Reviewerly/exHarmony huggingface dataset:
Oligopoly structure in the cryptocurrency market

Blockchain technology has been announced as the driving force behind the democratization of digital business. Various interest groups believed that cryptocurrencies would enable fast, cheap and anonymous payments over the Internet. The absence of a central institution and the possibility of the participation of the wider community in the maintenance of the system should have created electronic money adapted to individuals, not the financial elite. However, the question arises whether cryptocurrencies really provide equal opportunities for all participants. The subject of the paper is the degree of centralization of the most famous cryptocurrency systems in terms of wealth distribution and the possibility of participation in their maintenance. The goal of the paper is to determine the degree of inequality in various aspects of the functioning of the cryptocurrency system. The results of the analysis indicate that cryptocurrencies function separately from the traditional financial system, but do not enable the financial inclusion of marginalized social groups. No current cryptocurrency community provides equality of participants, neither in terms of mining, nor in terms of wealth distribution. It can be concluded that the mining of cryptocurrencies and their secondary circulation show clear characteristics of oligopolistic structures.
Literature Review of Research on Common Methods of Grapheme-To-Phoneme

Grapheme-to-phoneme (G2P) conversion techniques have been used in many fields, most notably speech synthesis (text-to-speech, TTS). Nowadays, the development of speech synthesis is facilitated with the continuous improvement of G2P conversion techniques. The purpose of the paper is to provide an review of grapheme-to-phoneme conversion methods. First, the grapheme-to-phoneme conversion methods in recent years are sorted out; then the relevant data sets and evaluation metrics are listed, and finally the problems and development trends faced by grapheme-to-phoneme conversion are described.
Mixed reality training application to perform obstetric pulsed-wave Doppler ultrasound

Abstract Pulsed-wave Doppler ultrasound is a widely used technique for monitoring pregnancies. As ultrasound equipment becomes more advanced, it becomes harder to train practitioners to be proficient in the procedure as it requires the presence of an expert, access to high-tech equipment as well as several volunteering patients. Immersive environments such as mixed reality can help trainees in this regard due to their capabilities to simulate real environments and objects. In this article, we propose a mixed reality application to facilitate training in performing pulsed-wave Doppler ultrasound when acquiring a spectrogram to measure blood velocity in the umbilical cord. The application simulates Doppler spectrograms while the trainee has the possibility of adjusting parameters such as pulse repetition frequency, sampling depth, and beam-to-flow angle. This is done using a combination of an optimized user interface, 3D-printed objects tracked using image recognition and data acquisition from a gyroscope. The application was developed for Microsoft HoloLens as the archetype of mixed reality, while a 3D-printed abdomen was used to simulate a patient. The application aims to aid in both simulated and real-life ultrasound procedures. Expert feedback and user-testing results were collected to validate the purpose and use of the designed application. Design science research was followed to propose the intended application while contributing to the literature on leveraging immersive environments for medical training and practice. Based on the results of the study, it was concluded that mixed reality can be efficiently used in ultrasound training.
The Application of Case Studies in Digital Citizenship Education: An Empirical Study

This study, underpinned by qualitative research, seeks to unravel the multifaceted concept of digital citizenship education through the lens of educators who leverage case studies in their teaching. In an era marked by rapid digitization, the cultivation of responsible and ethical digital citizenship has become crucial. This research explores the potential of case studies as a powerful pedagogical tool, providing tangible context to this intricate concept.Drawing on the collective experience and insights of 25 educators from diverse backgrounds, this study investigates their strategies for defining digital citizenship, selecting and applying case studies, and addressing contemporary issues in the field. Through semi-structured interviews and thematic analysis, key patterns emerge, highlighting the importance of clear definitions, comprehensive literature reviews, focused research questions, and active discussions for effective case study integration.The findings offer significant pedagogical insights and present a roadmap for educators, suggesting strategic incorporation of case studies to enrich digital citizenship education. It emphasizes the utility of case studies in linking theoretical knowledge to real-world applications, thereby fostering a comprehensive understanding of digital citizenship.The study also encourages future research to delve deeper into these practices to optimize their effectiveness further. Ultimately, the study contributes to the evolving discourse on digital citizenship education, providing valuable insights that can inform pedagogical approaches and curriculum design in our increasingly digital world.
Vectorizing and distributing number‐theoretic transform to count Goldbach partitions on Arm‐based supercomputers

Summary In this article, we explore the usage of scalable vector extension (SVE) to vectorize number‐theoretic transforms (NTTs). In particular, we show that 64‐bit modular arithmetic operations, including modular multiplication, can be efficiently implemented with SVE instructions. The vectorization of NTT loops and kernels involving 64‐bit modular operations was not possible in previous Arm‐based single instruction multiple data architectures since these architectures lacked crucial instructions to efficiently implement modular multiplication. We test and evaluate our SVE implementation on the A64FX processor in an HPE Apollo 80 system. Furthermore, we implement a distributed NTT for the computation of large‐scale exact integer convolutions. We evaluate this transform on HPE Apollo 70, Cray XC50, HPE Apollo 80, and HPE Cray EX systems, where we demonstrate good scalability to thousands of cores. Finally, we describe how these methods can be utilized to count the number of Goldbach partitions of all even numbers to large limits. We present some preliminary results concerning this problem, in particular a histogram of the number of Goldbach partitions of the even numbers up to 2 40 .
Great Achievements Made in College English Teaching Reform between the Years of 2014 -2024

According to “College English Teaching Guidance”, college English will transform from basic English to EAP and EOP. First , as for each university and college, they should arrange these courses, select proper textbooks and organize the examinations from their unique characteristics. Second, as college English teachers, we should arm ourselves with scientific and technological knowledge beforehand, familiarize ourselves with scientific documents, improve our teaching skills in this new field. Now it has been nearly ten years since the issue of “College English Teaching Guidance”, have the objectives been accomplished?
Refinement of fatigue curve parameters using fuzzy set theory and tomography images processing of censored composite samples

The fatigue curve is important in engineering practice. Since the task of constructing a fatigue curve is characterized, among other things, by the presence of qualitative (not numerical), inaccurate and incomplete information, the question arises of the appropriateness of applying the theory of fuzzy sets. To clarify information on censored, namely, removed from testing upon reaching the test base specimens, the use of computed tomography to study the degree of their damage is proposed. A fuzzy regression model has been developed to consider linguistic variable that describes the status of a sample (broken or censored). With fuzzy dependent variables of the of regression model, the coefficients of the equation also turn out to be fuzzy. For the fuzzy dependent variable of the time before failures of censored samples, the degree of damage is additionally evaluated based on the rating of experts by the analysis of tomography images. The model made it possible to estimate the interval of expected life for a given stress amplitude using an uncertainty accounting method that differs from the classical statistical description. An example of constructing a fuzzy fatigue curve for polymer composite materials is considered.
The Role of Parents in Learning Motivation and Student Achievement: a good achievement

This study formulates the problem, The Role of Parents in Learning Motivation and Student Achievement: a good achievement, So how does the influence of student achievement from the role of parents? and does parental motivation affect student achievement? The results of the study show that the participation of parents means that the progress and improvement of children's learning achievement and learning motivation at school can continue to increase, along with increasing age and children's reasoning power. The role of parents who teach and guide becomes an additional teaching outside of school for students in achieving their learning achievements. This study to collect data with literature sources.
MIGTNet: Metapath Instance-based Graph Transformation Network for heterogeneous graph embedding

Many real-world graphs are heterogeneous with various types of entities and relations. These heterogeneous graphs commonly inherent various structural and semantic information of sequences of node types, called metapaths; and many current heterogeneous graph embedding models learn not only the node features but also the metapaths. However, all of them only focus on the metapath instances (i.e., the sequences of nodes following the schema defined by a metapath) and ignore the relations between them when learning about the metapaths. To overcome this limitation, we propose a new model called the Metapath Instance-based Graph Transformation Network (MIGTNet) for heterogeneous graph embedding, which uses both metapath instances and relations between them. MIGTNet constructs a metapath instance-based graph, where a node represents a metapath instance and a link represents a relation between metapath instances, and inputs it to a hierarchical graph attention network to obtain meaningful node embeddings. Extensive experiments using real datasets show that MIGTNet outperforms state-of-the-art heterogeneous graph embedding models in node classification and node clustering.
The virtual reality technologies in the educational processes in Kemerovo State Institute of Culture

The virtual reality (VR) development made it suggestible to test and endorse these technologies in educational processes of Kemerovo State Institute of Culture (KSIC). The VR visualizes the knowledge, clarifies information on complex processes and phenomena, demonstrates buildings interiors, musical instruments, architecture, and geographic locations, creates the illusion of exposure to imaginary space. The purpose of the study is to demonstrate the possibilities of applying VR in educational, learning, creative and professional activities of the higher educational institution. The scientific originality of theoretical value of the study resides in the fact that the VR technology is approbated at the Kemerovo State Institute of Culture. The researchers applied the scientific methods of document flow analysis, content analysis of websites of RF Ministry of Culture academic institutions, content analysis of textual sources on designing and applying VR -technologies in education as well analysis of applying VR-technologies in KSICВ. They discuss VR-projects designed for key specialities of KSIC, and characterize the program “VR-technologies” for the students in various fields of study. The examples of using VR-technologies in professional and extra-curricular activities of the higher educational institution are provided. Applying VR-technologies to the educational processes permits to preserve cultural heritage and to make it attractive to the users. The program of “VR-technologies” supports building relevant skills of designing modern products with 3D-models. Further development of VR-projects for educational processes in Kemerovo State Institute of Culture is a promising area of studies.
Research on Multidimensional Evaluation Model and Algorithm of Effectiveness of New Power System Technical Standard System Based on Association Rules and Big Data

The current conventional evaluation method of the effectiveness of the technical standard system of power system is mainly through the extraction of key factors of technical standards, so as to construct an evaluation model. Due to the lack of mining processing of technical standard data, it leads t
Research on the Development Strategy of International Cooperation in Running Higher Vocational Colleges ----Taking the Sino-German Cooperation in Running Schools in the Area of Vocational Education as an Example

With the continuous development of economic globalization, the trend of strengthening international exchanges and cooperation and introducing high-quality international education resources is becoming more and more obvious. Exploring a reasonable mode of international cooperation in running higher vocational colleges is not only conducive to promoting the development of higher vocational colleges, but also conducive to enhancing the international competitiveness of China’s higher education.
Exploration and Practice of Hybrid Teaching Mode of Web Front-end Development Technology Based on OBE

To address the problems of traditional lecture-based teaching in the “Web Front-end Development Technology” course, in which students lack practical problem-solving ability and enterprises lack satisfaction with the front-end development talents cultivated by universities, this paper proposes a hybrid teaching mode for the Web Front-end Development Technology course based on OBE, with the educational outcomes of students as the main objective. The OBE teaching concept is used to reform the teaching mode; the teaching design is based on the framework of activity theory; the SPOC hybrid teaching mode is used to learn the theoretical basics and complete the corresponding experiments and exercises online; the task-driven method and project-based teaching are used to enhance students’ practical skills and innovative thinking offline. The method is then applied to our school’s teaching and learning programs. Subsequently, the model was applied to the teaching of “Web Front-end Development Fundamentals” course in our 2021 and 2022 computer science classes, and the experimental data were analyzed using multiple linear regression methods. The experimental results show that the OBE-based hybrid teaching model studied in this paper has achieved significant results.
Machine Learning for Rhabdomyosarcoma Whole Slide Images Sub-type Classification

The most frequent malignant soft tissue tumor in children is Rhabdomyosarcoma (RMS). RMS has several subtypes that differentiate treatment and patient outcomes. Because of variations in the appearance of histopathology images, manual subtype classification requires a high level of expertise and is time-consuming. While several machine-learning techniques have been developed to classify the most common tumor types in histology images, more must be understood about the automatic classification of tumor subtypes. Moreover, existing techniques for classifying whole slide image (WSI) histopathological subtypes rely on small, randomly selected image tiles or on representative tiles chosen by specialists from the considerably larger WSIs. These methods do not draw knowledge from the whole tissue region captured by a WSI, possibly missing significant tumor signatures. They also fail to account for the spatial distribution of patterns that could play a role in reliable subtype classification. This paper proposes a novel methodology that combines different methods to extract features from a whole slide image and generates a whole slide feature map (WSFM). This map and additional clinical features are then used to train various machine-learning models. We obtain 91.84% WSI tumor subtype classification accuracy on a diverse dataset. A direct advantage of our methodology is that it does not require any WSI-level annotation by pathology experts. Training and testing can be performed much faster computationally using simple machine learning algorithms instead of complex deep learning architectures.
Partitioning of an M-Part Weighted Graph with N Vertices in Each Part into N Cliques with M Vertices and the Total Minimum Sum of Their Edges Weights Using Ant Algorithms

An m-part weighted graph with n vertices in each of its parts is used for tracking objects in multi-camera vision systems. This problem is called: The General Maximum Minimal Clique Problem. Different ant colony optimisation algorithms are presented to solve this problem. New dynamic desire functions are proposed and the results of their influence on the obtained total sum of the weight of cliques are presented. Thus the best dynamic desire functions were determined, which were determined to be better than the existing desire functions in terms of the obtained minimum sum of weights of all cliques. This problem occurs in the area of object tracking on film frames in cameras.
Providing reproductive health information to rural women: The potentials of public libraries.

Access to reproductive health information is an integral part of the sustainable development goal3 of good health and well-being. The World Health Organization (WHO) recommends a gender-based approach to public health that should holistically address the physical, mental and emotional well-being of women. However, women in urban areas seem to be benefiting more from various laudable reproductive health programmes than those in rural areas. This scenario negates the concept of sexual and reproductive health and rights, which have fundamental bearing for sound economic development and poverty alleviation. It is against this background that this paper examined the provision of reproductive health information to rural women in Nigeria. It identified the reproductive health information needs of rural women, including information on fertility, management of unplanned pregnancies, sexually transmitted diseases, prenatal and postnatal care. It further discussed the problems hindering rural women from accessing reproductive health information. To alleviate these problems, the paper discussed various roles public libraries can play in helping rural women gain access to reproductive health information. It recommended adequate funding, community engagements, and collaboration with relevant agencies for public libraries to take their place in the dissemination of reproductive health information to rural women.
Blockchain for Agri-Business

Agri-food systems are continually evolving to feed an ever-growing population. However, several challenges such as quality products, inefficient supply chain, and excessive food wastage are needed to be faced in order to meet global demand. Among these constraints, supply chain traceability systems are one of the technology solutions that can identify and resolve the challenges of agri-food systems. The traditional supply chain lacks traceability, transparency, and information exchange. In this work, the blockchain projects utilizing the public and private blockchain platforms are discussed. Also, the product ownership management has been developed using Hyperledger composer for tracing the flow of agriculture products at every stage. It supports storing the supply chain activities as a ledger transaction in a permanent and immutable way. Agriculture products are mentioned as lots with a unique lot code provided for each batch of products. The chain of lot codes can be used to identify the product along the supply chain. This system ensures product ownership.
CrimedetGAN

Social networks play a dominant role in connecting communication devices, which play as essential role in exchanging a large amount of interpersonal data. As a result of exchange of messages, they leave behind some traces, which help in identifying the nature of the network. So, these are helpful in detecting crime users. The algorithms like SVM, Bayesian linear regression will not help in finding out the crime network. It also results in less accuracy for higher amounts of data. So, developing the trace learning system in GAN, which is a higher order of deep learning neural network, larger neural network dataset will be fed into the model, which has digital traces into the neural network done through proposed CrimedetGAN. A trained accuracy system model automatically identifies the digital traces which have been left in the crime natured social network. Experimenting with existing GAN frameworks, namely MaliGAN, seqGAN, LeakGAN, proposed CrimedetGAN came with a test score accuracy of 91.23% on the coherence NLP testing in tracing the relevant data fields for the given input datasets.
Examining the Impacts of Technology and Trust on I-Voting Acceptance in the COVID-19 Aftermath

This article presents an empirical study of influence of the domain-specific factors of trust in the electoral system and administration, trust in technology, citizens' attitudes toward voting, and COVID-19 conditions on the intention to use i-voting. The study examines and assesses the relationships and effects of the various factors that influence the intention to use i-voting. Survey data from 633 adult Slovenian citizens were collected in 2022, when voters were still burdened by the pandemic and needed to visit the polling place at least five times to participate in all scheduled voting. Structural equation modeling was used to analyze the data and develop the model. The study provides important findings, such as (1) trust in the voting system has a significant impact on voters' intention to use i-voting, (2) fear of COVID-19 does not persuade people to use i-voting, and (3) previous attitudes toward voting do not have a significant impact on voter intention to use i-voting.
Safe AI: Ensuring Safe and Responsible Artificial Intelligence

The increasing adoption of Artificial Intelligence (AI) in critical decision-making processes has highlighted the need for ensuring the security and reliability of AI systems. To this end, it is proposed the development of the Safe AI Framework, an evaluation system that assesses the overall health of an AI pipeline, including its Data, Model, and Environment components. Technically speaking, the evaluation system aims to identify potential vulnerabilities and provide suggestions for mitigation strategies to improve the health of the pipeline. The mitigation measures provided by the system can be categorized into data-related, model-related, and infrastructure-related mitigation measures. The proposed evaluation system is expected to provide several benefits to organizations, including the assessment of the security and reliability of their AI solutions, the identification of potential vulnerabilities, and compliance with regulations and standards related to AI.