检索结果-内蒙古大学图书馆

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

A survey on cryptographic techniques for protecting big data security: present and forthcoming

引用

Science China(information Sciences) 2022年第10期65卷 73-106页

作者： Siqi LU Jianhua ZHENG Zhenfu CAO Yongjuan WANG Chunxiang GU School of Cyberspace Security Information Engineering University Henan Key Laboratory of Network Cryptography Technology Institute of Security Technology Shanghai Key Laboratory of Trustworthy Computing East China Normal University Cyberspace Security Research Center Peng Cheng Laboratory Shanghai Institute of Intelligent Science and Technology Tongji University

Big data drive multidimensional convergence and profound innovations among industries and provide novel ways of exploring the world. As they significantly create economic and social value, big data meaningfully impact the implementation and management of information security and privacy *** technologies are used to protect the security and entire life cycle of big data. The demand for this technology is multiplied when the data are stored in the cloud. They are stored in the cloud in the form of ciphertext, and the driving requirement for data retrieval, sharing, and manipulation places the security of data at risk. The all-or-nothing approach of traditional cryptography systems cannot realize the release and processing of data information with flexible and increasingly fine granularity. Consequently, dealing with the relationship between privacy protection and data utilization, as well as navigating the blurry boundaries between subverting either plaintext or ciphertext, has become a research focus of the current cryptographic trend for protecting big data security. Presently, there are many studies designed to solve these ***, security requirements and source encryption mode for future big data systems and applications are elaborated. Then, focusing on the practical security and functionality of the big data life cycle, including storage, retrieval, sharing, calculation, statistical analysis, and utilization, the research being conducted based on those functions is reviewed. For each cryptographic technology that meets the requirement of each type of big data security or application, security and efficiency comments and sufficient comparison analyses of cryptography schemes or protocols are provided; moreover, the current general problems and development trends are expounded. Because the current innovative research on cryptographic technology was primarily based on the development or improvement of a single solution, the study on

关键词： big data security cryptographic techniques ciphertext-based data sharing and computing authenticated encryption functional encryption homomorphic encryption secure multi-party computing

来源：评论

学校读者我要写书评

暂无评论

GHTO: Improving Byzantine Robustness in Distributed Machine Learning Through Gradient Heterogeneity Twofold Optimization

GHTO: Improving Byzantine Robustness in Distributed Machine ...

引用

Frontiers Technology of information and Computer (ICFTIC), IEEE International Conference on

作者： Caiyun Li Ming Yang Jian Cao Xiaoming Wu Yunpeng He Xin Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center 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 Shandong Evayinfo Technology Co. Ltd Jinan P. R. China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Heterogeneous datasets induce local gradient variance and global gradient variance in distributed learning, collectively referred to as gradient heterogeneity. Gradient heterogeneity increases the system's vulnerability to Byzantine attacks, affecting performance. To address this issue, the paper proposes a Gradient Heterogeneity Twofold Optimization (GHTO) algorithm to enhance the Byzantine robustness of the system in non-independent and identically distributed environments. Specifically, a Local Gradient Correction (LGC) algorithm is proposed, which aims to reduce the local gradient variance. By further integrating LGC with momentum and bucketing techniques to achieve dual optimization of local and global gradient variance. Experimental results demonstrate the proposed LGC algorithm diminishes local gradient variance, and the GHTO method optimizes both local and global gradient variance, which further strengthens the robustness of the system against Byzantine attacks.

关键词： Computer aided instruction Machine learning algorithms Distance learning Machine learning Robustness Optimization Resilience

来源：评论

学校读者我要写书评

暂无评论

Research on Cross Domain Data Analysis and Data Mining Technology of power Grid Digital Benefits 4th

Research on Cross Domain Data Analysis and Data Mining Techn...

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 Dong, Aidi Lv, Changhui Zhao, Bo Pan, Jianhong State Grid Smart Grid Research Institute CO. LTD Nanjing210003 China State Grid Key Laboratory of Information and 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: (纸本)9789819912292

The digital power grid system integrates flexible resources such as traditional power sources and distributed power sources and has the characteristics of high time variability and high complexity. It urgently needs cross domain data support. However, the traditional evaluation methods can’t evaluate cross domain data, which is not targeted and effective, and its practicality needs to be improved. This paper analyzes the cross domain intelligent construction and business service requirements, the characteristics of power grid digital cross domain data, studies the internal coupling relationship between digital system application functions and cross domain data, and proposes a coupling evaluation model to measure the digital system and cross domain data. Research the process of cross domain data mining, cluster similarity measurement, historical data feature mining, and other technologies to achieve historical cross domain data feature analysis. Study the fitting analysis technology, and use correlation analysis and regression analysis to achieve the fitting analysis of spanning data, so as to support the technical and economic analysis of spanning data of power grid digital system. © 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

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：