Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/102637
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMa, Lijiaen
dc.contributor.authorTay, Wee Pengen
dc.contributor.authorXiao, Gaoxien
dc.date.accessioned2019-08-05T02:00:59Zen
dc.date.accessioned2019-12-06T20:58:01Z-
dc.date.available2019-08-05T02:00:59Zen
dc.date.available2019-12-06T20:58:01Z-
dc.date.issued2019en
dc.identifier.citationMa, L., Tay, W. P., & Xiao, G. (2018). Iterative expectation maximization for reliable social sensing with information flows. Information Sciences, 501621-634. doi:10.1016/j.ins.2018.10.008en
dc.identifier.issn0020-0255en
dc.identifier.urihttps://hdl.handle.net/10356/102637-
dc.description.abstractSocial sensing relies on a large number of observations reported by different, possibly unreliable, agents to determine if an event has occurred or not. In this paper, we consider the truth discovery problem in social sensing, in which an agent may receive another agent’s observation (known as an information flow), and may change its observation to match the observation it receives. If an agent’s observation is influenced by another agent, we say that the former is a dependent agent. We propose an Iterative Expectation Maximization algorithm for Truth Discovery (IEMTD) in social sensing with dependent agents. Compared with other popular truth discovery approaches, which assume either the agents’ observations are independent, or their dependency is known a priori, IEMTD allows to infer each agent’s reliability, the observations’ dependency and the events’ truth jointly. Simulation results on synthetic data and three real world data sets demonstrate that in almost all our experiments, IEMTD achieves a higher truth discovery accuracy than the existing algorithms when dependencies exist between agents’ observations.en
dc.description.sponsorshipMOE (Min. of Education, S’pore)en
dc.format.extent16 p.en
dc.language.isoenen
dc.relation.ispartofseriesInformation Sciencesen
dc.rights© 2018 Elsevier Inc. All rights reserved. This paper was published in Information Sciences and is made available with permission of Elsevier Inc.en
dc.subjectTruth Discoveryen
dc.subjectSocial Sensingen
dc.subjectEngineering::Electrical and electronic engineeringen
dc.titleIterative expectation maximization for reliable social sensing with information flowsen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doi10.1016/j.ins.2018.10.008en
dc.description.versionAccepted versionen
item.grantfulltextopen-
item.fulltextWith Fulltext-
Appears in Collections:EEE Journal Articles

SCOPUSTM   
Citations 50

5
Updated on Jan 22, 2023

Web of ScienceTM
Citations 20

6
Updated on Feb 2, 2023

Page view(s)

323
Updated on Feb 4, 2023

Download(s) 50

101
Updated on Feb 4, 2023

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.