Childern Literature Shaping Gender Identities

The aim of this paper is to analyze stereotype construction of gender roles in the text of children s stories which inculcate in the children s crude minds socially developed gender differences For this purpose study followed Dell Hymes speaking model This model has sixteen components that can be applied to different types of Discourse speech interaction message form message content setting scene Speaker sender address or the hearer receiver audience addressee purposes outcomes purposes goals key channels forms of speech norms of interaction norms of interpretation and genres Selected children s stories were analyzed to identify their role as primary thought developing sources in the mind of young learners thus shaping their gender identities This study would be beneficial in drawing the attention of authors editors and writers of children s literature to redefine gender roles in order to minimize gender difference

A proposed framework for irrigation management transfer in Iran: Lessons from Asia and Iran

Water resource management/ Land management/ Leaching/ Drainage/ Sodic soils/ Soil reclamation/ Supplemental irrigation/ Irrigation programs

A Monte Carlo Comparative Simulation Study for Identification of the Best Performing Panel Cointegration Tests

In this paper the performance of nine panel cointegration tests, having the null hypothesis of no cointegration, with respect to weighted average rank scores under the whole space of alternative using Monte Carlo simulations have been carried out. Our results indicate that PdPtp, PAWS and PdP_V tests are the only three best performing tests among all panel cointegration tests whether time and cross sectional dimensions are small, medium or large. However, PDFTstar, PDFTrhostar and PdGtp panel cointegration tests have also identified as best performer at large cross sectional dimensions

Most stringent test of null of cointegration: A Monte Carlo comparison

To test for the existence of long run relationship, a variety of null of cointegration tests have been developed in literature. This study is aimed at comparing these tests on basis of size and power using stringency criterion: a robust technique for comparison of tests as it provides with a single number representing the maximum difference between a test’s power and maximum possible power in the entire parameter space. It is found that in general, asymptotic critical values tends to produce size distortion and size of test is controlled when simulated critical values are used. The simple LM test based on KPSS statistic is the most stringent test at all sample sizes for all three specifications of deterministic component, as it has the maximum difference approaching to zero and lesser than 20% for the entire parameter space

Abstraction-Based Outlier Detection for Image Data

© 2021, Springer Nature Switzerland AG. Data plays an important role in all stages of training, and usage of machine learning algorithms. Outliers are the samples in data that are generated by a “different mechanism” and belong to unexpected patterns that do not conform to normal behaviour. Outlier detection techniques try to deal with such undesirable events. There have been exceptional success of deep learning over classical methods in computer vision. In recent years a number of works employed the representation learning ability of deep autoencoders or Generative Adversarial Networks for outlier detection. Basically, methods are based on plugging representation techniques to outlier detection methods or directly reported employing reconstruction error as an outlier score. The error distributions of inliers and outliers may be still significantly overlapped. This could be associated with variation of samples inside the class, or cases with high outliers ratios, etc. In these cases, simply thresholding reconstruction errors may lead to misclassification. Although the produced representation is perhaps effective in representing the common features of the normal data, it is not necessarily effective in distinguishing outliers from inliers. We present a method that is based on constructing new features using convolutional variational autoencoder (VAE) and generate abstraction based on these features. To identify anomaly detection we tested two scenarios: utilizing VAE itself as well as using abstractions to train an additional architecture. Results are presented in the form of AUC-ROC using four benchmark datasets