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When did your smartphone bother you last?

Published: 13 September 2014 Publication History

Abstract

This paper is prompted by the overall question 'what is the most effective way to recognise disruptive smartphone interruptions?'. We design our experiments to answer 3 questions: 'Do users revise what they perceive as disruptive incoming calls as time goes by?', 'How do different types of machine-learners (lazy, eager, evolutionary, ensemble) perform on this task?' and 'Can we restrict the initial amount of data and/or the number of features we need to make predictions without degrading performance?'. We consider these questions using Cambridge University's Device Analyzer dataset.

References

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Fisher, R., and Simmons, R. Smartphone interruptibility using density-weighted uncertainty sampling with reinforcement learning. In ICMLA '11 (2011).
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Granitto, P., Furlanello, C., and Gasperi, F. Recursive feature elimination with random forest for ptr-ms analysis of agroindustrial products. Chemometrics and Intelligent Laboratory Systems (2006).
[3]
Markitanis, A., Corapi, D., Russo, A., and Lupu, E. C. Learning user behaviours in real mobile domains. In Proc. of ILP'11 (2011).
[4]
Rosenthal, S., Dey, A. K., and Veloso, M. Using decision-theoretic experience sampling to build personalized mobile phone interruption models. In Proc. of Pervasive'11, Springer-Verlag (2011).
[5]
Smith, J., and Dulay, N. Ringlearn: Long-term mitigation of disruptive smartphone interruptions. In Symposium on Activity and Context Modeling and Reasoning (ACOMORE), PerCom '14 (2014).
[6]
Wagner, D. T., Rice, A., and Beresford, A. R. Device analyzer: Understanding smartphone usage. In Proc. of MobiQuitous '2013 (2013).

Cited By

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  • (2020)Can you Turn it Off?Proceedings of the ACM on Human-Computer Interaction10.1145/34151624:CSCW2(1-18)Online publication date: 15-Oct-2020
  • (2017)A personal visual analytics on smartphone usage dataJournal of Visual Languages and Computing10.1016/j.jvlc.2017.03.00641:C(111-120)Online publication date: 1-Aug-2017

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  1. When did your smartphone bother you last?

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    cover image ACM Conferences
    UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication
    September 2014
    1409 pages
    ISBN:9781450330473
    DOI:10.1145/2638728
    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: 13 September 2014

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    Author Tags

    1. interruptions
    2. learning
    3. notifications
    4. smartphones

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    UbiComp '14
    UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing
    September 13 - 17, 2014
    Washington, Seattle

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    Overall Acceptance Rate 764 of 2,912 submissions, 26%

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    Cited By

    View all
    • (2020)Can you Turn it Off?Proceedings of the ACM on Human-Computer Interaction10.1145/34151624:CSCW2(1-18)Online publication date: 15-Oct-2020
    • (2017)A personal visual analytics on smartphone usage dataJournal of Visual Languages and Computing10.1016/j.jvlc.2017.03.00641:C(111-120)Online publication date: 1-Aug-2017

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