Reviewerly/exHarmony huggingface dataset:
The practical significance of using a credit modular system in higher education

The article describes the basic concepts of the credit-modular system used today in the country's universities, its content and essence. The main tasks of the creditmodular system and the forms of organizing the educational process in this system are discussed. A module is a part of a curriculum that covers several subjects and courses. This is a set of disciplines (courses) aimed at developing students’ knowledge and skills, the ability to think analytically and logically.
Privacy Preservation Techniques and Models for Publishing Structured Data

In today’s era, almost every organization is adopting digitization by replacing paper-based storage of data with an electronic version of it. These collected data are useful for the development of various applications, business upliftment, providing better services, and to the research firms. However, it is likely to increase the possibility of security breaches and disclosing the private information of users. To take advantage of available data, it is necessary to protect the sensitive information of users before publishing. In this paper, the main focus is on the fundamentals of privacy preservation and review of literature where authors have recommended various methods to offer privacy to sensitive data of users.
Demography-Based Hybrid Recommender System for Movie Recommendations

Recommender systems have been explored with different research techniques including content-based filtering and collaborative filtering. The main issue is with the cold start problem of how recommendations have to be suggested to a new user in the platform. There is a need for a system which has the ability to recommend items similar to the user’s demographic category by considering the collaborative interactions of similar categories of users. The proposed hybrid model solves the cold start problem using collaborative, demography, and content-based approaches. The base algorithm for the hybrid model SVDpp produced a root mean squared error (RMSE) of 0.92 on the test data.
Parameter estimation and hypothesis testing the second order of bivariate binary logistic regression (S-BBLR) model with Berndt Hall-Hall-Hausman (BHHH) iterations

Bivariate Binary Logistic Regression (BBLR) is a logistic model that has two response variables where each variable depends on two categories with the response variables being correlated with each other. In this research, a development study will be conducted on a Bivariate Binary Logistic Regression model using the second order (S-BBLR). Furthermore, the S-BBLR will be applied to the problem of Sustainable Development Goals (SDGs) related to the Human Development Index (HDI) and Public Health Development Index (PHDI) data in East Java, Indonesia. The parameter estimation process uses the Maximum Likelihood Estimator (MLE) method. The problem in estimate the parameters of this model is that MLE cannot find an implicit analytical solution, so an iteration method will be used in the form of Berndt Hall-Hall-Hausman (BHHH) in the iteration process. Hypothesis test for the S-BBLR model include simultaneous and partial tests performed using the Maximum Likelihood Ratio (MLRT) and the Wald method. Based on the analysis, it was found that the percentage of poor people, the pure participation rate (APM), and the number of public health centers had a significant impact on PHI and PHDI with a classification accuracy of 86.84%.
Digital Teaching and Learning Resources of the Ukrainian Children’s Libraries Websites

The article reveals the influence of information society on changes that have taken place in regional libraries for children. Website filling condition of regional libraries of Ukraine for children with digital teaching and learning resources and the personalized access is analyzed. A literature review shows that these issues tend to be insufficiently studied. Monitoring method was used to analyze the websites of 22 regional libraries for children of Ukraine. The following results were attained through the implementation of monitoring method, namely 87% of regional libraries for children had websites, 9% of library sites become unusable, 4% did not have the library site. The results of the analysis of website traffic statistics show that Ternopil Regional Library for Children is the most popular among children. Subsequently it was found out, that 10 websites of regional libraries for children didn’t have traffic statistics. Therefore, failure to serve the information needs in a timely manner. The expert evaluation of websites aimed to determine types of digital teaching and learning resources by content, subject and usage. Headings and location for digital teaching and learning resources were identified: philosophy, psychology, social sciences, humanities, natural and mathematical sciences; art, architecture, games, sport. Moreover, the findings of the study showed that databases of information and bibliographic resources, namely digital catalogs, guides, bibliographic reviews played a key role in finding necessary information. Most regional libraries for children offer both their own information products and provide opportunity for children to use teaching and learning Internet resources. Although, there are no uniform classifiers of internet resources that make it hard for users to find information. The publication suggests ways of improving organizational structure of websites to place teaching and learning resources.
АНАЛИЗ ЖИЗНЕСТОЙКОСТИ И МОТИВАЦИИ У СТУДЕНТОВ В УСЛОВИЯХ ДИСТАНЦИОННОГО ОБУЧЕНИЯ

The article studies the relationship between resilience and motivation among students in the conditions of distance learning. The urgency of the problem is due to the growing interest in the problem of resilience in conditions of extreme social challenges as a mechanism for activating personal resources, in particular its role in overcoming stress during the transition to the distance learning format. A review of existing research in higher education highlights the important role of resilience in helping students, overcoming obstacles, managing their well-being and completing their studies. The survey of students was carried out through Google forms. The following methods of data collection were used: “A short version of the resilience test” (E.N. Osin, E.I. Rasskazova) and “Methods of motivation and orientation to professional activity among students” (A.V. Krylova, I.A. Ignatkova). Methods of mathematical statistics were used to analyze the data. The relationship between resilience and professional external positive motivation was found. Resilience is higher in students with pronounced positive external positive motivation. Social motives such as status, prestige, the desire to get a second higher education, increase the resilience of students in the conditions of distance learning, allow them to effectively cope with stress, as well as turn a potentially traumatic situation into opportunities and use it for personal development. The revealed results can be useful for the organization of distance learning, the educational process of students in order to increase resilience and motivation in conditions of extreme social challenges, as well as for the intensification of educational work on the role of resilience in overcoming distress, the perception of obstacles as sources of additional growth and development.
The Effect of Video- Mediated Communication on 7th Grade EFL Students’ Para-Linguistic Competence and Attitude

This study aimed to investigate the effect of Video-Mediated Communication (VMC) on 7th Grade EFL students' para-linguistic competence and attitude.The study sample consisted of thirty 7th-grade students at AL Manbar Privet School at Al-Mafraq during 2019/2020.The study sample was divided equally into two groups: an experimental and a control group.The control group was taught using a paper textbook, whereas the experimental group was taught using the VMC strategies.The study's findings indicated that using VMC in language teaching and learning significantly affects developing students' para-linguistic competence and attitude.However, the experimental group performed better than the control group in speaking due to the new method of using VMC.The results also showed that the experimental group has a positive attitude toward VMC, which might be due to using the VMC strategies through which the experimental group students have developed their para-linguistic competence and attitude.
A Study on the Effect of the Quality of In-Service Education on Teacher"s Self-efficacy

Recently, as the public interest in childcare service has been increasing, the expectation on the performance of daycare teachers has been increasing as well, as it is the key factor in the quality of childcare service. In order to improve the quality of childcare service, the expertise of daycare teachers has to be reinforced through high-quality in-service education. Therefore, this study verifies the effect of the quality of in-service education on the self-efficacy of daycare teachers with the purpose of increasing the self-efficacy of daycare teachers in childcare service sites.BR As the result, first, the quality of education service, which includes faculty, education program, and administration, positively affected self-directed learning. Second, faculty and education program showed a positive effect on learning flow. Third, self-directed learning and learning flow positively affected the self-efficacy of the teachers. Fourth, self-directed learning and learning flow positively affected the satisfaction of education. Fifth, the satisfaction of education positively affected the self-efficacy of the teachers. Lastly, faculty, education program, and administration showed a significant effect on the self-efficacy of the teachers through providing self-directed learning, learning flow, and satisfaction of education.BR This research holds practical significance in that it provides a fundamental resource that can aid increasing the expertise and capability of daycare teachers through in-service education service in this moment, when the expectation on daycare teachers to perform their roles successfully in childcare service sites are increasing.
CREATION OF A SOFTWARE DEVELOPMENT FOR THE IMPLEMENTATION OF MONITORING THE PROGRESS OF SCHOOLCHILDREN

It is highlighted that the performance of schoolchildren is a complex process that occurs within the framework of the implementation of many of the points discussed in the article. Also, student failure can occur not only due to the low intellectual development of schoolchildren, but also for a number of other reasons. The article mentions the presence of student failure due to an incorrect pedagogical approach, the use of outdated methods, a low level of interaction with students of the teacher himself, and inappropriate behavior. It was determined that modern schools do not have enough developed programs that would reveal the average performance of students, would allow teachers to control the educational process faster and more productively. A program has been developed to determine the average score of high school students; during the experiment, it has established itself as relevant and necessary in the educational process. It was determined that this program will help teachers switch to an electronic reporting format, unload work time in favor of productive learning, rather than drawing up a written report by hand.
Reduce Footprints of Multiport Interferometers by Cosine-Sine-Decomposition Unfolding

We present a novel 3D-unfolding method based on Cosine-Sine-Decomposition (CSD) to enable an alternative arrangement of unitary blocks towards plane normal, which reduces the planar footprints of universal multiport interferometers exponentially.
Mixed Reality and Kinect

In the beginning, it was the punch card. In less than 100 years, the world has gone from massive, room-sized computers to tiny headsets that can project holograms and understand the world around them. We are starting our journey from the early pioneers of the science of information technology. We are going to explore how the world transitioned from primitive dumb computers to Artificial Intelligence and Mixed Reality interfaces, creating miraculous devices such as the Azure Kinect.
Security Dynamics in Computer Science Technologies

The quantitative study of security dynamics in computer-science technologies is essential for understanding security-development patterns of information systems. Here, we specify and investigate security dynamics as (i) the relation between technological and security developments, (ii) the security development, modelled as the evolution of security considerations among technologies, and (iii) the effect of security development on the opinion given to technologies. We perform a scientometric analysis on arXiv e-prints (n=340569) related to 20 computer-science technology categories. Our empirical results are threefold. First, we provide evidence of a lack of relation between the technological and security developments: while most categories follow a sigmoid-growth curve of technological development, this latter is not a determinant of security development. Second, we find a security-attention pattern: over the lifetime of categories, security considerations appear more frequently, emphasising that security gains more attention at a later stage of technological development. Third, we find an opinion pattern: the experts' opinion related to each category is positively explained by the prevalence of security considerations. These results emphasise new methods for understanding, modelling, and benchmarking security dynamics of technologies, which brings new heuristics for considering changes related to the security of information systems.
Intelligent Blockchain-Enabled Applications for Sharing Economy

Sharing economy is one of the fastest-growing businesses in the last century. Therefore, it is a major benefit to utilize blockchain technology to enhance sharing economy applications’ transparency, security, and decentralization. We propose a framework for data management to support blockchain-based sharing economy applications. It supports indexing and querying blockchain data to facilitate the retrieval process instead of the conventional sequential scanning of the blockchain ledger. Furthermore, we implement an on-demand service for indexing future requests.
Pose Conditioned Human Motion Generation Using Generative Adversarial Networks

This work proposes a model that allows for the generation of human pose sequences represented by points in the 3D space based on artificial intelligence algorithms. A GAN framework AC-GAN variant uses pose-based labels with a simple discriminator and two auxiliary classifiers, one based on unsupervised k-means labels and the other on the 3DPCK metric. This model focuses on the emulation of the human body movement while cooperatively performing various activities. It successfully models human interaction behavior, achieving a 0.1507 in the Kolmogorov-Smirnov metric, 45.55 3DPCK of 150 mm, 4.4019 for the FID metric, and 0.0487 for the MMD metric, all for unseen motion data.
An Experimental Analysis for Credit Card Fraud Detection with Imbalanced and Machine Learning Techniques

Credit card fraud is a criminal misdemeanour and causes harm to banks, persons as well as financial institutions. Hence, it is substantial to detect and prevent fraudulent activities. Several significant approaches have been put forward in the literature to detect different types of fraud cases. However, most of the existing approaches face different challenges. Among them, the class imbalance problem is the most common. This problem consists of unequal distribution of observations across the classes. In this study, the ratio fraudulent:non-fraudulent is very small, and this poses a challenge for traditional classification algorithms to detect fraudulent activities. This paper presents a rigorous experimental analysis to tackle the class imbalance problem. The experiments were carried out in three categories: (a) experiments with traditional machine learning algorithms, (b) experiments with ensemble methods, and (c) experiments with traditional machine learning algorithms after imbalanced data pre-processing techniques. The imbalanced data pre-processing techniques include undersampling, oversampling, and a combination of both. The performance of the solutions has been evaluated using five different metrics: F1-score, precision, recall, area under precision–recall curve (AUPR), and area under receiver operating characteristic curve (AUROC). The experimental results show improved performance in detecting fraudulent cases.
Hybridization Preprocessing and Resampling Technique-Based Neural Network Approach for Credit Card Fraud Detection

Credit card fraud is a form of financial fraud growing every year, causing losses to financial multinationals as well as government sectors. Traditional methods like manual detection of credit card frauds are feasible only for small datasets, but with the rise of big data, these methods are of no worth. Data mining and machine learning (ML) techniques have been widely used for fraud detection, by drawing a pattern that separates two classes (fraud and legitimate). The only necessity of ML algorithms is vast sets of examples, but messy and complex datasets are tricky for ML techniques and thus bring challenges during formulating learning rules. Almost all real-world dataset is unclean which tends to produce inaccurate models. This chapter revealed various challenges possessed by ML classification 98algorithms because of complex imbalance dataset and therefore proposes a new hybridization preprocessing and resampling technique (HPRT), which solves two major issues: (a) cleans the dataset while (b) balancing the dataset. HPRT thus enhances the performance of ML algorithms. Several ML algorithms were observed to produce better result when combined with HPRT. HPRT-based neural network model is constructed for detection of credit card fraud and is compared with traditional neural network model. Confusion matrix and other matrices revealed moderately high and precise results for HPRT-based neural network-based model in comparison to traditional neural network model.
Dynamic Analysis of a Semi-automatic Telegraph Key

The first telegraph key model was invented in 1846 by Alfred Vai, a close collaborator of Samuel F.B. Morse. Since then the telegraph key has changed several designs from the mechanical semi-automatic or bug to automatic systems, representing for years the main means of communication as well as the first means of electrical communication invented. Today telegraphy replaced by the telephone and the Internet, however, these devices are still used by about 3 million Amateur radio operators. In this paper, a dynamic analysis of a precise telegraph key model has done. The model is the Vibroplex Original Deluxe which is a semi-automatic model made by Vibroplex Company Inc., a historical company manufacturer of these devices, founded by Horace Greeley Martin (1873–1937). The feature of this device is to automatically generate points (in Morse code sense) by means of an oscillating mass. The validated model will be useful to test different setting parameters and how to manage them to get comfort for the operator.
A Cluster Validity Protocol to Evaluate Internal Indices for Clustering of High-Dimensional Datasets

Clustering with high-dimensional data is a well-known challenge faced by many data scientists, especially when dealing with distance based methods that are often affected by increasing dimensionality: many dimensions may be irrelevant and can mask existing clusters in noisy data. Therefore, Subspace Clustering is used to find clusters within different subspaces of dimensions and its soft category of algorithms assigns a weight to each feature in different clusters without transforming or reducing its feature space. In this work, we have investigated whether or not Soft Subspace Clustering (SSC), by means of the Fuzzy Cluster Validity Indices (FCVIs), with the appropriate parameters, performs better than Fuzzy c-Means when dealing with high-dimensional data. Our main result is a novel protocol for evaluating FCVIs performances concerning SSC. To provide firm conclusions about our proposal, we compared the performance of four clustering algorithms over thirty-seven high-dimensional datasets validated by nineteen FCVIS.
Implementasi Support Vector Machine (SVM) dan Naïve Bayes untuk Analisis Sentimen Aplikasi KAI Access

Google Play Store is one of the platforms on Android to download an application, Google Play also provides a feature for the public to be able to provide comments/reviews of the downloaded application. Reviews of the application are in the form of perception, both positive and negative, a review of one of the applications on google play, namely the KAI access application, can be used as research material to find information. The technique that can be used for this research is sentiment analysis, the classification method that will be used for this sentiment analysis is the support vector machine and naive Bayes as a comparison to find better accuracy of the two algorithms, this research can help developers to find out the shortcomings and advantages that must be improved on the application. The results of the study using the Support Vector Machine (SVM) classification obtained an accuracy rate of 93% while using the Naïve Bayes method that was 89%. So, the Support Vector Machine method provides a higher level of accuracy than the Naïve Bayes method.
A NLP Framework to Generate Video from Positive Comments in Youtube

Video-sharing sites platforms like YouTube have unique architecture and atmosphere. The comments section is one of the evolution that attracted the users towards expressing opinions and sharing more about videos. Opinions can be used to examine knowledge, user behavior analysis and provide the creator with more ideas to create videos. This paper proposed a novel NLP framework to examine user comments on YouTube and use sentiment analysis to create a short video from positive comments. The results of this study suggest that the framework could be effective to promote the original video using classified community comments. In addition, the results of our implementation indicate that such as framework can be integrated to detect some comments on YouTube and remove negative comments before even posting them.
Computational linguistics and natural language processing

As in many other areas, computing systems and software have become increasingly important in the practice and scholarly studies of translation. This chapter reviews the main developments in the fields of computational linguistics and natural language processing, describing the tools they have produced and the uses these tools have been put to in translation and methodology. It briefly outlines the main lines of research in computational linguistics, namely symbolic methods and statistical learning methods, and then focuses on examples of applications of these methods in translation and translation studies. Tools developed specifically for such applications which combine these methods are described in concrete examples. These examples cover creation and indexing of corpora, language processing, and computational linguistics in analysis and visualisation of text. This is followed by a discussion of the main issues and topics regarding the use of computational linguistics methods in translation practice and research, and recommendations for researchers and practitioners who use or may wish to use these methods and tools in their work.