Reviewerly/exHarmony huggingface dataset:
Meta-Analysis of the Effect of Technologies on Primary School

The main purpose of this study is to show how much learning technologies increase the success in primary school with meta-analysis method. Advanced technologies such as virtual-augmented reality, animation-simulation, inverted classroom application, web-internet course applications, course and game teaching software, which are subject to experimental studies in computerized learning environments, have been included in this meta-analysis study, since they have features that can improve academic achievement. It is taken into account that having features such as; publication in a peer reviewed open-access magazines, the execution of the application by selecting the sample from the primary school in Turkey, listing the mean and standard deviation values and the number of sample group students for the final test, for the articles to be included in this research. It was concluded that advanced technology supported lesson applications in primary school have the ability to increase academic achievement at a very high level. The effect size found (md: 4.17239) is larger than the highest level value statements defined in the literature.
ياس أثر استخدام الافراد للإنترنت على النمو الاقتصادي في فلسطين (Measuring the Impact of Individuals Use of the Internet on Economic Growth in Palestine) (2000-2018)

Arabic Abstract:هدفت الدراسة لقياس أثر استخدام الافراد للإنترنت على النمو الاقتصادي في فلسطين خلال الفترة (2000-2018). واعتمد الباحثان على المنهج القياسي لتقدير علاقة الانحدار بين المتغير المستقل (نسبة استخدام الافراد للإنترنت) والمتغير التابع (معدلات النمو الاقتصادي ) في فلسطين .وتوصلت الدراسة الى وجود علاقة ايجابية طردية بين استخدام الافراد للإنترنت ومعدل النمو الاقتصادي ، حيث بلغ معامل الانحدار للنموذج القياسي 0.0068 ، وهذا يعنى ان زيادة 1% في نسبة استخدام الأفراد للإنترنت أدى الى زيادة في معدل النمو الاقتصادي بنسبة 0.0068% خلال فترة البحث . اضافة الى وجود علاقة ارتباط قوية بين المتغير المستقل والمتغير التابع حيث بلغت قيمة الارتباط 0.988 .English Abstract:The study aims to measure the impact of individuals use of the Internet on economic growth in Palestine from 2000 to 2018. The two researchers adopted the econometrics approach for estimating the regressive relationship between the independent variable (the percentage of individuals using the Internet) and the dependent variable (economic growth rates) in Palestine.The study found a direct correlation between individuals use of the Internet and economic growth rate, where the regression coefficient of the econometrics model reached 0.0068. This means that a 1% increase in the percentage of individuals Internet users will lead to a rise in the rate of economic growth by 0.0068% during the research period. Also, there is a strong correlation between the independent variable and the dependent variable, where the correlation value was 0.988.
Monitoring Method of Transmission Line Breaking Prevention Based on Deep Learning

With the rapid development of the national economy, the national power consumption level continues to increase, which puts forward higher requirements on the power supply guarantee capacity of the power grid system. The distribution range of the transmission line is wide and densely, most lines are exposed to the unguarded field without any shielding or protective measures, which are vulnerable to man-made destruction or natural disasters. Therefore, it is very important for the early monitoring and prevention of the external force breaking of the transmission lines. The method for preventing external breakage of transmission lines based on deep learning proposed in this paper utilizes the video data collected by the cameras erected on the transmission line roads to perform feature extraction and learning through 3D CNN and LSTM networks, and obtains a monitoring model for external breakage prevention of transmission lines. The model was tested on public data sets and verified that it has a good performance in the field of transmission lines against external damage. The method in this paper makes full use of the existing video acquisition equipment, and the process does not require human intervention, which greatly reduces the cost of line monitoring and the hidden dangers of accidents.
Ways to Enhance the Influence of Academic Journals Based on MOOC Resources

In the era of Internet media convergence, to further enhance the influence of academic journals in colleges and universities, the first thing is to pay close attention to the quality construction, strengthen the program planning, expand the quality of contributions, shorten the publishing cycle, and play the role of editorial board, strengthen the editorial department team construction and other directions. Secondly, it is necessary to explore a variety of communication channels and strengthen them in the direction of precise positioning of traditional publishing, develop digital publishing model, joining in digital publishing platform, and using academic conferences to expand publicity channels. Academic journals in colleges and universities especially can take advantage of unique conditions and campus MOOC resources to explore new ways to accurately and effectively enhance the brand effect and influence of journals through situational intervention.
Efficient Multi-Granularity Network based on Local Context-aware Correlation Feature for Person Re-Identification

The joint learning strategy of the global and local feature can further improve discriminative ability in complex realistic scenarios. The adjacent horizontal stripes regions of local features have the strong correlation characteristics. Considering the structure characteristics of human body, in this paper we design the Multi-Granularity Network based on Local Contextaware Correlation Feature (MGN_CACF) based on ResNet50-IBN-a backbone, which is split into four branches including a Top DropBlock branch, a global feature branch and two local feature branches. Experimental results shown that our proposed algorithm can achieve significant performance improvement, such as for Market-1501 and CUHK03 datasets Rank-l/mAP = 95.7%/88.5% and Rank-l/mAP=78.0%/73.8% respectively.
Context-Aware Residual Module for Image Classification

Attention module has achieved great success in numerous vision tasks. However, existing visual attention modules generally consider the features of a single-scale, and cannot make full use of their multi-scale contextual information. Meanwhile, the multi-scale spatial feature representation has demonstrated its outstanding performance in a wide range of applications. However, the multi-scale features are always represented in a layer-wise manner, i.e. it is impossible to know their contextual information at a granular level. Focusing on the above issue, a context-aware residual module for image classification is proposed in this paper. It consists of a novel multi-scale channel attention module MSCAM to learn refined channel weights by considering the visual features of its own scale and its surrounding fields, and a multi-scale spatial aware module MSSAM to further capture more spatial information. Either or both of the two modules can be plugged into any CNN-based backbone image classification architecture with a short residual connection to obtain the context-aware enhanced features. The experiments on public image recognition datasets including CIFAR10, CIFAR100, Tiny-ImageNet and ImageNet consistently demonstrate that our proposed modules significantly outperforms a wide-used state-of-the-art methods, e.g., ResNet and the lightweight networks of MobileNet and SqueezeeNet.
University studies after the pandemic. Challenges and solutions

The transition of higher education institutions to a distance format during the pandemic has become the most discussed topic in the teaching community. As a result of the pandemic, all Russian universities found themselves in a situation where the educational process and its management were switched to a remote format all at once. This transition has accelerated the development of the digital infrastructure of universities and the inclusion of all faculty and students in the online educational environment. The Coronavirus pandemic has highlighted that, following a gradual return to a traditional form of learning, the learning strategy and resources used must change. The new environment will require new skills from teachers and students and thus changes in learning technology. New learning areas include digital tools skills, team (group) working skills in a digital environment, and skills for sharing synchronous and asynchronous formats. The article presents an analysis of the implementation of new approaches to learning during the pandemic and the prerequisites for their use after returning to the traditional form of the learning process. An analysis of attitudes of teachers and students of Perm National Research Polytechnic University towards using new tools and technologies for sustainable development of the educational process is presented. Based on the analysis, barriers to implementing the new approach are identified, and recommendations for overcoming them are offered.
Solving Navigation and Obstacle Avoidance in Soccer Robot using Case-Base Reasoning Technique

Obstacle avoidance is an important and crucial issue for mobile soccer robots given the robot's dynamic environment. A robot faces obstacles in the playground, that are sometimes mobile, such as the opponent robots, teammate robots, and other obstacles such as the goals and flags, therefore its environment is in a completely dynamic state. The main idea of this article is to use case-based reasoning which uses past experiences to solve a new problem that is in some ways similar to a problem it has already encountered. The case-based reasoning database includes a series of ordered information on several previous cases that can be adapted to the new problem to the extent that the solution of the old problem can be adapted and modified to the new one. Traditional expert systems require problem-solving knowledge and Domain knowledge, while case-based reasoning does not need problem knowledge and focuses on finding similarities between the new problem and the previously solved ones. With this approach, learning is done through modifying existing laws and spending fewer resources.
Identification of parameters of Poisson distributions by the extreme order statistics

Let Poisson variables X1,⋯,Xm be independent and Y1,⋯,Yn be also independent such that each Xi∼P(λi),Yj∼P(μj). We prove that if Xm:m=max{X1,⋯,Xm} and Yn:n=max{Y1,⋯,Yn} have the same distribution, then m = n and {λ1,⋯,λm} = {μ1,⋯,μm}. The weak version in term of minimum statistics is also derived.
Solving TSP Problem in Cloud Computing using Improved Cultural Algorithm

Traveling Salesman Problem (TSP), despite its simple appearance, is one of the classic and complex problems in Combinatorial Optimization and it is difficult to find an accurate answer for large samples. This problem is so important that many real-world problems can be turned into a TSP and solved. Optimization methods for solving difficult problems, such as TSP, mainly involve a large number of variables and constraints that reduce their practical efficiency in solving large-scale problems. An optimization algorithm includes factors that increase the speed of convergence, which can be inherited as a culture to the next generation. The basic idea of cultural algorithms is based on the theory that in advanced societies, in addition to the knowledge that parsons have in their genetic code and inherited from their ancestors, there is another element called culture for evolution. Culture is a set of accepted beliefs of community leaders. Of course, one of the disadvantages of this type of algorithm is the formation of a false culture and the adherence of all people to the same culture, which occasionally leads to local optimizations during the evolution process. The solution proposed in this paper to overcome this shortcoming is to select diverse leaders and consequently produce different subpopulations. This increases the diversity of people in the population and thus distributes the search throughout the problem space, and breaks the problem into smaller problems, and reduces the complexity of problem-solving temporality. In the meantime, cloud computing, given scalability and accessibility, provides us with good facilities. Using the capabilities of cloud computing, one problem can be divided into smaller sub-problems and solved in several virtual machines. Each of the virtual machines uses the improved culture algorithm technique proposed to solve their dedicated sub-problem. In the meantime, the nodes assigned to each machine are hidden from the other machine. Finally, the result is obtained by combining the results of all virtual machines, according to the proposed algorithm.
Product strategy based on fuzzy analysis algorithm under big data

This article focuses on the qualitative and quantitative problems of rating and evaluation, and based on the discriminative relationship between text metrics and rating metrics. For problem one: By exploring the factors that may arise, we use analysis to determine the relationship between star ratings, reviews, and help ratings. For problem two: In Part A, we quantify customer reviews. Establish a review measurement system to determine the data metrics that can best be tracked by Sunshine Company. In part B, we add a time gradient function to the original functional relationship. In areas with large mutations, we believe that the product reputation has increased or decreased dramatically during this period. In Part C, we use fuzzy comprehensive analysis. The factor set U is set up: U:U= {u_1, u_2, u_3, u_4}, and the evaluation set V={v_1, v_2, v_3, v_4}, and the data transfer matrix is constructed by constructing it. The PSS value is used to measure the advocacy level of each type of product to determine whether the product is potentially successful or potentially failed. In part D, we obtain a dense function based on the problem b, and use the continuity of the dense degree to determine whether there will be a specific star rating affecting the review. In Part E, we sorted out the frequency of words appearing in the full text of the review to build a database of specific quality description words. By comparing reviews with the database, we divided customer reviews into a scalar with a scale of 5, and then scored them using cosine.
Mastering the competence “teaching in primary school” by bachelors in pedagogical education according to worldskills standard

The subject of the research is to find ways and methods of mastering the competence «Teaching in primary school» by bachelors of pedagogy according to Worldskills standards. The purpose of the research is to consider the features of the competence «Teaching in primary school» by bachelors of pedagogy according to Worldskills standards. The research methods include a competency-based approach. The results of the research is based on the basis of the methods «Motivation of professional activity» introduced by G.М. Bespalov and others. The authors revealed that 70% of the respondents identified as one of the important criteria the skills of a positive attitude towards their own personality, understanding of others, which allows them to show self-regulation in any situations, including those of the demo exam. The research results can be introduced into the practice of colleges and universities. Conclusions: Taking part in the WorldSkills movement, bachelor students can experience the versatility of the primary school teacher profession in practice. Only by plunging into practical activity, the knowledge and skills of students are revealed to the maximum, accompanying them with a creative approach, which allows the competitor to self-actualize in stressful situations, showing mobility, originality of thinking, depth of knowledge, efficiency.
Public key encryption with distributed keyword search

In cloud storage, data owners usually achieve confidentiality of information through giving encrypted data to the server and as a consequence, the possibility of searching over the database is lost. Searchable encryption (SE) is a method to encrypt data in such a way that keyword searching would still be possible. However, to the best of our knowledge, existing literature on SE schemes only supports search operations carried out by an individual users. In this paper, we introduce the concept of public key encryption with distributed keyword search (PEDKS) to provide a solution for scenarios where the search ability can be distributed among authorized subsets of users according to a predefined access structure. Therefore, by employing a PEDKS, group members realizing the specified access structure can cooperatively query to search. We further propose a concrete construction of a PEDKS scheme and prove its security under the intractability of ECDL and BDH problems. Performance analysis and comparison with related concepts are provided as well.
Object tracking based stereo vision

Abstract In this paper, we described a new method for moving objects (detection and tracking). The first step to determine the object tracking is the segmentation process through getting a background image and then comparing it with the foreground image. Separation of the background image from the foreground image is the result of this step to obtain an object. In further step, enhance the segmentation operation by using morphology to remove small noise. Finally, select the largest object, center and radius of the largest. Therefore, we can detect the object perfectly and then the tracking of all objects. In this process for finding out the location of the target, we observed less error in detection and was very accurate.
Attack and Defense Countermeasures of cognitive radio

Abstract with the development of cognitive radio theory and technology, its application in various fields will be more and more extensive. She’s trying to bring AI technology to the Internet. In this paper, the attack and defense of cognitive radio will become more and more intense. The mobile communication process of cognitive radio is summarized, and the attack and Defense Countermeasures of cognitive radio are analyzed.
A Simulation for Forecasting Compute Resource Usage

The usage of compute resources by data processing jobs may change over time, requiring careful resource planning when an organization operates these resources itself in an on-premise private cloud. Ideally, the currently available resources always match the need of jobs executed on them. This way the resources would neither be overutilized, which is usually undesirable as the jobs might take longer, nor underutilized, which causes unnecessary costs for unused resources. When an organization decides to extend its private cloud resources, it can still take months until the servers are bought, delivered, and installed. Thus, the resources have to be planned carefully in advance. Estimating the future resource needs is difficult and influenced by many factors. In our experience, creating the estimate is often a manual process supported by self-designed spreadsheets; these spreadsheets are maintained by a single person from time to time and might even be replaced completely if someone else assumes that person's responsibility. However, this approach does not lead to transparent and verifiable forecasts that enable collaboration and learning from past decisions.
Fast Real-time Semantic Segmentation Network with an Asymmetric Encoder-Decoder Structure

Currently, the-state-of-art semantic segmentation methods often bring the huge computational cost to achieve high performance, and it is difficult to balance the inference speed and model accuracy, and it is difficult to deploy on the equipment with limited hardware resources. In this paper, we propose an asymmetric high-resolution (1024*2048px) real-time semantic segmentation network that can balance accuracy and speed-Fast Real-time Semantic Segmentation Network(FRSSNet). We combine the existing multi-branch network and encoder-decoder structure and design a new bottleneck. Meanwhile, we use multiscale to get better segmentation results. By combining spatial details with semantic information, the accuracy can reach 69.6% on Cityscapes and the FPS can reach 200.
Performance Evaluation of Encryption Algorithms in Mobile Devices

The use of mobile devices increases rapidly nowadays and the mobile technologies, models and software are changing very fast. Mobile devices are vulnerable to several types of threats from many sources such as applications, mobile device, network and social resources. Several encryption algorithms are available to protect mobile data. This paper presents an evaluation study of encryption algorithm for mobile devices. The study focuses in preserving the confidentiality of mobile data without data availability and considering battery draining issue. The algorithms under study are AES, DES, TEA, RSA and REA. This paper presents two experiments in the evaluation process. The first experiment utilizes the execution time of encrypting 10 files using five encryption algorithms in three mobile devices. The second experiment handle the battery consumption of one mobile device to the same files using the same five encryption algorithms. The experimentation results revealed that AES is the fastest algorithm and REA is the slowest. AES is the least algorithm in battery consumption as it consumes less than 1 mAh to encrypt 20 times 1500 KB files.
The Limo-Powered Crowd Monitoring System: Deep Life Modeling for Dynamic Crowd With Edge-Based Information Cognition

To ensure the safety of people in massively crowded activities, crowd monitoring systems are very important. Presently, many researches focus on studying video for crowd monitoring, and realize the recognition of behavior patterns. However, most systems only implement a real-time display of the monitored scene to comprehensively model a place. Because the environment at crowd gatherings is complex and dynamic, an intelligent monitoring system is necessary. This paper proposes a limo-powered crowd monitoring system for modeling dynamic crowds. On one hand, limo-system realizes life modeling at the individual level, offering personalized cognition of each person. On the other hand, it uses data collected by multi-sensor devices and intelligent algorithms deployed on the edge cloud to dynamically perceive the environment. At the same time, considering the redundancy of monitoring data, the value of information can be recognized via information cognition components in the edge cloud. Finally, combining the results of environment awareness and life modeling, simulation experiments of crowd scenes are executed for multi-scenarios. Results show that the intelligent system can be effectively applied to crowd monitoring in comprehensive environments, with important significance for crowd perception and modeling.
Head-Driven English Syntactic Translation Model Based on Natural Language Processing

Natural language is an important symbol of human intelligence, so making computers understand natural language is one of the fundamental tasks of artificial intelligence research. The study of English syntactic analysis has important theoretical value and urgent practical needs. In this paper, a head-driven English syntactic translation model based on natural language processing is designed, which integrates automatically acquired semantic classes into the syntactic analysis model to solve the problem of data sparseness in the model with small samples. Using a semantic dictionary and applying the principle of minimum description length, a semantic class with granularity between parts of speech and vocabulary is automatically determined for the syntactic analysis model. In English chunk analysis task, the algorithm improves the accuracy of chunk analysis, and it can also be applied to other natural language processing tasks or other classification problems.