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Understanding and Developing Models for Detecting and Differentiating Breakpoints during Task Execution

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Title: Understanding and Developing Models for Detecting and Differentiating Breakpoints during Task Execution
Author(s): Iqbal, Shamsi T.; Bailey, Brian P.
Subject(s): computer science
Abstract: The ability to detect and differentiate breakpoints during task execution is critical for enabling defer-to-breakpoint policies within interruption management. In this work, we examine the feasibility of building models that are capable of detecting and differentiating three granularities (types) of perceptually meaningful breakpoints, without specifications of users. tasks. We collected an ecological sample of task execution data, and then asked human observers to review the collected videos and identify perceived breakpoints and their type. Statistical methods were applied to learn models that map features of the ongoing interaction to each type of breakpoint. Results showed that the models were able to detect and differentiate breakpoints with reasonably high accuracy across tasks. Among many uses, such models can enable interruption management systems to better realize defer-to-breakpoint policies for interactive, free-form tasks.
Issue Date: 2006-10
Genre: Technical Report
Type: Text
URI: http://hdl.handle.net/2142/11263
Other Identifier(s): UIUCDCS-R-2006-2778
Rights Information: You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS: 2009-04-21
 

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