Reviewerly/exHarmony huggingface dataset:
Formation of social and legal competence of future social teachers

Complex and contradictory processes of change in all spheres of public life in our country place special demands on the quality of professional competence of specialists. The article analyzes the process of solving theoretical and applied problems related to the task of developing the professional and legal competence of social educators. In accordance with the purpose of the study, in accordance with the direction of changes taking place in the world, in society, the main directions for increasing the professional legal competence of a social teacher and analyzing the professional qualifications of a social teacher are: done. The qualification characteristics of a social teacher, including a list of job responsibilities, determine the main types of assistance provided by a social teacher to a child, where social and legal competence is aimed at protecting the rights of adolescents and children and promoting the implementation of legal norms. The guarantees of various categories of children and the legal education of the younger generation were studied. To effectively implement these types of activities, social educators need to know the legal documents in the field of children’s rights, be able to use them in their professional activities, and know the socio-legal and socio-economic foundations of their activities. educational institutions providing assistance to children and their families; The pedagogical conditions that ensure the effectiveness of its implementation are described. An experimental study was conducted in which 47 undergraduate students of the L. Gumilev Eurasian National University took part. The formative experiment included 4 stages of development of socio-legal competence (informational-cognitive, value-oriented, regulatory and communicative stages). The dynamics of changes in the social and legal competence of a social teacher are shown. Key words: social teacher, socio-legal competence, professional competence.
Almost uniformly convergence on MV-algebra of intuitionistic fuzzy sets

The aim of this contribution is to formulate some definitions of almost uniformly convergence for a sequence of observables in the MV-algebra of the intuitionistic fuzzy sets. We define a partial binary operation ⊖ called difference on MV-algebra of intuitionistic fuzzy sets. As an illustration of the use the almost uniformly convergence we prove a variation of Egorov’s theorem for the observables in MV-algebra of intuitionistic fuzzy sets.
FORMATION AND PROMOTION OF THE IMAGE OF A MODERN UNIVERSITY IN THE CONTEXT OF STATE RATING PROJECTS

The relevance of the study of the theory and practice of reputation management of higher educational institutions is of an enduring nature. In the second and third decades of the 21st century, the conceptual and applied rethinking of this activity was signifi cantly infl uenced by the state rating projects “5-100”, “Priority-2030” and MosIUR, which presented a list of assessment requirements for the work of building the reputation of universities and allowed it to be focused.The formal indicators of their eff ectiveness contained in the projects do not exhaust the list of positions that make up the content of the attractive image of a higher education institution, but are the result of their implementation. In order to update this list, the article uses methods and structure that involve the analysis of conceptual theoretical foundations for building the image, reputation and brand of an organization – a subject of socio-economic relations, including a university; general overview of activities within the framework of the “5-100”, “Priority-2030” and MosIUR projects; as well as qualitative studies of the experience of their implementation by the 4 Russian Universities. The results of the study were analytical conclusions on optimizing approaches to the theoretical foundations of university reputation management and its applied components, which describe such as identifying a university using visual components, semantic content of messages to target audiences, studying the competitive environment, and creating an image in the media fi eld
English for Specific Purpose (ESP) Teaching Design Based on Accounting Students Needs Analysis

The Asean Economic Community (AEC) and the World Trade Organization (WTO) are challenges that need to be faced by higher education administrators. Higher education graduates are required to have the ability for facing the global competition. One of the skills that needs to be mastered is communication in using international languages. It must provide by study program to deliver in English teaching program. Currently, lecturers' perspectives serve to shape the teaching of English for Specific Purposes (ESP). Learning Needs Analysis is supposed to create the basis of ESP English instruction. Based on that problem, research was carried out to conduct a learning needs analysis for ESP English learners to determine appropriate learning design recommendations. This research uses a qualitative approach with interviews, observation, and closed-ended questionnaires as data collection techniques. This research shows the most appropriate English learning design for accounting students is to focus on speaking skill. It should be applied in active classes activity; students centered and the role of the lecturer as a facilitator. These elements aim to improve the self-quality of students and prepare them for future career.&#x0D;
Assessing the Robustness of Intelligence-Driven Reinforcement Learning

Robustness to noise is of utmost importance in reinforcement learning systems, particularly in military contexts where high stakes and uncertain environments prevail. Noise and uncertainty are inherent features of military operations, arising from factors such as incomplete information, adversarial actions, or unpredictable battlefield conditions. In RL, noise can critically impact decision-making, mission success, and the safety of personnel. Reward machines offer a powerful tool to express complex reward structures in RL tasks, enabling the design of tailored reinforcement signals that align with mission objectives. This paper considers the problem of the robustness of intelligence-driven reinforcement learning based on reward machines. The preliminary results presented suggest the need for further research in evidential reasoning and learning to harden current state-of-the-art reinforcement learning approaches prior to being mission-critical-ready.
Enhancing huBERT Model with Additional Convolutional and BiLSTM Layers for Hungarian Sentiment Analysis

This paper explores the enhancement of the huBERT model’s classification efficiency by integrating convolutional and BiLSTM layers. The authors train this modified model on categorically labeled user reviews from two websites, assessing performance via a confusion matrix and the F1 score. Results offer insight into the hybrid architecture’s efficiency in text classification and provide groundwork for future investigations into improving machine learning methods in language processing.
Digital Chain of Custody Operational Framework

Managing digital evidence is intricate due to the growing intricacies of digital data. It holds a crucial role in legal proceedings, but ensuring the credibility of digital evidence and upholding the chain of custody’s integrity is challenging. Conventional evidentiary standards inadequately audit digital evidence metadata, creating discrepancies between traditional and digital custody.Digital evidence presents unique features that further complicate its administration. These include the ease of duplication, transfer, alteration, and deletion of digital data, making it susceptible to contamination. Moreover, digital evidence can seamlessly cross borders and is often time-sensitive. Existing standards exhibit gaps in the digital chain of custody, causing inconsistencies among stakeholders and EU member states. Standardizing the digital chain of custody (dCoC) is vital for evidence consistency and court admissibility.This paper proposes a standardized dCoC process that safeguards digital evidence’s integrity, enhances transparency, and fosters accountability. It introduces novel concepts like digital custody metadata (DCM), specifying transfer purposes and prompting custodianship at Custody Transfer Points (CTPs). A case study demonstrates the concept’s application, developed within the STRATEGY project and submitted to the CEN/TC 391 standardization body. It offers a new European standard for consistent and reliable digital evidence management.
What is the perception of the Hungarian quantum community regarding the future of quantum computing?

Quantum mechanics revolutionized our understanding of the microscopic world and laid the foundation for quantum computing. Quantum companies are developing quantum technologies, with IBM, Google, Microsoft, and Rigetti Computing being prominent players. Quantum communities foster collaboration among researchers, academics, and enthusiasts. In current paper we introduce the results of research conducted in two phases. In the first phase, the members of the Hungarian quantum community have been identified through document analysis and keyword searches. The second phase involved interviews with 14 individuals to understand their attitudes toward quantum computing and gain an insight into the current state of affairs and future prospects. According to the research data, the community’s focus remains on fundamental research, creating physical qubits, and investigating quantum phenomena, with limited practical use of quantum algorithms and less focus is dedicated to developing algorithms, despite the fact, that some respondents believed in quantum supremacy having been reached.
Artificial Intelligence and Machine Learning Solutions for Attracting Investment and Financial Aid in the Digital Economy

In 2020, digitization and digital transformation required a new question for an entrepreneur in the startup ecosystem, and to answer it, the "Digital Transformation Guide" from Columbia Business School (Rogers, 2016) suggested that the company's activity in this area be revised. Digital age. Through the allegory of the matrix, this idea of the world of tomorrow was presented for startups that face new environmental, financial, social and social challenges. In this context, new technologies allow entrepreneurs to use artificial intelligence (AI) and machine learning (ML) in the financial sector and policy implications. A non-technical background on the evolution and capabilities of AI/ML systems, their deployment and use cases in the financial sector and the new challenges they present to the financial sector policy makers are used. AI/ML systems have made major advances in the past decade. Although it is not yet possible to develop a machine with the capacity to understand or learn every intellectual task that a human does, today's artificial intelligence systems can perform well defined tasks that typically require human intelligence. The learning process, a critical component of most artificial intelligence systems, is in the form of ML, which relies on mathematics, statistics, and decision theory. Advances in ML, and especially in deep learning algorithms, are responsible for most recent achievements such as self-driving cars, digital assistants, and facial recognition. To determine his path and choose the best path of growth to fulfill his mission with the desired success. The example Covid-19 vaccine(s) provides an opportunity to leverage research in the drug discovery and business development phases of international pharmaceutical companies such as Pfizer, Moderna, Astrazeneca, Johnson &amp; Johnson, Sanofi, etc. by new technologies for the commercialization and deployment of global vaccine campaigns. Therefore, an excellent knowledge of the digital world thanks to overgrowth, the scalability of a business model, the implementation of a strategy by a management team, allows to reduce uncertainty and increase the chances of success. The financial sector, led by financial technology (fintech) companies, has rapidly increased the use of AI/ML systems (Box 1). The financial sector's recent adoption of technological advances, such as big data and cloud computing, along with the expansion of the digital economy, has enabled the effective deployment of AI/ML systems. A recent survey of financial institutions (WEF 2020) shows that 77 percent of all respondents predict that AI will be of general or very high importance to their business over the next two years. McKinsey (2020a) estimates the potential value of AI in the banking sector at $1 trillion
A constructive parallel heuristic approach for the MWPSP

Algorithms for the problem of identifying a maximum edge-weight planar subgraph of a given edge-weighted graph G are relevant in a wide variety of application areas. In this paper, we propose a new local search constructive heuristic for this NP-hard problem, along with a GPU-based algorithm designed to accelerate its computational process. The experimental findings demonstrated that considerable speedup is attainable while maintaining good-quality results.
Além do Básico: Otimizando Aplicações Paralelas em Arquiteturas Modernas

In this book are the six mini-courses presented during the XXIV Symposium on High Performance Computing Systems, held from October 17th to 20th, 2023, in Porto Alegre, RS. The first chapter covers essential concepts of processing and analyzing large volumes of data, practicing the use of machine learning algorithms. In the second chapter, readers are introduced to understanding the operation of the OpenMP distributed programming model through the introduction of the OpenMP Cluster. Chapter three explores challenges and solutions associated with complex applications that deal with large volumes of data, aiming to optimize the performance and power consumption of parallel applications. Automated solutions that enable reproducible and shareable installation of packages entirely in user spaces, reducing costs and errors for system administrators, are presented in chapter four. Chapter five aims to present the concepts of an adaptive/evolutionary system, Evolutionary Hardware (EHW), characterized by a high level of parallelism. Finally, chapter six discusses storage systems, covering various connections such as SATA, SCSI, SAS, iSCSI and Fiber Channel, highlighting the advantages and disadvantages of each of these connections.
Abordagem Gamificada para Introdução em Arquiteturas de Hardware Evolutivo

In this book are the six mini-courses presented during the XXIV Symposium on High Performance Computing Systems, held from October 17th to 20th, 2023, in Porto Alegre, RS. The first chapter covers essential concepts of processing and analyzing large volumes of data, practicing the use of machine learning algorithms. In the second chapter, readers are introduced to understanding the operation of the OpenMP distributed programming model through the introduction of the OpenMP Cluster. Chapter three explores challenges and solutions associated with complex applications that deal with large volumes of data, aiming to optimize the performance and power consumption of parallel applications. Automated solutions that enable reproducible and shareable installation of packages entirely in user spaces, reducing costs and errors for system administrators, are presented in chapter four. Chapter five aims to present the concepts of an adaptive/evolutionary system, Evolutionary Hardware (EHW), characterized by a high level of parallelism. Finally, chapter six discusses storage systems, covering various connections such as SATA, SCSI, SAS, iSCSI and Fiber Channel, highlighting the advantages and disadvantages of each of these connections.
Towards scalability improvements of HybriD-GM quantum simulator

Currently quantum computers require specialized hardware to be used, until they become more feasibly the testing of quantum algorithms is done by simulation, which is a task of high temporal and spacial complexity. To address this issue, the HybriD-GM simulator, and many others around the world, are under research and improvement, this paper is an ongoing work on improvements for the multi-core support of the HybriD-GM simulator, the experiments are done with the Shor and Grover algorithms, while varying the number of cores and qubits for each, to improve the scalability the removal of critical regions by the usage of tasks and other OpenMP resources is exploited, and the study about the impact of others variables on the simulator is already planned, as well improvements for future works on the GPU, and GPU-CPU approaches and expansion to distributed computing for horizontal scalability of the system.
Forecasting Twitter's Favorite Cryptocurrencies Based on a Comparative Assessment of SARIMA, LSTM and Fb Prophet Machine Learning Approach

The complicated combination of long-term patterns, short-term seasonality, and uncertainty has made selecting an appropriate forecasting model for cryptocurrency prices challenging. To forecast the future prices of the cryptocurrencies, this study compared three machine learning models: the Seasonal Auto-Regressive Integrated Moving Average (SARIMA), the Long Short-Term Memory (LSTM), and the Facebook (Fb) Prophet for the period of 2017 till 2023. It was discovered that the FbProphet model works well in predicting the daily price forecasts of Bitcoin and Dogecoin with low mean squared error (MSE), using time series datasets from 2017 through 2023 as the training data to anticipate 12 months of unseen data. While predicting the future from 2023 to 2024, Fb Prophet stated that Dogecoin is most likely to remain stable while Bitcoin price is most likely to continue falling. The LSTM model performs better than the other models based on forecasting results.
Digital Gold in Emerging Markets: An Investor's Perspective

The digital revolution in the gold market has led to the emergence of digital gold as a form of investment. Digital When compared to the physical manner of investment, gold has a much greater potential to generate returns than any other asset type. It does, however, also have certain possible hazards, such as the possibility of fraud, market risk, expenditures, etc. The goal of the current study was to determine how well-informed retail investors were about digital gold. Additionally, it wants to research how investors view this new investment possibility. The findings indicate that a lack of understanding of the idea among investors prevents them from making Digital Gold investments. People who invest in digital gold say they do it because it is convenient and requires no storage.
Simulating Parametric and Nonparametric Models

The purpose of this paper was to investigate the performance of the parametric bootstrap data generating processes (DGPs) methods and to compare the parametric and nonparametric bootstrap (DGPs) methods for estimating the standard error of simple linear regression (SLR) under various assessment conditions. When the performance of the parametric bootstrap method was investigated, simple linear models were employed to fit the data. With the consideration of the different bootstrap levels and sample sizes, a total of twelve parametric bootstrap models were examined. Three hypothetical and one real datasets were used as the basis to define the population distributions and the “true” SEEs. A bootstrap paper was conducted on different parametric and nonparametric bootstrap (DGPs) methods reflecting three levels for group proficiency differences, three levels of sample sizes, three test lengths and three bootstrap levels. Bias of the SLR, standard errors of the SLR, root mean square errors of the SLR, were calculated and used to evaluate and compare the bootstrap results. The main findings from this bootstrap paper were as follows: (i) The parametric bootstrap DGP models with larger bootstrap levels generally produced smaller bias likewise a larger sample size. (ii) The parametric bootstrap models with a higher bootstrap level generally yielded more accurate estimates of the standard error than the corresponding models with lower bootstrap level. (iii) The nonparametric bootstrap method generally produced less accurate estimates of the standard error than the parametric bootstrap method. However, as the sample size increased, the differences between the two bootstrap methods became smaller. When the sample size was equal to or larger than 3,000, say 10000, the differences between the nonparametric bootstrap DGP method and the parametric bootstrap DGP model that produced the smallest RMSE were very small. (4) Of all the models considered in this paper, parametric bootstrap DGP models with higher bootstrap performed better under most bootstrap conditions. (5) Aside from method effects, sample size and test length had the most impact on estimating the Simple Linear Regression.
Influence Strategies of Early Childhood Parent-Child Reading on Primary School Students' Independent Reading in the Context of Early Childhood and Primary School Convergence

Parent child reading is a reading activity where parents and children share stories. According to the survey results, parent-child reading, it has a very positive impact on children's cognition, language development, reading interest, emotional communication, social skills, and independent reading habits. At the same time, it is conducive to establishing a closer relationship between parents and children. This study aims to explore the impact of parent-child reading on primary school students' autonomous reading ability in the context of preschool transition, and propose corresponding strategies. Experimental data shows that children who participate in parent-child reading activities from an early age are more likely to develop independent reading habits, indicating that parent-child reading is very important for cultivating children's independent reading ability. Therefore, research suggests that it is necessary to strengthen training in parent-child reading for children, in order to cultivate their independent reading ability and lay a solid foundation for their future. So parents, schools, and communities should all work together to promote parent-child reading activities, so that more children can enjoy the fun of reading.
A REVIEW ON THE IMPACT OF CYBERSECURITY CRIMES IN FINANCIAL INSTITUTIONS DURING THE TIME OF COVID-19

The COVID-19 pandemic significantly impacts every aspect of our life, including how we connect with others in our professional and personal lives and how we structure our work in general. Quarantines and the deployment of social isolation measures caused an astronomical rise in cybersecurity offences in this region. As a result, using the risky Internet network to run every aspect of daily life necessitates complete and direct reliance on it, which increases the risk of cyberattacks, which increased in the financial sector during COVID-19. This paper analysed the cyber security crimes during the pandemic of COVID-19, highliting the different types of cybercrime that occurred worldwide. The mechanism of cybersecurity crimes campaigns was demonstrated by analysing and evaluating cyberattacks in the framework of worldwide events. The systematic review methodology was used in this paper to summarise, compare, and critically analyse the findings of about 60 researchers, articles, and numerous reports from government organisations, security companies, as well as the academic journals that looked into cybercrimes during COVID-19 or suggested solutions to combat them. The influence of the COVID pandemic on cybersecurity is also discussed in this study, with an emphasis on the financial sector and how cybercrimes increased compared to the period prior to the pandemic to a stage where four to five separate cyberattacks on particular days were reported.