bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Generating Adversarial Examples in Applications
Authors: Zhao Yang , Wang Juan , Liu Yingjiang
Published: February 2021

The deep neural network is a highly expressive model, which plays an extremely critical role in modern artificial intelligence applications. Adversarial samples can change the prediction results of neural networks by imposing imperceptible perturbations on the original images, which brings new challenges to deep learning. In this paper we summarize the methods of generating adversarial samples in recent years, intuitively feel the development of adversarial samples from the time of publication, and briefly classify them from the perspective of algorithm principles. At the same time, for practical applications, the advantages and disadvantages of the algorithm are analyzed and summarized, and the conditions for the algorithm to be suitable for application are proposed: no need to know the specific structure of the algorithm, high quality of the adversarial samples and convenient migration. The analysis points out that among the typical methods, MI-FGSM, ONE-PIXEL, and methods using GAN are more practical and worthy of further study.
Pneumonia Recognition Based on Convolutional Neural Network Feature Map Fusion
Authors: Liu Xiaozhong , Wang Zaixing , Zheng Lijun , Gao Jinhui
Published: February 2021

Pneumonia recognition has important research significance in computer-aided diagnosis, and there is a problem of low accuracy for pneumonia recognition. In this paper, an improved network is based on the convolutional neural network AlexNet, the AlexNet_Branch network. The AlexNet_Branch network adds a parallel branch convolutional neural network to AlexNet, and it connects AlexNet and the branch convolutional neural network at the fully connected layer. During training, the same image is simultaneously obtained by AlexNet and the branch convolutional neural network to obtain different feature maps, and then the feature maps are merged together at the fully connected layer to improve the accuracy of recognition. Through design experiments, different AlexNet_Branch networks composed of different layers of branch convolutional neural networks were built, and the network was trained and tested on the chest X-ray image set respectively. The results show that the addition of a branch convolutional neural network greatly improves the accuracy of pneumonia recognition, and the AlexNet_Branch network test accuracy consisting of a 16-layer branch convolutional neural network is 98.01%.
SQL Injection Attack Detection and Prevention Techniques Using Deep Learning
Authors: Chen Ding , Yan Qiseng , Wu Chunwang , Zhao Jun
Published: February 2021

Web application brings us convenience but also has some potential security problems. SQL injection attacks topped the list of Top 10 Network Security Problems released by OWASP, and the detection technology of SQL injection attacks has been one of the hotspots of network security research. In this paper, we propose a SQL injection detection method that does not rely on background rule base by using a natural language processing model and deep learning framework on the basis of comprehensive domestic and international research. The method can improve the accuracy and reduce the false alarm rate while allowing the machine to automatically learn the language model features of SQL injection attacks, greatly reducing human intervention and providing some defense against 0day attacks that never occur.
Intelligent Recognition of Transmission Line Inspection Image Based on Deep Learning
Authors: Qi Yue , Mu Shile , Wang Jun , Wang Liangliang
Published: February 2021

Under the background of high-speed development of the power grid, the traditional manual detection method is low efficiency and high cost, which are not able to meet the current requirements. With the development of UAV technology, the method of using UAV to inspect transmission lines has a positive effect on improving detection efficiency and quality. In recent years, deep learning has developed rapidly, which provides a new solution for the analysis and processing of UAV aerial inspection images. This paper focuses on the deep learning algorithm to study the insulator location and fault identification in aerial photographs, which plays an important role in realizing the automation and intelligence of UAV detection.
Design and Application of Anti-interference Control System for the Concentration of Tobacco Paper Reinforcing Agent
Authors: Tai Guangfeng , Jiang Changhui
Published: February 2021

Aimed at the improvement of the preparation accuracy of the tobacco paper reinforcing agent, the concentration control of the tobacco paper reinforcing agent was considered. The concentration of reinforcing agents is affected by many factors, such as influent flow, powder feeding speed, external interference, and so on. The compounding process has typical nonlinear, multivariable, strong coupling characteristics, and the traditional control method has a poor effect. In order to improve the quality of tobacco paper pulp, the linear auto disturbance rejection controller model of enhancer was designed to achieve the purpose of active disturbance resistance and improve the control effect of enhancer concentration. Through three groups of simulation experiments, simulation of ADRC to concentration tracking, concentration tracking under disturbance, and comparison with traditional PID control, the linear ADRC is capable of estimating and compensating well. The simulation results show that the linear ADRC has a good anti-interference performance. At the same time, the PLC control system and human-computer interaction software are designed to realize the monitoring of each process parameter in the preparation process of the reinforcing agent, which provides a practical approach for the concentration control in the preparation process of reinforcing agent.
Research and Application of Spacecraft Product Inspection System Based on Smart Glasses
Authors: Bian Yuchuan , Huang Lei , Gao Jing , Hu Siyu , Li Xintong
Published: February 2021

cenarios, studies the construction of the digital test system based on intelligent glasses, based on the standard operating procedure operation guide, inspection data online and conformity intelligent decision, heterogeneous data packet generation and analysis of key technology, realized the application of spacecraft product inspection process, improves the working efficiency and quality.
Research on the Architecture of Convolutional Neural Network Accelerator
Authors: Liu Ying , Ding Haitao , Ma Jiali
Published: February 2021

Convolutional neural networks are widely used in human production and life, but due to their large amount of calculation and complex calculation mode, their calculation speed is slow, so it is necessary to design a dedicated hardware accelerator. This paper firstly analyzes the algorithm of the convolutional neural network and decomposes the algorithm into multiple basic operations. For the convolution operation with the largest amount of calculation and complex operation mode, a near calculation storage array is designed according to its operational characteristics. Furthermore, a convolutional neural network accelerator architecture is proposed to realize the fast operation of a convolutional neural network.
Vehicle Object Detection Based on Improved RetinaNet
Authors: Zhang Luyang , Wang Haitao , Wang Xinyao , Chen Shuai , Wang Huaibin , Zheng Kai , wang Hailong
Published: February 2021

Aiming at the low efficiency of vehicle object detection in real scenes, this paper proposes an improved RetinaNet. An octave convolution structure and a weight pyramid structure are introduced respectively to improve the detection performance of RetinaNet for vehicles. Specifically, we use octave convolution instead of the traditional convolution layer to improve the feature map’s representation of detailed information. In addition, in order to improve the quality of feature fusion, a weighted feature pyramid network (WFPN) structure is proposed to limit the propagation of gradients between different levels. The experimental results on the DETRAC data set show that the method has good detection results for vehicle targets of different scales in different scenarios, and can better meet the needs of practical applications.
A Survey on Feature Extraction Methods of Heuristic Malware Detection
Authors: Li Nuannuan , Zhang Zheng , Che Xin , Guo Zhimin , Cai Junfei
Published: February 2021

In the age of the Internet, while the network is sending a lot of information to people, hackers also transmit a lot of malicious code through the network. Hackers use these malicious codes to steal sensitive information from infected people and damage machines and devices to achieve their evil goals. Therefore, it is very important to accurately detect malware to protect users from loss. There are now three major methods of detecting malware, which are signature-based, behavior-based, and heuristic-based methods. However, with the rapid increase in the types and number of malware, signature -based method can’t detect unknown malware and behavior-based cannot guarantee the False Positive Ratio. So these detection methods can no longer meet the needs. Therefore, some researchers proposed some heuristic-based detection methods. In this article we overview the methods used to extract features and the features extracted in heuristic detection and discuss the advantages and disadvantages of the features
Interpretable Saliency Map for Deep Reinforcement Learning
Authors: Zheng Hong , Dai Yinglong , Yu Fumin , Hu Yuezhen
Published: February 2021

Deep reinforcement learning (deep RL) achieved big successes with the advantage of deep learning techniques, while it also introduces the disadvantage of the model interpretability. Bad interpretability is a great obstacle for deep RL to be applied in real situations or human-machine interaction situations. Borrowed from the deep learning field, the techniques of saliency maps recently become popular to improve the interpretability of deep RL. However, the saliency maps still cannot provide specific and clear enough model interpretations for the behavior of deep RL agents. In this paper, we propose to use hierarchical conceptual embedding techniques to introduce prior-knowledge in the deep neural network (DNN) based models of deep RL agents and then generate the saliency maps for all the embedded factors. As a result, we can track and discover the important factors that influence the decisions of deep RL agents.
Making AI available for everyone at anywhere: A Survey about Edge Intelligence
Authors: Zhang Yusen , Li Bao , Tan Yusong
Published: February 2021

With the rapid development of the 5thgeneration of wireless network communication technology, a high-speed data rate, broader bandwidth, and low-latency wireless network has already come to the truth. Meanwhile, Edge Computing which put data processing at the edge of the network has already been well developed, and it has changed the computing mode tremendously. And also, Artificial Intelligence (AI) has made a great breakthrough from Computer Vision, Unmanned Vehicle to Natural Language Processing. Nevertheless, with the increasing model size and model depth, the training data set and communication delay has become the bottleneck of AI popularization. As zillions of bytes of data are being generated at the network edge, and also lots of AI applications are being utilized by enormous customers lied at the edge, the combination of Edge Computing and AI is imperative. This paper has done a deep investigation into the new field about the confluence of Edge Computing and AI, aiming of discussing the concept, architecture, and research ideas on Edge Intelligence. To the end, this paper provides the road-map for future researching work.
Weighted User Goal Sampling for Dialog Policy Learning
Authors: Yin Kai , Wang Zhenyu
Published: February 2021

A deep reinforcement learning algorithm has been widely used for learning dialog policy in a task-oriented dialog system. Dialog agent collects training data and improves its policy by interacting with users. Interact with real users is time-consuming and not realistic. Consequently, we usually build a user simulator instead of a real user. At the beginning of each dialogue, the simulator will sample a user goal extract from training data. Then the simulator will communicate with the dialog agent to accomplish this user goal. The existing user simulator usually samples this user goal randomly. This will cause the dialog agent to waste a lot of time to learn what it already learned. To solve this problem, we propose two user goal weighting methods which give relatively large weight to the user goal the current dialog agent can’t accomplish. This lets the dialog agent pay more attention to those user goals. Experiment result in a movie-ticket booking task shows that the proposed weighted user goal sampling method can effectively accelerate the policy learning progress compares to the random sampling method.
Rare Earth Magnetic Materials Surface Defect Recognition via Multi-Scale Residual Neural Network
Authors: Zhang Bin , Li Zhixi , Hao Yan
Published: February 2021

The work aims to solve the problems of low detection accuracy, and poor reliability of magnetic material surface defects. A lightweight multi-scale feature ResNet (LMSF-ResNet) method was proposed in this paper. The method reduces the calculation and parameters of the neural network by using group convolutions and channel split operator and merging features from different branches of distinct scales by using the multi-scale model’s structure. The algorithm employs the strategy of using the channel attention model to improve accuracy at the same time. This paper takes the defect images of rare earth magnetic materials as an example to verify its effectiveness. The results show that the method has higher detection accuracy and stronger stability, and can be widely used to accurately detect surface defects of magnetic materials.
Intelligent Detection of Urban Road Underground Targets by Using Ground Penetrating Radar based on Deep Learning
Authors: Gao Yao , Pei Lili , Wang Sa , Li Wei
Published: February 2021

Ground penetrating radar (GPR) is widely used in the field of intelligent road detection because of its non-destructive detection method, which is based on an electromagnetic wave reflection mechanism. However, this method requires large-scale data processing and also relies on manual judgment, which is time-consuming and laborious. To solve this problem, this paper analyzes and studies the GPR images of underground pipelines and uneven settlement of urban roads by means of road surface measurement and laboratory tests. The radar image data set is constructed by collecting radar images and denoising and marking them. Then, Deep Feature Selection Net is adopted to improve the fast region-based convolutional neural network (Faster R-CNN) to enhance the network’s ability to extract features from radar images. Finally, by comparing with the improved faster R-CNN model, it is found that the automatic identification rate of underground pipelines and uneven settlement in urban roads increases, reaching more than 80%.
Flood Forecasting Method of Small and Medium-sized Watershed Based on Convolutional Neural Network
Authors: Wang Jimin , Cao Ying , Li Jiahuan , Ji Changzheng
Published: February 2021

A data-driven flood forecasting model based on a convolution neural network is proposed for the small and medium-sized watershed. Pearson correlation coefficient analysis was used to determine the time length of the input data. The historical rainfall and discharge were used to create the two-dimensional input data matrix. NAS was used to determine the structure of the model. The experiment results in Tunxi watershed in Anhui Province show that the accuracy is high under the 1˜3h forecast period, with the forecast period increased, accuracy would decrease.
An Efficient Methodology for License Plate Localization and Recognition with Low Quality Images
Authors: Xiao Sainan , Yang Wangdong , Cao Buwen , Zhou Honglie , He Chenjun
Published: February 2021

It is challenging to find an effective license plate detection and recognition method due to the different conditions during the image acquisition phase. This paper aims to develop a new accurate and efficient method based on color difference and SVM recognition model that yields better performance for vehicle images under low quality. The proposed method is tested with 200 images which involve many difficult conditions, such as low resolution, night-lighting, dirt, complicated background, and distortion problems. The experimental results demonstrate very satisfactory performance for license plate detection in terms of speed and accuracy and are better than the existing methods like edge detection or HSV color conversion method. The overall probability of localization is close to 100%, with a false recognition rate of 2%.
Semantic Matching for Short Texts: A Cross Attention Mechanism
Authors: Zheng Senhong , Chen Fan , Wang Xuejie
Published: February 2021

Semantic matching is a hot topic in the field of NLP. It is widely used in information matching, machine translation, intelligent Q&A, and answering system. With the rapid development of artificial intelligence, people pay more attention to friendly human-computer interaction and how to obtain accurate and effective information. Aiming at the problem of the diversity and structure of Chinese, this paper introduces the cross attention mechanism. Texts provide attention to each other, then to information retrieval. Experiments on aetc-nlp dataset show that the method is effective.
Research on Evaluation System and Threshold Value of Water Resources Carrying Capacity Based on Principal Component Analysis
Authors: Liu Lihuan , Liu Jingshuo
Published: February 2021

The traditional water resources carrying capacity evaluation system obtained by the fuzzy comprehensive evaluation method has strong subjectivity in factor selection and threshold calculation, and the factor analysis is relatively one-sided. Therefore, water resources carrying capacity evaluation system based on principal component analysis is established and its threshold is studied. First, the main influencing factors of water resources carrying capacity, including water resources endowment, society, ecology, and other resources are analyzed. Then, the water resources carrying capacity evaluation system is established by selecting the relevant indicators in the evaluation system, comprehensively considering the selection principles, and selecting the index with the highest score as the main component. Finally, different methods are used to calculate the threshold value of the evaluation index. So far, the establishment of the evaluation system of water resources carrying capacity based on principal component analysis and the calculation of the threshold value is completed. The results of the case analysis show that the established water resources carrying capacity evaluation system are more scientific and effective than the traditional system.
Signal Sorting Algorithm of Hybrid Frequency Hopping Network Station Based on Neural Network
Authors: Wang Zhongyong , Zhang Beibei , Zhu Zhengyu , Gong Kexian
Published: February 2021

In a non-cooperative frequency hopping communication system, the frequency hopping network station sorting of the received hybrid signals plays an important role and becomes an active research area in recent years. In order to solve the problem that the currently widely used clustering algorithm can not achieve satisfactory accuracy. In this paper, we propose a signal sorting method for hybrid frequency hopping network stations by applying the neural network to classify the frequency hopping description words of signals. Additionally, the conjugate gradient algorithm is utilized in the neural network training process to improve the convergence speed. Simulation results demonstrate that when compared with the clustering algorithm, the proposed algorithm converges with fewer iterations and delivers better sorting accuracy, especially in a low signal to noise ratio environment.
Combined Model for Short-term Wind Power Prediction Based on Deep Neural Network and Long Short-Term Memory
Authors: Xiong Bangru , Meng Xinyu , Wang Ruihan , Wang Xin , Wang Zhengxia
Published: February 2021

Wind power generation is affected by weather and historical wind power, which presents the characteristics of instability and high volatility. Most wind power prediction models ignore physics information. In this paper, a novel combined predicting model that simultaneously considers physics information and historical information is presented to address the drawbacks of existing models. First, the physical characteristics of wind speed, wind direction, and temperature are obtained by Deep Neural Network(DNN), and time-series characteristics from historical wind power are extracted by Long Short-Term Memory(LSTM). Then, the physical features and the time-series features are fully connected for feature fusion to obtain the final time-series physical features. Finally, the short-term wind power prediction is performed according to the obtained merged features. Experimental results demonstrate that the DNN-LSTM model proposed in this paper achieves high accuracy and stability, and provides technical support for wind power system dispatch.
Research on Face Recognition Algorithm Based on HOG Feature
Authors: Li Yalan , Lu Ruhua , Huang Rui , Zhang Wenfen
Published: February 2021

As a common biometric recognition technology, face recognition is also an important research direction in the field of computer. Although compared with the initial research, the current research has made great progress, but there are still many difficulties in practical application. In this paper, by extracting HOG features, after introducing the detailed steps of PCA and LDA subspace feature extraction methods, dimensionality reduction feature extraction method combing PCA with LDA is applied to extract face features. This method first uses PCA to reduce the dimension of face features, and then uses LDA for linear discriminant analysis. Finally, the feature extraction methods based on PCA and LDA are tested and compared in FERET standard face database and CAS-PEAL database of Chinese Academy of Sciences
Personalized Recommendation Method for English Teaching Resources Based on Artificial Intelligence Technology
Authors: Gao Meiyun , Xing Jun , Yin Chengbo , Dai Linlinm
Published: February 2021

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

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

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

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

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

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

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

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

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

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

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

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

Big 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

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

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

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

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

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

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

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

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

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
Research of Energy Consumption Monitoring System Based on IoT and Blockchain Technology
Authors: Qi Yue , Wang Xiaocui , Zhou Qian , Mu Shile
Published: February 2021

As the underlying platform for security applications of the Internet of Things, blockchain can provide reliable storage, data encryption, access control, identity authentication, and other service support for the security of the Internet of Things. Its immutability can ensure the authenticity of the information on the chain and the construction of shared accounts The foundation of trust is to ensure business transparency and traceability. Asymmetric encryption can ensure the information security of the Internet of Things. Smart contracts support automatic transactions of IoT devices to improve the operating efficiency of the system, thereby promoting the flow of IoT data and enabling timely warning and carry out effective control. The application research and industrial application of blockchain in the energy field will bring innovative application models to the Energy Internet and realize the flow of data value. This article focuses on the energy consumption monitoring of the Energy Internet consumption, applies blockchain technology to the construction of an energy consumption monitoring system for key energy-consuming units, analyzes the fit between business needs and blockchain technology, designs a blockchain platform architecture, and discussed the issues of energy consumption monitoring blockchain terminal equipment authentication and collection of trusted data.
Multi Channel Allocation Technology for large scale Connection Scenarios in Wireless Networks
Authors: Tian Hua , Jiao Ge , Xu Xiao Yan
Published: February 2021

The existing wireless network multi-channel allocation technology has the defects of low throughput and large average delay. Therefore, this paper proposes a multi-channel allocation technology for large-scale connection scenarios. Through the wireless network architecture, the large-scale connection scenario is described. Based on this, the interference model of wireless network is built. In order to simplify the process of multi-channel allocation in wireless network, Kruskal algorithm is used to form the minimum spanning tree of wireless network topology, and the link load weight is calculated. Based on the calculation results, the wireless network multi-channel is allocated according to the link load weight from large to small The multi-channel allocation of wireless network for large-scale connection scenarios is realized. Using the proposed technology to determine the value of link load weight, the multi-channel allocation scheme is obtained, and the simulation experiment is carried out. The experimental results show that: compared with the standard value, the proposed technology has large throughput and small average delay, which fully shows that the proposed technology has higher allocation performance.