bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intelligent Traffic Signal Control System Based on Edge Computing and Big Data
Authors: Yang Jinyan , Suo Hui 
Published: November 2020

Abstract
The traffic in big cities is becoming increasingly congested. Traffic jams need to be solved urgently. Intelligent transportation is the trend of future development. This paper proposes an intelligent traffic light control system based on edge computing and big data. Firstly, a large number of edge computing nodes are used to obtain all kinds of traffic related real-time data; secondly, big data is used to achieve the effective storage and processing of massive data; finally, neural network is used to integrate all the influencing factors to achieve the optimal control of signal lights. Experimental results show that the method is effective.
RETRACTED: Research on the Development of Network Economy under the Background of Big Data
Authors: Chen Gang 
Published: November 2020

Abstract
As the product of the development of Chinese network technology and big data era, network economy plays an important role in promoting the economic development of China. The development of computer network technology and the arrival of the era of big data have changed the mode of social production and consumption and triggered a revolution in the development of the network economy. Now, the network economy is gradually accepted by the masses due to its advantages of high efficiency, speed and convenience. However, compared to the real economy, its “virtual economy” characteristics will increase the risks of transactions and supervision. Based on this, this paper has studied the opportunities and challenges faced by network economy development under the background of big data and puts forward some suggestions and measures for the network economy to adapt to the challenges from the era of big data.
Research on the Science and Technology Collaborative Innovation Demonstration Zone Evaluation Based on Data Envelopment Analysis
Authors: Li Xiaosong , Tian Zhifeng , Li Zenghua , Peng Xinran 
Published: November 2020

Abstract
In recent years, studies on the Science and Technology Collaborative Innovation Demonstration Zone have increased notably. However, there were few works dedicated to the evaluation method, and our study was to fill in the blank. In this paper, the evaluation model based on Data Envelopment Analysis was built, 6 input and 4 output evaluation indexes were introduced, and a case analysis was conducted. The research result provides reference for objectively evaluating the construction effect of the Science and Technology Collaborative Innovation Demonstration Zone.
Implementation for Geological Environment Sub-node Data Sharing of Geological Cloud 2.0
Authors: Ren Xiaoxia , Yin Ming , Han Mingwei , Zhou Meng , Yu Mengliang , Huo Zhibin 
Published: November 2020

Abstract
Geological Cloud 2.0 is a comprehensive upgrade of data resources and systems on the Geological Cloud 1.0. As one of its distributed nodes, geological environment sub-node provides data sharing services in the geological environment for Geological Cloud 2.0. In order to better serve the comprehensive sharing of Geological Cloud 2.0 data resources, the specific technical implementation of Geological Cloud 2.0 geological environment sub-node data sharing was introduced with shared data. The construction effect of this node was introduced. The results show that the data sharing implementation technology of geological environment sub-node provides reference experience for Geological Cloud 2.0 construction and upgrading in the future.
Research on Multi-sensor Data Fusion Technology
Authors: Tong Yuqing , Bai Jiujun , Chen Xuebo 
Published: November 2020

Abstract
Multi-source sensor information fusion mainly collects various types of information from multiple independent decentralized sensors. Due to the different types of information, specific combinations of time and events are required to make the collected information more advanced and useful information. From the level of fusion, it will be merged from the three levels of data set, feature level and decision-making level. For different practical problems, it is necessary to use a certain level of fusion or a certain two levels of fusion according to the situation, so as to obtain the most optimal integration scheme.
Big Data Property Rights Confirmation Scenario and Its Applicability of Rights Confirmation Technology
Authors: Zhao Haijun , Wang Yanlin 
Published: November 2020

Abstract
The rapid development of the new generation of information technology constantly promotes the value appreciation and realization of big data, and the demand market for big data transactions is forming gradually. As the basis of big data transaction, the ownership of data property is worthy of wide attention in academic circles. This paper summarizes and analyses three scenarios of big data property rights confirmation and their tasks and work content, discusses five technical means and their applicability of big data property rights confirmation, and probes into the construction of large data information property rights evaluation model in the changeable dynamic environment. It is hoped that this paper will provide scientific reference and guidance for the future work of big data right confirmation.
Construction and Application of VR / AR-based STEAM Curriculum System in Primary and Middle Schools under Big Data Background
Authors: Hu Xiaoqiang , Wang Yuanyuan , Fan Xiaoting , Ma Zhimei 
Published: November 2020

Abstract
Virtual Reality Technology has been widely used in teaching to provide learners with a real sense of learning experience. At the same time, the new educational models, such as STEAM education and Creator Education, which are strongly advocated by our country, combine the two and use visualized big data to screen and analyze STEAM education, to understand the connotation and application of STEAM education and the role of virtual reality technology in STEAM education. This paper expounds the goal and practical curriculum structure of STEAM curriculum system in primary and middle schools based on virtual reality technology, designs and integrates the curriculum structure, constructs the concrete curriculum system, and gives the thematic teaching organization form, finally, the teaching application of the course system is discussed.
Evaluating Impacts of the COVID-19 Pandemic on China’s Container Ports Based on AIS Big Data
Authors: Zhu Jishuang , Qiu Weiwei , Jian Wentao 
Published: November 2020

Abstract
To quantitatively analyse the impacts of the COVID-19 pandemic on China’s container ports, the berthing ships are mainly investigated by the big data of Automatic Identification System (AIS). First, the methods of AIS data acquisition, cleaning and statistical analysis are introduced. Then the sample ports of Shanghai, Ningbo-Zhoushan and Tianjin are selected, and the monthly ratios of key indicators such as the number of berthing ships and berthing time are calculated by AIS analysis system. Finally, the numerical results show that although the COVID-19 epidemic does not significantly affect the number of container ships arriving at China’s ports, it has a significant impact on the average berthing time of container ships.
Metro OD Matching and Statistical Research Based on AFC Data
Authors: Jiang Ailin , Sun Qianqian , Peng Zhen , Wei Xinyi , Huang Qiang , Tong Ming 
Published: November 2020

Abstract
With the development of the smart city system, it is more and more import to improve China’s rail transit system and create a convenient traffic environment for passengers. This article will use the AFC data of Chongqing Regional Rail Transit for two months for OD(origin-destination) matching and passenger flow statistics. The main work includes AFC(Automatic Fare Collection) data cleaning, OD information matching, data conversion, passenger flow statistics and data visualization under the big data environment. At the same time, in the construction of the data platform, three schemes are currently adopted. One is to adopt the traditional method of java-Oracle when the amount of data is small. The second is to use the classic method of Hadoop-Hive when the amount is large. The third is to use parallel computing Maxcompute for large amounts of data to improve data calculation speed. Through the experimental comparison of the three schemes, the exact matching of the site, line, time and passenger flow statistics, in terms of speed, the parallel computing method is used to calculate the fastest data. This paper based on the AFC data of metro OD matching and statistical research can help the transformation of urban traffic structure from extensive to fine and provide support for urban transportation planning and decision-making.
The Application of Blockchain to Intelligent Port Logistics——Taking Hainan Free Trade Port as an Example
Authors: Liu Cuicui , Wu Shaoying 
Published: November 2020

Abstract
Blockchain technology promised a secured and trusted way to trade, and empowered traditional industries, such as the logistics industries. This paper first analyzed the researchers’ thought and real-world application of blockchain technology to the logistics industry, and then introduced the current development of information technology in intelligent port logistics in Hainan, an island that was transformed in to a free trade zone and was granted much more autonomy than that given to the other FTZs in China. Based on a case study of the possible application of blockchain in the lintelligent port logistics hubs of Hainan FTZ, this paper came to the suggestions that can be workable for the construction of ports alike in Hainan FTZ.
Study on the Prediction of Imbalanced Bank Customer Churn Based on Generative Adversarial Network
Authors: Li Bo , Xie Jiuzuo 
Published: November 2020

Abstract
The imbalanced commercial bank customer data will lead to the unpredictability of the minority class. Therefore, this paper proposes an imbalanced data method based on generative adversarial network to deal the problem of poor prediction performance of traditional classifiers for minority class. This paper method is based on the generative adversarial network to generate minority class samples to improve imbalanced data. Finally, the classifier is used to train the balanced data to improve the prediction performance of minority class. In this experiment, the data of a commercial bank customer were measured with indicators such as F1, Precision, and compared with traditional data sampling methods such as SMOTE, BSSMOTE. This method is feasible and applicable to the classification of imbalanced data of banks by observing the experimental results, which has better application value.
Threats to Wireless Sensor Networks and Approaches to Use Homomorphic Encryption to Secure Its Data
Authors: Babenko Ludmila , Tolomanenko Ekaterina 
Published: November 2020

Abstract
The article considers the basic principles of building modern wireless sensor networks. The general structures of such networks, their application spheres, their strengths and weaknesses are considered. Special attention is paid to the problem of security of processed and transmitted data, as well as existing approaches to providing reliable cryptographic protection of these data in wireless sensor networks. It is shown that at working out of mechanisms of maintenance of protection of the information in sensor networks first of all it is necessary to consider restrictions inherent in such networks on volume of memory, speed of used processors, and also power consumption. This leads to a more careful and weighted choice of encryption algorithms for use in sensor networks, both for direct encryption of data collected and transmitted by sensor networks nodes, and for auxiliary functions such as calculating the optimal network route by routers that are part of sensor networks. The article focuses on the possibility of using homomorphic encryption systems to protect data collected by sensors and transmitted via wireless connections. The advantages of full and partial homomorphic encryption systems in terms of their application to sensor networks with sensors measuring environmental parameters are shown. The analysis showed that using partial homomorphic encryption over the Paye Cryptosystem to hide data in sensor networks had reasonable grounds.
A Blind Watermarking Algorithm Based on Wavelet Transform for 3D Model
Authors: Sun Guangmin , Zhu Zixi , Zhang Zihao 
Published: November 2020

Abstract
In order to protect the visual effect of 3D model, watermarking algorithm should have good invisibility, thus a novel frequency-domain watermarking method is proposed in this paper. Robust regions of 3D model are selected as the watermarking regions in this algorithm, and watermarking regions are transformed into two-dimensional images depending on vertices distribution. Then the two-dimensional images are transformed into wavelet transform images by using wavelet transform algorithm and the watermark is embedded in low frequency coefficients of wavelet transform images. The experimental results show that the watermarking algorithm in this paper has high invisibility, and can resist common attacks such as noise, rotation, cropping and scaling.
Activation Detection Algorithms for Pattern Division Multiple Access Uplink Grant-free Transmission
Authors: Tang Wanwei , Kang Shaoli , Zhao Jinlong , Ma Zhuang , Zhang Xiurong , Wang Lixia 
Published: November 2020

Abstract
As a key technology of 5G, non orthogonal multiple access (NOMA) technology can significantly improve the spectrum efficiency compared with the traditional orthogonal multiple access technology. In addition, grant-free transmission technology can further reduce the transmission delay of the system and effectively improve the number of service users in the cell. In this paper, the pattern division multiple access (PDMA) technology (a typical NOMA technology) is taken as an example, PDMA technology based on Grant-free transmission (GF-PDMA) is studied, the uplink GF-PDMA pilot’s structure is designed and a user activation detection algorithms based on the received pilot’s power is proposed. The simulation results reviews that the algorithm proposed in this paper can fully meet the detection requirements of PDMA.
Friction Coefficient Inversion Calculation Based on Quasi - newton Method and Particle Swarm Optimization
Authors: Ren Yongliang , Zhang Guopeng , Sun Kai , Jiang Minzheng , Gao Sheng , Reifsnyder Sam , Zhang Ruijie , Wang Yan , He Shuwei 
Published: November 2020

Abstract
Establishing the pipeline network model of oilfield water injection under known partial pressure through fuzzy clustering method. In order to solve the inversion of pipe element friction coefficient in the case that the joint pressure of water injection pipe network is partly known, the simulation mathematical model of network node pressure and the optimal mathematical model of pipe element friction coefficient inversion are established respectively. By combining the simulation of nodal pressure by quasi-newton method and the inversion of friction coefficient by particle swarm optimization, the ideal pipe element friction coefficient of the pipe network with the partly unknown part of the node pressure can be inverted to achieve the partial accurate solution and partial estimation solution of the pipe element friction coefficient.
Multi-constraint UAV Fast Path Planning Based on Improved A* Algorithm
Authors: Kong Xiaodi , Pan Bin , Cherkashin Evgeny , Zhang Xiaoyang , Liu Linke , Hou Jian 
Published: November 2020

AbstractPath planning is an important part of UAV intelligent control technology. For the current UAV path planning, the A* algorithm has a large memory overhead during the planning process, and the search speed is slow. It cannot meet the path planning in a complex three-dimensional environment. The immediacy requirement and considering the constraints are less, this paper made the following improvements to the A* algorithm. The first, combining an anytime repair search framework with a weighted A* algorithm, it is possible to quickly find a feasible path during the search process. The second, aiming at the defect of relying on low heuristic function weights in the algorithm, a double ranking criterion is proposed to improve the efficiency of the algorithm approaching the optimal path in the iterative process. And third, to reduce the number of node expansions in the planning process, increase list storage constraints have been improved. Finally, the results of simulation experiments show that the improved algorithm proposed in this paper can quickly generate feasible paths, and can continuously optimize the approach to the optimal path within a specified time, which is far superior to the traditional A* algorithm in terms of planning efficiency and immediacy.
Research on Text Classification Algorithm Based on Machine Learning
Authors: Zhang Ping , Fang Yajie 
Published: November 2020

Abstract
In the era of big data, the data we analyze is no longer limited to numbers, while even more data analysis is based on text. Facing massive and complex data, traditional analysis methods cannot effectively process text data. Manual processing of text data not only consumes a lot of time and manpower, but also it is difficult to ensure the fairness and objectivity of the assessment. The development of machine learning provides us with an effective method of text mining. This article will introduce three classic algorithms in machine learning for elaboration, and apply them into a specific experiment to show the wisdom of machine learning in the field of text classification.
Research on Group Reverse Farthest Neighbour Query Algorithm
Authors: Liu Yongshan , Gong Xiang , Kong Dehan , Hao Tianbao , Yan Xiaoqi 
Published: November 2020

Abstract
In recent years, the demand for location-based services is growing. From resource tracking to personal life assistance, spatial data query technology plays an important role, especially the application of reverse nearest neighbor query technology in decision support, resource allocation optimization, data mining. By contrast, the reverse farthest neighbor query has become a more and more hot topic in the research of spatial database theory in recent years. The purpose of the reverse farthest neighbor query is to obtain the final result sets with the given point as its farthest neighbor, which is specially used to solve the problem of weak influence set in space. This paper gives a query optimization algorithm based on the least covering circle. The algorithm firstly introduces the minimal covering circle algorithm. Secondly, the p-ray pruning algorithm is used to filter the candidate set in the second level, and the candidate set is modified by the reverse far-neighborhood range query algorithm to obtain the final result set, which solves the problem in Euclidean effectively. Through specific comparative experimental analysis, c-grfn algorithm has good query performance not only in random distributed environment, but also in Gaussian distribution. It usually has 20% advantages of time in distributed space. As the data set increases, the time advantage becomes more obvious
Breast Cancer Prediction Based on K-Means and SOM Hybrid Algorithm
Authors: Lin Haoquan , Ji Zhenzhou 
Published: November 2020

Abstract
Breast cancer is one of the most serious diseases that threaten women’s health, affecting about 12.5% of women worldwide. Early detection of breast cancer is critical to saving lives. Therefore, If the physical examination indicators related to the human body can be extracted and the breast cancer can be analyzed through machine learning, which will play a key role in predicting and preventing breast cancer. As the high complexity and low precision of SOM neural network algorithm and shortcomings of K-means clustering algorithm needs to determine the number of clustering advanced and randomly select initial clustering centers, a hybrid algorithm combining K-means and SOM neural network is proposed in this study. The results show that the hybrid algorithm can accurately cluster the data sets, and compared with K means model and SOM neural network model, the performance of the hybrid algorithm model is better in clustering accuracy and computing speed.
An Automatic Detection Algorithm for Small Craters Based on Morphology
Authors: Ma Sushuang , Liang Jiarui , Tian Xiaolin 
Published: November 2020

Abstract
Study of impact craters on planetary surfaces has revealed secrets of the evolution of planets. Identifying craters on planetary surfaces has become fundamental in planetary researches, whereas time-consuming, e.g. months to years to collect crater database. Small craters, including a large number of secondary craters, have shown its importance in researches of planetary sciences, whose database requires construction. Whereas, few studies concentrate on detection of small craters. In this paper, the writer adopts morphology-based algorithm to detect and localize the crater candidates effectively. The methodology has shown its effectiveness by detecting 869 small crater candidates in a region covering 37500 × 56250 m2 on the Martian surface, with a precision of 79.29 %. Among those true positive detections, 91.25 % are craters with a diameter less than 1 km (not included). Compared with existing automatic detection methods on planetary impact craters, the method proposed in this paper performs better on detection of small craters, and it fills the blank of detection of craters with a diameter less than 200 m. Focusing on detection of small craters, it can provide a tool for quickly generating datasets for planetary scientific research in a targeted manner.
A novel Customer Service Recommendation Algorithm for Power Users
Authors: Ma Yuejiao , Liu Yongjun , Tao Ji 
Published: November 2020

Abstract
Aiming at the large amount of power user data and the fact that the collaborative filtering recommendation technology fails to consider the relationship between users and customer service staff, a k-means clustering and user portrait recommendation method is proposed. This method firstly uses clustering technology clustering the power users’ portrait label vectors to gather similar users together, and makes recommendations based on the cluster to which the users belong. Secondly, through calculating the user portrait label vector similarity between the attributes of the service feature vector to establish the connection between the user and the customer service, and improve the traditional grading forecast method, the user and the personnel of the service of similarity index is integrated into it. Finally, the personnel of the service will be recommended from the two aspects of service quality and service fits.
Research on Inverse Kinematics Algorithm of 6-DOF Industrial Robot Based on RBF-PID
Authors: He Dongkang , Shi Fusheng , Tan Shunxue , Deng Qigui 
Published: November 2020

Abstract
To solve the question of multi value and precision in the process of designing the inverse kinematics controller of 6-DOF Industrial robot, a radial basis function-proportional integral differentiation(RBF-PID) adaptive controller method has been proposed to train the inverse kinematics model of industrial robot. Compared with the traditional RBF neural network, the advantage of RBF-PID adaptive controller was that it had high search efficiency and was not easy to fall into local optimal solution. The simulation results showed that this method could not only overcome the influence of the initial value selection of the traditional RBF neural network learning algorithm on the network performance, but also had a good solution accuracy for the inverse kinematics of the 6-DOF Industrial robot. Compared with the traditional RBF neural network algorithm, the RBF-PID adaptive controller method had improved the performance of overshoot, adjustment time and anti-interference, and solved the problem of solving the multivalued inverse kinematics algorithm.