Reviewerly/exHarmony huggingface dataset:
Academic mobility development among university students during COVID-19 pandemic

The aim of the article is to study the academic mobility of students in the context of internationalization of education, exchange of experiences and gaining additional knowledge and skills between participants in the process of academic mobility. The research methodology is to use methods of questionnaires, surveys, interviews among students to study the prospects, advantages, disadvantages and opportunities of academic mobility. The results of the study cover the internal and external mobility of university students and provide an opportunity to assess the dynamics of quantitative indicators of international mobility. Factors influencing the academic mobility of students and graduate students are identified. The types of academic mobility that are the most common among university students are identified and the importance of academic mobility for the success of student learning and future career development is clarified. The internationalization of higher education stimulates the quality of education, the diversity of education, and the free exchange of educational resources. This, in turn, generates opportunities for students to experience a "global village" along with an appreciation for other cultures - one of the many "hidden" benefits.
Application of Multidirectional Mutation Genetic Algorithm and Its Optimization Neural Network in Intelligent Optimization of English Teaching Courses

Aiming at the problems existing in the traditional teaching mode, this paper intelligently optimizes English teaching courses by using multidirectional mutation genetic algorithm and its optimization neural network method. Firstly, this paper gives the framework of intelligent English course optimization system based on multidirectional mutation genetic BP neural network and analyses the local optimization problems existing in the traditional BP algorithm. A BP neural network optimization algorithm based on multidirectional mutation genetic algorithm (MMGA-BP) is presented. Then, the multidirectional mutation genetic BPNN algorithm is applied to the intelligent optimization of English teaching courses. The simulation shows that the multidirectional mutation genetic BP neural network algorithm can solve the local optimization problem of traditional BP neural network. Finally, a control group and an experimental group are set up to verify the role of multidirectional mutation genetic algorithm and its optimization neural network in the intelligent optimization system of English teaching courses through the combination of summative and formative teaching evaluations. The data show that MMGA-BP algorithm can significantly improve the scores of academic students in English courses and has better teaching performance. The effect of vocabulary teaching under the guidance of MMGA-BP optimization theory is very significant, which plays a certain role in the intelligent curriculum optimization of the experimental class.
Student Perceptions of Mobile Automated Speech Recognition for Pronunciation Study and Testing

This study utilized Automated Speech Recognition technology to determine the potential utility and acceptance of such technology in the English as a Foreign Language classroom. Learners were made aware of the Automatic Speech Recognition potential of their mobile devices and provided with some direction in, and incentive for, its use. Participants were then scored on their assessment of the technology according to the Technology Acceptance Model. Participants showed a marked appreciation for the ease and utility of the technology with over 72% agreeing that the technology was both accessible and useful. Support for the use of Automatic Speech Recognition as a testing method was somewhat mixed, with 75% of participants agreeing that the testing was fair, but only 60% reporting that they felt they did well on the test. As a secondary point of interest, this study examined the potential use of Automatic Speech Recognition technology for teaching and testing pronunciation.
Understanding the Contributions of Junior Researchers at Software-Engineering Conferences

Junior researchers play a key role in advancing research by providing diverse and novel points of view. However their participation in the scientific community and especially computer science is not well-understood. In this paper we describe our first steps towards understanding the contribution (i.e in terms of publications) of junior researchers to computer science. More precisely we investigated to what extent junior researchers contribute publications to four highly reputable software-engineering conferences. We collected data on 5 188 main-track research papers and the corresponding 8 730 authors. The incipient results indicate a decline in the proportion of junior researchers contributing to the main-tracks of these conferences. Moreover their ratio of contribution is highly related to collaborations with more experienced researchers. With this pilot study we aim to show that the analysis method we employed can foster a more detailed understanding of the status and development of junior researchers' contributions.
Algorithm of Trending Videos on YouTube Analysis using Classification, Association and Clustering

In this modern era, many gadget users often open YouTube, both from direct applications and through websites on the internet. YouTube is a video sharing website and allows users to watch or share videos with a specific purpose, usually videos that are spread on YouTube have three types of content, ranging from educational contents, entertainment, and information. Usually, YouTube website users, looking for videos with entertaining content such as in the categories of gaming, music, short movies, and many more. Contents in this category are usually in demand among those aged under 50 years. Not infrequently students also use YouTube as a reference and guide to find additional material in their lessons, by looking for videos with educational categories. Then adolescents to adults often also use YouTube as an international information medium, because YouTube is not limited to a particular country, but is connected to all countries in the world, so we can easily receive information from abroad so quickly. Authors using data mining technique methods, including classification, association, and clustering to find out the YouTube algorithm itself in determining which videos can be trending. Authors has been carried out with existing data mining software to meet the results, find out that rating given by YouTube for getting to the trending list are “views”, “likes”, “dislikes”, “comments”.
Application of Recommendation Algorithm and Big Data Technology in Computer English Corpus Database

Abstract Corpus linguistics is one of the branches of modern linguistics. It is also a cutting-edge research problem of modern linguistics spawned by the background of the information technology era. Corpus linguistics can reset the teaching role of English majors. College English vocabulary teaching makes use of the corpus’s abundant real language materials and the computer’s ability to analyze and process large-scale corpus, guide students to explore the usage of vocabulary in the context, and use the corpus to deepen students’ autonomous learning. This article will start with the development of corpus linguistics, explain the necessity of combining corpus and English language teaching, and propose a corpus linguistics-based English language learning platform system in colleges and universities, and combine with semantic recommendation algorithms to verify that the system is in English learning.
Modern LMS Platforms for Distance Education: Analysis and Comparison

The study aims to determine the main parameters and tools of learning management systems (LMS), identify their strengths and weaknesses using the example of several distance education platforms (Moodle, Google Classroom, Microsoft Teams, Open edX and ILIAS), which are widely used not only in Russia but also around the world, in order to bring out the pedagogical potential for online learning at higher education institutions. The paper lists the main elements of information technology support for learning, draws conclusions about the most effective LMS platforms. Scientific novelty lies in identifying the main characteristics of the platforms that are necessary for the successful implementation of online education in current conditions. As a result of the study, the researcher has determined the main parameters and tools of LMS.
Call for establishing benchmark science and engineering

Currently, there is no consistent benchmarking across multi-disciplines. Even no previous work tries to relate different categories of benchmarks in multi-disciplines. This article investigates the origin and evolution of the benchmark term. Five categories of benchmarks are summarized, including measurement standards, standardized data sets with defined properties, representative workloads, representative data sets, and best practices, which widely exist in multi-disciplines. I believe there are two pressing challenges in growing this discipline: establishing consistent benchmarking across multi-disciplines and developing meta-benchmark to measure the benchmarks themselves. I propose establishing benchmark science and engineering; one of the primary goals is to set up a standard benchmark hierarchy across multi-disciplines. It is the right time to launch a multi-disciplinary benchmark, standard, and evaluation journal, TBench, to communicate the state-of-the-art and state-of-the-practice of benchmark science and engineering.
TEACHERS’ PERCEPTIONS OF CULTURALLY APPROPRIATE PEDAGOGICAL STRATEGIES IN VIRTUAL LEARNING ENVIRONMENTS: A STUDY IN COLOMBIA

This article presents the results of the first phase of the project "Design, development and evaluation of an App to promote the development of Intercultural Competence and ICT Competence in teachers of Higher Education" that was carried out in the Department of Antioquia in Colombia.The main objective of the first phase of the study was to analyze the perceptions of higher education teachers about their Intercultural Competence "Culturally Appropriate Pedagogical Strategies" in Virtual Learning Environments.The study has a quantitative approach, with a descriptive and non-experimental design that allows observing and analyzing the phenomena of the study.To identify participants' perceptions, a survey was conducted in which 69 teachers from various educational institutions in the Department of Antioquia in Colombia participated voluntarily.
Nature Inspired Algorithm for Pixel Location Optimization in Video Steganography Using Deep RNN

The steganography is applied on text, image, video, and audio files. The steganography is useful for safe and secure data transmission. Video steganography is used to preserve confidential information of security applications. To improve security of the message, pixels locations are optimized using nature inspired algorithm. As conventional algorithms have a low convergence rate a new algorithm is proposed. A New algorithm is developed by combining two model algorithms namely, Water wave optimization (WWO) and Earth worm optimization (EWO) and is renamed as proposed Water-Earth Worm Optimization (WEWO) algorithm. The frames are preprocessed and extracted using Discrete Cosine transform (DCT) and Structured Similarity index (SSIM), respectively, as regular processing. For pixel prediction, the fitness function is obtained from neighborhood entropies in proposed algorithm. In this method, secret message is embedded with two level decomposition of Wavelet Transform (WT). In the proposed work is tested with ‘CAVIAR’ dataset. The Proposed WEWO-Deep RNN algorithm performance is tested with modular noises such as, pepper, salt and pepper noises. The proposed method gives enhanced performance, which is seen with the parameters, Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Correlation Coefficient (CC) which defines image quality indices.
When HEAAN Meets FV: A New Somewhat Homomorphic Encryption with Reduced Memory Overhead

We demonstrate how to reduce the memory overhead of somewhat homomorphic encryption (SHE) while computing on numerical data. We design a hybrid SHE scheme that exploits the packing algorithm of the \(\mathtt {HEAAN}\) scheme and the variant of the \(\mathtt {FV}\) scheme by Bootland et al. The ciphertext size of the resulting scheme is 3–18 times smaller than in \(\mathtt {HEAAN}\) to compute polynomial functions of depth 4 while packing a small number of data values. Furthermore, our scheme has smaller ciphertexts even with larger packing capacities (256–2048 values).
Context-Aware Support for Cardiac Health Monitoring Using Federated Machine Learning

Context-awareness provides a platform for healthcare professionals to assess the health status of patients in their care using multiple relevant parameters such as heart rate, electrocardiogram (ECG) signals and activity data. It involves the use of digital technologies to monitor the health condition of a patient in an intelligent environment. Feedback gathered from relevant professionals at earlier stages of the project indicates that physical activity recognition is an essential part of cardiac condition monitoring. However, the traditional machine learning method of developing a model for activity recognition suffers two significant challenges; model overfitting and privacy infringements. This research proposes an intelligent and privacy-oriented context-aware decision support system for cardiac health monitoring using the physiological and the activity data of the patient. The system makes use of a federated machine learning approach to develop a model for physical activity recognition. Experimental analysis shows that the federated approach has advantages over the centralized approach in terms of model generalization whilst maintaining the privacy of the user.
Efficiency of the Method of Student Thematic Online Debates in the Development of Professional Competences in Future Specialists

The transition to distance learning because of the COVID-19 pandemic has led teachers to adapt effective teaching methods to distance education. Theoretical analysis of scientific works allowed determining that student debates have significant potential for the development of critical thinking, communication skills. The aim of the study was to test the effectiveness of the method of student thematic online debates in the development of professional competencies in future specialists. In the experimental study, several complementary techniques were used to determine the level of future specialists’ professional competence. The reliability of the obtained conclusions on the basis of the used methods was ensured by comparing the data obtained by each method, as well as the representativeness of the sample size and statistical significance of the experimental data. It is determined that the following competencies can be developed in students through the method of student thematic online debates: readiness to work in a team, ability to generalize, analyse and adequately perceive information, ability to communicate according to language norms, reasoned and clearly build oral and written speech. The study allowed determining that there are certain methodological features of using the method of student thematic online debates in the distance educational process. The method of debate is an effective approach to the organization of online learning, which allows obtaining not only theoretical knowledge, but also gives the possibility of applying them in professional activities in the future. Proof of the effectiveness of the proposed model of using debate within distance learning is confirmed by the unequivocally positive dynamics in the experimental group to improve the Value-orientation criterion, Cognitive criterion Communicative-activity criterion (in particular, at a high level of 13.79%, 7.38%, 11.56% respectively). The novelty of the presented scientific intelligence in the experimental study of the potential of online debates for students of agricultural universities, but with the potential to adapt the presented model of organization of the pedagogical environment for students of other specialties. The research materials can be used by teachers in the development of distance online courses, heads of distance learning coordination departments in higher educational institutions. The prospect of further research is studying the effectiveness of thematic online debates to increase the level of internal learning motivation in future specialists.
A Robust DCT-Based Video Watermarking Scheme Against Recompression and Synchronization Attacks

Video transmission and sharing have become faster and easier due to the advancement of the Internet and social platforms. In this case, video watermarking is used for copyright protection and tracking the source of the video. Nevertheless, when the video is spread on the Internet or social platforms, the watermark is easily attacked and breached. For this reason, keeping the watermark alive under recompression and other attacks is an urgent and challenging issue. However, none of the existing technologies is robust to these hybrid attacks. In this paper, a DCT-based video watermarking scheme is proposed against recompression, synchronization attack, and hybrid attacks. The proposed method modifies the highest frequency DCT coefficient for watermark embedding. In addition, the fivefold watermarks are encoded as Bose Chaudhuri Hocquenghem (BCH) codes to reduce the bit error rate, and odd-even quantization is used to embed them into the U channel of the video frame. When the video is cropped, the synchronization code is used to find the positions of the watermarks. Moreover, the watermark extraction is blind. Experimental results show that the proposed method can resist compression, strong recompression, synchronization attacks such as cropping, inserting, and frame dropping/inserting. It can also maintain good imperceptibility.
Analyzing the Voice of Customer through online user reviews using LDA: Case of Moroccan mobile banking applications

Nowadays, most of the organizations make their mobile applications available through different stores, such as Google Play Store, Apple App Store, and Windows Phone Store. Banks and financial institutions have also provided mobile applications for their customers. These app stores not only allow applications to be downloaded, but they also permit users to leave comments and reviews. In this paper, we will start first by looking at eight Moroccan mobile banking applications in the Google Play Store. Data that hasn’t been exploited by Moroccan banks yet. Once the preprocessing phase is complete, we will examine and analyze user reviews using Latent Dirichlet allocation (LDA) to extract and identify topics. Topics discovered focus mainly on Security, services, quality, and interface. While customer reviews can influence future demand, they can also be used by managers to improve their services and customer experience.
ZAŠTITA POTROŠAČA NA TRŽIŠTU DIGITALNE IMOVINE

Increased importance and availability of different forms of digital assets have resulted in increased consumers’ interest to conduct transactions with this specific type of assets. Increased consumers’ presence on the markets for digital assets has become one of the major challenges that modern consumer law encounters. The main risk faced by consumers who trade with different forms of digital assets arises from their instability and volatility. For that reason, the possibility of a loss of invested means is significantly higher compared to other similar marketplaces. The main subject of this paper is a review of basic mechanisms of consumers’ protection on the markets for digital assets. The review has been based on a comparative analysis of the Serbian Law on Digital Assets and of the EU Proposal for a Regulation on Markets in Crypto-assets. Before the analysis of concrete legal provisions on consumer protection has been made, the author indicates to certain traits of digital assets and problems and risks that consumers face in this innovative and dynamic marketplace.
Dynamic Modeling and Simulation Analysis of Virtual Product Design and Creation Based on OSG Artificial Intelligence Platform

The creation of Koji Pottery art is a creative act that has risen rapidly in recent years. The difference in the proportion of ingredients in the glaze directly affects the roughness, luster, and glaze of the glazed surface of the work. The curve of heating and cooling is also the key to the success or failure of the work. Therefore, taking the Koji tea bowl shape as a research example, a virtual reality simulation system and an OSG (OpenSceneGraph) artificial intelligence platform are used to construct a Koji tea bowl shape simulation model, and then the physical production is performed according to the model. After the design and production of the Koji tea bowl shape, the RTM (resin transfer molding) model is used to evaluate the process quality standard requirements of the Koji bowl shape. According to the RTM model test results, the homogeneity test results show that the result is much larger than 0.10, from which it can be inferred that the Koji tea bowl shape has reached the national process quality standard requirements. At the same time, the OSG artificial intelligence platform uses the iterative random sampling method to extract and display the abnormal data in the simulation model, which greatly improves the success rate of ceramic creation. In this paper, the creation and simulation of virtual ceramics based on OSG artificial intelligence platform are deeply studied and analyzed, and the related processes are improved, which reduces the difficulty of software operation and improves the creation efficiency.
Corpus linguistics

Corpora are collections of authentic texts selected and assembled to study language. This chapter provides an introduction to corpus use for translation scholars, reviewing the theoretical assumptions underlying it and providing an introduction to the different corpus types (monolingual comparable, parallel, and composite designs) and the different tools available for studying translation through corpora (concordances, clusters, collocates, frequency lists, keywords, n-grams). Several application areas are identified as especially central, such as the search for typical features of translation, the investigation of translator style, and translator education. While providing an accessible introduction to the field, the chapter also aims to explore some more advanced notions. First, in terms of methods for corpus exploration, quantitative lexico-grammatical indices, annotation-based data extraction methods, and inferential and exploratory statistics for corpus comparison are briefly presented. Second, several critical issues and topics are discussed, namely the emergence of increasingly composite corpus designs, the wider adoption of quantitative methods and sophisticated techniques borrowed from natural language processing, the emergence of new hypotheses and research paradigms which combine corpus techniques with other data sources, the inclusion in the research paradigm of novel forms of translation, and the comparison of translation with other forms of bilingual communication.