Reviewerly/exHarmony huggingface dataset:
CURRENT TRENDS IN THE DEVELOPMENT OF DUAL EDUCATION IN GERMAN HIGHER SCHOOLS

The article describes that the functioning of dual educational institutions ensures the training of personnel in accordance with the requirements of employers and involves the participation of enterprises in providing the practical component of training and its partial financing. The implementation of dual programs in different countries of the world contributes to the improvement of the professional training of students, motivates those seeking education to acquire high-quality professional competencies, and provides confidence in employment after graduation. Studying the positive experience of different countries of the world on the implementation of dual programs will contribute to the modernization of the educational environment and the development of the domestic system of dual form of education. The article defines the stages of development of the dual system of education in Germany. The article provides a retrospective review and analysis of modern trends in the development of dual education in higher education in Germany. The article describes the positive results of the introduction of a dual form of education in different countries of the world: the development of an educational system that meets the requirements of the labor market and the needs of employers; introduction of comprehensive certification of production personnel; creation of general requirements for the development of dual curricula that ensure the training of education seekers in real industrial conditions; strengthening the role of vocational education; reducing the duration of studies in certain specialties; possible employment of graduates of educational institutions immediately after graduation; reducing the period of adaptation of young professionals at workplaces. The article proves that Germany's experience in the development of university dual education is important for Ukraine, for reforming the higher education system, producing specialists who are competitive on the domestic and global labor market, who meet the needs of society and modern demands of employers.
Variational Continuous Label Distribution Learning for Multi-Label Text Classification

Multi-label text classification (MLTC) refers to the problem of tagging a given document with the most relevant subset of labels. One of the biggest challenges for MLTC is the existence of class imbalance. Most advanced MLTC models suffer from this issue, which limits the performance of the models. In this paper, we propose a model-agnostic framework named variational continuous label distribution learning (VCLDL) to address this problem. VCLDL theoretically builds a corresponding relationship between the feature space and the label space to mine the information hidden in the observable logical labels. Specifically, VCLDL regards label distribution as a continuous density function in latent space and forms a flexible variational approach to approximate the density function of the labels with the collaboration of the feature space. Combined with VCLDL, MLTC models can pay more attention to the distribution of the whole label set, rather than specific labels with maximum response values, thus the class imbalance problem can be well overcome. Experimental results on multiple benchmark datasets demonstrate that VCLDL can bring significant performance improvements over the existing MLTC models.
Detecting Cross-site Scripting Attacks using Deep Neural Networks

The most frequently encountered attack in web applications is known as Cross-site Scripting (XSS). This type of attack is designed to compromise the sensitive information of users, and there exist three distinct types of XSS attacks. The main challenge to detect XSS, was that the attackers changes XSS pattern frequently. Convolutional Neural Network (CNN) model was proposed as an appropriate model for classifying XSS. Here a CNN model with Generative Adversarial Network (GAN) was proposed and tested as XSS detector model. We developed a methodology to train the CNN and generate training dataset using GAN. The proposed model is suited for XSS classification, it achieved 99.4% of precision, 99.3% of recall and 99.4 of accuracy. The proposed model was compared with other three deep learning methods and it achieved better performance in overall.
Generation of high-dimensional qudit quantum states via two-dimensional quantum walks

Several quantum protocols, with applications ranging from fundamental studies to cryptographic scenarios, can be enhanced through the generation and manipulation of quantum states that belong to high-dimensional Hilbert spaces. For this reason, it is worth devoting efforts to find more efficient methods for complex qudit-state generation. One-dimensional quantum walks have proved to be efficient and versatile platforms for the engineering of such complex states. Hitherto, however, using their two-dimensional counterpart for this task has remained unexplored. In this paper, we consider two-dimensional quantum walk evolution as a tool for the generation of high-dimensional qudit states. We theoretically prove that a suitable change of the coin operators at each step permits the generation of a subset of qudit states by using less resources with respect to the one-dimensional counterpart. Then, we successfully generate qudit states by exploiting two-dimensional quantum walks on an experimental photonic platform. The walker position is encoded on discrete sets of optical modes carrying quantized amounts of transverse momentum and the mode couplings are actively controlled via liquid-crystal devices. The obtained results provide insight into qudit generation for applications in quantum communication and quantum cryptography.
COVID-19 Transition to Online

The need to quickly transition from face-to-face teaching using engaging, content-based activities to a new, online platform in just two weeks during the COVID-19 pandemic, made it necessary to try to migrate familiar activities that the students enjoyed into a virtual classroom. The activity that proved to be the most successful in maintaining a sense of community, engaging students, and reviewing content was playing Quizlet Live while in Breakout Rooms in Zoom.&#x0D; Keywords: online teaching, gaming, pandemic, student participation, active learning, learner engagement, collaboration, educational technology, Distance Education.
Exploring the Predictive Variables on Adolescents Career Maturity Development Based on Mixed Effect Random Forest: Interpretation using SHAP

The purpose of this study is to explore predictive variables of career maturity during middle and high school. To do this, the mixed effects random forest that considers the hierarchical data structure of longitudinal data was applied to GEPS(gyeonggi education panel study) data from the 4th to 9th wave (7th-12th grades). To derive the major predictive variables which are more stable, the analysis through multiple imputation was repeated five times to identify the top 20 variables with a high SHAP (shapley additive expansion) importance index. As a result, 17 major predictive variables were derived by using SHAP value: ‘self-understanding as a learner’, ‘proactive initiative’, sub factors of self-directed learning ability, friend relationships, teacher relationships, and ‘career search during vacation’ were selected as key variables that predict development of career maturity. In addition, variables related to relationships between parent-child such as ‘parent parenting behavior’ and ‘attachment’, as well as students affective attitudes such as ‘self-esteem’, ‘self-efficacy’, and ‘learning motivation’ were selected as key predictive variables, too. The relationship between major predictive variables and career maturity was examined through the SHAP dependence plot, and educational implications for career development of adolescents were discussed.
Exploring Communicative Language Teaching to Investigate English Communication Willingness in EFL Learners’ Classroom

Being Willing to Communicate (WTC) in English is significant since communicating in another language is considered to be a crucial part of learning a foreign language. Without it, the students only learn English in a passive way. This study, therefore, aims to explore the Communicative Language Teaching (CLT) to investigate the Willingness to Communicate (WTC) in English of EFL learners. This research uses mixed methods, especially Embedded Design, to explore CLT and investigate WTC in English of EFL learners. Qualitative analysis begins this research through observation and interviews. The results of both are then strengthened by the results of quantitative analysis through the questionnaire. It was found that there were various communicative activities implemented by the teacher. Those are effectively conducted by the teacher to promote communicative language teaching in the classroom. Another finding is that there are six WTC patterns, and the pattern of voluntary participation in class activities has the highest percentage of occurrences. The conclusion that can be drawn from this research is that the willingness to communicate (WTC) in English for EFL learners is regarded as high or very willing.
Can an Awareness Campaign Boost the Effectiveness of Influencer Marketing Disclosures in YouTube Videos?

Answering the strong need for insights into how minors can effectively be informed about advertising (e.g., influencer marketing) in online content, we ran an online experiment (&lt;em&gt;N&lt;/em&gt; = 623 minors between 8 and 18 years old) testing the effects of two pictograms that were designed in co-creation with minors and the potential of an awareness campaign to boost the pictogram’s effectiveness. Our findings provide three important insights that have implications for theory, practice and regulations. First, we find that minors are able to distinguish between sponsored and non-sponsored videos, indicating that they have developed some level of advertising literacy in this context. Second, our study shows that the two pictograms informing minors about advertising in online videos went unnoticed by most viewers and did not enhance conceptual or attitudinal advertising literacy. Third, the awareness campaign did not lead to higher recognition of the pictograms nor enhanced advertising literacy. The campaign did increase minors’ understanding of the meaning of the pictograms. However, the majority of minors also understood the pictograms without the campaign. Based upon our findings, we argue that current disclosures (such as the tested pictograms) are not noticed by most minors and are ineffective in enhancing minors’ advertising literacy. Although an awareness campaign can make minors more familiar with the pictograms and their implementation in online videos, it does not seem to boost the pictogram effects on advertising literacy.
Web-Based Human Resource System Quality Analysis Using ISO 25010:2011 Method Based on Usability Characteristics

The application of information technology is a crucial aspect of Human Resources (HR) activities in an organization (company) to manage its employees, such as attendance data, personal data, and performance appraisals. This data can produce information to make a decision. To assist HR in carrying out their duties, an IT company implements a Human Resources Information System (HRIS), but it has never been tested. The purpose of this research is to test the Human Resource system that has been implemented using the ISO 25010:2011 method with usability characteristics. The research stage starts with analyzing the employees' problems with using the system. Tests are divided into 2 types, namely on employees who often use the system and employees who rarely use the system, and determining the research method. Afterward, determine the characteristics and sub-characteristics and their weight, make assessment indicators of the sub-characteristics, assess the usability characteristic testing, and at the end, total calculation of them. The results showed that the lowest ratings were still obtained for the employee learnability sub-characteristics, namely 77.2% and 65.3% for both types. Therefore, it is recommended that the company should make a detailed user guide and prepare training for all employees. So they will understand the system well.
Improving Literacy Skills through Letter Ball Games for Children 5-6 Years Old

Literacy skills is an indicator of language development that must be possessed by early childhood. The literacy skills of children in TKIT Champion are still low, such as children who are not yet able to read their own names, find it difficult to distinguish between several letters. Through the letter ball game it is hoped that it can improve the literacy skills of group B children at TK IT Champion. This research is a class action research. The subjects in this study were children aged 5-6 years in group B class Syawal. Data collection techniques, namely observation and documentation, are then processed using a percentage formula. The results of this study indicate an increase in literacy skills through letter ball games. It can be seen from the success of each cycle, cycle I first meeting was 46%, increased at the second meeting to 54% and increased to 63% at the third meeting. In the second meeting, the child's literacy ability was 69% at the second meeting, 74% at the second meeting, and 86% at the third meeting. From the results of the research in each cycle that was carried out, it can be concluded that the letter ball game can improve the literacy skills of Kindergarten B children
THE ROLE OF LEADERS IN THE CREATION OF NEW KNOWLEDGE AND THEIR IMPACT ON THE DEVELOPMENT OF INDUSTRIES AND SECTORS OF THE ECONOMY

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
Deep learning approach-based hybrid fine-tuned Smith algorithm with Adam optimiser for multilingual opinion mining

A deep learning-based Hybrid Fine Tuned Smith Algorithm with Adam optimiser (HFS-AO) is introduced for multilingual opinion mining. Initially, data are collected using the web scraping algorithm to collect three different languages data: Marathi, Hindi and English. After the data extraction, the annotation process is suggested to label the collected data using the Zero-shot instance-weighting technique. Further, pre-process the data to remove unnecessary noises and symbols. After that, text vectorisation is performed using Naïve-Bayes vectorisation with Laplace smoothing. Finally, the Fine Tuned Smith algorithm with an Adam optimiser is proposed for polarity classification. From the three languages, the article regulates that it was possible to determine whether an opinion is negative, positive or neutral. The Python Jupiter software is utilised for this research to evaluate the proposed method's performance. The findings illustrate that when compared to other languages English language accuracy is higher which is about 98.8%.
Explainable machine learning for labquake prediction using catalog-driven features

Recently, Machine learning (ML) has been widely utilized for laboratory earthquake (labquake) prediction using various types of data. This study pioneers in time to failure (TTF) prediction based on ML using acoustic emission (AE) records from three laboratory stick-slip experiments performed on Westerly granite samples with naturally fractured rough faults, more similar to the heterogeneous fault structures in the nature. 47 catalog-driven seismo-mechanical and statistical features are extracted introducing some new features based on focal mechanism. A regression voting ensemble of Long-Short Term Memory (LSTM) networks predicts TTF with a coefficient of determination (R2) of 70% on the test dataset. Feature importance analysis revealed that AE rate, correlation integral, event proximity, and focal mechanism-based features are the most important features for TTF prediction. Results reveal that the network uses all information among the features for prediction, including general trends in high correlated features as well as fine details about local variations and fault evolution involved in low correlated features. Therefore, some highly correlated and physically meaningful features may be considered less important for TTF prediction due to their correlation with other important features. Our study provides a ground for applying catalog-driven to constrain TTF of complex heterogeneous rough faults, which is capable to be developed for real application.
Security in Operating Systems

This chapter focuses on how the operating system (OS) supports security. There are two central functions: The chapter concludes with a discussion of why it is necessary to update the OS continually, and whether it is possible to trust that the OS (and potential updates) are genuine.
In the untact Era, the Effect of Instructor’s Image and Use of Non-Verbal Communication on Learner"s Class Satisfaction and Class Immersion

This study is to verify the effect of the instructor"s image and non-verbal communication on learners" class satisfaction and class immersion during non-face-to-face classes after COVID-19. As a result first, the impact on learners" class satisfaction according to the instructor"s image and non-verbal communication use in non-face-to-face classes had the greatest impact on the instructor"s professional image, social image, similar language, facial expression, and external image, and second, social images had the greatest impact on learners" class immersion due to the use of instructor"s images and non-verbal communication, followed by professional images, facial expressions, appearances, similar languages, and external images. Third, class satisfaction has a large influence on class immersion(β=.760), and fourth, as a result of verifying the mediating effect of class satisfaction, it was found that class satisfaction plays a mediating role in influencing the instructor"s image and non-verbal communication on class immersion. In the end, this study will need to prepare empirical measures for non-face-to-face lectures that meet the needs of learners in order to draw efficient learning methods between instructors and learners in non-face-to-face classes such as the post-COVID-19 era.
Rules for Forming of Bibliographic Record Heading in the New National Standard

The article presents the detailed comments of the standard developer and the text of the SIBID national standard – GOST R 7.0.80–2023 “Bibliographic record. Heading. General requirements and rules” which puts into action from August 1, 2023. This standard brings about a change in the structure and composition of bibliographic record heading thus enabling to actualize and unify the method of its compiliation for all kinds of information resources. The GOST is intended for use by centers of the national (state) bibliography, libraries, publishers, information services and other organizations and structures dealing with the bibliographic activities. In this GOST in comparison with its predecessor of 2000 a number of new clauses is introduced, as well as the volume of information is broadened about identifying features, punctuation requirements and fully renewed album of specific examples. The rules for presentation of names of secular leaders and ecclesiastics of different confessions are fully shown because they often cause difficulties for a bibliographer. The given document should improve the searching efficiency and velocity of bibliographic information exchange.
ResFed: Communication Efficient Federated Learning With Deep Compressed Residuals

Federated learning allows for cooperative training among distributed clients by sharing their locally learned model parameters, such as weights or gradients. However, as model size increases, the communication bandwidth required for deployment in wireless networks becomes a bottleneck. To address this, we propose a residual-based federated learning framework (ResFed) that transmits residuals instead of gradients or weights in networks. By predicting model updates at both clients and the server, residuals are calculated as the difference between updated and predicted models and contain more dense information than weights or gradients. We find that the residuals are less sensitive to an increasing compression ratio than other parameters, and hence use lossy compression techniques on residuals to improve communication efficiency for training in federated settings. With the same compression ratio, ResFed outperforms current methods (weight-or gradient-based federated learning) by over 1.4× on federated datasets, including MNIST, FashionMNIST, SVHN, CIFAR-10, CIFAR-100, FEMNIST, in client-to-server communication, and can also be applied to reduce communication costs for server-to-client communication.
Prevailing Technologies Augmented Reality Software with Hardware for E-Entertainment and E-Learning Purposes: A Survey

Augmented Reality (AR) technology is rapidly growing in recent years. AR hardware, software, and devices have become more general, accessible, and attractive for users. Therefore, many developers are researching the way to effective methods to be developed in the AR field. In this survey paper, we researched advanced technologies to improve AR games, applications, and training. With advanced hardware and software development tools. We introduce advanced software with hardware development tools for AR, flowcharts of advanced AR development, and details of AR hardware with functionality, for AR applications, games, and training.
Drafting a pro-antitrust and data protection regulatory framework

&#x0D; The Digital Personal Data Protection (DPDP) Act 2023 has significant implications for antitrust issues in digital markets. A consensus has emerged among competition regulators across jurisdictions that in markets underpinned by data-driven business models, antitrust concerns intersect with data protection regulatory issues. With the recent enactment of the law, the landscape in India is now ripe to ensure that the two regulatory tools of data protection and competition work in tandem. The paper provides an overview of why a new approach to antitrust law is required in the realm of data-driven digital platforms, and delves into the evolving antitrust cases in India with respect to digital platforms to lay out the jurisprudence on data-related anti-competitive practices. The main objective of this paper is to map the Digital Personal Data Protection Act in the context of India's jurisprudence to theoretically illustrate how regulation of personal data could impact antitrust enforcement. Finally, the paper outlines how the CCI needs to assess digital antitrust cases and how the personal data legislation will need to evolve to achieve the twin goals of data protection and fair competition.&#x0D;
THE CONCEPTUAL BASIS OF THE STRATEGIC POTENTIAL OF THE DEVELOPMENT OF THE EDUCATIONAL SECTOR

The article deals with the issue of managing the strategic development potential of the educational sector as a system-forming branch of the national economy. The strategic potential of the educational sector is considered as a system of interaction of all elements of socio-economic relations, as well as a set of available resources, opportunities and abilities necessary for the development and implementation of a strategy for the development of the educational sector and its segmental components.