THESIS
2015
viii, 35 pages : illustrations ; 30 cm
Abstract
As a new distributed computing model, crowdsourcing lets people leverage a crowds intelligence
and wisdom toward solving problems. This thesis aims to characterize various dimensions of
worker quality control, which is a crucial component of crowdsourcing systems, typically occupying
a large fraction of the time and money invested in crowdsourcing.
In this thesis, we implement several state-of-the-art works related to worker error rate estimates
in crowdsourcing using three large open datasets, by which we have a discussion about the efficiency
and accuracy of different methods under various circumstances. Besides that, we propose
to evaluate the crowd with confidence. This provides a confidence interval in addition to a simple
numeric worker error rate and further leads to va...[
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As a new distributed computing model, crowdsourcing lets people leverage a crowds intelligence
and wisdom toward solving problems. This thesis aims to characterize various dimensions of
worker quality control, which is a crucial component of crowdsourcing systems, typically occupying
a large fraction of the time and money invested in crowdsourcing.
In this thesis, we implement several state-of-the-art works related to worker error rate estimates
in crowdsourcing using three large open datasets, by which we have a discussion about the efficiency
and accuracy of different methods under various circumstances. Besides that, we propose
to evaluate the crowd with confidence. This provides a confidence interval in addition to a simple
numeric worker error rate and further leads to various potential applications.
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