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An improved algorithm of pattern matching for information security audit system

Journal of Computational Information Systems 2008年第5期4卷 2221-2228页

作者： Ding, Lei Yu, Fei Xu, Cheng Yue, Guangxue School of Physics Science and Information Engineering Jishou University Jishou 416000 China Provincial Key Lab. of Image Processing and Imagecom Communication Nanjing University of Post and Telecommunications Nanjing 210003 China College of Computer and Communication Hunan University Changsha 410082 China

At present, network information audit system is almost based on text information filtering. Text categorization is the basic technology of text information filtering. Pattern matching algorithms are very important for the rule based information audit system. It directly influences the accuracy and real-time performance of the system. The KMP and BM algorithms are introduced in this paper. Research is carried out to improve the BM algorithm and a better BM algorithm is proposed According to the situation that the system characters group is bigger and that characters of the pat tern are less, the thesis puts forward an improved string-matching algorithm. The results of our experiments prove that it promotes the precision of text categorization. © 2008 Binary Information Press.

关键词： Pattern matching

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Partial-order Algorithm of Model Checking in µ-Predicate Ambient Logic

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Journal of computers (Taiwan) 2018年第5期29卷 142-159页

作者： Jiang, Hua Lin, Rong-De Zou, Fu-Min Li, Ling-Xiang Key Lab of Granular Computing Minnan Normal University ZhangZhou China School of Mathematical Science Huaqiao university QuanZhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou Fujian350008 China School of Electronics and Information Engineering Hunan University of Science and Engineering Yongzhou425199 China

This paper introduces a kind of partial-order algorithm of model-checking in finite-control mobile ambients against μ-predicate ambient logic(Ambient logic based on first-order μ-calculus). Based on Tarski's fixpoint theorem, nested predicate equations and Block Dependency Graph, a group of partial ordering relation between inter-mediate results in the process of model checking is given, and thus a kind of local model-checking algorithm is proposed. To our knowledge, this is the first algorithm which exponent of time complexity is d/2+2, the exponent of space complexity is d/2 (d is the alternate nesting depth of fixpoint operator in the formula) and this is the third model-checking algorithm for predicate ambient logic with recursion. This paper's contributions are: (1) getting a group of partial ordering relation between intermediate results in the computing process of model checking for µ-calculus first-order predicate ambient logic based on Tarski's fixpoint theorem;(2) using the partial ordering relation to design an algorithm for model checking in mobile ambients;(3) analyzing the complexity of the algorithm. © 2018 computer Society of the Republic of China. All rights reserved.

关键词： Model checking

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Path ω-automaton

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Journal of Information Hiding and Multimedia Signal Processing 2017年第3期8卷 640-648页

作者： Jiang, Hua Zou, Fumin Lin, Rongde Li, Lingxiang Key Lab of Granular Computing Minnan Normal University ZhangzhouFujian China Beidou Navigation and Smart Traffic Innovation Center of Fujian Province FuzhouFujian China School of Mathematical Science Huaqiao university QuanzhouFujian China Fujian Provincial Key Laboratory of Big Data Mining and Applications FuzhouFujian China School of Electronics and Information Engineering Hunan University of Science and Engineering YongzhouHunan China

Automata theory is an important branch of theoretical computer science. ω-automaton is an important part of automata theory. The judgment standard for the equivalence of two automata is the equivalence of their acceptance language. Acceptance conditions for automata are expressed with state set. This paper tries to use path to express acceptance conditions for ω-automaton that is convenient for judging the automatons equivalence property and transforming between different types of automata. With the help of Path ω-automaton, Muller automaton is transformed into Büchi automaton. Compared with the existing algorithm, the state scale of Büchi automaton has been reduced and the problem of state space explosion has been relieved. The research findings in this paper contribute to enrich the existing ω-automaton theory and provide a new idea for ω-automaton theory and application research. © 2017.

关键词： Automata theory

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Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

arXiv

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

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Collaborative Edge Service Placement for Maximizing QoS with Distributed Data Cleaning 31

Collaborative Edge Service Placement for Maximizing QoS with...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Liang, Yuzhu Wang, Wenhua Zheng, Xi Liu, Qin Wang, Liang Wang, Tian School of Artificial Intelligence Beijing Normal University Beijing100875 China School of Computing Macquarie University NSW2109 Australia College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai519000 China BNU-HKBU United International College Guangdong Key Lab of Ai and Multi-Modal Data Processing Zhuhai519000 China

ISBN: (纸本)9798350399738

The proliferation of dirty data on Internet of Things (IoT) devices can undermine the accuracy of data-driven decision-making by affecting the distribution of original data. The Quality of Service (QoS) of data cleaning on these devices is heavily impacted by processing delay and accuracy. In this paper, we find that edge service placement is a key step aligned with data cleaning and consider the collaborative edge service placement with distributed data cleaning (SPDC) problem. To address this issue, we propose a novel distributed collaborative edge-based architecture that effectively balances the demands of storage, communication, computation, and load constraints. Experimental results show that the proposed approach significantly improves the accuracy of data cleaning by 0.31%-86.07% and reduces delay by 2.73%-58.71% compared to state-of-the-art baselines. © 2023 IEEE.

关键词： Edge computing

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Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

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2023 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2023

作者： Tong, Zhan Wu, Zhan Yang, Yang Mao, Weilong Wang, Shijie Li, Yinsheng Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China

ISBN: (纸本)9798350337488

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics. © 2023 IEEE.

关键词： computed tomography deep unsupervised learning denoising diffusion probabilistic model Metal artifact reduction physics-based consistency constraint

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Rapid detection of traditional Chinese medicine with neuraminidase inhibitory activities based on high-throughput and virtual screening strategy

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Phytomedicine Plus 2023年第1期3卷 100417-100417页

作者： Huang, Yuheng Wang, Zhen Fang, Senbiao Tan, Ying Chen, Jiajun Xie, Jiaming Tu, Zhengchao Huang, Weihuan Li, Ning Tian, Haiyan Institute of Traditional Chinese Medicine and Natural Products College of Pharmacy Jinan University Guangzhou 510632 China Department of Scientific Research KMHD GeneTech Co.Ltd. Shenzhen 518000 China Key Laboratory of Regenerative Medicine Ministry of Education Jinan University Guangzhou 510632 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha 410083 China

Background: Oseltamivir, a neuraminidase inhibitor (NAI), is the primary and first-line anti-influenza drug. In recent years, more and more oseltamivir resistant strains appeared frequently. Purpose: To identify anti-influenza candidates to overcome oseltamivir resistance from traditional Chinese medicine. Methods: High-throughput screening platform was used to screen candidates with oseltamivir sensitive and resistant neuraminidase (NA) inhibitory activities from traditional Chinese medicine (TCM) and in validation of potential active components in vitro. Molecular docking is used for virtual screening of TCM compound libraries and analysis of the molecular mechanism of active compounds. Results: Six out of 188 TCM formula granules were screened out with good inhibitory activities in oseltamivir sensitive (H5N1) and resistant NA (H274Y mutant). A compound library containing 679 candidates in the above six plants were docked into binding pockets of H5N1 NA and its H274Y mutant. The results indicated that diverse structural types, including flavonoids glycosides, alkaloids, caffeoylquinic acid derivatives, etc. showed good docking scores on the two NAs. Furthermore, 10 representative compounds tested in vitro showed inhibitory activities in both oseltamivir sensitive and resistant NA. The docking simulations revealed that natural TCM molecules bind with NA in an absolute different mode compared with oseltamivir, which occupy the extra cavities adjacent to the active site of NA, thus might make them non-susceptible to be influenced by mutation around the active sites. Conclusions: The combination of high-throughput screening and molecular docking can efficiently screen out candidates that have the potential to overcome oseltamivir resistance from TCM. © 2023

关键词： High throughput screening NA inhibitory activity Traditional chinese medicine Virtual screening

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Temporal Group Attention Network with Affective Complementary Learning for Gait Emotion Recognition

Temporal Group Attention Network with Affective Complementar...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Ziyi Zhu C. L. Philip Chen Haiqi Liu Tong Zhang School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Computational AI Models and Cognitive Intelligence Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Pazhou Lab Guangzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Skeleton-based methods in Gait Emotion Recognition (GER) that abstract gait as a spatial-temporal graph in a non-Euclidean space have achieved remarkable success. However, existing studies neglect capturing crucial spatial-temporal implicit dependencies and make insufficient use of manually crafted affective complementary information. In this paper, we propose a novel Temporal Group Attention Network with Affective Complementary Learning (T2A). Specifically, we propose a Temporal Group Attention (TGA) module, which captures spatial-temporal implicit dependencies and crucial features across space-time. Moreover, we design an affective complementary learning strategy, which first introduces an Affective Denoising (AD) module to reduce the impact of noise in weak emotion-related information and learn augmented affective features. Then, we adopt a decision-level fusion of the denoised affective features with the deep skeleton features to compensate for the discrepancies between different feature spaces. Experimental results on Emotion-Gait and EMOGAIT demonstrate that our proposed method significantly outperforms state-of-the-art methods.

关键词： Emotion recognition Noise reduction Noise Skeleton bioinformatics

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Fine-scale genetic structure of Tujia and central Han Chinese revealing massive genetic admixture under language borrowing

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植物分类学报 2021年第1期59卷 1-20页

作者： Guang-Lin He Ying-Xiang Li Meng-Ge Wang Xing Zou Hui-Yuan Yeh Xiao-Min Yang Zheng Wang Ren-Kuan Tang Su-Min Zhu Jian-Xin Guo Ting Luo Jing Zhao Jin Sun Zi-Yang Xia Hao-Liang Fan Rong Hu Lan-Hai Wei Gang Chen Yi-Ping Hou Chuan-Chao Wang Department of Anthropology and Ethnology Institute of AnthropologyNational Institute for Data Science in Health and Medicineand School of Life SciencesXiamen UniversityXiamen 361005FujianChina Institute of Forensic Medicine West China School of Basic Science and Forensic Medicine Sichuan UniversityChengdu 610041China Department of Anthropology and Ethnology Institute of AnthropologyNational Institute for Data Science in Health and Medicineand School of Life SciencesXiamen UniversityXiamen 361005FujianChina Institute of Forensic Medicine West China School of Basic Science and Forensic Medicine Sichuan UniversityChengdu 610041China Institute of Forensic Medicine West China School of Basic Science and Forensic Medicine Sichuan UniversityChengdu 610041China School of Humanities Nanyang Technological UniversityNanyang 639798Singapore Department of Anthropology and Ethnology Institute of AnthropologyNational Institute for Data Science in Health and Medicineand School of Life SciencesXiamen UniversityXiamen 361005FujianChina Institute of Forensic Medicine West China School of Basic Science and Forensic Medicine Sichuan UniversityChengdu 610041China Department of Forensic Medicine College of Basic Medicine Chongqing Medical UniversityChongqing 400000China Department of Forensic Medicine College of Basic Medicine Chongqing Medical UniversityChongqing 400000China Department of Anthropology and Ethnology Institute of AnthropologyNational Institute for Data Science in Health and Medicineand School of Life SciencesXiamen UniversityXiamen 361005FujianChina Department of Anthropology and Ethnology Institute of AnthropologyNational Institute for Data Science in Health and Medicineand School of Life SciencesXiamen UniversityXiamen 361005FujianChina Department of Anthropology and Ethnology Institute of AnthropologyNational Institute for Data Science in Health and Medicineand School of Life SciencesXiamen UniversityXiamen 361005FujianChina Department of Anthropology and Ethnology In

Archaeological,genetic,and linguistic evidence has supported the idea that northern China is the original center of modern Sino-Tibetan-speaking ***,the demographic history of subsequent southward migration and genetic admixture of Han Chinese with surrounding indigenous populations remain uncharacterized,and the language shifts and assimilations accompanied by movement of people,or just an adaptation of cultural ideas among populations in central China is still unclear,especially for Tibeto-Burman-speaking Tujia and central Han Chinese *** resolve this,we genotyped over 60K genome-wide markers in 505 unrelated individuals from 63 indigenous *** results showed both studied Han and Tujia were at the intermediate position in the modern East Asian North-South genetic cline and there was a correlation between the genetic composition and the *** observed the strong genetic assimilation between Tujia people and central Han Chinese,which suggested massive population movements and genetic admixture under language *** and central Han Chinese could be modeled as a two-way admixture deriving primary ancestry from a northern ancestral population closely related to the ancient DevilsCave and present-day Tibetans and a southern ancestral population closely related to the present-day Tai-Kadai and Austronesian-speaking *** ancestral northern population we suspect to be related to the Neolithic millet farming groups in the Yellow River Basin or central *** showed that the newly genotyped populations in Hubei Province had a higher proportion of DevilsCave or modern Tungusic/Mongolic-related northern ancestries,while the hunan populations harbored a higher proportion of Austronesian/Tai-Kadai-related southern ancestries.

关键词： demographic history gene flow genetic admixture language borrowing Tibeto-Burman Tujia

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The 5th International Conference on Biomedical engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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