bluuebunny/crossref_metadata_2025_split huggingface dataset:
HSI Denoising: Global Non-Location Low-Rank Factorization and Sparse Factorization Based on Iteratively Boost Method
Authors: Yan Hao 
Published: January 2021

Abstract
This study proposes global and non-location low-rank factorization and sparse factorization based on iteratively boost (GNS-ITboost) scheme from the perspective of the iteration method to improve Hyperspectral image (HSI) denoising results. First, this study establishes a novel iterative boost method by jointing global low-rank factorization, non-location low-rank factorization and sparse factorization to HSI denoising. Second, we propose the non-convex method, weight schatten p-norm method to more effectively approximate the rank function to obtain better non-location low-rank factorization. Third, this study provides a new noise estimation method by prior knowledge to accelerate obtaining a better denoising effect. Finally, proximal alternating minimization and weight sparse ideas are used to obtain the solution of denoising optimization problems. Furmore, evaluation results indicate that the proposed method can outperform the state-of-the-art methods in terms of FastHyDe, GLF, LRMR, SNLRSF, SSTV.
Spatial-Time Complementary Coding Signal Design for RadCom System
Authors: Zhang Chen , Yang Jin 
Published: January 2021

Abstract
Sharing signal design is an important challenge in radar communication integrated (RadCom) system. In this paper, a spatial-time complementary coding signal design method is proposed. Coding signal generation, radar receive processing and communication receive processing are presented sequentially. Numerical simulation results show that the proposed method achieves better sidelobe reduction than ordinary random coding signals.
Informatization Strategies of Education and Teaching Management in the Era of Cloud Computing and Big Data
Authors: Xiaona Ma 
Published: January 2021

Abstract
In the era of cloud computing and big data, the integrated processing of information and data has become the trend of various industries. College education and teaching management should be actively innovated and optimized, change traditional models and methods, build education and teaching management system, and improve management level. Based on this, this article will analyze the importance of the informatization of education and teaching management in the era of cloud computing and big data, and propose ways to construct the informatization of education and teaching management to help the relevant.
Personalized Recommendation Algorithm for books and its implementation
Authors: Chun-mei Li , yi-han Ma , Wei Pi , Yan Qi , Jie-teng Jiang , Shuo Dong 
Published: January 2021

Abstract
In recent years, with the completion of the new library of Qinghai University, the collection of books in the library has greatly increased. The library has a total collection of 880,000 volumes, covering a dozen disciplines such as science, engineering, agriculture, literature, history, economics, philosophy, law, education, management and medicine. It is difficult for users to find the books they are interested in among the numerous materials. Based on the actual situation of the library of Qinghai University, the differences of different professional users and their personal interests, this paper chooses the item-based collaborative filtering algorithm to realize personalized recommendation. First of all, in the calculation of book similarity, the traditional user score data is not chosen to calculate the similarity, but to calculate the similarity between books and books according to the feature vector of book name. Secondly, in order to avoid the problem of cold start, the system recommends the users who have no borrowing record, but the most borrowed books in their department. The combination of the two realized the personalized recommendation of books. By comparing with other traditional recommendation algorithms, it is found that the algorithm adopted in this paper has better recommendation effect.
Harbor Ship Detection Based on Channel Weighting and Spatial Information Fusion
Authors: Zhou Quancheng , Song Fei , Chen Zhenghao , Zhang Rui , Jiang Ping , Lei Tao 
Published: January 2021

Abstract
Aiming at the problems of false alarms and missed detection caused by large differences among ship types and complex background in harbor remote sensing images, a robust single-stage ship detection method is proposed. First, a Channel Weighting Mechanism is devised, which self-learns the weights of different channel to enhance valid features in channel dimension. Second, a Spatial Information Fusion Module is designed to enhance features in spatial dimension, which extracts more information of ship appearance from shallow feature maps, then excavates potential contextual information in deep semantic features. Finally, to promote the detection ability of ships of various scales, a Multi-stage Weighted Fusion Pyramid is applied to optimize the fusion of high-stage features and low-stage features. Extensive experiments conducted on self-established dataset for harbor ships show that the proposed method provides the performance by 3.08%mAP compared to RetinaNet.
A wireless network detection and control system for intelligent agricultural greenhouses based on NB-IOT technology
Authors: Ye Haitao , Yang Yongjie , Zhu Linyu 
Published: January 2021

Abstract
In order to reduce unnecessary labor input in agriculture and maximize production efficiency, a NB-IoT technology based wireless network detection system for intelligent agricultural greenhouse plants was designed. Through the emerging Internet of Things technology NB-IoT[1], the system can conduct remote real-time monitoring and intelligent control of the growing environment of crops in agricultural greenhouses[2]. In agricultural greenhouses layout based on stm32 microcontroller to shed internal environment parameters in a wireless sensor network for data acquisition, and then collected data through the
I

2

C
[3]protocol to micro controller, the controller will receive the data transmission to the NB - IoT module, uploaded to the cloud, finally received by remote terminal and displayed to the user. Similarly, users can also remotely issue control commands to the microcontroller in the greenhouse through the terminal interactive interface, so as to realize the unmanned intelligent control of the environment in the greenhouse.
A structured data preprocessing method based on hybrid encoding
Authors: Liu Chang , Yang Liu , Qu Jingyi 
Published: January 2021

Abstract
With the rapid development of civil aviation transportation industry, the passenger throughput in civil aviation is increasing, while the problem of flight delays is becoming more and more serious. For flight delay prediction under big data, deep learning methods can be applied to make high-precision predictions. Since data preprocessing is one of the most important parts, the method based on hybrid encoding is proposed in this paper. Firstly, the flight and meteorological data are fused with the associated primary key, Since weather data has a greater impact on flight delay. Then, the fused data is encoded according to different data types. Min-Max encoding is used for continuous features, and CatBoost encoding is adopted for discrete features respectively. Finally, the data set which has been preprocessed can be put into the deep convolutional neural network ResNet to verify the effect. The experimental results show that the prediction accuracy rate of flight delay level can reach 94.02% on the structured data set after hybrid encoding.
Quantum Algorithms for Solving Linear Regression Equation
Authors: Li Kai , Dai Hongyi , Jing Feng , Gao Mei , Xue Bin , Wang Pu , Zhang Ming 
Published: January 2021

Abstract
Linear regression is one of the most important and common analytical methods in mathematical statistics. The letter studies a general model of linear regression problem based on least squares method, and investigates the impact of quantum algorithms on the time complexity of solving linear regression problem when quantum algorithms can be implemented on quantum computers. With the help of the general model of linear regression problem in terms of least squares method, we propose a novel simplified quantum scheme for solving linear regression equation based on the sparsity-independent quantum singular value estimation algorithm. For a linear regression equation with dimension n, our scheme can reduce the time complexity from O(Nn
2) to

O
(

N

log
N

)

when both the condition number κ of related matrices and the reciprocal of precision ∈ are small in size O(play logN), where m is the dimension of the input u and N is the number of samples.
A Depth Multiple Hierarchical StereoNet by Using Thermodynamic Color Guidance
Authors: Kong Weiyi , Yang Menglong , Huang Qinzhen 
Published: January 2021

Abstract
Thermodynamics is devoted to the study of energy or materials, and few researchers have combined the laws of thermodynamics with stereoscopic vision. Thus, we propose Depth Multiple Hierarchical StereoNet (DMH-Net) by using thermodynamic color guidance, which present end-to-end deep architecture for real-time stereo matching, realize sub-pixel precision and produce high-quality refinement disparity on dataset benchmarks. At the same time, we use color thermodynamics to guide the image output of an accurate Hill disparity image. Our DMH-Net consists of four modules: the first module uses a deep spatial pooling method to extract multiple features that work on the initial left and right images. The second module use color guided to produce high-quality cost volume. We use the ahead of two modules to calculate depth disparity precision, which can segment coarse to fine to obtain background and edge of objects information. Thirdly module integrates the information about 3D cost volume and color channel to frame a 4D hierarchical cost volume. The fourth module is designed by depth hierarchical refinement to regress the final disparity. Finally, we achieve sub-pixel precision and real-time performance on benchmarks, which can produce high-quality thermodynamic disparity regression maps.
Multipath number estimation based on QR decomposition
Authors: Gao Xiangying , Liu Zhixin , Zhao Yongjun , Liu Chengcheng 
Published: January 2021

Abstract
Accurate multipath number estimation is the prerequisite for MUSIC algorithm to achieve excellent superresolution performance. To solve the problem that the existing multipath number estimation algorithms based on eigenvalue distribution usually require a large amount of computation, this paper proposed a fast multipath number estimation algorithm based on QR decomposition under the OFDM signal model. This algorithm only needs few times QR iteration decomposition to obtain the pseudo-eigenvalues of the matrix, and uses them to estimate the multipath number through two-step comparison. Due to the avoidance of accurate eigenvalues solution, compared with the traditional algorithms based on eigenvalue distribution, proposed algorithm’s computation is smaller. Simulation results showed that the proposed algorithm has better detection performance in low SNR and few snapshots than existing algorithms.
SOC prediction of lithium battery based on SA-PSO-BP neural network fusion
Authors: Yin Guoliang , Jiang Chengpeng , Yang Yi , Xiao Wendong 
Published: January 2021

Abstract
In electric vehicles, accurate SOC can provide accurate and reliable residual power for drivers, which is the target value of a judgment method to effectively avoid overcharge or over discharge of battery pack[4]. However, due to the nonlinear characteristic of SOC value, its specific value cannot be directly measured by instruments and equipment. The final estimation of SOC value can only be carried out by monitoring battery parameters such as current and voltage. In order to achieve high-precision prediction of the state of charge (SOC) of lithium iron phosphate (LiFePO4) batteries, this paper uses an algorithm combining particle swarm optimization optimized by simulated annealing and BP neural network. Through MATLAB simulation, it can be seen that this method is more accurate than particle swarm optimization used to optimize BP neural network.
A Browser-based Cross Site Request Forgery Detection Model
Authors: Zhang Jingyu , Hu Hongchao , Huo Shumin 
Published: January 2021

Abstract
CSRF is one of the most serious cyber attacks, and has been recognized as the main threat and one of the ten most serious vulnerabilities in web applications. The CSRF attack is when the attacker uses the implicit authentication mechanism of the HTTP protocol and the cookies cached in the browser to pass the authentication, and then executes the attack on the target website. In this article, we propose a browser-based CSRF detection model, which can detect CSRF attacks by analysing HTTP requests and web page content, thereby identifying the attacker’s CSRF attacks.
Research and improvement of energy balance in wireless sensor network based on LEACH
Authors: Tong Yulin 
Published: January 2021

Abstract
In view of the problem that LEACH routing algorithm does not consider the remaining energy of candidate nodes when electing cluster head nodes, the factors affecting the energy balance of nodes are analysed from the election process of cluster head nodes and transmission energy consumption. Regarding the deficiencies of LEACH routing algorithm in the cluster head election process, a new cluster head election and cluster head communication transmission path selection algorithm is proposed. The results show that the improved algorithm proposed in this paper can not only achieve node energy balance, but also effectively extend the life of wireless sensor networks and strengthen the integrity of the network in the working phase.
On Mongolian College Students’ Chinese-English Word Recognition Based on Eye Movement
Authors: Guan Guiyun , Zhang Hewei 
Published: January 2021

Abstract
For Mongolian college students, the typical trilinguals, the word processing is quite different from that for bilinguals. The article designed two experiments, including visual and auditory word recognition (the priming words were Chinese, while competitive words were English). Eye tracker was used to capture the data and SPSS was utilized to analyze the results, finding that the cross-language word recognition (from Chinese to English) for Mongolian college students was completed through phonological mediation.
An adaptive multi-sensor resource allocation algorithm based on efficiency function
Authors: Li Jian , Lu Xuanmin , Zhang Yiwen , Mou Zhiying 
Published: January 2021

Abstract
In this paper, combined with the Kalman filtering algorithm, the linear weighted sum of the normalized prediction information increment DI and the norm of target filter error covariance matrix ║ cP ║ is used to represent the pairing function of the target-sensor combination and the assignment and automatic updating of the pairing function between sensor and target are realized. Based on the analytic hierarchy process (AHP), an adaptive sensor resource allocation algorithm is proposed. The simulation results show that the algorithm can effectively integrate various evaluation factors and is compatible with different types of targets. The algorithm has simple structure and good versatility. The limited sensor resources can be dynamically adjusted according to the priority of targets, making the sensor allocation strategy more reasonable and effective.
Research on small moving target detection algorithm based on complex scene
Authors: Zhang Wei , Sun Wenhua 
Published: January 2021

Abstract
In recent years, small moving target detection based on complex scenes is widely used in many fields, such as computer vision, image processing and pattern recognition. It is widely used in military navigation, intelligent video monitoring system, video retrieval and other fields. Due to the dynamic background in the complex scene, the moving object has the characteristics of long distance, small area, unclear texture and color features, and partial loss of detail features. Therefore, how to extract small moving objects quickly, accurately and completely from complex scenes is the key of subsequent image analysis and processing. Aiming at the problem of strong background noise in more complex scenes, this paper proposes a neighborhood frame search denoising algorithm based on time consistency, which can effectively remove the strong background noise disturbance. The experimental results show that the algorithm can effectively remove strong background noise.
A router abnormal traffic detection strategy based on active defense
Authors: Li Xin , Yi Peng , Jiang Yiming , Yu Jing 
Published: January 2021

Abstract
With the rapid development of network attacks, traditional security protection technology is difficult to deal with unknown threats and persistent attacks. Active defense improves the ability to defend against network attacks by building a dynamic, heterogeneous and redundant endogenous security system. Aiming at the problem of single abnormal arbitrament information of routers in mimic defense, a router abnormal traffic detection strategy based on active defense is proposed. By clustering the traffic information of multiple heterogeneous redundant routing function entities and comparing the distance measurement between them, the routing function entities in abnormal state are determined. The experimental results show that the proposed strategy effectively detects the security threats of routing functional entities and expand the method of mimic router arbitrament.
Malware Detection Model Based on Deep Convolution Generation Adversarial Network
Authors: Tang Cong , Shi Jiangyong , Yang Yi , Yang Yuexiang 
Published: January 2021

Abstract
At present, malware is one of the biggest threats to Internet security. In this paper, a new static malware analysis algorithm MSG is proposed based on DCGAN. The algorithm transforms the disassembled malware code into a gray image based on SimHash, and uses DCGAN to generate countermeasure samples for training to detect unknown malware variants. The experimental results show that the detection rate of our algorithm for malware can reach 96.67%, and the dodge rate of generated malicious samples can reach 0.92 under the detection of CNN discriminator.
Conceptual characteristics and analysis of typical application scenarios of energy blockchain
Authors: Zhang Xinsheng , Xu Qichao , Lu Jianfei , Xu Jingce 
Published: January 2021

Abstract
In recent years, blockchain has gradually moved from a technical concept to reality, and has increasingly shown its important potential to empower industrial innovation and boost economic construction. Blockchain technology has shown strong application potential in the field of energy and power with its technical characteristics such as traceability and nontampering. At present, many energy companies at home and abroad have used the energy blockchain to implement various scenarios, but they have not yet formed a target systematic research on energy blockchain. This research deeply analyzes the conceptual and technical characteristics of energy blockchain, proposes a screening model for application scenarios, and analyzes typical application scenarios of energy blockchain based on this, aiming to provide a useful reference for energy companies to develop blockchain applications.
Research on Augmented Reality (AR) Intelligent Maintenance Guide System
Authors: Sheng Yi , Gu Jiayan 
Published: January 2021

Abstract
The augmented reality (AR) intelligent maintenance guide system is researched. To do the maintenance with high efficiency and low cost, AR technology is a solution. The system is to provide a guide system with interactive display of virtual and reality. The test results show that the system has the function of reading, scanning, communication. The system can combine the virtual and reality.
Quick Debugging Method for Internet of Things Communication Module
Authors: Shuai Chen , Renyi Shu , qing Cheng 
Published: January 2021

Abstract
When developing the application system of the Internet of Things communication module, the short-range wireless communication module must be ensured to work normally in the first. In order to quickly debug short-range wireless communication modules, this paper proposes a module debugging method using USB to TTL serial port. The method uses a USB to TTL serial port module to connect the wireless communication module with the personal computer, and directly debugs in the Serial Port Tester, thereby avoiding the delay of writing the program and playing the role of quick debugging. The effectiveness of the debugging method is verified by experiments.
Research on security protection of OPC UA PubSub Protocol
Authors: bu Tianyu , Li Xi , Huang Jinfeng , Sun Zhigang 
Published: January 2021

Abstract
As a new generation of OPC protocol, OPC UA provides a standardized interoperability solution for industrial automation. In order to accelerate the application of OPC UA in industrial control networks, OPC Foundation published OPC UA PubSub protocol. With the gradual opening of industrial control network, the security risks of deep manufacturing facilities are greatly increased, so it is urgent to study the security protection of PubSub protocol. Based on PubSub protocol, we study the characteristics of its deployment environment and the security threats it faces, and point out the defects and solutions of PubSub protocol's own security protection mechanism. At the same time, we propose a security gateway model OSG, which is established between the field equipment layer and the field control layer, in order to deal with the security risks existing in the PubSub protocol.
Application and improvement of capture-recapture model for crowdsourced testing
Authors: Yao Yi , Liu Yuchan 
Published: January 2021

Abstract
Crowdsourced testing has become an effective alternative to traditional testing. However, crowdsourced testing is inherently difficult to manage. In this paper, objective information such as defect count is considered to evaluate the task process. We researched how the capture-recapture model estimates the total number of defects in a crowdsourced testing scenario. By comparing the difference between crowdsourced testing and traditional testing, incremental sampling methodology and the best estimator for crowdsourced testing were proposed to estimate the number of defects, so as to improve the ability to evaluate the completion degree of crowdsourced testing task.
A Fast Deep Learning Method for Network Intrusion Detection Without Manual Feature Extraction
Authors: Yue Wang , Yiming Jiang , Julong Lan 
Published: January 2021

Abstract
Many deep learning models have been used in network intrusion detection to improve the accuracy of intrusion detection. In the existing deep learning methods, the input sample is either the feature vector extracted from the raw traffic data in advance, or directly the raw traffic data. These two categories of methods have their own limitations. In order to overcome the limitations, this paper proposes a preprocessing method based on multi-packets input unit and the raw traffic data is significantly compressed to improve the computational efficiency. To improve the computational efficiency and the detection accuracy, a deep learning model called F-CNN is designed. Experimental results on the benchmark dataset show that the proposed method has significantly improved detection accuracy and training efficiency compared with existing models with good performance.