bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Non-structural Big Data Analysis Algorithm of Communication Network Based on Feature Fusion
Authors: Meng Xin 
Published: August 2021

Abstract
Nowadays, Along with the fast development of information technology, the scale of data is increasing in an exponential way, and the worth of big data has been paid increasingly interested. At present, there are two key problems in the category of big data: how to delegate big data as a integrate model and what efficiently reduce dimensions of big data. Although the processing technology of various mold of data has been studied, the efficiency of parallel query has not been optimized deeply. Especially, it is more difficult to analyze the information of online users with widely dispersed information. A communication network non-structural big data analysis algorithm based on semantic relevance feature fusion is proposed, which reconstructs the high-dimensional phase space of the distributed information flow of cloud storage big data, extracts the semantic relevance dimension feature quantity of big data from the reconstructed phase space, and conducts adaptive learning training with the extracted feature quantity as the test set. Knowable from the simulation experiment that the algorithm effectively reduces the generation of redundant messages and improves the network running environment.
Application of BERT+Attention Model in Emotion Recognition of Metizens during Epidemic Period
Authors: Wang Xuyang , Tong Yixuan 
Published: August 2021

Abstract
In 2020, SARS-CoV-2 will affect the hearts of people all over the country, and Weibo will become the representative of people expressing their feelings on the Internet. Traditional emotion dictionary and machine learning methods have poor text emotion recognition effect, while BERT pre-training model is based on bidirectional Transformer model, which can better obtain the emotion expressed by the text and effectively improve the accuracy of the model. On the basis of improving BERT pre-training model, attention mechanism is introduced, and the key features are weighted to make emotion classification more accurate. According to the analysis of emotions expressed by netizens on Weibo during the epidemic, compared with textCNN model, BILSTM model and BILSTM+Attention model, the accuracy rate has increased by 6.25%, 4.69% and 2.67% respectively. The overall performance of this model is the best, and it can effectively recognize text emotion.
Evolutionary modeling algorithm of ordinary differential equations based on genetic modeling
Authors: Chen Zhiqin 
Published: August 2021

Abstract
Differential equations are often used to describe complex systems and nonlinear systems related to time, but it is difficult to establish an ideal model for such systems based on some observed data. Especially when dealing with unknown chaotic system data, it is blind and difficult to combine existing nonlinear analysis results with relevant experience. In this paper, through the study of genetic modeling, the optimization process of model parameters using GA is embedded in the optimization process of model structure using GP, and the local search process of neighborhood solution generated by GP-based standard mutation operator is carried out for some individuals in each evolution generation, and the evolution modeling algorithm of ordinary differential equations is designed and implemented, and an application example is given.
Coarse-to-Fine Siamese Network for Tooth Object Tracking
Authors: Wang Xiaofeng , Ao Houjun , Shen Hongbin , Cai Hongbin , Lu Guanghui 
Published: August 2021

Abstract
At present, object tracking algorithms based on deep learning have been widely used in the computer vision industry and has great application value. In this paper, we track the tooth, which has the problem of poor texture and high similarity, with the object tracking algorithm. In general, previously existing single object tracking algorithms are derived from the siamese network architecture. These algorithms have the problem of losing a tracked object with poor textures and high similarities. In order to address the issue of object loss, we put forward a novel coarse-to-fine siamese network (CFSN) for tooth object tracking. The coarse detection network is based on the features extracted by the Feature Pyramid Networks (FPN) and the Region Proposal Network (RPN). The fine detection network has a similar structure to the coarse detection network, and the main purpose is to fine-tune the results of the previous stage. Based on the coarse-to-fine network, a framework of multi-task is proposed, that is, the mask prediction branch is added to the fine detection network, which lead to significant improvements in tracking efficiency. Through the comprehensive experimental evaluation of the tooth dataset, we find that the CFSN model is more effective in tracking tooth objects than the existing methods.
Design and Implementation of Virtual Host Based on Machine Learning Algorithm
Authors: Ji Xuan 
Published: August 2021

Abstract
The research of face modeling and animation technology has a history of 30 years. With the development of virtual reality, video entertainment, videophone, human-computer interaction and other applications, this research field has attracted more and more attention. Based on machine learning algorithm, this paper studies the personalized 3D face modeling and real-time rendering of virtual hosts. The research lines of this paper are the establishment of neutral face model data structure, face feature extraction, improvement of personalized face model matching and real-time rendering in scene roaming. A realistic 3D facial lip animation system based on MPEG-4 standard and driven by voice files is designed and implemented. The system is easy to operate, versatile and efficient, and meets the needs of real-time voice-driven lip animation.
English Teaching Ability Evaluation Based on Fuzzy K-means Clustering Algorithm
Authors: Li Yan 
Published: August 2021

Abstract
With the continuous progress of my country’s social economy and the continuous improvement of people’s material living standards, people’s demand for teaching standards is increasing. In recent years, many higher vocational colleges in our country have hired many foreign teachers when opening English classes. Higher vocational colleges are different from traditional schools. They pay particular attention to cultivating students’ skills. Using foreign teachers in English classrooms can enable students to understand English knowledge and improve their later use ability. However, in English classroom teaching, too many foreign teachers have brought a lot of problems to the teaching ability evaluation system of English foreign teachers and caused difficulties to the teaching management of higher vocational colleges. Based on the fuzzy K-means clustering algorithm, this paper has launched a series of research and discussion on the evaluation of English teaching ability. In the context of the modern big data era, in order to improve the teaching ability of English teachers, improve the quality of college teachers, and provide students with better teaching services, teachers can start with the main factors that influence and influence the improvement of college English teaching ability. In this paper, by designing a college English teaching ability evaluation system, based on the establishment of the constraint parameter index analysis model, the ability evaluation of the big data information model is carried out, and the ability constraint function information is extracted. A combination of K-means clustering algorithms, clustering and integrating proficiency index parameters, completing the preparation of an educational resource allocation plan, and achieving scientific evaluation of the English language ability. The experimental results show that the use of the K-means fuzzy clustering algorithm to evaluate the English language proficiency has an excellent information fusion analysis capability. This improves the accuracy of educational ability evaluation and the efficiency of educational resources.
Research on Detection and Tracking Technology of Moving Objects in Sports Video Based on Data Mining
Authors: Fang Wu 
Published: August 2021

Abstract
The uniqueness of sports video brings a great challenge to target detection and tracking technology. The tracking of moving objects in sports videos is characterized by complexity, which is manifested in complex scenes, complex moving directions and so on. In the process of tracking moving objects, there will be various disturbances, which will affect the tracking accuracy. To improve the tracking accuracy, it is necessary to choose the appropriate target detection and tracking algorithm. In order to improve the tracking accuracy of moving targets, an intelligent tracking method of moving targets in sports video based on data mining technology is proposed, and the improved background update difference method is used to detect moving targets. This method can track the moving target in sports video accurately and quickly, and the real-time performance of moving target tracking is better than other tracking methods.
Research on Application Technology of Computer Communication and Network Development Based on Data Mining Technology
Authors: Fu Ning , Zhang Dongxia 
Published: August 2021

Abstract
With the development of computer technology and internet communication, every country in the world began to implement information management, and applied technical means to upgrade the development scale of social industry, so as to promote the development of national economy in an all-round way. Computers break the limitation of people’s spatial communication, can give full play to the role of network resource sharing, and contribute to the high-speed processing of information. Scientific and rational application of computers can quickly transmit data, which plays an important role in promoting China’s economic development. With the advent of the Internet era, the rapid development of information technology has provided convenience for people’s lives and greatly improved their comprehensive national strength. With the development of digital science, computers and networks are widely used. The development of computer communication and network has been widely used in various fields. From the perspective of technology research and development, this paper explores and analyzes the application and function coordination of computer and network based on data mining technology.
Research on Load Balancing of Cloud Storage Server Based on Fully Connected Group
Authors: Chen Xiaodan , Pang Shuanglong , Wang Haiyan , Zeng Desheng 
Published: August 2021

Abstract
The scheduling problem of online cloud storage traffic is a hot topic in current network research. Aiming at the scheduling problem of file upload requests in online cloud storage systems, the existing schemes rarely meet the requirement of providing users with different bandwidth guarantees. One of the purposes of using multiple volume servers is to let multiple volume servers share the system load evenly and avoid the system performance bottleneck. Once the load is aggregated and one of the multiple volume servers is overloaded, the volume server will become the system bottleneck again, which will greatly affect the overall performance of the system. On the basis of analyzing the existing scheduling algorithms of server clusters, this paper proposes a dormant scheduling algorithm of fully connected groups, which can automatically equip servers with I D, redirect tasks according to the actual processing capacity, and ensure load balance by unlimited increase. The experimental results show that the load balancing strategy among multi-volume servers ensures the overall reading and writing performance of the system, and effectively improves the I/O throughput and I/O times per second.
Information Automatic Sharing Model Based on Big Data Mining Algorithm
Authors: Xiaqiu Long 
Published: August 2021

AbstractThe advent of the era of big data has promoted the transformation of traditional financial management models, and the automated sharing of public financial information has gradually appeared in the public’s field of vision. Gradually recognized by business managers, there has been an upsurge in the application of public financial information automated sharing models. Especially it has been widely used in large enterprises and multinational enterprises, and it has also attracted the attention of many large enterprises. With the continuous maturity of big data technology, financial information has shown diversified characteristics, and more accurate financial analysis capabilities are required. The management of enterprises has gradually realized that applying big data mining algorithms to the automated sharing of public financial information should be possible. Bring new progress to sharing centers and enterprises. Slowly, with the continuous development of technology, more and more senior corporate managers realize the importance of automated sharing of public financial information and the practicality of big data mining algorithms. Research shows that this article mainly uses the literature research method to study the origin, definition and implementation process of the public financial information automation sharing model by domestic and foreign scholars, and proposes the process modules that need to be improved and optimized under the big data environment. Studies have proved that the company’s products have gradually gained advantages after 2015, reflecting that the company’s profitability is gradually recovering, and the utility of the automatic sharing model of public financial information based on big data mining algorithms has begun to gradually come into play. At the same time, the main business income and net profit respectively showed a downward trend in 2015, and showed a steady increase after 2009. In 2008, the company’s public financial information automatic sharing model based on big data mining algorithms incurred relatively large costs in the early stage, which also had a greater impact on the company’s related profit indicators.
Information System Construction and Prediction Model of Leaving Using Big Data Mining and Random Forest
Authors: Zhao Qiaoyue 
Published: August 2021

Abstract
University talents play an important role in building the core competitiveness of universities and play a decisive role in improving the quality of university education. With the opening up and progress of society, the flow of talents in colleges and universities has become more and more frequent. Therefore, more and more attention has been paid to the analysis of factors affecting the turnover of outstanding college teachers. This paper establishes a random forest model to find out the key factors affecting the turnover of outstanding teachers in colleges and universities, and integrates these variables into an index to help colleges and universities understand which outstanding teachers need to be focused and predict the outstanding teachers on the job, and judge the probability of their departure., Which can formulate effective talent retention measures for the university managers. The experimental results verify that the work itself, work pressure and welfare remuneration are the most important factors affecting the turnover tendency of college teachers, followed by leadership management and development prospects. Relatively speaking, the impact of interpersonal relationships is the least. The evaluation model of university teachers’ turnover intention based on the random forest model proposed in this paper can be further applied to the study of teacher turnover intention.
Information System Establishment and Prediction Analysis for SVM Model of RBF Kernel Function
Authors: Wu Siyun 
Published: August 2021

Abstract
The effective prediction of Shanghai Composite Index can provide much useful information for relative organizations to analyze the changes of stock market and then make right decision. Therefore, the prediction of the Shanghai Composite Index is meaningful. In this study, supported vector machine (SVM) algorithm is employed to construct the prediction model, and MSE R2 are used for error test. The result shows SVM model has high accuracy, which indicates the prediction model established in this study is reliable.
Research and application of privacy protection technology based on big data environment
Authors: Ding Zhiping 
Published: August 2021

Abstract
With the development of the Internet of Things, the popularization of the mobile Internet, and the rapid promotion of social networks, the growth of data has entered a big explosion, and the development of information technology has caused a torrent of big data. The continuous development of technology brings people convenience, speed and comfort, but also hides hidden worries. Combined with differential privacy and clustering, a clustering-based differential privacy universal dataset release method for mixed datasets is proposed: using k-prototype clustering algorithm to group records in the hybrid dataset to reduce differential privacy Query sensitivity and the amount of noise to be added to improve data utility while providing data privacy protection; perform attribute difference calculations for numerical attributes and categorical attributes combined with weights, and measure their information loss separately. Finally, Experimental results show that this algorithm can improve the usability of data publishing.
Research on the Method of Landscape Creation Based on Virtual Reality
Authors: Ren Wang 
Published: August 2021

Abstract
The landscape creation process based on the concept of virtual reality, the landscape creation as a dynamic process, from subjective and objective two aspects discusses the evolution history of landscape creation, system creation process model. Based on the concept of process method, this paper summarizes and puts forward the concept and form, rules and operation, output and result oriented process method and related technology import mode. The overall analysis of the whole process of the innovative landscape creation has formed a comprehensive, innovative, holistic and process of the system creation method, to provide overall guidance for the further research of landscape space and form creation method. This paper discusses and analyses the application of VR technology from the aspects of landscape creation and landscape expression. On the one hand, the application of virtual reality technology in landscape creation can promote the diversification of design thinking and creation process, stimulate the multi direction thinking, further emancipate the creative thinking and improve the design quality. On the other hand, the virtual reality technology has created a more powerful expressive force with its vividness, interactivity, authenticity and accuracy, and expanded the content and meaning of the architectural expression. In addition, the author also discusses the landscape design and the essence of landscape in the virtual reality era.
Research on Energy Saving Distribution in Cloud Computing Environment
Authors: Zheng Bigeng 
Published: August 2021

Abstract
With the huge increase of cloud infrastructure and the continuous expansion of cloud computing scale, the consumption promotion role of IT resources has been increasing, which has greatly hindered the development of IT industry. Energy consumption has become an important factor restricting the development of cloud computing. How to reduce energy consumption and improve the efficiency of energy utilization in cloud computing has become a new problem. At present, most of the cloud computing task scheduling algorithms are also carried out in job scheduling, which does not consider the allocation and energy consumption of resources. Therefore, the rational allocation of resources is an effective way to improve the efficiency of energy utilization and energy utilization. In this paper, the concept of resource waiting and resource consumption is proposed. It is considered that resource allocation is an effective way to improve the efficiency of platform software. In this paper, the resource space time energy consumption of intelligent energy is defined. The optimal ratio of known energy consumption to 0 and the resource needed for resource tasks and tasks is called map / task allocation and resource allocation. In this paper, the best resources than W test and wait for the relationship between the specific energy and resource; then, to verify that the map / mountain ice task division rationality by experiment; then the experimental data to verify the R2 algorithm can reduce the energy consumption of resources, improve the utilization rate of node resources, optimizing the energy efficiency of Map/Reduce task. The proposed resource ratio model and resource W method can be applied to existing map reduction system, which is of theoretical significance and practical value for improving and optimizing energy efficiency in cloud computing models.
Personalized adaptive online learning analysis model based on feature extraction and its implementation
Authors: Lihua Bi 
Published: August 2021

Abstract
Modern people pay more and more attention to individualized learning. The traditional teaching method is to explain all the learning contents in a unified way. The setting of teaching contents and courseware are relatively fixed, which can not provide individualized choices for different learners. The core of the adaptive learning system based on feature extraction studied in this paper is that the system recommends personalized learning content for learners according to the learner model. To establish and personalize the self-adaptive learning engine mechanism, a personalized self-adaptive learning content presentation based on clustering is proposed. This study can analyze the data of students’ learning behavior and knowledge mastery, recommend reasonable learning path and learning resources with appropriate difficulty, give timely and accurate feedback to students’ learning effect, provide personalized service intervention, and promote teaching and learning.
Design and Research of Digital Painting Online Learning and Communication System Based on Visual Semantics
Authors: Niu Zhuke 
Published: August 2021

Abstract
This paper mainly describes the design and implementation of digital painting online learning and communication system. Based on the research results of publishing and matching mechanism of visual services and the needs of service resource management, this paper designs a publishing and matching model of visual services, and implements a prototype system by using technology, which enables users to conveniently publish, query, manage and share visual service resources, and the prototype system has good scalability and is easy to upgrade. And the overall operation flow of the system and the data flow diagram of the three user function modules, i.e. administrator, teacher and student, are designed in detail. Finally, each information data table of the background database of the digital digital painting online learning and communication system is designed.