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EFusion: correcting unreliable labels with expert...
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EFusion: correcting unreliable labels with expert knowledge for mobile crowdsensing

Abstract

With technological advances in mobile devices, mobile crowdsensing (MCS), a special crowd sourcing paradigm, has attracted unprecedented interest. Traditionally, MCS recruits a crowd of mobile users to capture data of interest and input their own judgment (i.e., intelligence) to facilitate the processing of big data. Recently, there is a surge of interest in another form of MCS where the sensing crowd consists of “smart” devices/programs that possess machine intelligence. One example is the smart cameras that recognize human faces or detect urgent events such as a car collision.

Authors

Jiang J; Wu K; Wang H; Zheng R

Pagination

pp. 1-2

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication Date

May 1, 2019

DOI

10.23919/ifipnetworking46909.2019.8999406

Name of conference

2019 IFIP Networking Conference (IFIP Networking)

Conference proceedings

2019 IFIP Networking Conference (IFIP Networking)

ISSN

1861-2288
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