Article

Learning and reasoning about interruption

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Johnson ApacibleAuthors Info & Claims

ICMI '03: Proceedings of the 5th international conference on Multimodal interfaces

Pages 20 - 27

https://doi.org/10.1145/958432.958440

Published: 05 November 2003 Publication History

Abstract

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.

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Li ZCheng YYan YLindlbauer D(2024)Predicting the Noticeability of Dynamic Virtual Elements in Virtual RealityProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642399(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642399
Lenz LFender AChatain JHolz C(2024)Comparing Synchronous and Asynchronous Task Delivery in Mixed Reality EnvironmentsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.337203430:5(2776-2784)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3372034
Ying LJha KAarya STenenbaum JTorralba AShu T(2024)GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS58592.2024.10802144(7099-7106)Online publication date: 14-Oct-2024
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Index Terms

Learning and reasoning about interruption
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding

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Published In

cover image ACM Conferences

ICMI '03: Proceedings of the 5th international conference on Multimodal interfaces

November 2003

318 pages

ISBN:1581136218

DOI:10.1145/958432

Conference Chair:
Sharon Oviatt
Oregon Health & Science University
,
Program Chairs:
Trevor Darrell
Massachusetts Institute of Technology
,
Mark Maybury
MITRE
,
Wolfgang Wahlster
DFKI, Germany

Copyright © 2003 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 05 November 2003

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ICMI-PUI03

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ICMI-PUI03: International Conference on Multimodal User Interfaces

November 5 - 7, 2003

British Columbia, Vancouver, Canada

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ICMI '03 Paper Acceptance Rate 45 of 130 submissions, 35%;

Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Cited By

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https://dl.acm.org/doi/10.1145/3613904.3642399
Lenz LFender AChatain JHolz C(2024)Comparing Synchronous and Asynchronous Task Delivery in Mixed Reality EnvironmentsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.337203430:5(2776-2784)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3372034
Ying LJha KAarya STenenbaum JTorralba AShu T(2024)GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS58592.2024.10802144(7099-7106)Online publication date: 14-Oct-2024
https://doi.org/10.1109/IROS58592.2024.10802144
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Krajchevska EPetreska NHandjiski OAndovska SIlijoski BLameski PRibarski PTojtovska B(2022)Digital Shift: Assessment of Mental States Through Passive Mobile SensingICT Innovations 2021. Digital Transformation10.1007/978-3-031-04206-5_15(198-220)Online publication date: 12-Apr-2022
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