bluuebunny/crossref_metadata_2025_split huggingface dataset:
Video fire recognition based on multi-channel convolutional neural network
Authors: Zhong Chen , Shao Yu , Ding Hongjun , Wang Ke 
Published: October 2020

Abstract
A video fire detection method based on multi-channel convolutional neural network(CNN) is presented in this paper. With improved OTSU and connectivity analysis, an adaptive threshold segmentation for flame image is implemented. According to the flame combustion characteristics, the feature of flame area is extracted using parameters such as colour, roundness and area change. Using TensorFlow platform, the CNN and the video database is formed. In order to improve the ability of dynamic fire detection, the method of combining random gradient descent and momentum correction is used to train CNN. Experimental results show that the accuracy of CNN fire detection method is 86%, compared with 68.2% of the traditional k-nearest algorithm (KNN).
Design and Implementation of a Web Crawler System based on an Adaptive Page-Rank algorithm
Authors: Zhang Xin , Cheng Zhi , Zhang Chen 
Published: October 2020

Abstract
Web crawlers have the ability to automatically extract web page information, but there exists the issue that some pages reuse keywords to improve their search rankings. Therefore, we propose an adaptive Page-rank algorithm to build a crawler system to resolve the issue mentioned above. Specifically, we generate a relationship matrix based on the crawled web page access relationships, and then an probability matrix based on the number of web pages is generated iteratively, and finally the web pages crawled are displayed in descending order of calculated weights. Besides, we propose to control the iterative process in Page-rank with the coherence of anchor texts. The system uses Python language to realize the functions of web crawling. Experimental results demonstrate that this system has a high speed in data collection. Comparing with Hints and classical Page-rank crawler systems, The results show that the proposed method outperforms in precision and recall.
Design of a Web-based Personalized E-learning Platform
Authors: Xue Chengfeng , Zeng Xiting 
Published: October 2020

Abstract
It is important to carry out the distant learning especially in the COVID-19 period when most of schools have to close down. Therefore, a Web-based personalized e-learning platform is presented to satisfy the requests of the middle school student. Firstly, the features of the learner’s needs for e-learning are analysed as the base of the platform. Secondly a novel architecture is proposed that describes the components necessary for the distribution of courses and knowledge in Internet. Thirdly the design of the platform is enabled by the real time collaboration system, multimedia transmission and knowledge repository to help the middle school student study in their own natural ways such as gaming and blogging with friends from interests. The experiment results show that the designed e-learning platform can improve the performance of network throughput and the learner’s satisfactory level.
The feature selection based on AndroidManifest.xml
Authors: Guan Jun , Mao Baolei , Jiang Xu 
Published: October 2020

Abstract
Nowadays, Android malicious applications are rampant, and information security is facing serious challenges. To prevent being decompiled, Android malicious applications often use software packers to protect themselves. After decompilation, the only available file is the AndroidManifest.xml file. Therefore, when detecting shell applications, the researchers can only rely on the features provided by the AndroidManifest.xml file, including permissions and intent filters. How to select effective features from the AndroidManifest.xml file is the key to detect Android malicious applications. This paper proposes the feature selection method for Android malware detection based on AndroidManifest.xml.
Research on related properties of two types of self-similar networks
Authors: Zhong Wang , Mafuxiang  
Published: October 2020

Abstract
Self-similarity is the whole and the part of a complex system, that is, the similarity of structure or property between the part and that part. Studying self-similar networks is helpful for us to better understand the complex networks in the real world. Because the fractal networks have self-similarity, a type of modified Koch network and a type of Austria network were firstly described by this paper, and the exact expressions of degree distribution and aggregation coefficient of the two types of networks were secondly derived, finally, the relationship between the Randic indicator of the two types of network and other invariants is studied.
Recognition of Common Insect in Field Based on Deep Learning
Authors: Cao Xu , Wei Ziyi , Gao Yinjie , Huo Yingqiu 
Published: October 2020

Abstract
Traditional insect taxonomy methods have high technical requirements, and the ability of artificial identification of insects is insufficient. In order to solve the problem, this paper proposes a method of common insects recognition in field based on transfer learning. A total of 9 kinds of insects, such as mythimna separata, rice borer, rice plant hopper, mole cricket, mantis, locust, grass fly, ladybug, and ditch beetle are collected for classification and identification, those include the main insect pests and some natural enemies of the main food crops in the field, such as wheat, rice, corn, etc. Then we use the digital image processing technology and the confrontation generation network to expend the insect dataset, and build a model based on transfer learning to transfer the knowledge learned by VGG16, VGG19, InceptionV3, and InceptionV4 on the ImageNet dataset to the insect classification and recognition. Experimental results show that the transfer learning training model has better classification performance and higher convergence speed, and data expansion can help extend sample and avoid overfitting. The highest recognition accuracy is up to 97.39% among models, which adopt the VGG19 convolutional neural network to pretrain the model for transfer learning. This method has the high recognition accuracy, less time consumption, simple and convenient, robustness in particular for the translation and rotation, which provides a reference for the identification and classification method of field insects.
Research on Construction and Application of Power Distribution Internet of Things
Authors: Shao Siyang , Ma Xiang , Yuan Wei 
Published: October 2020

Abstract
As an important part of power grid aggregation and distribution, the distribution network is becoming more and more important and intelligent with the development of the Internet of Things technology. As a high-priority system for power supply services, the distribution network faces customers directly, so it also faces greater challenges. On this basis, based on the traditional distribution network, this paper studies and comparatively analyzes the advantages of the new distribution Internet of Things model, proposes a four-tier architecture system for the construction of distribution Internet of Things. Furthermore, the application and development process of the Internet of Things technology in the distribution network is explained from the three key technologies of comprehensive perception technology, diverse communications, and edge computing. Finally, from the perspective of operation and maintenance management and fault diagnosis and early warning, the application prospects of the Internet of things for power distribution are discussed.
Analysis on the Mechanism of Superconducting Quantum Computer
Authors: Wang Yuhui 
Published: October 2020

Abstract
Nowadays the quantum computer has been drawing public attention, and how to realize a quantum computer is one of the difficult problems. However, superconducting circuit is a good system for solving the physics of quantum computers, as it has a solid development history and a perfect fit with quantum computers. In this paper, the development history of the superconducting quantum computer will be reviewed. In addition, the Josephson junction is the basis of superconducting quantum computation, based on which as well as in combination with DiVincenzo’s five requirements, superconducting quantum computers have a great potential. For the main part, the advantages and disadvantages of superconducting a quantum computer are analyzed. Some suggestions are also given and the future of superconducting computer is predicted. In conclusion, the superconducting system is found to be a suitable system for quantum computers.
1553B self-check abnormal analysis of a certain computer
Authors: Wang Yongchao 
Published: October 2020

Abstract
During the factory test of a certain computer, the 1553B self-test is abnormal. Through the analysis, it is found that the RAMLB signal pin constraint error in the FPGA firmware caused the failure. In the case of a certain difference in the characteristics of the chip, the 1553B extended SRAM storage function is unstable, and the existing drivers and applications use the extended storage area, which eventually leads to unstable communication. This problem was solved by modifying the 1553B driver. The research in this paper enriches the research of computer 1553B communication and provides theoretical support and basis for subsequent development, design, and solving practical problems.
Design of Financial Early Warning System Based on Elman_Adaboost Algorithm
Authors: Wu Guoqiang , Li Qingping 
Published: October 2020

Abstract
This article analyzes the relevance of the Elman_Adaboost algorithm and combines the design points of the financial early warning system. This article includes data integration platform design, operating system platform design, data warehouse platform design, system integration platform design, system logic structure design, system software function design, financial warning level division, financial risk warning situation division, etc. The author of this paper continuously improves the financial information system, the financial risk responsibility system, etc. The purpose of this article is to improve the practicality of the financial early warning system and lay the foundation for the stable development of the enterprise economy.
Algorithms on Graph Coloring Problem
Authors: Yang Mingtao 
Published: October 2020

Abstract
Graph coloring problem is one of the most famous NP-complete problem and it is an important problem in the study of set theory and graph theory. Since it was raised, lots of people has tried many methods to solve the problem or to optimize the solution to this problem. In this article, the author will introduce the concept of the graph coloring problem and two solutions of the problem. One is based on genetic algorithm and the other is based on greedy algorithm. Each solution has its own traits. Through the analysis, this paper aims to reduce the complicity of the algorithms for making them useful in various applications.
Content analysis and topic correlation of financial news related to big data concept
Authors: Teng Yuanyang , Wu Xiaobo , Li Yicun 
Published: October 2020

Abstract
This paper combines the traditional content analysis method with the emerging natural language processing algorithm, LDA, and takes the financial news related to big data as the research object of content analysis, so as to extract the topics and vocabulary concerned by different categories of financial news related to big data, and judge the correlation of different categories of financial news on topics.
Virtual simulation of communication between KUKA robot and PLC
Authors: Shen Bin , Yang Zhihang , Wang Yanwu 
Published: October 2020

Abstract
Virtual simulation can effectively provide the training environment for the robot. It can solve many problems in robot practice, such as complex equipment, high price and so on. Virtual simulation and communication of KUKA robot can increase more possibilities. Based on the study of its trajectory planning and kinematics, this paper further increases the research content of virtual policy. This paper takes uniy3d virtual reality engine as the development platform, and studies the virtual motion simulation and control of industrial robot from the perspective of interaction between virtual robot and real robot,
Research on algorithm and implementation of Fourier transform in the case of N < M
Authors: Gao Yang , Zhu Jia Li , Li Yun Fei , Liu Jian 
Published: October 2020

AbstractIn this paper, Fourier transform (FT) is studied under a special situation in which the transform lengthNis less than the number of input dataM. First, the corresponding Fourier transform formula is deduced, and then an implementation example is given as well as performance simulation and implementation analysis. The results show that the mixed radix FT method has less resource consumption and less processing latency compared with fast Fourier transform (FFT) and discrete Fourier transform (DFT) method, respectively. The proposedN<MFT method increases the flexibility of traditional Fourier transform algorithm.
Hamiltonian claw-free graphs involving induced cycles
Authors: Yin Jun , Xiong Liming 
Published: October 2020

Abstract
The hamiltonian problem is an important topic in structural graph theory, which is closely related to Four Color Problem. Hence lots of graph scholars are dedicated to this topic. There are many authors working for finding some sufficient conditions for hamiltonian property of graphs. Let G be a claw-free graph with n vertices and δ(G)≥3. In this paper, we show that if G has an induced cycle of length more than (4n – 2δ(G)–4)(δ(G)+2)−1, then G is hamiltonian. The result is best possible if δG
is 3 or 4.
Optimization of the Recovery Time of Pyramid Code in Distributed Storage System
Authors: Geng Wei , Zhang Hang , Liu Longxiang , He Rui , Tang Dan 
Published: October 2020

Abstract
In large scale distributed storage systems, erasure code is a basic technology that provides high reliability at low cost. Compared with traditional redundancy technology, erasure coding technology has low redundancy and high flexibility. Therefore, it is a good choice for distributed systems to construct Pyramid codes that are flexible and suitable for a variety of application scenarios, but its disadvantage is that the data recovery time is still long. To address the above problems, in this paper, we propose an Active Fault-Tolerant Pyramid (AFTP) based code, which dynamically adjusts the length of the group in the Pyramid code and the original data block correlation with redundant blocks by using the hard disk fault prediction model based on the decision tree to reduce the length of the group of data blocks in a potentially faulty hard disk, which can be used for multiple hard disk failures. All read and recovery operations are performed within the group, reducing recovery time without adding additional storage overhead. To verify the validity of the AFTP code, we conduct intensive experiments on the distributed storage system based on Ceph. The results show that, compared with Basic-Pyramid (BP), the recovery time of AFTP code is reduced by 8%-64%, and compared with the commonly used classic block codes, the recovery time of AFTP code is reduced by 11%-52%.
Group strategy of dissertation defense based on greedy retrospective hybrid algorithm
Authors: Su Pei , Luo Bing Xue , Deng Fang Yi , Xia Ai Xue , Guo Yan 
Published: October 2020

Abstract
Thesis defense is an important link to examine the quality of graduate thesis. In order to ensure the fairness of evaluation, the examiners and students are subject to many restrictions when the thesis defense is grouped. Meanwhile, with the popularization of higher education and the increasing number of graduates, grouping becomes very complicated. This paper designs a paper defense grouping strategy based on greedy backtracking hybrid algorithm. In this paper, the best solution of the group of judges and the group of students is found by using the idea of greed, and then the solution space tree is established. Then combined with the idea of backtracking algorithm, the search was carried out from the root node, and the schemes that did not meet the constraints were eliminated until all the results that met the requirements were found. In this way, a grouping strategy can meet the constraints of the arrangement of thesis defense in universities and improve the efficiency and fairness. Finally, the correctness of the algorithm strategy is verified by experiments.
Nuclide Spectrum Peak Searching Algorithm Based on Multiple Morphological Structuring Elements
Authors: Huang Yao , Liu Mingzhe , Liu Xianghe , Luo Rui 
Published: October 2020

Abstract
Considering the characteristics of the traditional peak searching methods of nuclide spectrum and morphological transformation in the image processing, a nuclide spectrum peak searching method based on multiple morphological structuring elements is proposed. Four flat structural elements at an angle of 0, 45, 90, and 135 to the horizontal line are utilized, then the morphological transformation of the nuclide spectrum is performed, and the details of the different directions of the nuclide spectrum curve are better controlled. The algorithm proposed in this paper improves the ability of the morphological peak searching and the peak information is preserved well, meanwhile, the method has a better peak recognition effect.
Exploration and Practice of online teaching mode of operating system course
Authors: Zhang Wenhua 
Published: October 2020

Abstract
In the spring of 2020, the Ministry of Education proposed to suspend classes but keep learning and carry out online teaching. In this paper, after analyzing the advantages and disadvantages of online teaching, combined with the ongoing operation system, some ideas of operating system online teaching reform are put forward from three aspects of leading to learn, live to learn in class and after class superintendent. Course evaluation and network teaching platform evaluation pay attention to the usual inspection and evaluation of learning process, which makes the evaluation more reasonable and fair.
A Facial Expression Recongnition Method Based on Dlib, RI-LBP and ResNet
Authors: Yang Jianxing , Adu Jianhua , Chen Hongguang , Zhang Jie , Tang Jianfeng 
Published: October 2020

Abstract
A method of facial expression recognition using a composite feature is proposed. The method combines the expanded Dlib facial feature detector, the rotation-invariant local binary pattern (RI-LBP) and the 50-layer ResNet neural network model (ResNet_50). First, the expanded Dlib was used to locate 83 feature points on the face, obtainting the Dlib feature after preprocessing and dimentionality reduction (PCA). Then, the rotation-invariant LBP feature was extracted from 8 important regions after tilt correction. Furthermore, a 50-layer ResNet neural network was used to extract the low level features from the images. Finally, the three features were combined and extreme learning machine (ELM) was used to classify the composite facial features. The experimental results on Jaffe and CK+ datasets showed that the proposed method performs better compared with other methods.
Corporate Performance, Agency Costs and Non-financial Information Disclosure
Authors: Wu Jing , Yuan Fangying 
Published: October 2020

Abstract
Taking Shanghai and Shenzhen A-share listed companies as samples, this paper studies the relationship between corporate performance, agency cost and non-financial information disclosure. It is found that non-financial information disclosure is positively correlated with corporate performance and negatively correlated with agency cost. Further analysis shows that the higher the level of non-financial information disclosure, the stronger the ability to control risks. Based on the stakeholder - agency theory, this paper studies the non-financial information disclosure of listed companies in China in reducing the agency cost among stakeholders. The conclusion shows that the managers should promote good non-financial information disclosure, which can not only improve the performance of the company, but also reduce the risk and agency cost.
Garbage Sorting System Based on Composite Layer CNN and Multi-Robots
Authors: Liang Binyan , Wang Yanbo , Wang Yao , Chen Zhihong , Lin Junqin 
Published: October 2020

Abstract
Object detection technology is the key problem in the garbage sorting system. There are many difficulties in identifying the target in the garbage, such as poor visibility and targets being coverage. The existing image processing methods are difficult to detect the target objects in the garbage sorting system. To solve this problem, this paper proposes a garbage sorting system based on convolution neural network and robots. The CNN network combines the features of three different network layers and predicts the location and angle information of the target objects at the same time, which we called composite layer. Coupled with the training solution of classification regression and angle prediction, the performance of object detection is further improved. The entire sorting system is composed of three subsystems: vision system, electrical system and mechanical system. The vision system is responsible for the image processing and send the position and attitude information to the electrical system. The electrical system controls the different robots to do sorting motion. We construct a TestSet which has 23983 objects to be detected. The experiment results show that our system’s Drop rate is 1.7% and False rate is 4.8%, which is good enough to meet the needs.
Analysis and Forecast of Production Safety Accidents of Housing Municipal Engineering in China
Authors: Zheng Kaixia 
Published: October 2020

Abstract
The problem of production safety accidents in housing municipal engineering has always been a big trouble in China, which has clearly rebounded in recent years. This shows that China’s management performance in this regard still needs to be improved, and there is still a lot of work to be done to prevent and reduce the occurrence of accidents.
This article collected data on 11,948 cases of production safety accidents in housing municipal engineering in 31 provinces, municipalities, and autonomous regions in China from 2003 to 2018. It performs data preprocessing and makes preliminary analysis; uses SPSS software to perform factor analysis, variance analysis, and cluster analysis based on the time and space dimensions of the data. Results show the total number of such production safety accidents is still very large, but there are certain regularities in the time and space dimensions. Relevant government departments and enterprises should focus on controlling the time and region where accidents occur frequently. Besides, this paper establishes the ARIMA model to predict the number of security accidents from 2020 to 2022, and the number of security accidents will continue to increase, so we should be vigilant and prevent it in advance.
A New Profile Least-Squares Approach for Partially Linear Nonparametric Models with Measurement Errors
Authors: Yin Yumeng 
Published: October 2020

Abstract
Partially linear nonparametric models, as a generalization of partially linear models and nonparametric models, have good adaptability and robustness. In this paper, based on the selection of this model, and a new method of contour least squares estimation, the focus is on the estimation of partially linear nonparametric models with measurement errors of covariance. Because when the measurement error is ignored, the parametric and non-parametric components are usually biased estimates. On this basis, we propose a modified contour least squares estimation of measurement error, and also obtain the error variance estimation results. Finally, through numerical simulation, the feasibility of the model proposed in this paper when the covariant has additive measurement error is verified.