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International Symposium on Electronic Commerce and Security

作者： Ling Zhan Jiguang Wan Peng Gu Key Laboratory of Data Storage System School of Computer Science and Technology Huazhong University of Science and Technology Wuhan Hubei China School of Electrical Engineering and Computer Science University of Central Florida Orlando FL USA

ISBN: (纸本)9780769532585

As a result of the multimedia rapid growth, there has been a huge increase in the amount of information generated and shared by people all over the world. Demand for large-scale multimedia storage system is growing rapidly. This paper describes the design and implementation of the Two-level metadata server for distributed multimedia storage system (DMSS). The DMSS divides the logical view of the stored data from the physical view. The logical view is managed by the metadata server which is called GMS (global metadata server), and the physical view is managed by a component of storage servers which is called LMS (local metadata server). Adopting LMS, each storage server can maintain its own storage resources and metadata and data independently, and can offer storage service independently. The DMSS allows the application servers to access the storage servers directly and in parallel providing very high performance.

关键词： Servers Multimedia communication Multimedia systems Least squares approximation System performance File systems Heart beat

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An Empirical Evaluation of NVM-aware File Systems on Intel Optane DC Persistent Memory Modules

An Empirical Evaluation of NVM-aware File Systems on Intel O...

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International Conference on Information Networking

作者： Guangyu Zhu Jaehyun Han Sangjin Lee Yongseok Son CAU Institute of Innovative Talent of Big Data Dept. of Computer Science and Engineering and Chung-Ang University Seoul South Korea School of Computer Science and Engineering Chung-Ang University Seoul South Korea

ISBN: (数字)9781728191010

ISBN: (纸本)9781728191027

Cloud computing arouses the need for large-scale data processing which in turn promises vigorous developments on distributed file systems. The performance of the underlying storage nodes determines the performance of the overall system. Emerging byte-addressable Non-volatile memories (NVM) are promising techniques that can greatly improve storage performance. Researchers have already investigated NVM or NVMaware file systems to take advantage of the characteristics of NVM. However, previous researchers usually perform the studies based on simulations or emulations. In this paper, we provide an empirical evaluation of NVM-aware file systems on the first commercially available byte-addressable NVM (i.e., the Intel Optane DC Persistent Memory Module (DCPMM)) We first evaluate the performance of Ext4, XFS, F2FS, Ext4-DAX, XFSDAX, and NOVA file systems on DCPMM, Optane SSD, and NVMe SSD. Then we compare them in terms of throughput and different access patterns. Second, we observe how remote NUMA node access and device mapper striping affect the performance of DCPMM. We anticipate that the experimental results and performance analysis will provide the implications on various memory and storage systems.

关键词： Performance evaluation Nonvolatile memory File systems Sockets Memory modules Throughput Performance analysis

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Impact of individual behavioral changes on epidemic spreading in time-varying networks

arXiv

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arXiv 2021年

作者： Wang, Bing Xie, Zeyang Han, Yuexing School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China

Changes in individual behavior often entangles with the dynamic interaction of individuals, which complicates the epidemic process and brings great challenges for the understanding and control of the epidemic. In this work, we consider three kinds of typical behavioral changes in epidemic process that is, self-quarantine of infected individuals, self-protection of susceptible individuals, and social distancing between them. We connect the behavioral changes with individual’s social attributes by the activity-driven network with attractiveness. A mean-field theory is established to derive an analytical estimate of epidemic threshold for SIS models with individual behavioral changes, which depends on the correlations between activity, attractiveness and the number of generative links in the susceptible and infected states. We find that individual behaviors play different roles in suppressing the epidemic. Although all the behavioral changes could delay the epidemic by increasing the epidemic threshold, self-quarantine and social distancing of infected individuals could effectively decrease the epidemic size. In addition, simultaneous changes in these behaviors and the timing of implement of them also play a key role in suppressing the epidemic. These results provide helpful significance for understanding the interaction of individual behaviors in the epidemic process. Copyright © 2021, The Authors. All rights reserved.

关键词： Time varying networks

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Accessible Authentication Using Keyword Search through Homomorphic Encryption 1

Accessible Authentication Using Keyword Search through Homom...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Vekariya, Vipul Manjunath, R. Gill, Jasmeen Gopianand, M. Kumar, Avneesh Jha, Amaresh Parul University Department of CSE PIET Gujarat Vadodara India Department of Data Science & Mathamatics Bangalore India RIMT University Department of CSE Godindgarh Mandi India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India Galgotias University Department of Computer Application Uttar Pradesh Greater Noida India School of Modern Media UPES Dehradun India

ISBN: (纸本)9781665456876

data storage presents substantial issues due to concerns about data security and privacy. In this study, we provide a search-based, interoperable, verified public key cryptography system. The searchability is significantly improved by our recommended method, which allows the cloud servers to create invert crypto indexes while needing a query trap and leveraging asymmetric cryptography developed by van Vries, Miller, Showing different, and Vaikuntanathan. Contrarily, the recommended method offers a brand-new permitted dataset built on the inversion decryption scheme and shows how it may be used to evaluate the thoroughness and correctness of serps. Additionally, using the provided method, many users may do encrypted searches on cryptography. Finally, it is shown that the offered technique is secure utilizing the approximate-GCD problem. The experiment's results showed that the proposed technique had less computational complexity than the methods currently in use. © 2022 IEEE.

关键词： Public key cryptography

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MRI Brain Tumor Segmentation using Cuckoo-based Dimensionality Reduction and Ensemble Convolutional Neural Network

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Open Biomedical engineering Journal 2024年第spl1期18卷

作者： Shenbagarajan, A. Ragavan, K. Shenbagalakshmi, G. Venkatesh, R. Department of Artificial Intelligence and Data Science Mepco Schlenk Engineering College School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering Mepco Schlenk Engineering College Department of Computer Science and Engineering Ramco Institute of Technology

Background: Brain tumor identification at an early stage is a challenging task that increases the lifetime of patients. Specialists' conclusions on recognizing brain tumors are difficult, as they are based on their theoretical knowledge. It takes a huge amount of time to diagnose the patient. Recently, research has suggested an automated technique that is dependent on convolutional neural networks. Medical pictures are a set of accumulations of data that are hard to store and process, expending broad registering time. The decreased infiltrated systems are normally utilized as an information pre-preparing venture to make the picture information less mind-boggling with the goal that high-dimensional information may be recognized by a fitting and apt low-dimensional portrayal. Objective: This study proposes an optimization-based dimensionality reduction and brain tumor segmentation using ensemble convolutional neural networks in MRI images to enhance disease diagnosis and extend healthcare accessibility. Methods: Cuckoo-based dimensionality reduction and Ensemble CNN are proposed to segment the tumor region. The cuckoo-based optimization search technique is used to reduce the dimensionality of MRI Brain Images to perform better segmentation. The proposed technique is evaluated on the BRATS database, which contains two datasets: the Leaderboard and Challenge datasets. The outcomes are estimated utilizing the Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), and Sensitivity. Results: The Experimental analysis shows promising results on the leaderboard dataset and the BRATS Challenge dataset. The proposed method outperformed the leaderboard dataset with a greater 91% Dice Similarity Coefficient (DCE), 95% Positive Predictive Value, and 87% Sensitivity of High-Grade Glioma (HGG). Seventy-two percent Dice Similarity Coefficient (DCE), 70% Positive Predictive Value, and 93% Sensitivity of Low-Grade Glioma (LGG). 88% Dice Similarity Coefficient (DCE), 90% Po

关键词： Dimensionality reduction Ensemble convolutional neural networks (ECNN) High-grade glioma Low-grade glioma MRI Segmentation

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Impact of individual behavioral changes on epidemic spreading in time-varying networks

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Physical Review E 2021年第4期104卷 044307-044307页

作者： Bing Wang Zeyang Xie Yuexing Han School of Computer Engineering and Science Shanghai University Shanghai 200444 P.R. China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai 200444 P.R. China

Changes in individual behavior often entangle with the dynamic interaction of individuals, which complicates the epidemic process and brings great challenges for the understanding and control of the epidemic. In this work, we consider three kinds of typical behavioral changes in epidemic process, that is, self-quarantine of infected individuals, self-protection of susceptible individuals, and social distancing between them. We connect the behavioral changes with individual's social attributes by the activity-driven network with attractiveness. A mean-field theory is established to derive an analytical estimate of epidemic threshold for susceptible-infected-susceptible models with individual behavioral changes, which depends on the correlations between activity, attractiveness, and the number of generative links in the susceptible and infected states. We find that individual behaviors play different roles in suppressing the epidemic. Although all the behavioral changes could delay the epidemic by increasing the epidemic threshold, self-quarantine and social distancing of infected individuals could effectively decrease the epidemic outbreak size. In addition, simultaneous changes in these behaviors and the timing of implement of them also play a key role in suppressing the epidemic. These results provide helpful significance for understanding the interaction of individual behaviors in the epidemic process.

关键词： Epidemic prevalence Social networks Evolving network models Monte Carlo methods Spreading models Threshold & cascade models

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MHRNet: A Multi-stage Image Deblurring Approach with High-Resolution Representation Learning

MHRNet: A Multi-stage Image Deblurring Approach with High-Re...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wenfu Liu Junjie Peng Haochen Yuan Luming Zhang Zesu Cai School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China

Image deblurring is a classic task in computer vision that aims to recover clean images from blurred images and is widely used in surveillance, medical imaging and so on. Existing methods typically apply a multi-stage encoder-decoder architecture to learn features at different scales, and they have achieved remarkable results. However, these methods usually map the input to a low-resolution (LR) image to expand its receptive field and then gradually reverse this image to the original resolution. Although these approaches obtain rich semantic information via spatial resolution reduction, they lose a large amount of spatial information, which is essential for image deblurring and extremely difficult to recover. To solve this problem, we propose a novel multi-stage model with high-resolution (HR) representation learning (MHRNet). In this model, HR representations are always preserved to reduce the loss of spatial information, and the features across all the scales at each resolution are fused to obtain spatially accurate and semantically rich features. Extensive experiments conducted on the GoPro, HIDE and RealBlur datasets demonstrate that MHRNet outperforms the state-of-the-art (SOTA) methods and reaches a 33.99-dB peak signal-to-noise ratio (PSNR) on the GoPro dataset.

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Sentiment Analysis in Social Media Using Deep Learning Techniques

Sentiment Analysis in Social Media Using Deep Learning Techn...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Babu, M. Shinde, Pravin Vishnu Agrawal, Neha Bhaskar, M. Vijaya Hemabala, K. Priya, Sweta Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai600124 India Shah and Anchor Kutchhi Engineering College Affiliated to University of Mumbai Department of Artificial Intelligence & Data Science Maharashtra India Maharaja Agrasen Institute of Technology Department of It New Delhi110085 India SriVenkateswara Institute of Technology Department of Computer Science & Engineering Andhra Pradesh Anantapur India Department of Freshmen Tiruvathi517502 India Amity University Department of Amity School of Communication University Patna801503 India

ISBN: (纸本)9798331543617

This work offers a thorough investigation of sentiment analysis in social media using deep learning techniques. Understanding stoner sentiment has become essential for colourful operations like marketing, public opinion research, and recommendation algorithms due to the rapid expansion of social media networks. Deep learning approaches have emerged as crucial tools for sentiment analysis tasks because of their capacity to extract intricate patterns from massive datasets. The usefulness of deep learning models, such as convolutional neural networks (RNNs), and intermittent neural networks (RNNs), and their variations, in extracting complex sentiment information from social media textbooks is investigated in this study. Additionally, the study looks at model infrastructures, point representations, and pre-processing techniques to improve sentiment analysis performance. The results show how effective deep learning techniques are in extracting sentiment directly from various social media posts, opening the door for more sophisticated sentiment-apprehensive digital processes © 2024 IEEE.

关键词： Adversarial machine learning

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OCI Runtime Comparison and Analysis Study 5

OCI Runtime Comparison and Analysis Study

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5th IEEE International Conference on Artificial Intelligence and Knowledge engineering, AIKE 2022

作者： Jeon, Sung Hwan Shim, Simon Chung, Haejin Nah, Yunmook Dept. of Data Science Graduate School Dankook University Yongin-si Korea Republic of Dept. of Computer Engineering San Jose State University San Jose United States Cha Mirisa College Duksung Women's University Seoul Korea Republic of Dept. of Applied Computer Eng. Dankook University Yongin-si Korea Republic of

ISBN: (数字)9781665471206

ISBN: (纸本)9781665471206

Containers are one of the technologies required for cloud services. Because containers use resources directly on the host, they are lighter and faster than traditional virtual machines. However, it has the disadvantage of poor security and isolation. To solve this problem, security-oriented runtimes such as Kata, gVisor, and Firecracker have emerged. Each runtime is created differently, so performance and resource usage are different. In this study, performance and evaluation of each lowlevel runtime are conducted. © 2022 IEEE.

关键词： Containers

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Deep policy hashing network with listwise supervision

arXiv

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arXiv 2019年

作者： Wang, Shaoying Lai, Haijiang Yang, Yifan Yin, Jian School of Electronics and Information Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Data and Computer Science Sun Yat-Sen University Guangzhou China

Deep-networks-based hashing has become a leading approach for large-scale image retrieval, which learns a similarity-preserving network to map similar images to nearby hash codes. The pairwise and triplet losses are two widely used similarity preserving manners for deep hashing. These manners ignore the fact that hashing is a prediction task on the list of binary codes. However, learning deep hashing with listwise supervision is challenging in 1) how to obtain the rank list of whole training set when the batch size of the deep network is always small and 2) how to utilize the listwise supervision. In this paper, we present a novel deep policy hashing architecture with two systems are learned in parallel: A query network and a shared and slowly changing database network. The following three steps are repeated until convergence: 1) the database network encodes all training samples into binary codes to obtain whole rank list, 2) the query network is trained based on policy learning to maximize a reward that indicates the performance of the whole ranking list of binary codes, e.g., mean average precision (MAP), and 3) the database network is updated as the query network. Extensive evaluations on several benchmark datasets show that the proposed method brings substantial improvements over state-of-the-art hashing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Binary codes

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