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arXiv 2020年

作者： Fan, Haoyi Zhang, Fengbin Wang, Ruidong Xi, Liang Li, Zuoyong School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

Unsupervised anomaly detection aims to identify anomalous samples from highly complex and unstructured data, which is pervasive in both fundamental research and industrial applications. However, most existing methods neglect the complex correlation among data samples, which is important for capturing normal patterns from which the abnormal ones deviate. In this paper, we propose a method of Correlation aware unsupervised Anomaly detection via Deep Gaussian Mixture Model (CADGMM), which captures the complex correlation among data points for high-quality low-dimensional representation learning. More specifically, the relations among data samples are correlated firstly in forms of a graph structure, in which, the node denotes the sample and the edge denotes the correlation between two samples from the feature space. Then, a dual-encoder that consists of a graph encoder and a feature encoder, is employed to encode both the feature and correlation information of samples into the low-dimensional latent space jointly, followed by a decoder for data reconstruction. Finally, a separate estimation network as a Gaussian Mixture Model is utilized to estimate the density of the learned latent vector, and the anomalies can be detected by measuring the energy of the samples. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed *** Codes 68T30 Copyright © 2020, The Authors. All rights reserved.

关键词： Anomaly detection

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Syndrome Classification Based on Multi-Graph Attention Network 12

Syndrome Classification Based on Multi-Graph Attention Netwo...

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12th International Conference on information technology in Medicine and Education, ITME 2022

作者： Ma, Jishun Lu, Weikai Zhou, Changen Teng, Shenghua Li, Zuoyong Shandong University of Science and Technology College of Electronic and Information Engineering Qingdao266590 China Fujian University of Technology School of Electronic Electrical Engineering and Physics Fuzhou350118 China Fujian University of Traditional Chinese Medicine Research Base of Traditional Chinese Medicine Syndrome Fuzhou350122 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

Syndrome classification is an important step in Traditional Chinese Medicine (TCM) for diagnosis and treatment. In this paper, we propose a multi-graph attention network (MGAT) based method to simulate TCM doctors to infer the syndromes. Specifically, the complex relationships between symptoms and state elements are aggregated using graph attention networks (GAT) and syndromes are classified by a multilayer perceptron (MLP). To verify the effectiveness of the model, extensive experiments are conducted on the Treatise on Febrile Diseases dataset. The experimental results show that the proposed method outperforms several typical methods in terms of accuracy, precision, recall, and F1-score. The MGAT model has high accuracy in syndrome classification and has practical application value. © 2022 IEEE.

关键词： computer aided diagnosis

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Leveraging Knowledge Distillation for Improved Event Extraction in QA Models

Leveraging Knowledge Distillation for Improved Event Extract...

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Frontiers technology of information and computer (ICFTIC), IEEE International Conference on

作者： Peiyao Zhou Zhikui Hu Kangli Zi Dawei Zhang School of Computer Science Jiangsu University of Science and Technology Zhenjiang China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Event extraction is an important task in natural language processing, and it is widely utilized in intelligence domains such as business and military for information extraction. Recently, many works have successfully transformed document-level event extraction into Question-answering (QA) tasks with remarkable results. This approach embeds event argument information within the questions, introducing prior knowledge into the process. Jin et al. highlighted the importance of high-quality QA pairs for practical QA tasks, emphasizing that constructing these pairs remains a key challenge[1]. In this study, we propose a Prompt-based question generation method to automatically generate questions containing event arguments, which converts event extraction into a QA task. We introduce an event ontology-based information retrieval module to enhance answer accuracy and select the most relevant document segments as input text. Additionally, we employ knowledge distillation to build the QA model, transferring knowledge from a large pre-trained model to a more compact and efficient one, improving the student model's performance. Experiments show that our model performs strongly in mainstream benchmarks, with 4.8 improvements on WikiEvents.

关键词： Accuracy Benchmark testing information retrieval Question generation Question answering (information retrieval) Data mining Business

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Process Calculus with Data Structure and its Model Checking Algorithm

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Physics Procedia 2012年 33卷 782-789页

作者： Qing Zheng Zi-Ning Cao Department of Computer Science and Technology Nanjing University of Aero. & Astro. Nanjing 210016 P.R. China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 P.R. China National Key Laboratory of Science and Technology on Avionics System Integration Shanghai 200233 P.R. China

Model checking is one of the most important technology for automatically verification. So this paper generally proposed a method of combining CCS and Z language, to perform model checking. It combines the advantage of current CCS and Z systems, where CCS is good at describing concurrent systems, also Z is a good tool for data structure. As a result, form the new system CCS-Z. Then we give the syntax and semantics of it, at last give the model checking *** advantage of this paper is combined data stucture on the famous system CCS, and increase the capacity of system description.

关键词： Model Checking CTL Z Language CCS

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Automated segmentation of intraretinal cystoid macular edema based on Gaussian mixture model

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Journal of Innovative Optical Health Sciences 2020年第1期3卷 35-47页

作者： Jinghong Wu Sijie Niu Qiang Chen Wen Fan Songtao Yuan Dengwang Li Shandong Key Laboratory of Medical Physics and Image Processing&Shandong Provincial Engineering and Technical Center of Light Manipulations School of Physics and Electronics Shandong Normal University Jinan 250358P.R.China School of Information Science and Engineering University of Jinan Jinan 250022P.R.China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 P.R.China Department of Ophthalmology the First Affiliated Hospital with Nanjing Medical University Nanjing 210094P.R.China

We introduce a method based on Gaussian mixture model(GMM)clustering and level-set to automatically detect intraretina fluid on diabetic retinopathy(DR)from spectral domain optical coherence tomography(SD-OCT)images in this ***,each B-scan is segmented using GMM *** original chustering results are refined using location and thickness ***,the spatial information among every consecutive five B-scans is used to search potential ***,the improved level-set method is used to obtain the accurate *** high sensitivity and accuracy demonstrated here show its potential for detection of fluid.

关键词： Gaussian mixture model level-set spectral domain optical coherence tomography(SD-OCT) segmentation

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Solving clustering problem by hybrid ant colony algorithm

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Journal of information and Computational Science 2009年第4期6卷 1839-1846页

作者： Gao, Shang Zhang, Zaiyue Zhang, Xiaoru Cao, Cungen School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang 212003 China Key Laboratory of Intelligent Information Processing Chinese Acad. of Sci. Beijing 100080 China

An optimization model of clustering problem is given in this paper. The advantages and shortages of K-Means algorithm, simulated annealing algorithm and basic ant colony algorithm are analyzed. The ant colony algorithm is then extended to use K-means clustering to seed the initial solution and the information pheromone is adjusted according to them. The hybrid ant colony algorithm is proved effective than others through examples. © 2009 Binary information Press.

关键词： Simulated annealing

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Multiscale fast correlation filtering tracking algorithm based on a feature fusion model

Multiscale fast correlation filtering tracking algorithm bas...

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作者： null null null null null null null School of Computer and Communication Engineering and Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China School of Information Science and Engineering Fujian University of Technology Fujian China Hunan Valin Lianyuan Iron & Steel Corporation Loudi China Electronics & Information School Yangtze University Jingzhou China Hunan Institute of Scientific and Technical Information Changsha China Technical Quality Department Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited Changsha China

关键词： Object tracking

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An Optimizing Parameters and Feature Selection in SVM Based on Improved Cockroach Swarm Optimization 16th

An Optimizing Parameters and Feature Selection in SVM Based ...

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16th International Conference on Intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of information technology, Applications and Tools, FITAT 2020

作者： Nguyen, Trong-The Yu, Jie Nguyen, Thi-Thanh-Tan Lai, Quoc-Anh Ngo, Truong-Giang Dao, Thi-Kien Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou China Information Technology Faculty Electric Power University Hanoi Viet Nam Department of Pattern Recognition & Image Processing Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam

ISBN: (纸本)9789813367562

This study improves a classifier of the support vector machine (SVM) by optimizing its parameters by adjusting cockroach swarm optimization (CSO). Classification system design includes data inputs, pre-process, and classification. The relief technique selects the feature subset, and the kernel parameters used as input for the system. The feature selection and classification of data sets were figured out by the combination of ICSO and SVM with its optimized parameters synchronous. Simulation results of the proposed scheme are compared with other systems, e.g., GA-SVM, PSO-SVM, and CSO-SVM, which show that the proposed project can achieve better classification accuracy than the different algorithms. It is promising that the proposed scheme can be applied to high-dimensional data classification, such as image classification, signal classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Support vector machines

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Image reconstruction of concrete based on Filtered Backprojection method using ultrasonic time of flight data

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computer Modelling and New Technologies 2014年第6期18卷 72-78页

作者： Fan, Honghui Zhu, Hongjin Han, Qingbang School of Computer Engineering Jiangsu University of Technology Changzhou Jiangsu China Sichuan Province Key Laboratory of Bridge non-destructive testing and engineering computing Zigong Sichuan China Key Laboratory of Cloud Computing Intelligent Information Processing of Changzhou City Changzhou Jiangsu China College of IOT Engineering Hohai University Jiangsu China

This research aims to recognize the defect of concrete materials using an ultrasonic computed tomography imaging technique. Filtered Backprojection method was used to reconstruct concrete images in this paper. Ultrasonic time of flight data was measured to reconstruct computer tomography images. 306 data paths were obtained in total by manual scanning for one computer tomography image. We examined the effect of the interpolation data as the density of time of flight data has a considerable effect on image quality. The feasibility of concrete reconstruction system and time of flight data interpolation were examined in detail using numerical and concrete phantoms.

关键词： Interpolation

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Building an efficient intrusion detection system based on feature selection and ensemble classifier

arXiv

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arXiv 2019年

作者： Zhou, Yuyang Cheng, Guang Jiang, Shanqing Dai, Mian School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Jiangsu Provincial Key Laboratory of Computer Network Technology Southeast University Nanjing China National Key Laboratory of Science and Technology on Information System Security Beijing China

Intrusion detection system (IDS) is one of extensively used techniques in a network topology to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning approaches from the field of machine learning have been used to increase the efficacy of IDSs, it is still a problem for existing intrusion detection algorithms to achieve good performance. First, lots of redundant and irrelevant data in high-dimensional datasets interfere with the classification process of an IDS. Second, an individual classifier may not perform well in the detection of each type of attacks. Third, many models are built for stale datasets, making them less adaptable for novel attacks. Thus, we propose a new intrusion detection framework in this paper, and this framework is based on the feature selection and ensemble learning techniques. In the first step, a heuristic algorithm called CFS-BA is proposed for dimensionality reduction, which selects the optimal subset based on the correlation between features. Then, we introduce an ensemble approach that combines C4.5, Random Forest (RF), and Forest by Penalizing Attributes (Forest PA) algorithms. Finally, voting technique is used to combine the probability distributions of the base learners for attack recognition. The experimental results, using NSL-KDD, AWID, and CIC-IDS2017 datasets, reveal that the proposed CFS-BA-Ensemble method is able to exhibit better performance than other related and state of the art approaches under several metrics. Copyright © 2019, The Authors. All rights reserved.

关键词： Data mining

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