Reviewerly/exHarmony huggingface dataset:
Digital Technologies in the Training of Psychologists in Vladivostok

In the context of the information volumes continuous growth, the digitalization of education is considered a necessary tool for the modernization of the educational system. There is a pronounced specificity of training specialists in the various fields of science - natural, humanitarian, technical and exact. How does work differ in the different forms of training specialists - full-time and part-time. There are peculiarities, opportunities and traditions in every particular university. In this regard, the problem arises of the need to digitalize education, determining the measure, and sufficiency in each case. The functioning of the current digital educational environment is due to both the psychophysiological capabilities of the human body, moral and ethical principles. The purpose of the study is to identify the saturation of digital educational technologies in the process of full-time training of students-psychologists and conflictologists at FEFU. Research methods: problematization, survey, observation. The author examines two components of digitalization at the university - the learning process, and its organization and concludes the sufficiency of the information technologies use in the training of psychologists and conflictologists at FEFU. The author also draws attention to the active use of technical means in the educational process - the formalization of education, which manifests itself in a decrease of students’ motivation, loss of interest, involvement, “non-appropriation” of information. Prevents direct transfer of distance learning formats to full-time. Requests the need for informational support of teachers. Emphasizes attention to ensuring the high-quality functioning of technical means.
L’apprendimento linguistico cooperativo in contesti plurilingui Strategie dell’apprendente e dell’insegnante

This article takes into account results from two classroom research projects and analyses validity and effectiveness of dialogical cooperative techniques for plurilingual education. The first experimental study focuses on language learning strategies developed by pupils in plurilingual classrooms, such as translanguaging, repetition, avoidance and metalinguistic strategies, whereas the second study concerns teachers’ strategies in modifying and adapting their talk to support the language learning process of students.
Computer-Vision Based Expression Recognition in Classroom

Human emotional facial expressions play an important role in interpersonal relations. This is because humans demonstrate and convey a lot of evident information visually rather than verbally. Although humans recognize facial expressions virtually without effort or delay, reliable expression recognition by machine remains a challenge as of today. To automate the recognition of the emotional state, machines must be taught to understand facial gestures.This paper gives a survey of different techniques for recognizing facial expression. The abstract view of the proposed system is given here for recognizing the emotions with higher accuracy. It helps to detect the face from the input image using Haar-Cascade face detection algorithm and to evaluate the face and Emotion from Students Face using the SVM classifier.
A Design of Autonomous Remote Control Car Using Convolutional Neural Network and Haar-like Features Classifier

The autonomous vehicle is a type of vehicle that can drive safely without any human intervention. This safety is related to the vehicle's capability to keep moving on its track without disturbing the other lines, detect objects in front of it, and estimate the distance to that object to prevent an accident. However, only a few researchers develop autonomous vehicles that can follow the predetermined path, detect objects, and estimate the distance to said objects. In this research, the author wants to make an autonomous remote control car with those three features. This project develops an autonomous remote control car controlled by a convolutional neural network to keep the car in a track. The device has features to classify three object classes (i.e. pedestrian, car, and stop sign) using Haar-like classifier. Besides, the device can estimate the distance to the object by using pinhole imaging theory. The device takes images from a mobile phone attached to the car as its only input and processes the images in MATLAB2019a. The final device can follow the track with the accuracy ranging from 86.67% to 100.00% and classify three object classes with the accuracy ranging from 53.33% to 86.67%. Besides, the device can estimate the object distance with average error equals to 2.43 cm
Understanding Urban Devotion through the Eyes of an Observer

If and how an individual’s social, economic, and cultural backgrounds affect their perception of the built environment, is a fundamental problem for architects, anthropologists, historians, and urban planners alike. Similar factors affect an individual’s religious beliefs and tendencies. Our research addresses the intersection of personal background and perception of sacred space by examining people’s responses to a virtual replica of a “madonella,” a street shrine in Rome. The shrine was virtually recreated using photogrammetry. It was optimized for user studies employing VIVE Pro Eye. The study looked at the gaze behavior of 24 participants and compared their gaze patterns with demographic background and social-communal responses. The study finds that certain religious habits of an individual could predict their fixational features, including the number and total duration of fixations, on pivotal areas of interest in the shrine environment (even though these areas were placed outside of immediate sight). These results are a promising start to our ongoing study of the perception and received meaning of sacred space.
Real-Time Coarse-to-Fine Depth Estimation on Stereo Endoscopic Images With Self-Supervised Learning

Fast and accurate depth estimation is an essential task in computer-assisted surgery and robotics, especially for endoscopic and microscopic procedures. We propose a real-time stereo matching model using a staged, coarse-to-fine architecture to estimate disparity from medical stereo camera data with self-supervised learning. Our model processes images with a resolution of $1280 \times 1024$ pixels beyond 60 fps, with similar accuracy to the semi-global matching algorithm, and does not require any ground truth depth for training. We evaluated our model on two stereo endoscopic datasets from the literature. A mean absolute error below 1.5 mm and root mean square error below 1.9 mm were identified.
Single-Core Multiscale Residual Network for the Super Resolution of Liquid Metal Specimen Images

In a gravity-free or microgravity environment, liquid metals without crystalline nuclei achieve a deep undercooling state. The resulting melts exhibit unique properties, and the research of this phenomenon is critical for exploring new metastable materials. Owing to the rapid crystallization rates of deeply undercooled liquid metal droplets, as well as cost concerns, experimental systems meant for the study of liquid metal specimens usually use low-resolution, high-framerate, high-speed cameras, which result in low-resolution photographs. To facilitate subsequent studies by material scientists, it is necessary to use super-resolution techniques to increase the resolution of these photographs. However, existing super-resolution algorithms cannot quickly and accurately restore the details contained in images of deeply undercooled liquid metal specimens. To address this problem, we propose the single-core multiscale residual network (SCMSRN) algorithm for photographic images of liquid metal specimens. In this model, multiple cascaded filters are used to obtain feature information, and the multiscale features are then fused by a residual network. Compared to existing state-of-the-art artificial neural network super-resolution algorithms, such as SRCNN, VDSR and MSRN, our model was able to achieve higher PSNR and SSIM scores and reduce network size and training time.
Investigation and Improvement of Distributed Differential Evolution Algorithm Cloudde

As a kind of new emerging optimization technology, distributed evolutionary computation (DEC) algorithms have fast developed in recent years. The DEC algorithms, which make use of multiple computers or resources to enhance the optimization capabilities of algorithms, have received widespread attention. Among the DEC algorithms, a cloud-based distributed differential evolution (Cloudde) algorithm has shown excellent performance. The Cloudde has a double-layered heterogeneous distribution structure, which can run different differential evolution (DE) variants with various parameters and/or operators in different populations. Moreover, the Cloudde can adaptively migrate individuals among the populations to make best use of the computational resources among multiple populations. However, since the proposal of the Cloudde, there are still some questions remained to be discussed. The first is how to choose the basic DE algorithms to form various DE variants (i.e., the various populations). The second is how to evaluate the performance of different populations of individuals hence we can rank the populations. The third is how to design an efficient migration strategy to make full use of computing resources among multiple populations. This paper makes investigation on these issues and studies the performance of Cloudde variants with various configurations for these three aspects. The experimental results in this paper are useful for researchers who want to conduct further research on Cloulde and other related DEC algorithms. Moreover, based on the investigation results, an improved Cloudde (I-Cloudde) is proposed and the experimental results show the superiority of I-Cloudde when compared with Cloudde.
Statistical Model of a Thin Film Formation

This paper presents a model of a thin film formation process of an amorphous alloy as a sequential procedure when a conditional unit of substance is randomly thrown onto a substrate at each next step. The islands of a precipitant are generated on the substrate with an increase of number of steps (density defects of substance). We determine the probability distribution of an island area, which shows the maximum informational entropy. An algorithm for computing estimates of parameters of this distribution is obtained. The results of processing experimental data are presented. We demonstrate that the proposed distribution is more consistent with the experimental data than the Pareto distribution.
Comparison of options and optimization of sorting and collection of waste at demolition sites during renovation

The most famous and relevant innovative type of construction reorganization of urban areas is renovation that is defined in the directive documents of the Moscow Government as the demolition and dismantling of buildings in conditionally autonomous urban areas, defined by the well-known Resolution of the Moscow Government and legal documents explaining it, without taking into account the degree of physical or moral aging of these buildings and structures with the aim of erecting multi-storey residential buildings with increased comfort living in these territories. Thus, a certain meaning of renovation makes it possible and necessary to purposefully study and model the life cycle of this socio-technical and organizational-technological construction process, which has certain formal differences from the well-known model of the traditional life cycle of a construction facility and the sequence of stages in the implementation of this life cycle. The difference in the configuration of the life cycle of a construction facility in the traditional approach and in the innovative one in the conditions of renovation has been determined. In the study, this difference highlights the stage of demolition and dismantling, with which the problem of disposal of construction waste is inextricably linked. In this regard, an urgent task of comparing options and optimization of sorting and collection of waste at demolition and dismantling facilities at the first stage of renovationarises. Three options for solving this problem and projects for the production of work have been developed for each of the three options for the demolition and dismantling of buildings. The most rational is the second option.
Principles of Creating an Integrated Development Environment for Educational Computer Systems

The various questions of creation of integrated development environment for computer training systems are considered in the given paper. The information technologies that can be used for creation of the integrated development environment are described. The different didactic aspects of realization of such systems are analyzed. The ways to improve the efficiency and quality of learning process with computer training systems for distance education are pointed.
On a parallel spark workflow for frequent itemset mining based on array prefix‐tree

Abstract Extracting frequent itemsets from datasets is an important problem in data mining, for which several mining methods including FP‐Growth have been proposed. FP‐Growth is a classical frequent itemset mining method, which generates pattern databases without candidates. Many improvements have been made in the literature due to the high time complexity and memory usage of FP‐Growth. However, most of them still suffer from performance issues on large datasets. In this paper, we design an auxiliary structure, Array Prefix‐Tree (AP‐Tree), and propose a new algorithm, Array Prefix‐Tree Growth (APT‐Growth), which is further parallelized as a Spark workflow, referred to as PAPT‐Growth. Based on a density threshold, we incorporate an adaptive algorithm selection process into PAPT‐Growth to ensure its running time performance. We conduct extensive experiments on different thresholds and multiple datasets, and experimental results show the performance superiority of PAPT‐Growth in comparison with several state‐of‐the‐art methods such as PFP, YAFIM, and DFPS. The analysis on density reveals a changing point, which justifies the necessity and validity of adaptive algorithm selection.
Steganographic Resources of FPGA-based Systems for Approximate Data Processing

The program code controls the operation of FPGA (Field Programmable Gate Arrays) chips.Through the use of program code it is possible to interfere with the operation of these chips.Operational monitoring (integrity, authenticity, distribution paths) of program code is one of the most effective means of preventing illegal interference with FPGA-based systems.Traditional methods of monitoring program code have the problem of storing control data.A promising approach is to embed the monitoring data into the FPGA program code in a steganographic manner.In this case, the fact of the availability of monitoring data and the fact of monitoring is not obvious to an external observer.There are methods for steganographic embedding of additional data into the FPGA program code.Methods of this kind are based on equivalent transformations of the program code.This paper proposes and experimentally substantiates the presence in the FPGA program code of new resources for steganographic data embedding.These resources arise in the programming code of FPGA systems that perform approximate data processing.In the paper, inessential LUT (Look Up Table ) FPGA units are classified as such resources.These units are involved in the calculation of the least significant (discarded during rounding) bits of the result and do not participate in the calculation of the most significant (retained) bits.Also inessential bits of the program code of LUTs are referred to new resources.These bits are bits that are not accessed during the calculation of the result of the operation.The experiments performed have shown the possibility of organizing stego containers in the FPGA program code using the identified additional resources.The volume of such stego containers has been estimated.It is shown that this volume is sufficient for storing monitoring data, which are used for operational monitoring of the FPGA program code.
Visual Paths in Creation of User-Oriented Security Protocols

In modern security systems very often are used personal features, what allow to define user-oriented security protocols. Among such procedures we can consider approaches based on application of eye movements or blinking actions analyzed using eye tracking sensors. In this paper will be described such solutions dedicated for security purposes, and creation of personal security locks using visual paths. In particular new procedures will be defined, which will be based on visual cognitive skills, and possibilities of defining semantic commands during evaluation of visual patterns.
Semi-supervised Clustering and Classification

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TEE-based Privacy-Preserve in Collaborative Traffic Policy Compilation for Programmable Devices

Maintaining the integrity of network devices policy across a different organization is very challenging since the devices are shared for multiple traffic forwarding purposes, including public Internet access. An organization's administrator can put unnecessary (i.e., wrong) policy that may leak the private traffic between the organizations to a public network. It can be avoided by exchanging the network traffic policy between the organizations but keeping the confidentiality of the policies among them (i.e., to avoid honest-but-curious adversary) is very challenging. Furthermore, there is also no guarantee that the policy is properly enforced into the network device. An administrator can intentionally put malicious policies that allow the attacker to enter the organization's network (i.e., malicious adversary). This paper proposed a cross-organization network traffic policy compilation that preserves the policy privacy and ensures its enforcement to the network devices. It utilizes a trusted execution environment (TEE) to compile the high-level traffic policies into low-level rules for the programmable network device. Then, the rules are easily pushed and optimized by using hardware programming abstraction.
Remote Usage Data Collection and Analysis for Mobile Accessibility Applications

Conducting experimental research in the field of mobile accessibility and assistive technologies is difficult due to the low numerosity of the representative population. To address this issue, a possible approach is remote collection and analysis of usage data through publicly available mobile applications. This method is useful for performing large scale evaluations and acquiring knowledge of the target population and their behavior. The acquired knowledge can be used to advance future research and to improve the mobile applications themselves.
OPPORTUNITIES FOR USING CLOUD SERVERS IN EDUCATION

The article discusses the possibility of implementing cloud technologies in the edu cational process. The principle of operation of a cloud server, its functionality, as well as the ad vantages of using cloud services are described
Frequencies Wave Sound Particle Swarm Optimisation (FPSO)

PSO is a remarkable tool for solving several optimisation problems, like global optimisation and many real-life problems. It generally explores global optimal solution via exploiting the particle – swarm’s memory. Its limited properties on objective function’s continuity along with the search space and its potentiality in adapting dynamic environment make the PSO an important meta-heuristic method. PSO has an inherent tendency of trapping at local optimum which affects the convergence prematurely, when trying to solve difficult problems. This work proposed a modified version of PSO called as FPSO, where frequency-wave-sound is employed to exit from any encountered local optimum; if it is not the optimal solution. This FPSO mimics the characteristics of the waves by using three parameters, namely amplitude, frequency and wavelength. FPSO is then compared and analysed with other renowned algorithms like conventional PSO, Grey Wolf Optimisation (GOW), Multi-Verse Optimiser (MVO), Moth-Flame Optimisation (SL-PSO), Sine Cosine Algorithm (PPSO) and Butterfly Optimisation Algorithm (BOA) on 23 bench marking test bed functions. The performance is evaluated using various measures including trajectory, search history, average fitness solution and best optimisation-solution. The obtained results show that the FPSO algorithm beats other metaheuristic algorithms and confirmed its better performance on 2-dimensional test functions.
An Efficient Shape Adaptive Techniques for the Digital Image Denoising

The existing state-of-the-art in image denoising is reflected by patch-based approaches such as Block Matching and 3D collaborative filtering (BM3D) algorithms. BM3D, however, still suffers from performance degradation in smooth areas as well as loss of image information, especially in the presence of high noise levels. Integrating shape adaptive techniques with BM3D increases the denoising effect, including the denoted image's visual quality; and retains image information as well. We proposed a system in this study that generates multiple images using different shapes. These images were aggregated for both stages in BM3D at the pixel or patch levels and, when properly aggregated, resulted in an average of 1.15 dB better denoising output than BM3D.
Research on battlefield target recognition method based on machine learning

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An End to End Method for Scene Text Detection and Recognition

Scene text detection and recognition is a hot research area. In this paper, we introduce an end-to-end text detection and recognition method. In the text detection part, we use the resnet50 network to extract features, and we use different data sets to detect the text, and evaluate the detection results under different models. At the same time, text correction and text recognition are integrated with text detection to complete detection, correction and recognition, in order to achieve end-to-end text recognition. Experiments show that the detection and recognition results of this method are good and fast, and it is an efficient and accurate scene text detection and recognition device.