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A Heuristic Sampling Method for Maintaining the Probability Distribution

Journal of Computer Science & Technology 2021年第4期36卷 896-909页

作者： Jiao-Yun Yang Jun-Da Wang Yi-Fang Zhang Wen-Juan Cheng Lian Li Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei 230601China National Smart EldeTca/re International Science and Technology Coopemtion Base Hefei University of Technology Hefei 230601China School of Computer Science and Information Engirwering Hefei University of TechnologyHefei 230601China School of Mathematics Hefei University of TechnologyHefei 230601China

Sampling is a fundamental method for generating data *** many data analysis methods are developed based on probability distributions,maintaining distributions when sampling can help to ensure good data analysis ***,sampling a minimum subset while maintaining probability distributions is still a *** this paper,we decompose a joint probability distribution into a product of conditional probabilities based on Bayesian networks and use the chi-square test to formulate a sampling problem that requires that the sampled subset pass the distribution test to ensure the ***,a heuristic sampling algorithm is proposed to generate the required subset by designing two scoring functions:one based on the chi-square test and the other based on likelihood *** on four types of datasets with a size of 60000 show that when the significant difference level,a,is set to 0.05,the algorithm can exclude 99.9%,99.0%,93.1%and 96.7%of the samples based on their Bayesian networks-ASIA,ALARM,HEPAR2,and ANDES,*** subsets of the same size are sampled,the subset generated by our algorithm passes all the distribution tests and the average distribution difference is approximately 0.03;by contrast,the subsets generated by random sampling pass only 83.8%of the tests,and the average distribution difference is approximately 0.24.

关键词： Bayesian network chi-square test sampling probability distribution

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Research on a Technology of Mail Anti-virus Gateway for Industrial Internet

Research on a Technology of Mail Anti-virus Gateway for Indu...

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Image Processing and Computer Applications (ICIPCA), IEEE International Conference on

作者： Yuhong Wu Yunshan Lv Ling Zhang School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication Key Laboratory of Public Big Data Security Technology Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

With the rapid rise of the Industrial Internet, emails that convey a large amount of information require a strong network security environment. Aiming at the increasingly complex mail virus prevention and control work under the background of the industrial Internet, this paper designs and implements a mail anti-virus gateway system. First, design the overall process and structure of the mail virus processing system; then, design an extended mail receiving and sending protocol on the basis of the public framework, thereby ensuring the convenience of the system for use in different roles and environments. In the detection and filtering part of mail virus, an anti-virus engine containing multiple sub-function modules is designed to detect and filter viruses in mail text and attachments. Through the function test of 1000 kinds of virus mails, it is found that the mail anti-virus gateway system designed in this article can detect and filter most types of mail viruses. Compared with the virus detection on the client and server, the anti-virus gateway system has better performance And efficiency.

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Magnetic activity and orbital parameters of CC Com based on photometric data,LAMOST low-and medium-resolution spectra

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Research in Astronomy and Astrophysics 2021年第4期21卷 129-138页

作者： Zhong-Zhong Zhu Li-Yun Zhang Gang Meng Yao Cheng Liu Long Xianming L.Han Qing-Feng Pi Lin-Yan Jiang College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China College of Physics Guizhou UniversityGuiyang 550025China Guizhou Provincial Key Laboratory of Public Big Data Guizhou UniversityGuiyang 550025China Dept of Physics and AstronomyButler UniversityIndianapolisIN 46208USA College of Medicine Guizhou University of Traditional Chinese MedicineGuiyang 550025China

In this paper,we present four sets of photometric VRI light curves,and several LAMOST low and medium resolution spectra of contact binary CC *** revised the orbital parameters by simultaneously combining with previously published radial velocity measurements using the WilsonDevinney *** used light curves at different observational times to obtain the starspot *** values of the starspot radius are variable in short-and long-term scales,and their longitudes are *** updated the orbital period change of CC Com,and analyzed the periodic *** period of CC Com decreases at a rate of 4.66(±0.20)×10^(-11)d yr^(-1),which may be due to mass transfer from the secondary component to the primary *** oscillation of its orbital period with a period of17.18(0.08)years and amplitude of 0.0018(1)d may be caused by the light time effect(LITE)via a third body of 0.06 M☉dwarf or magnetic activity ***,we obtained one optical spectrum from the LAMOST survey,which gives the spectral type of CC Com as K7±2 *** emissions exist in the Ha,and Ca 11 H&K lines in the observed spectrum,indicating strong chromospheric activity on CC *** the 12 LAMOST medium-resolution spectra,the EWs of Ha line are variable along the phase and time,which may be a plage or flare event.

关键词： binaries:magnetic activity stars:eclipsing stars:individual(CC Com)

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Computational Intelligence in Remote Sensing Image Registration:A survey

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International Journal of Automation and computing 2021年第1期18卷 1-17页

作者： Yue Wu Jun-Wei Liu Chen-Zhuo Zhu Zhuang-Fei Bai Qi-Guang Miao Wen-Ping Ma Mao-Guo Gong Key Laboratory of Big Data and Intelligent Vision School of Computer Science and TechnologyXidian UniversityXi′an 710071China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial IntelligenceXidian UniversityXi′an 710071China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Electronic EngineeringXidian UniversityXi′an 710071China

In recent years,computational intelligence has been widely used in many fields and achieved remarkable *** computing and deep learning are important branches of computational *** methods based on evolutionary computation and deep learning have achieved good performance in remote sensing image *** paper introduces the application of computational intelligence in remote sensing image registration from the two directions of evolutionary computing and deep *** the part of remote sensing image registration based on evolutionary calculation,the principles of evolutionary algorithms and swarm intelligence algorithms are elaborated and their application in remote sensing image registration is *** application of deep learning in remote sensing image registration is also *** the same time,the development status and future of remote sensing image registration are summarized and their prospects are examined.

关键词： Computational intelligence evolutionary computation neural network deep learning remote sensing image registration

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Thermal conductivity prediction with Fully Connected Neural Network 3

Thermal conductivity prediction with Fully Connected Neural ...

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3rd International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2022

作者： Zhang, Xingxing Li, Shaobo Yu, Liya Dong, Rongzhi Song, Qisong Wang, Zhongyu State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Mechanical Engineering Guizhou University Guiyang China Department of Computer Science and Engineering University of South Carolina SC United States Guizhou University Guiyang China

ISBN: (纸本)9783800759330

In this paper a Fully Connected Neural Network (FCNN) model is developed for the prediction of the material thermal conductivity. The traditional theoretical experimental research and computational simulation could not meet the needs of scientists' exploration of new materials. In recent years, data-driven machine learning algorithm has been used in material screening and performance prediction. Thermoelectric materials have become a hot topic in the field of materials research because of their ability to realize the interactive conversion of electric energy and thermal energy, small size, light weight, long service life and environmental friendliness. The hypothesis verification method based on a large number of experimental observations and the method of system calculation using the first principle are difficult to meet the requirements for rapid discovery of new materials in the current era due to the long research period. Therefore, the use of machine learning algorithms to assist in the development of new thermoelectric materials is of great significance. In this paper, a material thermal conductivity prediction model based on Fully Connected Neural Network (FCNN) is established, which is applied to the thermal conductivity dataset in the Materials Project database. The model is evaluated by ten-fold cross-verification, and good prediction results are obtained. The mapping model between descriptive and thermal conductivity of materials is established by using machine learning algorithm, which can be used for large-scale material screening and guiding experimental research. © VDE VERLAG GMBH.

关键词： Forecasting

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Learning Self-Supervised Vision Transformers from Scratch for Aerial Person Re- Identification

Learning Self-Supervised Vision Transformers from Scratch fo...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Houfu Peng Lili Xu Xing Lu Daoxun Xia School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Key Laboratory of Information and Computing Science Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang China

In recent years, person re-identification (Re-ID) as a widely studied computer vision task, has reached a saturation state under closed-world setting, which encourages researchers to further explore more realistic scenarios. Among them, person Re- Idin aerial imagery is proposed and improved due to its unique practical importance in public security. However, since the aerial person images are taken by unmanned aerial vehicles (UAV s), influenced by camera height and angle of view, there are more serious problems such as weak appearance feature and occlusion than ground person images. Most of the current state-of-the-art person Re-ID methods on closed-world datasets are based on local convolution neural network, and hardly works well when applying them to aerial person Re- Idtasks directly. In this paper, we improve the emerging vision transformer (ViT) and apply it to the person Re- Idin aerial imagery. It should be noted that a large amount of data is required to be pretrained for ViTs to achieve competitive performance. Considering the limitations of data, computing power and flexibility in practical scenarios, we improve the pre-training process based on self-supervised learning, and achieve training ViTs from scratch with limited data. Specifically, in pre-training stage, the self-supervised paradigm based on parameter instance discrimination is applied to capture feature alignment and instance similarity, which alleviates the data-hungry of ViTs caused by the lack of inductive bias. Extensive comparative evaluation experiments are conducted on the aerial Re- Iddataset. Our method achieves a Rank-1 accuracy of 65.29% and a mean average precision (mAP) of 57.31%, which proves its effectiveness in aerial person Re-ID tasks.

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Detection of Pine Wood Nematode Infestation Based on Improved YOLOv5 5

Detection of Pine Wood Nematode Infestation Based on Improve...

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2023 5th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2023

作者： Miao, Jiaming Zhang, Chunhui Yuan, Mengfei Gu, Shenming School of Information Engineering Zhejiang Ocean University Zhoushan316022 China Evaluation Department Binzhou Safety Evaluation Center Co. Ltd. Binzhou256600 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China

Pine wilt disease is a destructive forest disease caused by the parasitism of pine wood nematodes inside pine trees. Once infected, pine trees quickly wither and die due to the inability to drain water, hence it is also known as pine tree withering disease. It is mainly spread by pine sawyer beetles, with the characteristics of fast dissemination, rapid onset, and high mortality rate, making it a significant global plant disease. Failure to effectively control the pine wilt disease in a timely manner will result in the massive death of pine trees in a short period of time in forest areas. With the continuous development and popularization of artificial intelligence and UAV (Unmanned Aerial Vehicle) technology, combining various methods can timely detect diseased trees, and performing timely treatment and protection can greatly save the time and personnel costs of biological pest control. While it is also conducive to promoting the work of multiple related disciplines, such as bioengineering and greening engineering. In this paper, we first conducted a large number of aerial surveys on the forest area through UAV, collected relevant data, and preprocessed the obtained data, including data augmentation, cleaning, filtering, deduplication, formatting, etc., to ensure data quality and accuracy. Then, we labeled the data, and made certain improvements to the original YOLOv5. We added a new RRAM (Recurrent Residual Attention Module) to the original network model, which enables the network to timely focus on important information in redundant data, thus improving the network's performance. Compared with the original YOLOv5, our network has a stronger performance. © Published under licence by IOP Publishing Ltd.

关键词： Antennas

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High-Quality Noise Detection for knowledge Graph Embedding with Rule-Based Triple Confidence 18th

High-Quality Noise Detection for Knowledge Graph Embedding w...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Hong, Yan Bu, Chenyang Wu, Xindong Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Mininglamp Academy of Sciences Mininglamp Technology Beijing China

ISBN: (纸本)9783030891879

knowledge representation learning is usually used in knowledge reasoning and other related fields. Its goal is to use low-dimensional vectors to represent the entities and relations in a knowledge graph. In the process of automatic knowledge graph construction, the complexity of unstructured text and the incorrect text may make automatic construction tools unable to accurately obtain the semantic information in the text. This leads to high-quality noise with matched entity types but semantic errors. Currently knowledge representation learning methods assume that the knowledge in knowledge graphs is completely correct, and ignore the noise data generated in the process of automatic construction of knowledge graphs, resulting in errors in the vector representation of entities and relations. In order to reduce the negative impact of noise data on the construction of a representation learning model, in this study, a high-quality noise detection method with rule information is proposed. Based on the semantic association between triples in the same rule, we propose the concept of rule-based triple confidence. The calculation strategy of triple confidence is designed inspired by probabilistic soft logic (PSL). The influence of high-quality noise data in the training process of the model can be weakened by this confidence. Experiments show the effectiveness of the proposed method in dealing with high-quality noise. © 2021, Springer Nature Switzerland AG.

关键词： knowledge graph

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Reliability Analysis of Heterogeneous Sensor-Cloud Systems against Targeted Attacks

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China Communications 2022年第8期19卷 181-197页

作者： Hao Peng Zhen Qian Guangquan Xu Kejie Mao Kangtian Li Dandan Zhao Department of Computer Science and Engineering Zhejiang Normal UniversityJinhua Zhejiang 321004China Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal UniversityJinhua Zhejiang 321004China The Big Data School Qingdao Huanghai UniversityQingdao Shandong 266427China College of Intelligence and Computing Tianjin UniversityTianjin 300072China

Based on the wide application of cloud computing and wireless sensor networks in various fields,the Sensor-Cloud System(SCS)plays an indispensable role between the physical world and the network ***,due to the close connection and interdependence between the physical resource network and computing resource network,there are security problems such as cascading failures between systems in the *** this paper,we propose a model with two interdependent networks to represent a sensor-cloud ***,based on the percolation theory,we have carried out a formulaic theoretical analysis of the whole process of cascading *** the system’s subnetwork presents a steady state where there is no further collapse,we can obtain the largest remaining connected subgroup components and the penetration ***,this result is the critical maximum that the coupled SCS can *** verify the correctness of the theoretical results,we further carried out actual simulation *** results show that a scale-free network priority attack’s percolation threshold is always less than that of ER network which is priority ***,when the scale-free network is attacked first,adding the power law exponentλcan be more intuitive and more effective to improve the network’s reliability.

关键词： sensor-cloud systems targeted attacks heterogeneous networks reliability analysis system security

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A Task Scheduling Approach based on Particle Swarm Optimization for the Production of Remote Sensing Products

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2023年第1期50卷 23页

作者： Hou, Yan-e He, Wenwen Zuo, Xianyu Dang, Lanxue Han, Hongyu Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Kaifeng475004 China Computer and Information Engineering Henan University Henan Kaifeng475004 China

The production of remote sensing products is very time-consuming, because it contains a large amount of remote sensing data to be processed. To increase the production efficiency of remote sensing products, a remote sensing processing job is usually divided into multiple tasks that are executed in parallel in the cluster system. So efficient task scheduling is the core problem of remote sensing products production system. This paper issued this problem and proposed a hybrid particle swarm optimization algorithm to improve the production efficiency of the remote sensing products. First, the task scheduling model of remote sensing products was established to minimize the maximum task execution time. Then, in the framework of particle swarm optimization, an inertia weight parameters adjustment strategy based on linear decreasing was adapted. For the solution obtained in each iteration, a local search procedure of task movement between the production nodes was employed to improve the quality of the solution. In addition, a probability acceptance rule was also introduced to allow the acceptance of poor solutions avoid trapping into local optimum prematurely. Finally, we carried out several experiments in the CloudSim simulation platform. The experimental results demonstrate that the proposed algorithm is effective and more competition when compared with the existing scheduling algorithms, such as ant colony optimization, tabu search and other classical scheduling algorithms © 2023, IAENG International Journal of Computer *** Rights Reserved.

关键词： Remote sensing

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