Sensing techniques for mobile interaction
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Leveraging characteristics of task structure to predict the cost of interruption
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FeedMe: a collaborative alert filtering system
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Matching attentional draw with utility in interruption
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Shape changing device for notification
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Private aggregation for presence streams
Future Generation Computer Systems
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We present methods for inferring the cost of interrupting users based on multiple streams of events including information generated by interactions with computing devices, visual and acoustical analyses, and data drawn from online calendars. Following a review of prior work on techniques for deliberating about the cost of interruption associated with notifications, we introduce methods for learning models from data that can be used to compute the expected cost of interruption for a user. We describe the Interruption Workbench, a set of event-capture and modeling tools. Finally, we review experiments that characterize the accuracy of the models for predicting interruption cost and discuss research directions.