Outlier detection is an integral part of robust evaluation for crowdsourceable Quality of Experience (QoE) and has attracted much attention in recent years. In QoE for multimedia, outliers happen because of different test conditions, human errors, abnormal variations in context, etc. In this paper, we propose a simple yet effective algorithm for outlier detection and robust QoE evaluation named...

background: the aim of the study was assessment of defaults and conducted meta-analysis of the efficacy of single-dose oral albendazole against t. trichiura infection. methods: we searched pubmed, isi web of science, science direct, the cochrane central register of controlled trials, and who library databases between 1983 and 2014. data from 13 clinical trial ar­ticles were used. each article w...

IDDQ test-based outlier rejection becomes difficult for deep sub-micron technology chips due to increased leakage and process variations. The use of Neighbor Current Ratio (NCR) that uses wafer-level spatial correlation for identifying outlier chips has been proposed earlier as a means of coping with these issues. Due to the slow speed of IDDQ test, there is a strong motivation to reduce the nu...

We propose an inlier-based outlier detection method capable of both identifying the outliers and explaining why they are outliers, by identifying the outlier-specific features. Specifically, we employ an inlier-based outlier detection criterion, which uses the ratio of inlier and test probability densities as a measure of plausibility of being an outlier. For estimating the density ratio functi...

We have proposed replicator neural networks (RNNs) for outlier detection [8]. Here we compare RNN for outlier detection with three other methods using both publicly available statistical datasets (generally small) and data mining datasets (generally much larger and generally real data). The smaller datasets provide insights into the relative strengths and weaknesses of RNNs. The larger datasets...

This work presents a novel algorithm of finding correspondence using a relaxation labeling. For the variance experiments, the variance of all algorithms except the proposed algorithm is large. The largest variance of the proposed algorithm is +0.01 in the 0.08 deformation test of a character. Overall, the proposed algorithm outperforms compared to the rest of algorithms. Except the proposed alg...

With increasing circuit complexity and reliability requirements, screening outlier chips is an increasingly important test challenge. This is especially true for IDDQ test due to increased spread in the distribution. In this paper, the concept of current ratio is extended to exploit waferlevel spatial correlation. Two metrics – current ratio and neighbor current ratio – are combined to screen o...

Outlier detection and environmental association analysis are common methods to search for loci or genomic regions exhibiting signals of adaptation to environmental factors. However, a validation of outlier loci and corresponding allele distribution models through functional molecular biology or transplant/common garden experiments is rarely carried out. Here, we employ another method for valida...

Clustering is an extremely important task in a wide variety of application domains especially in management and social science research. In this paper, an iterative procedure of clustering method based on multivariate outlier detection was proposed by using the famous Mahalanobis distance. At first, Mahalanobis distance should be calculated for the entire sample, then using T -statistic fix a U...