bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detection and Prevention of Cyber Crime Based on Diamond Factor Neural Network
Authors: Li Jin , He Ping 
Published: January 2020

Abstract
The purpose of this paper is to provide a quantitative analysis method for cybercrime research, and also provide a new mathematical research method for network behavior analysis. Firstly, a novel factor space research method is established by using the “medium scale”. On this basis, the concept of factor discovery of cybercrime behavior is proposed, and the corresponding cybercrime behavior analysis model is established, namely the criminal factor neural network. Secondly, the learning mechanism of network behavior neural network is discussed by using the factor discovery principle. At the same time, a network behavior learning algorithm based on diamond thinking is obtained. Finally, factor discovery thought, factor neural network learning system are applied to the research of cybercrime model analysis and prevention strategy to provide guiding decision support and problem solution for public security departments.
Human Posture Recognition in Intelligent Healthcare
Authors: Yang Xinyue , Ren Xiaoyang , Chen Meng , Wang Luqi , Ding Yuhao 
Published: January 2020

Abstract
With the rapid growth of medical technology, intelligent healthcare has gradually integrated into our lives, and its core technology is the human body motion state detection. According to the basic tools being used, we divide this technology into two categories: vision-based detection and sensor-based detection, and take the fall monitoring as an example for elaboration. Finally, different methods are compared and analyzed, the difficulties that need to be solved in this field are summarized, the future trends are included too.
Research on Online Learning Resource Recommendation Method Based on Wide & Deep and Elmo Model
Authors: Liu Jie , Zhang Hong , Liu Zihui 
Published: January 2020

Abstract
In order to improve the accuracy of online learning platform recommendation for learner learning resources and to alleviate the cold start problem, this paper proposes an online learning resource recommendation method based on Wide&Deep and Elmo model. The method uses the real learning data set of a university network and educational learning platform, and uses Wide&Deep to deeply explore the deep features of learner characteristics and course content features under the condition of high-dimensional data sparseness, that is, automatic learning combination features, the learner-course feature vector is constructed as the input of the recommendation algorithm. In addition, for the learner’s text feature, it will use the ELMo language model to pre-train the feature vector to improve the recommendation accuracy.

CCS Concepts

•Computing methodologies➝Cognitive science • Theory of computation➝Online learning algorithms
Analysis of Initial Value Effect of Differential Chaotic Shift Keying Communication System
Authors: Tao Sui , Hao Wang , Fanna Zeng , Yongxin Feng , Qiang Jiang 
Published: January 2020

Abstract
Chaotic signals have many unique properties, chaotic digital modulation technology has many advantages over traditional digital modulation technology, so it has good application value in the field of information security. In order to analyze the differential chaotic shift keying technology more comprehensively, based on the research of the DCSK, FM-DCSK and RM-DCSK differential chaotic shift keying techniques, the MATLAB simulation platform is used to simulate the balance of the different sequences and the initial values of different chaotic keying systems are used to simulate the error performance of different systems. The experimental results show that the peak of the balance of chaotic map may lead to the peak value of the system error rate. Therefore, the peak point should be avoided when the initial value of the sequence is selected. Different digital modulation techniques and chaotic mapping systems have their own characteristics, and when the reasonable parameters are selected according to the characteristics, the system performance can be optimized.
Simulation of Multiple Scattering of THz Wave Propagation in Sandstorm
Authors: Hongxia Wang , Honghui Sun , Xuanke Zhao , Xiaofang Shen , Lianfen Wang , Yunfang Zhao 
Published: January 2020

Abstract
Terahertz wave has important application prospects in communication, but its transmission in the atmosphere will be affected by the atmospheric environment. Based on Mie scattering theory and Monte Carlo method, the attenuation characteristics of THz wave multiple scattering caused by dust storms in Tengger desert are studied in this paper. The relationship between the attenuation rate and visibility and frequency of THz wave are obtained by calculation. The results show that the multiple scattering of THz wave in sandstorm is very significant. There is a big error in the calculation of attenuation rate based on single scattering. The difference between the two methods decreases with the increase of visibility. The attenuation rate of THz wave varies with the frequency in the sandstorm. When the frequency of THz wave is less than 2.4 THz, its attenuation rate increases with the increase of frequency, and when the frequency is more than 2.8 THz, its attenuation rate decreases with the increase of frequency.
Research on Strategies to Improve Service Quality of College Teacher Development Center in the Age of “Internet +”
Authors: Wang Lei 
Published: January 2020

Abstract
Against the backdrop of integration of big data and Internet technologies with higher education, higher education reforms have raised standards for teachers’ teaching ability and information literacy, hence higher standards for the service quality of college teacher development centers. In this context, this paper analyzes the service quality and problems of private college teacher development centers in the system of “Internet + training”, constructs the service quality gap model of the college teacher development center, and explores the causes for poor service quality in the training process. At last, according to the causes, this paper builds the data platform, develops the training teams (program development team and technical support team), relies the virtual community of “E-home of college teacher development”, optimizes the training mode to propose the strategies to improve private college teacher development center in the age of “Internet+”.
Research on User Experience of VR Games Based on ISM Theory
Authors: Feng Yu-Liang , Chen Chun-chin , Wu Shu-Ming 
Published: January 2020

Abstract
This paper explores the starting point of VR (virtual reality) game experience by 5 “W” of care (What, Why, Who, Where and When), uses STEEP method to analyze the problems in different aspects of the user experience, and invites two senior people to evaluate 11 important factors. Then, the factor relationship hierarchy analysis graph is obtained with the ISM theoretical operation, and the VR game users’ problems and goals that urgently need to change are obtained. The main issue in the research is that the quality of the game story determines the experience of the users, and the side issues are the sound effect, art effect, and special effect.
Analysis of the Influence of Mass Media on Music Communication Based on Digital Conditions
Authors: Kong Wenting 
Published: January 2020

Abstract
Based on digital conditions, mass media as a carrier enters people’s field of vision and has an important impact on music communication. With the continuous development of the media industry, the relationship between mass media and music art has become extremely complex, and has an impact on the production, dissemination and popularity of music art. This paper mainly discusses the role of mass media and the influence of mass media on music communication, and analyzes the path of mass media to spread music.
Travel Attractions Recommendation based on Max-negative the Gated Recurrent Unit trajectory mining Representation
Authors: Zhang Shunyao , Yochum Phatpicha , Bin Chenzhong , Chang Liang 
Published: January 2020

Abstract
Although the traditional recommendation algorithm has achieved good results in the field of travel recommendation, due to the lack of data, the cold start and data sparseness problems and the neglect of the semantic problems hidden in the travel track, the low recommendation accuracy remains unresolved. Recently, the RNN model performed very well in recommending system sequence learning. We use RNN to model the travel sequence in the travel recommendation. In the pairwise, we can achieve a more accurate recommendation by effectively processing the negative samples and then training to generate a smaller loss function. Our method (Max-GRU) is optimized by adding additional negative sample and finding Max-negative on the Gated Recurrent Unit trajectory mining Representation Model. On the Shanghai tourism data and Guilin tourism data, both MRR@10 and RECALL@10 have been significantly improved compared to the use of the RNN model and baselines.
Identification of Tomato Diseases using Skip-gram and LSTM Based on QA(Question-Answer) System
Authors: Hang Xiao , Gao Hongju , Jia Shaopeng 
Published: January 2020

Abstract
In the field of agricultural information processing, automatic identification and diagnosis of common diseases of tomatoes play an important role. Deep learning is a hot research topic in the field of pattern recognition and machine learning. It can effectively solve some problems of vegetable pathology, such as disease identification, automatic control and production prediction. In this paper, the tomato pest and disease experiments were carried out with natural language data set of 1.12G pathological and healthy tomatoes crawled from Doctors of Agriculture Website as source data. Five common tomato diseases’ symptoms were identified by training Skip-gram algorithm. Finally, symptoms identified above can be classified using LSTM algorithm with classifiers. The research shows that the tomato pest and disease corpus identification models based on LSTM algorithm with classifiers achieve an average accuracy which are over 60%. The simulation results of tomato diseases identification show feasibility and effectiveness of the methods. This article aims at integrating our methodology into working systems that can be used in the identification fields.
Research on Financial Portfolio Analysis in the New Era
Authors: Yu Jiang Nan 
Published: January 2020

Abstract
Financial investment is a unique form of investment. It must have an orderly and standardized institutional framework so that the innovation, development and improvement of the financial system can be the rules of conduct. This paper focuses on the unique investment mode of financial investment, and discusses the main characteristics of financial investment in detail, and then derives the prototype of financial investment theory. At the same time, it analyzes the function of financial investment from three aspects, and provides a powerful financial investment for the new era.
Research on the Construction of Army Command System Based on “Cloud Top”
Authors: Wang Jian , Wei Wei 
Published: January 2020

Abstract
With the development of information technology, combat mode tents to be intelligent. As the most complex and pluralistic military force, the Army has higher requirements for its command system which must have the characteristics of parallel,efficient and real-time. The traditional command system can no longer meet the requirements of future operational command. This paper though researches Construction of Operational Command Architecture Based on “Cloud Top” and Technical System of Army Command System to provide some reference for the Army to gain command superiority.
Inflation persistence investigation of china based on quantile autoregressive model
Authors: Qiu Dan , Gbolonyo Patrick Kweku , Zhang Xiuqi , Yang Xiaorong 
Published: January 2020

AbstractIt has been noted that high inflation has a significant threat to many countries developments and the persistence of inflation increases the cost of monetary policy in controlling inflation. Therefore, this paper proposes a unique method to estimate inflation persistence by using quantile autoregressive (QAR) model and to show how various shocks may affect the rate of inflation within different quantile levels. Based on the QAR model, the monthly inflation persistence in China and its dynamics over time are considered in the paper. The data illustrated here are the monthly year-on-year Chinese consumer price index from January 1987 to May 2019. The results indicate that the Chinese inflation series is globally stationary, but exhibits non-stationary behavior in about 44.85% of the observations. Again, it is evidence that the Chinese inflation rate has asymmetric characteristics at different quantile levels in its conditional distribution. Our research indicates that the QAR model outperforms the ordinary least square (OLS) method in terms of heterogeneity association of the inflation dynamics.
An Improved Frequent Pattern Growth Based Approach to Intrusion Detection System Alert Aggregation
Authors: Sun Yuxi , Chen Xiaomei 
Published: January 2020

Abstract
This paper introduces different approaches to intrusion detection system (IDS) alert aggregation and proposes an improved frequent pattern growth (FP-growth) algorithm for it. This approach can be divided into three parts, which are removal of noisy data, mining association rules and text similarity check. According to the experiment on Snort alarm dataset provided by an enterprise, all the association rules found by the proposed approach are valid. Therefore, compared with FP-growth algorithm, the proposed approach can increase the precision of the result and is useful for alert aggregation.
Student Network Behavior Analysis and Relevance Research Based on Optimal Decision Tree Algorithms
Authors: Han Geng 
Published: January 2020

Abstract
Based on the optimal decision tree algorithm, this paper proposes a student network behavior analysis model. Through training and predicting the academic performance of Beihang Grade 2013 undergraduates, it is found that students’ online behavior has a profound impact on students’ academic performance. To maintain a good learning state, students must strictly limit the time spent on the Internet in idle time, effectively control the time spent on the internet, and ensure that their daily sleep is not affected by the Internet behavior. The regularity of students’ life is positively correlated with their achievement ranking.
Research on Quality Analysis Model of Additional Comments Based on Text Analysis
Authors: Dong Yali , Wang Suyang , Ye Shixuan 
Published: January 2020

Abstract
Based on the idea of text analysis, this study analyzes the relevance of text topics, the emotional consistency of fist comments and additional comments, and the information contained in the text, then get out of the online quality assessment model. The calculation results can reflect the quality of additional comments, helping consumers and businesses to identify important information and make correct judgments.
Research on Lean Analysis Algorithm for Equipment Centralized Monitoring in Big Data Era
Authors: Chen Tan , yong Wang , Xiaomin Lu , Jiang Wu , Guangcheng Zhang , Ziwei Bai 
Published: January 2020

Abstract
The equipment monitoring brought by the smart grid big data is difficult to effectively supervise the operation status of the whole network substation, and the typical defects (familial defects) are difficult to classify and locate. This paper proposes the substation operation state evaluation algorithm and typical defect classification algorithm. The operating state evaluation algorithm of the substation is based on different operation data generated by the substation. By normalizing the data of different dimensions, the substation is divided into different operating state levels. The typical defect classification algorithm establishes and maintains the historical experience database, and calculates the conditional probability of each information item to realize the correlation between the signal and the defect, and finally judge whether the signal is from a typical defect. These two algorithms are effective means for equipment monitoring professionals to realize intelligent supervision of substations and equipment in the era of big data.
Bivariate matrix-valued Newton-Thiele-type mixed rational interpolants
Authors: Dongyin Wang , Youtian Tao , Xiangguo Liu 
Published: January 2020

Abstract
By means of Newton’s difference quotient and Thiele type continued fraction combined with the generalized inverse matrix, we present a kind of bivariate matrix-valued blending difference quotient. On this basis, we construct the bivariate Newton-Thiele type rational interpolants and give its iterative algorithm.
A Multipitch Tracking Algorithm Based on Wavelet Packet Analysis
Authors: Liting Chen , Weiping Hu , Jialing Mo , Yixuan Zhang 
Published: January 2020

Abstract
In view of the limitation of many multipitch detection methods in single-channel speech, a robust and accurate multipitch estimation method for multiple voices is processed. In this study, our approach is based on the spectral analysis of the wavelet packet analysis. It utilizes the quasi-periodicity in a short time frame of speech, and gets candidate pitche in every frame by using peak selection and searching algorithm. The multipitch envelope of the mixed signal is obtained by neighborhood analysis and determined from these candidates by virtue of the fact that pitch should change rather smoothly in consecutive frame. Simulation results showed that the proposed method can robustly estimate fundamental frequency for mixed speech from single-channel speech.
Analysising the influence factors of single task pricing based on public packet system: An Empirical Study in China
Authors: Sun Jiang-Jie , Ping-Wang  , Du Ya-Nan , Jun-Liu  
Published: January 2020

Abstract
This study takes single task pricing as an example, analyzes the key factors of task pricing and constructs an index model of single task pricing through the data of 835 task cases. Through the empirical analysis of the new pricing model, we draw the conclusion: the new pricing model reduces the cost of the task and increases the completion rate. It has a certain guiding significance for the development and management of the public crowdsourcing platforms.
Rolling bearing diagnosis method based on WMKPCA-LSTM network
Authors: Cui Bo , Chu Jizheng , Cao Ning 
Published: January 2020

Abstract
The monitoring data from large machinery have characteristics of high dimensions, non-linearity and correlation. In the traditional fault diagnosis, it is hard to extract features and complex to build the model. In order to solve these problems, the rolling bearing diagnosis method based on a weighted mixed kernel principal component analysis (WMKPCA) and long-short-term memory (LSTM) network is proposed. The method uses WMKPCA algorithm to reduce the dimensionality of the vibration signal. The method not only effectively reduces the correlation of the original data, but also improve the diagnostic accuracy; The LSTM neural network has a memory function that can be used to extract temporal features. Experimental results show that the proposed algorithm has higher diagnostic accuracy and higher efficiency than traditional methods of principal component analysis (PCA), kernel principal component analysis (KPCA) and multiway kernel principal component analysis (MKPCA).
Looking at the Demand of Bank Big Data Talents from the Perspective of Big Data
Authors: Wei Wei 
Published: January 2020

Abstract
Big data is the foundation of artificial intelligence. In the financial field, as a very large and very convenient field for applying big data, there has been a lot of research on big data and artificial intelligence. From intelligent customer service to smart investment consultants to smart counters and unmanned banks, the penetration speed of big data and artificial intelligence in the financial field has reached an unimaginable level. We use big data to make money, and we use big data to understand the dynamics and trends of the bank. This article uses the reptile software Octopus to analyse the recruitment data of Zhonggong Financial Online to understand the needs of banks for big data talents, to explore its real dynamics and implementation in artificial intelligence, financial technology and big data.
Medical insurance participation and the ageing population subjective well-being: an empirical analysis based on CGSS 2015 data
Authors: Li Minya 
Published: January 2020

Abstract
Using the Chinese General Social Survey (CGSS 2015) data, the current research is to examine the relationship between medical insurance and subjective well-being for elderly people in China. To test our hypothesis, we first generated and tested an ordered probit model, as the dependent variable in our research is subjective well-being, additionally, as in China we have different medical insurance types, we integrated them and classified them as social basic medical insurance, and commercial medical insurance. Also, as the causal identification problem and endogenous problem exist, we introduced another main dependent variable as the individual’s social security satisfaction in our research. The implications derived from this study are summarized as conclusions.
Application Research of Face Recognition Technology in Smart Campus
Authors: Zhou Xiang 
Published: January 2020

Abstract
Promoting the degree of campus informationization has become an important part of the construction of smart campus. Face recognition, with its uniqueness and lifelong invariance, has natural security advantages and practical advantages in the application of smart campus, but it also has the disadvantages of high equipment cost and data leakage risk. The face recognition technology can be used to build safe educational environment, create convenient business processes and develop personalized technology service in the construction of smart campus. This paper designs and implements a dormitory management system based on face recognition. The test results show that the use of face recognition technology as a source of information collection for people entering and leaving dormitory area greatly improves the management efficiency and ensures the accuracy and reliability. The application of face recognition in campus management system will break the traditional mode of teachers and students’ work and life, and become an important part of smart campus.
Research on TV imaging casing damage detection and classification method based on C4.5 decision tree
Authors: Cuan Ying , Wang Zeshang , Han Jiaxin 
Published: January 2020

Abstract
The casing damage is one of the important problems in oilfield development. The television imaging technology can get enough images of the inner wall of casing to detect the casing, but the efficiency of manually identifying the images is very low. Through the mapping relationship between image and casing damage type, the image features can be obtained. Then, the continuous data threshold selection of C4.5 decision tree algorithm is improved. Finally, the improved C4.5 decision tree algorithm is used to classify casing damage types. Experimental results show that the detection and classification method proposed in this paper can be well implemented.