Reviewerly/exHarmony huggingface dataset:
Visual Management of Sports Based on Intelligent Analysis of Big Data

After years of development, big data technology has become more mature and reliable in its application in various fields. With the explosive growth of data and the rise of data complexity, the processing and application of data have actually changed our daily life. The development of data in sports is very active in recent years, and the natural dependence of sports on data determines that the visualization of sports has a great creative space in data sports. Therefore, it is necessary to deeply study the visual management of sports under the background of big data technology. As we all know, in sports, competitive games occupy the vast majority, and the data generated by this is not limited to the score of the game. People are paying more and more attention to information related to sports, but these information are generally very complicated and huge. This is also an area with the widest audience, and the core of competitive sports is data. The final results and progress of most sports competitions are reflected through changes and comparisons of data. With the continuous development of big data information collection technology, the available sports data are more detailed, which also makes sports develop toward visualization. Therefore, with the help of the excellent ability of big data in screening data, this article proposes algorithms such as DTW and recurrent neural networks to reasonably and reliably analyze and process a large number of data generated in the process of sports and embeds an error analysis module in the designed model to ensure the accuracy requirements in data processing to a greater extent. The model designed in this article has been able to improve the accuracy by more than 86%. This will greatly facilitate our management and have a far-reaching impact on sports visualization.
THE IMPORTANCE OF ICT IN SENIOR PRESCHOOLERS’ MORAL EDUCATION

The research features the results of the effective introduction of information and communication technologies (ICT) into the process of senior preschoolers’ moral education in preschool educational institutions (PEI). The benefits of ICT implementation in preschool education in different countries are discussed, and the balance between ICT and moral education is clarified. The necessity to find new approaches to increase the level of senior preschoolers’ moral education was emphasized, which determined the topicality of the raised scientific problem. An experimental study of ICT implementation in the senior preschoolers’ (n = 432) moral education was conducted, which included constitutive, formative and control stages. Criteria (cognitive, emotional and value-oriented, behavioral), their indexes and levels of senior preschoolers’ moral education were specified. Based on the diagnostic method “Me and My Friends”, the initial levels (high, sufficient, medium and low) were determined. An experimental method of ICT implementation in senior preschoolers’ moral education is presented. This method was developed considering the requirements of the Basic Component of Preschool Education of Ukraine (2021) and partial program (additional thematic program on the organization of moral education in preschool education) “Treasury of Morality” (2020). The method is aimed at building children’s positive attitude to ICT as a source of information that promotes the consolidation of knowledge about moral norms and development of skills of moral behaviour in society. The educational potential of ICT is analyzed, which should be implemented to increase the level of senior preschoolers’ moral education, namely: videos, multimedia presentations, cartoons, video tales, online games, smart cards etc. The sequence of the method implementation at the information-introductory, reconstructive-formative and affirmative-consolidating stages is revealed. The effectiveness of ICT implementation in the senior preschoolers’ moral education was assessed at the control stage. Positive dynamics of high and sufficient levels in senior preschoolers’ moral education in the experimental group (EG) is proved to have risen. Importance of the ICT introduction into the modern system of senior preschoolers’ moral education of PEI is confirmed. The following prospective directions of the initiated study are outlined: interaction with parents in the context of children’s moral education involving ICT, ensuring consistency of PEI and primary school in children’s moral education, as well as the development of moral competence of university students majoring in “Preschool education”.
Accurate 3D hand mesh recovery from a single RGB image

This work addresses hand mesh recovery from a single RGB image. In contrast to most of the existing approaches where parametric hand models are employed as the prior, we show that the hand mesh can be learned directly from the input image. We propose a new type of GAN called Im2Mesh GAN to learn the mesh through end-to-end adversarial training. By interpreting the mesh as a graph, our model is able to capture the topological relationship among the mesh vertices. We also introduce a 3D surface descriptor into the GAN architecture to further capture the associated 3D features. We conduct experiments with the proposed Im2Mesh GAN architecture in two settings: one where we can reap the benefits of coupled groundtruth data availability of the images and the corresponding meshes; and the other which combats the more challenging problem of mesh estimation without the corresponding groundtruth. Through extensive evaluations we demonstrate that even without using any hand priors the proposed method performs on par or better than the state-of-the-art.
Seeking sigma: Time-of-the-day effects on the Bitcoin network

This research investigates and tests for the presence of time-of-the-day effects on the Bitcoin network. Results indicate that NYSE trading sessions lead Bitcoin trading activity, both on the blockchain and centralised exchanges. Effects are found to have strengthened over time, however, simultaneously diminished at the weekend indicating significant exchange interactions, and that Bitcoin has developed somewhat outside its intended design parameters and is influenced by other forces such as those originating from NYSE trading. While proponents consider Bitcoin trading to be ‘24/7’, our findings suggest that both transaction and on-chain network activity are best described to be, at best, ‘12/5’, presenting significant implications for traders, with regards to centralised exchange liquidity and the speed of their transaction inclusion on the blockchain. Finally, the role and influence of both algorithm and volatility traders cannot be eliminated.
Paradigm gaps are associated with weird "distributional semantics" properties: Russian defective nouns and their case and number paradgim

This study investigates the phenomenon of defectiveness in Russian case and number noun paradigms from the perspective of distributional semantics. We made use of word embeddings, high-dimensional vectors trained from large text corpora, and compared the observed paradigms of nouns that are defective in the genitive plural, as suggested by Zaliznjak (1977), with the observed paradigms for non-defective paradigms. When the embeddings of about 20,000 inflected forms were projected onto a two-dimensional space, clusters of case and number within case were found, suggesting global semantic similarity for words with the same inflectional features. Moreover, defective lexemes were characterized by lower semantic transparency, in that inflected forms of the same lexeme are semantically less similar to each other, and their meanings are also more idiosyncratic. Furthermore, compared to non-defective lexemes, inflected forms from defective lexemes are further away from the idealized average case-number meanings, obtained by averaging over the vectors of all inflected forms of the same case-number combination. As a consequence, the semantics of defective forms are predicted less precisely by a simple model of conceptualization that assumes that the meaning of a given Russian inflected form is approximated well by the sum of pertinent embeddings of the lexeme, case, and number within case. We conclude that semantics, at least the kind captured by word embeddings, also contributes to the defectiveness of Russian noun paradigms.
A Hybrid Bayesian-Convolutional Neural Network for Adversarial Robustness

We introduce a hybrid Bayesian-convolutional neural network (hyBCNN) for improving the robustness against adversarial attacks and decreasing the computation time in the Bayesian inference phase. Our hyBCNN models are built from a part of BNN and CNN. Based on pre-trained CNNs, we only replace convolutional layers and activation function of the initial stage of CNNs with our Bayesian convolutional (BC) and Bayesian activation (BA) layers as a term of transfer learning. We keep the remainder of CNNs unchanged. We adopt the Bayes without Bayesian Learning (BwoBL) algorithm for hyBCNN networks to execute Bayesian inference towards adversarial robustness. Our proposal outperforms adversarial training and robust activation function, which are currently the outstanding defense methods of CNNs in the resistance to adversarial attacks such as PGD and C&W. Moreover, the proposed architecture with BwoBL can easily integrate into any pre-trained CNN, especially in scaling networks, e.g., ResNet and EfficientNet, with better performance on large-scale datasets. In particular, under l∞ norm PGD attack of pixel perturbation ε=4/255 with 100 iterations on ImageNet, our best hyBCNN EfficientNet reaches 93.92% top-5 accuracy without additional training.
On the issue of the sources of alluvial diamonds of the Ebelyakhsky region (Arctic zone of Russia)

The work considers the identification of potentially diamond areas based on the geological and statistical analysis of data from heavy mineral concentrate sampling of alluvial deposits of the Ebelakh River and its confluents. The researchers compress the information of the diamond placer mineral field of the Ebelyakh River and identify the main components based on information entropy (K. Shannon). The spatial heterogeneity of polymineral fields, revealed using the entropy method, allow outlining areas of possible localization of intermediate hosts (or their former development) in the Ebelyakhskiy region.
Quality Evaluation of Professional Training of Secondary Vocational Education Graduates in the Field of Information Technology

The study aims to design a model for quality evaluation of professional training of secondary vocational education graduates in the field of Information Technology. The paper considers the specificity of the secondary vocational education stage based on students’ age particularities and the regulation of the educational process by the requirements of the state, the features peculiar to education in the field of Information Technology characterised by dynamic changes in the means and technologies used, which affects the need for a quick response of the education system to changes in the field of Information Technology. Taking into account the considered features of secondary vocational education in IT, the paper identifies the elements of the quality evaluation system of students’ training. Scientific novelty of the study lies in developing and describing a model for quality evaluation of professional training of secondary vocational education graduates with account of the specificity of Information Technology training. As a result, three blocks of the formed model have been identified and described: the target block, the content-procedural block and the performance-evaluative block. In addition, pedagogical conditions for a successful application of the developed model for training IT specialists in secondary vocational education programmes in the field of Information Technology have been determined.
A comprehensive analysis of consumer decisions on Twitter dataset using machine learning algorithms

An exponential growth posting on the web about the product reviews on social media, there has been a great deal of examination being done on sorting out the purchasing behaviors of the client. This paper depends on utilizing twitter for sentiment analysis to comprehend the customer purchasing behavior. There has been a significant increase in e-commerce, particularly in persons purchasing products on the internet. As a result, it becomes a fertile hotspot for opinion analysis and belief mining. In this investigation, we look at the problem of recognizing and anticipating a client's purchase goal for an item. The sentiment analysis helps to arrive at a more indisputable outcome. In this study, the support vector machine, naive Bayes, and logistic regression methods are investigated for understanding the customer's sentiment or opinion on a specific product. These strategies have been demonstrated to be genuinely for making predictions using the analysis models which examine the client's conclusion/sentiment the most precisely. The exactness for each machine learning algorithm will be analyzed and the calculation which is the most precise would be viewed as ideal.
SOLAR IRRADIANCE PREDICTION USING BAGGING DECISION TREE-BASED MACHINE LEARNING

Solar energy is one of the most widely used renewable energy sources to generate electricity. However, the amount of solar radiation reaching the earth's surface is variable, creating uncertainty in the output of electrical power generation systems that use this source. Therefore, solar irradiance prediction becomes a critical process in planning. This study presents a short-term prediction of solar irradiance using bagging decision tree-based machine learning. As the inputs of the proposed method, air temperature, hour, day, month, and previous solar irradiance values were determined. The performance of the proposed method is tested on the measured data. The R2 and RMSE values are 0.87 and 91.282, respectively, according to the results obtained. As a result, it has been revealed that the varying solar irradiance can be predicted with acceptable differences with this method.
FORMATION OF CYBER SECURITY SKILLS THROUGH METHODS OF HACKING, BYPASSING AND PROTECTING THE PROCEDURE FOR GRANTING ACCESS IN MICROSOFT WINDOWS OPERATING SYSTEM

The article looks into the problematic issue of forming/developing teaching staff’s cyber security skills (a case study of the teachers from the National University of Defence of Ukraine named after Ivan Cherniakhovskyi). The importance of this issue is fueled by the analysis of cyber security of user information on personal computers through the prism of vulnerabilities in the security mechanisms implementation for Microsoft Windows operating system (versions 7, 10), including access procedures. The key steps and specificities of the procedure for granting access (identification, authentication and authorization) in Microsoft Windows operating system are described. A survey among the teachers-respondents was conducted to figure out whether they understand the essence of the procedure for granting access and have some idea about methods of hacking, bypassing and protecting this access. The survey revealed that the teaching staff totally misunderstand the concepts and procedures. The issue of forming/developing teaching staff’s cyber security skills becomes even more relevant amid the implementation of basic cyber security principles in Ukraine, adopted by the Verkhovna Rada in 2017. Accordingly, the authors describe typical modern tools of hacking, bypassing and protecting the procedure for granting access in Microsoft Windows operating systems (versions 7, 10), which will enable everyone to master some practical steps in order to realize the importance and necessity of key tools and techniques of ensuring personal cyber security. In doing so, the authors intended to visualize possible ways of cyber security violation and increase awareness about them with the aim of preventing cyber risks. In addition, the authors seek to inform different categories of people that contemporary information and communications technologies not only expand the capabilities of our global digital society, but also increase exponentially the number of objects vulnerable to cyber threats. In addition, our task was to promote the issues of forming/developing teachers’ skills in supporting their cyber security by training them to implement some cyber security tools and techniques aimed at reducing cyber risks. Our attention is also paid to some ethical aspects in reviewing the outlined outcomes, presented for educational purposes in an effort to raise public awareness of the described vulnerabilities, which pose cyber risks to those involved in information sphere.
WHERE ARE WE IN SPACE: EXAMINING TEACHERS’ ICT COMPETENCY IN TEACHING LEARNERS IN SPECIAL SCHOOLS IN KENYA

&lt;p&gt;Information Communication Technology (ICT) plays a central role in facilitating education in the 21st century. For successful integration of ICT in teaching and learning teachers play a key role. As such, there is a need for competent teachers in applying ICT skills to teach and facilitate learning. This study sought to examine teachers’ ICT competency in implementing Digital Literacy Programme (DLP) in public primary special schools in Nairobi County ii Kenya. The study used a semi-structured questionnaire tool to collect data from 99 teachers and 41 head teachers from public primary special schools in Nairobi County. Mixed-Methods Research (MMR) where both Statistical Methods and thematic analysis approaches were used to analyse the data. Statistical Methods used include descriptive analysis such as frequencies and percentages and bivariate analysis approaches including Chi-square tests of hypothesis regarding the relationship between teacher’s ICT competency and implementation of DLP. The study found that the role played by DLP in building teacher ICT skill capacity is 28.4% lower in special schools compared to regular schools. As a result, teachers of children with disabilities and special educational needs have limited relevant ICT skills such as assistive technology to support teaching and learning. The study also found that whilst there may be a significant proportion of teachers skilled with ICT skills, lack of access to digital devices and internet connectivity makes it difficult for them to demonstrate their competency and apply the same to the teaching of children with disabilities. Based on the results of this study, recommendations for policy enhancement and improvement of practice have been made.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0978/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
MACHINE LEARNING ALGORITHMS FOR ASL IMAGE RECOGNITION WITH LENET5 FEATURE EXTRACTION

American Sign Language is used by mute and deaf people so that they can interact with the people around them. It is used by approximately 2,50,000-5,00,000 Americans (and some Canadians) of all ages. Over the period of time, many have proposed different methods for recognition of ASL. Sign language Recognition is a complex technical problem due to the difficulty of visual analysis of hand motions and the highly structured nature of sign language. Hence the accuracy is not achieved. To enhance this accuracy, the proposed system compares different machine learning classification algorithms using Lenet5 architecture for feature extraction. Lenet5 is one of the architectures of Convolution Neural Network (CNN). The proposed system uses machine learning algorithms like Neural Network, Decision Tree Classifier, K-Nearest Neighbour and Support Vector Machine. There is a raise in accuracy using Neural Network algorithm when compared to other machine learning algorithms. The proposed solution was tested on data samples from ASL data sets and achieved an overall accuracy of 99.99% using Neural Network.
Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning

Exploiting the properties of quantum information to the benefit of machine learning models is perhaps the most active field of research in quantum computation. This interest has supported the development of a multitude of software frameworks (e.g. Qiskit, Pennylane, Braket) to implement, simulate, and execute quantum algorithms. Most of them allow us to define quantum circuits, run basic quantum algorithms, and access low-level primitives depending on the hardware such software is supposed to run. For most experiments, these frameworks have to be manually integrated within a larger machine learning software pipeline. The researcher is in charge of knowing different software packages, integrating them through the development of long code scripts, analyzing the results, and generating the plots. Long code often leads to erroneous applications, due to the average number of bugs growing proportional with respect to the program length. Moreover, other researchers will struggle to understand and reproduce the experiment, due to the need to be familiar with all the different software frameworks involved in the code script. We propose QuASK, an open-source quantum machine learning framework written in Python that aids the researcher in performing their experiments, with particular attention to quantum kernel techniques. QuASK can be used as a command-line tool to download datasets, pre-process them, quantum machine learning routines, analyze and visualize the results. QuASK implements most state-of-the-art algorithms to analyze the data through quantum kernels, with the possibility to use projected kernels, (gradient-descent) trainable quantum kernels, and structure-optimized quantum kernels. Our framework can also be used as a library and integrated into pre-existing software, maximizing code reuse.
Clinical Knowledge Mining Based on Image Enhancement Algorithm: Endoscopic Clinical Analysis of Peptic Ulcer in Children

The incidence rate of peptic ulcer is increasing gradually. Medical images can meet the needs of patients as an auxiliary diagnosis and treatment method for peptic ulcer. However, in the long-term treatment, the actual effect is average, and the diagnosis effect of gastrointestinal diseases will gradually deteriorate. In this paper, we use an image enhancement algorithm to study the mechanism of peptic ulcer from the perspective of a medical image. In this paper, 56 images of children with peptic ulcer were selected, and the gastroscopy based on the image enhancement algorithm provided technical support for the rapid diagnosis of patients with peptic ulcer. Experimental results show that the clinical features of peptic ulcer have different characteristics according to the age difference of patients, which can play a positive role in promoting the treatment of patients of different ages.