bluuebunny/crossref_metadata_2025_split huggingface dataset:
Personalized Recommendation Method for English Teaching Resources Based on Artificial Intelligence Technology
Authors: Gao Meiyun , Xing Jun , Yin Chengbo , Dai Linlinm 
Published: February 2021

Abstract
Traditional recommendation methods decompose keywords and key sentences less frequently, resulting in low accuracy of recommended content. Therefore, a personalized recommendation method for English teaching resources based on artificial intelligence technology is proposed. Web mining technology is used to collect user personalized data and mine teaching resource rules and patterns that can represent user characteristics. Artificial intelligence technology is used to simulate the intelligent behavior of users searching for resources, to obtain words with a higher frequency. The words with higher relative contribution are selected as keywords, thereby calculating the similarity between keywords and user characteristics, then the teaching resources with high similarity are taken as the recommendation target, and the recommended format is selected to implement resource recommendation. Experimental results show that compared with traditional methods, this method improves the accuracy of recommended content and enables teaching resources to better meet the individual needs of users.
Frequency Management Method Based on Cloud Computing, Big Data and Artificial Intelligence
Authors: Qiao Liuyuan , Zhang Xiaoxue 
Published: February 2021

Abstract
Today’s electromagnetic environment is still not optimistic enough, and spectrum resources are relatively few. In the actual allocation, unevenness will inevitably occur. In addition, the current level of intelligent monitoring is limited, and it is impossible to fully grasp the dynamics of frequency use. Spectrum monitoring emergency maneuverability still needs to be further improved, and there is a lack of refined spectrum resource management measures. In order to solve this problem as soon as possible, the article proposed a solution to the electromagnetic spectrum monitoring problem based on Cloud Computing, Big Data and Artificial Intelligence technology, proposed construction plans for various main applications and corresponding monitoring services, mainly strengthening the construction of handheld monitoring systems, electromagnetic spectrum monitoring and control systems, cloud monitoring systems, Big Data analysis systems, and intelligent monitoring and dispatching systems.
RETRACTED: Research on Big Data Integration Method for Investment Statistics Based on Artificial Intelligence Technology
Authors: Wang Yanru 
Published: February 2021

Abstract
Aiming at the problems of big data resources of investment statistics in the management process, such as unstructured, massive, and multi-type, the research on the big data integration method for investment statistics based on artificial intelligence technology is conducted. A new method of big data integration is proposed through the correlation analysis of investment statistics big data, the retrieval of investment statistics information resources, and the autonomous integration of big data based on artificial intelligence technology. Experiments have proved that compared with traditional integration methods, this method can effectively reduce the loss of information resources, ensure the integrity of the integrated investment statistics big data resources, and provide theoretical and technical explorations for the compilation of enterprise investment statistics, also provide a reference for big data integration in other related fields.
Adopting a new sample strategy to predict miRNA-disease associations
Authors: Zhong Xing , Lei Tian , Rong RongHui 
Published: February 2021

Abstract
Exploring unknown miRNA-disease associations by computational tools is a new way to study the correlations between genes and diseases. in this paper, we proposed a new model named ABPUSVM, which consisted of a new sample strategy based on Positive-Unlabeled learning and a prediction model that combined AdaBoost and SVM. When ABPUSVM was applied to predict unknown associations of miRNA-disease, the AUC of 0.9383 improved greatly based on 5 folds cross-validation and showed its excellent performance, which indicated ABPUSVM was significantly better than other classic models. Afterward, a case study confirmed that 46 out of the top predicted 50 miRNAs of breast cancer by ABPUSVM were supported by three databases. whose results showed the dataset by ABPUSVM were significantly better than that of the other methods. All results have shown that ABPUSVM is a promising and potential tool for exploring the associations of miRNA-disease.
Holiday load forecast based on combination of multi-scale features
Authors: Liu Shaobo , Wu Haiwei , Lu Jixiang , Zhang Qipei , Wu Lin 
Published: February 2021

Abstract
Aiming at the difficulty of load forecasting due to the current holiday load jump, a holiday load forecasting method with multi-scale feature combination is proposed. Feature extraction and recoding of date information, load information and weather information, and effectively use of historical data information, reconstruct load forecast feature combination. This method is used to reconstruct the electric load data of a certain area in Jiangsu Province. XGBoost and LSTM were used to predict the holiday load in the reconstructed multi-scale feature combination dataset and the traditional feature combination dataset. The experimental results show that in both load forecasting models, this feature combination method can effectively mine the latent relationship contained in historical data, represent more refined prior knowledge, and improve the accuracy of holiday load forecasting.
A Public Opinion Analysis System Based on Emotion Analysis in Linyi
Authors: Zhou Jiaqian , Wang Jihou , Li Qintao , Wen Yulian , Zhang Yan , Lin Peiguang 
Published: February 2021

Abstract
This article, the official news site of Linyi five services in Shanghai, Yiwu GanZhou Shenzhen national logistics hub of news as the data source, through Word2Vec and construction LSTM emotion classification model, with positive or negative emotion in general categories, calculating to analyze its emotional value, from the emotional category and time series analysis and word frequency vector to Linyi public opinion analysis of logistics hub
News Crawling Based on Python Crawler
Authors: Wang Jihou , Li Qintao , Zhou Jiaqian , Wen Yulian , Zhang Yan , Lin Peiguang 
Published: February 2021

Abstract
News is an important form to reflect current politics, which attracts people’s attention. The emergence of crawlers provides a convenient way for people to obtain useful information from mass news. Through Python language, this paper takes the relevant news of business logistics hub as an example to carry out the experiment of network news crawling. This paper introduces the workflow, design and implementation of the crawler in detail. The experiment proves that the designed crawler can get news quickly and can provide people with the information they need.
LDA-based Topic Mining of Microblog Comments
Authors: Liu Xu , Gao Ying , Cao Ziyi , Sun Guang 
Published: February 2021

Abstract
Microblog comments express the public’s views and attitudes on Microblog hot topics and hot events. Therefore, by excavating the theme of Microblog comments, we can help people understand the trend of public opinion and the attitude of the public towards the event. In view of Microblog comment topic mining, the problem we need to solve is how to use simple methods to quickly and efficiently obtain Microblog comment content, and how to use what methods to mine the theme of Microblog hot events to a great extent. First of all, we construct dynamic links through static web pages, write Microblog comment web crawler program in Python language, and solve the problem of crawling Microblog dynamically loading web page comments. Secondly, we use the Latent Dirichlet Allocation model, which is the implicit Dirichlet distribution, to mine the text.
Intelligent Acquisition and Analysis of Commodity Review Data
Authors: Deng Lianwu , Song Jiaxin , Zhang Zejun , Deng Bochu , Cao Songlin 
Published: February 2021

Abstract
When shopping on online e-commerce platforms as they can only contact products through photos and other means, it is easy to have cognitive deviations on the products on online platforms. This paper makes intelligent acquisition and analysis of commodity review data by using web crawler tools. Through a series of operations such as comment data capture, data preprocessing, Chinese word segmentation, emotional tendency analysis, LDA model analysis, it analyzes buyers’ emotional tendency of this commodity to help consumers choose the commodities they really like.
Application of Data Classification Method Based on Non-Negative Matrix Factorization
Authors: Li Ling Jiao , Liu Ying Lin , Huang Hong Yu , Fan Xiao Ping 
Published: February 2021

Abstract
At present, there are many methods for data dimensionality reduction, but most of the results of decomposition methods allow negative values. Obviously, these negative values have no physical meaning in practical problems. The non-negative matrix factorization method is a dimensionality reduction under the condition of ensuring non-negative values. This paper mainly uses a non-negative matrix factorization algorithm to perform dimensionality reduction representation and local feature extraction of data, and then classify. Experiments show that the algorithm in this paper is reasonable and effective.
Vulnerability Mining Based on Hook Mechanism
Authors: Wang Longjuan , Cao Chunjie , Zhong Wenjie , Tao Fangjian , Tang Chaosheng 
Published: February 2021

Abstract
IOT devices are easy to be attacked by hackers because of their intelligence and openness. The vulnerability mining of IOT devices has become a key research field in the security field. This paper studies a method of vulnerability mining based on function hook mechanism, which can make AFL QEMU mode is suitable for the binary fuzzy test of firmware CGI program of IOT devices, it can effectively test the firmware CGI program. Through the experiment, we successfully found the common vulnerability of IOT router equipment and through the reverse analysis of firmware, we trace back to the assembly code of the corresponding vulnerability point, and then carry out the corresponding analysis.
Multi-Angle Movie Reviews Analysis Based on Multi Model
Authors: Liu Yanzhe , Liu Bingxiang , Yu Jiajia , Yu Zhijian 
Published: February 2021

Abstract
In this study, movie reviews are used as data sets to extract related phrases, topics, and sentiment scores from the text. Based on users’ information, users’ behavior preferences and their influences are analysed, and text semantic information is mined from multiple perspectives. A variety of data processing and machine learning methods including text segmentation, Apriori association rule mining algorithm, sentiment analysis, linear fitting, TFIDF algorithm, PCA dimensionality reduction, and LDA topic model is used in the research. At the same time, due to the coarse granularity of the topic extraction in the LDA algorithm, it is not suitable for short text, this paper proposes a new topic model based on improved k-means and TextRank and gets good results on this dataset. This paper uses multiple data mining models to analyse film reviews and presents an empirical study of the efficacy of machine learning techniques in text semantic mining.
The Value of Hybrid MTS/MTO Supply Chain Sharing Demand Forecasts under Big Data
Authors: Cao Yu , Wu Kan , Hu Hanli 
Published: February 2021

AbstractBig data technology provides convenience for all entities in the supply chain to obtain demand forecasts and share the information. This article considers a supply chain composed of a manufacturer and two competitive retailers and analyzes the value of sharing demand information in the supply chain. In this supply chain, the manufacturer has a hybrid MTS/MTO production system and sells products to the MTS retailer and the MTO retailer. Both the manufacturer and the retailers have private demand information. We established a no-information sharing model, a full information-sharing model, and two partial-information sharing models, to study the value of sharing information. The results show that the full information sharing strategy cannot benefit all entities. However, if the demand forecasts of the two retailers are very different and lower than the manufacturer’s forecast, sharing information between the manufacturer and the retailer who has high demand prediction can benefit all entities.
Design of Mobile Learning Service Platform Based on Data Mining
Authors: Yu Yi 
Published: February 2021

Abstract
The current mobile learning service platforms are mostly self-centered, and they provide exactly the same learning content, and their personalized service capabilities are insufficient to meet the different learning needs of different students at the same time. To this end, combined with data mining technology, the research on a mobile learning service platform that can provide personalized services is conducted in this paper. The platform design includes four parts, namely frame design (development frame design and network architecture), main hardware design (microprocessor, switch, memory), main software design (user registration and login program, personalized mining analysis program, online learning procedures, online communication procedures) and system testing. The test results show that the concurrent performance and compatibility performance of the system meets the requirements after testing, allowing multiple users to use different mobile clients to access the platform at the same time.
Loss Correction of Wireless Model in Complex Multi Wall Environment
Authors: Zhang Yaokun , Yan Tianfeng 
Published: February 2021

Abstract
K-M (Keenan motley) model calculates the total loss value of indoor wireless signal penetrating multiple walls by adding fixed loss values of single wall, without considering the influence of wireless signal polarization and incident angle on wall loss. In order to solve this problem, considering the polarization and incidence angle of the beam, the wall and its direction can be detected automatically from the blueprint image by Hough transform in image processing. By obtaining the direction and angle of the wall, the loss attenuation is defined as a function of the incident angle. The improved model can define various types of walls with different attenuation characteristics. Through the comparison of actual measurement, the error between the improved model and the measured road strength loss is reduced, and the root mean square error between the measured value and the prediction value of the improved model is obviously improved, and the prediction accuracy of the model in indoor environment is improved.
Short-term Photovoltaic Power Prediction based on Sparse Representation Method
Authors: Zheng Dan Yang , Li Jun 
Published: February 2021

Abstract
In the research of solar power prediction, providing accurate prediction data in real time is one of the most effective means to enhance the capacity of wind power acceptance and improve the power reliability and economy. The existing prediction models based on statistical methods are often unavoidable in data preprocessing and model training stage, and their adaptive ability needs to be improved. Considering that the sparse coding method does not require model training, and has the characteristics of high solving efficiency and strong self-adaptability, an online solar energy prediction model using sparse coding is proposed.Firstly, the historical time series data is composed of input-output pairs with delay, and the dictionary is respectively constructed in atomic form. Then, the sparse weight is calculated for the delay input data vector to be predicted, and the corresponding predicted output is obtained by borrowing the dictionary. Taking the actual solar power data of Alberta, Canada as sample, the simulation was carried out in MATLAB. The simulation results show that the model can accurately predict the solar power and improve the effectiveness and practicability of the prediction
Design of Comprehensive Service System for Fresh Food E-commerce under the Background of “Rural Revitalization”
Authors: Guan Xin , Huang Lejia , Zhang Liyun 
Published: February 2021

Abstract
The traditional system takes a long time to adjust the fresh food storage temperature. Therefore, under the background of “village revitalization”, the comprehensive service system for fresh food e-commerce is designed in this paper. First of all, on the hardware side, by optimizing the physical architecture, the application layer and the data server layer are separated; on the software side, the system database is designed to extract customer order data. Then the ant algorithm is used to select the optimal logistics configuration path, and the temperature control algorithm is used to adjust the temperature environment so that the fresh food reaches the optimal storage temperature in the transportation path. Finally, the hardware and software design are combined to realize the comprehensive service system design. The experimental results show that compared with the traditional system, the designed system in this paper shortens the control time of fresh storage temperature and strengthens the service control of cold chain logistics.
Research on Node localization Algorithm in WSN Based on TDOA
Authors: Zhao Qiaomei , Cheng Yun , Zhou Taoyun 
Published: February 2021

Abstract
Many applications of wireless sensor networks (WSN) require nodes to know their location information. Monitoring data without node location information is meaningless in many situations. Since the node positioning efficiency of WSN directly affects the performance of the network, designing an efficient positioning algorithm is the focus of WSN research. Based on the principle of TDOA positioning in WSN, it studies two main positioning algorithms: Chan and Taylor algorithm. Meanwhile the least square (LS) method is used to provide the initial position for Taylor algorithm and the first calculation result for Chan algorithm. Simulation results under different conditions show that the Chan algorithm has a good positioning accuracy and less computation than that of Taylor algorithm in the line of sight (LOS) environment. However, under the actual channel, due to the influence of No Line of Sight (NLOS), the positioning accuracy of Taylor algorithm is better than that of Chan algorithm, but its computation is large.
An enhanced encryption algorithm with key update scheme for Internet of Things
Authors: Chen Hong 
Published: February 2021

Abstract
The protocols in the Internet of Things are more complex than the Internet. Once the Internet of Things devices have security problems, it will lead to a decline in user experience and huge economic losses. Therefore, this paper proposes a secure communication scheme for the Internet of Things based on a dynamic key update mechanism. This paper uses a variety of chaotic equations with good random characteristics to generate the session key library, and then uses the lightweight symmetric encryption algorithm Present-80 to encrypt the communication data. The simulation test results show that under the condition of ensuring communication efficiency, the security communication protocol of the Internet of Things based on the dynamic key update mechanism proposed in this paper has higher security.
Identification of Cybersecurity Elements Based on Convolutional Attention LSTM Networks
Authors: Hu Yingying , Liu Runjie , Ma Ziyu 
Published: February 2021

Abstract
As the first step of cybersecurity situational awareness, the accuracy of cybersecurity element recognition will directly affect the results of situational understanding and situational prediction. In this paper, we propose a network element recognition method based on the convolutional attention mechanism combined with a long- and short-term memory network. The input network traffic data is successively passed through the convolutional neural network, attention mechanism, and long- and short-term memory network, which not only takes into account the influence degree of different network attributes on different network behaviors but also realizes that the feature information extracted in the early stage can be circulated in the network, thus providing a discriminant basis for the final network behaviors To verify the effectiveness of our proposed method, we perform experimental validation on the KDD-Cup 1999 (kdd-99) dataset. The results show that our proposed method achieves an accuracy of 98.48% in the identification of network security elements. In addition to this, we also compare and analyze our proposed algorithm with other mainstream algorithms, and the results also validate the effectiveness of our proposed method.
Research on Vehicle Network Security Situation Prediction Based on Improved CLPSO-RBF
Authors: Li Ruxiang , Li Fei , Wu Chunwang , Song Jiaqi 
Published: February 2021

Abstract
In order to improve the accuracy and efficiency of the vehicle network security situation prediction, a prediction algorithm based on improved CLPSO-RBF is proposed. Firstly, for speeding up the optimization efficiency of CLPSO, a reasonable speed monitoring variable has been introduced. Secondly, we use the improved CLPSO algorithm to optimize the clustering radius of the RBF neural network, so that the optimal RBF network structure can be determined. Finally, we use the optimal RBF network to predict the security situation of the vehicle network. Simulation experiments have proved that the improved algorithm has higher accuracy and faster convergence rate in situation prediction, and has better prediction effects.
An Optimized Encryption Algorithm of Fruit And Vegetable Product Traceability Code Based on Embedded System
Authors: Mu Shile , Wang Jiamin , Qi Yue , Tian Jianying 
Published: February 2021

Abstract
The quality and safety of fruits, vegetables, and other agricultural products is the lifeline of agricultural development. In recent years, the state departments concerned have strengthened the whole process block on the quality of fruits and vegetables, continuously optimized the measures, and continuously improved the quality control level of fruits and vegetable products. Traceability code is the key technology to realize the whole process block and traceability of the whole industry chain. There is a risk that the traceability code will be modified privately in the application process. Once the traceability code is cracked and modified, its important role in the traceability system will be destroyed. In order to prevent the traceability code from being arbitrarily modified, this paper designs and studies an optimization algorithm of traceability encryption based on an embedded system