Reviewerly/exHarmony huggingface dataset:
Online Learning in the Context of Pandemic in Russia

The urgent transfer of education in Russia to a distance format in the context of a pandemic has significant differences from a properly planned online learning based on wide-scale open online courses. Educational organizations that are forced to work with students remotely in order to reduce the risks of the spread of coronavirus should be aware of this difference when assessing the effectiveness of so-called "online learning" using distance learning technologies. Such a drastic measure was forced and urgent. Not all universities were ready for this radical restructuring of the educational process based on objectively different levels of development of information infrastructure, provision of disciplines with electronic educational resources, and readiness of teachers to use digital platforms and services in the educational process. This article analyzes the capabilities of online educational platforms and the opinions of students and teachers, who teach and study online in conditions of the pandemic.
The Effect of the Augmented Reality Applications in Science Class on Students’ 21st Century Skills and Basic Skills

This study investigates augmented reality (AR) applications on 21st-century skills and basic skills of elementary school students. The research method is determined as a mixed method. The sample group of the research consisted of 62 randomly selected elementary school 4th-grade students. In the experimental group, courses were taught using AR, and in the control group, the lessons were taught with activities included in the science class. In the research, the 21st-century learning and innovation scale and basic skills scale was used as a quantitative data collection tool, and semi-structured interview and researcher's journal were used as a qualitative data collection tool. According to the results, the study of science using augmented reality applications has positively affected the skills of elementary school 4th-grade students. The qualitative results of the research concluded that students' creativity and innovation, critical thinking, making inferences, problem-solving, collaboration, and communication skills developed. During the interviews with students and the classroom teacher, they stated that the AR application makes information more permanent by embodying the information in their minds. The lessons are fun and exciting; their interest in technology is increased and should be used in other lessons.
The Origin of Eight Trigrams: Explaining the Mechanism of Soil Response to Temperature Change

The Eight Trigrams has held a high position in the Eastern culture, but its origin of has been a mystery for thousands of years, thus it has not yet entered the scientific system. This research reveals the possible origin of the Eight Trigrams and the purpose of drawing the hexagrams. Research has shown that due to changes in the solar radiation there exist annual and diurnal soil temperature variable zones (STVZs). The heat conducts and interactions between the double-layer also form two different statuses of warming and cooling. In this paper the hydrometeorological factors such as temperature, relative humidity, absolute humidity and earth-air pressure were monitored or calculated in the STVZs. If the anural/diurnal STVZ is divided into three levels, and we associate soil cooling processes (Yin) with “--” and warming processes (Yang) with “—”. It is not hard to find that the distribution of soil status in soil profile at different times and locations clearly reflects the soil response to temperature change and reveals movement mechanism of earth-air and water. Furthermore, this model can be seen to be the source of the Eight Trigrams in the Yi-jing. The double-layer structure of the soil constitutes the foundation of the double trigrams. The Yin and Yang processes both form the primary driving force for phreatic water moves upward. Fu Xi invented the method of hou-qi, in which the monitoring of earth-air pressure in the closed system and applying Yin-Yang to draw Eight Trigrams and analysis of spatiotemporal status changes in the soil, which was then used to enact a calendar used as a guide to time for farming usage. The Yin-Yang and Eight Trigrams perfectly explain the response of earth-air to solar terms and mechanisms of water vertical cycle on daily/yearly timescales.
EDUCATION IN THE ERA OF THE NATIONAL TECHNOLOGICAL INITIATIVE: ALIGNING THE GOALS OF THE PARTICIPANTS IN EDUCATIONAL RELATIONS

Currently, the study of the factors that influence the readiness of participants in educational relations to learn and develop in new technological conditions is acquiring special relevance. In this regard, in this article, the authors analyze the factors influencing the goal-setting of participants in educational relations. In the 2020-2021 academic year, a computer survey was conducted in the Udmurt Republic, during which the opinions of participants in educational relations about goal-setting in the “era of the National Technological Initiative” were analyzed. The interviews were attended by 182 schoolchildren, parents of primary and secondary school students - 174 people, and 154 subject teachers. The article presents the results of answers to questions about the awareness of the markets of the National Technological Initiative and the willingness to implement them in the life. The awareness of schoolchildren about the technologies of the National Technological Initiative is higher than that of parents and teachers. The tendencies of the coordination of the goals of the participants in educational relations were identified, which ensure the readiness of graduates of a general educational organization to solve the problems of the National Technological Initiative. A positive relationship was revealed between the level of teachers' readiness to introduce innovations and the level of parents' awareness of NTI technologies. And there is a positive relationship between the readiness of schoolchildren to implement the technologies of the National Technological Initiative in their lives and the willingness of parents to use the technologies of the National Technological Initiative. The results of this study can be useful for further studying the goal-setting of participants in educational relations, as well as teachers and parents interested in the technologies of the National Technological Initiative.
The Main Factors Influencing the Socialization of Students in PostPandemic Period

Successful reform of the higher education system in the context of COVID-19, both in a single country and in the world in the context of its development toward democratization, requires updated approaches to assessing the results of socialization of various categories of young people, and especially students. Since student age is a period of active formation of the inner need of a person to correlate his own aspirations with the interests of society, it is sensitive for the assimilation of social experience, socially significant activities, and the formation of an active creative personality. Higher education in the context of COVID-19 faces an important task - to ensure the education of "vital and socially competent person who can make independent choices and make responsible decisions in various life situations" the formation of their own motives and interests. It is important how much the student as a subject of socialization, independent in the information space, what is the level of his social competence, how quickly he chooses the field of activity in which he can achieve high professionalism. The article deals with the problem of socialization of student youth in modern conditions of a higher educational institution, associated with the processes of development and education of the individual. The features, stages and socio-pedagogical conditions of successful socialization of students in the context of COVID-19 are revealed.
Removal of orange G via environmentally friendly GO@Fe3O4-COOH@NH2-MIL-101(Fe) nanocomposite

Attractive environmentally friendly magnetic metal-organic framework (MOF) nanocomposite GO@Fe 3 O 4 -COOH@NH 2 -MIL-101(Fe) was synthesized by a solvothermal method and characterized by X-ray diffraction, Fourier transform infrared, scanning electron microscopy, Brunauer-Emmett-Teller, and vibrating sample magnetometer, and used as an effective adsorbent for the removal of orange G from aqueous solution.The various operating parameters that effect the adsorption process, such as adsorbent dosage (0.01-0.2 g), pH (1.0-10.0),initial concentration (50-400 mg L -1 ), and temperature (298.15-318.15K) were studied.The percent removal of orange G increases with the increment of the adsorbent dosage and decrease of the initial concentration, and the optimum pH is 6.0.The Redlich-Peterson model can be favorably applied to describe the adsorption equilibrium data, indicating that the surfaces of GO@Fe 3 O 4 -COOH@NH 2 -MIL-101(Fe) nanocomposite for the orange G adsorption are made up of homogeneous adsorption patches.The adsorption kinetics indicate that the adsorption process of GO@Fe 3 O 4 -COOH@NH 2 -MIL-101(Fe) toward orange G obeys the pseudo-second-order model and the activation energy is 16.5 kJ mol -1 .Thermodynamic parameters, ΔH = 39.81 kJ mol -1 , ΔS = -46.80J mol -1 K -1 , and ΔG < 0, exhibit that the adsorption is exothermic and spontaneous.The maximum adsorption capacity of orange G onto GO@Fe 3 O 4 -COOH@NH 2 -MIL-101(Fe) nanocomposite is 128.8 mg g -1 .Therefore, magnetic GO@ Fe 3 O 4 -COOH@NH 2 -MIL-101(Fe) nanocomposite can be employed as an environmentally friendly and efficient adsorbent for the orange G removal from wastewater.
ANALYSIS OF MATLAB SYSTEM APPLICABILITY FOR SYNTHESIS OF CONTROLLED BUTTERWORTH DIGITAL RECURSIVE IIR FILTERS

A number of digital signal processing applications use controlled IIR digital recursive filters.The word " controlled " refers to filters whose structure ratios clearly depend on the cut rate orboundary frequencies. Controlled IIR filters can be synthesized using a variety of tools to computetraditional, uncontrolled IIR filters. The article dealt with the synthesis uncontrolled IIR filters andanalyzed the suitability of the presentation of the results of synthesis for the construction of controlledIIR filters. The design techniques based on MATLAB (2021a) and the fundamental concepts of IIRButterworthdigital filters were described and explained. The composited signal was processed by theanalyzed filter to find whether it met the filtering progress criteria. To check the calculated filters, theSimulink prototype was used, as well as FDA tool of signal processing toolbox. Based on the resultsobtained, a conclusion was made about the applicability of the MAT LAB (2021a) system for thesynthesis of controlled digital recursive IIR-Butterworth filters. The analyzed technique was moreefficient, faster, decreased the tasks and found the results are satisfying.
Performance of Equilibrium Optimizer for the Traveling Salesman Problem

This paper presents a new method based on Equilibrium Optimizer (EO) algorithm that is inspired from the mass balance of a control volume for traveling salesman problem (TSP).For enhancing the efficiency of EO, the 2opt movement algorithm is used to update the solution generated by EO.The efficiency of the proposed EO for the TSP problem has been compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) on different instances consisting of the 14-city, 30-city, 48-city and 52-city.The calculated results show that for the large scale instances such as 48-city and 52-city, EO has found the better tour than PSO.In comparison with GA, EO has ability finding the best tour with the smaller mean and standard deviation.The comparisons with previous methods in literature have also demonstrated that EO has ability to search the better tour than other methods.Thus, the proposed EO can be a potential method for the TSP problem.
Facial Expression Recognition Using Python Using CNN Model

Facial expressions are a vital part of human life. Each day has a number of instances and all instances include numerous amounts of communication. Every communication expressed with emotion tells us about the state of the person. The interpersonal as well as security purposes are solved through facial expressions. The mischievous intention of a person can be caught by his expressions.&#x0D; The human mind can capture visual information faster. So, a machine recognizing it will be a challenge. As the saying goes- “A picture is worth a thousand words”- only when it is represented well. A machine being able to detect the atmosphere by the means of expression is less of a manual work.&#x0D; This paper detects the faces, extract the features as well classify them into different categories which ultimately lead to expression recognition. We evaluate our proposed method with the dataset which we used and the recall of angry, fear, happy, neutral, sad, and surprise is 60%, 31%, 84%, 22%, 57% and 58% respectively and the f1-score is 51% 35%, 82%, 25%, 51% and 64% respectively. Experimental results demonstrate the competitive classification of our proposed system.
Incrementally Stochastic and Accelerated Gradient Information Mixed Optimization for Manipulator Motion Planning

This paper introduces a novel motion planner, incrementally stochastic and accelerated gradient information mixed optimization (iSAGO), for robotic manipulators in a narrow workspace. Primarily, we propose the overall scheme of iSAGO informed by the mixed momenta for an efficient constrained optimization based on the penalty method. In the stochastic part, we generate the adaptive stochastic momenta via the random selection of sub-functionals based on the adaptive momentum (Adam) method to solve the body-obstacle stuck case. Due to the slow convergence of the stochastic part, we integrate the accelerated gradient descent (AGD) to improve the planning efficiency. Moreover, we adopt the Bayesian tree inference (BTI) to transform the whole trajectory optimization (SAGO) into an incremental sub-trajectory optimization (iSAGO), which improves the computation efficiency and success rate further. Finally, we tune the key parameters and benchmark iSAGO against the other 5 planners on LBR-iiwa in a bookshelf and AUBO-i5 on a storage shelf. The result shows the highest success rate and moderate solving efficiency of iSAGO.
Hand Motion Analysis using CNN

-Hand motion detection and gesture recognition research has attracted large interest due to its wide range of applications in the field of Human computer interaction such as sign language recognition, 3D printing, virtual reality. There have been several approaches to create a robust algorithm to ease human computer interaction and perform in unfavourable environments. The real time recognition and learning of the model are big challenges. In this work, we use Convolutional Neural Network architecture to detect and classify hand motions, the region of interest of the image is passed through the neural network for the hand motion analysis and detection. Our system has achieved testing accuracy of 98%.
Statistical guarantees for local graph clustering

Local graph clustering methods aim to find small clusters in very large graphs. These methods take as input a graph and a seed node, and they return as output a good cluster in a running time that depends on the size of the output cluster but that is independent of the size of the input graph. In this paper, we adopt a statistical perspective on local graph clustering, and we analyze the performance of the l1-regularized PageRank method~(Fountoulakis et. al.) for the recovery of a single target cluster, given a seed node inside the cluster. Assuming the target cluster has been generated by a random model, we present two results. In the first, we show that the optimal support of l1-regularized PageRank recovers the full target cluster, with bounded false positives. In the second, we show that if the seed node is connected solely to the target cluster then the optimal support of l1-regularized PageRank recovers exactly the target cluster. We also show empirically that l1-regularized PageRank has a state-of-the-art performance on many real graphs, demonstrating the superiority of the method. From a computational perspective, we show that the solution path of l1-regularized PageRank is monotonic. This allows for the application of the forward stagewise algorithm, which approximates the solution path in running time that does not depend on the size of the whole graph. Finally, we show that l1-regularized PageRank and approximate personalized PageRank (APPR), another very popular method for local graph clustering, are equivalent in the sense that we can lower and upper bound the output of one with the output of the other. Based on this relation, we establish for APPR similar results to those we establish for l1-regularized PageRank.
Extraction of Knowledge from Web Server Logs Using Web Usage Mining

Information on internet increases rapidly from day to day and the usage of the web also increases, thus there is the need to discover interesting patterns from web. The process used to extract and mine useful information from web documents by using Data Mining Techniques is called Web Mining. Web Mining is broadly classified in to three types namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this paper our focus is mainly on Web Usage Mining, where we are applying the data mining techniques to analyse and discover interesting knowledge from the Web Usage data. The activities of the user are captured and stored at different levels such as server level, proxy level and user level called as Web Usage Data and the usage data stored at server side is Web Server Log, where it records the browsing behavior of users and their requests based on the user clicks. Web server Log is a primary source to perform Web Usage Mining. This paper also brings in to discussion of various existing pre-processing techniques and analysis of web log files and how clustering is applied to group the users based on the browsing behavior of users on their interested contents.
Klasifikasi Citra Burung Lovebird Menggunakan Decision Tree dengan Empat Jenis Evaluasi

Lovebird is a pet that many people in Indonesia have known. The diversity of species, coat color, and body shape gives it its charm. As well in this lovebird bird has its uniqueness of various rare colors. However, many ordinary people have difficulty distinguishing the types of lovebirds. This research is needed to improve previous study performance in classifying lovebird images using the Decision Tree J48 algorithm with 4 types of evaluation. In this case, also to reduce the stage of feature extraction to speed up the computational process. Based on available comparisons, the results obtained at the same split ratio with a comparison of 60:40 in Decision Tree J48 have the precision of 1,000, recall of 1,000, f-measure of 1,000, and accuracy value of 100%. Then the Artificial Neural Network with a split ratio of 60:40 has a precision of 0.854, recall of 0.843, f-measurement of 0.841, and an accuracy value of 84.25%. These results prove that by testing the first-level extraction on color features, Decision Tree J48 is superior in classifying images of lovebird species, and Decision Tree J48 can improve performance and produce the best accuracy.&#x0D;
Security Concerns and Privacy Preservation in Blockchain based IoT Systems: Opportunities and Challenges

Blockchain is a decentralized network consisting of peer-to-peer nodes which perform all the operations within a network without the need of a central authority unlike IoT where information exchange, validation and authentication of data is done using a central authority. The trivial security and privacy protocols in place for IoT suffer due to the decentralization and limited resource capabilities of IoT devices. Blockchain in IoT with its cryptographic functions and decentralized nature could prove significantly useful. This paper discusses the motivation for IoT using Blockchain, the attacks and challenges in Blockchain, the challenges in the integration of Blockchain in IoT and the opportunities of such an integration. Some applications of Blockchain in IoT are also discussed to provide an insight into the future prospect of this integrated technology.
Performance evaluation of an adopted model based on big-bang big-crunch and artificial neural network for cloud applications

High-performance computing is changing the way we compute. In the past decade, the cloud computing paradigm has changed the way we compute, communicate, and technology. Cover real-world problems. There are still many complex challenges in the cloud computing paradigm. Improving effective planning strategies is a complex problem in the service-oriented computing paradigm.In this article, our research focuses on improving task scheduler strategies to improve the performance of cloud applications. The proposed model is inspired by an artificial neural network-based system and astrology base scheduler Big-Bang Big-Crunch. The results show that the proposed strategy based on BBBC and neural network is superior to the method based on astrology (BigBang BigCrunch costaware), genetic cost and many other existing methods.The proposed BB-BC-ANN model is validated using standard workload file (San Diego Supercomputer Center (SDSC) Blue Horizon logs). The results show that the proposed BB-BC-ANN model performs better than some of the existing approaches using performance indicators like total completion time (ms), average start time (ms), average finish time(ms), scheduling time(ms), and total execution time(ms).
Semantic Extraction of Basketball Game Video Combining Domain Knowledge and In-Depth Features

The team sports game video features complex background, fast target movement, and mutual occlusion between targets, which poses great challenges to multiperson collaborative video analysis. This paper proposes a video semantic extraction method that integrates domain knowledge and in-depth features, which can be applied to the analysis of a multiperson collaborative basketball game video, where the semantic event is modeled as an adversarial relationship between two teams of players. We first designed a scheme that combines a dual-stream network and learnable spatiotemporal feature aggregation, which can be used for end-to-end training of video semantic extraction to bridge the gap between low-level features and high-level semantic events. Then, an algorithm based on the knowledge from different video sources is proposed to extract the action semantics. The algorithm gathers local convolutional features in the entire space-time range, which can be used to track the ball/shooter/hoop to realize automatic semantic extraction of basketball game videos. Experiments show that the scheme proposed in this paper can effectively identify the four categories of short, medium, long, free throw, and scoring events and the semantics of athletes’ actions based on the video footage of the basketball game.