bluuebunny/crossref_metadata_2025_split huggingface dataset:
RETRACTED: Preliminary Study on Credit Risk Management under Internet Finance Based on Big Data Age
Authors: Ma LiYun 
Published: June 2020

Abstract
Under the support of modern technology, Internet finance as a new financial model gradually improves the work structure and changes the traditional forms of financial work, so that the work becomes more convenient. After the emergence of Internet finance, due to the short time of its development, we should also introduce modern technology in the process of carrying out related work according to financial management, and use big data to analyze working data, and find that many work will be difficult to carry out because of lacking of scientific management mechanisms during the period of Internet financial application so that it cannot achieve the desired effect. There are great security risks in the Internet financial market because of lacking of credit risk management. In order to improve our financial system, we need to pay more attention to the credit risk management. According to the problems existing in the operation of Internet finance, we put forward some solutions.
Detection of Malicious Domain Name Based on DNS Data Analysis
Authors: Zhang Kunsan , Ji Wen , Li Nan , Wang Yiting , Liao Shengyang 
Published: June 2020

Abstract
This paper describes the research background of malicious domain name detection based on DNS data analysis, obtains DNS data through active domain name data or passive domain name data, then obtains DNS data through knowledge-based methods, detection methods based on machine learning and hybrid methods, and finally puts forward research suggestions.
Research on Data Acquisition System of EMU Structure Health Monitoring Based on DSP and FPGA
Authors: Li Chao , Zhang Bin 
Published: June 2020

Abstract
According to the characteristic that structural health monitoring have many monitoring sensors and monitoring signal types, with DSP and FPGA used as the core controller, a data collection system was designed. The designed system can realize multi-channel parallel data collection, flexible port configuration and good scalability, which meets the requirements of high-speed collection and processing and real-time online monitoring. The designed data acquisition system has the advantages of high performance, low cost and convenient application. In addition, the system has a wide sampling frequency range, which can adapt to a variety of sensor signal sampling requirements, is easy to install, easy to achieve network configuration, and can better meet the structural health monitoring of railway vehicles.
Attribute Reduction of Coal Mine Fire Incidents Based on Finding Maximum Mutual Information
Authors: Zhang Manting , Feng Weibing 
Published: June 2020

Abstract
There are many redundant attributes for the original data of the emergency system with uncertainty and data loss, and the existing incomplete information system attribute reduction algorithms have the disadvantages of high time complexity and completeness of the reduction result, an attribute reduction method for incomplete emergencies based on finding the maximum mutual information is proposed. Firstly, the original data of the emergency was preprocessed, and the situation and the consequences were regarded respectively as conditional attributes and decision attributes, thereby an incomplete decision information table was constructed; then, according to the characteristic that the mutual information of the conditional attribute set and the decision attribute is equal to the information entropy of the decision attribute in the consistency incomplete information system, the attribute reduction algorithm based on finding the maximum mutual information was obtained; finally, this method was used to reduce the attribute of coal mine fire data and compared it with other three methods. The results indicate that this method can effectively reduce the redundant attributes of coal mine fire emergencies, and can ensure that the reduction results have strong completeness when the algorithm time is low, thus decision-making for the emergency department is provided.
Research on Precision Planting Management System Based on Agricultural Big Data
Authors: Li Yu 
Published: June 2020

Abstract
Through the use of agricultural intelligent sensing equipment and intelligent control equipment, the precision planting management system builds the Internet of Things monitoring network in the agricultural production site to realize real-time data collection, monitoring of the climate environment, soil conditions, crop growth, pests and diseases, and remote automation control of various agricultural facilities and equipment on the site. The collected agricultural big data is uploaded to the planting management cloud platform in real time, and statistical analysis is performed instantly, which addresses the staff’s demand for data collection and analysis. Through data mining and analysis, the rules and expertise of high-quality and high-yield crops are obtained, which further guides the regulation of agricultural varieties and production environment, so as to achieve intelligent planting.
Overview and Prospect of secure communication based on Federated learning
Authors: Chen Qin , Xu Nan 
Published: June 2020

Abstract
With the increasing amount of data, the in-depth application guided by data prediction has ushered in the climax of development However, due to the limited depth and breadth of the data owned by each unit, the data among different units are not common, so more and more data islands are formed in the city. Data sharing has become a major obstacle to the construction of smart city. To solve the problem of data sharing, in 2016, Google first proposed the concept of Federated learning. However, the bandwidth of wireless communication has not increased significantly. As a result, the bottleneck has shifted from previous computing to today’s communication problems. During my internship in a company, I participated in a project to provide platform support for federal learning. The purpose of the platform is to facilitate the transformation of federal learning academic achievements into commercial products. Based on this project and the current situation of federal learning, this paper summarizes the secure communication problems of federal learning.
Weibo user attribute analysis method based on multi-feature
Authors: Cheng KeFei , Shan Fengchi , Song ZhiWen , Zhao ZhenNi , Guo XiaoYong 
Published: June 2020

Abstract
In view of the deficiency of domestic Weibo user attribute analysis, the imperfection of Weibo feature extraction and the problem that the classification accuracy needs to be improved, a Weibo user attribute analysis method based on multi-features is proposed. This paper first uses the word2vec model to build text features from Weibo content, then constructs Weibo user features from Weibo information and user information, and finally sends multi-feature sets into the improved three-tier stacking model to build Weibo user attribute analysis model. The experimental results show that this method has better classification effect than the text-based classification method and the traditional stacking model.
Performance Research of LoRa at High Transmission Rate
Authors: Zhang Zhihao , Zhang Baiguo , Zhang Xiaobo 
Published: June 2020

Abstract
In recent years, most of Lora’s research in the field of wireless technology has focused on the experimental exploration of its stability and communication range, without much attention to the impact of the actual transmission rate. However, as the length of Lora transmission load increases, the growth rate of low-speed transmission time is faster than that of high-speed transmission time. Therefore, it means that for the transmission of large capacity packets, the lower the transmission rate, the more time it takes; the longer the time, the more power it consumes. In this paper, the SNR, RSSI and packet loss rate of Lora over 3 Kbps in urban area and mountainous area were tested and explored respectively, and the RSSI curve with distance in different environment was obtained. The test results show that the actual communication distance of Lora with high transmission rate of 21875 bps in line of sight environment can reach 3 Km, packet loss rate is less than 1%. In addition, the influence of Lora on communication quality under different occlusion degrees in a certain range was tested. In the range of 500 m to 3 km, when the occlusion ratio was 0-5%, the highest speed reference value was 21875 bps, 5-10% reference value was 6250 bps, and more than 10% reference value was 3125 bps. In this paper, Lora SX1278 transceiver was used, in the same transmission of 100 KB data, the actual transmission rate is further improved by using the maximum duty cycle and improving the request response mechanism. Compared with a Lora system for image transmission, the transmission time is reduced by 81.3% and the transmission speed is increased by 4.3 times, which greatly improves the transmission efficiency of large capacity data such as image.
Space Attack Technology Overview
Authors: Liu Zhenhua , Lin Chuanwen , Chen Gang 
Published: June 2020

Abstract
With the development of aerospace technology, space security has become the focus of national attention, and space offensive and defensive technologies have become increasingly important. This article summarizes the attack methods of space satellites and aircraft. It focuses on analyzing three types of space attack technologies: kinetic energy attacks, directed energy attacks, and communication interference. In-depth analysis of anti-satellite missiles, common-orbit satellite interceptors, high-energy lasers, microwaves, particle beams, communication suppression, communication deception and other attack technologies, introduced the maturity and test situation of each technology, and sorted out for further development of space attack and defense technology research Several research directions.
TAC: A unified trust anchor framework based on consortium blockchain
Authors: Zhang Feng , Jiang Wenbao , Shi Boxuan 
Published: June 2020

Abstract
In the current network, there are still many security issues, such as identity fraud and address spoofing. In traditional Public Key Infrastructure(PKI), most trust models adopt a centralized trust model with a single-point trust anchor. The existing PKI system lacks a unified authentication framework and is low efficiency. Aiming at the problems of how to store public keys efficiently and how to make the network secure and trustworthy, this paper proposes a new trust anchor framework based on consortium blockchain, named Trust Anchor Chain(TAC). This study uses the form of consortium to manage TAC, proposes a hierarchical naming ID, named TAC

ID
to uniquely identify the user in TAC, and proposes a new data structure, named Struct Radix Trie, to store public key information in TAC to improve efficiency. Section 4, TAC has superiority through the comparison between local experiments and remote experiments. In TAC, users can access each other in the TAC, get the public key, and ensure the correctness of the identity. All users work together to maintain TAC to achieve equal network interconnection, security and credibility.
Decision-making Risks of Operational Tests from a Statistical Point of View
Authors: Bo Yun , Bai Yu , Liao Xuejun 
Published: June 2020

Abstract
The operational test, along with the evaluation based on it, is the most important decision-making basis for the Department of Defence in acquiring weapons and equipment. However, there has been relatively little attention to the credibility of its conclusions. In order to evaluate the decision-making risks quantitatively, the relationship between the two types of errors in the operational tests is studied. By analysing the interaction between the factors affecting decision risk errors, it is pointed out that it is of great practical significance to ensure that the tests have sufficient statistical power, which is usually overlooked before in operational tests.
Pollution of soil environment by oil exploitation in loess hilly region
Authors: He Lirong 
Published: June 2020

Abstract
This study selected the five typical Wells area as the research object in the town of Li Qu, Baota district, Shaanxi province. The petroleum hydrocarbon content of soil samples collected from 0~10m, 10~30m and 30~50m outside the oil well center was studied and analyzed, the results showed that the well surrounding soil samples of petroleum hydrocarbon concentration in the range of 73-1090 mg/kg, much higher than that of petroleum contaminated soil (500 mg/kg), and petroleum hydrocarbon concentration are radiation pollution radius distribution. According to the standards of oil contaminated soil remediation stipulated by Oklahoma (500mg/kg) and Canada (450mg/kg), the soil around the oil contaminated soil remediation needs to be remediation in different years of production. In addition, due to the specific degradation effect of indigenous microorganisms on petroleum hydrocarbons in the soil, the soil pollution level of the oil Wells in operation is higher than that of the abandoned ones. The soil oil pollution in 2 # and 5 # oil wells is about the critical value in the soil remediation standard.
Research on Reliability Assessment Method of Emergency Escape Parachute
Authors: Ma Fangyun , Lei Huajin , Wang Wensheng , Liu Jing , Jin Liying 
Published: June 2020

Abstract
Aiming at the problem that emergency escape parachute cannot meet the requirements of the index using the binomial evaluation method, combined with the characteristics of emergency escape parachute products, comprehensively using emergency escape parachute components and system test data, the emergency escape parachute Bayesian reliability assessment method is proposed to solve the problem of reliability assessment in the case of small sample size of emergency escape parachute system test, which effectively reducing emergency escape parachute test expenses and development cycle.
Research on Task-oriented Dialogue Based on Modified Transformer
Authors: Jiang ZongLi , Zhang Shuo 
Published: June 2020

Abstract
The traditional end-to-end task-oriented dialogue models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. But in the case of large amounts of data, there are many types of questions. It performs poorly when answering multiple types of questions, memory information cannot effectively record all the sentence information of the context. In view of the above this, this article uses a modified transformer model to overcome the problems mentioned in dialogue tasks. Transformer is a model constructed using attention mechanisms, which completely discards the method of RNN (recurrent neural networks), and its structure includes two sub-parts of Encoder and decoder. It uses residual network, batch normalization, and self-attention mechanism to build the model structure, uses Positional Encoding to capture sentence information, which can speed up model training convergence and capture Longer sentence information. In this paper, we modified the activation function in the transformer and use label smoothing to optimize the training to make the model’s expressive ability better than previous.
Analytics for the Sustainable Use of Resources in Hospitals: LOS classification for nephropathy patients
Authors: Zhang Xinli , Shen Xue 
Published: June 2020

Abstract

Aim: Exploring the impact of nephropathy patient characteristics on length of stay (LOS) grading, proposing a path of LOS classification based on the characteristics of patients, and providing suggestions for the accurate management of shunting of patients with nephropathy patient to promote the sustainable development of the hospital.

Methods: The data of inpatients from the Department of Nephrology of a large hospital in 2016 were used, including five variables: gender, age, patient type, medical insurance type, and LOS. Based on quantifying patient attribute variables, We use three steps to finish the grading. Firstly, using the factor analysis to extracte the common factors of patient characteristics. Sencendly, according to the results of factor analysis, using k-means clustering analysis to classify the patients. Finally, According to the characteristics of different types of patients and the law of LOS differences, a LOS classification path based on patient characteristics is proposed.

Results: The factor analysis shows that the LOS common factor characteristics are disease characteristics, attribute characteristics and reimbursement ratio characteristics. The k-means clustering indicates that the patients are divided into 5 categories: the mean LOS in category 1 is 15.78, Patient characteristics: Mostly elderly women with the blood resuscitates patients(38.2%) or tumor recovery patients(30.3%), city medical insurance(50%);the mean LOS in category 2 is 10.5, Patient characteristics: Mostly strong men with the ordinary patients(62.5%), City medical insurance(79.2%);the mean LOS in category 3 is 7.62, Patient characteristics: Mostly young men with the other patients(99.7%), Provincial medical insurance(73.1%);the mean LOS in category 4 is 13.7, Patient characteristics: Mostly women in pre-old age with the Ward daytime patient(38.9%) or other patients(31.8%), Urban rural medical insurance(60.6%);the mean LOS in category 5 is 6.73, Patient characteristics: Mostly young men with the other patients(99.3%), Provincial medical insurance(54.4%).According to the characteristic differences among patients and the law of LOS differences, a model of patients’ LOS classification path was proposed.

Conclusion: The LOS classification path based on patient characteristics can realize the pre-classification management of patients, which has practical significance for early intervention of hospital resources.
Review article: the key technologies of brain-computer interface
Authors: Cui Hongmei , Jiang Qin , Lin Wenzhang , Liu Xiujun 
Published: June 2020

Abstract
As an important branch of intelligent robots, brain-computer interface (BCI) technology has great application prospects in the fields of neurological rehabilitation, functional prosthesis assistance and neurological monitoring. Many breakthroughs have been made in the research of brain-computer interface. Based on the existing research literature, this paper has done the following work: outlined the research significance and development status of brain-computer interface technology; introduced the working principle and structure of brain-machine interface system; compared EEG signals of different frequencies, and classified their application fields; analyzed five preprocessing methods, and listed their advantages and disadvantages; summarized the theories and algorithms involved in feature extraction and pattern classifier. Finally, after a detailed introduction of the non-intrusive brain-computer interface components, the paper summarized the current technical challenges faced by the brain-computer interface system.
Study on Control Scheme of Quartz Fiber Prefabricated Rod Production Line
Authors: Sun Donghui , Qin Weiguang , Wang Hongyu , Ma Guang 
Published: June 2020

Abstract
PSOD process has serious hysteresis phenomenon and belongs to the large hysteresis system. On the basis of analyzing the working mechanism of the system, the PID control, Smith predictive control and fuzzy control are combined to control the system. Simulation results show that the proposed method has better dynamic performance than the traditional PID control method, regardless of the given input or interference. The conclusion is valuable for the safe operation and control of time-delay systems.
Comparison of Backbones for Semantic Segmentation Network
Authors: Zhang Rongyu , Du Lixuan , Xiao Qi , Liu Jiaming 
Published: June 2020

Abstract
As for the classification network that is constantly emerging with each passing day, different classification network as the backbone of the semantic segmentation network may show different performance. This paper selected the road extraction data set of CVPR DeepGlobe, and compared the performance differences of VGG-16 as the backbone of Unet, ResNet34, ResNet101 and Xception as the backbone of AD-LinkNet. When VGG-16 is used as the backbone of the semantic segmentation network, it performs better in the face of long and wide road extraction. As the backbone of the semantic segmentation network, ResNet has a higher ability to extract small roads. When Xception is used as the backbone of the semantic segmentation network, it not only retains the characteristics of ResNet34, but also can effectively deal with the complex situation of extracting target covered by occlusions.
Analysis of China’s Regional Economic Vitality and Its Influential Factors Based on System Cluster Model and Computer Multiple Regression Model
Authors: Pang Jinfeng , He Jintao , Luo Lan , Li Yao , Mao Yiren , Zhang Hanrui 
Published: June 2020

Abstract
The economic vitality of China’s regions (provinces or cities) is affected by many factors. In this paper, the influencing indicators are listed by referring to the relevant factors that affect economic vitality. Based on the data obtained from the Internet, a systematic (hierarchical) clustering model of relevant indicators in Chinese provinces and cities is established, introducing the Elbow Rule and Factor Analysis to analyze and summarize the correlation of various indicators. A computer-based multiple regression model is used to establish a relationship model that affects economic vitality. Based on this relationship model, effective and reasonable suggestions for improving economic vitality are proposed.
RETRACTED: Research on the Construction of Legal Mechanism of Socialist Harmonious Society Based on Computer Simulation
Authors: Li Sumin 
Published: June 2020

Abstract
The perfect computer simulation legal mechanism has an important safeguard function and plays a key role in building a socialist harmonious society. Chinese current legal system is still imperfect in terms of legislation and law enforcement, such as lagging legislation, unfair legislation, unfair justice, etc. Building a harmonious society is a topic of common concern to the whole society and is widely recognized by the public. Machine learning is an algorithm that computer programs can make judgments and inferences through learning data, thus simulating human intelligence activities and improving the performance of the system itself. The application of machine learning has brought opportunities for social development and injected vitality into legal reform. Therefore, based on computer simulation, this paper introduces the important role of perfecting computer simulation legal mechanism in building a socialist harmonious society, and elaborates four kinds of computer simulation legal mechanisms in building a socialist harmonious society.