检索结果-内蒙古大学图书馆

IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Mingxuan Li Dongdong Huo Chao Liu Han Wang Yazhe Wang Yu Wang Ping Zou Yandong Li Zhen Xu Chinese Academy of Sciences Institute of Information Engineering Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Beijing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

As permissioned chain technology is getting more and more important in blockchain technology, Hyperledger Fabric-one of its representative-has received extensive attention. However, previous works just focused on plaintext transmission and made TPS optimization that cannot be applied in ciphertext transmission situation, which is usually required on permissioned chain under actual circumstances. This paper will be the first to focus on private data transaction flow to optimize Fabric TPS, without changing the normal call to the Fabric SDK (Software Development Kit). We modify the process of writing data to the transient database after the completion of the simulated transaction, optimize the data structure in transient database, and simplify the process of verifying hashes. At the same time, professional SmartContract experiment cases and experiment plans are designed to simulate large-scale commercial applications, which are used to measure the TPS before and after optimization in the Fabric private data transaction mode. The results show that the optimized Fabric model can greatly promote the performance of the private data transaction flow.

关键词： Peer-to-peer computing Fabrics Blockchain Distributed ledger Optimization Organizations Transient analysis

来源：评论

学校读者我要写书评

暂无评论

An Acute Kidney Injury Prediction Model Based on Ensemble Learning Algorithm

An Acute Kidney Injury Prediction Model Based on Ensemble Le...

引用

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

作者： Yuan Wang Yake Wei Qin Wu Hao Yang Jingwei Li College of Computer Science and Information Engineering Tianjin University of Science and Technology Population and Precision Health Care (Tianjin) Ltd Center for Cyber Security University of Electronic Science and Technology of China Department of critical care medicine Sichuan University State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences

Acute Kidney Injury (AKI), a common disease in Intensive Care Unit (ICU) patients, is related to high cost, morbidity and mortality. The early prediction of AKI is critical for improving patients' outcome. However, sparse clinical data and highly imbalanced dataset bring great challenges to AKI prediction. Among existed machine learning algorithms, ensemble learning often stands out with its good performance on complex classification problems. Boosting algorithm is one of the best ensemble learning algorithms. Therefore, we develop a prediction model based on it aiming to forecast AKI ahead 24 hours and 48 hours. We also adopt the way that is analogous to text modeling transforming heterogeneous time series reflecting patients' medication information into multidimensional vector to overcome problem brought by sparse data. Additionally, since the imbalanced dataset would affect predictive performance, we artificially construct a more balanced dataset based on the original dataset to initialize the model. According to the experimental results, our model works well on the ICU patients dataset (AUC 24h ahead: 0.80 48h ahead 0.77). We also verify that medication information improves model performance (24h ahead: AUC 0.75 to 0.80 48h ahead: AUC 0.75 to 0.77) and find the optimal ratio of number between classes when initializing model for AKI prediction.

关键词： Predictive models Sensitivity Prediction algorithms Drugs Feature extraction Machine learning algorithms Kidney

来源：评论

学校读者我要写书评

暂无评论

SLBot: A serverless botnet based on service flux 3

SLBot: A serverless botnet based on service flux

引用

3rd IEEE International Conference on Data Science in cyberspace, DSC 2018

作者： Wu, Di Fang, Binxing Yin, Jie Zhang, Fangjiao Cui, Xiang Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Cyberspace Institute of Advanced Technology Guangzhou University China Institute of Electronic and Information Engineering of UESTC in Guangdong China School of Cyberspace Security Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781538642108

Today, botnet continue to be a significant threat to Internet, and is still responsible for most large scale cyber-attacks. An obvious strategy for preventing these activities is detecting Command-and-Control (C&C) servers of the botnet. Once C&C servers have been discovered, the botnet would face the risk of the whole structure's failure, and defenders can easily trace the owner. Recently years, attackers have begun to exploit social network websites (e.g., ***) as their C&C infrastructures, which turns out to be quite stealthy but still can be detected through the application-centric approach, such as monitoring the behavior of particular usernames. In this paper, we assess the efficacy and feasibility of abusing multiple different public services to construct botnets, and propose a serverless C&C channel model using a novel strategy named Service Flux, which contains three subchannels: addressing channel (AC), command channel (CC), and upload channel (UC). We implement the model based botnet prototype named SLBot, and evaluate the resilience and efficiency of it. © 2018 IEEE.

关键词： Botnet

来源：评论

学校读者我要写书评

暂无评论

Erratum to:Recent Advances of Chimp Optimization Algorithm:Variants and Applications

引用

Journal of Bionic engineering 2024年第3期21卷 1618-1618页

作者： Mohammad Sh.Daoud Mohammad Shehab Laith Abualigah Mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva Mohammad A.Alia Raed Abu Zitar College of Engineering Al Ain University112612 Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman 11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq 25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied Science Research Center Applied Science Private UniversityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio′n Basada en la Informacio′n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Erratum to:J Bionic Eng https://***/10.1007/*** this article the statement in the Funding information section was incorrectly given as‘22UQU4361183DSR03’and should have read‘23UQU4361183DSR03’.

关键词： statement section correctly

来源：评论

学校读者我要写书评

暂无评论

Dense attention fluid network for salient object detection in optical remote sensing images

arXiv

引用

arXiv 2020年

作者： Zhang, Qijian Cong, Runmin Li, Chongyi Cheng, Ming-Ming Fang, Yuming Cao, Xiaochun Zhao, Yao Kwong, Sam The Department of Computer Science City University of Hong Kong Hong Kong The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The School of Computer Science and Engineering Nanyang Technological University Singapore The College of Computer Science Nankai University Tianjin300071 China School of Information Technology Jiangxi University of Finance and Economics NanchangJiangxi330032 China The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains an open and challenging problem. In this paper, we propose an end-to-end Dense Attention Fluid Network (DAFNet) for SOD in optical RSIs. A Global Context-aware Attention (GCA) module is proposed to adaptively capture long-range semantic context relationships, and is further embedded in a Dense Attention Fluid (DAF) structure that enables shallow attention cues flow into deep layers to guide the generation of high-level feature attention maps. Specifically, the GCA module is composed of two key components, where the global feature aggregation module achieves mutual reinforcement of salient feature embeddings from any two spatial locations, and the cascaded pyramid attention module tackles the scale variation issue by building up a cascaded pyramid framework to progressively refine the attention map in a coarse-to-fine manner. In addition, we construct a new and challenging optical RSI dataset for SOD that contains 2,000 images with pixel-wise saliency annotations, which is currently the largest publicly available benchmark. Extensive experiments demonstrate that our proposed DAFNet significantly outperforms the existing state-of-the-art SOD competitors. https://***/ rmcong/DAFNet_TIP20 © 2020, CC BY-NC-SA.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

iShake: Imitation-Resistant Secure Pairing of Smart Devices via Shaking

iShake: Imitation-Resistant Secure Pairing of Smart Devices ...

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Congcong Shi Lei Xie Chuyu Wang Peicheng Yang Yubo Song Sanglu Lu State Key Laboratory for Novel Software Technology Nanjing University State Grid Key Laboratory of Information & Network Security Global Energy Interconnection Research Institute co. Ltd School of Cyber Science and Engineering Southeast University

ISBN: (纸本)9781728125848

In conventional device-to-device (D2D) communication through wireless channels, it is an essential demand to authenticate with each other and establish spontaneous secure connections among the smart devices. In this paper, we propose an imitation-resistant mutual authentication and key generation framework for smart devices, by shaking these devices together. According to the multi-sensor data collected from smart devices, these devices are able to authenticate each other and generate a unique and consistent symmetric key if and only if they are shaken together. We have conducted comprehensive experimental study on shaking various devices, illustrated several novel observations and extracted some important clues for efficient key generation. We propose a series of novel techniques to make the key generation robust to noise and privacy-preserving, and generate highly distinctive and fully randomized symmetric keys among these devices. Realistic experiment results indicate that our solution is able to authenticate with each other and generate the symmetric keys with high accuracy and time-efficiency.

关键词： Symmetric key generation Mutual authentication Smart device Shaking Imitation resistant Vibration mutual authentication Smart devices Secure Sensor Data

来源：评论

学校读者我要写书评

暂无评论

Dynamical Interaction Between Information and Disease Spreading in Populations of Moving Agents

引用

Computers, Materials & Continua 2018年第10期57卷 123-144页

作者： Lingling Xia Bo Song Zhengjun Jing Yurong Song Liang Zhang Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China College of Telecommunications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing210003China Global Big Data Technologies Centre University of Technology SydneySydney2007Australia College of Computer Engineering Jiangsu University of TechnologyChangzhou213001China College of Automation Nanjing University of Posts and TelecommunicationsNanjing210003China

Considering dynamical disease spreading network consisting of moving individuals,a new double-layer network is constructed,one where the information dissemination process takes place and the other where the dynamics of disease spreading *** the basis of Markov chains theory,a new model characterizing the coupled dynamics between information dissemination and disease spreading in populations of moving agents is established and corresponding state probability equations are formulated to describe the probability in each state of every node at each *** Carlo simulations are performed to characterize the interaction process between information and disease spreading and investigate factors that influence spreading *** results show that the increasing of information transmission rate can reduce the scale of disease spreading in some *** infection period and strengthening consciousness for self-protection by decreasing individual’s scope of activity both can effectively reduce the final refractory density for the disease but have less effect on the information *** addition,the increasing of vaccination rate or decreasing of long-range travel can also reduce the scale of disease spreading.

关键词： Complex networks Markov chains theory interaction process spreading dynamics double-layer network

来源：评论

学校读者我要写书评

暂无评论

Symmetrical slot line with a dielectric insert in the slot 10

Symmetrical slot line with a dielectric insert in the slot

引用

10th All-Russian Conference on Irreversible Processes in Nature and technology

作者： Klyuev, S.B. Nefyodov, E.I. Potapov, A.A. RPC Istok 2A Vokzalnaya Str Fryazino Moscow Region141190 Russia Fryazino Branch of kotel'Nikov Institute of Radio Engineering and Electronics of RAS Vvedenskiy Sq. Fryazino Moscow Region1 141195 Russia Kotel'Nikov Institute of Radio Engineering and Electronics of RAS Mokhovaya St. Moscow125009 Russia College of Information Science and Technology / College of Cyber Security Guangzhou510632 China Cooperative Chinese-Russian Laboratory of Informational Technologies and Signals Fractal Processing Guangzhou510632 China

The electrodynamic properties of a symmetric slot line with a dielectric insert are investigated. A numerical analysis of the transmission line is performed using the finite element method. The dispersion matrix elements of the transmission line are presented. It is shown that the model of a symmetric slot line with a dielectric insert can extend the operating frequency band more than 2 times. © Published under licence by IOP Publishing Ltd.

关键词： Electric lines

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to “A novel trust-based security and privacy model for Internet of Vehicles using encryption and steganography” CAEE, Volume 102, September 2022, 108205

引用

Computers and Electrical engineering 2022年 103卷

作者： Manjari Singh Rathore M. Poongodi Praneet Saurabh Umesh Kumar Lilhore Sami Bourouis Wajdi Alhakami Jude Osamor Mounir Hamdi Oriental Institute of Technology Bhopal Madhya Pradesh India Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Mody University of Science and Technology Lakshmangarh Rajasthan India KIET Group of Institutions Delhi-NCR Ghaziabad Uttar Pradesh India Department of Information Technology College of Computers and Information Technology Taif University P.O. Box 11099 Taif 21944 Saudi Arabia Department of Cyber Security and Networks Glasgow Caledonian University Cowcaddens Road Glasgow Scotland G4 0BA United Kingdom

来源：评论

学校读者我要写书评

暂无评论

GraphNAS: Graph neural architecture search with reinforcement learning

arXiv

引用

arXiv 2019年

作者： Gao, Yang Yang, Hong Zhang, Peng Zhou, Chuan Hu, Yue Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence University of Technology Sydney Australia Ant Financial Services Group Hangzhou China

Graph Neural Networks (GNNs) have been popularly used for analyzing non-Euclidean data such as social network data and biological data. Despite their success, the design of graph neural networks requires a lot of manual work and domain knowledge. In this paper, we propose a Graph Neural Architecture Search method (GraphNAS for short) that enables automatic search of the best graph neural architecture based on reinforcement learning. Specifically, GraphNAS first uses a recurrent network to generate variable-length strings that describe the architectures of graph neural networks, and then trains the recurrent network with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation data set. Extensive experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that GraphNAS can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network. On node classification tasks, GraphNAS can design a novel network architecture that rivals the best humaninvented architecture in terms of test set accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：