Reviewerly/exHarmony huggingface dataset:
Texture Representation and Application of Colored Spun Fabric Using Uniform Three-Structure Descriptor

Abstract The local binary pattern (LBP) and its variants have shown their effectiveness in texture images representation. However, most of these LBP methods only focus on the histogram of LBP patterns, ignoring the spatial contextual information among them. In this paper, a uniform three-structure descriptor method was proposed by using three different encoding methods so as to obtain the local spatial contextual information for characterizing the nonuniform texture on the surface of colored spun fabrics. The testing results of 180 samples with 18 different color schemes indicate that the established texture representation model can accurately express the nonuniform texture structure of colored spun fabrics. In addition, the overall correlation index between texture features and sample parameters is 0.027 and 0.024, respectively. When compared with the LBP and its variants, the proposed method obtains a higher representational ability, and simultaneously owns a shorter time complexity. At the same time, the algorithm proposed in this paper enjoys ideal effectiveness and universality for fabric image retrieval. The mean Average Precision (mAP) of the first group of samples is 86.2%; in the second group of samples, the mAP of the sample with low twist coefficient is 89.6%, while the mAP of the sample with high twist coefficient is 88.5%.
A Brief Study on Data Communication and Computer Networks

A computer network, sometimes known as a data network, is a kind of telecommunications network that enables computers to communicate with one another. Data is passed between networked computing devices through data links in computer networks. Cable or wireless media are used to establish connections (network links) between nodes. The Internet is the most well-known computer network. Network nodes are computer devices that originate, transport, and terminate data on a network. Hosts such as personal computers, phones, servers, and networking gear are examples of nodes. When one device can share information with another device, whether or not they have a direct connection, they are said to be networked together. Access to the World Wide Web, shared usage of application and storage servers, printers, and fax machines, and the use of email and instant messaging apps are all supported via computer networks. The physical medium used to carry signals, the communications protocols used to arrange network traffic, the network's scale, topology, and organizational intent all vary across computer networks.
Gaussian Based Active Learning Algorithm for Image Classification Problem

One of the relevant problems in deep learning is data efficiency. In the active learning approach, we have a large set of unlabeled data, a small set of labeled data and a limited budget for labeling. The model for training on labeled data is defined. The task is to select the most relevant samples to increase model quality on the test data set. In this work, we overview some active learning algorithms and propose a novel algorithm for active learning based on Gaussian distribution. The main idea of the algorithm is to use reference samples from each class and compute the distribution parameters (and) for each embedding coordinate. We use Gaussian function as a measure of distance between unlabeled samples and class representation, so we can combine it with any confidence-based algorithm. We tested our approach on a part of ImageNet task (20 random classes from original ImageNet 2012 dataset). We used a Gaussian distribution-based measure combined with a confidence-based sample selection method. This combination achieved significantly better results.
The Relationship Between Student Teacher's Relationship To The V Grade V's Student's Achievement Motivation Ibtidaiyah Madrasah Darussalam Jogosatru Sukodono

This research is motivated by the low achievement motivation of students such as not doing homework on time, not doing assignments in class on time. This study uses a quantitative approach. The type of research used is correlational which aims to determine the relationship between variables, namely the relationship between teachers and students with achievement motivation. The sampling technique in this study used a saturated sampling technique, namely 30 students of class V Madrasah Ibtidaiyah Darussalam Jogosatru Sukodono. The instrument used in this study was a questionnaire. The technique used in this research data analysis is statistical technique using Pearson Product Moment correlation calculation. with the help of SPSS 17.0 software for windows. Based on the test results obtained a correlation coefficient of 0.639 with a significance of 0.001 (&lt;0.05). In conclusion, the hypothesis is accepted, that there is a positive relationship between teacher-student relationships and achievement motivation in fifth grade students of Madrasah Ibtidaiyah Darussalam Jogosatru Sukodono. This means that the higher the teacher-student relationship, the higher the student's achievement motivation, and vice versa
Content Filtering in Streaming Video Using Domain Adaptation

This paper addresses the problem of content filtering in live streaming video. We consider the case where positive data, content to be filtered, is not readily available on the target platform. We therefore use positive data from other sources and apply domain adaptation to classify new data on the target platform. In order to map features of source and target domains into a common feature space, we optimize a Wasserstein distance (WD) loss and binary cross entropy loss, such that class distributions remain separated in the new feature space. Our baseline model achieves state-of-the-art results on the public NPDI dataset, and we show that WD-based domain adaptation improves the accuracy in the absence of positive samples in the target domain.
Anonymous Mutual Authentication: An Information Theoretic Framework

We consider the anonymous mutual authentication problem, which consists of a certificate authority, single or multiple verifiers, many legitimate users (provers) and any arbitrary number of illegitimate users. The legal verifier and a legitimate user must be mutually authenticated to each other using the user’s key, while the identity of the user must stay unrevealed. An attacker (illegitimate prover) as well as an illegal verifier must fail in authentication. A general interactive information theoretic framework in a finite field is proposed, where the normalized total key rate as a metric for reliability is defined. Maximizing this rate has a trade-off with establishing anonymity. The problem is studied in two different scenarios: centralized scenario (one single verifier performs the authentication process) and distributed scenario (authentication is done by N verifiers, distributively). For both scenarios, achievable schemes, which satisfy the completeness, soundness (at both verifier and prover) and anonymity properties, are proposed. Increasing the size of the field, results in the key rate approaching its upper bound.
TV Viewing Behaviour: Analysis Using Machine Learning Algorithms

Television is becoming an important part of our everyday life as a mass medium for communication. With its dramatic and demonstrative strength, to change it is a true source of knowledge, education and entertainment. It is convincing to take into account the promotion of social values and norms in a civilized society. The structure and shape of the shape play a major role. Here, we propose a new method called multi-class classification with the help of big data analytics. In this study, we have provided a framework for comparing the quality of various classification methods using statistical simulation when individuals belong to one of the two groups that are mutually exclusive. Here we compare naïve Bayes classification, multi-layer perceptron classification and decision tree classification as a test case. From the results, we found that the classification accuracy of multi-layer perceptron classification is higher than the other two in analysing the television viewing behaviour. The data for the study is collected through a direct questionnaire survey. Here, accuracy is assessed over correctly and incorrectly categorized instances. The analysis found that MLP is the best algorithm to predict TV viewing behaviour.
Automatic combination strategy search in segmentation of Cardiac T1 mapping image

Fusion of multiple medical images may improve image quality and ease feature extraction for the diagnosis and therapy of diseases. In cardiac magnetic resonance (MR) images T1 mapping, multiple MR images are reconstructed for the estimation of T1 mapping; therefore, heart myocardium should be segmented from images for further analysis using T1 values. Traditionally, segmentation is performed for each image separately, and the per-image segmentation results are combined in a complicated way to get a final segmentation result. Traditional combination methods require the segmentation results of all images satisfy certain criteria, and therefore often fail because of low image quality. However, it is very difficult to design a combination strategy to integrate the information of multiple images for segmentation. In this study, we designed the Segfuse network based on convolutional neural networks to get strategies for improving the segmentation performance of T1 mapping images even when some images were of low quality. After validating with 112 images from 42 subjects, the segmentation accuracy based on the Segfuse had a mean dice of 80.2%, nearly equivalent to that of a traditional method.
Optimization-Based Neural Networks Compression

This paper presents a method for constructing a size compressed neural network with better or similar accuracy than a given dense neural network, therefore the compressed network requires less memory and computational resources. The presented method relies basically on learning successive mappings between the given dense neural network (teacher) hidden features and the size-compressed neural network (student) hidden features, where the latter is learned also to solve the initial task of the teacher network. The presented method is particularly compared to baselines where we specifically show that the additional learned mappings significantly improve the performance (accuracy and computation) of the student network.
Using Ontologies for the Development of the University Educational Ecosystem

The paper considers the possibility of improving the educational ecosystem by using ontologies in the architecture of the university electronic information environment. Various types of actors of the modern educational ecosystem, interacting within the educational process with educational resources, are presented; their needs that are relevant for its development shortly are analyzed. This paper describes a general approach to using ontology as an element of the IT architecture of a university. Formalizing objects of the educational process in the form of ontology (OWL, RDF models) allows creating various applications for its intellectual support, ensuring the uniformity of architectural solutions and further evolutionary development of the university information environment based on the IEEE LOM and TinCanAPI standards.
A Study on the Influence of Computer-aided Japanese Learning Mode on College Students

Abstract In modern daily life, the number of college students has become a sharp increase in the pattern. And with the continuous progress and development of social science and technology, learning to understand and use the language of other countries has become a new national demand. This paper studies the Japanese learning mode of college students based on computer-aided technology. With the increasing demand, many universities and colleges in China have made Japanese study as an independent major for college students. As a result, the people of Japanese level has been greatly improved. At present, the situation is ever-changing, there are still a lot of advanced, good knowledge is the carrier of Japanese, there are many places worth learning. There is no denying that Japan has a lot of profound culture, which will broaden our horizons. Through the study of Japanese, we can speed up our integration into a collective, a culture.
Two-level chaotic system versus non-autonomous modulation in the context of chaotic voice encryption

In this paper, two methods are introduced for securing voice communication. The first technique applies multilevel chaos-based block cipher and the second technique applies non-autonomous chaotic modulation. In the first approach, the encryption method is implemented by joining Arnold cat map with the Lorenz system. This method depends on permuting and substituting voice samples. Applying two levels of a chaotic system, enhances the security of the encrypted signal. the permutation process of the voice samples is implemented by applying Arnold cat map, then use Lorenz chaotic flow to create masking key and consequently substitute the permuted samples. In the second method, an encryption method based on non-autonomous modulation is implemented, in the master system, and the voice injection process is applied into one variable of the Lorenz chaotic flow without modifying the state of controls parameter. Non-autonomous modulation is proved to be more suitable than other techniques for securing real-time applications; it also masters the problems of chaotic parameter modulation and chaotic masking. A comparative study of these methods is presented.
Unveiling multiple facets of design degradation in modern code review

Software design is a key concern in code review through which developers actively discuss and improve each code change. Nevertheless, code review is predominantly a cooperative task influenced by both technical and social aspects. Consequently, these aspects can play a key role in how software design degrades as well as contributing to accelerating or reversing the degradation during the process of each single code change’s review. However, there is little understanding about such social and technical aspects relates to either the reduction or the increase of design degradation as the project evolves. Consequently, the scarce knowledge on this topic helps little in properly guiding developers along design-driven code reviews. Our goal in this Doctoral research is three-fold: (1) to characterize the impact of code review and their practices on design degradation over time; (2) to understand the contribution of technical and social aspects to design degradation; and (3) to propose a conceptual framework to support design-decision making during code review. Our preliminary results show that the majority of code reviews had little to no design degradation impact, and that technical and social aspects contribute to distinguishing and predicting design impactful changes.
The Walkthrough Method

Nowadays the social scientists are called to integrate within their studies new tools that modify and innovate the scientist's typical toolbox. Digital platforms, media, and especially apps pose further challenges to social scientists today, as they are an important place of significant socio-cultural, economic, health, relationships, and entertainment transformations. When studying digital technologies, in fact, it's important to pay attention to both their socio-cultural representations and technological aspects – since even design and data outputs have social and cultural influences. In this context, new research questions arise; among all the possible tools in the digital method toolbox, the walkthrough method is a noteworthy way to answer them. Starting from these considerations, this chapter aims to analyze, through a review of the literature, the birth and development of the walkthrough method in its various meanings to identify the innovative aspects and fields of application.
Word Embedding Techniques for Sentiment Analyzers

Sentiment analysis is perceived to be a multi-disciplinary research domain composed of machine learning, artificial intelligence, deep learning, image processing, and social networks. Sentiment analysis can be used to determine opinions of the public about products and to find the customers' interest and their feedback through social networks. To perform any natural language processing task, the input text/comments should be represented in a numerical form. Word embeddings represent the given text/sentences/words as a vector that can be employed in performing subsequent natural language processing tasks. In this chapter, the authors discuss different techniques that can improve the performance of sentiment analysis using concepts and techniques like traditional word embeddings, sentiment embeddings, emoticons, lexicons, and neural networks. This chapter also traces the evolution of word embedding techniques with a chronological discussion of the recent research advancements in word embedding techniques.
Classification of underground engineering surrounding rock based on Gaussian cloud model

Abstract Based on the Gaussian cloud model, an integrative evaluation method for the stability classification of surrounding rock was presented. Selecting five factors, i.e. rock quality designation, uniaxial compressive strengthen, integrality coefficient, strengthen coefficient of structural plane and seepage measurement of groundwater to be the evaluation indicators. According to the Gaussian cloud model and the information entropy theory, the data were normalized and the comprehensive certainty was calculated, then a Gaussian cloud model was obtained to classify the stability classification of surrounding rock. 14 samples of the first stage project in Guangzhou pump accumulator electricity station and 16 samples produced by the interpolation method were taken as the training samples. 12 samples of the second stage project were selected to verify the model. Compared with the artificial neural network (ANN) method and support vector machine (SVM) method, the results show the gauss cloud method has excellent performance and high prediction accuracy, and the method is of practical value, can be applied to evaluate the stability of surrounding rock in underground engineering.
Analysis of Multi-Features Combination of Unsupervised Content Based Image Retrieval with Different Degrees of Accuracy

The method of identifying related images in an image database is known as image retrieval. Text-based and content-based data processing are the two main forms of image retrieval processes. The visual characteristics of an image, such as color, texture, shape, and spatial design, are used in the content-based approach of image retrieval. In unsupervised mode, the images are retrieved using a cluster-based graph partitioning algorithm. The efficiency of different content based image retrieval systems is contrasted in this paper by fusing multiple image characteristics. The creation of four versions resulted from the integration of several features. Compute the union of all four models by normalizing the value between 0 and 1. The data comes from the COREL image database, which includes 1000 images of the same resolution. According to this article, images can be best recovered using three model-based features rather than two features. The accuracy of the union is thought to be superior.
Digital Literacy for Early Childhood Education Teachers in Society 5.0

Education in the era of disruptive characterized by their change of paradigm that is learning centered on student and learning throughout life that goes beyond the limits of space and time. The concept of society 5.0 more pressing on aspects of human or source power human that is how humans are able to do in anticipation against the trend globally from result appears era disruptive. A teacher has a role which is important in the world of education of children of age early in order to optimize the six aspects of the development which the child ages early as optimal. The purpose of this study was to determine the knowledge and skills of teachers Education Children Age Early on literacy digital as a medium of learning in children age early in the era of society 5.0. The research methodology is a case study.. Subject research 30 early childhood teachers as the city of Pontianak, the instruments are used in research of this questionnaire, interview and documentation. Results of the research showed the level of knowledge and skills of literacy digital for early childhood teachers throughout the city of Pontianak on the dimensions of the search engines the average - average 66%, dimensions instructions directions hyipertext average average 63%, and the dimensions of the evaluation of the content of the information average average 62%. conclusions of research is that the ability of the knowledge and skills of literacy digital early childhood teachers belonging pretty well, solutions are offered in order to improve the understanding and skills of literacy digital must be held training literacy digital.
Copy and remove as dynamic operators

In this article, we present a modal logic that extends the basic modal logic ML with two dynamic operators: copy (cp), which replicates the current model, labelling each copy with a different propositional symbol and respecting accessibility relations even between distinct copies; and remove (rm), which deletes paths in the model that satisfy certain intermediate conditions. We call the resulting logic ML(cp,rm). We study its computational complexity, and its relative expressivity with respect to (static) modal logics ML and ML(◻−), and the dynamic epistemic Action Model Logic, AML.
Face-Mask and Social Distance Detection System

Recently the widespread outbreak of the Coronavirus (Covid-19) has brought global crisis with its deadly spread and has put the world to a halt. During this pandemic, WHO has made wearing masks and practising social distancing compulsory to protect against this deadly virus. These are the necessities of today’s world. The conditions in which we lived a few months prior is completely different than what it is now. This paper presents a system for detecting whether the people around are following the Covid-19 basic safety measures like maintaining social distancing and practicing wearing masks. The proposed system utilizes the YOLO v3 object detection model for social distance detection and caffe based face detector for Face mask recognition.
Development of a Neural Network Chatbot for User Support

The paper focuses on the operation principle of a simple chatbot, explaining its necessity and its development complexity. The method of natural speech analysis (based on mathematical approaches) is described and subsequently classified. A model based on a feedforward neural network is used as a classifier. The authors describe the method of transforming a text into parameters (both input and output) for training a neural network, discuss the training method and its subsequent application in a finished program. A target model was obtained during the work on test data in a special development framework, which showed its accuracy and adequacy on the examples. Improving indicators and increasing the list of generated answers, the authors argue, require big data, and the mandatory nature of their heterogeneity, is explained.