Reviewerly/exHarmony huggingface dataset:
Optimization of general education networks structure in rural areas of the Russian Federation and the Samara Region from the late 1990s to 2020

This paper discusses the optimization of rural educational networks structure in the Samara Region and Russia from the late 1990s to 2020. Documents of the federal level that contributed to the development of the countrys educational system, including the development of a general education network in the countryside, are presented. The dynamics of changes in the number of general educational organizations in the country in comparison with the number of schoolchildren for the period from 1995/1996 academic year to 2019/2020 academic year is given. The author compares approaches to the optimization of rural general education network structure used in the experiment on restructuring the network of general educational institutions located in rural areas which was conducted in the 2002/2003 and 2003/2004 academic years and during the implementation of complex projects for education modernization by the Russian regions in 20072009. On the example of the Samara Region, differences and similar moments of rural general education network structure optimization in this region with all-Russian trends are considered. The basic regional principles of optimization are revealed. Models of changes in the structural elements of the general education network of the Samara Region in comparison with the models used in the framework of the experiment on restructuring the network of rural schools are presented.
Performance Evaluation of Head Pose Estimation Methods for Digital Signage Systems

This paper addresses the problem of head pose estimation (HPE) for digital signage systems. Because the HPE which estimates the roll, pitch and yaw angles of human heads can extract not only the existence of human faces but also additional information, it is expected that the HPE can play an important role in digital signage systems. However, for the quality of the additional information, the HPE needs to be accurately conducted. This paper presents various performance evaluation results of the HPE methods which are non-deep learning-based and deep learning-based methods. The HPE methods in this work are implemented based on the existing open-sources. Their accuracy was evaluated with the public datasets by comparing the estimated angles and the true angles in the public datasets.
A Probabilistic Semantics for Belief Logic

Belief logic is one of the most important branches of philosophical logic. The standard approach for characterizing belief is based on the so-called possible worlds model within which the problem of logical omniscience is considered a significant defect. We present an alternative semantics for belief logic based on a probabilistic model, which formalizes the so-called Lockean thesis in philosophical literature. The Lockean thesis states that a person believes a proposition whenever they see that the probability of the proposition has reached or exceeded a prespecified threshold. We give a complete deductive system for probabilistic belief logic as well as detailed comparisons to existing frameworks of belief logic.
Future Directions

What does the future hold for motion-based interaction methods? Will increasingly popular concepts and inventions, such as immersive virtual reality, hasten their already rapid development? Or will they be supplanted by other solutions, such as interfaces capable of reading brain activity directly, or those that recognize voice commands? Neither of these require movement. Each enables new perspectives for both users and technology, but also carry many risks. The development of technologies related to the recording of movement, such as immersive virtual reality, is also of high importance for other branches of science, including psychology. To date, no solution has facilitated the analysis of human behaviors by psychologists in such a detailed manner, nor in such natural environments.
Web Service for Visualization Numerical Data

The article presents the developed web-service for visualization of numerical data. Such service allows quickly make and get visualization in different formats for further use. The developed solution does not require knowledge of programming and is focused on people who do not know how to work with powerful software packages for data visualization.
Correlation arrays, polytopes and the CHSH inequality

Raffles and correlation arrays for experiments to test the CHSH inequality • Deriving the CHSH inequality and the Tsirelson bound for this setup.
Radar Multi-Target Detection and Tracking Based on LMB Filter

Multi-target detection and tracking in complex environment is a hot difficulty topic in radar field. Aiming at the problem of radar detection and tracking of static multitarget, this paper aims to provide a complete multi-target detection and tracking scheme based on high-resolution range profile (HRRP). In the stage of target detection, the HRRP matching template is established by using priori database, the HRRP matching filter is constructed, and the constant false-alarm rate (CFAR) algorithm is fused to realize target detection; In the multi-target tracking stage, the multi-target tracking technology based on the labeled multi-Bernoulli (LMB) is used to filter the detection set, and the sequential Monte Carlo (SMC) based on adaptive newborn density is used to obtain multiple target tracks to realize multi-target tracking. The test results show that the fusion detection method of HRRP template and CFAR realizes the detection of complex weak target, and keeps low false alarm rate. LMB filter can effectively filter out false alarm and extract effective target tracks.
DETERMINATION OF GALAXY DISTRIBUTION WITH STATISTICAL MOMENTS

In this report we discuss topological studies of large scale structure of the Universe (LSS) from XMM-Newton, Sloan Digital Sky Survey and simulated data of galaxy distribution. Early works in this mentioned field were based on genus statistics, which is averaged curvature of isosurface of smoothed density field. Later, significant number of other methods was developed. This comprise Euler characteristics, Minkowski functionals, Voronoi clustering, alpha shapes, Delanuay tesselation, Morse theory, Hessian matrix and Soneira-Peebles models. In practice, modern topology methods are reducedto calculation of the three Betti numbers which shall be interpreted as a number of galaxy clusters, filaments and voids. Such an approach was applied by different authors both for simulated and observed LSS data. Topology methods are generally verified using LSS simulations. Observational data normally includes SDSS, CFHTLS and other surveys. These data have many systematical and statistical errors and gaps. Furthermore, there is also a problem of underlying dark matter distribution. The situation is not better in relation to calculations of the power spectrum and its power law index which does not provide a clear picture as well. In this work we propose some tools to solve above problems. First, we performed topology description of simple LSS models such as cubic, graphite-like and random Gaussian distribution of matter. Our next idea is to set a task for LSS topology assessment using X-ray observations of the galaxies. Although, here could be a major complication due to current lack of detected high energy emitting galaxies. Nevertheless, we are expecting to get sufficient results in the future encouraging comprehensive X-ray data. Here we present analysis of statistical moments for four galaxy samples and compare them with the behavior of Betti numbers. Finally, we consider the options of applying artificial neural networks to observed galaxies and fill the data deficiency. This shall enable to define topology at least for superimposed superclusters and other LSS elements.
Study on a visual coder acceleration algorithm for image classification applying dynamic scaling training techniques

Abstract Nowadays, image classification techniques are used in the field of autonomous vehicles, and Convolutional Neural Network (CNN) is used extensively, and Vision Transformer (ViT) networks are used instead of deep convolutional networks in order to compress the network size and improve the model accuracy. The ViT network is used to replace the deep convolutional network. Since training ViT requires a large dataset to have sufficient accuracy, a variant of ViT, Data-Efficient Image Transformers (DEIT), is used in this paper. In addition, in order to greatly reduce the computing memory and shorten the computing time in practical use, the network is flexibly scaled in size and training speed by both adaptive width and adaptive depth. In this paper, we introduce DEIT, width adaptive techniques and depth adaptive techniques and combine them to be applied to image classification examples. Experiments are conducted on the Cifar100 dataset, and the experiments demonstrate the superiority of the algorithm on image classification scenarios.
Character Recognition using Perpendicular Distance on Sweep Line and Chi-Square Statistic as classifier

In ordered to identify an object in an image it is considered a single unit and this process is known as image processing. So, In this paper, we have proposed a novel feature extraction (FE) technique for character/digit recognition (CR) by applying perpendicular distance onto a sweep line from borders of the input character. Proposing a robust FE technique is crucial for any CR system for better performance. CR plays crucial role in many Image Processing (IP) applications. Before extracting the features of the image, process it by converting into grey scale and subsequently to binary image. A scan line is generated in the binary image and traversed perpendicularly from each point on the scan line to both directions to get the extreme end points which is taken as perpendicular distance. The extracted features are in a DB/text file for recognition of input characters. A data set containing 10, 000 images have been used for performance analysis and separated them into 2 different categories as training, testing sets and stored the extracted features in the DB/text file along with the label which it specifies while training and test the efficiency of the model. Chi-square statistic is used as classifier in this paper. We have achieved encouraging results using the proposed CR FE technique when compared with the other CR FE techniques from the literature.
SDR-based Speed Measurement with Continuous-Wave Doppler Radar

This paper presents a single frequency Continuous-Wave Doppler Radar capable of estimating the velocity of mobile agents. This kind of radar can be implemented at less complexity and a relative low cost by using Software Defined Radio (SDR). Moreover, open source software (GNU Radio) and hardware (Adalm-PlutoSDR) are used because of their flexibility in operation and integration. Compact parabolic antennas operating at 5.5 GHz are employed for radar signals transmission and reception. From the obtained signal in reception, further low pass filtering and Fast Fourier Transform (FFT) is performed in order to measure the Doppler shift. Experiments were performed using a car as the moving target taking advantage of its speedometer for measurements and error determination. The theory of the Doppler mathematical model is verified showing error calculation over the obtained results from theory and the realized tests.
Application of the «pecha-kucha» format in the organization of practical classes at the university

An important component of the organization of the educational process is currently the use of innovative educational technologies, including modern tools of pedagogical activity. In addition to professional competencies in their field of activity, a university teacher must be able to creatively implement innovative pedagogical technologies. In the presence of various methods, forms and means of information and digital pedagogical technologies, the problem of the effectiveness and adequacy of their application becomes urgent. The article discusses the use of the pecha-kucha format in the training of undergraduates in the field of study 44.04.01 Pedagogical education (Secondary general education (Teacher of basic and secondary general education (by subject area)) of the Sevastopol State University discipline Standardization of school education. Based on the results of the study, it was concluded that the use of the pecha-kucha format is effective. The article provides practical recommendations for creating presentation material, organizing a speech and conducting a discussion.
Traffic travel service selection based on multi-objective optimization algorithm

With the development of big data and cloud computing, traffic travel services are increasing and showing diversified development. For traffic travel service selection, this paper establishes a multi-objective optimization model from the two dimensions of quality of service (QoS) and transaction. Based on this model, an improved multi-objective algorithm was proposed. The optimization algorithm adopts adaptive fireworks explosion to implement local search, and uses heuristic crossover and mutation operators to ensure population diversity and eliminate inferior individuals. Finally, the solution in the external archive is used as the Pareto optimal solution set. Experimental results show that the multi-objective optimization algorithm can effectively solve the multi-objective service optimization problem.
Analysis of the data influence on the training of Haar cascades for face detection

This paper is devoted to the problem of the sampling influence on the result of the detector, which is based on the Haar cascades. Trained Haar cascades give more stable detection results. On the given data sets, the spread of correct detection decreased by an average of 1.55 times. Based on the results obtained the criteria for selecting the training sample are formulated. The best results for training were obtained: 28-29 stages of the cascade training, with the maximum false positive rate being 0.5-0.51, the minimum hit rate for each stage being 0.999.
A Decentrlized Trading Model Based on Public Blockchain with Regulatable Bi-Tierred Identities

Traditional network trading platforms process transactions and store data on their centralized servers. Usually, users deem them as the third parties worth to be trusted, while exploitations and leaks of user privacy never cease. Emerging cryptocurrency systems based on blockchains provide a P2P trading model in a decentralized way, avoiding many problems inherent in traditional centralized platforms. However, the intrinsic anonymity makes it hard to trace identities and regulate the blockchain system when something goes wrong with the transactions, always causing financial losses to users.In this paper, we propose a blockchain trading prototype based on a traceable two-tier identity structure, which combines a personal CA-based identity with anonymous addresses to enable appealings without centrally keeping transaction data, hence protecting privacy and regulating transactions simultaneously. We develop an off-chain transaction confirmation protocol and build an on-chain regulatory agency based on smart contracts. The separation of the on-chain part from the off-chain part makes it suitable to be deployed in many cryptocurrency system that supports smart contracts. Experiments show that all the transaction data can be stored by users themselves and the regulatory agency still works independently. A security analysis on the supervision smart contract is also made. We recommend this model to be an alternate to the traditional centralized electronic business, if privacy preserving and regulations are both considered.
Intelligent Classification Method of Low Occupancy Big Data Based on Grid Index

In order to enhance the load balance in the big data storage process and improve the storage efficiency, an intelligent classification method of low occupancy big data based on grid index is studied. A low occupancy big data classification platform was built, the infrastructure layer was designed using grid technology, grid basic services were provided through grid system management nodes and grid public service nodes, and grid application services were provided using local resource servers and enterprise grid application services. Based on each server node in the infrastructure layer, the basic management layer provides load forecasting, image backup, and other functional services. The application interface layer includes the interfaces required for the connection between the platform and each server node, and the advanced access layer provides the human-computer interaction interface for the operation of the platform. Finally, based on the obtained main structure, the depth confidence network is constructed by stacking several RBM layers, the new samples are expanded by adding adjacent values to obtain the mean value, and the depth confidence network is used to classify them. The experimental results show that the load of different virtual machines in the low occupancy big data storage process is less than 40%, and the load of each virtual machine is basically the same, indicating that this method can enhance the load balance in the data storage process and improve the storage efficiency.
Improving Impact and Dependency Analysis through Software Categorization Methods

Software requirements specifications serve as instructions for any software development engagement. These instructions are mostly written in natural language for ease of manual analysis and comprehension. Since natural language is inherently ambiguous, software requirements analysis plays a pivotal role in enhancing clarity during the software development life cycle. There are several methods of software requirements analysis. We focus on analysis methods which categorize requirements. We present a comparison of the performance of three common categorization techniques of software requirements documents, using three different datasets. We evaluate three bag of words models: count vectorization, term frequency - inverse document frequency (TF-IDF), and a word embeddings technique. We report the similarity of the categories obtained using cosine similarity as a measure of similarity between the requirements vectors produced by the different methods. Syntactic techniques outperformed semantic techniques for some datasets. These results suggest that syntactic techniques produce comparable categories to semantic techniques for some requirements categorization tasks.
Avatar Control System Use Based on Bioinformatics in E-Business Research of Entrepreneurs

This Avatars Control System use Based Bioinformatics in E-Business Research &amp; deals with issues related to the construction and development of the digital economy in one particular region -Republic of Armenia. This country is considered, the digital sector of which may become the largest in the country in the next five years. The digital revolution affects all areas of the Armenian economy. Prior to this, the Armenian economy was known as the mining industry, a food producer and a food. Today, according to statistics, Armenia's leading mining and energy companies are already one step ahead of their colleagues in the economically successful implementation of digital technology. Naturally, in such successes of digital transformations, there are key components. The authors hope that the examples of development and thought about the driving springs of these transformations, chosen in consideration of the interests of the development of the Russian E-Business Research of Entrepreneurs , can be interesting and useful for Russian enterprises that have started their digital transformations.
Introduction

Since the 1970s, the sustained optimization of microelectronics and the microcomputer has deeply changed and continually improved automation technology, in particular the scope of functionality and performance of machine tools. It thus became possible to create flexible, programmable control technology that could perform complex switching and control operations using software systems tailored specifically for the purpose.
An empirical evaluation of ensemble bagging-based model for authorship attribution on Twitter

Authorship Attribution (AA) of short texts like SMS, chat, social media posts has become a relevant study issue, adding new dimensions to this field. However, AA of Arabic Tweets is not well-investigated and left behind compared to longer texts such as ancient books, poems, news articles, or even similar short text like the fatwa (i.e., a legal decree in the religion of Islam). This paper presents the advantage of using a bagging ensemble model over a single learner model to increase the accuracy of AA of Arabic tweets. In doing so, we evaluated the performance of a bagging ensemble model using three state-of-the-art classification approaches as base classifiers, namely Naïve Bayesian (NB), Support Vector Machines (SVM), and Decision Trees (DT). According to the experiments conducted, the proposed bagging classifier that used the SVM algorithm as a base model achieved the highest accuracy rate (i.e., 95,03%) among the other classifiers. This accuracy is among the highest ever published in similar studies.
Internet Big Data Mining and Research on Intangible Cultural Heritage Activation with Smart Devices

Internet big data mining and the research on intangible cultural heritage activation with the smart devices is conducted in this paper. For the security concern, the blockchain model is then integrated. In the public chain, sharing refers to the supply of knowledge to all participants, and in the alliance chain, it reflects the provision of more in-depth research materials for researchers. The members of the alliance chain are composed of professionals and researchers in various levels of institutions, and the standards set by the core alliance members are used as the consensus mechanism. This process relies on the modern technologies such as database technology, artificial intelligence technology, mathematical statistics and the smart systems. The designed model is validated on the real data. The performance under different conditions have been tested.
Analysis of user experience during interaction with selected CMS platforms

The purpose of this study was assessing user experience while working with two popular CMS systems: WordPress and PrestaShop. The evaluation was done using a questionnaire and an eye tracking technique. Average task completion time, the number of fixations, the percentage of correctly completed tasks and the SUS index were used for comparisons. On the basis of the obtained results which, were collected during and after the users' interaction with a given system, it is difficult to clearly state which CMS proved to be better.
Decomposition

Software is complex, one of the most complex endeavors humankind has ever undertaken. When you first read the requirements document for a large-scale programming project, you may feel overwhelmed. That’s expected; the task is overwhelming! For this reason, large-scale programming projects typically begin with analysis.
Researching Machine Learning Methods for Diagnosing Women's Health

This article reviewed the existing methods for solving the problem (decision trees, neural network training), identified their advantages and disadvantages. The proposed method differs from the existing ones in that in order to identify the most significant signs affecting the assessment of the probability of getting a disease, the selection of signs was carried out on the basis of a genetic algorithm, as well as comparison and identification of effective methods of neural network learning. As a result of the comparison, the backpropagation Bayesian regularization method was chosen, since it showed the smallest recognition error with a relatively short training time. Thus, a neural network was trained to predict cervical cancer in women.