Reviewerly/exHarmony huggingface dataset:
An Analysis on Factors That Influence Students’ Anxiety in Speaking English

This research aims at exploring some theories and previous findings related to students' speaking anxiety and identifying the factors and the phenomena that influence students’ speaking anxiety as well as finding out the way students to anticipate reducing their anxiety while speaking English.This research used library research in nature. In conducting the research, the researcher used two theories as the framework of the study. The first theories used by Worde (2003) and the second theory was from Rajitha & Alamelu (2019).  The researcher analysed this data using descriptive qualitative. The results of this research found that there were some influential factors that contribute to the students' anxiety in speaking English namely: speaking activities, inability to comprehend, pedagogical practices, error correction, fear of negative evaluation, external factors and internal factors. Meanwhile, several strategies from Kondo and Ying-Ling (2004)used toreduce students' anxiety in learning foreign languages, especially when appearing in front of the class, are preparation, relaxation, positive thinking, making friends, and resigning. Therefore, students are expected to be able to minimize their anxiety and improve their English skills.
Assessment on image segmentation development techniques by thresholding strategies

Image segmentationis among the incredible regions of development in the current situation. It is a procedure commonly used to improve the crude picture acquired from different materials. Division of pictures is a critical advance in picture examination and pre-preparing. Over the span of the work, standard staggered thresholding strategies are viable because of its low computational cost, dependability, decreased combination times and exactness. Nature-propelled techniques for enhancement assume essential function in the handling of pictures. Numerous streamlining strategies have been proposed for various picture handling applications. It likewise expands picture division, picture rebuilding, picture edge identification, picture upgrade, picture design acknowledgment, picture age, picture thresholding, picture combination and so on This paper incorporates diagram of numerous metaheuristic Firefly calculation, Differential development, Particle swarm enhancement, Genetic calculation, Artificial honey bee state advancement, NNA and the outcomes are contrasted and entropy approaches for Shannon or Fuzzy. The recommended approach shows preferred proficiency in target factor over condition of - the-craftsmanship draws near, auxiliary comparability list, PSNR and standard inference.
A Joint Model for Representation Learning of Tibetan Knowledge Graph Based on Encyclopedia

Learning the representation of a knowledge graph is critical to the field of natural language processing. There is a lot of research for English knowledge graph representation. However, for the low-resource languages, such as Tibetan, how to represent sparse knowledge graphs is a key problem. In this article, aiming at scarcity of Tibetan knowledge graphs, we extend the Tibetan knowledge graph by using the triples of the high-resource language knowledge graphs and Point of Information map information. To improve the representation learning of the Tibetan knowledge graph, we propose a joint model to merge structure and entity description information based on the Translating Embeddings and Convolution Neural Networks models. In addition, to solve the segmentation errors, we use character and word embedding to learn more complex information in Tibetan. Finally, the experimental results show that our model can make a better representation of the Tibetan knowledge graph than the baseline.
Diversity or Uniformity

Emoji is a Japanese term that reminds users in the digital world about history, community, attitudes, appearances, economics, and politics while texting. This study aims to address identity representations that are focused on the demands of distinction in the postmodern era and offered by emoji in the digital world. The thesis also attempts to challenge whether the structure of multiple identities is feasible in the postmodern era and/or whether identities are re-uniformized in a symbolic language.
A Improved Generative Adversarial Networking Architectures for Crowd Counting

Crowd counting on computer vision targets at the statistics for the number of people within an image. At present, the problem generally solved by estimating the amount of probability annotation dots within density map generated from the crowd scene. We propose a IGAN(Improved Generative Adversarial Networking) architecture to solve the accuracy of generative density map. The Improved GAN included two parts: generater and discriminator. In the generater, we predict the density maps by inputting images to the generative network. And the work of discriminator is to distinguish features between the generative density map and corresponding given veritable map, and force the generater to produce rational density map as close as the ground truth. The Improved GAN is trained collaboratively by blending together density loss and adversarial loss. Through experiments on five popular public datasets(ShanghaiTech PartA and PartB, WorldEXPO'10, UCF_CC_50 and UCSD), we validate the preferable performance of the Improved GAN in the complex scene.
Improving the Generalised Few-shot Learning by Semantic Information

Human beings can learn novel vision categories with a few images. To mimic the ability, few-shot learning task becomes a hot topic recently. But it seems that most prior works neglect the fact that we as humans will not forget our original knowledge after learning novel categories. To reach this goal, we investigate the generalised few-shot task. Besides, when we learn new vision categories, the semantic information is usually helpful to link the new categories with our prior knowledge. Therefore, we expect the life-long learning model that can also link to the textual semantic information as human beings do. In this paper, we propose a two-head model including visual learning and textual learning. In visual learning, we use a classifier weights generator to help recognise the novel categories without changing the pre-trained feature extractor and the classifier weights for the base categories. In the textual learning component, we construct a classifier for all the categories using graph convolutional neural networks with the help of the knowledge graph. Finally, we use a simple weighted fusion technique to combine them to get the final prediction. To prove the validation and effectiveness of our model, we conduct experiments on ImageNet-FS and MiniImagenet to show that our result surpasses the previous state-of-the-art methods in this setting.
ACCURACY OF NONPARAMETRIC DENSITY ESTIMATION FOR UNIVARIATE GAUSSIAN MIXTURE MODELS: A COMPARATIVE STUDY

Flexible and reliable probability density estimation is fundamental in unsupervised learning and classification. Finite Gaussian mixture models are commonly used for this purpose. However, the parametric form of the distribution is not always known. In this case, non-parametric density estimation methods are used. Usually, these methods become computationally demanding as the number of components increases. In this paper, a comparative study of accuracy of some nonparametric density estimators is made by means of simulation. The following approaches have been considered: an adaptive bandwidth kernel estimator, a projection pursuit estimator, a logspline estimator, and a k-nearest neighbor estimator. It was concluded that data clustering as a pre-processing step improves the estimation of mixture densities. However, in case data does not have clearly defined clusters, the pre-preprocessing step does not give that much of advantage. The application of density estimators is illustrated using municipal solid waste data collected in Kaunas (Lithuania). The data distribution is similar (i.e., with kurtotic unimodal density) to the benchmark distribution introduced by Marron and Wand. Based on the homogeneity tests it can be concluded that distributions of the municipal solid waste fractions in Kutaisi (Georgia), Saint-Petersburg (Russia), and Boryspil (Ukraine) are statistically indifferent compared to the distribution of waste fractions in Kaunas. The distribution of waste data collected in Kaunas (Lithuania) follows the general observations introduced by Marron and Wand (i.e., has one mode and certain kurtosis).
Social Media Data Analysis: Twitter Sentimental Analysis on Kerala Floods Using R Language

Web-based social networking has turned out to be essential specialized devices for ordinary discussions. Because of their ubiquity, person to person communication destinations comprise a rich wellspring of data about the general supposition. Social information analysis is the examination of individuals’ connection in Internet-based life on how they are communicating their perspectives. The information broke down here is gathered through long range informal communication site Twitter which is one of the greatest stages for data sharing. Wistful investigation permits us think about individuals’ perspectives in support or against of any subject. In this paper, we target August 2018, Kerala Floods as a contextual investigation for feeling examination. Our thought behind is to watch, look at and break down how individuals express their perspectives. To get the better results with these tweets, R-programming is used to develop the proposed system. In this analysis, the data from tweets are separated utilizing catchphrase-based learning extraction. The exploratory outcomes demonstrate that how individuals everywhere throughout the world remains for Kerala and contribute relief reserve to CMO Kerala.
Improving Speaker Recognition with Quality Indicators

Nuisance factors such as short duration, noise and transmission conditions still pose accuracy challenges to state-of-the-art automatic speaker verification (ASV) systems. To address this problem, we propose a no reference system that consumes quality indicators encapsulating information about duration of speech, acoustic events and codec artifacts. These quality indicators are used as estimates to measure how close a given speech utterance would be to a high-quality speech segment uttered by the same speaker. The proposed measures when fused with a baseline ASV system are found to improve the performance of speaker recognition. The experimental study carried on a modified version of the NIST SRE 2019 dataset shows a relative decrease of 9.6% in equal error rate (EER) compared to the baseline.
A Study on The Reform Strategis of Foreign Language Teaching in Uuniversities Under the Background of Intelligent Information

At present, the social demand for foreign language talents is increasing, and higher requirements have been put forward for foreign language talents. In order to meet the requirements of society, language teaching reform for colleges and universities is required. In this process, it needs to pay attention to the application of modern information technology, give full play to the advantages of advanced technology, stimulate students' interest and promote the improvement of foreign language teaching quality. Based on this, this paper analyzes the reform strategies of foreign language teaching in colleges and universities under the background of intelligent information.
Coupling Modifies the Quantum Fluctuations of Entangled Oscillators

Coupled oscillators are among the simplest composite quantum systems in which the interplay of entanglement and interaction may be explored. We examine the effects of coupling on fluctuations of the coordinates and momenta of the oscillators in a single-excitation entangled state. We discover that coupling acts as a mechanism for noise transfer between one pair of coordinate and momentum and another. Through this noise transfer mechanism, the uncertainty product is lowered, on average, relatively to its non-coupled level for one pair of coordinate and momentum and it is enhanced for the other pair. This novel mechanism may be explored in precision measurements in entanglement-assisted sensing and metrology.
Construction of the Evaluation Index System for Smart Learning Environment in Colleges and Universities Based on Multi-Space Integration

The concept and connotation of the smart learning environment based on multi-space integration was firstly introduced. Also, a smart learning environment system model based on multi-space integration was demonstrated through taking the Central China Normal University as an example. Then, the evaluation indexes for smart learning environment in colleges and universities based on multi-space integration were proposed initially and verified by the Delphi method. According to the verification result, three first-level evaluation indexes were determined. Moreover, 26 second-level evaluation indexes were also determined on the basis of the first-level evaluation indexes. In the end, an evaluation index system for smart learning environment in colleges and universities based on multi-space integration was constructed. It can provide a reference for constructing and evaluating smart learning environment in colleges and universities, and effectively promote the reform and development of classroom teaching.
Correcting Low Illumination Images Using PSO-Based Gamma Correction and Image Classifying Method

In this work, the authors have proposed a method for improving the visual quality of 2D color images suffering from low illumination. The input image is converted to HSV (Hue, Saturation, Value) color space, and the V component is subjected to high pass Laplace filter. The filtered output is then made to undergo a two-stage classifier and a brightness correction process. Finally, the resultant image obtained is gamma-corrected using an optimum gamma value computed using a well-known meta-heuristic based optimization technique namely, particle swarm optimization (PSO). The corrected V component is combined back with the H and S components to reconstruct the final result. The authors have tested this method on a number of 2D color images of natural scenes and the result is found to be satisfactory. Also, the experimental results are compared with similar methods in terms of subjective and objective metrics.
The German additive particle <i>noch</i>: Testing the role of topic situations

The particle noch (‘still’) can have an additive reading similar to auch (‘also’). We argue that both particles indicate that a previously partially answered QUD is re-opened to add a further answer. The particles differ in that the QUD, in the case of auch, can be re-opened with respect to the same topic situation, whereas noch indicates that the QUD is re-opened with respect to a new topic situation. This account predicts a difference in the accommodation behavior of the two particles. We present an experiment whose results are in line with this prediction.
Noisy Effect on Nonlinear Quantum Search Algorithm

Abstract In this paper, we investigate the effects of three single‐qubit quantum noise channels on the quantum nonlinear search algorithm proposed by Abrams and Lloyd. We focus on the gates of the nonlinear part of the algorithm and consider the case where the quantum noise channel only affects the qubit on which the gate is proceeding. Assuming that quantum noise effects occur after using the gates of the nonlinear part of the algorithm, we find that the quantum nonlinear search algorithm appears to be very resilient to the quantum noise when the desired state does not exist in the first register and is subject to the phase damping channel or the depolarising channel.
The Construction of Cognitive Graph of Integrated War Emergency System

The emergency response system is an important part of the national strategic system, an important support for national strategic capacity, and a basic support for effectively responding to various crisis situations and war scenarios. The effectiveness evaluation of emergency response system is an inspection evaluation of the system construction effect and an important part of supporting the construction of the emergency response system. In order to deal with the problems such as the integration of the military and the local government systems, the unavailability of expert subjective evaluation data, the difficulty of transferring and applying a large number of effective evaluation frameworks and high human cost of evaluation, this paper proposes the main technical approaches for the design and construction of the cognitive graph of the integrated war emergency system, which provides the basis for the design and data integration of the operational effectiveness evaluation model of the integrated emergency response system.
EyeTell: Disabled Patients Communication Application

Eye-tracking technology is a growing field used to detect eye movements and analyze human processing of visual information for interactive and diagnostic applications, especially in human and computer interaction. In this paper, we are presenting EyeTell as a system of gaze tracking through the front-facing camera of android devices to help use an application that solves the miscommunication problem between disabled patients and their caregivers. EyeTell application allows severely disabled patients to communicate easily and effectively through easy to use interfaces.
Performance Analysis of K-ELM Classifiers with the State-of-Art Classifiers for Human Action Recognition

Recent advances in computer vision have drawn much attention toward human activity recognition (HAR) for numerous applications similar to video games, robotics, content recovery, video surveillance, etc. The enlightening and pursuing of human actions recognized by the wearable sensor devices (WSD) generally used today face difficulty in precision and reckless automatic recognition due to regular change of body movements by the human. Primarily, the HAR system will preprocess the WSD signal, and then, six sets of features were extracted from wearable sensor accelerometer data that are viable from the computational viewpoint. In the end, after the crucial dimensionality reduction process, the selected features were utilized by the classifier to ensure high human action classification results. In this paper, to analyze the performance of the K-ELM, classifiers-based deep model for selected features is predominantly focused with the state-of-the-art classifiers such as artificial neural network (ANN), k-nearest neighbor (KNN), support vector machines (SVM) and convolutional neural network (CNN). The experimental results obtained by analyzing performance using the metrics such as Precision, Recall, F-measure, specificity and accuracy shows that K-ELM outperforms with less time for most of the above-mentioned state-of-the-art classifiers.
Challenging the Uniqueness of Being by Cognizing

Humans become powered enough to question the further types of their being while there are no ways to resolve the mystery of being of cellular realities (cellulars) predetermined by a type of programs, genomes, and their universal processors. Acknowledging that genomic reproduction can not be originated by a chance, the kernel of effective cognition is universal for being in the universe and mental models can be reduced to basic classifiers, in what follows, we continue to challenge the uniqueness of human cognizing arguing possibility of origination of basic classifiers in frame of fundamentals of physicists followed by constructive formation of mental systems composed from those basic classifiers
Object Color Estimation with Dominant Color in Automated Image Semantic Description Generation Tasks

The automated image tagging is an important part of modern search engines. The generated image tags can be constructed from object names and their attributes, for example, colors. This work presents an object color name detection real-time algorithm. It is applicable to any automatic object detection and localization systems. The presented algorithm is fast enough to run after the existing real-time object detection system, without adding visible overhead. The algorithm uses k-means to detect the dominant color and selects the correct name for the color via Delta E (CIE 2000).
DEVELOPMENT OF CONTENTS FOR MASTER’S PROGRAM IN PSYCHOLOGICAL AND PEDAGOGICAL DISCIPLINE

The master’s educations is one of the novelties actively developing in the last decade. During the current period, the certain experience has been gained in masters’ training whose skill level shall be higher than bachelor’s and specialist’s one. In parallel with practical work, the researches on issues of the masters’ education are developing. Several directions of this research are presented in this article. This article is dedicated to psychological and pedagogical education. The article gives description for master’s education «Technology of inclusive education», it gives examples of the work programs developed in accordance with requirements of the educational standards for their studying results. The topic of work programs reflects the needs for competence-based development of modern professional in psychological and pedagogical educational profile. The article gives the general characteristics of specialists studying the master’s education programs. It justifies the need for psychological and pedagogical master’s educational profile selected by the author and her colleagues. Its relevancy is based on development of the inclusive education in this country and around the world, on the demand for the high-skilled staff to work under the circumstances of inclusion. The article justifies the relevancy of development of contents for educational program in connection of its result orientation. The article promoting the experience of the particular educational institution might be interesting to educational organization implementing the programs of psychological and pedagogical direction.
Corrective Maintenance of Manufacturing System Using Artificial Intelligence Techniques Support for Engineering Process

Corrective Maintenance (CM) take an important role on automated manufacturing process in many industrial areas. However, CM process depends mainly on operator’s knowledge and their decision of locating failure components or its causes in order to make corrections on their equipment. The common knowledge - based system as Failure Mode and Effect Analysis (FMEA) has especially used since many years, but it has required an integrated approach which exploit production data to generate a more effective maintenance procedure in recent periods. In this context, this paper proposes an integrated protocol of diagnosis process by combining expert’s system with Pearson correlation (R) model and using AI technique: Bayes Learning. This protocol toward exploiting historical production data problem to identify and diagnosis drift production or bad quality product for dealing good correction decision. Therefore, our key contribution to support maintenance tool machine for increasing life-cycle engineering.