Reviewerly/exHarmony huggingface dataset:
Development of digital competencies of high school students In the school informatics course by means of case technologies

In the context of digitalization of education, the article actualizes the problem of finding new and developing existing methods and means of teaching. Currently, case technologies have received a new update, which can be effectively used for the purpose of propaedeutics and the development of digital competencies in high school students, including in the context of specialized training. The article describes both the essence and features of this method as applied to the educational process of a modern school, and the experience of using specific cases of a controlling nature when students study information technologies as part of a school informatics course. From a methodological point of view, the presented material will be useful not only for school teachers, but also for university teachers involved in the professional training of future teachers of informatics.
PERCEPTION OF TEACHER’S LEADERSHIP BY PRE-SERVICE AND IN-SERVICE TEACHERS IN KAZAKHSTAN

The study describes the content and structure of the concept of teachers’ leadership from the pre-service and in-service teachers’ perspective. The method of obtaining data is a free-associative experiment with the registration of answers given to the verbal stimulus “teachers’ leadership”. The results are presented in the form of an associative field where we distinguish the nucleus, near, far, and outer periphery, compiled based on frequency analysis of the respondents’ reactions. By cognitive analysis of the associative field “teachers’ leadership” in the verbal consciousness of the pre-service and in-service teachers, semantic zones in its structure are distinguished.
KRB01 Method for Securing the Data

Data is more readily available through social media, hospitals, populations, and other places. The importance of digital data in current and future world cannot be overstated, because data is the only factor that determines the survival of human lives in the world. Despite the hype, digital data is growing more vulnerable to hackers due to a lack of effective security. This research work implements KRB01, a new approach to solve this problem. There are four steps to implement the proposed procedure. The first step applies the T-test technique; The second step applies the odd operations; The third step represents even operations; and the final fourth step applies the equations. The KRB01 method gives high security when compared to ChaCha method.
Gym Hero: A Research Environment for Reinforcement Learning Agents in Rhythm Games

This work presents a Reinforcement Learning environment, called Gym Hero, based on the game Guitar Hero. It consists of a similar game implementation, developed using the graphics engine PyGame, with four difficulty levels, and able to randomly generate tracks. On top of the game, we implemented a Gym environment to train and evaluate Reinforcement Learning agents. In order to assess the environment's capacity as a suitable learning tool, we ran a set of experiments to train three autonomous agents using Deep Reinforcement Learning. Each agent was trained on a different level using Deep Q-Networks, a technique that combines Reinforcement Learning with Deep Neural Networks. The input of the network is only the pixels of the screen. We show that the agents were capable of learning the expected behaviors to play the game. The obtained results validate the proposed environment as capable of evaluating autonomous agents on Reinforcement Learning tasks.
IMMPDA Algorithm for Missile Tracking in Clutter

Missiles have the characteristics of fast speed, high maneuverability, random and uncertain maneuvering changes. In addition, clutter interference exists in the surrounding environment, which makes it difficult to track the trajectory of multiple missiles at the same time. To solve this problem, this paper proposes a missile angle tracking method based on the interactive multiple model — probability data association (IMMPDA) algorithm that combines the interactive multiple model algorithm and the probability data association algorithm. The CV, Singer and CT are selected as basic models, and the three models are interactively mixed through IMM to adapt to the changeable motion states of the missile. Then the PDA algorithm is used to associate the target measurements and clutter points obtained by radar detection with the established track. Since the measurement system is nonlinear, extended Kalman filter is selected for filtering. The experimental simulation results show that this method can realize the angle tracking of high-speed and high-maneuvering missiles based on proportional guidance in clutter environment.
Estimation of the Accuracy of a Control System with a Fuzzy PID Controller Based on the Approximation of the Static Characteristic of the Controller

The problem of estimating the accuracy of an automatic control system with a fuzzy PID controller is solved. To describe a fuzzy controller, its static characteristic is used, which is approximated by two piecewise-linear and one piecewise-constant sections. This approach makes it possible to study the system as a linear one at each section of the approximated characteristic, and accordingly develop the calculation methods known in control engineering, taking into account the features of the system under consideration. In the article, to calculate the error in the steady state, the theorem on the final value of the original is used. For two different types of second-order control objects — static and astatic — on the basis of this theorem, analytical expressions are obtained that relate the accuracy of the control system with the values of the target and disturbance with a different structure of the controller (P-, PI-, PD-). When conducting experimental studies, the fuzzy PID controller was compared with a linear one tuned by the method of the maximum stability. Research results show that a fuzzy controller ensures the accuracy of the control system is not worse than a linear one, while increasing the dynamics of the system. The analytical expressions presented in the article make it possible to assess the accuracy of a control system with a fuzzy controller and can be used as a technique for adjusting the controller based on the accuracy requirements.
Single-round Lattice-based Multisignatures

This work presents a provably-secure lattice-based multisignature scheme which requires only a single round of communication, whereas the existing works need two or three rounds. The reduction in the number of rounds for the proposed scheme is achieved by utilizing lattice trapdoors. In order to generate multisignatures securely, our scheme however requires an honest centralized server that maintains the trapdoor of a shared matrix used in the scheme.
Comparative study of models solar radiation in Sub-Saharan environment

The depletion of fossil and natural energy resources such as oil, gas and uranium is decreasing due to the current high consumption and the development of the industry in recent years. Climate change and pollution require a reflection on the energy need by a rapid transition to renewable energies. To cover energy needs, research is being conducted on renewable energy. One of the renewable energies which can fulfill the demand of the world until now, is solar energy, It is the direct source of generation of heat, light, and electricity and which is free and inexhaustible in most of regions of the world, this energy can be used in different ways, either in photovoltaic systems for the production of electricity, or in thermal systems for the production of hot water in the housing sector. In this article we will do a comparative study. One consists in determining the two empirical models (Davies & Hay and Ashrae) which will allow us to estimate the daily irradiations on a horizontal plane, and on the other hand we compare it by the results measured in the site of Adrar at the time of four seasons. A comparative study of the results obtained by simulation of the two models showed that the Ashrae model gives a better estimate of the global solar components, the absolute mean error does not exceed 6% and the correlation coefficient was greater than 0.97, as did the relative coefficient of the bias error does not exceed 5% in absolute value, relative RMSE does not exceed 9%.
Matrix-variate Lindley distributions and its applications

Restring on the fact that the definition of multivariate analogs of the real gamma distribution is replaced by the Wishart distribution on symmetric matrices, and based on the notion of mixture models which is a flexible and powerful tool for treating data taken from multiple subpopulations, we set forward a multivariate analog of the real Lindley distributions of the first and second kinds on the modern framework of symmetric cones which can be used to model waiting and survival times matrix data. Within this framework, we first construct a new probability distributions, named the matrix-variate Lindley distributions. Some fundamental properties of these new distributions are established. Their statistical properties including moments, the coefficient of variation, skewness and the kurtosis are discussed. We then create an iterative hybrid Expectation-Maximization Fisher-Scoring (EM-FS) algorithm to estimate the parameters of the new class of probability distributions. Through simulation as well as comparative studies with respect to the Wishart distribution, the effectiveness and reliability of the proposed distributions are proved. Finally, the usefulness and the applicability of the new models are elaborated and illustrated by means of two real data sets from biological sciences and medical image segmentation which is one of the most important and popular tasks in medical image analysis.
Adversarial Erasing method based on graph neural network

Abstract Semantic segmentation is a traditional task that requires a large number of pixel-level ground truth label data sets, which is time-consuming and expensive. Recent developments in weakly-supervised settings have shown that reasonable performance can be obtained using only image-level labels. Classification is often used as an agent task to train deep neural networks and extract attention maps from them. The classification task only needs less supervision information to obtain the most discriminative part of the object. For this purpose, we propose a new end-to-end counter-wipe network. Compared with the baseline network, we propose a method to apply the graph neural network to obtain the first CAM. It is proposed to train the joint loss function to avoid the network weight sharing and cause the network to fall into a saddle point. Our experiments on the Pascal VOC2012 dataset show that 64.9% segmentation performance is obtained, which is an improvement of 2.1% compared to our baseline.
Information space as a factor of optimization of the future specialists’ self-education

The training of a specialist at a university is determined by the quality of the educational process, during which it is necessary to take into account the situation on the labor market. Successful training largely depends on the students self-education, which is difficult and limited in the context of a pandemic. The conditions of todays realities dictate the methods and forms of the process of self-education of students. During the pandemic, students practice self-education on a remote basis. We consider this form as a complex functional system with its own logic of development and relatively independent stages of learning via the Internet. Changes in various fields of activity are observed daily, which increases the requirements for the quality of education of a competitive individual; the idea of online education using electronic information platforms that serve as a means of self-education of students at the university is analyzed. The process of organizing the educational process in disciplines and tasks from the position of digitalization, through the introduction of modern digital technologies in the process of education of academic courses at the time of the pandemic in the country and the world, becomes significant. The conceptual apparatus considered in the paper: information space, educational space, self-education, competence, competence approach. It clarifies the need to teach the discipline in combination to acquire professional experience; the idea of revealing and enriching the inner potential of each person, his development and self-education throughout his life is substantiated; a detailed analysis of psychological and pedagogical literature is given. The results of a comprehensive study conducted at Chelyabinsk State University confirm the dependence of communicative competence on the students self-education during the pandemic.
Algorithm analysis based on machine learning in alarm information of metering system

Abstract Due to the huge scale of the metering warning system and the wide variety of warning information, manual identification can no longer meet the real-time monitoring needs of modern power grids. Through the use of machine learning, data mining technology, linear regression method, naive Bayes method, automatic classification analysis of alarm information from the massive alarm information. Construct an SVM prediction model to improve the accuracy of alarm information analysis and improve the effectiveness of power grid monitoring and management. The experimental results show that it can provide support for the normal operation of the grid metering system.
Improvements in the computing efficiency of the probabilities of the LIL test for the PRNG evaluation

The evaluation of randomness in pseudorandom numbers generators is of great importance in cryptography. For such an endeavor, there are numerous randomness testing tests. Many have been designed based on important laws of randomness, such as the central limit theorem and the law of large numbers. Recently, a novel test with three variants was proposed by Yongee Wang, based on the law of the iterated logarithm for the evaluation of pseudorandom numbers generators. Wang found severe vulnerabilities in known PRNG utilizing this test's application, which can be of great importance in evaluating cryptographic algorithms and random numbers generators. In this work, a simplification of the expressions given by Wang is presented to calculate the theoretical probabilities applied in the test to allow more efficient implementations for its three variants.
Secure Allocation for Graph-Based Virtual Machines in Cloud Environments

Cloud computing systems (CCSs) enable the sharing of physical computing resources through virtualisation, where a group of virtual machines (VMs) can share the same physical resources of a given machine. However, this sharing can lead to a so-called side-channel attack (SCA), widely recognised as a potential threat to CCSs. Specifically, malicious VMs can capture information from (target) VMs, i.e., those with sensitive information, by merely co-located with them on the same physical machine. As such, a VM allocation algorithm needs to be cognizant of this issue and attempts to allocate the malicious and target VMs onto different machines, i.e., the allocation algorithm needs to be security-aware. This paper investigates the allocation patterns of VM allocation algorithms that are more likely to lead to a secure allocation. A driving objective is to reduce the number of VM migrations during allocation. We also propose a graph-based secure VMs allocation algorithm (GbSRS) to minimise SCA threats. Our results show that algorithms following a stacking-based behaviour are more likely to produce secure VMs allocation than those following spreading or random behaviours.
Improved butterfly algorithm optimizing ELM network parameters and its application in tennessee-eastman process

accurate diagnosis and identification of chemical system faults is of great significance to ensure the long-term safe operation and high-quality production of the system. Aiming at the problems of low fitting degree and parameters easily falling into local optimum in ELM model prediction, an improved butterfly algorithm (FBOA) is proposed to optimize ELM network parameters (FBOA-ELM) to improve its performance. Then the fault diagnosis of standard Tennessee Eastman (TE) process is carried out. The results show that the FBOA-ELM method can effectively classify the 16 kinds of faults selected in TE process. Compared with the other four algorithms, the accuracy is significantly higher than the four algorithms, which indicates that the FBOA-ELM model has better classification effect and higher accuracy.
Strategies of Collaborative Filtering Recommendation Mechanism Using a Deep Learning Approach

Nowadays, recommendation systems are widely used to help users locate the items they want. Collaborative filtering (CF) is a commonly used method for the recommendation. CF techniques use user-item ratings for prediction but suffer from the problems of data sparsity, cold start, and scalability. Though the Matrix Factorization (MF) techniques like Singular Value Decomposition (SVD) or Principal Component Analysis (PCA) overcome the above-mentioned problems, these methods are possible to deliver unmeaningful results in the condition of a low ranked approximation and denser singular vectors. In this paper, we review strategies of collaborative filtering recommendation mechanisms and propose an approach based on an autoencoder of convolutional neural network. Autoencoders are unsupervised learning methods in which neural networks are supported for the task of representation learning. We identify the user’s features through learning, and then use these features to combine the collaborative filtering algorithm to recommend items. The experimental results show that the convolutional autoencoder can effectively reduce the computations when the amount of data is huge and benefited from the performance of its convolutional neural network.
Dynamic Co-attention Network for Visual Question Answering

Visual Question Answering (VQA) embraces driving answers to questions about a particular image. It requires a fine-grained understanding of both the image content and the question content. Recently, VQA models use attention mechanism to find appropriate visual features based on the given question, this requires multi-step inference. The majority of the present work focus on the visual attention only and ignores the role of the textual attention in VQA. In this paper, we propose a dynamic capsule co-attention (CapsCoAtt) where the visual and textual features are treated as capsules and the attention weights are obtained through an iterative process inspired by the capsule network (CapsNet). In addition to that, to achieve a deep understanding for the questions, we propose a hierarchal question representation through three levels. We evaluate the proposed model on the benchmark dataset VQA 2.0. The results show a significant improvement in the VQA performance with the lowest number of parameters compared with the baseline models.
Free Form Plenoptic Video Acquisition and Visualization System

Recently, with the wide use of VR/AR devices and stereoscopic display, demand for realistic content is increasing. Immersive content should be realized based on content filmed with live-action, not existing content that was VIRTUALLY created. In this paper, we propose a free form plenoptic camera and visualization system as a base technology to provide such immersive live-action content. Most of all, various objects can be freely photographed in various ranges by relaxing the constraints of the existing system. This paper also suggests a refocus technology that enables a user to convert the focus of captured data as he/she desires, and an arbitrary view generation technology that allows a user to change the viewpoint as desired. The superiority of the proposed method was demonstrated by comparing the existing arbitrary view generation technology and the proposed method.
Hierarchical Multi Label Classification of News Articles Using RNN, CNN and HAN

Labeling or tagging of any online news article is very important nowadays for quick information retrieval. In real world, news may belong to multiple categories. Moreover, those categories can be organized hierarchically. Such news classification is termed as hierarchical multi label classification. Hierarchical multi label classification in case of news domain has been very rarely explored. The base article for this paper created a pre-defined taxonomy of Bio-NLP labels, and articles were then categorized into multi labels based on pre-defined hierarchy. In this work, similar approach is used. The labels are placed in a pre-defined hierarchy, and then articles are classified in multi label manner with the pre-defined hierarchy in place. HAN (Hierarchical Attention Network) has rarely been used for hierarchical multi label classification. In this work, combination of CNN and GRU is implemented at first, and then HAN alone is implemented for the task. When CNN (Convolutional Neural Network) alone is used for classification as in the base paper, the average F1-score for different sets of experiments results in approximately 0.86. When CNN is combined with GRU (Gated Recurrent Unit), the average F1-score improvement is 1%.When HAN (Hierarchical Attention Network) is used then average F1-score improves by 3.35%. The models were trained and tested using news articles of the guardian newspaper published in 2014.
Optical methods development for banknote deterioration evaluation

We have developed a model of the medium color of the image, according to which the image moves away from the viewer at a certain distance, at which all the colors of the image are mixed, forming an inseparable color spot. That is, the image is converted into a conditional pixel, which is a mixture of colors of all its pixels. Based on the proposed model, it is possible to estimate the degree of closeness of the optical characteristics of the two images by the deviation of the average color. The method of medium color was realized with a specially designed program application for Kazakh Tenge as well as for Ukrainian Hryvnia evaluation. The measurement of optical characteristics was performed in the non-printed area near the watermark on both the face and reverse of the banknotes. The results show that the evaluation of the image by the deviation of the medium color according to the developed model is a fairly accurate indicator of wear of the printed layer, has an arithmetic mean error less than traditional optical evaluation methods - brightness change and color difference. Thus, the deviation of the medium color describes the human perception of banknote soiling fairly well. This method of medium color, which aims to eliminate the subjectivity of image evaluation, can be used to process both digital images and imprints.