bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tomato Pests and Diseases Classification Model Based on Optimized Convolutional Neural Network
Authors: Jia Shaopeng , Gao Hongju , Hang Xiao 
Published: January 2020

Abstract
In the field of agricultural information processing, automatic identification and diagnosis of common diseases of tomatoes play an important role. The emergence of deep learning can help people simplify the process of image feature extraction, reduce network complexity and improve recognition accuracy. This paper built the Inception-v3 model to identify and classify five classes of tomato pests and diseases. We collected some images of tomato diseases uploaded by farmers from the online diagnosis platform for crop pests and diseases. In the experiment, the combination of the moving average model and the regularization was used to adjust the parameters in the model and enhance the robustness of the model on the test data. Experiments showed that the Inception-v3 model based on regular expression and moving average optimization can effectively avoid regularization and has higher accuracy, which can reach 86.9%.
Research on personal credit scoring model based on multi-source data
Authors: Zhang Haichao , Zeng Ruishuang , Chen Linling , Zhang Shangfeng 
Published: January 2020

Abstract
In the Internet financial personal credit loan business, it is necessary to construct a credit scoring model for users, and the problems of unbalanced user categories, high data dimensions and sparse features make it difficult to model the credit situation of users. This paper adopts the idea of grouping modeling. It proposes an improved BIV value feature screening method and a weighted average model based on Logistic Regression, Random Forest and Catboost, which provides a set of solutions for user modeling in this scenario. The grouping modeling idea pre-groups the customers and reduces the feature sparsity problem. The improved BIV value shows the influence of each feature on the results and points out the mutation threshold. The oversampling method alleviates the category imbalance problem. AUC is used as the model result evaluation index, and the results show that the classification effect of the model is good. The results show that customers with a long history of credit history and a history of good credit behavior have lower credit risk.
A virtual examination evaluation system design and implementation of equipment maintenance
Authors: Qiu Lei , Xie Cun , Zhang Xuezhi , He Peng 
Published: January 2020

Abstract
This paper presents a design method of assessment system for equipment virtual maintenance training, introduces the framework of the system, the components of each module and the scoring basis of the assessment link, and puts forward the scoring weight ratio according to the characteristics of equipment. Finally, taking the equipment as an example, the virtual assessment system is implemented.
Competitiveness Analysis of Hebei Electric Power Industry Based on Factor Analysis and Cluster Analysis
Authors: Huang Yuansheng , Zhu Lifeng 
Published: January 2020

Abstract
Based on the “Diamond Model” which incorporates the factors of power industry, and starting from the industrial chain structure of power industry development, an evaluation system including three dimensions of power industry demand side, power generation side and power industry resource side is constructed. The cross-sectional data of 31 provinces in China in 2017 are selected, and factor analysis and cluster analysis are used. The Empirical Study on the competitiveness of Hebei electric power industry shows that the competitiveness of Hebei electric power industry is the fourth gradient among 31 provinces (cities and districts) in China, and the overall competitiveness of the electric power industry is weak. At the same time, factor analysis shows that the competitiveness of power industry on generation side is weak, while the competitiveness of upstream and downstream power market of power industry has advantages over other provinces, and the development coordination of the overall supply and demand structure of power industry is unbalanced, which hinders the development of the competitiveness of the whole power market. On the basis of the above analysis, this paper combines the strengths and weaknesses of Hebei electric power industry, and gives relevant suggestions for its development.
Practice of Rural Revitalization Planning Based on Baidu Index’s Attention to Behavior Tendency: A Case Study of Jinyi House in Gutian County, Fujian Province
Authors: Shan Ke , Hui Pan 
Published: January 2020

Abstract
At present, the era of network information explosion, people use search engines to obtain more comprehensive and detailed information while leaving relevant records. In spite of these network drawbacks, this paper mainly focuses on the network search engine, which has brought a new database and platform for the theoretical and practical research of social sciences. Based on Baidu Index, a popular statistical analysis platform which can reflect the facts more comprehensively, this paper analyses the trend of people’s attention and choice on rural revitalization. The most relevant key words of Baidu Index for Rural Revitalization are summarized and sorted out: culture and nostalgia. In response to the strategy of “cultural renaissance” put forward by our country, this paper constructs a cultural revitalization based on Baidu Index’s research on behavior tendency. Taking Golden Wing House in Gutian County, Fujian Province as an example, this paper explores the practical ways of cultural revitalization, hoping that this research can provide relevant rural revitalization construction units and managers with information. Reference.
Research on the application of Naive Bayes and Support Vector Machine algorithm on exercises Classification
Authors: Li Qi-na , Li Ting-hui 
Published: January 2020

Abstract
In this paper, exercises on electronic major courses of college are classified, further, compared with the accuracy of algorithms based on the Naive Bayes and Support Vector Machine (SVM) in the classification of exercises. Data sets from exercises related to the content on the course of Data Structure are selected. According to knowledge content of course and characteristics of exercises, judgment and multiple-choice questions are used in experimental data, and these exercises are divided into seven categories depending on content of chapters. Firstly, the package of Jieba participle is applied to segment text of exercises, and the proper nouns of knowledge content are identified by importing a custom dictionary. Because characteristic words of short text are quite sparse, weight method about TF-IDF is directly adopted to feature representation of text instead of feature selection. Finally, algorithms based on the Nave Bayes and SVM are employed to train and classify text of exercises. The experimental results show that algorithm based on SVM has lower error rate and higher classification accuracy than the Naive Bayes.
Water Depth Inversion based on Landsat-8 Date and Random Forest Algorithm
Authors: Zhang Jin , Li Shujun , Wang Mo 
Published: January 2020

Abstract
In view of the fact that the traditional water depth inversion model is susceptible to water quality and environmental factors, the water depth inversion model is constructed using the random forest algorithm using the high resolution remote sensing image of the Huangyan Island area and the corresponding measured water depth data. The influence of the number of training sets on the inversion of remote sensing water depth is explored. The results show that the inversion accuracy of random forest is higher, and for this method, the more features in the training set, the better the effect of remote sensing inversion.
Research on Science and Technology Information Dissemination Based on Homogeneous Networks
Authors: Guanhui Wang , Yu Chen 
Published: January 2020

Abstract
With the advancement of science and technology and the advent of the era of knowledge-based economy, knowledge sharing has become an important factor in the development of science and technology. This paper constructs a model of public sentiment information dissemination in the context of homogeneous networks, revealing the influence and control effect of the connection relationship between enterprise nodes in science and technology network on the dissemination of science and technology information. The alteration of critical thresholds helps to control the degree and speed of public opinion information dissemination by adjusting the parameters in the model.
Partial Discharge Pattern Recognition of Transformer Based on Deep Forest Algorithm
Authors: Guan Jie , Guo Minkai , Fang Sen 
Published: January 2020

Abstract
In order to improve the accuracy of pattern recognition of transformer partial discharge type, solving the current pattern recognition based on machine learning algorithm requires artificial extraction of description features, poor adaptability and low recognition accuracy. In this paper, the deep forest algorithm is introduced into the partial discharge pattern recognition of transformers. In this method, the partial discharge images collected by partial discharge inspection instrument are processed by gray-scale and bilinear interpolation as input of deep forest model. The feature extraction of PD images is realized by using multi-grained scanning structure, and the classification of PD is realized by using cascade forest structure as classifier. By setting up the partial discharge experiment platform of transformer, the algorithm is tested with the sample data obtained from the experiment, and the results show that the pattern recognition accuracy of this method is high, and the recognition accuracy increases with the increase of sample data.
Negative Effects of Complex Promotion Strategy in Online Shopping Evidence from Lab Experiment
Authors: Huang Zhanbing , Zhuli  , Gao Fangyuan 
Published: January 2020

Abstract
This study discusses the negative Effects of the complex promotion strategy on consumers. Based on the dual-systems theory, we conducted lab behavioral experiments to obtain real decision data with Mouselab, a decision-making tracking technology. A sample of 116 students in Shaanxi Province participated in the experiment. The results show: (1) Under the complex promotion strategy, the quality of consumer decisions deteriorates. (2) Under the complex promotion strategy, the cognitive cost of consumers increases.
HRCE: Detecting Food Security Events in Social Media
Authors: Gao Wang , Fang Yuan , Wang Yuwei , Zhang Fan 
Published: January 2020

Abstract
Analyzing food security events shared on social networks not only helps people deepen their understanding of food security events, but also helps managers cope with these events. In this paper, we propose a model that utilizes task-specific features and a deep learning model to detect food security events from tweets, called HRCE. Specifically, the proposed model leverages a hierarchical Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) that takes word embeddings as inputs, and combines contextual embeddings to identify food security events from social media. We collected a novel food security related dataset from Twitter, and manually annotated 2,418 tweets. We conducted experiments on this dataset and concluded that HRCE outperforms baseline methods in terms of precision, recall and F1-score.
Research On Production Classification Of Coalbed Methane Well Based On MATLAB
Authors: Tang Yifang , Zhang Tianxiang , Guo Zixi , Zhu Weiping , He Pengbo 
Published: January 2020

Abstract
China’s coalbed methane resources are very rich, but the coalbed methane mining effect is not good. Fracturing is an important means of coalbed methane mining. It is very important to select coalbed methane wells with mining value before fracturing. For most fracturing wells in China, CBM wells are selected according to the experience of the site and workers. It has blindness and increases the cost of coalbed methane mining. In order to reduce the loss, this paper proposes a prediction model based on support vector machine coal fracturing effect. First, pretreatment studies of fracturing well data are performed to ensure the quality of fracturing well data. Secondly, the gray correlation analysis is carried out on the pre-processed fracturing well data, and eight main controlling factors affecting the coal cracking effect are extracted. Finally, using the support vector machine classifier, the coalbed methane wells are divided into three categories: low-yield wells, middle-production wells and high-yield wells. At the same time, the support vector machine nonlinear regression model is used to predict the stable production of coalbed methane wells. Using the support vector machine model to predict, predict the stable production value of coalbed methane wells is basically consistent with the field data, which has important guiding significance for on-site coalbed methane mining.
The Affecting Factors Analysis of Adolescents’ Acknowledgement of Personal Data in China
Authors: Sun Tianjian 
Published: January 2020

Abstract
The swift development of information technology brings people to the big data era and puts the issue of personal data privacy on the agenda. This research focus on the affective factors(gender, age, living environment, school type) of adolescents’ acknowledgement of personal data privacy in China. The samples were selected from five different schools in Shaanxi province, China. Descriptive statistics are used to analyze the result from the questionnaires. T-test and multiple linear regression analysis are applied to test the hypotheses, and it is examined that hypotheses involving age, region and school type are supported by both tests.
Design of Quality Management and Inspection Platform for Industrial Control Integration Based on AI
Authors: Lin Lin , Wang Libo , Li Hui 
Published: January 2020

Abstract
With the application and development of industrial internet, the third-party software testing is facing new problems. In order to solve the problem of multi-region, distributed, multi-role and dynamic testing in third-party testing and realize the functions of centralized control, distributed management, traceability, resource sharing, paperless and networked office, this paper designs an integrated quality management testing platform based on AI for industrial control. The establishment of this platform will fill the gaps in the technology of integrated testing platform for third-party software testing and evaluation of China’s industrial Internet, and further promote the construction of testing capability and quality management level of China’s industrial Internet enterprises.
Study on the decision considering imperfect maintenance of multiple components based on the exponential distribution
Authors: Pang Guoying , Liu Jia , Liu Jun , Guo Yan , Yuan Xuemei 
Published: January 2020

Abstract
The real-time monitoring and the high reliability system of multiple components are studied in the mechanical system as background information. The characteristics of copula among multiple component of different systems are considered. The information of affected components is monitoring as covariate. With the prediction model of remaining useful life (RUL)based on stochastic filtering theory, and the strategy of opportunity maintenance, the maintenance decision model of dynamic is studied on the prediction information of remaining useful life. Lastly, the relevant theories are verified by simulation.
Loan risk assessment based on Pythagorean fuzzy analytic hierarchy process
Authors: Song Peng , Li Li , Huang Ding , Wei Qingjie , Chen Xue 
Published: January 2020

Abstract
Risk assessments are increasingly being applied to financial institutions and banks, especially on loan risk assessments. When deciding to approve the applicant’s loan or not, financial institutions or banks need to assess whether the lender has the loan conditions. Usually this is a very difficult decision. In this paper, a new integrated approach Fuzzy Pythagoras Scoring (FPS) model, including Pythagorean fuzzy analytic hierarchy process (PFAHP) and fuzzy comprehensive evaluation (FCE), is used for personal loan risk assessment. The risks of decision analysis in loans are assessed by the proposed method. Comparing the results of the traditional analytic hierarchy process (AHP) and PFAHP, it shows that the method can obtain reliable evaluation results and evaluate personal loans more accurately.
Design and Development of High School Curriculum Learning System Based on the Core Quality of Mathematics
Authors: Chen Jie-chao 
Published: January 2020

Abstract
Mathematics plays a unique and irreplaceable role in forming human rational thinking, scientific spirit and promoting human intellectual development. It is worth studying how to cultivate the core quality of high school students. Under the background of Internet +, many Internet education service industries have further developed, and efforts have been made to study the effective integration of subject knowledge and information technology to improve students’ learning effectiveness. Mathematics, as one of the core courses of high school education, highlights its importance. Based on the core quality of mathematics, this paper constructs a new model of high school mathematics knowledge, and designs and implements the framework of high school curriculum learning system. Teachers adjust teaching content and strategies according to the actual situation of students, improve classroom efficiency, gradually achieve high-level teaching goals, and train new talents who can adapt to the needs of the new era in modern teaching.
Improvement of sphericity test for large-dimensional covariance matrix
Authors: Yuan Shoucheng , Ma Zhengqi 
Published: January 2020

Abstract
This paper discusses the problem of testing the sphericity of population covariance matrix when the sample size n and the dimensionality p both tend to infinity with p / n → c ∈ (0,1). A new test statistic is presented by utilizing an inequality, and asymptotic properties of the proposed statistic are derived for generally distributed population under the null hypothesis. Numerical simulations demonstrate that the proposed statistic has a significant improvement on test powers under the alternative hypothesis by comparing with some congeneric statistics.
A Solution to Defend Cloud Auditing Protocols from A Potential Attack
Authors: Lv Jiaxian , Wang Yi , Liu Jinze 
Published: January 2020

Abstract
PRISM, a monitoring plan carried out by American government and revealed to the public in 2013, brought subversion attack back to the centre of cryptography research. In this work, we propose a kind of subversion attack on the cloud auditing protocols, where the cloud server plays the role of subversion attacker, manages to recover the secret message stored by the users of the auditing protocol. Then we propose a general frame of defence solutions with experiments to evaluate the practicability of our theme.
Research on the Breach of Relationship Norms by Opportunistic behavior in the Context of Big Data
Authors: Chen Dai-fen , Zou Fang , Zhang Li , Yi Jiao 
Published: January 2020

Abstract
In this paper, psychological contract violation is introduced as a mediate variable to study the breach of relationship norms by opportunistic behavior of e-commerce platform. Based on the data analysis of 400 B2C e-commerce platform consumers, the results show that the opportunistic behavior of the platform has a negative impact on relationship norms, and psychological contract violation will reduce consumers’ willingness to follow the relationship. It plays a mediate role between platform opportunistic behavior and relationship norms, and user stickiness can reduce the negative impact of psychological contract violation on relationship norms. However, opportunistic behavior and psychological contract violation have no negative influence on conflict resolution norms. The findings provide theoretical guidance for e-commerce platform managers to make up for the destruction of relationship norms caused by opportunistic behavior through the realization of consumer psychological contract.
Stratum Differences in Health Behaviour of Elderly Group—An empirical research based on harmonized CHARLS
Authors: Hua Jie 
Published: January 2020

Abstract
Based on the harmonized CHARLS data, this paper discussed the impact of social class on the health behaviour of the elderly. Through negative binomial panel regression and logit panel regression with fixed effect, the study found that older people with higher social strata pay more attention to maintaining their health and thus tend to avoid health risk behaviours. Among them, education has a specially significant impact on the choice of health behaviour, and elder people with higher education have a healthier behaviour. In addition, retirement also has a significant impact on the health of the elderly, mainly reflected in the possibility of their exercise habits disappearing after retirement.
Optimize Logistics cost model for shared logistics platform based on time-driven activity-based costing
Authors: Xu Sheng , Niu Jingjing , Cai Xin 
Published: January 2020

Abstract
Transportation cost management plays an important role in both traditional logistics management and supply chain logistics management. Time-driven activity-based costing bypasses the expensive, time-consuming and subjective activity investigation and is more suitable for the intermediate link of enterprise cost accounting. Linear programming is a mathematical theory and method to study the extreme value problem of objective function under linear constraints, and the optimal decision is made by using limited human, material and financial resources. Based on the concept of Shared logistics, this paper studies transport enterprises to solve problems such as information asymmetry in the process of transport. By establishing a linear programming time-driven activity-based costing accounting model, the allocation of scarce resources in transport enterprises can be reasonably solved, and the efficiency of cost accounting can be improved to achieve considerable cost savings.
Analysis of Influence of Family Status on Dietary Behavior of Preschool Children through Data Samples: A Case Study of Eating Frequency of Western Fast Food
Authors: Tan Fang , Zhao Xin , Yi Ruokun , Xu Nanxin , Zhang Jiaqiong 
Published: January 2020

Abstract
Children’s eating habits are closely related to not only their own growth and development, but also their psychological development. In order to explore the influence of family status on the frequency of Western fast food eating for preschool children, this study selected five aspects of preschool education, including the cognitive level of adopters and family income, as the research dimension, and used SPSS software to analyze nearly 2000 groups of data. The study found that the low cognitive level of adopters in Western fast food behavior caused children to eat Western fast food more frequently. Children with high family income, decent parents, high educational level of parents and family living conditions had higher frequency of eating Western fast food. Finally, it is proposed to improve children’s dietary behavior by improving their dietary education, education ability and their own dietary behavior, developing diversified and interesting dietary health, health education activities, and strengthening the publicity of dietary education in kindergartens.