bluuebunny/crossref_metadata_2025 huggingface dataset:
A Dynamic Load Balancing Strategy Based on Improved Ant Colony Algorithm
Authors: Shi Hang Qi , Hao ZiLi , Shi Hang Qi
Published: April 2021

Load balancing technique plays a fundamental role in distribution, while facing numerous challenges. It is difficult for ordinary load algorithms to cope with complex scenarios, and it is likely that uneven load will lead to downtime of a single server, thus causing an avalanche effect and eventually breaking down the whole cluster. This paper proposes a load balancing strategy based on improved ant colony algorithm, considers the influence of task length and processing efficiency in the algorithm based on the original ant colony algorithm, and takes the load rate of nodes as an important index for ants to select path. In addition, the load coefficient includes CPU, memory, IO, and network bandwidth into the calculation range, which makes the obtained server load condition more accurate. Finally, test data of different algorithms are obtained through experiments, and the advantages and disadvantages of this load balancing strategy are verified after horizontal comparison.
Research on the Application of Computer Music Making Technology in New Media Environment
Authors: Zhang Meili , Hou Kewen
Published: April 2021

The rapid development of science and technology makes the use of new media technology more and more common. In the 21st century, the communication mode of new media is becoming more and more diversified, and its convenient and efficient communication mode can meet the needs of people’s modern office and life. Digital audio has a variety of production technology, and the production accuracy and controllability are very high, so it occupies the main position in the information composition of new media. As a sensory media, sound is mainly manifested in voice and music in daily life. At the same time, computer music production technology mainly includes digital processing technology and MIDI production technology. It can be seen that computer music production technology has become the core technology of sound editing, production and processing under the background of new media. This paper takes the application of computer music production technology as the research object, analyzes the internal relationship between new media and music production technology, the application and development of music production technology and so on, so as to provide the development direction for computer music production technology.
A Brief Analysis of the 5G+4K/8K+AI Strategic Layout of Central Radio and Television Station —— Taking the 2019 National Day Campaign Publicity Report as an Example
Authors: Wang W H , Du C X
Published: April 2021

On August 20th, 2019, at the Beijing International Radio, TV & Film Exhibition, the Central Radio and Television General Station announced the 5G+4K/8K+AI strategy, including four main parts: integrated broadcasting, ultra-high-definition television, mobile new media and artificial intelligence. CCTV is now comprehensively abandoning standard production system to achieve comprehensive high-definition, comprehensively promoting the development of 4K/8K, building the first national 5G new media platform, combing artificial intelligence with media, and building a new strategic layout. On the occasion of the 70th anniversary of the Chinese National Day, the Central Radio and Television Station integrated various television channels, broadcast frequency, and website platforms, carried out panoramic and three-dimensional live and broadcast, realized the whole process of 5G+4K ultra-high-definition and 5.1 stereo live broadcast, and provided a new audio-visual experience for end users. This article will take 2019 National Day 70th Anniversary publicity report as an example, excavate the characteristics and advantages of 5G+4K/8K+AI strategic layout, and discuss the development trends and predicament of the initial stage of strategic layout.
Image Inpainting of Multi-Spectral Image with Laser Lines Based on Generative Adversarial Network
Authors: Lu L , Zhong G , Dong J
Published: April 2021

This paper presents a Generative Adversarial Network based on image in-painting, which can reconstruct the shape using a multi-spectral image with a laser line. One of the difficulties in multi-spectral photometric stereo is to extract the laser line, because the required illumination for multi-spectral photometric stereo, e.g. the red, green, and blue lights, may pollute the colour of the laser line. In this paper, we presents a method, which uses the Generative Adversarial Network based on image in-painting, to separate a multi-spectral image with a laser line into a clean laser image and an uncorrupted multi-spectral image without the laser line, to reconstruct the shape using a multi-spectral image with a laser line. To make the proposed method applicable to real-world objects, a rendered image dataset obtained using the rendering models in ShapeNet has been used for training the network, and the evaluation shows the superiority of the proposed approach over several previous methods, on both rendered images and real-world images.
Pyramid and Similarity Based Feature Enhancement Network for Person Re-identification
Authors: Chu Chengguo , Qi Meibin , Jiang Jianguo , Chen Cuiqun , Wu Jingjing
Published: April 2021

Person re-identifification (Re-ID) has becomes an increasingly important task due to its wide applications in the field of intelligent video surveillance. However, Because of the camera shooting angle, the different proportions and pose changes of pedestrians in images are also different, such problem substantially hinders the model’s capability on further improving feature extraction. In this paper, we propose a new end-to-end architecture named Pyramid and Similarity Based Feature Enhancement Network (PS-Net) which includes Pyramid Joint Attention(PJA) and Similarity Feature Fusion Branch(SFF) to enhance the feature maps performance. Among them, PJA contains spatial attention at different receptive fields and channel attention to enhances the discriminative area of pedestrian features by combining the advantages of two different attentions. SFF fuses the features which have different levels of information but have congruent relationships, enriches the information contained in pedestrian features and generates more robust feature. Extensive experiments have been conducted to validate the superiority of our PS-Net for Re-ID over a wide variety of state-of-the-art methods on two large-scale datasets: Market-1501 and DukeMTMC-ReID.
Research on Vehicle Appearance Damage Recognition Based on Deep Learning
Authors: Zhu Qianqian , Hu Wei , Liu Yingnan , Zhao Zihao
Published: April 2021

Economic development has promoted the booming of the auto industry. With the increase of the number of cars, car insurance has become the largest type of insurance in the insurance industry with more than half of the market share. After the emergence of traditional vehicles, professional loss assessment personnel need to go to the scene to investigate the accident and complete the loss assessment. In recent years, With the rapid development of science and technology, the insurance industry has been changing from artificial and information to automation and intelligence. This paper presents a vehicle appearance damage recognition algorithm based on deep learning and its model evaluation method, which can accurately judge the vehicle damage in the image. The research shows that the Mask R-CNN model based on KL-loss performs well in vehicle damage detection and has good robustness; at the same time, the accuracy of the evaluation model results is greatly improved by replacing the traditional IOU calculation accuracy method with the component position.
A Context-Aware Gated Recurrent Units with Self-Attention for Emotion Recognition
Authors: Li Lin , Yang Miaomiao
Published: April 2021

Text-oriented emotion recognition research has made significant research progress, however, very few works pay attention to learn the highquality long-distance contextual information for the utterance emotion recognition. In this paper, we proposed a context-aware gated recurrent units with self-attention for emotion recognition. The two bidirectional gated recurent units can obtain the sequence ralationship between words and utterances. Compared with the self-attention mechanism that cannot capture long-distance contextual information, three contexts are used in the self-attention mechanism, namely global, deep and deep-global context, which can capture the highquality long-distance contextual information. And the connection mechanism can enhance the embedding information of words and utterances. Experimental results on public Friends and EmotionPush datasets demonstrate that the three contexts in the self-attention mechanism outperforms several baselines on some emotion types recognition and indicate the effectiveness of the designed model, especially the deep-global context increased by 1% and 0.2% on UWA compared with all models.
A Multi-Channel LSTM Model for Sea Surface Temperature Prediction
Authors: Lin Yidi , Zhong Guoqiang
Published: April 2021

Sea Surface Temperature (SST) plays an important role in marine ecology. SST prediction raises considerable attention in ocean-related fields. Recently, deep learning models are widely used in SST prediction, but it is not easy to obtain optimal prediction results using historical observation data directly. More than temporal information, SST data also contains other features, such as trend, periodicity and disturbance. In this paper, we proposed a Multi-Channel LSTM (MC-LSTM) model to improve SST prediction. Firstly, a wavelet transform is used to decompose time-sequence data into multiple sequences representing trend, period and disturbance respectively. Secondly, we use multiple LSTM channels to train these data in parallel, and then obtain the combined prediction results. MC-LSTM can predict Sea surface temperature only by historical SST data, without the help of other spatial and climatic information, so it is easy to get the data. Compared with the direct use of LSTM for prediction, MC-LSTM can improve the prediction accuracy by 26%.
Research on Replica Placement Strategy for New Building Intelligent Platform
Authors: Yang Y L , Zhu J C , Zhang R , Zhu X L , Wang M Y , Wang C
Published: April 2021

The new building intelligent platform adopts single point storage in the data storage mode, which does not reflect the distributed characteristics of the platform. Once the node is damaged, the data will be lost. To solve this problem, this paper improved the data storage mode of the new building intelligent platform, combined Hadoop distributed file system (HDFS) with the platform, and realized the reliable backup of the platform data. For the deficiency of HDFS default replica placement strategy, the paper advanced a algorithm of replica placement for the new building intelligent platform based on PSO, and obtained the optimized replica placement strategy by considering the storage space residual rate of nodes, bandwidth residual rate and network distance between nodes. Simulation was on the CloudSim platform. The results of experiment show that the proposed scheme was superior to the default replica placement strategy in load balancing, which can reduce the load pressure of single node to a certain extent and improve the load balancing of the whole platform.
Design and Realization of Modern Smart Home System Based On Multimedia Network Technology
Authors: Yuan Hongmu
Published: April 2021

With the development of science and technology and the progress of multimedia network technology, various intelligent electronic products are more and more widely used in people’s lives. High-tech and intelligent technology have moved from intelligent buildings to residential home users, and home intelligent technology has become a cutting-edge technology topic. In this regard, the purpose of this article is to study the design and implementation of modern smart home systems based on multimedia network technology. This article first studies the development status of domestic and foreign multimedia network technology and smart home technology, and conducts a questionnaire survey on some residents to understand the current status of smart home. Finally, based on this, this article uses high-level programming language to code the system. According to the method and technology of test theory, the function and performance of the software system are tested completely, and compared with the traditional smart home system. The experimental results show that the modern smart home system using multimedia network technology is more recognized and loved by residents, and the residents’ preference for smart home has increased by about 20%, which has played a significant role in increasing the utilization rate of smart home systems and is a multimedia network the future development of smart home systems provides an important reference.
Big Data Analysis of Threat Intelligence Based on IP
Authors: Xu Fajian , Chen Shuzhen
Published: April 2021

This paper first discusses the concept of network threat intelligence and related resource integration, and proposes a three-tier architecture model of threat intelligence analysis system based on IP. Then, it discusses the diversified data acquisition technology, information index extraction technology, data mining and data modeling analysis technology, the key technology of threat intelligence credible intelligence analysis model based on machine learning and privacy protection. Finally, this paper considers and summarizes the legal standardization of threat intelligence analysis.
Analysis of Difference for Gully Density and the Relationship between Terrain Factors and Gully Density in the Losses Plateau Based on Google Earth and GIS
Authors: Zhu Jiangshan , Hou Chenglei
Published: April 2021

Based on ASTER GDEM V2 (30 meter) and the corresponding Google Earth high-resolution images data, small-catchments artificial interaction and random points sample method were used to estimate gully density of five selected catchments in the Losses Plateau and analysis the relationship between the difference for gully density from two methods and the areas of catchments. Besides the gully density, terrain factors such as slope, flow accumulation as well as the distribution probability of gully were calculated by using spatial analysis method. The statistical analysis method was used to analyze the correlation between gully density and terrain factors and obtain gully characteristic distribution prediction model. The results of the study indicate the following: (1) the average difference of gully density extracted by random point method and artificial interactive method is 7.86%, and the difference appears to increase first and then decrease with the watershed area. When the area of catchment is 76 km2, the difference is closed to 0; when the area is 131 km2, the difference is closed to maximum value, which is 0.182. The time-saving and labor-saving random point method can replace the tedious artificial interaction method. (2) The gully slope on Loess Plateau is mainly distributed at 10°∼30° (74%), and the flow accumulation of gully distribution is mainly distributed at 0∼0.07 km2 (95%); 10°∼30° and 0∼0.07 km2 can be used as critical reference values for, respectively. (3) The gully distribution probability showed a when slope gradient was lower than 30 degree, while a negative correlation of gully density and the flow accumulation were found in this study.
Garment Textile Correction System Based on Artificial Intelligence under Computer Parameter Optimization Design
Authors: Yan Lifeng , zhang Qianyun , Liu Wei
Published: April 2021

As artificial intelligence technology is widely used in more and more fields, it has become the frontier technology of social innovation. Artificial intelligence based on the development of computer parameters has been widely used in the clothing textile industry, and the technology of the clothing textile correction system has appeared. This article will discuss in detail the development trend of my country’s artificial intelligence technology and the establishment of computer parameters for clothing textiles in combination with the development, model status, and development status of domestic and foreign clothing textiles. Trend and production models will also be developed and analyzed for apparel textile correction system models. The clothing system’s artificial intelligence and production integration, computer parameters, product specifications and other aspects are analyzed. The purpose of this article is to create a clothing textile correction system, which can provide a useful reference for the development of artificial intelligence in the clothing textile industry in our country. Based on relevant knowledge and modern theories in practice, this article is based on a clothing textile correction system using artificial intelligence in computer parameter optimization design. In the course of the experiment, the implementation of the garment textile correction system summarized the technical value and function, as well as the technical purpose and development prospects, through rigorous experiments in theory and practice, to innovate and develop the traditional garment textile production mode. Experimental data shows that under the optimized design of computer parameters, the creation of artificial intelligence-based clothing textile correction system can provide a more convenient way for the clothing textile industry, integrate artificial intelligence into clothing textile production, and improve management efficiency and production efficiency.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
M-YOLO: A Nighttime Vehicle Detection Method Combining Mobilenet v2 and YOLO v3
Authors: Huang Shan , He Ye , Chen Xiao-an
Published: April 2021

Vehicle detection at nighttime plays a vital role in reducing the incidence of night traffic accidents. In order to further improve the accuracy of nighttime vehicle detection, and to be suitable for constrained environments (such as: embedded devices in vehicles), this study proposes a deep neural network model called M-YOLO. First, M-YOLO’s feature extraction backbone network used the lightweight network MobileNet v2. Second, the K-means algorithm is reused to cluster the dataset to obtain the anchor boxes which are suitable for this paper. Third, M-YOLO uses the EIoU loss function to continuously optimize the model. The experiments showed that the average precision (AP) of proposed M-YOLO can reach to 94.96%. And ten frames per second (FPS) were processed in a constrained environment. Compared with YOLO v3, the proposed model performs better in detection accuracy and real-time performance.
Research on the Detection Method of Information System Access Abnormal Behaviour
Authors: Zhang Linghao , Wang Sheng , Xiang Siyu , Zhang Jie , Liang Yunhui
Published: April 2021

The security of the information system is very important to the production of electric power companies. However, at this stage, there are still problems such as the inability to detect abnormal/malicious behaviors and traffic of the terminals in the network, and the inability to detect and deal with abnormal behaviors in a timely manner, leading to increased risks. This paper chooses to start with the detection of abnormal traffic as the research goal, and realizes the detection and classification of abnormal network traffic through the convolutional neural network algorithm to achieve the detection of abnormal behavior of information system access. It plays a fundamental role in many network security-related tasks, such as the design of a network traffic anomaly detection system, and has very important practical significance.
A Fine-Grained Data Mining Model Based on Psychometric Data
Authors: Wang Zhenzhen
Published: April 2021

As social development and the pace of life gradually accelerates, psychological pressure and conflict become more and more prominent, and mental health becomes an issue of growing concern for the country and its people. In order to study the relationship between people’s mental health problems and their own personal information factors, the authors use a fine-grained mining model of psychometric data. First, the model clusters user groups through the k-means++ clustering algorithm, determine which group the user belongs to, and then use the C4.5 decision tree algorithm to make predictions in the corresponding group.
Design of an automatic weighing check system for small material feed
Authors: Liu Zhaoxia , Chen Yan
Published: April 2021

Small material is an important part in feed production, and it is one of the keys to guarantee product quality. Nowadays, small materials are putting into small batches artificially in many feed enterprises, and the small materials are weighing mannually before putting. In order to improve the automation of the weighing and putting process of small materials, this study designs an automatic weighing check system for small materials. The system is composed of Arduino electronic development platform equipped with weighing sensor, liquid crystal display module (LCD), and relay module. The results show that the relative error of the weighing check measure is less than 0.2%, and it demonstrates that the system has realized the precise control of the weighing check of small materials, which is beneficial to reduce the work intensity of workers, and improve the automation degree of the feed production industry.
Linguistic Question-Answering Reasoning Based on Intelligent Perception of Attribute Weight
Authors: Xiao Liang , Wei Dan
Published: April 2021

Intelligent Question Answering System aims to realize the communication between human and machine. Based on the real data of question-answer, of front end design, and of daily chat in Chinese, this paper proposes a design method of Intelligent Question-Answering System based on deep neural network and optimizes it. Through real data from authentic view and online daily chat, the paper constructs the property weight, laying foundation for the system to play its role in the real scene such as text cleaning and word segmentation, part of speech tagging, word vector representation, word weight adjustment, etc. By using the mixed model of BOW and Skip-gram to represent the word vector, the paper describes the correlation between words and makes for the shortcomings of BOW. Meanwhile, it retains Bow’s excellent ability of discrete-feature processing.
Research on College English Teaching Model Reform Based on Flipped Classroom
Authors: Xie Jingjing
Published: April 2021

With the rapid development of science and technology, information technology has been widely applied in modern education. It is an urgent need to further enhance College Foreign Languages teaching reform and cultivate high-quality and versatile international talents. College English teachers should exploit and construct courses based on computer and the Internet in order to provide students with better language learning environment and condition. Flipped Classroom teaching mode actually applies modern information technology, especially the network technology, and makes College English teaching and learning unrestrictive anytime and anywhere to some extent, in order to develop towards individuality and autonomous learning. This paper introduces current College English teaching model reform trend. Then, theoretical basis of College English Flipped Classroom and methodology are exhibited. It makes quantitative and qualitative analysis of questionnaire, interview and test and discusses the results due to statistical data. Finally, it draws the conclusion that College English teaching model reform based on Flipped Classroom is feasible and effective.
RETRACTED: The Application of Journalism in the Era of Big Data
Authors: Yang Jiangkejie , Xiong Zhihua
Published: April 2021

The application of big data in journalism is of great significance and plays a subversive role in traditional journalism. It can also promote the development of journalism, including the paradigm transformation of news narrative and so on. It can also provide a good development opportunity for journalism. In addition, the bias of big data will also make news lose authenticity, and the bias of news will become more difficult to detect, which will make news become less objective and cause damage to news to a certain extent. At the same time, the excessive mining of individual information by news will also lead to the invasion of individual privacy and abuse of personal information. Data in and of itself is not harmful, but the absence of a set of principles for how data is applied can have serious consequences. Therefore, it is very important to establish a sound application principle of news data. of news data.