SUMMARY Nonparametric Bayesian methods have proven to be extremely useful for providing flexible models that are capable of fitting an extraordinarily wide array of data sets. Two of their most natural uses are in providing distributions for random effects and in providing large classes of models that elaborate on a para-metric model. These models are appropriate for a great many data sets, all...

Social question and answer sites (SQAs) are increasingly popular knowledge sharing platforms. In this paper, we outline how an SQA site functions as a social learning community. The success of an SQA site depends not only on effectively organizing and delivering information, but also on whether it can provide the cues needed by community members to successfully learn to be productive contributo...

A pioneering constructivist approach to building a robot that reproduces a developmental process of infants’ vowel acquisition has been conducted by Yoshikawa et al. [1] inspired by the observation in infant study. They have constructed a mother-infant interaction model with robot learning capability and parrot-like teaching by caregiver. However, the robot has not listened his/her own voice, t...

Recently, deep learning methods have made a significant improvement in compressive sensing image reconstruction task. However, it still remains a problem of block effect which degrades the reconstruction results. In this paper, we propose a fully-convolutional network, where the full image is directly measured with a convolutional layer. Thanks to the overlapped convolutional measurement, the b...

the present study aimed to study guidelines for jihad-e-keshavarzi organization of gilan province to become learning organization through strengthening organizational citizenship behavior and attitudes of employees. a questionnaire based survey was applied to 159 employees estimated through stratified random sampling. the face and content validity of questionnaire was confirmed by panel of expe...

The problem of residual variance estimation consists of estimating the best possible generalization error obtainable by any model based on a finite sample of data. Even though it is a natural generalization of linear correlation, residual variance estimation in its general form has attracted relatively little attention in machine learning. both theoretically and experimentally to understand bet...