Journal of Interconnection Networks

Traditional teaching methods are limited to time and place, and the performance of dance teaching resource management is poor. This paper designs a dance teaching resource management system based on cloud computing. The functional structure of the system includes the core cloud computing teaching and teaching management application. The data management module is used to store the processed data in the data file, and respond to the retrieval request of dance teaching content publishing module and the remote image resource positioning request of multi-media management module. Design user courseware on demand process, using the existing dance teaching resources to learn. The experimental results show that the designed system has good performance, high stability and compatibility, and strong data storage capacity of teaching resources.

Data management is an administrative mechanism that involves the acquisitions, validations, storage, protection, and processing of data needed by its users to ensure that data are accessible, reliable, and timely. It is a challenging task to manage protections for information properties. With the emphasis on distributed systems and Internet-accessible systems, the need for efficient information security management is increasingly important. In the paper, artificial intelligence-assisted dynamic modeling (AI-DM) is used for data management in a distributed system. Distributed processing is an effective way to enhance the efficiency of database systems. Therefore, each distributed database structure’s functionality depends significantly on its proper architecture in implementing fragmentation, allocation, and replication processes. The proposed model is a dynamically distributed internet database architecture. This suggested model enables complex decision-making on fragmentation, distribution, and duplication. It provides users with links from anywhere to the distributed database. AI-DM has an improved allocation and replication strategy where no query performance information is accessible at the initial stage of the distributed database design. AI-DM findings show that the proposed database model leads to the reliability and efficiency of the enhanced system. The final results are obtained by analyzing the dynamic modeling ratio is 87.6%, increasing decision support ratio is 88.7%, the logistic regression ratio is 84.5%, the data reliability ratio is 82.2%, and the system ratio is 93.8%.

In order to improve the competitiveness of high-tech enterprises, the investment in scientific and technological innovation of Chinese high-tech enterprises has been gradually improved. Therefore, the correlation between financial subsidies and research and development investment of high-tech enterprises is analyzed in 6G environment. The incentive form can promote the research and development investment of enterprises and improve the economic benefits of enterprises. Through the research on the relationship between the incentive of senior executives’ shareholding, the allocation of senior executives and the relationship between research and development investment and financial subsidy, the paper analyzes the incentive intensity and the relationship between incentive period and research and development investment. After using the correlation and regression analysis, we can see that incentive can promote research and development investment and strengthen the correlation between research and development investment and financial subsidy.

Pole Vault is one of the most difficult and complicated sport in athletic and tracks events; it is high jumping action with vaulting pole stick. In the study of Pole Vault technology, the rapid advancement of Augmented Reality (AR) technology has considerable significance for the visual error correction of the pole vault take-off action images in the simulated world. The key objective is to evaluate the correction algorithm of the visual errors of take-off action images of pole vault sport. This study analyzes the AR framework, features, and pole vault, as well as province methods for correcting visual defects. Pole vault take-off action images have been simulated with AR computation technique, and the AR images of continues pole vault made. This paper primarily provides an overview of augmented reality (AR) technology, including its properties and benefits, as well as a few visual error correcting methods. AR discusses how to choose an aerobics instructor who can help you maintain an aerobics regimen on a regular basis. Augmented reality has been used to model these pictures, and AR images of aerobics have been created. The visual error correction of the longitudinal direction, transverse direction, and height direction of AR simulated continue pole vault images with Scale Invariant Feature Transform (SIFT) algorithm, Mean Absolute Deviation (MAD) algorithm, Normalize Cross-Correlation (NCC) algorithm, and Sobel Edge Detection (SED) algorithm is proposed in this study. This revealed that poor performance in the pole vault sport is due in large part to a lack of a well-planned structure for implementing a corrective mechanism for visual mistake correction in take-off action images. The statistical result of perfect pole vault technique dissected and described, including the best manage, takeoff, drive and swing, and bar separation. Along with technical work, a pole vaulters training regimen should involve sprinting, weight lifting, and gymnastics as well.

A social activity that uses certain ideas, concepts, political views, and moral values in a society or social group enriches students’ ideology and allows learners to form ideological and moral qualities that correspond to their social and political establishment. The continuous improvement of their complete quality and technical skills is at the heart of social and economic growth. In ideological and political education, risk factors are widely influenced, including the impact of educational purposes and education providers. In this paper, Deep Learning-Based Innovation Path Optimization Methodology (DL-IPOM) has been proposed to strengthen data awareness, improve the way of thinking in ideological and political education. The political instructional collaborative analysis is integrated with DL-IPOM to boost Ideological and political education excellence. The simulation analysis is conducted at (98.22%). The consistency of the proposed framework is demonstrated by efficiency, high accuracy (98.34%), overshoot index rate (94.2%), political thinking rate (93.6%), knowledge retention rate (80.2%), reliability rate (97.6%), performance (94.37%) when compared to other methods.

Mobile technology can make lessons of physical skills for the students to prepare them more vibrant. Its multicultural, consolidated, and communicative characteristics contribute to the improving teaching performance, excitement of learners, reformation of learning, and a basic idea of teaching mode. The challenge facing college physical education professionals is emotionally labile students, repetition of their education, and the lack of appropriate technology, resources, school violence, and behavioral issues. Because of the high levels of bullying experienced by students with learning and attention difficulties, misbehavior and absence are more likely to occur. The school environment and negative emotions can both aggravate academic difficulties. The proposed methodology involves evaluating student’s physical training based on big data analysis (ESPT-BDA) to compensate voluntary sport behavior by neglecting its unwanted attitude or values and solving the problem of uniform learning to meet the student’s requirements. Repetition helps hone a talent by putting it to use repeatedly. The interval at which a skill is performed is another critical component of repetition. Using spaced repetition as a learning strategy involves gradually increasing the time between repetitions of the same piece of knowledge. The regulating intelligent control framework is used to compute an effective method of directing the insufficient and improper system to achieve a specific objective in an uncertain context. A convolution neural network is implemented to automatically detect vital physical activity characteristics without a need for human monitoring. Various experiment’s findings demonstrate the effect of different sequences up to 96.51% properly classified test data and improve evaluation of intelligent control framework, intelligent practicing intensity control 94.3%.

The traditional teaching system uses local area network to realize online teaching, which leads to high stability and hardware load of the teaching system and makes it difficult to meet the teaching demand. To solve the above problems, a dance movement distance learning system based on wireless network communication technology is designed. On the B/S architecture, the hardware control module and communication module of the system are designed. The fuzzy set principle is used to evaluate the students’ dance cognitive ability, so as to personalize the recommended teaching contents. The teaching video is compressed according to H.264/AV compression standard to reduce the system transmission and processing load. The system functionality test results show that the maximum transmission packet loss rate of the designed system is 8.3%, and the lost data does not interfere with teaching, has low computer memory consumption, and has superior performance.

According to the existing literature, the existing engineering ideological and political curriculum distance education system has the problems of long response time and few concurrent users, which cannot meet the development needs of the professional talents of electronic engineering. Therefore, the design and research of the long-distance education system of the ideological and political course of electronic engineering specialty dependent on big data is proposed. Based on the analysis of the demand of distance education system, the overall structure of the distance education system is designed. The design of the system business functions mainly includes login verification module design, instant communication module design, online classroom module design, video answer module design and video back-view module design, and design the server cluster load balancing strategy according to big data technology, so as to realize the operation of the remote education system of the ideological and political courses of electronic engineering. The design system is shorter than response time while compared with existing system. and the number of users is more. It is fully proved that the design system has better application performance.

In view of the noise of the data collected by the traditional multilateral cooperation mode of international migration governance, which leads to the small value of the actual task completion degree of the final mode, this paper designs a multilateral cooperation mode of international migration governance under the background of the community of human destiny based on big data sharing. Taking the governance process as the guidance, the multilateral cooperation database of international migration governance is constructed. In the multi direction of development, we should rely on the background of the community of human destiny to set up the legal construction system. The middleware data sharing model is built on the model of international migration governance, and the multi-lateral cooperation model structure of international migration governance is created by using big data sharing technology. After building the big data sharing architecture, simulate and set the data group of multilateral cooperation mode, and prepare two traditional multilateral cooperation modes and the designed multilateral cooperation mode for experiment. The results show that the designed multilateral cooperation mode has the highest task completion degree.

This article explains the knowledge properties for characterization and research outcomes. It deals with developing and integrating the Hadoop cloud computing platform. The platform utilized in this paper is a one-piece learning algorithm, a statistical model and a cloud-based selection model. Hadoop supports this model, which is suitable for data size computing. We develop Cloud Computing integrated Autocorrelation Function (CCiACF) computation model based on Hadoop learning and implement an efficient teaching platform research data processing and correlation system. Multiple simulations are conducted on the Hadoop platform under various operating conditions to check the training ability’s exactness and characteristics. The Spark Structure of this research is to successfully and effectively develop computational system performance and improve teaching platform research models using Hadoop based cloud computing. Various experimental studies have been performed, and findings indicate that the method proposed is highly successful in data collection, governance and analysis.

The definition of a general assessment of the efficiency of the power grid through an education test system is presented in this article. The paper expands an existing test scheme through the development of the requisite transmission and delivery networks. Major equipment such as generation, switching stations, transmission, and radial delivery networks within a functional framework has a comprehensive test system. Therefore, the trial machine is limited to analyzing reliability models and measurement methods using hand measurements or designing small computer programs. The overall reliability assessment of the power grid is to provide the customer with adequate service. This paper provides a basis for modeling and constructing intelligent and adaptive interfaces for Human-Computer Interaction In Power System Service Using A Genetic Algorithm (HCI-PS-GA). Integration of two-way connectivity and two-way power flow between utilities and customers turns the conventional infrastructure into an intelligent grid renowned for its intelligent grid (IG). IG is committed to ensuring reliability, flexibility, and sustainability. IG offers cyber threat and natural catastrophe surveillance, control, and endurance. It takes into account both storage and generation choices. The interfaces and their interaction styling framework play a vital role in the massive use of technology, search and explore multimodal, cloud system, adaptive, and intelligent interfacing styles of the next generation with good expression and natural characteristics for greater user acceptability, usability, and satisfaction which offers the accuracy rate of 89%.

Depending on the rapid growth of technology and computer science and globalization, increasing universities and various training programs are expected to achieve increasing lifelong education currently. Based on the increase of visual saturation, one of these programs is undoubtedly visual arts creation. Nowadays, there is a need to reconsider the content of art education and arts programs in the light of modern art productions, changing aesthetic judgments, and social changes.One of the most challenging tasks an art teacher faces in today‘s classroom is explaining the meaning behind the art that is sometimes deemed meaningless or shocking. Hence, in this paper, Deep Learning Algorithm (DLA) has been proposed for modern art education.Profound learning outcomes in the art world shifted people’s traditional views; more and more people consider that when AI eventually eliminates artists, it is not a long way. To examine the algorithm’s performance in practise, experimental assessment exposes it to learning tasks. Such algorithmic features are frequently evaluated independently in terms of performance while learning a model, i.e. at training time, and performance while applying a learnt model, i.e. at test time. This article discusses the implementation of deep learning in drawing, music, literature, and describes the classical models and algorithms in those fields of art. One of the most significant aspects of utilising a deep learning technique is its capacity to perform feature engineering on its own. In this technique, an algorithm searches the data for characteristics that correlate and then combines together to facilitate quicker learning without being expressly directed to provide it. The proposed method supports a comprehensive approach to teaching and learning in the arts and outlines standards and skills for the art teacher in the content of art, knowledge of the students, instruction, curriculum development, and evaluation of student learning outcomes, program, and teacher effectiveness.

University students face immense challenges in current situations in ideological and political research. Therefore, the way ideological work constantly needs to be adapted, and the exchange of advanced experience strengthened to increase ideological and political education (IPE) in universities. Specific methods of university administration may include only ideological and political courses. Courses information and student grades did not conduct an ideological or political evaluation of the student. They assessed the psychological behaviors of the student based on their success, nor did them include clear information on the course schedule for specific ideological and political courses. This article, Supervised learning-based teaching evaluation approach (SL-TEA), has been proposed to focus on supervised learning from ideas about machine learning technology and the current IPE status, to be developed using a brief analysis procedure. Supervised learning uses a practice set to provide the necessary quality through teaching models. Inputs and correct outputs that allow the model to learn over time are part of this training data. The study uses the system of experts to manage, operate and monitor model evaluation data and create a related database for a real-time update. Besides, to check the impact of the model and to run simulation tests. This study SL-TEA model follows the real needs of the system that the ideological and political teaching content of colleges and colleges can be evaluated. Thus, the experimental results show the better performance through the highest student accuracy ratio of 97.1 %, a high-performance ratio of 94.3%, improved political thinking rate of 92.8%, improved actual positive rate of 90.2%, the false-positive rate of 92.2%, enhance learning rate of 96.6% and reduce the error rate 21.2%, compared to other methods.

Education is one of the areas with a higher impact on digitalization, which takes various forms such as education through digital devices and technology to improve the learning process. Online and tangible computing platforms have become more interested in pursuing active teaching through educational technologies within the curriculum. Some common problems and considerations can be addressed, such as access, capacity, financing, periodic progress measurements, and evaluation results. The productive learning activity can be immediately and constantly monitored by proposing Digital Tangible Intelligent Monitoring systems (DTIMS). The decision tree method’s teaching methodology can efficiently perform this proposed system, which monitors the former challenges. At the same time, the next is resolved by a dynamic evaluation process based on the Internet of Things (IoT). The research is evaluated using the education systems currently adopted. The results highlighted the potential of the proposed model and helped to gain information in digital teaching. The simulation analysis is performed based on accuracy 97.69%, vulnerability 91.09%, and efficiency, proving the proposed framework’s reliability of 85.10%.

Artificial intelligence and robots are changing the economic and entrepreneurship environment in the industrial revolution. Artificial intelligence and robotics have become prevalent in modern economic, professional, social, and daily lives. As a function of its ability to update and develop business processes and innovative ideas, services, and products and resolve difficult tasks to achieve new, entrepreneurship has experienced massive development. Significant changes are occurring in entrepreneurship and economic growth due to artificial intelligence. Therefore, this paper aims to understand the effect and components of data entrepreneurship overall with the help of an artificial intelligence-based feasibility evaluation model (AI-FEM). Robot, edge and physical resource layers are described in depth in this document. We first deploy an edge node near the data sources to combine multiple devices’ interfaces and function as a raw data filter. Then it provides opportunity recognition, opportunity development, and opportunity implementation processes are part of the framework’s processes described in this paper. This paper aims to develop a basic framework for evaluating AI’s potential implications for the interaction between entrepreneurship and economic growth. The economic growth of industrial robots reduces basic labor costs. However, it increases hourly compensation, suggesting that the productivity-enhancing advantage of industrial robots equals the wage-increasing influence. The results show that the system is feasible and performs better in real-time and network transmission than in an AI-based industry scenario. The experimental results of AI-FEM show the high-performance ratio of 95.5%, productivity ratio of 96.3%, reliability ratio of 93.4%, the employment rate of 92.6%, an efficiency ratio of 93.6%, industrial management ratio of 90.3%, and cost-effectiveness ratio of 20.3% compared to other methods.

In recent years, with the continuous improvement of high-performance computing hardware platform, the application of high performance for a wide range of high-performance, traditional parallel programming environment tools cannot meet the growing demand for high-performance applications and the face of parallel software development environment in a relatively backward state. The heterogeneous system of the new coprocessor with a high performance and power ratio can have high performance computing needs to provide the task to accelerate. As a result, heterogeneous systems are becoming a new trend in high performance computing. However, with the introduction of the new coprocessor and the expansion of the system scale, heterogeneous systems are difficult to program the problem is also increasingly prominent. In view of this problem, this paper adopts the abstract technology to enhance the granularity of parallel programming, and designs a structured parallel programming prototype system which can achieve different levels of implicit parallelism.

The professional painting industry has experienced a dramatic breakthrough with the rapid expansion of computer science and technology. In the current digital era, digital painting art is extending the more significant creative space to add new content. Digital painting is the modern trend of mainstream painting presented to the public as a new generation of visual art. Creativity may show up, and new techniques of creating art can arise infinitely with the assistance of computer intelligence technology. This article explains how computer image processing is used in the production of art. The report offers a painting technique based on Image Rendering (IR), which does not rely on human expertise in the past, and a color image is turned into a photo with a painting effect automated. Image-based rendering is a novel way in which computer graphics and picture processing are drawn and combined with the requirement to build geometric models, get information from the input image simply by interpolating views, deforming images, and reconstructing the desired action. This article proposes the indirect use of picture processing technology and computer technology to produce oil painting. It will investigate the application of contemporary digital picture technology in order not only to maintain traditional tastes, and to keep pace with the pace of the times, to create traditional optimization.

In the special performance prediction model of dance cadets, the physical form quality index is regarded as the dependent variable and the special achievement is regarded as the dependent variable. The results of association analysis and cluster analysis showthat there are 6 specific items of higher sensitivity in body shape, physical quality and special quality. Neural network is adopted to build prediction model, and reliability and error analysis are carried out for this model. The test results show that the model constructed by the neural network is more accurate than the regression model to predict the dance performance and the development level of the physical quality.

Nowadays, it is essential to capture and evaluate student action in the physical education classroom to assess their behavior. Every student’s performance is unique in physical activity. Every time, the staff or trainer cannot watch and evaluate the students individually. At the university level, the use of classroom capture systems is becoming more widespread. However, due to technology’s recent growth and application, the research on classroom capture systems’ efficacy in university classrooms has been minimal. This paper is proposed for the student action capture and evaluation system. Image preprocessing is the process of preparing pictures for use in model training and inference. This covers resizing, orienting, and color adjustments, among other things. As a result, a change that can be an augmentation in certain cases can be better served as a pretreatment step in others. The DL-IF uses cloud technology for data storage and evaluation. DL-IF uses the imaging technology to monitor students’ actions and responses in the classroom. The image data are evaluated based on the trained set of data provided in DL-IF’s Artificial Neural Network (ANN). The evaluation of unique individuality in every student’s performance is reported to the respective trainer. The simulation analysis of the proposed method DL-IF proves that it can monitor, capture and evaluate every student’s action in all physical activity classrooms. Hence, it proved that this framework could work with high accuracy and minimized mean square error rate.