Reviewerly/exHarmony huggingface dataset:
Modeling Human Performance in Complex Search and Retrieve Environment Using Supervised and Unsupervised Machine Learning Techniques

Prior research has evaluated human performance in complex search-and-retrieve environment. However, little is known about how supervised and unsupervised machine learning techniques could account for human performance in such complex scenarios. The primary objective of this research is to develop supervised and unsupervised machine learning models to account for human actions in a complex search-and-retrieve environment. A total of 50 participants were recruited to participate in the study to perform as human agents in a simulation developed using Unity 3D. The environment consisted of four building with targets (having positive reward) and distractors (having negative reward) present inside those buildings. Participants were tasked to explore the environment and maximize their score by collecting as many target items while avoiding distractor items present in the environment. The experiment consisted of training and test phases which differed in the availability of feedback, items present in the environment, and the duration. Next, machine learning models were developed to account for human actions using supervised and unsupervised techniques such as Decision Tree, Random Forest, Support Vector Classifier (SVC), Multilayer Perceptron (MLP), and K-nearest neighbor (KNN). These models were trained using data collected in the training phase of the experiment and their performance was evaluated on data collected in the test phase. Results revealed that KNN, an unsupervised learning model, performed better in predicting human actions on the test dataset compared to supervised learning models such as decision tree, random forest, MLP, and SVC. We highlight the main implications of our findings for the human factors research community.
Malware Detection in URL Using Machine Learning Approach

The most common technique to host fraudulent or harmful content, such as spam, malicious ads, etc., is using a Uniform Resource Locator (URL). Malicious URLs hold harmful contents that cause loss of information, malware installation, and monetary loss of the victims. Hence, it is necessary to detect such URLs and take some action on such threats. Earlier, one database is maintained to blacklist such URLs and a URL is compared with the available database of blacklisted URLs. If the URL is found in the database then the browser considers the URL suspicious and blocks it. But, this method is ineffective in finding newly discovered URLs. By suggesting a solution based on machine learning, this problem can be resolved. This research aims to investigate how machine-learning techniques can be used to identify harmful URLs.
Creating a Virtual (Digital) Educational Environment for Teaching Biology in the Conditions of Technoparks and Quantoriums at School and University

The article describes the features of the interaction of a general education school and a technopark of a pedagogical university in the implementation of modern biological education. The relationship of biology with other sciences is considered, the types of educational technologies used in the process of interaction are presented, the significance of the digital laboratories of the technopark are revealed. The article considers as a scientific problem the identification of promising areas of didactic research, the expansion of their research field. In the article, the provisions of general didactics are concretized in relation to new objects included in its research field, the problems of didactics of higher education are considered. Also, the article deals with virtual reality, more precisely, its concept for creating a more powerful and useful virtual learning environment than existing approaches to learning based on traditional technologies, by incorporating new technologies and new learning criteria. A method of vector modeling of virtual reality, presented for use in various types of self-education, based on information and communication technologies, educational technologies for blended teaching of biology in high school is proposed and substantiated.
Digital tools in education as an integral part of teaching a foreign language in higher school in the context of large-scale digitalization of society

One of the pillars of the United Nations 2030 Agenda for Sustainable Development is quality education. It aims to provide inclusive and equitable quality education for all. Digital technologies have become an important tool to achieve this goal. Technological advances in education have made life easier for students. Instead of using pen and paper, students now use a variety of software and tools to create presentations and projects. Unlike a heavy book, browsing an e-book is easier. These methods contribute to increasing interest in research. The article briefly talks about the need to use digital technologies in education. Higher education institutions are undergoing major changes. They are caused by many factors, the main of which is the large-scale digitalization of society. Digital technologies demonstrate a number of tools chosen for teaching a foreign language in higher education. The purpose of this study is to provide an overview of the distinctive features of the digital transformation process that has taken place in higher education institutions. In addition, the article considers digital learning as part of the ecosystem of modern higher education.
Formation of Ecological Knowledge of Schoolchildren Through Information and Communication Technologies

The article considers the process of formation of ecological knowledge of students through information and communication technologies, presents the main types of ICT and their features. Also, the features of the formation of ecological knowledge in children of primary school age are revealed. Psychological characteristics of children of this age, contributing to the assimilation of environmental knowledge and skills. The specifics of the variable programs for the course “The World Around” for elementary school are considered, which allowed us to conclude that it is necessary to systematically use information and communication technologies in the process of forming environmental knowledge in elementary school children. The paper summarizes typical eco-knowledge formed in children of primary school age; the stages and criteria for their formation are defined (motivation-target, creative-cognitive, evaluative-corrective and cognitive, emotional-value, practical-effective, respectively). The results of the study will allow for in-depth work in the direction of the formation of environmental knowledge of schoolchildren. A set of activities using ICT, aimed at the formation of ecological ideas among schoolchildren, can be applied by teachers when working in the field of environmental education.
XACML: Explainable Arrhythmia Classification Model Using Machine Learning

Cardiac arrhythmia, an irregular heart rhythm has a high mortality rate. Early diagnosis may redeem the patient from death. Many reliable decision support systems have recently been built as “black boxes,” or systems that conceal their internal logic from the user. This absence of justification raises both ethical and practical problems. The literature describes a variety of strategies for resolving this significant flaw, often, accuracy is sacrificed for interpretability. Trust among clinical experts, particularly in medicine, can be established through the justification of decisions provided by automated medical expert systems. This work aims to integrate explainability with the Boosting model in the classification of cardiac arrhythmia diagnosis. The reasons behind the model’s output are explained using the familiar explainable AI tool LIME in a simple and comprehensible manner.
Implementation of a Comprehensive Concept of Education in the System of Environmental Education

The article is devoted to the problems of environmental education. The idea is expressed that the ecological education of the population is the most important means of overcoming the ecological crisis. A brief review of the definitions of the concept of “environmental education”, as well as some concepts of environmental education, is made. The main attention is paid to the complex concept of education, developed on the basis of the Moscow International University and the Laboratory for Cognitive Research of Consciousness named after Said Makhdikhon Sattorov under the guidance of Professor of RAE (RANS) Maira Makhaev. Methodological bases of the complex concept of training are revealed. The complex concept is based on the allocation of four blocks (the “knowledge” block, the methodological block, the skills and abilities block, the responsibility block), which are the parameters of any training. An idea was expressed about the possibility of introducing this concept into the system of environmental education, which will improve the quality of environmental education.
About Some Features of the Existence of the Humanities in the Digital Age

In the digital age, the humanities have undergone changes as new technologies have opened up new opportunities for research and interaction. Scientists now have access to huge amounts of data and can use computational methods for analysis, leading to new discoveries. However, the digital age also poses challenges, such as the need for long-term data retention. As the field of humanities continues to evolve, scientists must cope with these challenges and use the opportunities provided by the digital age to advance their research and interact with society in new and innovative ways.
Unsupervised Learning Method for Better Imputation of Missing Values

The main objective of any clustering method is to improve the clusters quality. Such an improvement can be achieved using graph partitioning algorithm which partitions the graph into maximum components with minimum cut which represents the optimality of spectrum partitioning. The commonly used graph portioning algorithm is spectral algorithm called known popularly as spectral clustering. Such a unsupervised method can be used during imputation for identifying the optimal clusters. Optimal clusters reduce the search space during imputation and thus achieve dimensionality reduction. The proposed method uses MKNNMBI imputation method in which the non-missing dataset used for imputing the missing values is reduced. The reduction is achieved by using spectral partitioning method for which the non-missing dataset is represented as a graph. The spectrum of a Laplacian graph is obtained using spectral clustering from which the optimal l eigen values are identified as optimal cluster centers for imputation. The imputation is done using this reduced optimal non-missing dataset. The imputed dataset is evaluated by comparing the accuracies of classifiers like SVM, C4.5, NB and kNN. Proposed method has improved the accuracies of the imputation on optimal reduced datasets.
Sentence Classification Using Quantum Natural Language Processing and Comparison of Optimization Methods

The natural language processing (NLP) demands high computing resourses to process language related problems. The evolution of quantum computing hardware and simulators create avenues to design and develop NLP methods to test on these platforms. This research uses quantum natural language processing (QNLP) to simulate the sentence classification and compares optimization techniques (Simultaneous perturbation stochastic approximation: SPSA and convergent optimization via most-promising-area stochastic search: COMPASS) for small dataset and simple sentence structures and the results are promising to show that with the COMPASS optimization technique the model performs better Table 1. When the ansatz depth is less and vice-versa. The research in quantum natural language processing is infancy, the simulation results are promising and the better results can be expected for large datasets with different sentence structures if real quantum computers are available in near future.
Digitalization of the Russian Education System: Opportunities and Prospects

The purpose of the article is to determine the current state, opportunities and prospects for the digitalization of the Russian system of (higher) education while maintaining the existing approach to state regulation of this process and developing a new (improved) approach to speed it up. For this purpose, in this study, a method is used to assess the degree of digitalization of the system of (higher) education. As a result, it was found that at present the greatest degree of digitalization is considered through the characteristics of additional indicators of the functioning of the system of (higher) education in modern Russia, such as the work of library resources, state and public control. At the same time, the degree of digitalization of the main indicators (educational and scientific activities of universities) is at a low level. At the same time, the digitalization infrastructure of the Russian system of (higher) education is sufficiently developed, which determines significant opportunities for the further development of this process. The final degree of digitalization of the Russian system of (higher) education is estimated at 25.8% and is very low. The reason for this problem lies in the insufficient state regulation of the process of digitalization of the Russian system (higher education).
Cognitive Theory of Psychological Types in Solving Problems of Intercultural Communication

The purpose of this article is to give an overview of the cognitive concept of psychological types, which makes a significant contribution to solving the problems of intercultural communication. Scientists from Moscow International University and the Laboratory of Cognitive Research of Consciousness named after Said Makhdikhon Sattorov (Laboratory of Cognitive Research of Consciousness Said Makhdikhon names) under the guidance of Professor of the Russian Academy of Natural Sciences (RANS) Maira Makhaev have developed a cognitive theory of psychological types, which allows analyzing intercultural communications with positions of psychotypology. The cognitive concept is based on the main provisions of Carl Jung’s concept of psychological types (Jungian Psychological Types Theory). In this concept, a new terminology is proposed to designate mental functions (S, I, E, L), classes of mental functions (class of constructive functions and class of receptive functions), directions of work of functions (exoversion and endoversion instead of extraversion and introversion) and 16 psychological types. The psychological type is described as a cognitive system for receiving, transforming, storing and transmitting information.
VR, NFT, Metaverse... How digital innovations affect the development of marketing and PR communications

This study aims to determine innovative global trends in communications and their impact on the development of the marketing and PR industry. For this, we applied trend analysis, one of the modern forecasting methods, which is based on the examination of secondary sources and news publications and allows the identification of weak information signals from various markets based on the mention of digital innovations. Innovative tools guide companies to construct a new reality for stakeholders, improve interaction methods with stakeholders, and form their favorable corporate image and brand positioning. We identified six trends that will impact the development of communications in the coming decades: machine learning; big data; virtual reality technologies; digital gamification; blockchain and NFT; migration to the metaverse. We concluded that these digital innovations would enrich the promotion toolkit, form a new socio-communication dimension, and produce a creative and meaningful context. In particular, we conclude that VR technologies will encourage further penetration of advertising and PR into the field of integrated virtual communications. Gamification will become more and more deeply rooted in all areas of marketing and PR activities, diversify interaction with stakeholders, and increase its effectiveness thanks to novelty, emotionality, and user involvement. Finally, NFT will become a symbol of innovation from the image perspective and a thematic platform for stakeholder communications. The article also outlines the features of marketing and PR in the metaverse
Nonlinear Graph Wavelets via Medianfication

Graph wavelet transforms allow for the effective representation of signals that are defined over irregular domains. The transform coefficients should be sparse, and encode salient features of a signal. In many situations, these salient features appear as discontinuities in the signal, e.g. physical edges in natural images. The transforms facilitate the development of various graph signal processing tasks, e.g. feature extraction or compression. The graph wavelets proposed in the literature are linear transforms, and employ linear filters that are essentially mean operations. We propose the construction of nonlinear graph wavelets that are based on the nonlinear median operator. The construction process converts existing linear transforms that are based on the critically sampled two-channel graph filter bank. There are two parts to the conversion process. The first part derives a lifting structure similar to that found in classical wavelet transforms. Given a pair of polynomial filter functions representing the filter bank, a polyphase factorization technique, that is based on the Euclidean algorithm for polynomials, will be developed. The resulting lifting structure enables a more modular design of graph wavelet transforms, but is still linear. The second part of the process, termed medianfication, converts certain linear operators, that are associated with the graph shift, into corresponding median operators. The resulting nonlinear transform shares many desirable properties of the corresponding linear transform. The nonlinear transform has the perfect reconstruction property. Though strictly nonlinear, the transform has similar spectral characteristics to the linear counterpart. It is able to separate low-pass features, which are smooth approximation of a signal, from high-pass features, which are rapid variations in a signal. A graph transform has the edge-aware property if filtering across signal discontinuities is avoided, and this leads to better performance in some applications. For linear transforms, this requires some preprocessing algorithm to generate an edgemap that encodes the locations of the discontinuities. However the nonlinear transform has an in-built edge-aware property, i.e. an edgemap is not required. Applications of the nonlinear transform to nonlinear approximation and denoising will also be considered.
DBCR-YOLO: improved YOLOv5 based on double-sampling and broad-feature coordinate-attention residual module for water surface object detection

Unmanned missions have become more and more popular in recent years. The related technologies of unmanned ground vehicles and unmanned aerial vehicles are growing rapidly, but research on unmanned surface vehicles (USVs) is rare. Water surface object detection algorithms play a crucial role in the field of USVs. However, achieving an object detection algorithm that balances speed and accuracy in the presence of interference is a difficult challenge. We proposed a network, DBCR-YOLO, that improved the detection accuracy while meeting real-time requirements. Based on YOLOv5, we added an additional detection head for detecting tiny objects. Then, we replaced the downsampling in YOLOv5's backbone network with the proposed double sampling mechanism to solve the problem that paying attention to the key features of objects cannot be done in the downsampling process of YOLOv5. Finally, we substituted the proposed BCR neck for YOLOv5's neck, thus improving the fusion of features between different scales based on fewer parameters and fewer calculations. We tested our network on the water surface object detection dataset. Compared with YOLOv5, DBCR-YOLO improved the detection accuracy by 3.4%. At the same time, DBCR-YOLO achieved the highest accuracy in comparison with other networks.
A Variational AutoEncoder (VAE)-based Deep Learning Anomaly Detection Model for Industrial Products with Dynamic Weights Assigned to Loss Function

In the industrial field, deep-learning-based image anomaly detections are attracting attention because of some of their advantages.The deep-learning-based models can overcome the shortcomings of traditional methods, such as human eye detection and rule-based machine detection.When using deep learning, which has many advantages, one of the limitations is that anomalous products are difficult to obtain.Since most industrial products do not have defects, unsupervised learning detection models are strongly required.We propose a new model based on the variational autoencoder (VAE), which is a generative model applicable to detection by unsupervised learning.VAE is a model for optimizing parameters or latency based on a loss function that is the sum of several terms, and in our proposed method, original weights are given to these terms.In addition, our model dynamically and adaptively explores a ratio of weights.We have developed a dynamic weighted VAE adapted to area under the receiver operating characteristic curve (AUROC, AUC) using validation data.We have already reported the efficiency of the AUC-adapted VAE; however, this method is not unsupervised learning, and a method that does not use validation data was desired.In this paper, we discuss the previous method in more detail and describe the new method, which is fully unsupervised learning, by conducting additional experiments.The results of several experiments show that the proposed method is potentially effective for some actual industrial product image datasets while maintaining unsupervised learning.
Open-set federated adversarial domain adaptation based cross-domain fault diagnosis

Abstract Data-driven fault diagnosis techniques utilizing deep learning have achieved widespread success. However, their diagnostic capability and application possibility are significantly reduced in real-world scenarios where fault modes are not fully covered and labels are lacking. Owing to potential conflicts of interest and legal risks, industrial equipment fault data usually exist in the form of isolated islands, making it difficult to carry out large-scale centralized model training. This paper proposes open-set federated adversarial domain adaptation (OS-FADA) to achieve collaborative evolution of fault diagnosis capabilities among cross-domain data owners while protecting privacy. The OS-FADA is a general fault diagnosis framework that employs two-phase adversarial learning. First, faced with the data distribution shift caused by variable working conditions, a generative adversarial feature extractor training strategy is designed to achieve domain-invariant fault feature extraction by approximating the feature distributions of clients to a unified generated distribution. Second, considering the label distribution shift of unknown faults occurring in the target client, an adversarial learning method is proposed to establish decision boundaries between known and unknown faults. Ultimately, the co-evolution of fault diagnosis models between clients is achieved by combining two-phase adversarial learning and federated aggregation. Results from an industrial gearbox case demonstrate that our proposed method achieves over 20% diagnostic accuracy improvement and has excellent potential for cross-domain fault diagnosis tasks with unknown faults when the data silos problem cannot be ignored.