bluuebunny/crossref_metadata_2025 huggingface dataset:
The Applicable Conditions of Incentive Contract for Knowledge Sharing in Industrial Construction Supply Chain Based on Evolutionary Game Theory
Authors: Qiao Shi , Wang Qiankun , Guo Zeng , Shen Chuxiong , Xu Wei
Published: August 2020

Knowledge sharing is fundamental for improving knowledge system of industrial construction, and incentive contract is considered important means to promote knowledge sharing. This study aims to analyze the applicable conditions of incentive contract for knowledge sharing and provide guidance for core enterprises to formulate incentive contracts to promote knowledge sharing between member enterprises. Based on evolutionary game theory, an incentive model of knowledge sharing is developed. By solving the model, it is shown that the preconditions for applying incentive contract in industrial construction supply chain are that output capacity of knowledge sharing is higher than the cost, and incentive coefficient is more than the ratio of costs to outputs; and if knowledge sharing is implemented in industrial construction supply chain, the conditions should be satisfied, which are that the output capacity of knowledge sharing is higher than the sum of cost and risk, and the incentive intensity is higher than the ratio of the sum of cost and risk to the output capacity. These conclusions help us to formulate reasonable incentive mechanism.
Forecasting the Exchange Rate for USD to RMB using RNN and SVM
Authors: Liao Ruofan , Boonyakunakorn Petchaluck , Harnpornchai Napat , Sriboonchitta Songsak
Published: August 2020

One of the most important mechanisms supporting world trade is the exchange rate. Depreciation or appreciation of any currency, especially those main currencies such as US dollar, Pound sterling, Renminbi, could greatly affect international trade leading to greater impact to businesses and people’s wellbeing. Recently, researchers have been exploring the use of machine learning techniques to forecast time series data in the financial area. This paper will use machine learning techniques, namely Recurrent Neural Network (RNN), Support Vector Machine (SVM), and a traditional model, namely the ARIMA model which is selected as a benchmark. The result shows that RNN has the best performance compared with both SVM and ARIMA. This paper aims to forecast the exchange rate for USD to RMB, which could give the involved country, institution or people the foresight of the situation and prepare for risk.
Performance Analysis and Optimization of Spaceborne Multi-baseline InSAR Systems
Authors: Wang Yilin , Tan Xintong , Zhu Jubo , Liu Jiying
Published: August 2020

The spaceborne interferometric synthetic aperture radar (InSAR) technique has become a useful tool to obtain the surface elevation or deformation. The multi-baseline InSAR technique is capable of fusing data from multiple channels for interferometric processing and improving the accuracy of elevation estimation of the observed scenes. In this paper, we study the joint distribution to statistically characterize the interferometric phases of multi-baseline InSAR. The baseline parameters are sought based on the second-order moment of the interferometric phase joint probability density function (PDF). The study done in this thesis provides a basis for the design of parameters for multi-baseline interferometric systems.
Keyword-aware Route Search for Various User Requests
Authors: Wang Shuang , Zhang Qiaoqiao , Wang Yinzhe , Zheng Xueliang
Published: August 2020

With the popularity of smart mobile devices and social network, there are numerous location-based data with semantic information. This paper proposes a new route search algorithm which can learn from users’ historical record to meet different requests from various users. In this algorithm, point matching and candidate route generation should be done firstly. Secondly, the ranking scores of candidate routes are calculated. Also, the distance of routes is considered in this part. For users’ requests, it is calculated by users’ historical record including keyword record and distance record. Keyword record represents users’ requests for keywords, and it is comprised of queried record and selected record. Distance record represents users’ tolerance of route distance. Finally, route suggestions are given to users after calculation. Meanwhile, the record is updated after users’ selections. At the end of the paper, the experiments are conducted on real location-based datasets, and the result of the experiments shows that the proposed algorithm demonstrates good performance for different users’ requests.
An Improved Stream Cipher Algorithm Based on NFLSR
Authors: Feng Guanghui , Zhang Chunfu
Published: August 2020

To overcome defects of traditional stream cryptographic algorithms based on XOR operation, this paper proposes a new stream cipher algorithm with higher security and suitability for encrypting short information, which is based upon traversal operation and zipper-like random dynamic hash mapping, has the same time and space complexity as the traditional one. It generates the key stream through an application of Nonlinear Feedback Shift Register (NLFSR) and Bits Scrambling.
Financial Risk Assessment Based on Factor Analysis Model
Authors: Han Dan , Ren Xinchang
Published: August 2020

Based on factor analysis,this paper establishes a financial risk assessment model at the company level,and determines the influence degree of the solvency, operation ability, profitability, development ability and the ability to obtain cash flow on the model,and collects a large amount of relevant information and data, calculates the index weight. Finally, based on the analysis of the actual situation of each real estate company in China,the SPSS software is used for empirical analysis, and divides the risk levels of these 120 real estate companies.And then we put forward corresponding suggestions.
R&D Investment, Innovation Output and Enterprise Competitiveness: A Perspective of Employee Education
Authors: Wang Liangcheng , Wang Shengkun , Peng Tao
Published: August 2020

The study examines whether employee education plays a role in firm’s competitiveness, particularly in innovation activities. The innovation activity has two paths: technologic output and the transformation of scientific achievements. Using the sample of 174 listed companies of top 500 companies in china, we find that innovation output and enterprise competence have significant “inverted U” relationship; constrained by efficiency in achievement transformation path, R&D investment has significant negative effect on enterprise competence. Innovation output plays a completely mediating role in the relation between R&D investment and enterprise competence. Employee education has negative regulating effect on the first path, but the study has not found significant regulating effect on achievement transformation path.
The Game Analysis of Low Carbon Supply Chain Emission Reduction Considering Channel Power under the Concern of Fairness
Authors: Wang Hongqi , Li Jing , Qiu Xingbo
Published: August 2020

The emission reduction strategies of different channel power structures is studied under the fair, neutral and fair concerns of manufacturers, and compares the emission reduction levels, wholesale and retail prices, and the changes and differences in the profits of supply chain members under various circumstances. The research shows that the increase of emission reduction cost factor is not conducive to the improvement of emission reduction level, price and profit. The profit level of manufacturer under the manufacturer-led structure is not conducive to the manufacturer-led structure due to other structures, but is conducive to the improvement of the profit level of manufacturer under other structures. Manufacturer’s equity concern behaviour is beneficial to the improvement of manufacturer’s utility, in which the equity concern factor should be kept at a certain level under Nash vertical structure. Equity concerns and emission reduction strategies under decentralized decision-making are not conducive to the improvement of the overall supply chain effectiveness.
Research on the Influence of Internal Control on Enterprise Credit Risk
Authors: Wang Xiaohui , Yuan Fangying
Published: August 2020

This paper empirically tests the relationship between internal control and credit risk by taking a-share listed companies from 2014 to 2018 as samples. It is found that the quality of internal control is positively correlated with the credit risk. Further analysis shows that the five elements of internal control are significantly related to the size of credit risk, among which the internal environment, information and communication elements have a more significant impact on credit risk.
Exact analytic solutions of nonlinear equations
Authors: Zhong Baoming , Jiang Jun
Published: August 2020

The improved sub-equation method is used to obtain the exact analytical solution of (2 + 1) - dimensional BBM Equation. Some traveling wave solutions such as soliton solution and periodic wave solution of trigonometric function are obtained. Practice shows that the sub-equation method is widely used in nonlinear evolution equations.
Research on O3 Concentration Monitoring Data in Aircraft Cabin
Authors: Xu Enhua , Xu Yan
Published: August 2020

The paper explored the hourly and daily characteristics of O3 concentration monitoring data in aircraft cabin by micro air monitor, and proposed the additive model for O3 considering the influence of internal and external factors. Linear interpolation filling the missing values could effectively solve the problem of data missing and improved the effect of the addictive model of ARIMA and multiple regressions. The addictive calibration model by ARIMA and Multiple regressions for O3 was reconstructed based on linear interpolation filling. The error analysis showed that the accuracy of O3 was improved. The prediction effect was also improved by considering the interaction effect.
An Improved K-means Distributed Clustering Algorithm Based on Spark Parallel Computing Framework
Authors: Lu Xin , Lu Huanghuang , Yuan Jiao , Wang Xun
Published: August 2020

Traditional K-means distributed clustering algorithm has many problems in clustering big data, such as unstable clustering results, poor clustering results and low execution efficiency. In this paper, a density based initial clustering center selection method is proposed to improve the K-means distributed clustering algorithm. The algorithm uses the sample density, the distance between clusters and the cluster compact density, defines the product of the three as the difference weight density, and finds the sample point with the maximum difference weight density as the initial cluster center, so as to solve the problem of randomness and low quality of initial cluster center selection. At the same time, this paper uses spark parallel computing framework to implement the improved algorithm to further improve the processing performance of the algorithm in big data clustering.The experimental results show that the improved k-means distributed clustering algorithm based on spark parallel computing framework has higher execution efficiency, accuracy and good stability in big data clustering analysis.
Comparison of Research on Social Media in China and Foreign Countries Based on Bibliometric Analysis
Authors: Yunan Peng , Zongshui Wang , Yueyang Feng , shengqin Yang
Published: August 2020

This paper aims to compare the similarities and differences of Chinese and foreign research on social media. Cluster analysis, co-word analysis and social network analysis have been used to compare the research trends, content relevance and network structure of research on social media. (1) Similarities: Social media is a hot topic for scholars both in China and foreign countries; the development of social network technology leads to many new areas; social media research tends to study privacy, commerciality and service; hotspots of social media research in China and foreign countries both include social media, platform and social network. (2) Difference: Chinese research mainly focuses on big data, Wechat and Weibo, while foreign research mainly focuses on Facebook, Twitter and other kinds of social network media; Chinese research is clustered while foreign research is extensive; Chinese research pays more attention on macroscopic perspectives, such as media convergence, big data and libraries; foreign research tends to microcosmic perspectives, such as emotional analysis, crisis information dissemination, social media marketing.
Application of statistical analysis method in scale validity test
Authors: Wang Gang , Zhou Huaren , Yao Jia
Published: August 2020

In order to facilitate research and improve application efficiency, scales based on various functions were designed and developed. However, in the process of using the scale, people often ignore the validity of the scale for a specific purpose, resulting in the loss of significance of subsequent studies. This paper briefly introduces the application of correlation analysis and factor analysis in scale validity test, which is enlightening to improve people’s understanding of statistical analysis methods.
Value at Risk of the Exchange Rate in Southeast ASEAN-3 Based on Bayesian Markov-Switching GARCH Approach
Authors: Li Mingyang , Liao Ruofan , Sriboonchitta Songsak
Published: August 2020

This study analyzes Bayesian Markov-Switching of the single regime and the two regimes to forecast the risk of the exchange rate in three ASEAN countries, and various GARCH family and distribution are selected by DIC to find the best fitting models. This study will help governments to prevent the recurrence of events like the 1997 financial crisis. The study finds that Thailand has the best exchange rate stability and the lowest risk and is most suitable for foreign investors seeking stability.
Additive Calibration Model for NO2 Based on Linear Interpolation
Authors: Xu Yan , Xu Enhua
Published: August 2020

The paper proposed the additive model for NO2 considering the influence of internal and external factors. Linear interpolation filling the missing values could be effectively solved the problem of data missing and improved the effect of the addictive model of ARIMA and multivariate linear regression. The addictive calibration model by ARIMA and Multiple linear regression for NO2 was reconstructed based on linear interpolation filling. The error analysis showed that the accuracy of NO2 was improved. The prediction effect was also improved by considering the interaction effect.
Research on the comprehensive evaluation method of college students’ achievements based on multi-view RSR
Authors: Ao Xiqin , He Aixiang
Published: August 2020

In view of the problems and shortcomings of the current college students’ achievements evaluation methods, a multi-view rank sum ratio(RSR) comprehensive evaluation method of students’ achievements was proposed. The method integrated the two factors of curriculum importance and students’ learning ability when determining the weight. Then combined with the RSR ranking method, the comprehensive evaluation ranking of students’ achievements was carried out. This method overcomes the subjectivity of weight setting and maximally reflects the integrity of information. Finally, through the empirical analysis, the method was compared with the average score method and intellectual education evaluation method, and the effectiveness of the method was verified through the ranking results comparison.
Producer-consumer Model Based Thread Pool Design
Authors: Wang Liangzhou , Wang Chaobin
Published: August 2020

The concurrency of the server has a crucial impact on the efficiency of processing requests on the server side, so the thread pool technology has been widely applied to the server. Different thread pools play an important role in improving the performance of highly concurrent servers. This article first introduces socket technology, thread pools, etc., which are high-concurrency server development technologies in Linux systems, then proposes a thread pool model based on the producer consumer model, and gives key codes. Finally, the performance is compared through experiments . Experimental results show that lightweight servers can achieve higher concurrency by using this thread pool model.
Research on Stock Returns Forecast of the Four Major Banks Based on ARMA and GARCH Model
Authors: Hu Yuanwei , Tao Zheng , Xing Dongao , Pan Ziyi , Zhao Junyi , Chen Xiaoling
Published: August 2020

This paper uses the prediction method of time series analysis to fit and predict the stock daily logarithmic return series of the four major banks. The ARMA model and the GARCH model are constructed and compared empirically. The results show that the GARCH model is better than that of the ARMA model in the fitting effect. In the prediction effect, The ARMA model is the best, followed by the GARCH model.
Skew-normal median regression model with applications
Authors: Cao Xingyun , Wang Gege , Wu Liucang
Published: August 2020

Skewed/asymmetrical data often appear in many research fields such as finance, economy, climate science, environmental science, engineering technology, social sciences and biomedicine. Little research has been carried out to establish median regression model to investigate the influence of some covariates on distributional characteristics. Since the median can capture the “medium” level of the skewed data, the main objective of this paper is to construct the median regression model for the analysis of skew-normal data. We develop an expectation-maximization (EM) algorithm facilitated by Newton-Raphson algorithm to estimate the regression coefficients and parameters. The proposed methods are evaluated by some Monte Carlo simulations and are illustrated by a real data analysis.
Analysis of Convertible Bond Investment Value
Authors: Liu Zhaoyang
Published: August 2020

During the vigorous development of convertible bonds in China, it has become a hot investment product recently. This paper introduces the investment methods and risks of convertible bonds to investors with various risk tolerance through the explanation of the background and important clauses. Convertible bonds, as a combination of creditor’s rights and options, can be said to be applicable to all kinds of investors at the same time. Investors with low risk tolerance can obtain fixed income regardless of market conditions under the condition that they have stable and low-risk coupon rate as guarantee. Investors with high risk tolerance can obtain returns with greater risks and greater returns through stock conversion under the same stable fixed income background. Under the protection of many important clauses (the specific clauses are as follows), the interests of various types of investors are stabilized. At the same time, this paper also puts forward several investment suggestions according to the risks of convertible bonds.
Population and Cultural Disasters Caused by Sea Level Rise Based on Regression Model
Authors: Wang Jiapan , Gao Yi , Lin Haoqi
Published: August 2020

With the worsening environment, more people become EDPs since their homeland become inhabitable. Leaving the place they once called home, their rights may not be guaranteed. Meanwhile, their unique culture are also facing the edge of extinction. To determine the scope of the issue, we discuss the number of people at risk and specify the cultural loss. When predicting EDP population, we start by using the regression model to predict the trend of sea level rise due to the greenhouse effect and find out by 2120, the world’s average sea level will probably rise by 60mm. Considering the effect of greenhouse gas makes this model more persuasive than if we only use sea level data to predict. Then, we build a spherical cap model to resemble to geographical features of tropical islands. It greatly simplifies the complexity of island topography and extends our prospective from a few islands to the whole world. With the help of the spherical cap model, on conservative estimate, the current number of EDP is about 67 million; by 2050, the potential EDPs will reach 130 million and over 400,000 square kilometers of land will be submerged. In the aspect of culture, with island residents becoming refugees, the potential lost of religious rituals, folk stories, traditional techniques, intangible arts and unique languages lead to the huge loss of cultural heritage.
Towards Student Learning Ability Estimation and Truth Discovery in Japanese Online Course
Authors: Han Lanling , Liu Yanwei , Shi Chunhui , Bi Yang , Yu Liang , Shen Hua
Published: August 2020

This paper focuses on an important task in online courses, which is to estimate student learning ability from students’ answering records. The challenge of this task is to automatically estimate the learning ability for students and infer the true answer for each question without any supervision. Most of the existing methods solve these challenges by designing an optimization objective function. However, these approaches ignore the characteristics of students from different groups. Intuitively, outstanding students always provide correct answers and should be assigned higher weights compared with ordinary students. Based on this intuition, this paper proposes a new optimization framework by dividing students into two groups: an authoritative group and an ordinary group. The losses of the objective are from both authoritative students and ordinary students. Through optimizing both losses simultaneously, the proposed model can automatically estimate learning ability for authoritative students and ordinary students and infer the right answer for each question. Two experiments conducted on two datasets show that the proposed model outperforms state-of-the-art baselines.
Research On Optimal Search Efficiency Of Rocket Wreckage Based On UAV
Authors: Zhang Wei , Yao Shengsheng , Wang Qiang , Zhong When an , Wang Yuchao
Published: August 2020

In order to improve the efficiency of UAV search for debris targets in the area of debris. The paper discusses the relationship between the flight altitude, photoelectric turret parameters, searching area and resolution of UAV. Then discusses the characteristics of rocket debris distribution based on historical debris distribution data.by building the model of UAV operating area and regularities of debris distribution,this paper calculate the probability of finding the debris targets in one flight with optimal searching method by Monte Carlo calculation method.this method has high practical value for using UAV to search rocket debris.
Analysis and design of intelligent environmental protection IOT system architecture based on GIS
Authors: Min Huang
Published: August 2020

With the third great change of the world’s information industry, IOT will open an information age of interconnection of all things. The environmental protection Internet of things combines the traditional environmental protection industry with the Internet of things technology, realizes the practice of environmental protection informatization, is conducive to the construction of an all-round environmental protection governance system, speeds up the process of China’s environmental protection informatization, and promotes the rapid transition from “digital environmental protection” to “intelligent environmental protection”. With the continuous progress of cloud computing, big data and other cutting-edge technologies, environmental protection Internet of things has begun to integrate these technologies to better meet the needs of the public. In this paper, through the analysis and research of the application example of environmental protection Internet of things, summarizes the overall architecture of intelligent Internet of things system based on GIS for environmental protection. The overall architecture of the intelligent Internet of things system can be divided into four levels: field equipment, transmission network, IOT equipment management platform, environmental protection big data analysis and application platform. Using the IOT system architecture to design the IOT system platform can solve the repeated construction of infrastructure and software application system in the field of environmental protection, improve the traditional environmental governance mode and meet the public’s right to know the environmental conditions. People can understand the real-time environmental information through mobile terminals or environmental protection cloud platform, and enterprises that cause pollution can use the network Anonymous reporting is carried out so that the public can participate in the national cause of ecological environment governance.
Design of a WeChat Mobile Learning Platform for Multi-modal Language Learning and its Application
Authors: Lei Qian-qian , Liu Hai-yang
Published: August 2020

To follow the increasingly popularity of MALL (Mobile Assisted Language Learning) in the mobile multi-media era, the study probes into the design of a multi-modal language mobile learning platform supported by WeChat. The study also practically applies the platform to an English course for mobile learning and analyses the learning results with the quasi experimental research method. The experiment shows that the platform can effectively improve learners’ language ability, especially listening, writing and speaking skills.
Research on Baidu Street View Road Crack Information Extraction Based on Deep Learning Method
Authors: Zhang Min , Liu Yang , Luo Shaohua , Gao Siyan
Published: August 2020

Rapid and effective identification and extraction of road cracks has always been a major difficulty in road detection and maintenance. This paper applies the Deeplabv3 + model to road crack extraction, and proposes a new joint identification method for road cracks based on open network data and open source convolutional neural network. Based on the semantic segmentation theory and Deeplabv3+ neural network, this method uses baidu street view map as the training data set. By adjusting the proportion weight of road cracks and background, the training network model can quickly and accurately identify road cracks. Experimental results show that this method has achieved a good effect on crack segmentation: Mean Intersection over Union(MIOU) of this method is more than 70%, and the processing speed is 1 second/sheet, which is better than FCN algorithm.The results of road crack information extraction are similar to those of manual interpretation, which indicates that this method is feasible for quality evaluation of municipal roads.
Empirical Research on Youth National Security Cognition Level Based on Cloud Model
Authors: Fan Jiangyan , Zhang Hongbing , Liu Ying
Published: August 2020

Focusing on the development level, existing problems and cultivation path of the overall national security concept of young people has become an urgent issue of the times. In order to evaluate the national security cognition level of young people, this paper constructs a systematic evaluation system and improves the existing evaluation methods. Based on fuzzy comprehensive evaluation, grey relational analysis and the TOPSIS, a combined evaluation model based on cloud model is constructed, which takes into account the fuzziness and randomness in the evaluation process. Through the analysis of the examples and the verification of the mean square error, this new evaluation method has obvious superiority over the traditional evaluation method. The results show that the current national security awareness of young people is relatively weak, and the overall level of awareness is at a moderately low level.
The Application of Geographical Analysis in Serial Crimes
Authors: Xin Wang , Xinshe Qi , Guo Li , GaoCuicui  , Na Wang , Ruiping Huang , Rong Liu
Published: August 2020

In this paper, we use bayes’ theorem to generate two mathematical models for a series of crimes. This series of crimes is a dynamic process, so we regard it as JTC model. In this model, there are some major factors, such as the individual’s vulnerability to crime, the attractiveness of the place of the crime and the utilization of the crime from the place of origin to the place of destination. We combine the bayesian theorem with the JTC model to obtain the mathematical model of geographical distribution. We made full use of the model to predict the location of the next perpetrator in the series.
Research on Electronic Product Storage Reliability Based on Similar Product Prediction
Authors: Cui Zenghui , Xuan Zhaolong , Li Tianpeng , Yang Qingxi
Published: August 2020

For the long-term storage of some electronic products, the life value of the electronic product is calculated in combination with the non-work failure rate model, and the life value is applied to the similar product prediction method to obtain information about the storage reliability of the electronic product . The calculation process of the non-work failure rate model and the prediction method of similar products is introduced, and the application examples are used to verify, reduce the error when using the entire system life information for estimation, and improve the accuracy of storage reliability.
Research on Tourism Question Answering System Based on Xi’an Tourism Knowledge Graph
Authors: Yang Lu , Cao Han , Hao Fei , Zhang WeiZhen , Ahmad Muhib
Published: August 2020

Question answering (QA) system provides a direct, efficient and accurate way for people to obtain information. At present, open domain QA systems such as Siri and Cortana are widely used in the general field, but they cannot meet the demand of some professional fields. This paper focuses on the background and needs of QA in the tourism field, researching the relevant technologies required for the implementation of QA system, and finally completes the construction of QA system based on the knowledge graph of tourism. The main research contents of this paper are as follows: 1.An algorithm to identify the tourism entities in questions is proposed according to the characteristics of the tourism entities. 2. Referring to the ideas of Liu et al., a convolutional neural network (CNN) model is introduced into attribute linking, but in order to improve the accuracy of attribute linking, we move the similarity calculation of questions and attributes from the outside of the model to the input layer of the model, and also introduce Attention mechanism. Integrate the technology of each module to design and implement a QA system for tourism. We experiment with the system on the constructed Xi’an tourism knowledge graph, and the results prove that the system we designed can answer the natural language questions raised by users about tourism.
Research and Application of High-Performance Data Storage and Transmission Technology in Microservice Environment
Authors: Li Kunying , Ding Yu , Shi Ying , Wang Liling , Zhen Zebing
Published: August 2020

With the development of the Internet, various types of application systems are emerging, which brings more and more server access, more and more content, and more and more strong interaction between users and the system. The new generation of microservice software development framework brings great pressure for data transmission and storage. In order to improve the stability of data storage and read and write speed. This paper proposes a cache service architecture designed by sentinel mode. Redis is used as the core intermediary layer of high-performance storage to solve the lightweight and high concurrent data, and multiple linear regression analysis is used to determine those data that need asynchronous transmission and heavyweight transmission with RabbitMQ message queue for transmission. Finally, it realizes the efficient storage, expansion and effective and timely information communication of data in the whole microservice environment.
A Financial Fraud Detection Model Based on Organizational Impression Management Strategy
Authors: Zhang Jie , Yao Jianrong , Wang Lu , Chen Yuangao , Pan Yanqin
Published: August 2020

Financial fraud misleads investors into making wrong decisions based on incorrect information, especially for listed companies’ financial statements fraud. It damages investors’ interests, disturbs the economic order, and creates a crisis of trust, which is extremely harmful. Therefore, it is of great significance to build an effective financial fraud detection model for listed companies. This study uses a sample of 126 Chinese listed companies from 2013 to 2017 to examine the relationship between two organizational impression management strategies (promotion strategy and defense strategy) and financial fraud using the integrated learning methods. This study innovatively analyzes financial fraud using social media data and annual reports’ readability data as non-financial features. The results show that companies implementing the defensive/protective strategy were more likely to commit financial fraud. In addition, the average number of tables in a company’s annual report can significantly help researchers to judge fraud.
Research on Classroom Attendance System Based on Face Recognition
Authors: Pu Jingyu , Zhang Liang , Zhang Jinqian , Xue Ziwei , Zhang Xixin , Yang Jie
Published: August 2020

Deep learning face recognition technology has become one of the most popular technologies. The explosive growth in the use of face recognition has brought a variety of practical needs. Face recognition can be applied to identity authentication, bank security, forensic investigation, face scanning payment, access control system and so on. Existing face recognition methods have achieved remarkable results. With the continuous development of deep learning technology, many depth methods show better accuracy than human recognition. Based on the in-depth study and discussion of the deep learning method, this paper applies the theory to practice, and designs and develops a set of face recognition system for classroom attendance.
Estimation Method of Blurred Parameters in Moving Blurred Image
Authors: Zhang Guanyang , Zheng Tianxiong
Published: August 2020

In order to improve the quality of image restoration and remove the blur caused by motion in the image, a blurred parameter recognition algorithm with point spread function is proposed. This algorithm combines the Fourier spectrum of the image with the edge detection based on phase consistency and identifies the blurred angle by determining the direction of the edge of the center bright stripe. Then bilinear interpolation is used to generate the sub-pixel image of the spectrum, calculate the distance between the dark bands, and estimate the blurred length. Experimental results show that, compared with the traditional algorithm, the proposed method can not only obtain higher precision of blurred angle and blurred length, but also have better stability, and can correctly detect blurred parameters of blurred images with different scales and contents.
Research on Weapon Equipment Purchase Performance Evaluation Based on Support Vector Machine
Authors: Li Xiaosong , Peng Xinran , Xiao Zhenhua
Published: August 2020

In this paper, we present an efficient performance evaluation method of weapon equipment purchase based on support vector machine. Firstly, the concept of weapon equipment purchase performance evaluation was explained and the existing problems of weapon equipment purchase performance evaluation were analysed. Then, a 12-dimension performance evaluation indicator system was constructed (these dimensions are: budget progress, cost-benefit ratio, expenditures reduction rate, supplier satisfaction, purchaser satisfaction, customer satisfaction, purchase cycle rationality, qualification rate, price rationality, competition, success rate, and policy completeness). And lastly, a weapon equipment purchase performance evaluation model based on support vector machine was established, and a case analysis was conducted. The result provides a reference for building the indicator system and model for weapon equipment purchase performance evaluation and the research conclusion can help complete and improve the evaluation method.
The framework study on mimic defense technology in power web service system
Authors: Cai Yuxiang , Pan Dan , Wang Yiting
Published: August 2020

Aimed at the features of the power grid web system for the State Grid Corporation of China (SGCC), the paper analyzes the confronting security risks. A mimic defense security protection system for the power grid web application has been designed by combining the features of network attack and introducing the mimic defense core technology. The defense framework with layered ideas and dynamic heterogeneous redundancy has implemented effective protection at multiple attack phase and expanded the defense range so as to provide the security protection capacity to the power grid web application.
Fall into the trap of phishing for fools—Based on behavioral analysis of smoking and drinking
Authors: Huang Zhanbing , Gao Fangyuan , Lu Yu
Published: August 2020

Behavioral Economics puts forward that the weakness of human decision-making is an important cause of the vulnerability of the poor. If the external environment of decision-making is bad, it is easy to fall into the trap of “phishing for fools” set by the economic subject, which causes economic loss or psychological burden and increases the risk of returning to poverty. This paper studies whether the low-income groups are more likely to fall into the trap of “phishing for fools” and make irrational decisions based on the fact that the excessive consumption of tobacco and alcohol is harmful to their health but the consumption is increasing year by year. Based on the data on the 2015 China Health and Retirement Longitudinal Study, this paper Constructs Logit model, linear regression model and ordered Probit model to empirically analyze the important factors influencing the smoking and drinking behavior of rural residents. The results showed that, under the control of individual characteristics and regional living habits, income level had a significant impact on low-income groups’ alcohol and tobacco consumption behavior.
CNN-Based Surface Defect Detection of Smartphone Protective Screen
Authors: Chen Hailang
Published: August 2020

With the advancement of the intelligent era of Industry 4.0, in order to solve the problem of low-efficiency and high miss-detection rate using manual visual inspection for the detection of surface defects on smartphone protective screens, surface defects on smartphone protective screens based on convolutional neural networks are introduced as the detection method. This detection method exhibits excellent feature extraction capabilities and powerful target classification performance during the preprocessing, model design, model training, and detection of smartphone protective screens images. Experimental results show that the detection method can achieve accurate detection of defects such as punctures, bright spots, and scratches on the surface of the smartphone protective screens. The detection verification rate is as high as 98%, and the accuracy is high, which meets the actual needs of enterprises.
Evolutionarily stable strategy analysis of Quantitative investment strategy
Authors: Li Yicun , Xiao Weilin , Teng Yuanyang
Published: August 2020

Based on Chinese Stock Index Future, this study focuses on two types of market price changes, trends and shake. How investors’ strategies evolve to influence the market from trend to shake or from shake to trend to obtain the “evolutionary stable strategy” (ESS) of the market is conducted. The results show that trend strategies are more favourable in most market environments, and find a single strategy cannot survive in the market, and trading frequency should be appropriately reduced to reduce transaction costs, so as to survive in the market for a long time.
Research on System Log Analysis Based on Bidirectional LSTM
Authors: Zhao Jiantao , Wang Ke
Published: August 2020

System logs often record important information in the system and are an important source of information on user behavior, system abnormalities, and system operating conditions. With the stacking of system time and the surge of users, the data generated by the system has also exploded. In order to achieve intelligent system management, this paper proposes a system log analysis method based on bidirectional LSTM. The method uses neural networks to extract keywords and data vectors based on the logs generated by the system, and uses the data sequence corresponding to the keywords as input to make judgments about system abnormalities. The experimental results show that the method can effectively judge the abnormality of the system.
Research on Missile Intelligent Penetration Based on Deep Reinforcement Learning
Authors: Lei Sen , Lei YongLin , Zhu Zhi
Published: August 2020

Penetration capability is one of the most critical measures for assessing the effectiveness of missile systems. In the face of an increasingly sophisticated antimissile system, this article explores the use of artificial intelligence to achieve missile intelligent penetration. The applicability of the deep reinforcement learning method to the missile intelligent penetration problem is elaborated. The Actor-Critic deep reinforcement learning algorithm is studied. The deep reinforcement learning environment model for the missile penetration scenario is developed based on the generic combat effectiveness simulation system, WESS. A multi-sample collaborative training method is designed. On this basis, a missile intelligent penetration training system framework based on WESS and deep reinforcement learning is proposed to guide the missile penetration intelligence learning.
Attention-BLSTM-CRF Based Method for Named Entity Recognition in Judicial Domain
Authors: Wang Chen , Li Bo , Zhang Wenjing
Published: August 2020

Although the texts in the judicial field are relatively standardized, the entity categories are rich and the structure is different, and the entity expression is special in some legal documents, electronic files and guiding cases. In order to improve the effect of named entity recognition in the field of justice, this paper entity type can be divided into four categories, and presents a model of named entity recognition based on attention mechanism, structure improvement of input vector fusion CNN embed mode, BLSTM neural network at the same time we constantly study the characteristics of the context, finally by CRF decoding output sequence. The experimental results show that the method is helpful to the application research in the judicial field, and the experiment on the self-built corpus test set has achieved a good entity identification effect.
Short Text Classification and Clustering based Mobile Application Traffic Identification Method
Authors: Li Yuanhao , Chen Shuhui , Zhao Shuang
Published: August 2020

With the rapid development of mobile network, mobile traffic accounts for a large proportion of network traffic nowadays, and mobile application traffic identification is becoming increasingly important in network security. For mobile application traffic identification, recent works have focused on proposing supervised classifiers that have shown promising performance. However, it is difficult to obtain labeled traffic in practice and most of the traffic is unlabeled. In this paper, we propose a semi-supervised mobile application traffic identification method based on short text classification and clustering, which requires only a small number of labeled samples to classify traffic. First, plain text in mobile traffic is regarded as short text and its features are extracted using a short text classification algorithm. Then K-Means++ is used to cluster the samples and give the predictions. Experimental results show that the proposed method is more effective than the semi-supervised traffic identification methods that use statistical features.
Robustness Analysis of Interdependent Networks under Grey Information Attack
Authors: Wang Zhe , Li Jianhua , Kang Dong , Ran Haodan
Published: August 2020

In modern society, many infrastructures are interdependent owing to functional and logical relations among components in different systems, and they can be characterized as complex interdependent networks. Interdependency makes network so fragile that even a slight initial disturbance may lead to a cascading failure of the entire systems. Previous research on interdependent networks has focused on two types of initial attack: random attack and degree-based targeted attack. In this paper, a grey information attack strategy with the information incompleteness and complexity by adjusting the tunable parameters was designed, and a cascading failure process is studied considering their effects on failure propagation. Moreover, the robustness of two types of interdependent networks (BA - BA, ER - ER) is evaluated under grey attacking strategies. Interesting conclusions could be obtained. The simulation results show that the proposed method can identify the key nodes more effectively, and the robustness of the network decreases with the increase of the grey attack coefficient. In addition, the relation between weight coefficient and robustness under various strategy is quite different. Our results can be very useful for safety design and protection of interdependent networks.
Improving Robustness and Defense with Signal Augmentation for Modulation Identification
Authors: Zou Chuanjin , Yu Chenqiu , Guo Jibin , Guo Ronghai , Pang Junzhong , Zeng Weijun
Published: August 2020

Based on the Convolutional Neural Network, we consider finding an automatic recognition method of signal modulation. Electronic warfare (EW) is one of the most important forms of war, and the outcome of the war depends largely on it. Fast, accurate, secure and continuous communication is increasingly important, and the identification of signal modulation is one of the key links to ensure communication. The traditional recognition method is artificial recognition, which is inefficient and error-prone. In this paper, aiming at several common modulation signals, the automatic recognition system of modulation signals based on Convolu-tional Neural Network is designed by analyzing mathematical models and important parameters, then upgrade and improve the feasibility and effectiveness of the project.
Functionalized PEG-oligo(L-lysine)-PCL micelle system for the delivery of bioactive agents based on pH-sensitive degradation
Authors: Kang Jungkyu , Jeon Seong Ik , Chung In Jae , Ahn Cheol-Hee
Published: August 2020

Methoxy poly(ethylene glycol) amine with a molecular weight of 5 K and ϵ-caprolactone with a molecular weight of 3 K were conjugated to five lysine residues with molecular weight and M
w/M
n of 9.6 K and 1.04, respectively. The shift of peak molecular weight and narrow molecular weight distribution in a gel permeation chromatography (GPC) trace without any noticeable shoulder as well as 1H nuclear magnetic resonance analysis confirmed the successful synthesis of the copolymer. Polymeric micelles, of size around 60 nm, were formed by dialysis and crosslinked micelles (CMs) were prepared by adding a crosslinker, terephthalaldehyde, to generate weak acid-labile benzoic-imine bonds in the interface of the micelle-forming amphiphilic copolymer. The critical micelle concentrations of non-crosslinked micelles and CMs were determined to be 4.26 × 10–2 mg ml−1 and 7.01 × 10–3 mg ml−1, respectively. The hydrolysis rate of the CMs is highly pH-dependent and much more rapid at mild acid than physiological conditions. Doxorubicin was successfully loaded into the CMs and a controlled pH-dependent release behavior was observed. The enhanced micelle stability opens a way for preparing long-circulating delivery systems encapsulating poorly water-soluble drugs.