Reviewerly/exHarmony huggingface dataset:
COVID-19 Pandemic’s Impact on E-learning Platforms: A Survey

After the announcement of the World Health Organization (WHO) COVID-19 (Corona Virus Disease) as a global pandemic, many governments start following some limitations by following a lockdown or applying a curfew according to the number of cases recorded in each country. Many industries and organizations start to come up with alternative ways to accomplish tasks after the sudden lockdown either by splitting teams into smaller groups or by working from home (if this is applicable) according to the organization's policy. Like many other aspects of everyday life, COVID-19 had a serious impact on students, instructors, and all educational organizations worldwide. The pandemic caused schools, colleges and universities across the globe to shut down their campuses so that students could follow social distancing measures (Muhammad Adnan1and Kainat Anwar1, Online learning amid the COVID-19 pandemic: Students perspectives, Journal of Pedagogical Research - June 2020). Because of the pandemic, many possible solutions and alternatives were suggested to solve the dilemma of the education system amidst the global pandemic (Christian Paul Chua, Lawrence Dale Valencia, The Role of Artificial Intelligence in Education Amidst of the COVID-19 Pandemic, conference paper, August 2020.). Schools and universities start to work online using open tools technologies to be able to deliver the course contents including the course objectives and the intended learning outcomes of each course. Many online educational platforms were available to use by universities. This paper discusses some of the educational platforms and the impact of COVID-19 on these platforms.
The PC-Algorithm of the Algebraic Bayesian Network Secondary Structure Training

Algebraic Bayesian networks and Bayesian belief networks are one of the probabilistic graphical models. One of the main tasks which need to be solved during the networks’ handling is the model structure training. This paper is dedicated to the automation of this process for algebraic Bayesian networks. This work relates to the PC-algorithm for algebraic Bayesian network secondary structure training. The algorithm is based on the PC-algorithm for Belief Bayesian networks training. The algorithm pseudo-code and usage example are described. The provided algorithm helps investigate the full-automated machine learning of algebraic Bayesian networks. Earlier, the structure was provided by experts.
INTERACTION OF KINDERGARTEN AND FAMILY IN THE MATHEMATICAL DEVELOPMENT OF PRESCHOOLERS

The relevance of the study of the interaction of kindergarten and family in the mathematical development of preschoolers is due to the fact that the unification of the family and kindergarten in one educational space implies the cooperation of teachers and parents. The mathematical development of preschool children will be successful if the teacher interacts with parents. Only cooperation between the kindergarten and the family can ensure the effectiveness of educational activities in the mathematical development of a preschool child. It is necessary to select the forms and methods of organizing this interaction, taking into account the age characteristics of children and the settings of the Federal State Educational Standard (complex tasks, integration of different types of activities, individualization of the child's mathematical development, etc.) in the mathematical development of preschool children. The work of a preschool teacher on the mathematical development of children is particularly time-consuming and requires a lot of pedagogical attention. Mathematical knowledge of preschoolers, such as counting down to ten in ascending and descending order; the ability to recognize numbers in a row and randomly, quantitative (one, two, three...) and ordinal (first, second, third...); to study and present the basic geometric shapes (triangle, square, circle, and others); the basic forms of measurement: the ability to measure length, volume, width, height using a conditional measurement measure; comparison of objects: heavier-lighter, larger-smaller, wider-narrower, higher-lower; the ability to navigate in space and time, etc., will later be the basis for school education.
Protecting Sensitive Data in Web of Data

Over the last few years, the web of data has been increased. It permitted sharing and an interconnection of a large data in several domains and it keeps increasing day by day. However, certain sectors such as health, economic, government, etc., have a limited participation, due to the confidential nature of data. In order to develop the web of data, taking in consideration the confidentiality and the utility of data, we propose in this paper a framework of confidentiality preservation and sharing of the linked data. Our approach provides the means to generate privacy policies that specify the sensitive data to be protected in an Resource Description Framework (RDF) triple. Subsequently the application of the policy on the graph will allow their replacement by their encryption, thus ensuring a balance between confidentiality and utility of data.
CONTRIBUTION TO IMPROVING THE PERFORMANCES OF ELECTRO-MECHANICAL ACTUATORS BASED ON SQUIRREL CAGE INDUCTION MOTOR

In recent years, digital images are in use in a wide range of applications and for multiple purposes. There are many types of image forgery, the most important and popular type is called copy move forgery, which uses the same image in the process of forgery. Copy–Move and cut-paste forgeries are common type of tampering, where part of an image is copied and pasted to another place to add a new object or to hide/remove an object already present. Most of the copy move forgery detection methods are interest point-based, where the significant key points are extracted and compared to each other to locate similar regions and another category of method called block-based, where the image is divided into overlapping blocks and then features are extracted and compared to find similar regions. In this paper a new hybrid approach is proposed that exploits the block based and the key point based approach for copy-move forgery detection hence it is considered as “hybrid” method. In this paper two approaches have been presented, one with regular regions and another one with irregular image regions for forensic detection of copy move forger and their performance is analyzed and reported. In the first approach, the key points are extracted from the image, and a set of connected triangles are built onto these points to model the foreground regions. Then the triangle region matching is done using their inner angles, color information and area of the triangle regions to locate the copy m
AI-based content filtering system using an age prediction algorithm

Computer vision mainly focuses on the automatic extraction, analysis, and understanding of useful information from a single image or video. On the other hand, authenticity is emerging as one of the primary requirements in today's world by developing a system for computer vision complexity. Generally, two robust techniques such as age estimation and face recognition are required to maintain authenticity. In reality, fraud and scams are getting increased, so here this paper has proposed a new combined model for face recognition and age prediction. Face recognition has been implemented and presented in this paper by using a Deep Neural Network. The authenticity problem can be handled by using either facial recognition or age prediction alone; this study has presented a method that employs both of them together to enhance the system's robustness. So, first, this model detects the person's face, and then it predicts the person's age. If the individual is eligible to view the information or perform a task, their access will be limited; otherwise, their access will be restricted. So it helps to solve two difficulties in this case: the person's identification cannot be faked, and their age is also confirmed by the system. (CNN for the face, and mention technique for the age.)
DEVELOPMENT OF FUNDAMENTAL KNOWLEDGE AND COMPETENCIES BASED ON PBL ACTIVITIES

It is stated that the level of project based thinking and group-learning activity, including the use of synergistic approaches is still poorly developed in higher education system. However, the active involvement of students, teachers and specialists in the implementation of large projects should allow mastering new knowledge and skills, including the ability of teamwork, system thinking and leadership. At present, when evaluating the competencies of a specialist, it is necessary to pay attention to his ability to project thinking.
The Computer Measurement Method Research on Shaft’s Size by the Platform of the Optoelectronic Imaging

The measurement of shaft’s size by computer is the significant basis of the automatic industrial measurement and detection, this paper establishes the optoelectronic imaging instrument of the shaft, and the image data of shaft neck is acquired by the industrial camera. In the part of the image pre-processing, we propose the enhancement algorithm of edge line based on the binarization of the gradient operator to enhance the image’s edge line. After the selection of the edge lines by the user in the local region, the paper proposes a measurement method based on the paralleled edge line’s fitting and computing, which can measure the diameter of the shaft neck with the accuracy of sub-pixel. This method contains some key techniques such as the modification of the local region chosen by the user, the selection of the sample points, the fitting of the edge line, the computing method of the diameter size and so on. With the comparison of the conventional line detecting algorithm such as Hough transformation, this proposed algorithm can improve the measurement accuracy of the geometry size. In the part of the experimental results’ exhibition, we use the proposed algorithm to measure another shaft neck’s parameter and verify that the algorithm has the robust characteristic, with the comparison of the optoelectronic measurement result and the artificial measurement result, the reliability of the algorithm is verified.
Auxiliary Guidance System of Physical Training Based on Action Recognition and Motion Detection Model

Auxiliary guidance system of the physical training based on the action recognition and motion detection model is designed and implemented in this manuscript. Based on the model of the complex scene understanding and motion data structure optimization, according to the distribution probability of the entire contour shape, the spatial layout model of the three-dimensional reconstructed image of the human body movement is obtained, and the average pixel set of the detailed feature distribution of the human body movement is obtained. At this time, it is judged that the lens is shaken, and then the global motion parameters of the background point are estimated. With these models, the framework is designed. I order to test the performance; the model is applied into the training system.
Mathematical Model of Soil and Groundwater Contamination by Nitrogen Dioxide Taking Into Account the Factors Influencing the Diffusion Coefficient

The article considers the task of soil and groundwater contamination by harmful emissions of nitrogen dioxide from motor vehicles. The harmful emission concentrations measurement a mobile complex Sniffer4D Hyper-local Air Quality Analyzer is used. Based on these results, a mathematical model of diffusion of this harmful substance in the upper layers of soil and water resources taking into account the factors influencing the diffusion coefficient is proposed. As a result of the computations, the distribution of concentrations of soil and water contamination in the neighborhood of separate observation points has been built. Based on this model, a series of experiments investigated the process of diffusion of this harmful substance in the upper layers of soil for different soil moisture and for two types of soils. The obtained research results are fully consistent with the practical and theoretical results on the diffusion of gases into soils taking into account the factors influencing the diffusion coefficient.
Smart Contracts for User Registration on Ethereum Technology: Systematic Literature Review

The security and application of new technologies will not always be easy to integrate them, due to different characteristics, especially the benefits they will offer and their cost, among others; due to this, the search for security will always be a difficult point to achieve. In the present systematic literature review (SLR), the way to develop smart contracts within the user registration module of a system is sought, in addition to detecting which are the tools that will provide a better application of the contracts. Barbara Kitchenham methodology was applied with all its phases and stages, which defined Remix IDE as one of the most used tools for the development, testing and debugging of smart contracts. This review also provided us with guidelines for the development of contracts in different environments such as cloud, fog and storage systems.
Coding and Quality Evaluation of Affordable 6DoF Video Content

We present a coding and quality evaluation method for Six Degrees of Freedom (6DoF) video content. Firstly, the 6DoF video content is generated using an affordable capturing setup, and represented as the commonly used 6DoF format 3-D mesh. To overcome the difficulty in exploiting the temporal redundancy in mesh with different connectivity at every frame, we transform the mesh sequence into colored point clouds for compression, which saves the bit budget for connectivity while keeping the photo-realistic characteristics of the 3-D object. We compress the point cloud sequence using MPEG video-based point cloud compression structure, which can achieve the bit rate close to common media format. We also conduct projection-based and point-based quality evaluation to examine the compression distortion effect. Particularly, we employ PCA to find the representative viewing perspective for projection. We will release some data set and source codes to foster further research in this area.
Phase Extraction of Electronic Speckle Interference Fringe Image based on Convolutional Neural Network

Electronic speckle pattern interferometry (ESPI) is a kind of full-field, non-contact nondestructive measurement technology, which is suitable for deformation measurement and nondestructive testing of optical rough surface. Fringe phase is an important information of interference fringe image, and the accuracy of phase estimation plays an important role in the extraction of fringe information. However, because of the discontinuity of fringe image and the influence of noise, phase extraction has always been a challenging problem. In this paper, we propose a fringe image phase extraction technique based on convolutional neural network, and experimental results show that this method has a good effect on phase estimation of the interference fringe.
A Size Adaptive Neural Network for Nucleus Segmentation

The analysis of nucleus shape is useful for the pathological diagnosis or prognosis. The shape of the nucleus in the digital pathological images from different sources may vary greatly. Although, Convolutional Neural Network (CNN) has proven its success in automatic nucleus segmentation, segmentation performance may be reduced due to varies in nucleus size. In this paper, we proposed a CNN based size adaptive nucleus segmentation method. This method adapts the CNN model to image clusters with similar estimated nucleus size to increase its nucleus segmentation performance. Furthermore our method can automatically segment nuclei and the only parameter that needs to be set is the number of clusters. We compared this method to several existing methods on two datasets from the nucleus segmentation challenge. The proposed method achieved satisfactory results with mean Intersection-over-Union (IoU) 0.718 and 0.798 and mean F1-score 0.780 and 0.864. In addition, compared with the method without size adaptation, the proposed method improves the segmentation performance and is easy to implement.
Online Scene Text Tracking with Spatial-Temporal Relation

Scene texts in video are not fixed in color, size, format and are easily confused with the background, which imposes significant challenges in video scene text tracking. The trajectories are often be fragmented caused by these. Most tracking methods focus on the matching of the appearance features and the temporal information across frames, treating each text as a separate object. However, the relations among all texts are also important cues. In this paper, we propose a novel online video scene text tracking approach with the spatial-temporal relation module utilizing multiple cues, i.e. appearance, geometry and temporal. The spatial-temporal relation module enhances appearance features by modeling the relations between texts with each other in the same frame, which can avoid the influence of bad detection results, and track text stably and consistently. We achieved more tracked texts and more complete trajectories on IC15 with the spatial-temporal relation module.
Синтез техник игры на бас-гитаре в исполнительской практике (середина 1980-х годов – начало XXI века)

This article discusses the processes of synthesis of existing techniques of playing the bass guitar used in professional practice (mid 1980s – the beginning of the XXI century). The set of techniques of sound production is studied on the basis of the work of the leading bass guitarists of our time, who had the greatest influence on the evolution of performing on the bass guitar. The author pays special attention to the design features of stringed bass instruments in the context of the formation of innovative ways of sound production.
Exploiting Time Dynamics for One-Class and Open-Set Anomaly Detection

In this paper we describe and compare multiple one-class anomaly detection methods for Cyber-Physical Systems (CPS) that can be trained with data collected only during normal behaviors. We also consider the problem of detecting which group of sensors is most affected by the anomalous situation solving an open-set classification task. The proposed methods are domain independent and are based on a temporal analysis of data collected by the system. More specifically, we use different flavours of deep learning architectures, including recurrent neural networks (RNN), temporal convolutional networks (TCN), and autoencoders. Experimental results are conducted in three different scenarios with publicly available datasets: social robots, autonomous boats and water treatment plants (SWaT dataset). Quantitative results on these datasets show that our approach achieves comparable results with respect to state of the art approaches and promising results for open-set classification.
A Densely Connected Neural Network Based on SSD for Multiscale SAR Ship Detection

Synthetic Aperture Radar (SAR) ship detection plays an increasingly important role in marine applications. With the development of deep learning methods, many object detection algorithms based on deep neural networks have emerged. However, these methods only obtain detection scores from a high-level convolutional feature map, ignoring the multiscale characteristic of objects. To solve the above problem, this paper proposes a densely connected neural network based on SSD. Theoretically, the high-level convolutional feature maps contain more semantic information, while the low-level convolutional feature maps collect the detailed features of the image. Thus, we combine the high- and low- level feature maps by adding dense connections to SSD, achieving the multiscale and multiscene SAR ship detection. In addition, considering the characteristics of SAR images, we discard the image augmentation strategy and use K-means clustering to design the multiscale candidate bounding boxes, improving the object detection accuracy. Finally, experiments carried out on SAR Ship Detection Dataset (SSDD) verify that the proposed method achieves satisfactory performance on multiscale and multiscene SAR ship detection.
Analysis on the Applicability of High-resolution Remote Sensing Images for Highway Construction

Abstract This paper first analyzes and determines the scope and contents of highway engineering environmental supervision from the aspects of highway engineering composition, construction stage division, impact environmental factors and their characteristics, environmental protection requirements, etc., combines with the characteristics of high-resolution remote sensing images, proposes the construction process and supervision precision requirements for the highway engineering in which environment protection control can be carried out by using satellite remote-sensing images and UAV (unmanned aerial vehicle) remote-sensing images. Through the environmental investigation experiment of high-resolution satellite remote sensing images commonly used in typical road sections, the satellite remote-sensing images meeting the highway engineering environmental supervision requirements are selected through comparative analysis from the perspectives of spatial resolution, revisit cycle, spectral waveband characteristics, environmental supervision precision requirements and image purchase cost rationality.
Design and Implementation of Trajectory Planning Algorithm for SCARA 4-DOF Manipulator

In recent years, with the rapid development of automobile, steel, construction industry, production efficiency has become the key to the development of these industries, industrial manipulator with its high speed, high precision, long labor time plays a huge role. In view of the important role of trajectory planning in the efficient and stable operation of SCARA manipulator, this paper studies the trajectory planning algorithm with a four degree of freedom manipulator as the control object, and uses trajectory planning to effectively improve the control accuracy and work efficiency of the manipulator.
Feat ures of continuing formati on of stochastic cult ure of schoolchildren in the conditions of dista nce learning

The article describes the essence of continuity in the formation of a stochastic culture of students in the conditions of distance learning. The author’s definition of the concept “continuity in the formation of stochastic culture of students of a general education school in the conditions of distance learning” is given. There are the basic conditions of a teacher’s possession of the highest level of stochastic culture, due to which the formation of an integral personal quality, which is a generalized indicator of stochastic competence, takes place in schoolchildren. Modern information and communication technologies used in a series of developed distance classes in statistics, combinatorics and probability theory for grade 11 students are proposed. These ICT contribute to the successive formation of elements of stochastic culture in the conditions of distance learning. The article emphasizes that the use of ICT in teaching stochastics is an effective methodological tool, through which significant results can be achieved in the educational process.
Deep Learning in Image Recognition for Easy Sketching Techniques

Inventing learning algorithms and developing information systems mean that the advancement of AI is feasible in many areas due to the growth in the volume of data. This paper discusses the learning models technique of an in-depth sketch classifier. For a basic approach, we use the depth convolution layer and use the deep neural network. The results would reveal that around one-fifth of the equation has been changed. To train and validate the network, we are using Google Quick Dataset; it can have 98% precision in ten categories and 85% precision in ten categories and for 100 types, accuracy can be improved by STM32F429I showing Production Board for Discovery. The software is capable of applying the grouping of sketches in real time.