13,452 research outputs found
Exploring Outliers in Crowdsourced Ranking for QoE
Outlier detection is a crucial part of robust evaluation for crowdsourceable
assessment of Quality of Experience (QoE) and has attracted much attention in
recent years. In this paper, we propose some simple and fast algorithms for
outlier detection and robust QoE evaluation based on the nonconvex optimization
principle. Several iterative procedures are designed with or without knowing
the number of outliers in samples. Theoretical analysis is given to show that
such procedures can reach statistically good estimates under mild conditions.
Finally, experimental results with simulated and real-world crowdsourcing
datasets show that the proposed algorithms could produce similar performance to
Huber-LASSO approach in robust ranking, yet with nearly 8 or 90 times speed-up,
without or with a prior knowledge on the sparsity size of outliers,
respectively. Therefore the proposed methodology provides us a set of helpful
tools for robust QoE evaluation with crowdsourcing data.Comment: accepted by ACM Multimedia 2017 (Oral presentation). arXiv admin
note: text overlap with arXiv:1407.763
Monitoring Networked Applications With Incremental Quantile Estimation
Networked applications have software components that reside on different
computers. Email, for example, has database, processing, and user interface
components that can be distributed across a network and shared by users in
different locations or work groups. End-to-end performance and reliability
metrics describe the software quality experienced by these groups of users,
taking into account all the software components in the pipeline. Each user
produces only some of the data needed to understand the quality of the
application for the group, so group performance metrics are obtained by
combining summary statistics that each end computer periodically (and
automatically) sends to a central server. The group quality metrics usually
focus on medians and tail quantiles rather than on averages. Distributed
quantile estimation is challenging, though, especially when passing large
amounts of data around the network solely to compute quality metrics is
undesirable. This paper describes an Incremental Quantile (IQ) estimation
method that is designed for performance monitoring at arbitrary levels of
network aggregation and time resolution when only a limited amount of data can
be transferred. Applications to both real and simulated data are provided.Comment: This paper commented in: [arXiv:0708.0317], [arXiv:0708.0336],
[arXiv:0708.0338]. Rejoinder in [arXiv:0708.0339]. Published at
http://dx.doi.org/10.1214/088342306000000583 in the Statistical Science
(http://www.imstat.org/sts/) by the Institute of Mathematical Statistics
(http://www.imstat.org
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