bluuebunny/crossref_metadata_2025_split huggingface dataset:
English Teaching Courses for Students Majoring in Occupational Health in Higher Vocational Education Based on Virtual Reality
Authors: Wen Jin , Fu Fengyang 
Published: April 2021

Abstract
With the rapid development of modern technology, virtual reality technology is also used in our lives. This article discusses the virtual reality technology to simulate the English teaching courses for students majoring in occupational health in higher vocational schools. Virtual reality technology is a new technology derived with informatization. It is a process of building a virtual environment to make people feel the five senses. In English teaching, building a direct English environment is the best way to learn English. For non-native English students, constructing a real English language environment can make English teaching more effective. Through the introduction of virtual reality technology, this paper explores the possibility of using virtual reality technology in English teaching, using constructivism to construct a real language environment, allowing learners to truly experience the native English environment, increasing their interest in learning, and improving teaching effects. Aiming at the boring characteristics of the English learning process, based on virtual reality technology, an English learning education system is designed to encourage teachers to use interactive, graphical, game-like examples to teach and motivate students to use words and sentences It also promotes students’learning and understanding of the course through game teaching. This article conducts research on the performance and interest of students in English teaching by studying the English teaching courses of students majoring in virtual reality technology in health. Virtual reality technology is penetrating into various fields of daily life at an unprecedented speed. The combination with various disciplines is becoming more extensive and deeper. For traditional English linguistics and related disciplines, the huge impact of virtual reality technology is self-evident, but it also expands the research space for traditional system functional linguistics and pragmatics.
Content Characteristics and Limitations of Original Short Video Based on Depth Data
Authors: Wang Yingming 
Published: April 2021

Abstract
Short video is short video, which mainly relies on mobile intelligent terminal to realize fast shooting and beautification editing, and can be shared in real time and seamlessly connected on social media platform. Different from traditional media, short videos have low production and dissemination threshold, and do not need professional shooting skills. The disseminated content conforms to the characteristics that modern people absorb information fragmentation, which is why short videos are very popular at present. In the face of these changes, original short video came into being, and constantly innovated. Based on in-depth data analysis, this paper analyzed the content characteristics of original short video in narrative technique, form style, production subject, communication mode, etc., which has certain reference significance for the further development of original short video.
Research on Collaborative Innovation Platform of Internet of Things Industry Based on Data Mining Technology
Authors: Wang Yetong , Ku Junhua 
Published: April 2021

Abstract
The Internet era has entered a new historical development stage, and the technology and application of Internet of Things have become the new core of the future development of information society. Information technology has been integrated into every corner of production and life, and various information industries are constantly undergoing transformation, facing new opportunities and challenges. To enhance the technological innovation capability of China’s Internet of Things industry from the perspective of collaborative innovation, the key is to face the characteristics and needs of the Internet of Things industry and explore the interactive mode of process synergy and behavior synergy in technological collaborative innovation. There are more and more data generated in the process of human interaction with sensors, and more intelligent applications can be developed through deep data mining and analysis. This paper focuses on collaborative innovation of Internet of Things and smart service industry, and studies the combination mode of collaborative innovation of Internet of Things and smart service industry in China based on data mining technology, in order to provide valuable theoretical basis for the development of smart service industry and the improvement of collaborative innovation of Internet of Things in China.
Characteristics and Function Analysis of Swimming Life Saving System Based on Machine Vision Technology
Authors: Du Juan 
Published: April 2021

Abstract
As an integral part of the social system, swimming life-saving activity has become a system, so it is possible to study it from the perspective of system theory. Swimming lifesaving system is composed of two elements: people and things. The elements of people include lifeguards, drowning people and swimmers, while the elements of things include environment and lifesaving appliances. These factors have the relationship of mutual influence, interaction and promotion. Swimming is a sport with poor safety coefficient. It is far from enough to blindly pursue the improvement of swimming skills, so swimming should be analyzed and explored as a system. As an integral part of the social system, swimming life-saving activity has become a system, so it is possible to study it from the perspective of system theory. Based on machine vision technology, this paper analyzes the swimming pool positioning lifesaving system, hoping to provide guarantee for people’s life safety.
Construction and Application of Machine Learning Model in Network Intrusion Detection
Authors: Qi Guanglei , Chen Zhijiang , Zhao Haiying , Wu Chensheng 
Published: April 2021

Abstract
The modeling of network intrusion detection is a kind of significant network security protection technology. Nowadays, the network intrusion detection model can not accurately describe the intrusion behavior, so that incomplete network intrusion detection will occur. Therefore, based on machine learning algorithm, a network intrusion detection model is designed in the paper, which supports the fact that vector machine (SVM) fits the mapping relationship between network intrusion detection characteristics and network intrusion behavior. Meanwhile, a network intrusion detection model that reflects the relationship between the two aspects is established in the paper too. As a result, the experimental results show that the model proposed in the paper can not only accurately identify the network intrusion behavior, but has a very fast detection speed. Moreover, the model proposed in the paper has obtained much better network intrusion detection results than that of other models, which is of wide application prospect.
Moving Target Detection Classifier for Airborne Radar Using SqueezeNet
Authors: Li Guifeng , Tong Ningning , Zhang Yongshun , Feng Weike , Liu Chengliang 
Published: April 2021

Abstract
Conventional moving target detection methods for airborne radar always need many training range units. To solve this problem, this paper transforms the target detection problem into a multi-classification problem. Firstly, the training dataset is constructed based on a small amount of training range units. Then, a multi-class classifier based on SqueezeNet is constructed. Finally, the trained classifier is used to extract the characteristics of the received space-time data for target detection and parameter estimation. Simulation results show that the SqueezeNet-based airborne radar moving target detection method proposed in this paper can effectively detect the target and estimate its distance, doppler frequency, and other parameters. Compared with the conventional space time adaptive processing method, the proposed method can significantly reduce the number of needed training range units. Compared with the existing target detection method based on classification, the proposed method can effectively improve the accuracy of target detection and parameter estimation.
Research on Machine Learning Algorithm Based on Contour Matching Modal Matrix
Authors: Ye Junjie , Xu Wenjie 
Published: April 2021

Abstract
For the problem of contour matching modal matrix optimization, a combination of contour matching and gradient algorithm is proposed as an optimization search method. The basic idea of regression estimation and machine learning set is the same, both of them reconstruct the estimation of samples by estimating the machine learning clustering coefficients, and the main difference is the different models chosen. Although contour matching has the advantage of global optimization search, it has the defects of easy prematureness and poor local optimization search performance. Therefore, this paper combines it with the gradient algorithm, which not only speeds up the search in the gradient algorithm and ensures that the method converges to the global optimal solution, but also achieves the global convergence of the method and the high efficiency of the computational speed by keeping the optimal solution of the iterative process.
Sequence labeling model based on hierarchical features and attention mechanism
Authors: Yao Lijuan , Cheng Yanfen , Li Chao 
Published: April 2021

Abstract
Sequence labeling is a basic task in natural language processing, which is of great help to processing text information. Conventional sequence labeling approaches heavily rely on hand-crafted or language-specific features, which requires a lot of time. Therefore, most of the existing methods are based on the BiLSTM-CRF model, but how to use a neural network to extract useful information for each unit or segment in the input sequence becomes the main factor limiting the efficiency. Several BiLSTM-CRF based models for sequence labeling have been presented, but the major limitation is how to use neural networks for extracting useful representations for each unit or segment in the input sequence. In response to this problem, this paper proposes a sequence labeling algorithm based on hierarchical features and attention mechanism, which uses a hierarchical structure to integrate character-level and word-level information, and applies different attention mechanisms to these two layers of information. According to the structural characteristics of different levels, excavate more potential information. Finally, the previously captured and guided features are used for sequence tag prediction using CRF. Finally, the proposed model is subjected to comparative experiments and the results obtained are analyzed.
A dynamic resource chain task unloading method based on improved greedy algorithm
Authors: Bo Hu , Xiaolei Tian , Chao Yang , Wei Jiang , Jie Dong Shi Dan , Minghui Chen , Wu Chen , Shu Chen , Zhangjun Weng 
Published: April 2021

Abstract
the computing network based on cloud computing is becoming more and more complex. How to effectively unload the computing tasks is the key problem to improve the service ability of the computing network. In this paper, a mathematical model of task unloading problem in computing network is established, and an optimal solution based on improved greedy algorithm is proposed, which can quickly find the way of resource chain task dynamic unloading and realize task fast allocation and calculation. In this paper, the search space for greedy algorithms is too large and there is a lack of support for HPC LAN. Therefore, the greedy algorithm search is improved from one layer to two layers, which are respectively searched by the strongly connected graph and strongly connected graph of the virtual graph, which can adapt to the computing network with complex structure, and has friendly characteristics for high-performance computing LAN.
Evaluation Method of Pipe Corrosion Based on the Natural Frequency II: Corrosion of internal surface of pipe
Authors: Chen Yonghong , Ming Tingtao , Shen Bin , Zuo Huizi , Li Zhongying 
Published: April 2021

Abstract
Corrosion of internal surface of pipe is serious because it is difficult to observe. So it is very important to put forward a new method to evaluate the average corrosion rate of pipe which corrosion occurred in internal surface of pipe. Based on the damage of corrosion to the mechanical properties of materials, the mass loss factor of corroded pipe is defined, and the elastic modulus of pipe materials is equivalent. According to the modal analysis theory of elastic materials, a modal analysis model of corroded pipe was established, and the relationship between the modal of the corroded pipe and the mass loss factor was analyzed. The results indicate that it is effective and feasible to equivalence the elastic modulus of corroded pipe, and external surface corrosion is more destructive to the pipe than internal surface in the case of the same corrosion rate. At the same time, the model can be used to evaluate the corrosion rate of corroded pipe by monitoring and analyzing the modal..
Methods to Predict the Rest Life of Gear System
Authors: Zhang Yichi , Shu Tao , Li Wanli , Ding Rixian 
Published: April 2021

Abstract
Under the development of large-scale, complicated and automatic rotating machinery, people’s requirements for efficiency, safety and reliability are increasing daily. It has an important impact on prolonging equipment life, improving equipment utilization rate, increasing equipment safety, shortening maintenance time and reducing maintenance cost based on condition monitoring, identification and diagnosis of rotating machinery at the initial stage of fault. Taking the gear transmission system as the research object, this paper collects and enumerates the methods of gear system life prediction, and proposes a combined mathematical model of WD-GM (1,1) to predict the degeneration trend and failure time of gears. The prediction accuracy can reach 95%.
Research on the application of university teaching management evaluation system based on Apriori algorithm
Authors: Zhao Yang 
Published: April 2021

Abstract
University teaching quality evaluation is supported by big data, which can collect rich and comprehensive data materials as the basis of evaluation. However, it is difficult to find accurate and effective information from the huge data materials. This paper gradually establish “the whole process of implementation, multi stakeholder participation in the teaching quality evaluation system”. This paper take “the collection of data to achieve standardization, the application of data to achieve diversification and the management of data to achieve institutionalization” management measures. This paper make full use of the advantages of big data to improve the teaching quality of undergraduate teaching in local colleges and universities. This paper mainly expounds the Apriori algorithm in association rules of data mining, and uses it to further analyze the teaching evaluation results, trying to dig out the potential valuable information. These information can be used as a reference for further decision-making of teaching management department, so as to effectively improve the quality of education and teaching and accelerate the realization of education modernization.
Optimized frequent pattern mining algorithm based on Can Tree
Authors: Yong Ning Shi , Xiao Zhao 
Published: April 2021

Abstract
Due to the continuous dynamic changes of data in the current era, research on incremental association rules is necessary. Among them, frequent pattern mining has always been the subject of research. The research found that among the existing algorithms, Can Tree is very suitable for incremental mining because of its superior nature that it does not require adjustment, merging, and/or splitting of tree nodes during maintenance. In this paper, a new method of mining Can Tree is proposed to solve the problem of time consuming caused by repeatedly traversing paths when obtaining conditional mode basis. The path only needs to be traversed once to meet the requirements and verify it. Experimental results show that the performance of the algorithm is better than the traditional Can Tree algorithm, reducing time consumption to a certain extent.
Bibliometric Analysis based on Highly Cited Papers in Computer Science
Authors: Jiao Fangyu , Fang Ji , Ci Yutong , Tu Wenyan 
Published: April 2021

Abstract
From the perspective of bibliometrics, the important indicators of 4282 ESI highly cited papers in the field of computer science are analysed in this article, including publication time, countries/regions, institutions, journals, and keywords. China Mainland and the USA occupy a dominant position. The institutions with high number of papers and citations are mainly from China Mainland and the USA. The number of highly cited papers in China Mainland is increasing year by year. Most of the highly cited papers are dominated by international cooperation. Clusters of keywords through VOSviewer is to show the research hotspots that focus on algorithm optimization, isogeometric analysis, 5G communication, cloud computing, group decision making, etc.
Research on Spatial-Temporal Behavior Characteristics of Tourists Based on Digital Footprints: A Case Study of Guilin, China
Authors: Jiansheng Yao , Yanling Liu 
Published: April 2021

Abstract
Taking Guilin as an example, this paper constructs a digital footprint database based on online travel notes, and analyzes the temporal and spatial behavior characteristics of Guilin tourists from the aspects of time, space and network structure by using the methods of season concentration index, tourism preference index, nearest neighbor index and visualization technology. The results show that: (1) there is a certain seasonality in the tourist time, and the stay days are mainly 3-4 days; (2) the spatial distribution of Guilin scenic spots is unbalanced, and the visiting preference is uneven. The popular scenic spots include Li River, Yulong River, West Street, Two Rivers and Four Lakes, and Longji Terrace. Basis on this, this paper discusses the image promotion, path planning and marketing of the scenic spots Finally, the paper puts forward the optimization measures of Guilin tourism development.
Research and Implementation of New Distributed Information Interactive Platform
Authors: Wang Nan , Pan Aimin 
Published: April 2021

Abstract
The new distributed information interaction platform is based on the information system that is already in existence or under construction (or even to be built), so that the design and construction of the system can better meet the needs of various information(data) interactive processing in the future. In order to deal with the personalization problems that exist in various information systems, it provides custom means for the customization of event (business flow, workflow, data flow, etc.) processes and realizes a process automatic execution mechanism that is completely transparent to the operator.
Question answering system based on tourism knowledge graph
Authors: Sui Yuan 
Published: April 2021

Abstract
Nowadays tourism information services only provide users with massive and fragmented information returned by independent network search which makes users often need to spend a lot of time and energy to find what they really want from the massive data. As a result, route designing is very complicated. In view of this situation, this study builds a tourism knowledge graph based on neo4j and constructs a question answering system (QA). Also, we carry out the model and system performance evaluation, trying to improve the user satisfaction with query experience. According to the structure of question answering system (QA), this research designed and implemented named entity recognition (NER) model based on Bert-BiLSTM-CRF and matching reasoning model based on templates. With the above methods, natural language questions were successfully transformed into cypher query statements recognizable in graph database, and the corresponding answers will be captured and returned from tourism knowledge graph. According to the experiment, the method of Bert-BiLSTM-CRF obtains the state of art and QA system performs quickly and efficiently. For the purpose that artificial intelligence helps the development of tourism industry, this study has a certain significance.
NDN Producer Regional Content Synchronization Method Based On ChronoSync
Authors: Zhang Chaowu , Wang Hui , Fan Zhihui , Li Peiyu 
Published: April 2021

Abstract
In order to solve the problems of high handover delay, loss of interest packets and reduction of content hit rate caused by producer movement in naming data networks, a method of NDN Producer Regional Content Synchronization (NPRCS) based on ChronoSync protocol was proposed. In this method, the content of producers is synchronized to the cache of neighboring nodes in the process of producer movement to improve the availability of content after producer movement. Simulation results show that compared with the operating mechanism of NDN, the NPRCS method reduces the handover delay and retrieval time, and improves the consumer hit rate.
The application of imperfect information game theory in social games
Authors: Guo Ni , Qu Zehui 
Published: April 2021

Abstract
The research on game AI has always been one of the focuses of artificial intelligence research. More complex real-world problems can be solved by the study of game AI. Since our cognition and information acquisition of the world are imperfect, we proposed single deep counterfactual regret minimization method with advantage baselines, a deep reinforcement learning algorithm based on imperfect information game. The new approach combines the single deep counterfactual regret minimization approach with the baseline network to achieve a better performance than the benchmark in poker. At last, this paper applies the new algorithm to two Chinese poker games with imperfect information game, and obtains better performance. Meanwhile, it also provides the possibility for the extension to real life.
Research on Key Technologies of Industrial Internet Data Security
Authors: Yan Min , Huang Haibo , Li Jun 
Published: April 2021

Abstract
At present, industrial Internet data is facing a severe security situation in storage, transmission, and use. This paper proposes data security management and security protection strategies by studying the data security risks and status quo of industrial Internet platforms. Among them, the security management strategy includes data classification and classification, data security assessment and data security threat emergency management. The security protection strategy deploys industrial data security protection from the technical aspects of collection, storage, transmission, processing, sharing, and deletion, providing a reference for further strengthening the industrial Internet data security.
Classification method of IPv6 traffic based on convolutional neural network
Authors: Ying Pan , Shihui Wang , Xing Xu 
Published: April 2021

Abstract
This paper proposes a method of IPv6 traffic classification based on convolutional neural network. It aims to solve the problems of low accuracy, high dependence on traffic feature selection, inefficiency in IPv6 traffic classification technology based on machine learning, and the lack of research on IPv6 traffic classification methods. The method transforms IPv6 traffic into two-dimensional matrix as input of convolution neural network. Then, it constructs a traffic classification model of feature autonomous learning. The model consist of one input layer, two convolutional pooling layers, one full connection layer and one output layer. The experimental results show that the designed model has a good performance in IPv6 traffic classification.