Test with a focus on test design, test harness development, experiment execution, and analysis. Results for the 1.1 version of Radar illustrate the measurement and diagnostic capability of the test. General lessons on such efforts are also discussed. Report Documentation PageForm Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. OverviewThe RADAR (Reflective Agents with Distributed Adaptive Reasoning) project 1 within the DARPA PAL (Personalized Assistant that Learns) program is centered on research and development towards a personal cognitive assistant. The underlying scientific advances within the project are predominantly within the realm of machine learning (ML). These ML approaches are varied and the resulting technologies are diverse. As such, the integration result of this research effort, a system called Radar, is a multi-task machine learning system. Annual evaluation on the integrated system is a major theme for the RADAR project, and the PAL program as a whole. Furthermore, there is an explicit directive to keep the test consistent throughout the program. As such, considerable effort was devoted towards designing, implementing, and executing the evaluation. This document describes this process, protocol, and some of the results for the Radar 1.1 test. Note that this document is not centered on Radar features or the actual machine learning methods used.It is also important to note that the RADAR project differs from the bulk of its predecessors and its companion PAL program project, CALO 2 , in that humans are in the loop for both the learning and evaluation steps. Radar is trained by junior members of the team who are largely unfamiliar with ML methods. Generic human subjects are then recruited to use Radar while handling a simulated crisis in a conference planning domain. This allows concrete measurement of performance using a h...
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