Reviewerly/exHarmony huggingface dataset:
A Comprehensive Deep Semi-supervised Graph Learning Approach Incorporating Node Re-weighting and Manifold Regularization

Deep Graph Neural Networks (GNN) have gained increasing prominence across various fields and applications in recent years. In particular, Graph Convolutional Network (GCN), a specialized GNN, has emerged as a powerful tool for obtaining data representations through the processes of graph-based node smoothing and implementing transformations through neural networks layer by layer. Although GCN excels in the area of semi-supervised learning, it also has its weaknesses. In particular, it 1) tends to overlook the implicit manifold structure of graph data, 2) focuses primarily on graph convolutions, often neglects the graph construction, and 3) rarely addresses topological imbalances. In response to these limitations, we present a comprehensive deep semi-supervised Graph Learning approach incorporating Node Re-weighting and Manifold Regularization (ReNode-GLCNMR). This innovative approach corrects topological inconsistencies in the graph and adaptively reweights labeled nodes. It also seamlessly combines graph learning and graph convolution within one GCN architecture. Moreover, it imposes label smoothing by utilizing an unsupervised loss item. Our experiments on eight benchmark datasets demonstrate that our ReNode-GLCNMR model surpasses the performance of the most advanced semi-supervised GNN methods available.
The place of the higher education system in the innovation sphere in solving the problem of staffing the innovative economy. Part II

This article continues the study of the Innovation sphere role in the higher education system for innovative economy personnel training. It is devoted to the problem of the network project-based learning development within the framework of the entrepreneurial university model implementation. The core of the problem is development of entrepreneurial competencies in technical universities to promote technological startups through usage of expertise acquired by the Innovation students. The following article, as well as the previous one, is based on Basadur simplex method to find a range of factors which contribute to the solution of non-standard management problems. Identification of barriers to interaction is the most important factor in the development of a successful collaboration. Barriers of insufficient acquaintance with a partner, or low innovation susceptibility to the collaboration, are considered serious. The purpose of the study is to determine the external factors of innovative susceptibility to network interaction by universities that provide innovations. Low demand in the labour market is said to be the main barrier to the innovative susceptibility towards an innovation manager. There is no standardisation of his activity under the conditions of a high role of the Russian labour market regulation aspect. As a result, the author’s vision of key requirements set for the development of professional innovation standards is proposed to promote the innovation manager and increase innovative susceptibility of the market to his qualification level.
Usability Analysis of “SiPasar”, a Web-Based Application for Mapping Traditional Markets in Yogyakarta

SiPasar is a web-based application that maps traditional markets in the city of Yogyakarta. Its purpose is to provide visitors with information about traditional markets so they can have a more local experience. This study employs usability testing to obtain a better understanding of what is lacking and must be improved. We utilised four performance indicators: effectiveness, time on task, efficiency, and satisfaction. In addition, we summarized qualitative data collected through user observation and feedback. A significant proportion of the respondents were able to complete the tasks below the predetermined threshold, contributing to an overall effectiveness of 93%. However, tasks involving finding stall location using the floor plan took longer than other tasks. The result for Time-Based Efficiency (TBE) indicates that tasks related to locating information about famous stalls, facilities, and building locations on a floor plan, received high scores. Finding stalls in the floor plan has the lowest score, which is consistent with the effectiveness result. The Overall Relative Efficiency (ORE) is 100% for seven tasks, 34% and 87% for market floor plan-related tasks, and 65% for the search function-related task. Using SUS, we discovered that SiPasar has a total score of 68.1, which corresponds to a grade scale of D. Based on user observations and feedback, we identified four key areas for improvement: SiPasar’s floor plan, the visibility of its search function, the completeness of its content, and its overall design.
Evolution of Data Management and Common Data Models for Additive Manufacturing

Abstract Additive manufacturing, as the first fully digital manufacturing process, is critically dependent on data, including the input materials, the process parameters guiding the three-dimensional printing process execution, any postprocessing steps, and any inspections performed on the printed coupons and/or parts, to name just a few examples. This article presents the standards to enable findable, accessible, interoperable, and reusable (FAIR) data. It then discusses three main types of data models that are used to capture different levels of detail and granularity of data: conceptual, logical, and physical. Different approaches and techniques with their own strengths and weaknesses are developed to model data. Four of the major types of data models include hierarchical, relational, object-oriented, and network/graph-based. The article also presents the evolution of data management approaches. It then describes the characteristics of effective logical data models.
A Survey on Feature Selection in Imbalanced Data for Software Defect Prediction

In software defect prediction, unbalanced data poses a substantial obstacle due to the considerably lesser count of corrupted instances in comparison to non-damaged instances. When developing precise defect prediction models in such circumstances, the selection of features must be executed with extreme precision. Nevertheless, knowledge regarding feature selection techniques specifically developed for unbalanced data in the situation regarding the prediction of software errors prediction remains restricted. We combed through pertinent studies from the IEEE Xplore, Springer, Elsevier, Google Scholar, MECS Press, and Wiley databases as part of a systematic literature review. The objective of this investigation was to investigate and assess a range of feature selection methods that have been modified to function with unbalanced data in the domain of software defect prediction. Furthermore, we conducted an analysis of the accuracy, precision, recall, F1 score, and AUC-ROC performance metrics in order to evaluate the efficacy of the feature selection methods. From 2017 to 2023, the review assessed the twenty-one most pertinent articles from twelve periodicals and nine conference proceedings. Based on the comprehensive analysis, it can be inferred that the effectiveness of the prediction model is enhanced by the major studies that were chosen because of the feature selection approaches they used. Chi-Square is an extensively observed feature selection technique. It has been reported that the accuracy of predictions can be enhanced by ensemble models as opposed to a singular model or method. Nonetheless, the application of this method to software defect prediction remains restricted.
Unlocking Security : The World of Ethical Hacking

As nowadays all the information is available online, a large number of users are accessing it, some of them use this information for gaining knowledge and some use it to know how to use this information to destroy or steal the data of websites or databases without the knowledge of the owner. The purpose of this paper is to tell what is hacking, who is hackers, what is ethical hacking, what is the code of conduct of ethical hackers and the need of them. A small introduction of Linux Operating System is given in this paper. All the techniques are performed on the Linux operating system named Kali Linux. After this some basic hacking attacks covered in the paper are MiTM Attack (Man in The Middle Attack), Phishing Attack, DoS Attack (Denial of Services Attack). Further what is Wi-Fi, what are the techniques used in the Wi-Fi protection and the methods used by the hackers to hacks Wi-Fi passwords is covered in the paper.
The Influence of Neurosciences on Understanding the Bodily Conditioning of Cognitive Processes: a Socio-Anthropological Aspect

The study presents a conceptual analysis of the main approaches to the study of the human brain and consciousness from the standpoint of modern domestic and foreign neuroscience. Relevant interpretations of such problematic issues and concepts as “the boundary of the human body”, “embodied knowledge”, “general artificial intelligence”, “self”, etc. From the standpoint of a body-oriented approach, the problem of co-evolution of the body, consciousness, technology and social environment is considered. The idea of the body as an artifact is updated in the context of technological change in human nature, which entails the elimination of the living human body. On the example of the Homeric epic, it is shown that attempts to overcome vulnerability demonstrate one of the fundamental human needs. The realization of the need to expand and push the boundaries of the body in the anthropotechnological environment is presented in the form of a cyborg. It is suggested that, in terms of content, the idea of the “embodied mind”, as well as the project of creating a Hybrid reality and pan-communication, stem from the immanent human need for bodily involvement in the surrounding world. The features of the creation of Artificial Intelligence and Artificial General Intelligence at the present stage are revealed. The expediency of studying the results of neuroscientific comprehension of the organization of mental processes, first of all, in the field of visualization of brain processes and the mapping method, is noted. The main modern projects for the study of the brain, as well as the most effective methods for obtaining data on brain structures, are considered. The actual results of the research are described in such areas of neuroscience as: the study of mirror neurons and their systems; “reading the brain”, the study of neural correlates of consciousness. The results of studies of representatives of domestic neuroscience are presented. The human “I” is considered as a unique structure of the Ego-system of the brain (“self”). In conclusion, a conclusion is made about the significant influence of modern neuroscience on understanding the features of the functioning of the human brain, consciousness and cognitive processes; the effectiveness of applying the body-oriented approach to comprehending the integrity of “body-consciousness-technologies-social environment” is revealed.
Vladimir Mayakovsky’s Notebooks in Academic Interpretation

The paper reviews the results of the project ‘Poetics and Textology of Vladimir Mayakovsky’s Notebooks (1917–1930)’. In the course of the research, we studied manuscripts of the Mayakovsky State Museum (68), the Russian State Archive of Literature and Art (4), and private collections (2). On this basis, and with account of Varvara Arutcheva’s work Mayakovsky’s Notebooks (1958), theoretical and practical experience of preparing ego-documents of other writers, the author developed principles relevant to Mayakovsky’s notebooks. In previous complete collections of Mayakovsky’s works, the notebooks were only partially used in the sections ‘Variations and Discrepancies’ and ‘Unused Lines and Rhymes’. The study noted the genre diversity of creative writings. The notes reveal about 500 names from Mayakovsky’s social circle. The author crea­ted a unified digital archive of Mayakovsky’s notebooks. The methodology of their transcribing, copying and commenting validated during the study of notebooks was put to practical use in the Vladimir Mayakovsky’s Complete Works in 20 volumes, where the notebooks have been first published in their entirety as an independent section.
QAOA-based MRMR Algorithm for Feature Selection

As the volume of data for classification tasks in machine learning grows, feature selection plays an increasingly crucial role in enhancing the efficiency and effectiveness of these tasks. Existing classical feature selection algorithms often encounter challenges, such as high computational complexity and vulnerability to local optima. Quantum algorithms have been proposed to overcome these limitations. Here, our work proposes a new approach that combines the Quantum Approximate Optimization Algorithm (QAOA) with the classical feature selection algorithm Maximum Relevance-Minimum Redundancy (MRMR). First, we transform the feature selection problem into the Quadratic Unconstrained Binary Optimization (QUBO) problem. Then, we map the QUBO formulation to Hamiltonian with the Ising model. Finally, we employ QAOA with a shallow circuit to search this Hamiltonian's ground state, which matches the specific solution for feature selection. We conducted numerical experiments on eight real-world datasets for classification tasks from diverse domains, including finance, medicine, and natural hazards. Experiment results suggest that QAOA-selected feature subsets size are typically less than half the complete set while achieving comparable or better classification accuracy than classical MRMR, demonstrating its preference for smaller, more efficient feature subsets.
Investigating the Influence of Convolutional Operations on LSTM Networks in Video Classification

Video classification holds a foundational position in the realm of computer vision, involving the categorization and labeling of videos based on their content. Its significance resonates across various applications, including video surveil-lance, content recommendation, action recognition, video indexing, and more. The primary objective of video classification is to automatically analyze and comprehend the visual information embedded in videos, facilitating the efficient organization, retrieval, and interpretation of extensive video collections. The integration of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks has brought about a revolution in video classification. This fusion effectively captures both spatial and temporal dependencies within video sequences, leveraging the strengths of CNNs in extracting spatial features and LSTMs in modeling sequential and temporal information. ConvLSTM and LRCN (Long-term Recurrent Convolutional Networks) are two widely embraced architectures that embody this fusion. This paper seeks to investigate the impact of convolutions on LSTM networks in the context of video classification, aiming to compare the performance of ConvLSTM and LRCN.
Pre-trained CNNs: Evaluating Emergency Vehicle Image Classification

In this paper, we aim to provide a comprehensive analysis of image classification, specifically in the context of emergency vehicle classification. We have conducted an in-depth investigation, exploring the effectiveness of six pre-trained Convolutional Neural Network (CNN) models. These models, namely VGG19, VGG16, MobileNetV3Large, MobileNetV3Small, MobileNetV2, and MobileNetV1, have been thoroughly examined and evaluated within the domain of emergency vehicle classification. The research methodology utilized in this study is carefully designed with a systematic approach. It includes the thorough preparation of datasets, deliberate modifications to the model architecture, careful selection of layer operations, and fine-tuning of the model compilation. To gain a comprehensive understanding of the performance, we conducted a detailed series of experiments. We analyzed nuanced performance metrics such as accuracy, loss, and training time, considering important factors in the evaluation process. The results obtained from this study provide a comprehensive understanding of the advantages and disadvantages of each model. Moreover, they emphasize the crucial significance of carefully choosing a suitable pre-trained Convolutional Neural Network (CNN) model for image classification tasks. Essentially, this article provides a comprehensive overview of image classification, highlighting the crucial significance of pre-trained CNN models in achieving precise outcomes, especially in the demanding field of emergency vehicle classification.
EDUCATION AS CREATION OF A FUTURE HUMAN: A FEW LINES

Informatization /technologization of society (information and technology boom) causes fundamental changes not only in the field of communication, but also conditions deep culture changes – culture balance is damaged. "Lower" (mass) culture begins to dominate over "higher" (elite) culture. Does this manifest itself in education? Will really the concepts of deconstructivism and postmodernism determine the future of education? Probably so. It is obvious and inevitable that the transformation of education takes place under the influence of new technologies and human practice, which changes the entire social order. Can you imagine the school of the future? Is it possible to provide equal education to all students? Does this not contradict the general systemic logic (e.g., educated, but illiterate)? Education for the future is a complex construct that includes physical, intellectual, personal, technological education, etc. It is a multifaceted process that combines different approaches, different (sometimes contradictory) methodologies, systems, forms, etc. First, the education of the future will be clearly stratified and will be determined by access possibilities. Thus, the educational system will have to distinguish itself by two essential characteristics – mobility (ability to change quickly) and reactivity (ability to react to changes). What if a teacher is not able to cope with the competitive struggle in the education market today? What is the point of announcing that teaching is and will be a prestigious profession? There are many questions, but there is a lack of answers as usual. Here emerges the same classical balance problem. It seems that we all strive to create an efficient, safe, healthy, etc. teaching/learning environment. However, we should not forget that there is a balance in everything. It is a balance between individual and collective (group) teaching/learning, theory and practice, assessment and feedback, automated (machine) teaching/learning and “live” education, and so on. Subject balance can be mentioned as an example. Curricula should be designed to emphasise general education rather than focusing only on certain areas such as exams (e.g., STEM). The most important thing remains (should remain) the development of students’ abilities in various fields, including creative, critical thinking, problem-solving, and adequate communication.
Innovative Games as a Means of Developing the Methodological Competence of Students - Future Primary School Teachers

The article presents the theoretical justification and practical implementation of an educational environment aimed at developing the methodological competence of future teachers. Purpose of the study: to theoretically substantiate, practically define and experimentally create an educational environment aimed at developing methodological competence in future primary school teachers through game practice. Research methods: a) theoretical: analysis, synthesis, interpretation of scientific literature on the research problem; b) empirical: pedagogical experiment. The article describes approaches to defining the categories “competence”, “methodological competence”, “gaming practice”. The study shows the feasibility of using innovative games in teacher education and formulates the requirements for the development and implementation of innovative games. The results of the pedagogical experiment prove the effectiveness of using innovative games as a means of developing students' methodological competence. The practical significance of the study lies in the development of conditions for the use of innovative games in the methodological training of students - future teachers. The conclusions based on the results of the empirical study give reason to talk about the advisability of using elements of game practice in order to develop the methodological competence of future teachers.
Short-term effect of the Internet use on memory of adults

The relevance of the research is due to the increasing use of the Internet and the lack of reliable information about the Internet exposure on higher mental functions of the users of different age. The aim of the study is to detect the short-term effect of the Internet use on indirect memorization in adults. The study has been conducted in terms of intraindividual quasi-experiment, that assessed the accuracy of memorization in two conditions: traditional reading (a book), web-search (a mobile phone). The pictogram test in the author’s variant was the main tool to assess the memory of adults. The research was held among teachers during classes of training courses (n=50; 12 males, 38 females; average age 44,56±4,11). To implement a scheme of a positionally equalized sequence of sampling, the sample was divided into 2 groups. In the results, one third of the sample showed the same level of accuracy in memorizing words in different conditions. Most of the participants demonstrated positive trends while reading (р≤0,01). While the number of mistakes was three times more for the web-search.
Formation of general professional competencies in bachelores of pedagogical education in the process of mastering additional educational programs

The emergence of new public policy priorities in the field of education changes the role of lifelong education in the system of socialization of the individual, as well as the nature of interaction between various subjects within the educational process and beyond. To increase competitiveness in the labor market, students of higher educational institutions need to be prepared to quickly master new knowledge and skills to meet the requirements of professional activity in the labor market. One of the forms of obtaining specific knowledge and skills for students in parallel with higher education is additional education. The purpose of the study is to analyze the pedagogical conditions for the formation of general professional competencies among bachelors of pedagogical education using additional education programs. Materials and methods of research. The research materials are the methodologically based on a scientific review of research approaches developed in domestic pedagogy in the field of improving the system of training generalist specialists in the development of additional education programs. The research methods are the study and analysis of scientific and methodological literary sources, as well as questioning of students. Research results. At the Chuvash State Pedagogical University named after I.Ya. Yakovlev Center for Continuing Education. To determine the possibility of additional professional education, a study was conducted among students of this university in order to identify their awareness of the possibilities of mastering additional education programs. The results of the survey, according to which many students plan to undergo additional education in standard programs, 20% plan to receive certificates or certificates in a whole range of programs. Conclusions. The system of additional education adapts future teachers to the specifics of modern socio-economic conditions, high demands from the labor market and, ultimately, forms a highly qualified specialist with a wide profile, capable of a continuous process of self-improvement in the process of professional activity.