334 research outputs found
A Double Joint Bayesian Approach for J-Vector Based Text-dependent Speaker Verification
J-vector has been proved to be very effective in text-dependent speaker
verification with short-duration speech. However, the current state-of-the-art
back-end classifiers, e.g. joint Bayesian model, cannot make full use of such
deep features. In this paper, we generalize the standard joint Bayesian
approach to model the multi-faceted information in the j-vector explicitly and
jointly. In our generalization, the j-vector was modeled as a result derived by
a generative Double Joint Bayesian (DoJoBa) model, which contains several kinds
of latent variables. With DoJoBa, we are able to explicitly build a model that
can combine multiple heterogeneous information from the j-vectors. In
verification step, we calculated the likelihood to describe whether the two
j-vectors having consistent labels or not. On the public RSR2015 data corpus,
the experimental results showed that our approach can achieve 0.02\% EER and
0.02\% EER for impostor wrong and impostor correct cases respectively
The Effect of Interest Rates on Bank Risk-Taking: Evidence from Banks in China
There is a belief among some economists that 2008 financial crisis was caused by continuous low interest rates environment. They argue that low interest rate environment from the early to mid-2000s lead to the increase of banks’ risk-taking appetite. Many empirical studies conducted in western countries have proven the negative relationship between interest rates and bank risk-taking. In this paper, we examine whether or not this connection exists within the Chinese economy. We measure bank risk-taking behaviour based on the ratio of non-performing loans to total loans, and we find its relationship with two different kinds of interest rates: legal and market interest rates. In addition, we divided control variables into internal and external variables. We analyzed more than 800 observations made on Chinese banks between 2003 and 2012. Consistent with similar studies conducted in western countries, we found that low level interest rates substantially increased bank risk-taking behaviour
Research on Image Retrieval Optimization Based on Eye Movement Experiment Data
Satisfying a user's actual underlying needs in the image retrieval process is a difficult challenge facing image retrieval technology. The aim of this study is to improve the performance of a retrieval system and provide users with optimized search results using the feedback of eye movement. We analyzed the eye movement signals of the user’s image retrieval process from cognitive and mathematical perspectives. Data collected for 25 designers in eye tracking experiments were used to train and evaluate the model. In statistical analysis, eight eye movement features were statistically significantly different between selected and unselected groups of images (p < 0.05). An optimal selection of input features resulted in overall accuracy of the support vector machine prediction model of 87.16%. Judging the user’s requirements in the image retrieval process through eye movement behaviors was shown to be effective
Variable-Based Fault Localization via Enhanced Decision Tree
Fault localization, aiming at localizing the root cause of the bug under
repair, has been a longstanding research topic. Although many approaches have
been proposed in the last decades, most of the existing studies work at
coarse-grained statement or method levels with very limited insights about how
to repair the bug (granularity problem), but few studies target the
finer-grained fault localization. In this paper, we target the granularity
problem and propose a novel finer-grained variable-level fault localization
technique. Specifically, we design a program-dependency-enhanced decision tree
model to boost the identification of fault-relevant variables via
discriminating failed and passed test cases based on the variable values. To
evaluate the effectiveness of our approach, we have implemented it in a tool
called VARDT and conducted an extensive study over the Defects4J benchmark. The
results show that VARDT outperforms the state-of-the-art fault localization
approaches with at least 247.8% improvements in terms of bugs located at Top-1,
and the average improvements are 330.5%.
Besides, to investigate whether our finer-grained fault localization result
can further improve the effectiveness of downstream APR techniques, we have
adapted VARDT to the application of patch filtering, where VARDT outperforms
the state-of-the-art PATCH-SIM by filtering 26.0% more incorrect patches. The
results demonstrate the effectiveness of our approach and it also provides a
new way of thinking for improving automatic program repair techniques
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