bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Post-Quantum Cryptosystem Based on IoT Devices
Authors: Wang Song 
Published: April 2021

Abstract
With the emergence of quantum computers, public-key cryptographic algorithms commonly used in classical computers are no longer secure. However, the current post-quantum cryptographic algorithms are mainly concentrated on the desktop CPU of Intel and AMD. For the Internet of Things, which is increasingly widely used, the research on post-quantum cryptographic algorithms is still scarce. Based on the research of post-quantum cryptosystem, this paper explores the post-quantum key algorithm based on Internet of Things devices.
Design and optimization of two-step verification scheme for form data
Authors: Aihua Wang 
Published: April 2021

Abstract
In the era of big data, we submit a lot of data to the server through the web form interface every day. Data validation is an indispensable link in the process of submitting data. The current verification scheme has some problems, such as untimely verification data, heavy load on the server. This paper analyzes the defects in the single step method in the form data verification by case analysis. On this basis, the two-step scheme of data verification is proposed, the design and optimization ideas of the two-step scheme are given, and the process and principle of the two-step verification are described. The two-step method of form data validation provides an available solution for efficient and fast data validation.
Construction of Intelligent Logistics System Based on Internet of Things Technology
Authors: Rong Tan , Hulin Zhai 
Published: April 2021

Abstract
With the promotion of information technology, the Internet of Things develops rapidly and is gradually applied in all walks of life. Intelligent Logistics has become an inevitable trend in the development of logistics industry. This paper expounds the related concepts of the Internet of Things and Intelligent Logistics, introduces the key technologies in the Internet of Things, and constructs the Intelligent Logistics system based on the hierarchical structure and key technologies of the Internet of Things, so as to promote the sustainable development of Intelligent Logistics.
Modeling and security analysis of CBTC signal failure scenario based on ScOLA
Authors: An Xiaotong , Zou Jinbai , Shen Zhukai , Yao Shunyu 
Published: April 2021

Abstract
For the entire phase of the design, manufacture and operation of the Communication Base Train Control (Communication Base Train Control, CBTC) signal system, safety is the primary issue that needs to be considered. This article introduces ScOLA’s modeling language, combined with the situation of the wireless loss fault of the vehicle-mounted signal equipment, and carries out a formal description and step-by-step simulation of the system. It further illustrates that the application of the ScOLA modeling language can perform the safety analysis of the CBTC signal system faster and more accurately.
Meta-path Guided Heterogeneous Graph Neural Network For Dish Recommendation System
Authors: Zhang Jiarui , Zhu Yonghua 
Published: April 2021

Abstract
The traditional recommendation system can hardly utilize the ingredients and flavor characteristics of the dishes, and faces the problems of sparse data and cold start, resulting in inaccurate results of the recommendation system. This paper addresses these problems by constructing a heterogeneous graph network with the interaction data between users and dishes in the food domain and taking the main and auxiliary ingredients as nodes. Also, we capture the higher-order structural information among users, dishes, and main and auxiliary ingredients by using meta-path guidance. In the meantime, we assign weights to the edges associated with user nodes and main and auxiliary ingredients nodes, which can obtain users’ preferences for main and auxiliary ingredients by using weighted GCN networks. The experiments conducted on a food domain dataset demonstrate that the meta-path-guided heterogeneous graph dish recommendation algorithm proposed in this paper is improved over the traditional recommendation algorithm.
Research Hotspots Mining and Trend Analysis of Blockchain
Authors: Fang Ji , Tu Wenyan , Jiao Fangyu , Ci Yutong 
Published: April 2021

Abstract
In this paper, the bibliography included in Web of Science is taken as the data sample. This paper analyzes articles, reviews and proceedings papers published from 2013 to 2020 in the field of blockchain to reveal the major countries and institutions. We use co-word method to identify research hotspots in the field of blockchain. The results show that the research on blockchain is an emerging research area. China and The United States are always in the leading position. Beijing University of Posts & Telecommunications publish most papers in the field of blockchain. Smart contract, Internet of Thing, bitcoin, security and privacy are the hottest research topics in the field of blockchain.
Research on Unknown Threat Detection Method of Information System Based on Deep Learning
Authors: Zhang LingHao , Liang Yunhui , Tang Yong , Wang Seng , Tang Chao , Liu Chun 
Published: April 2021

Abstract
Enterprise information system may be threatened and attacked at any time from the external and internal environment, so how to improve the enterprise information system from the outside world unknown threat detection is particularly important. This paper is based on the method that is applied to detecting and identifying unknown threat, so as to protect and improve the information system’s safety and stability with technical support and guarantee. It can avoid the impact of unknown threat on information system security to the greatest extent.
Research on Control System of Delta Robot Based on Visual Tracking Technology
Authors: Wu Hongxia 
Published: April 2021

Abstract
Delta parallel robot has the advantages of fast motion speed, accurate positioning, low cost and high efficiency. In this paper, OMRON Adapt Hornet 565 3 axis parallel robot is taken as the research object and the control system of Delta robot based on visual tracking technology is studied. The control flow such as camera configuration, model configuration, and process configuration is described in detail, the control system was tested finally. Through the tests, The parallel robot can grasp different shapes of models accurately and quickly under the action of visual tracking.
An Automatic Parking Model Based on Deep Reinforcement Learning
Authors: Junzuo Li , Qiang Long 
Published: April 2021

Abstract
When parking a car, it is crucial to ensure the car constantly approaches the parking point, gets an excellent heading angle, and avoids significant losses caused by line pressure. An automatic parking model based on deep reinforcement learning is proposed. A parking kinematics model is built to calculate the different states of its movement. Steering angle and displacement are used to achieve interaction as actions; A comprehensive reward function is designed to consider the focus of action and safety in different parking stages. Through training, the car’s automatic parking is realized, and a comprehensive analysis of the various stages and situations in the parking process is given. Besides, it is showed by a further generalization experiment that the model has good generalization.
Image Denoise Methods Based on Deep Learning
Authors: Wu Changhe , Gao Tianhan 
Published: April 2021

Abstract
Image denoising is widely used in image, video, nuclear magnetic imaging and so on. In the application scene, camera jitter, the rapid motion of objects, dark light environment and so on may cause the captured photos to be unclean, so the research of image denoising has essential research value. This paper reviews the related research in this field in recent years, introduces the basic theory of image denoising, lists the common image noise, and then summarizes some classical denoising algorithms from traditional denoising methods. In addition, the shortcomings of traditional methods are analyzed. After that, the image denoising method based on depth learning is summarized, including the image denoising method based on REDNet, DnCNN, CBDNet, GAN, Noise2Noise structure, the principle and structure of various methods are introduced. Finally, the challenges of image denoising are analyzed, and the future research direction has prospected.
Research on Network Big Data Mining Technology Based on Structured Similarity
Authors: Wang Lei 
Published: April 2021

Abstract
At present, most institutions in my country mainly use clustering algorithms for data mining activities, but low accuracy is one of the common problems they face. The article proposes a network big data mining technology based on structured similarity. It is to convert the target data set in the network system into nearest neighbor network (kNN). It then use the network big data mining technology based on structured similarity. The target data is clustered to obtain a big data result with a slightly poorer objective function but higher clustering accuracy, so as to meet our needs for accurate data statistical results.
Research on FT-IR spectrum data mining of diesel engine lubricating oil
Authors: Li Jing , Tian Hongxiang , Ming Tingfeng , Sun Yunling , Zhang Shuai 
Published: April 2021

Abstract
Aiming at the difficulties in quantitatively describing the pollution component information contained in FT-IR spectral data of diesel engine lubricants, wavelet transform and fractal box dimension are used to process FT-IR spectral data and get fractal dimension curve. Multiple peaks can be obtained by analyzing the curve. Two groups of lubricating oil samples with water content of 0.10%, 0.22%, 0.44% and 0.88% respectively and with ethylene glycol content of 0.10%, 0.22%, 0.44% and 0.88% respectively. For the above two groups of oil samples, the corresponding relation between peak position and contaminate ratio can be obtained, and a new method based on FT-IR prediction of contaminate ratio is feasible.
Neuron classification with a data-driven workflow
Authors: Li Longfei 
Published: April 2021

Abstract
The shape feature of a neuron reveals the behavior of a neuron cell. Usually, a set of features of neuron anatomy can define the morphological characteristics of a neuron for comparison or analysis of a group of neuron cells. At present, the best set of neuron morphological description characteristics cannot be determined in the field of brain science research. Based on a data-driven strategy, this paper establishes a workflow from neuron feature processing to morphological classification.
Fuzzy Weighted Entropy Attention Deep Learning Method for Expression Recognition
Authors: Yao Lisha , Zhao Haifeng 
Published: April 2021

Abstract
Deep learning network has been widely used in facial expression recognition. However, due to the complexity and variability of expression images and the influence of various factors such as illumination and individual differences, the recognition effect of existing methods needs to be improved. In order to improve the expressive ability of deep learning network, an attention mechanism based on fuzzy weighted entropy is introduced into deep learning network and applied to multi-channel facial expression recognition. Firstly, the convolutional neural network model is constructed to extract the convolutional layer multi-channel features of facial image. Then, the fuzzy weighted entropy of different channel features is calculated as the attention weight. Finally, the attention weight and multi-channel features were fused to extract the high-dimensional features of the facial expression image and sent to the full connection layer to complete the facial expression classification. Experiments in CK+ and JAFFE databases show that the average recognition rates of this method obtained 94.86% and 96.88%. It is better than the mainstream methods in recent years.
RETRACTED: A Probe into the Professional Development Path of College English Teachers under the Background of Big Data
Authors: Zhang Ping 
Published: April 2021

Abstract
the concept of big data originated in the field of computer Internet, with people’s increasing understanding of big data, a new diversity of data and information assets has arrived in an all-round way. The arrival of the big data era has not only brought a profound impact on the field of education, but also brought new changes to college English teaching. By combing the current situation and characteristics of college English teachers’ professional development and the path of college English teachers’ professional development under the background of big data, this paper probes into the future professional development of college English teachers, so as to improve the teaching effect and students’ learning ability.
Point of Interest Recommendation Based on Graph Convolutional Neural Network
Authors: Zhai Fubo , Li Baozhu 
Published: April 2021

Abstract
The rapid development of the mobile Internet makes location-based social networks (LBSNs) play an increasingly important role in practical applications. Among them, point of interest(POI) recommendation is a research hotspot in the current context. As a kind of graph data, social network can naturally express the data structure in real life. In view of the current POIs recommendation research ignoring the diversity of graph data, we proposed a POI recommendation based graph convolutional neural network (PBGCN) model, which used the check-in information, popularity characteristics of interest points, and users’ social behaviors to recommend interest points through graph convolutional neural networks(GCN). Compared with other latest recommendation methods, our model has improved accuracy. This proves the feasibility of GCN in point of interest recommendation.
Lip Reading using CNN Lip Deflection Classifier and GAN Two-Stage Lip Corrector
Authors: Zhang Chengwei , Zhang Senyao 
Published: April 2021

Abstract
Lip reading is a task to infer the content of speech through the movement of the lips, which is a technology with wide application prospects. Thanks to the development of deep learning technology and the proposal of large-scale datasets, lip reading has made great progress. However, these large-scale datasets contain only small angle lip deflection which is different with the wild environment. Most networks based on these datasets take some simple methods to correct the lip deflection that led to performance degradation when in the wild. In this work, we proposed a very challenging dataset with large angle lip deflection and multiple speakers to simulate the wild environment, and we designed a lip deflection classifier based on Convolutional Neural Network and a two-stage corrector based on Generative Adversarial Network. We use these two models to correct the lip deflection and improve the recognition accuracy. Our proposed method achieves an absolute improvement of 18.3% and 7.4%, respectively, compared with no preprocessing and just face alignment, which shows that our proposed network is more suitable to correct the large angle lip deflection in the wild.
Research on Lightweight Improvement of Sonar Image Classification Network
Authors: Zhou Yan , Chen Shaochang 
Published: April 2021

Abstract
According to the constructed sonar common target detection dataset derived classification dataset, the convolutional neural network is used to classify the target. And in order to be able to better apply actual engineering, the lightweight of the network has been studied in depth. First, the performance of the ordinary convolutional neural network VGG-16 and the lightweight convolutional neural network MobileNet on the derived classification dataset is compared. The results show that the lightweight network achieves better results at a smaller cost. Then use the dilated convolution and bottleneck structure to improve the MobileNet network to obtain the IMDNet network and the IMBNet network. Through experimental comparison and analysis, the two improved networks have a significant reduction in the amount of parameters and calculations compared to the MobileNet network. The performance of the IMBNet network is basically the same as that of the MobileNet network, and the accuracy of the IMDNet network is 2.02% higher than that of the MobileNet network. This shows that in the field of sonar image classification research, lightweight convolutional neural networks have good performance, and have certain practical application prospects and values.
Traffic Crash Severity Prediction with Deep Learning
Authors: Chen Xining 
Published: April 2021

Abstract

This paper is based on deep learning to explore the accuracy forecasting model of traffic crash severity. A prediction model in terms of convolutional neural networks is proposed in the paper. The first is to collect the basic information of all traffic crash data of the entire urban road network in Chicago from 2016 to 2020. Then using the PivotTable to observe the distribution of each variable, and filtering them. The required prediction model is based on Python and it mainly includes three parts, the input layer, the hidden layer, and the output layer. Besides, using SGD optimizer to accelerate the training of the network model is a need. Aiming to avoid problems such as over-fitting, the dropout technique is adopted, and its parameter value taking 0.1 is better. Then comparing and analyzing with three traditional machine learning models, the decision tree classification prediction model, the logistic regression prediction model and the support vector machine prediction model, it is obvious to show the better performance of prediction model based on the convolutional neural network method. After optimization, its highest accuracy is 89.2%.
A Deep Learning Method for Chinese writer Identification with Feature Fusion
Authors: Xu Yang , Chen Yuehui , Cao Yi , Zhao Yaou 
Published: April 2021

Abstract

Writer identification is one of the research hotspots of computer vision and pattern recognition, and it is of great significance in the fields of judicial authentication, file security protection, historical document analysis, and so on. However, many problems are still challenging due to the different writing sources, the common features of learning local features, and the implicit features of handwriting style. This paper uses the fusion of depth features and manual features to obtain handwriting style features from handwriting pictures. Firstly, the handwriting picture is pre-treatment and divided into small pixel blocks, and then the depth feature and LPQ feature information are extracted from each pixel block, and the depth features are reduced by PCA, and then the local features are encoded into global features through the Vlad algorithm. So far, the depth global feature and the LPQ global feature of a page of handwriting pictures are obtained, and the two features are combined as the global feature vector of the page of handwriting. And our method has achieved good results on the CASIA-HWDB data set.
Research on the Construction of Smart Cities by the Big Data Platform of the Blockchain
Authors: Ma Qianwei , Yang Yanxia 
Published: April 2021

Abstract
In the process of rapid development of information technology, to promote the further construction of new smart cities, comprehensive utilization of cloud computing technology, big data technology and Internet of Things technology will help improve the convenience of residents’ lives. The development of blockchain technology provides more possibilities for the construction of new smart cities. The use of blockchain and other new technologies to build a smart city big data platform can realize a point-to-point communication mechanism. At the same time, this can also establish a more reliable trust agreement. It is conducive to the realization of applications such as smart IoT, government affairs, and big data. Using the blockchain big data platform to build a new type of smart city can break information islands and achieve the purpose of data sharing and information security. This is a key issue that must be paid attention to in the current construction of new smart cities. In this link, it is necessary to strengthen the research and analysis of blockchain technology to understand the problems that exist in the process of building a smart city on a big data platform. This article discusses the application advantages and related strategies of a smart city big data platform based on blockchain technology.
The Development Tendency of Artificial Intelligence in Command and Control: A Brief Survey
Authors: Niu Yukai , Jin Xiaoxi , Li Jinhui , Ji Gang , Hu Kai 
Published: April 2021

Abstract
Google’s DeepMind’s AlphaStar has achieved great success in the real-time strategy game StarCraft 2, and it has also made the combination of artificial intelligence and command and control, attracted widespread attention. Artificial intelligence theory and its engineering practice accumulated over many years have injected new development momentum into traditional fields, such as command and control. We download more than 900 high-level papers from Web of Science (WoS) that combine artificial intelligence and command and control as the research object, and employ bibliometric, CiteSpace, VOSviewer, and other bibliometric analysis software. From the number of documents, co-citing, co-occurrence of keywords and research hotspots, the four aspects are studied and visualized analysis, which clarifies the research status and key documents of artificial intelligence in command and control, and reveals the frontier hotspots and research directions in this field. Finally, a summary of the four visual analysis aspects points out important references for choosing research directions, exploring the frontiers of disciplines, and assisting scientific decision-making in this field.
A deep learning model for positioning perforated stent
Authors: Wang Erhui , Zheng Liancun 
Published: April 2021

Abstract
How to determine the window position of the aortic stent is an important issue in cardiovascular disease research. Currently, this process is viewed in two dimensions, and a lack of information is prone to errors. Here, the real-time frame is suggested to enumerate the three-dimensional shape using the two-dimensional perspective image. A robot-assisted window-opening stent implantation with deep learning is geometrically aligned ratios in window-opening aortic endovascular repair. First, place markers during the windowed stent. Then, we located the three-dimensional pose of each stent segment. Third, the overall three-dimensional shaped stent-graft is achieved by graft gap in terpolation. It has been proposed to separate the markers from the two-dimensional fluorescence microscope for semi-automatic label detection of the image. In this way, we can better understand the opening position of the aortic stent.
A Study on Website’s Column Optimization by User Preference Analysis: Taking “Donghu Circle” as an Example
Authors: Yan Jingjing , Yan Yu , Cao Sheng 
Published: April 2021

Abstract
In the era of big data, the development of localized information platform has made a certain breakthrough, which can be operated and optimized with the help of data analysis and around user preferences and column classification. Through the user’s use track and browsing preference on the platform, the reaction degree of users to different column content is explored, and the most concerned and interested sections are found, so as to optimize the column classification and content filling.