检索结果-内蒙古大学图书馆

IEEE Transactions on Sustainable computing 2024年第3期10卷 547-561页

作者： Xiao, Ziheng Zhu, Chenlu Feng, Wei Liu, Shenghao Deng, Xianjun Lu, Hongwei Yang, Laurence T. Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Artificial Intelligence and Intelligent Transportation Joint Technical Center of HUST and Hubei Chutian Intelligent Transportation Company Ltd United States Network and Industrial Control Information Security Technology Department China Nuclear Power Operation Technology Company Ltd. 1011 Xiongchu Avenue Hubei Wuhan430070 China The Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of

Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation. © 2016 IEEE.

关键词： Data reliability

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Feature Processing Based on Attention Mechanism for Co-Salient Object Detection

Comprehensive Feature Processing Based on Attention Mechanis...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohua Lv Mao Yuan Zengbin Zhang Zhengyang Zhang Zhenhui Ding Guangxiao Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Co-salient object detection (CoSOD) aims to detect common salient objects across multiple related images. However, existing methods often struggle with limited attention coverage, missing some co-salient objects. To address this, we propose a two-stage feature processing module (FPM) comprising comprehensive feature extraction module (CFE) and feature enhancement module (FEM). CFE extracts comprehensive cosalient features while reducing background noise, and FEM enhances feature representation and adjusts attention weights for full object coverage. Additionally, we introduce an adversarial learning module (ALM) to improve prediction quality by reducing noise in the co-salient regions. Extensive experiments on three benchmark datasets—CoCA, CoSOD3k, and CoSal2015—demonstrate that our model significantly outperforms state-of-the-art methods. The source code is available at https://***/yaobaimiao/CFPAM.

关键词： Attention mechanisms Source coding Noise Object detection Benchmark testing Feature extraction Adversarial machine learning Finite element analysis Background noise Speech processing

来源：评论

学校读者我要写书评

暂无评论

Research on Classification Method of Sensitive Structural Data of Electric power 12

Research on Classification Method of Sensitive Structural Da...

引用

12th IEEE International Conference on Electronics information and Emergency Communication, ICEIEC 2022

作者： Guan, Xiaojuan Zhou, Cheng Cao, Wantian Security State Grid Smart Grid Research Institute Co. Ltd State Grid Key Laboratory of Information & Network Power Grid Digitizing Technology Department Jiangsu Province Nanjing China

ISBN: (数字)9781665407540

ISBN: (纸本)9781665407540

This paper analyzes the necessity of classification of sensitive data from the importance of electric power structured data, and puts forward the classification identification method of structural sensitive data of electric power enterprises from data survey, data classification to construction of sensitive field database. © 2022 IEEE.

关键词： Sensitive data

来源：评论

学校读者我要写书评

暂无评论

Joint Entity and Relation Extraction Based on Prompt Learning and Multi-channel Heterogeneous Graph Enhancement

Joint Entity and Relation Extraction Based on Prompt Learnin...

引用

International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Haiqing Lv Xiaohui Han Peipei Wang Wenbo Zuo Jing Guo Lijuan Xu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China Information Research Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Joint extraction of entity and relation is crucial in information extraction, aiming to extract all relation triples from unstructured text. However, current joint extraction methods face two main issues. Firstly, they rarely consider the semantic information of entity and relation labels, leading to models that fail to fully understand and utilize the rich semantics in these labels, thereby limiting their performance. Secondly, although table-filling methods are widely used, they focus only on the start or end positions and ignore deep interactions between tables, relying solely on word-level information. To address these issues, we propose the P-MHE framework based on prompt learning and multi-channel heterogeneous graph enhancement. First, we use prompt templates to construct semantic nodes for entity and relation type labels, initializing them along with words as nodes in a heterogeneous graph. We iteratively fuse these semantic nodes through a message-passing mechanism to obtain node representations suitable for entity and relation extraction tasks. Secondly, we design a multi-channel heterogeneous graph to model node relationships from different perspectives, enhancing feature interactions among different types of nodes. Finally, we aggregate the semantic node information of entity and relation type labels after iteration, constructing separate decoding tables for each entity and relation type to better adapt to their respective characteristics. We evaluated our model on four public datasets. Experimental results show that P-MHE outperforms existing models on multiple public datasets. Extensive additional experiments further validate the effectiveness of our model.

关键词： Training Adaptation models Limiting Fuses Semantics Natural languages information retrieval Feature extraction Data mining Iterative decoding

来源：评论

学校读者我要写书评

暂无评论

LDVNet: Lightweight and Detail-Aware Vision network for Image Recognition Tasks in Resource-Constrained Environments 29

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Yuchen Liang, Hu Qiu, Ran Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network's lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments. © 2023 IEEE.

关键词： Detail-aware Image recognition Lightweight

来源：评论

学校读者我要写书评

暂无评论

Visual malware classification and detection method based on convolutional neural network

Visual malware classification and detection method based on ...

引用

IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Hua Huang Hua Sheng Xingwang Han Wenbin Zhang Zilin Zhao Information and Telecommunications Company State Grid Shandong Electric Power Company Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the rapid development of Internet technology, leading to the evolution of malware variants, and malicious threats are also increasing. Therefore, rapid and accurate classification of malware becomes crucial. In response to this challenge, this paper proposes a malware classification method based on deep learning and visualization techniques. The method first extracts sample features, then converts malware binary samples into image format, thus transforming abstract binary data into images with visual information. Subsequently, color mapping technique is applied to convert the processed images into color images. Furthermore, to address imbalanced datasets, this paper introduces data augmentation methods to ensure more balanced and comprehensive training of the model across different sample categories. Finally, an improved convolutional neural network architecture is employed for the classification detection of malware families, achieving accurate classification and identification of different malware families through learning and analysis of sample features. To evaluate the effectiveness of the proposed method, experiments are conducted on the Google Code Jam (GCJ) benchmark dataset. The experimental results demonstrate an accuracy rate of 99.29% on the GCJ dataset. Additionally, we also compared the performance of the improved method before and after, and found that the proposed method can provide more comprehensive feature representation and significantly improve classification accuracy and detection efficiency.

关键词： Deep learning Training Visualization Accuracy Color Static analysis Feature extraction Malware Internet Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Workflow Scheduling Method in Multi-Cluster Environments Using Tasks' Spatiotemporal Attributes

Workflow Scheduling Method in Multi-Cluster Environments Usi...

引用

Electronic information Engineering and Computer Communication (EIECC), International Conference on

作者： Yudong Geng Jing Chen Honghua Yan Qingshui Hao Aimin Zhang Tianxiang Zhang Mingchao Ji Tonglin Fu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Greenthink Electric Power Technology Co. Ltd Jinan China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

Nowadays, computing resources are widely distributed and diverse, and the demand for efficient execution of various workflow tasks on wide-area resources has been raised. multi-objection scheduling method based on space-time attribute of tasks. A scheduling scheme is generated through the three stages of workflow scheduling queue generation, resource selection combined with task spatiotemporal attributes and task and resource matching, and the selected resources of workflow are run according to this scheduling scheme. This method constructs a scheduling problem model to minimize workflow completion time, cost and resource matching degree, and finds the optimal scheduling scheme to improve task execution efficiency. In the process of the experiment, the workflow is actually run in the multi-cluster environment, and the experiment proves that it has a good effect in reducing the completion time and cost of workflow.

关键词： Sequential analysis Costs Processor scheduling Filtering Optimal scheduling Scheduling Spatiotemporal phenomena

来源：评论

学校读者我要写书评

暂无评论

GAF-GAN: Gated Attention Feature Fusion Image Inpainting network Based on Generative Adversarial network 29

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Liang, Hu Liu, Yuchen Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures. © 2023 IEEE.

关键词： Dense feature fusion module Gated attention mechanism Image inpainting Underwater environment

来源：评论

学校读者我要写书评

暂无评论

Research on Dynamic Analysis Technology of Quantitative Control Oriented to Characteristics of power Grid Digital Application Scenarios 1

引用

4th International Conference on 3D Imaging Technologies- Multidimensional Signal Processing and Deep Learning, 3D IT-MSP and DL 2022

作者： Wang, Gang Pan, Jianhong Lv, Changhui Zhao, Bo Dong, Aidi State Grid Smart Grid Research Institute CO. LTD Nanjing210003 China State Grid Key Laboratory of Information & Network Security Nanjing210003 China State Grid Jilin Electric Power Company Limited Changchun130021 China Power Economic Research Institute of Jilin Electric Power Co. Ltd Changchun130011 China

ISBN: (数字)9789819911455

ISBN: (纸本)9789819911448

The digitalization construction of power grid companies will gradually be carried out in depth, while the research on the quantitative control dynamic analysis for the characteristics of power grid digitalization application scenarios has not yet been carried out. In this paper, the dynamic analysis technology of quantitative control of power grid is studied, and the expected project costs and benefits are preliminarily calculated using the functional analysis method;Based on expert scoring method, the quantitative influencing factor set of digital application scene target features is constructed;Based on fuzzy analytic hierarchy process, the quantitative impact index set of digital application scenario features is constructed;Based on K2 structure algorithm and Markov Monte Carlo structure algorithm, the application model of Bayesian network computing is constructed, and a dynamic technical and economic evaluation model adapted to different application scenarios is formed;Based on the calculation application model, according to the corresponding background and characteristics of different projects, carry out data analysis, predict the cause level of the factors, match the data statistical results with the initial network data, and finally calculate the possible costs and benefits of the project by calculating the expected value of the nodes, so as to realize the dynamic technical and economic evaluation of the power grid adapted to different application scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Economic analysis

来源：评论

学校读者我要写书评

暂无评论

Research on 5G-Based Zero Trust network security Platform 4th

Research on 5G-Based Zero Trust Network Security Platform

引用

4th International Conference on Big Data and security, ICBDS 2022

作者： Dai, Zaojian Zhang, Jidong Li, Yong Li, Xinyi Lu, Ziang Fang, Wengao Grid Digitizing Technology Department State Grid Smart Grid Research Institute Co. Ltd Nanjing210003 China State Grid Key Laboratory of Information & Network Security Nanjing210003 China State Grid Jibei Electric Power Company Limited Information & Communication Dispatch Beijing100053 China

ISBN: (纸本)9789819932993

The number and types of network access devices and functions in the cloud computing and big data environments are continuously increasing, so the ability to actively expand network services is needed to ensure that the security and privacy protection needs of network practices are met on a continuous basis. The traditional physical isolation network security protection architecture is facing challenges in new technology scenarios such as big data and mobile Internet, and urgently needs to be changed. Based on this, this paper proposes a 5G-based zero-trust network security platform to better adapt and serve the dynamic network application environment. The security architecture and model are firstly studied to provide security principles of the proposed platform. The implementation scheme of proposed platform is then further analyzed where security algorithms are suggested. The results show that the proposed platform demonstrates better performance in identity, program, configuration and behavior detection. Therefor, it is expected to better deal with the network risks under the new technology scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： network security

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：