Reviewerly/exHarmony huggingface dataset:
Application of recommendation system in educational Field

The article is devoted to the issue of the application of recommender system technology in the educational sphere. It was determined that certain tasks of the educational field can be described in terms of recommendation systems: the user who is given a recommendation is the education applicant; the objects of his interest are disciplines from the catalog of optional disciplines, sources from the library of the educational institution, topics (or research area) of qualification papers, potential supervisors of qualification papers, external courses for the organization of self-education. A list of resources was created to determine the characteristics of the education applicants’ objects of interest and the education applicants themselves within the framework of formal education, and an analysis of their content and method of formation was carried out. The structure of the recommendation system has been developed; its feature is that each object of interest of the education applicant corresponds to a separate module of recommendations. At the same time, the recommendation modules use shared resources, including databases of the educational institution with direct information about the objects of interest and the education applicant, as well as survey databases consisting of general regular mandatory surveys for education applicants at the level of the educational institution and contextual surveys regarding objects of interest. A hybrid approach was chosen to form the recommendations. The structure of the system provides for the use of collaborative filtering for processing survey results, and data presented in the form of semi-structured or arbitrary texts in natural language is proposed to be processed using Natural Language Processing and Text Mining methods, which will allow taking into account semantically similar values of the characteristics of objects of interest and users and thus to improve the quality of the recommendation system.
The distributed programming and data processing methods for computational task optimization in cloud environments

Cloud computing environments have become an essential component for executing computational tasks of various scales and complexities in the modern information-driven world. The use of distributed programming and efficient data processing in cloud environments is a topical and strategically important issue. The analysis of recent research and publications in the field of cloud computing indicates a continuous development and expansion of capabilities in these environments. Special emphasis is placed on container-based distributed programming and orchestration, particularly using Kubernetes. These technologies enable scalability and automation of the computational processes in cloud environments. The aim of this article is to investigate methods for optimizing computational tasks in cloud environments. The research encompasses the analysis of distributed programming, real-time data processing, and the integration of state-of-the-art technologies to achieve optimal efficiency. One of the key aspects of the research is the study of distributed programming. This approach allows the distribution of computational tasks among various cloud nodes, leading to improved performance and fault tolerance. Real-time data processing plays a pivotal role in addressing tasks where time is a critical factor. The integration of cutting-edge technologies, such as artificial intelligence and machine learning, aids in achieving optimal results. The conclusions of this study underscore the importance of combining distributed programming and efficient data processing to optimize computational tasks in cloud environments. This approach enables increased productivity and reduced resource costs. Future research in this direction envisions further development of methods and tools to achieve the best possible outcomes in cloud computing. Exploring new opportunities in cloud computing environments will ensure a continuous enhancement of the quality of computational services and contribute to the advancement of the modern information society.
Pengaruh Pembelajaran Berbasis Daring Terhadap Hasil Belajar Mahasiswa Pendidikan Akuntansi Universitas Negeri Makassar

This study aims to determine the effect of online-based learning on student learning outcomes of Accounting Education at, the State University of Makassar. The variables of this research are online-based learning as the independent variable (X) and student learning outcomes as the dependent variable (Y). The population of this study was the 2018 and 2019 students of the Accounting Education Study Program consisting of 224 students, while the sample was 69 students who were taken using the proportionate stratified random sampling technique. Data collection is done by observation, questionnaires, and documentation. The data analysis used an instrument test and a hypothesis test. The results of this study indicate that online-based learning is included in the good category. Meanwhile, the student learning outcomes of Makassar State University Accounting Education are in a good category. From the results of simple linear regression analysis obtained online-based learning Y = 3.063 + 0.009X, which means that for each addition of 1 value of online-based learning, the value of learning outcomes has increased by 0.009. While the product-moment correlations obtained the coefficient of determination (r²) the value of r² = 0.070 or 7%, which means that online-based learning has a contribution to learning outcomes of 7 percent, and the remaining 93 percent is influenced by other factors. Meanwhile, the results of the t-test analysis obtained a significant value of 0.000 &lt;0.05, which means that online-based learning has a significant effect on learning outcomes, thus the hypothesis is accepted.
Youth Science?

The problem of the need to single out youth science as a special component of the scientific space is discussed.
Speeches of participants of the General Meeting of RAS members

Paricipants in the meeting discussed the presented reports by G.Ya. Krasnikov and M.V. Dubina and various problems facing the Russian Academy of Aciences and its individual divisions and branches
Optimization of multi-request single-processor COMPUTING

Background. The effectiveness of multi-query execution in single-processor computer system databases is considered. One of the methods of increasing the performance of databases of computer systems is the simultaneous execution of several queries that form a multi-query. Methods. Methods of analysis and criterial optimization are used in the paper. Results. The paper analyzes the processing of a conjunctive multi-query (queries are formed by the conjunction of elementary queries, from which a number of elementary queries are repeatedly included in the queries). With the growing importance of operational analytical data processing, the technique of more complex query optimizations has become crucial. In order to be effective, optimizers must adapt to new operators, changes in cost estimation methods, etc. The query execution time is usually used as a criterion for multi-query optimization, while subdividing the time spent working with data in RAM, buffer, and external memory. An optimization plan for conjunctive multi-query execution time when accessing the database based on the ordering of elementary queries is proposed. Two methods of performing a conjunctive multiquery are considered: independently of each other and jointly for unordered and ordered data. It is shown that the execution time of a conjunctive multi-query with joint processing is not always less than with independent processing. The conditions under which the joint processing of a conjunctive multi-query ensures no longer execution time in relation to independent processing are proved. Сonclusions. Implementation of the proposed optimization procedure allows one to increase efficiency of single-processor computer systems that support data bases.
Review of Dynamic Decision Making ForConnected VehiclesIn IotSystem

By leveraging real-time data and sophisticated algorithms, dynamic decision-making for connected vehicles elevates safety and efficiency to the level of a paradigm shift in transportation technology. This article examines the present state of dynamic decision-making, emphasizing the possible advantages and difficulties it may present. The utilisation of critical elements including data analytics, machine learning, and artificial intelligence empowers interconnected vehicles to accurately and promptly traverse intricate surroundings. However, widespread adoption is hindered by infrastructure integration, cybersecurity vulnerabilities, and public acceptability, all of which pose substantial obstacles. The prospective advantages, such as decreased accidents, alleviated traffic congestion, and enhanced mobility, emphasize the criticality of confronting these obstacles. In the future, the potential for dynamic decision-making in connected vehicles is contingent upon connectivity advancements, including the integration of autonomous features and 5G technology. These advancements hold the potential to revolutionize transport within smart cities by streamlining traffic circulation and delivering customized experiences. Sustained investigation, stakeholder collaboration, and the establishment of regulatory frameworks are imperative in order to fully harness the capabilities of dynamic decision-making in connected vehicles.
ON THE ACTIVITIES OF THE URAL BRANCH OF THE RUSSIAN ACADEMY OF SCIENCES IN 2022 &lt;i&gt;SPEECH BY THE CHAIRMAN OF THE URAL BRANCH OF THE RAS, RAS ACADEMICIAN V.N. RUDENKO&lt;/i&gt;

The institutes of the Ural Branch of the Russian Academy of Sciences operate over a vast territory stretching from Arkhangel'sk to Orenburg from north to south and from Tobol'sk to Izhevsk from east to west. Under the scientific and methodological leadership of the RAS Ural Branch, there are 32 scientific organizations, including 6 federal research centers, as well as 18 higher education establishments. The branch includes 115 members of the Russian Academy of Sciences, of which 41 are academicians. In July 2022, academic science in the Urals celebrated its 90th anniversary.
Current trends and prospects for the development of the university sector of Russian science

This study is aimed at identifying the features and prospects for the development of the university sector of Russian science, including with account for modern foreign trends. Some topical aspects in the development of university science are analyzed in detail proceeding from statistical data and the results of sociological surveys on the degree of involvement of universities and their units in RD performance, as well as on the cost structure, types and sources of research funding, and the dynamics of the development of HR potential and certain indicators of the postgraduate system of training highly qualified personnel. Based on the international comparisons presented in this article, the authors consider the expediency and validity of the broad participation of universities in the implementation of RD and, ultimately, the ability to provide a high level of graduate training under such conditions. Since Russia lacks higher education establishments specializing mainly in basic research, it is concluded that it is necessary to create research universities on the basis of divisions and scientific centers of the Russian Academy of Sciences, endowed with the right to develop standards and programs for training highly qualified scientific personnel independently. It is argued that to implement the relevant tasks of the country’s socioeconomic development, it is advisable to develop alliances and consortia of scientific, educational, and business organizations.
Code Modernization Strategy and Tool for Rapid Modernization of Legacy Banking Applications

Modernizing banking applications can bring about numerous benefits, both for financial institutions and their customers. Modernizing banking applications will provide following benefits such as enhanced Customer Experience, increased efficiency and productivity, improved Security and Compliance, scalability and flexibility, cost reduction, agility and innovation and customer retention. Modernizing banking applications is essential for staying competitive, meeting customer expectations, and navigating the challenges of a rapidly evolving financial landscape. It enables financial institutions to provide efficient, secure, and innovative services while optimizing operational costs. Modernized applications can incorporate advanced security features such as biometric authentication, multi-factor authentication, and encryption to protect sensitive customer data. It helps to compliance with industry regulations is easier to achieve through modernization, reducing the risk of legal and financial penalties. It helps to automate the manual processes and the incorporation of advanced technologies streamline banking operations. It also helps organization to reduce manual intervention leads to faster transaction processing, minimizing errors and improving overall operational efficiency. Banking Legacy systems can be expensive to maintain and upgrade. Modernizing banking applications can lead to cost savings in terms of maintenance, support, and hardware requirements. Also modernized application can be hosted in cloud and organization can save more money and will be able to maintain high performing applications. The challenges associated with modernizing banking applications are having outdated technology with high maintenance cost. Also, legacy systems are often rigid and inflexible, making it challenging to introduce new features or respond quickly to market changes. Also, older systems may have security vulnerabilities that are difficult to patch or mitigate, exposing sensitive customer data to potential breaches. Legacy systems may struggle to comply with evolving regulatory requirements, exposing the institution to legal and financial risks. Addressing these challenges through a well-planned and efficient modernization strategy is essential for banks to remain competitive, secure, and responsive to the dynamic demands of the financial industry. Since I came from Technology Background and having 14 years of banking experience, I have provided a custom tool that is efficient and have modernized legacy application in quick time.
Soviet experience in the management of applied and academic science: In memory of Academician G.I. Marchuk

Ten years ago, on March 24, 2013, Academician G.I. Marchuk died; his creative path includes outstanding results of basic and applied research in the field of computational mathematics and computer modeling, the creation of world-class scientific schools in atmospheric and ocean physics, nuclear reactor calculations, and mathematical immunology, as well as unique experience in managing industrial and mathematical science at the state level. The scientist’s behests, concentrated in his book The Science of Managing Science, indicate ways to reform the Russian Academy of Sciences, designed to ensure scientific and technological progress and sustainable development of society in our country.
Academician M.I. Rostovtsev on the relationship between state power and science: Unknown article of 1917

Recently, a small, undeservedly forgotten article by Academician M.I. Rostovtsev was discovered in the newspaper Svobodnyi Narod (Free People) of the Cadet Party (Petrograd) under the title “New System and Science.” It was published in August 1917. Its leitmotif is that only firm state power can guarantee the preservation and development of culture and science in Russia. The weakening of the state as a result of powerful revolutionary upheavals, according to the scientist, is the main danger to the scientific life of the country. This publication is interesting even now, during the difficult period of reforming science and education in modern Russia.
Integrated Accounting Education In Iraq Between Reality and Expectations: An Empirical Study

The research aims to provide an integrated conceptual framework on the most critical challenges and problems facing integrated electronic accounting education in Iraq and to identify the extent of its importance and effectiveness and the most significant proposed solutions to the issues facing both students and faculty. Two questionnaires were created and distributed electronically: one for accounting students in universities and institutes affiliated with the Ministry of Higher Education, with 161 responses, and the other for accounting professors in colleges and institutes, with 100 responses, and they were analyzed. According to the correlation coefficient, blended education has a considerable impact on education quality. Before the COVID-19 epidemic, universities and institutions in the Ministry of Higher Education and Scientific Research did not use e-learning. Modern electronic means include all advanced technical means that can be used and benefited from in the blended learning process, including computers as one of the main pillars, all devices, equipment, and all means of communication, such as computer networks of all kinds, and it is necessary to continue e-learning for some subjects, such as electronic and practical theoretical lectures. Therefore, electronic elements supplement my presence. Accounting education is an integrated system with several connected aspects to fulfill its aims, and e-learning is becoming more popular in education. Blended Learning is one way to apply this notion to e-learning
A Comparative Analysis of Symbolic and Deep Learning Based RDFS Materialization

The advancement of both symbolic reasoning and deep learning techniques have opened new fronts for knowledge graph (KG) processing. Knowledge graphs commonly store data that adheres to the Resource Description Framework (RDF) model, the cornerstone of the semantic web vision. As reasoning is one of the core functionalities of KGs, RDF-Schema, a set of semantics for the enablement of reasoning over RDF graphs, is often a first-class citizen in these graphs. Within that context, To reason is to infer new data based on existing ones, alongside a set of rules, such as RDF-Schema. This process however is very computationally expensive, to the point that it cannot happen when the KG is being queried, as that would inquire too much latency to the response. An alternative is to materialise the data before query time, which is to compute all inferences, and store them alongside the original data.
Token-Level Adaptation of LoRA Adapters for Downstream Task Generalization

This paper introduces a method for adapting LoRA adapters in smaller-sized language models to arbitrary downstream tasks. Unlike standard mixture-of-expert architectures, our method employs a gradient-free routing function to choose a weighted combination of experts without increasing the compute requirements for training or inference. The results show that token-level adaptation of LoRA adapters outperforms the base Llama-2-7b model across mathematical (GSM8K), scientific (ARC-Challenge), reading comprehension (SQuAD), and coding (CodeAlpaca-20k) tasks. Further evaluations also show that the average performance of token-level adaptation outperforms individual models fine-tuned for each of the tasks with the best performance observed in the adaptation of every other token during inference. The code for this study is made available through a public repository.1
Enhancing Deepfake Detection: Spatial-Temporal Preprocessing and Self-Attention ResI3D Model

Deepfake technology is the outcome of employing deep learning techniques to overlay the face of one individual onto the video of another. As deep learning technology advances rapidly, the proliferation of high-quality deepfakes for malicious digital activities is notably on the rise. With growing concerns about the misuse of deepfake technology, there is an increasing demand for research into deep learning-based methodologies to detect and counteract it. While Deepfake detection using deep learning has been a subject of prior research, these approaches primarily rely on images hence not utilizing temporal information. Additionally, research combining CNN and RNN has inherent limitations. It operates with compressed data, resulting in the loss of spatial information and the utilization of the inherent temporal characteristics in pixel-to-pixel temporal data. In this study, we propose a detection model that harnesses the inherent attributes of video data through self-attention on both the spatial and temporal axes, using the ResI3D model along with the Non-Local Block. Additionally, we conducted experiments during the preprocessing phase to validate and implement methods that facilitate the model's effective learning of both temporal and spatial information. As a result, our model demonstrated enhanced performance when compared to existing deepfake video detection models.
Exploration of Explainable AI for Trust Development on Human-AI Interaction

In recent years, the revolutionary impact of Artificial Intelligence (AI) cannot be overstated. This groundbreaking technology has radically transformed how we perform our daily tasks, thereby redefining the very fabric of our society. However, as our reliance on AI systems continues to grow, the need for calibrated trust becomes increasingly pressing. To address this concern, the concept of Explainable AI (XAI) has been introduced to provide human-level explanations. The primary goal is to offer cognitive information that prompts informed trust decisions. Nevertheless, it is essential to recognize that trust is a multidimensional construct, involving various means of processing beyond mere explanations. To fully understand and explore these dimensions within the context of XAI, this research aims to uncover and comprehend the additional facets of trust. Through an exploratory survey, it was confirmed that XAI serves a vital purpose in facilitating trust, and it can be effectively processed through affective means. Furthermore, the presentation of information beyond the depth of explanation was found to play a significant role in moderating trust formation during human-AI interactions.
Data Mining in Mobile Learning: Uncovering Insights for Enhanced Vocabulary Acquisition

The integration of mobile learning and machine learning, driven by the proliferation of smart handheld devices and the vast amount of learning resources available on the internet, has revolutionized education. This study explores the use of mobile learning, specifically leveraging mobility and big data mining techniques, to improve college English vocabulary acquisition among university students. By addressing the challenges faced in mobile learning and applying innovative methodologies, we have developed a personalized recommendation system based on data mining algorithms. This system suggests English vocabulary words to students based on their past performance, harnessing the power of big data mining. Through a comprehensive evaluation, we have demonstrated the effectiveness of our approach in enhancing vocabulary acquisition and retention compared to traditional book-based learning methods. The findings of this research not only contribute to the advancement of mobile learning but also provide valuable insights for educators and policymakers on the effective utilization of mobility and big data mining for enhanced student learning outcomes.