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3rd International Conference on Intelligent, Interactive Systems and Applications, IISA2018

作者： Wang, Gang Zhang, Xiaodong Zhu, Chengzhi Wang, He Peng, Lin Xu, Min Global Energy Interconnection Research Institute Co. Ltd NanjingJiangsu210003 China State Grid Key Laboratory of Information and Network Security Nanjing210003 China State Grid Corporation of China Beijing100031 China State Grid Zhejiang Electric Power Co. Ltd Hangzhou310007 China

ISBN: (纸本)9783030028039

The development of information display interaction from planar 2D to stereoscopic 3D has become increasingly mainstream. In the future, there will be more and more 3D content creators and consumers. The core model engine is standardized, lightweight, cross-platform and 3D-based. The interactive content library has become a hot topic in the IT industry. This paper studies the mainstream model file conversion compression technology based on the characteristics of the power grid business, and realizes the compression and weight reduction of the three-dimensional model. In this paper, compressive sensing-based 3D model compression method is used to achieve rapid compression of server-side models, which can greatly reduce the model space and increase the loading speed of the model. Compressed sensing compresses data at the beginning of data acquisition. When sampling, it does not follow the frequency twice as high as the maximum cut-off frequency of the initial signal as described by Shannon Nyquist Theorem, which greatly reduces the amount of data sampled, and does not require subsequent data recompression, which can greatly increase the model’s compression efficiency and quality. First, get the original model and split the model data into material data, animation data, grid data, node tree data, and map data. According to the model data classification, compression-sensing algorithm based on the business characteristics of power grid services was used to compress the model for sampling. Get a discrete representation of the model after sampling. Obtain the compressed model, and combine the material data, animation data, grid data, node tree data, and map data according to the characteristics of the power business and associate it with the unique identifier of the business model. The compression of mainstream model files based on the characteristics of power grid services can effectively improve the operational efficiency of 3D models. The research results lay a solid fou

关键词： Efficiency

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Multiple Spatial information Weighted Fuzzy Clustering for Image Segmentation

Multiple Spatial Information Weighted Fuzzy Clustering for I...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiangdao Liu Jin Zhou Hui Jiang C. L. Philip Chen Tong Zhang Lin Wang Shiyuan Han Yuehui Chen Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan China Development and Test Center Chinabond Fintech Information Technology Co. Ltd. Beijing China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

For image segmentation, fuzzy clustering methods with single spatial information cannot ensure robustness to the image corrupted by different noises. In this paper, to figure out this problem, we propose a multiple spatial information weighted fuzzy clustering method, in which the original pixel intensity and its two spatial information, the mean and median of neighbors within a local window, are combined with different weights to obtain precise segmentation results of noise images. And the entropy-regularized method is employed to optimize the weight of each term to handle the images with different noise. What's more, the kernelization of the proposed method is presented to relief the impact of outliers. It is worth noting that our methods can be further extended by combining with other spatial information. Experiments on synthetic images and natural images show the superiority and efficiency of the proposed methods.

关键词： Image segmentation Clustering methods Scalability Conferences Robustness Entropy Cybernetics

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Deep Unsupervised Workload Sequence Anomaly Detection with Fusion of Spatial and Temporal Features in the Cloud

Deep Unsupervised Workload Sequence Anomaly Detection with F...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Mengqing Wang Zhihua Zhang Jialei Liu Li Duan Chunhong Liu School of Computer and Information Engineering Henan Normal University Xinxiang China School of Software Engineering Anyang Normal University Anyang Henan China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Engineering Lab of Intelligence Business & Internet of Things Xinxiang Henan China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

The abnormal detection of the workload sequence is designed to achieve intelligent operation and management of the cloud platform to improve operational efficiency. Due to the diversity of workload sequence variation patterns in the large-scale cloud, it is difficult for the traditional abnormal detection methods to extract features effectively, which leads to detecting anomalies inaccurately. In this paper, a deep unsupervised anomaly sequence detection model with fusion of spatial and temporal features of workload sequence (TS-DeepSVDD) is proposed. To extract the spatial and temporal features of the workload sequence, the model introduces convolutional recurrent neural network (CRNN) to improve network architecture with deep support vector data description (DeepSVDD). First, a convolutional neural network (CNN) module extracts the spatial features of the workload sequence. Second, for the acquired spatial features vectors, the bidirectional long short-term memory (BiLSTM) module extracts temporal features. Finally, the features vectors training support vector data description (SVDD) classifier fuses with the deep features of the workload sequence spatial and temporal features. TS-DeepSVDD extracts the deep features of the workload sequence from the spatial and temporal dimensions. It achieves a comprehensive description of the inherent law of the workload sequence, increasing the differentiation between normal and abnormal sequences. The simulation dataset and Google trace dataset are used respectively for verification. The results show TS-DeepSVDD can detect different abnormal sequences more accurately than the traditional unsupervised anomaly detection methods.

关键词： Training Cloud computing Recurrent neural networks Support vector machine classification Feature extraction Task analysis Anomaly detection

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A Smart Content Caching and Replacement Scheme for UAV-Assisted Fog Computing network

A Smart Content Caching and Replacement Scheme for UAV-Assis...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Xujie Li Jichang Shen Ying Sun Ziya Wang Xuedong Zheng College of Computer and Information Engineering Hohai University Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Key Lab for Wireless Sensor network and Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China Nanjing China-Spacenet Satellite Telecom Co.Ltd China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

For unmanned aerial vehicle (UAV)-assisted fog computing system, content caching is an effective way to reduce network delay. How to smartly sense the future requirement of the user equipment and cache the contents in advance is a big challenge. Meanwhile, the impact of cache content size and other related factors on cache performance are often overlooked, which results in low cache hit ratio. Therefore, this paper proposes a smart caching and caching replacement scheme based on valuation function of content caching for UAV-assisted fog computing network. Firstly, the effect of the content popularity, content size and available cache storage space on system performance is analysed. Then the valuation function combing these factors is defined. Based on the presented valuation function, a smart content caching and caching replacement scheme for UAV-assisted fog computing network is proposed. Finally, the simulation results validate the effectiveness of the proposed scheme. The scheme can be applied into the design the deployment of the UVA-assisted fog computing network.

关键词： Edge computing Cost accounting Unmanned aerial vehicles Performance evaluation Delays Indexes Quality of service

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Lightweight design of SM4 algorithm and realization of threshold scheme

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Journal of Physics: Conference Series 2021年第1期1871卷

作者： B W Chen X Xia Q M Liang W D Zhong Academy of cryptographic engineering Engineering University of PAP Xi'an Shanxi 710086 China Academy of information engineering Engineering University of PAP Xi'an Shanxi 710086 China Key Laboratory of PAP of Network and Information Security of Engineering University of PAP Xi'an Shanxi 710086 China

Aiming at the lightweight design of SM4 cryptographic algorithm, a lightweight cryptographic algorithm scheme is proposed and implemented in hardware. Based on the work of Li et al., a 4-bit S-box is selected. The original 8-bit look-up table S-box in the SM4 algorithm is replaced with a 4-bit cyclic shift S-box, and the same circuit structure is used to save resources in hardware implementation. A threshold scheme based on the principle of secret sharing is proposed to improve the overall security of the scheme against side-channel attacks. The input information is divided into three groups, and all operations are converted from GF(24) to GF(22) through a multiplier and a squarer, which further reduces the circuit consumption and improves the security of the algorithm. In the realization of the threshold scheme, virtual values are intro-duced to ensure that the scheme meets the uniformity, and the decomposition method is used in the inverter to re-duce the number of operations and the proportion of space. After theoretical deduction, the threshold scheme as a whole has the ability to resist first-order and second-order DPA attacks. The overall hardware implementation of the program uses a total of 2803 logic elements, which proves that it achieves the goal of lightweight while ensuring safety.

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Constructing Benchmarks for Supporting Explainable Evaluations of Static Application security Testing Tools

Constructing Benchmarks for Supporting Explainable Evaluatio...

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Theoretical Aspects of Software Engineering (TASE)

作者： Gaojian Hao Feng Li Wei Huo Qing Sun Wei Wang Xinhua Li Wei Zou Institute of Information Engineering Chinese Academy of Sciences Key Laboratory of Network Assessment Technology Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences

When evaluating Static Application security Testing (SAST) tools, benchmarks based on real-world softwares are considered more representative than synthetic micro benchmarks. Generated from real-world software, the test cases in such kind of benchmarks usually contain multiple syntactic features which affect the vulnerability detection results reflecting SAST tools' capabilities in real-world settings. However, most existing benchmarks based on real-world software pay little attention to these syntactic features so that only limited information about the capabilities of SAST tools can be obtained from the evaluation results. In this paper, we provide a method of constructing benchmarks and evaluating SAST tools, which leverages the syntactic features to support the evaluation to be more explainable. To demonstrate the effectiveness, we applied our method to the benchmark built by Misha Zitser et al., generated 10 groups of test cases, and evaluated 2 SAST tools with them. The result shows that, with the benchmark constructed by our method, the evaluation could be more explainable which helps us to gain more information about the SAST tools' capabilities of vulnerability detection.

关键词： Benchmark testing Tools Syntactics Feature extraction Software Application security

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Batch face alignment using a low-rank Gan

arXiv

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arXiv 2019年

作者： Huang, Jiabo Xie, Xiaohua Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China Guangdong Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

This paper studies the problem of aligning a set of face images of the same individual into a normalized image while removing the outliers like partial occlusion, extreme facial expression as well as significant illumination variation. Our model seeks an optimal image domain transformation such that the matrix of misaligned images can be decomposed as the sum of a sparse matrix of noise and a rank-one matrix of aligned images. The image transformation is learned in an unsupervised manner, which means that ground-truth aligned images are unnecessary for our model. Specifically, we make use of the remarkable non-linear transforming ability of generative adversarial network(GAN) and guide it with low-rank generation as well as sparse noise constraint to achieve the face alignment. We verify the efficacy of the proposed model with extensive experiments on real-world face databases, demonstrating higher accuracy and efficiency than existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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An Enhanced Feature Selection Approach based on Mutual information for Breast Cancer Diagnosis

An Enhanced Feature Selection Approach based on Mutual Infor...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Nawel Zemmal Nabiha Azizi Amel Ziani Nacer Eddine Benzebouchi Monther Aldwairi Labged Laboratory Badji Mokhtar University Annaba Algeria Computer science department Lri laboratory: Information Research Laboratory Annaba Algeria Department of Network Engineering and Security Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9781728131573

Breast cancer is the most feared disease in the female population. Early detection plays an important role to improve prognosis. Mammography is the best examination for the detection of breast cancer. However, in some cases, reading mammograms is difficult for radiologists. For this reason, several researches have been conducted to develop computer Aided Diagnosis tools (CAD) for this disease which aims to interpret mammography images. This paper investigates a new CAD system based on Transductive scheme and Mutual information for breast abnormalities diagnosis. In the proposed method, a feature vector contains a combination of two features extraction method: Grey Level Co-occurrence Matrix and local Binary Pattern. In the next step, a novel scheme combining Mutual information and Correlation-based feature selection was applied for selecting the most relevant features. Finally, the classification was achieved using a Transductive Support Vector Machine classifier. The effectiveness of the proposed CAD is examined on the DDSM dataset using classification accuracy, recall and precision. Experimental results demonstrate that the proposed CAD system is clinically significant and can be used to classify the abnormalities of the breast.

关键词： Breast cancer computer aided diagnosis (CAD) Transductive support vector machine (TSVM) Semi supervised learning (SSL) Mutual information Correlation-based feature selection Breast Cancer computer aided design system breast neoplasm diagnosis Feature extraction Digital Mammography Eigenvector malformations Breast

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Data Storage Protection of Community Medical Internet of Things 7th

Data Storage Protection of Community Medical Internet of Thi...

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7th EAI International Conference on Cloud Computing, CloudComp 2016 and 1st EAI International Conference on security and Privacy in New Computing Environments, SPNCE 2016

作者： Zhang, Ziyang Chen, Fulong Ye, Heping Zhu, Junru Zhang, Cheng Liu, Chao Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal University 189 Jiuhua South Road WuhuAnhui241002 China

ISBN: (纸本)9783319696041

With the improvement of people’s living standard, people have put forward higher requirements on medical services. The effective combination of the traditional community medical systems and the modern Internet of things technologies can help to build a community medical Internet of things, which involves a large number of important information for health care and patient staff, and these information face the risk of privacy disclosure and information damage. From the point of view of data storage, we proposes a data storage protection method for preserving privacy data in the community medical Internet of things. Through analyzing the data integrity and security of the practical scheme, it is proved that the medical data can be protected effectively in the process of storage. © 2018, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

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WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma

arXiv

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arXiv 2022年

作者： Han, Chu Pan, Xipeng Yan, Lixu Lin, Huan Li, Bingbing Yao, Su Lv, Shanshan Shi, Zhenwei Mai, Jinhai Lin, Jiatai Zhao, Bingchao Xu, Zeyan Wang, Zhizhen Wang, Yumeng Zhang, Yuan Wang, Huihui Zhu, Chao Lin, Chunhui Mao, Lijian Wu, Min Duan, Luwen Zhu, Jingsong Hu, Dong Fang, Zijie Chen, Yang Zhang, Yongbing Li, Yi Zou, Yiwen Yu, Yiduo Li, Xiaomeng Li, Haiming Cui, Yanfen Han, Guoqiang Xu, Yan Xu, Jun Yang, Huihua Li, Chunming Liu, Zhenbing Lu, Cheng Chen, Xin Liang, Changhong Zhang, Qingling Liu, Zaiyi The Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The Department of Pathology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi541004 China The Department of Pathology Guangdong Provicial People's Hospital Ganzhou Hospital Ganzhou China The College of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The School of Computer Science and Engineering South China University of Technology Guangzhou510640 China The Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China The School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education Research Institute of Beihang University in Shenzhen Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China The Department of Radiology Guangzhou First People's Hospital Hospital of South China University of Technology Guangzhou510180 China The Zhejiang Dahua Technology Co. Ltd Zhejiang310053 China The School of Biomedical Engineering University of Science and Technology of China Hefei230022 China The Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Electronic and Computer Engineering & Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong The School of Automation Science and Engineering Xi'An Jiaotong University Xian710049 Chi

Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential value of the histopathology images can promote precision medicine in oncology. Tissue segmentation is the basic upstream task of histopathology image analysis. Existing deep learning models have achieved superior segmentation performance but require sufficient pixel-level annotations, which is time-consuming and expensive. To enrich the label resources of LUAD and to alleviate the annotation efforts, we organize this challenge WSSS4LUAD to call for the outstanding weakly-supervised semantic segmentation (WSSS) techniques for histopathology images of LUAD. Participants have to design the algorithm to segment tumor epithelial, tumor-associated stroma and normal tissue with only patch-level labels. This challenge includes 10,091 patch-level annotations (the training set) and over 130 million labeled pixels (the validation and test sets), from 87 WSIs (67 from GDPH, 20 from TCGA). All the labels were generated by a pathologist-in-the-loop pipeline with the help of AI models and checked by the label review board. Among 532 registrations, 28 teams submitted the results in the test phase with over 1,000 submissions. Finally, the first place team achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919). According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS. Cutmix data augmentation has been widely adopted to generate more reliable samples. With the success of this challenge, we believe that WSSS approaches with patch-level annotations can be a complement to the traditional pixel annotations while reducing the annotation efforts. The entire dataset has been released to encourage more researches on computational pathology in LUAD and more novel WSSS techniques. © 2022, CC BY.

关键词： Semantic Segmentation

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