bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and Implementation of Dynamic Expansion of Vehicle Real-Time Data Transmission Architecture Based on Kubernetes
Authors: Chen Chen , Cai Jianjun , Ren Nver , Li Kunhao 
Published: November 2019

Abstract
In order to deal with the overload problem of smart car server, a dynamic capacity expansion technology of vehicle real-time data transmission architecture based on Kubernetes cluster is proposed. The technology uses Docker to split the architecture into multiple micro-services, and uses the HPA feature of Kubernetes to periodically monitor server CPU resource utilization, trigger expansion rules, and automate the increase and decrease of nodes to achieve capacity expansion. It has been proved by experiments that the dynamic expansion technology of the real-time data transmission architecture based on Kubernetes can realize the dynamic expansion of the architecture. It solves a series of problems caused by server service overload caused by the sudden increase of data sent by the car in a short period of time, and ensures the stability of the service.
Derivations of transient thermal Green’s functions in three-dimensional general anisotropic media
Authors: Zhou Jiakuan , Han Xueli 
Published: November 2019

Abstract
In this paper, three-dimensional transient thermal Green’s functions in general anisotropic media are derived in relatively concise forms via the Radon transform. Both situations in full-space and half-space are provided. For the case in full-space, the governing equation of heat conduction problem in three-dimension is reduced to a similar one in one-dimension whose solution is existent. For the case in half-space, both Dirichlet and flux-free boundary conditions are considered, and the solutions are derived by an image method. Applying the inverse Radon transform to solutions in transform domain, Green’s functions in physical domain are subsequently expressed as an integral over a unit sphere. If written in terms of usual spherical coordinate, moreover, these solutions are regular integrals over finite intervals and can be evaluated easily and effectively. Numerical examples are presented to verify the accuracy and applicability of the present derivations, and to demonstrate the effects of distinguishing boundary conditions.
Real-time Training Model for Elderly People Balance Ability
Authors: Xue Jie , Wu Qiong , Mu Wenfei 
Published: November 2019

Abstract
Falling happens when people’s body (excluding feet) unexpectedly touches the ground, which causes serious injuries for old people. Therefore, it is of great significance to assessing old people’s balance characteristics and help them prevent form falling. Based on this, we constructed a feature extraction model and obtained 25 factors that have the greatest influence on body balance by our model. We found that the most influential parts mainly distribute on people’s trunk, left arm, right foot and head, which can be applied in further study of falling prevention and balance supervision.
Dual-band MIMO Antenna with Band-stop Decoupling Network
Authors: Liu Qi , Gao Peng 
Published: November 2019

Abstract
A dual-band MIMO antenna operating at 2.5 GHz and 5.8 GHz which can be used for WLAN application is presented in this paper. Simulation, fabrication and measurement are accomplished to verify the antenna performance. As a simplified design process, firstly a band-stop filtering network is presented to achieve port isolation. And a MIMO antenna is designed through appropriate adjustments. The antenna can provide more than 20 dB isolation at both operating frequencies.
Fast-Flux Detection Method Based on DNS Attribute
Authors: Wang Jiajia , Chen Yu 
Published: November 2019

Abstract
Fast-flux service network provides redirection/proxy services for a large number of malicious and illegal content based on botnet, and provides a high level of protection for botmaster. In this paper, a method is proposed to distinguish fast-flux service network from legal website. Firstly, the differences between the attributes of the above two are analyzed in detail, then the detection features are constructed, and decision tree algorithm is used to detect the existence of fast-flux service network.
A Novel Timing-based Network Covert Channel Detection Method
Authors: Lu Shoupu , Chen Zhifeng , Fu Guangxin , Li Qingbao 
Published: November 2019

Abstract
Network stealth events are endless, and covert timing channel is one of the most difficult means to prevent. In order to further improve the detection rate of covert timing channel, several typical network covert timing channel construction algorithms are analyzed. On the basis of the above analysis, a detection method based on IPDs multidimensional features was proposed in this paper. IPDs of covert timing channels from three dimensions: shape, change rule and data statistics are analyzed. Respectively, polarization feature, autocorrelation feature, clustering feature are proposed, and the three features are unified into a model. The threshold method is used to determine whether the channel to be detected is a normal channel. Experiments show that the method can detect the existing covert timing channel with less time cost and compared with the traditional detection method has a certain rate of improvement.
Variational Bayesian Method for Time-Varying Linear Prediction Modelling
Authors: Wan Hongjie , Zhang Haiyun 
Published: November 2019

Abstract
The speech signal is produced by vocal tract of human beings. The characterics of the vocal tract can be modelled by time varying linear prection analysis. In this paper, a hierarchical Bayesian model is proposed to model the time varying linear prediction model. Then Variational Bayesian method is used to estimate the parameters of the model. Experiments are carried out on real speech to demonstrate the performance of the proposed method.
Identification of Frequency Hopping Radiation Sources Based on the Feature of Ambiguity Function
Authors: Wang Shaobo , Qi Zisen , Yang Xin 
Published: November 2019

Abstract
Aiming at the problem that the traditional feature parameters are difficult to meet the fastness and effectiveness of frequency hopping radiation sources identification, a feature extraction method based on radar ambiguity function theory is proposed. The algorithm applies the radar ambiguity function theory to the identification of frequency hopping radiation sources. Firstly, the single pulse ambiguity function of each hop signal is calculated. Then the particle swarm optimization algorithm is used to extract the main ridge slice of ambiguity function of the signal (MRSAF). Based on the noise reduction processing of MRSAF and the stability of MRSAF envelope, the difference sum, difference maximum value and difference distribution entropy characteristics of MRSAF are extracted. The experimental results of fuzzy cluster mean (FCM) show that the extracted feature parameters can be used to identify signals with high accuracy under dynamic SNR conditions.
Research on the Countermeasures of College Students’ innovation and entrepreneurship development under the perspective of “Internet +”
Authors: Mei Han 
Published: November 2019

Abstract
“Internet +” innovation and entrepreneurship education is a new educational concept, and it is the inherent requirement of university education reform. In this paper, we take the cultivation of College Students’ “Internet +” innovation and entrepreneurship capabilities as the core, in-depth analysis of the major problems encountered by college students in “Internet +” innovation and entrepreneurship, and realize the profound educational reform from four levels of government, universities, enterprises and university students. Based on the evaluation index of graduate innovation and entrepreneurship education, this paper constructs an evaluation model of graduate innovation and entrepreneurship education based on analytic hierarchy process (AHP), which provides basis and reference criteria for the training and evaluation of graduate innovation and entrepreneurship education.
Research on Image Segmentation Method Based on Fuzzy Clustering
Authors: Feng Guilian 
Published: November 2019

Abstract
Image segmentation is to divide an image into several continuous non-overlapping regions with similar characteristics, such as gray level, color and texture information. Through image segmentation, we can extract specific objects from complex background. The image has great uncertainty and fuzziness. Traditional segmentation methods often fail to achieve ideal segmentation effect, which directly affects the subsequent target feature extraction and result analysis. However, the fuzzy theory can well describe the characteristics of the image. Therefore, more and more scholars have studied image segmentation and proposed various related segmentation methods. Firstly, this paper analyses the hierarchical structure of image engineering. Then, this paper analyses the basic concepts of the fuzzy clustering algorithm. Finally, this paper analyses the image segmentation effect of the fuzzy algorithm by experiments.
Analysis and prediction of traffic flow based on Wavelet-BP neural network
Authors: Wang Zhuo , Zhao Lei 
Published: November 2019

Abstract
Through on-the-spot investigation, collected data of traffic flow from a certain section of 3rd Ring Road in Beijing, the investigation results were analyzed from the perspective of time and space, macro rules of traffic flow and the influence of incidental traffic events on roads was summarized. The MATLAB software was used to decompose traffic flow data by three-layer wavelet, and the variation characteristics of the detail coefficients were obtained by decomposing the parameters of traffic flow by wavelet. Analyzed the abrupt change phenomenon of the traffic flow parameters, so as to judge whether some traffic incident occurred and which kind of it. With the level of road traffic service, the same moment which anomalous traffic flow data produced theoretically and traffic service levels were greater than or equal to 4 levels was chosen, so as to verify whether the anomalous time point analyzed by wavelet-analysis were precise. Used MATLAB software to establish the prediction model by a neural network, and determined the various parameters, functions, training and testing samples. Coefficients of different parameters in the BP neural network were used to predict the changes of traffic flow in the future. Compared the predicted data with the actually observed traffic flow, the starting time of traffic incident and the anomalous mutation starting point of three traffic flow parameters was consistent. This research had great significance to the future intelligent traffic decision-making.
A multi-Cross Matching Network for Chinese Named Entity Linking in Short Text
Authors: Ouyang Xiaoye , Chen Shudong , Zhao Hua , Hao Yuexing , Li Wei , Li Shaojie , Wang Rong , Liang Xiaohu 
Published: November 2019

Abstract
Named entity linking(NEL) for Chinese short text is regard as a ranking task. Current research on NEL is mostly about long text and rich language, which may unfit Chinese short text situation. We propose a multi-cross matching network(CMN) to solve these problems. CMN first matches a candidate and the mention with the text in three different degree, and extracts important matching information through a couple of convolution and pooling layers. Then sequence vectors are accumulated through a Gated Recurrent Unit(GRU) which distill relationships among words in texts. Finally we use dynamic average algorithm to calculate the mention-entity similarity. Experimental results demonstrate that CMN exhibits the outstanding performance on several Chinese datasets for the Chinese short text entity linking problem.
Predicting sales by online searching data keywords based on text mining: Evidence from the Chinese automobile market
Authors: Li Yi , Yu Liangru , Wen Rui 
Published: November 2019

Abstract
Online searching data reflects consumers’ real footprints in information collection and purchase decision-making processes, which is greatly valued in understanding their needs. This paper which is at the background of China’s automobile market, studies the relationship between online searching data and automobile sales through approaches that differ from existing research to extract keywords. First the online searching data keywords are determined, primarily by using text-mining technology to extract them, and specifically: i) Jieba was used to tokenize crawled automotive forum posts’ text into segmented words; ii) All word-segmented Chinese corpus were segmented into word vector space by Word2vec model; and iii) Similar keywords were discovered by calculating the word vector’s similarity indexes. A fixed effect model was then built based on 108 months of long panel data. Finally, combing with panel vector autoregressive model (PVAR), we used rolling window to predict Chinese automobile sales from January to December 2015.The empirical results demonstrate that: a long equilibrium exists between online searching data and automobile sales; our regression model can explain 76% of the variance. The holdout analysis suggests that online searching data can be of substantial use in forecasting Chinese automobile sales.
An Action Recognition network for specific target based on rMC and RPN
Authors: Li Mingjie , Feng Youqian , Yin Zhonghai , Zhou Cheng , Dong Fanghao , Lin Yuan , Dong Yuhao 
Published: November 2019

Abstract
The traditional methods of action recognition are not specific for the operator, thus results are easy to be disturbed when other actions are operated in videos. The network based on mixed convolutional resnet and RPN is proposed in this paper. The rMC is tested in the data set of UCF-101 to compare with the method of R3D. The result shows that its correct rate reaches 71.07%. Meanwhile, the action recognition network is tested in our gesture and body posture data sets for specific target. The simulation achieves a good performance in which the running speed reaches 200 FPS. Finally, our model is improved by introducing the regression block and performs better, which shows the great potential of this model.
Slope reliability analysis based on PSO-RBF neural network
Authors: He Yongbo , Li Qing , Zhang Ning , Li Chuangjiang 
Published: November 2019

Abstract
In this paper, a reliability analysis method based on ABAQUS and particle swarm optimization radial basis function neural network is proposed. The strength reduction method based on ABAQUS is used to calculate the safety factor corresponding to the selected random variable. The data fitting function of the radial basis function neural network is used to establish the model and map the relationship between the safety factor and the random variable to construct the response surface function. A large number of random samples generated by Monte Carlo are substituted into the function to obtain the corresponding safety coefficient,in order to calculate the instability probability and reliability index of the slope.
Knowledge Acquisition for the Air Combat Based on GWO
Authors: Huang Changqiang , Wei Zhenglei , Yang Yuanzhi , Ku Shuo , Zhang Hongpeng 
Published: November 2019

Abstract
The problem of knowledge acquisition for the air combat of fighter is studied, and a way to acquire knowledge from massive flight parameters data is put forward. First, expert system of air combat is built; then for flight maneuver knowledge extraction, a method based on grey wolf optimization (GWO) is proposed, in order to obtain simple and effective knowledge, and design a new evaluation function of the GWO algorithm. Finally, the simulation results of horizontal right turn and single plane loop verify that the knowledge extraction method of flight maneuver is effective and feasible.
Rehabilitation system for children with cerebral palsy based on body vector analysis and GMFM-66 standard
Authors: Wang Ziqi , Ding Zanhan 
Published: November 2019

Abstract
This paper mainly proposes a rehabilitation training system that uses motion recognition technology to help children with cerebral palsy complete the evaluation of gross motor functions. This article makes a comprehensive introduction to this product with regard to the system functional architecture, application technology principles and its application scope.
OGRU: An Optimized Gated Recurrent Unit Neural Network
Authors: Wang Xin , Xu Jiabing , Shi Wei , Liu Jiarui 
Published: November 2019

Abstract
Due to the structural problem, the traditional neural network models are prone to problems such as gradient explosion and over-fitting, while the deep GRU neural network model has low update efficiency and poor information processing capability among multiple hidden layers. Based on this, this paper proposes an optimized gated recurrent unit(OGRU) neural network.The OGRU neural network model proposed in this paper improves information processing capability and learning efficiency by optimizing the unit structure and learning mechanism of GRU, and avoids the update gate being interfered by the current forgetting information. The experiment uses Tensorflow framework to establish prediction models for LSTM neural network, GRU neural network and OGRU neural network respectively, and compare the prediction accuracy. The results show that the OGRU model has the highest learning efficiency and better prediction accuracy.
An early warning method for structural safety based on EEG
Authors: Li Jailong , Zheng Wei , Zhao Qing 
Published: November 2019

Abstract
It is an important part in the field of structural safety detection to warn the monitored results accordingly. Studying the effect of visual environment on EEG can provide theoretical evidence for structural security warning. Most of the existing studies on the effect of visual environment on EEG are based on a single factor of color or frequency, and seldom involve the effect of shape on EEG. In order to solve these problems, this paper carried out an experiment to explore the effect of color, frequency and shape on EEG. The EEG signal was pretreated by EMD-HHT algorithm. It was found that the effects of visual signals on brain waves were concentrated in the first 20 seconds. By the observed β power in the first 20 seconds we found that when the color is red or yellow, the frequency is 10 Hz, the shape is circular or triangular, the β power will increase significantly, it’s the electroencephalogram most relevant to attention.
Opportunities and Challenges of Unmanned Aircraft Vehicles Networking in Confrontation Environment: Collaborative and Reliable Communication with Intelligence
Authors: Wang Guisheng , Huang Guoce , Ren Qinghua , Zhang Wenhe , Wang Ye , Gao Weiting 
Published: November 2019

Abstract
With a wide range of applications of artificial intelligence (AI) technology in communication networks, collaborative and reliable communication with intelligence has enormous potential to improve the performance of unmanned aerial vehicles (UAVs) in complex confrontation environment. Two typical application scenarios of UAVs, assisted communication and swarm communication, are introduced after discussions of characteristics. In details, the preliminary network architecture is given to improve the adaptability in different scenarios. Then, combined with the characteristics of the UAVs and the anti-UAV countermeasures, the opportunities and challenges in UAVs cooperative communication are analyzed. Finally, highlighting the key design considerations of the AI technology gives possible solutions correspondingly, which deserves to be exploited for the cooperative and reliable UAVs’ communication.
Disease Regions Recognition on Mural Hyperspectral Images Combined by MNF and BP Neural Network
Authors: Lin Yuxian , Xu Chenqi , Lyu Shuqiang 
Published: November 2019

Abstract
Many murals are suffering from some serious diseases such as deep loss, mud besmirch and sootiness nowadays. And it is necessary to identify these regions before conservation and restoration. The purpose of this study was to explore the disease region recognition method using hyperspectral images. A method combined Minimum Noise Fraction(MNF) rotation and Back Propagation (BP) neural network was developed to classify the mural images to several kinds of damaged regions and normal parts. The MNF was adopted to select feature bands while the BP network was constructed and trained to classify the mural images. Some mural images collected in Beijing were utilized for the experiment. And according to the error matrix and sampling analysis, the overall accuracy of this method was 86.6% and the Kappa coefficient was 0.864. And they were higher than the maximum likelihood classification method.
The Selection and Optimization of a Negative comment Identification Method
Authors: Luo Haibo , Dong Jianhu , Luo Xianlu 
Published: November 2019

Abstract
This paper introduces the research background and significance of negative comments, and analyzes the present situation and development trend of negative comments identification at home and abroad. Several popular classification algorithms of naive Bayes, support vector machines and neural network are introduced. The word bag model, TF-IDF model and Word2Vec/Doc2Vec model feature model are also introduced. The data of feature extraction and classification algorithm in IMDB movie review website are simulated, and the results are compared and analyzed, and an optimal combination of feature extraction and classification algorithm is obtained.
Research on Users’ Emotion in the Later Period of the Inversion of public opinion--Taking the case of Wang Fengya as an example
Authors: Zhang Yuan , Zheng Yingli 
Published: November 2019

Abstract
Online public opinion reversal events occur frequently. The emotional characteristics and reasons of users in the later period of online public opinion reversal events are still not clear. Therefore, it is necessary to analyze the user emotions in the later period of the online public opinion reversal event, so as to help strengthen the supervision of public opinions and avoid the negative impact on the society in the process of public opinion reversal as far as possible. In this paper, Wang Fengya’s public opinion reversal event was taken as an example. Python crawler was used to capture the user comment data of the later period of this network public opinion reversal event. Combined with BosonNLP emotion dictionary, HowNet emotion dictionary and the characteristics of data capture, a custom emotion dictionary was constructed to realize the emotion analysis based on the custom emotion dictionary. In the later period of the reversal of public opinion on the Internet, user emotion has the characteristics of scattered user group structure, extreme and irrational emotion, and a high proportion of negative emotional comments. This is related to China’s economic development, the composition of Weibo network users and the diversity of media communication means.
The Business Logistics Security Research on Web Application
Authors: Tan Kangyu , Xie Yongjie 
Published: November 2019

Abstract
The rich functions of web applications have played an important role in the rapid development of the Internet. Different web applications have different functions. Compared with traditional XSS vulnerabilities, SQL injection vulnerabilities, and command execution vulnerabilities, the security issues involved in different server functions of Web applications vary widely, which leads to the absence of general solutions for specific security vulnerabilities in business logic process. After analyzing the security of the business logic process, it can be found that when the various security points of the business logic process are used by the attacker, and the behaviour of the attacker does not have any obvious difference from the normal user, which leads to the inability to achieve effective protection by directly using various types of scripts. Therefore, it is of practical significance to research a penetration detection idea of a logical vulnerability.