bluuebunny/crossref_metadata_2025_split huggingface dataset:
On transient heat analysis of a two-step convective reactive cylinder
Authors: Lebelo R S , Waetzel M , Mahlobo R K , Moloi K C , Adesanya S O 
Published: February 2021

Abstract
This article looks at the analysis of heat transfer due to combustion of reactive materials in a stockpile modelled in a cylindrical domain. The transient heat analysis is studied through the investigation of effects kinetic parameters, embedded on the differential equation governing the problem, have on the temperature of the system. The temperature patterns due to the parameters effect, are depicted in graphs generated through Maple software. The complicated nonlinear partial differential equation is solved numerically using Finite difference method (FDM). The results show that parameters such as the order of reaction slow down the exothermic chemical reaction, while parameters like the reaction rate enhance the combustion process.
Summary of Target Detection Algorithms
Authors: Li Wei , Feng Xiang Sheng , Zha Kaiwen , Li Shuning , Zhu Hua Sheng 
Published: February 2021

Abstract
In recent years, the soaring development of CNN has facilitated the maturity of the Computer Vision Algorithm. This paper will briefly introduce some representative Target Detection Algorithm, and systematically analyze the underlying problems, the modified methods, and the prospective direction of the algorithm in accordance with its merits and demerits. It is generally divided into a single-stage detection model and a double-stage detection model in terms of whether candidate areas need to be extracted during target detection for further tasks. Featured with scale in the double-stage detection model, the algorithm is divided into single-scale detection and multi-scale detection based on whether it can appropriately integrate with a network structure, which enhances the accuracy of the network model towards small targets. Meanwhile, it can also be divided into anchor-base and anchor-free in a single-stage detection model on the basis of the anchor bolt. A predictable development of the Target Detection Algorithm will show to us in the future.
A Temporal Dual Graph Convolutional Network for Social Unrest Prediction
Authors: Jiang Yijie , Zhou Bin , Tu Hongkui , Gao Liqun 
Published: February 2021

Abstract
Social unrest is endemic in many societies. It is a vital task to predict social unrest events be-cause these events can lead to societal changes and endanger public security. We combine two data sources, using social media data as the historical texts and news media data as the ground truth of unrest events. We propose a temporal dual graph convolutional network (TDGCN), which extracts the contextual semantic information and the communication relationship between social network users from the historical texts and constructs two dynamic graphs to capture the implied semantic and temporal features. The TDGCN can predict the occurrence of unrest events. Experimental results on a specific data set of unrest show that the proposed method has better performance than other state-of-the-art social unrest event predictions.
An Application of Automatic Text Revision for Power Defect Log
Authors: Li Hao , Deng Feng , Lu Jixiang , Zhang Tao , Li Hong 
Published: February 2021

Abstract
There are a large number of unstructured texts without data cleaning in the field of electric power. It is expensive to rely on manual ways to process the large amount of text data. In order to reduce the workload of data cleaning, we propose an intelligent method of automatic revision of power defect logs in this paper by adopting natural language processing technologies. We utilize entity recognition technology to recognize electrical equipment words on the text and utilize word similarity calculation to find out words with similar meaning in the standard vocabulary, which is the main process to revise Abnormal text. With the outstanding performance of entity recognition, the workload of data cleaning is reduced approximately 70% through our proposed method, which greatly improves the efficiency of unstructured data processing.
Merging Naive Bayes and Causal Rules for Text Sentiment Analysis
Authors: Luo Yongjian , Yang Xiaohua , Ouyang Chunping , Wan Yaping , He Sixi 
Published: February 2021

Abstract
Trad itional machine learning sentiment analysis models are d ifficult to achieve good classification results from small sample data. This paper proposes to merging naive Bayes and causal rule(MNBACR) for small sample data sentiment analysis scenarios. This model is based on the causal analysis theory, and introduce the causal inference algorithm into the field of text sentiment analysis. The causal inference algorithm extracts the causal ru les of Chinese texts, and the causal rules can be used as the features of the naive Bayes algorithm to predict the sentiment polarity of small sample texts. In experiments, the model in this paper is evaluated on financial news datasets which have a small number for sample, and the results show that the proposed method achieves the best performance compared to the existing state-of-the-art models on the small sample data onto sentiment analysis
An Overview on Fine-grained Text Sentiment Analysis: Survey and Challenges
Authors: Guo Xiaoting , Yu Wei , Wang Xiaodong 
Published: February 2021

AbstractAmong various natural language process tasks, sentiment analysis has always been a research hotspot. From the initial sentence-level and document-level coarse-grained sentiment analysis to recent fine-grained sentiment analysis on the aspect word level, researchers are committed to applying diverse methods to obtain better sentiment analysis results, ranging from lexicon-based, statistical machine learning methods to deep learning models. In the change of technology, several benchmark datasets that can be used for model performance comparison are gradually yielded. This article summarizes the current research status of aspect-level text sentiment analysis from multiple dimensions such as dataset, mainstream methods, and evaluation indicators, finally, it puts forward the challenges facing and potential research directions from a unique perspective.
Research on Personalized Learning Based on Collaborative Filtering Method
Authors: Hu Xingguang , Wang Yingchao , Chen Qi Bo , Liu Qian , Fan Xiaoping 
Published: February 2021

Abstract
With the popularization of the Internet, the rapid development of intelligent education is promoted. They are a new platform for students to learn, and they can quickly help students improve and consolidate knowledge, as well as unlimited time, place, and space. However, due to a large number of learning resources, how to find the resources you need, and suitable from the massive resources is a problem that needs to be solved in this field. The individual characteristics of each learner are different, so when meeting their own needs, they have different requirements for individualization. The personalized learning resource recommendation system was born, according to the different characteristics of students to recommend corresponding learning resources. Since most of the recommendation systems currently recommend a large number of test questions or a large number of books, but each person’s needs are different, this article focuses on the above problems and selects a small piece of content course knowledge points in the learning resources, using course knowledge Come as a recommendation point and solve the above problems by designing a personal personalized learning mechanism. The main research contents of this article are as follows: (1) It is proposed to take curriculum knowledge points as the recommended objects. (2) Use collaborative filtering methods and cognitive diagnosis methods to design individual learning mechanisms. (3) Experiment. Through related experiments, the use of collaborative filtering method and cognitive diagnosis method based on curriculum knowledge points recommendation to confirm whether it is feasible.
Research on Intrusion Detection Method Based on Pearson Correlation Coefficient Feature Selection Algorithm
Authors: Chen Pengtian , Li Fei , Wu Chunwang 
Published: February 2021

Abstract
The current era is the era of big data and 5G. The network security data in the network is different from the past, and the network security data is growing exponentially. As an important line of defense for network security, intrusion detection technology can efficiently detect and process massive amounts of security data has become an important factor restricting its development. The feature selection method of intrusion detection data directly affects the efficiency of intrusion detection. Therefore, this paper proposes a feature selection algorithm based on pearson correlation coefficient, which performs feature specification on many features, which greatly reduces the amount of security data that needs to be processed, and effectively reduces the dimensionality of the data to increase the intrusion. Detection efficiency.
Exploration and Practice of Dance Teaching Mode under the Internet Background
Authors: Chu Jun , Feng Aiyun 
Published: February 2021

Abstract
In early 2020, the new crown pneumonia epidemic swept in, in order to block the spread of the epidemic to the campus, the Ministry of Education issued the “spring semester delayed school opening” request, “stop classes do not stop” the voice immediately rang. At one time, how to combine modern information technology with the teaching content of various subjects to maximize the efficiency of online teaching has become the primary problem for every teacher to think about, and in this environment, the dance educators who are mainly based on the traditional teaching form of “oral teaching” also ushered in a great test. By analyzing the advantages and disadvantages of traditional dance teaching methods and based on the author’s online teaching practice activities for one semester, this paper makes a comprehensive analysis of the application value and significance of modern information technology in Chinese dance teaching, in order to further enrich the Chinese dance teaching mode, optimize the dance teaching resources, make the dance teaching methods and contents more in line with the requirements of China’s modern technology, and provide theoretical basis and practical operation guidance for the future modern dance teaching mode.
Application of Convolutional Neural Networks in Radio Station Link Establishment Behaviors Recognition
Authors: Wu Zilong , Chen Hong , Lei Yingke 
Published: February 2021

Abstract
For the problem that it is difficult to recognize the type of link establishment (LE) behaviors of radio station whose communication protocol is unknown, a method is proposed to solve the problem by using convolutional neural network (CNN) to recognize LE behaviors. Themethod processes physical layer signals directly and breaks through limitation of unknown protocol standard. Three classical CNN models are optimized through experiments so that they become more suitable for recognition of one-dimensional time series signals. Experimental results show CNN can effectively recognize the different link establishment behaviors with a large number of training samples. Moreover, DenseNet whose recognition accuracy can reach 96% when the signal-to-noise ratio (SNR) is 0dB has the best performance compared to GoogLeNet and ResNet.
From Data to Evidence: Multi-Source Evidence Association Model Based on Bayesian Network
Authors: Zhang Li , Xu Feng , Liu Ming , Cui Tong 
Published: February 2021

Abstract
Recent years have witnessed the boom of artificial intelligence. With the advancement of judicial information, using artificial intelligence (AI) technologies to mine judicial big data is of great significance in smart courts. However, the reasoning of the evidence chain mainly relies on the judge’s manual work in the litigation process. How to model the multi-source evidence association (MSEA) and reason credible evidence chains (CEC) automatically is largely unexplored. In this paper, we propose an MSEA model based on the Bayesian network. Firstly, we construct an MSEA network in which each evidence element serves as a node, and the node correlation probability is calculated via the association relationship among the evidence elements. Subsequently, with the guidance of the event judgment chain, the MSEA model is constructed based on Bayesian networks. In the end, we use a genetic algorithm to optimize the Bayesian network and select credible evidence chains. To the best of our knowledge, this is the first time that using a probability graph model to mining the association of multi-source evidence. Experiments and the case analysis prove the effectiveness of our method.
Research on Improved YOLOv3 Fire Detection Based on Enlarged Feature Map Resolution and Cluster Analysis
Authors: Yue Conghan , Ye Jun 
Published: February 2021

Abstract
Fire detection can effectively prevent the occurrence of fire. For the current fire detection methods, traditional image processing techniques such as grayscale image processing and feature extraction processing have poor anti-interference ability, weak generalization ability, and the detection results are more sensitive to data fluctuations. At present, based on the concept of deep learning, the proposed convolutional neural network processing of extracted image features has been widely used. On this basis, an improved YOLOv3 fire detection algorithm is proposed in this paper: The K-Means++ algorithm is used for clustering analysis to obtain the corresponding anchor boxes dimension, which reduces the error detection rate caused by the bounding boxes not matching the label; secondly, the resolution of the feature image is improved and the receptive field is enlarged; the image data are sharpened and the contrast is enhanced to make the data features more prominent. The experimental results show that the detection precision of the method is 97.7%, the recall rate is 98.5%, and the fps is 19, effectively solving the problem of high error detection rate and high missing rate of traditional image processing and general CNN networks for suspected flame objects.
Mumps prediction based on improved support vector machine
Authors: Li Yongna , Song Huamin , Jiang Jiulei , Jiang Shaoping 
Published: February 2021

Abstract
The prevention of d isease, has the important guiding significance to safeguard the health of the people. In order to improve the accuracy rate of mumps disease forecast of Yinchuan City, Ning xia, joined the combined kernel function idea based on regression prediction model of the traditional Support Vector Machine, the polynomial and radial basis kernel function combination instead of the traditional single kernel function model, proposed a combination of kernel function of support vector machine algorithm, with mumps Ning xia disease for the practice of Yinchuan City, to conduct research and forecast for the pathogenesis of rules combined with the local weather changes. The meteorological data and the number of diseases using correlation analysis method for processing, select the factor related coefficient ordering large from, then these factors should be selected using a single kernel and mixed kernel function for processing, training and prediction using support vector machine. The experimental results show that the mixed kernel function of Support vector machine prediction model results than the single kernel function regression is better
Narrow Density Window and Risk Assessment Based on Big Data
Authors: Li Pengfei , Guan Zhichuan , Han Chao , Hu Sicheng , Xiao Fan , Guan Yunyun 
Published: February 2021

Abstract
For the deep strata with fracture zone, fault zone and various permeability and fracture loss, the geological conditions are particularly complicated, which makes it particularly difficult to predict the safety density window and engineering risk, through investigation and research, combined with the actual situation of a deep oil and gas reservoirs in northwest China, for the formation of three pressure section in the regions was determined, and through the actual leakage occurs correction of formation fracture pressure of drilling fluid density as a standard of lost circulation project risk evaluation, the author combined with big data, based on a certain algorithm, got the regionalization of the region narrow safe density window, on narrow density window combination of generalized stress and strength interference theory, based on the real-time drilling fluid density in the region one of the key Wells project risk evaluation, the project risk is match with the actual project risk situation
A Study of Big Data Application in Agriculture
Authors: Chen Jia , He Shouwu , Li Xiaoying 
Published: February 2021

Abstract
Agricultural data is typically characterized by at least 3 characteristics: massive data volume, various data sources, and complex structure. With the popularization of next-generation information technology, precision agriculture has become an emergent development direction in agricultural informatization. This paper proposed a big data application framework for precision agriculture. The model includes data sources, data integration, and data analytics. Use of big data application provides better guidance for precision agricultural.
A continuous variable quantum key distribution protocol based on multi-dimensiondata reconciliation with Polar code
Authors: Feng Bao , Lv Chao , Liu Jinsuo , Zhang Tianbing 
Published: February 2021

Abstract
The continuous variable quantum key distribution protocol (CV-QKD) has the advantages of easy generation of quantum signals, convenient measurement and high communication capacity. However, the multi-dimensional data reconciliation is mainly implemented by binary LDPC code as quantum error correction code, which is greatly affected by complex environments and the bit error rate is high. In order to solve this problem, acontinuous variable quantum key distribution protocol based onmulti-dimension data reconciliation with Polar code, that can be applied to long-distance transmission in power systems, is proposed. First, we prepare an EPR quantum state, and perform heterodyne detection on one of the quantum states, and obtain the values of two orthogonal components at the same time which is the coherent state of the information carrier. Then the continuous variable quantum key distribution is realized by six steps (i.e., quantum state preparation, gaussian random detection, data filtering, calculation of bit error rate, data reconciliation, privacy amplification).In data reconciliation, we use Polar code as the error correction code and use reverse coding technology to achieve key error correction, which can effectively improve the negotiation efficiency. Numerical simulation verifies the feasibility of the protocol, and the protocol is more efficient than the multi-dimensiondata reconciliation protocol based on LDPC code under the same conditions.
Research on Python Data Visualization Technology
Authors: Cao Shengjia , Zeng Yunhan , Yang Shangru , Cao Songlin 
Published: February 2021

Abstract
In recent years, researchers at home and abroad have accumulated a lot of experience in the research of data visualization technology, and they have played animportant role in scientific discovery, medical diagnosis, business decision-making, and engineering applications. As a library developed using Python language, Matplotlib has a concise language, high drawing accuracy, and simple and easy-to-understand code. This article first introduces data visualization and related technologies used and then uses Python’s Matplotlib library and pyecharts library to realize data visualization. Through representative examples, combined with the use of correct charts, visual processing of data in different fields, so as to further analyze the effect of visualization.
Review of Rational Drug Use Based on Apriori Algorithm
Authors: Luo Zhangming , Cheng Binjie , Tian Sen , Hong Anding , Zhang Jin , Li Qiang 
Published: February 2021

Abstract
One of the important reasons for irrational drug use is that the medical industry has been facing the problem of complex and redundant information. In order to solve this problem, a multitude of researchers carried out research on rational drug use by data mining technology and tried to apply it to real medical treatment. Apriori is one of the most frequently used and valuable algorithms. Based on the Apriori algorithm, we discussed the basic theory and the improved result. We elaborated on the detail and analyzed the development of the applied scenarios in modern medicine and traditional Chinese medicine. Then we discovered the applied scenarios of the Apriori algorithm mainly included medication law, dose research, and experience inheritance. Through the analysis of the literature on the use of the Apriori algorithm in rational drug use and the current development trend, this paper pointed out the existing problems in this field and put forward the future work and research focus.
An Overview: Maritime Spatial-Temporal Trajectory Mining
Authors: Jin Jialong , Zhou Wei , Jiang Baichen 
Published: February 2021

Abstract
In recent years, the sources of maritime spatial-temporal trajectory data have been increasing. With the widespread application of detection equipment such as radars, ship-based platforms, and satellite, maritime trajectory data has shown feature of volume, variety, velocity and valueless. The increase in data types and amount of data has led to complexity and difficulty of data processing. Traditional data mining algorithms are facing serious difficulty. This paper reviews researches for maritime spatial-temporal trajectory mining in recent years on trajectory clustering, anomaly detection, and trajectory prediction. Difficulties current researches faced are pointed out and prospects of deep learning are discussed in this paper.
Data Correlation based Feature Selection Model for Children’s Growth and Development Assessment
Authors: Zhao Changhua , Mi Chunqiao , Shi Yuanquan 
Published: February 2021

Abstract
The paper focuses on children’s data present inElectronic Health Records(EHRs) starting fromraw data gathered from Southwest Medical Data Center in China. We designed a systembased on workflow to analyzefeatures that affect children’s growth. The system is able to relate health caredata to diagnosis codes and with additional information integrated and correlated to EHRs data. Finally, we propose prediction models based on Recursive Feature Elimination (RFE), which can identify features thatare important to detect children’s growthand findcorrelations among features.
Research and Implementation of Clock Synchronization Technology Based on PTP
Authors: Yuan Kexin , Guo Xuebing , Tian Jianying 
Published: February 2021

Abstract
With the rapid development of power system, the requirement of network clock synchronization accuracy is becoming higher and higher, in many cases, it needs to reach microsecond level. In order to meet the increasing demand, based on the analysis of the basic principle of PTP clock synchronization protocol, a physical layer clock synchronization system based on STM32 MCU and DP83640 chip is proposed. The system synchronization time error is less than 1 microsecond by using the timeacc-007 time precision measuring instrument. The scheme meets the requirements of accurate clock synchronization for all distributed nodes in the power system.