检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

2,906 篇 会议
39 篇 期刊文献
31 册 图书

馆藏范围

2,976 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,029 篇 工学
- 1,807 篇 计算机科学与技术...
- 831 篇 软件工程
- 528 篇 信息与通信工程
- 388 篇 电气工程
- 160 篇 控制科学与工程
- 90 篇 生物工程
- 69 篇 电子科学与技术（可...
- 59 篇 动力工程及工程热...
- 54 篇 网络空间安全
- 41 篇 机械工程
- 33 篇 生物医学工程（可授...
- 29 篇 仪器科学与技术
- 26 篇 环境科学与工程（可...
- 22 篇 交通运输工程
- 22 篇 安全科学与工程
- 20 篇 建筑学
389 篇 理学
- 222 篇 数学
- 97 篇 生物学
- 59 篇 统计学（可授理学、...
- 52 篇 物理学
- 43 篇 系统科学
- 35 篇 化学
260 篇 管理学
- 161 篇 管理科学与工程(可...
- 123 篇 图书情报与档案管...
- 88 篇 工商管理
59 篇 法学
- 44 篇 社会学
37 篇 医学
- 32 篇 临床医学
- 24 篇 基础医学(可授医学...
29 篇 教育学
- 21 篇 教育学
25 篇 经济学
- 25 篇 应用经济学
10 篇 文学
8 篇 农学
4 篇 军事学
2 篇 艺术学

主题

303 篇 cloud computing
151 篇 distributed comp...
132 篇 social networkin...
90 篇 computational mo...
75 篇 computer archite...
74 篇 distributed comp...
69 篇 resource managem...
66 篇 distributed data...
65 篇 servers
63 篇 grid computing
60 篇 protocols
58 篇 mobile computing
53 篇 wireless sensor ...
51 篇 security
50 篇 monitoring
49 篇 edge computing
41 篇 big data
40 篇 computer science
40 篇 data models
39 篇 internet

机构

15 篇 beijing univ pos...
13 篇 state key labora...
8 篇 key laboratory o...
7 篇 institute of com...
7 篇 school of cybers...
6 篇 college of compu...
6 篇 beijing univ pos...
5 篇 school of comput...
5 篇 shandong normal ...
5 篇 school of comput...
5 篇 ibm thomas j. wa...
5 篇 school of comput...
5 篇 tu wien distribu...
4 篇 univ sci & techn...
4 篇 school of inform...
4 篇 poznan supercomp...
4 篇 north china inst...
4 篇 beijing universi...
4 篇 aalto univ dept ...
4 篇 school of engine...

作者

12 篇 dustdar schahram
9 篇 yang yang
8 篇 buyya rajkumar
7 篇 wehrle klaus
7 篇 soman k. p.
7 篇 wu xu
6 篇 liu yang
6 篇 xu wu
6 篇 zhang peng
6 篇 jin hai
5 篇 wang jing
5 篇 yao wenbin
5 篇 tomforde sven
5 篇 liu pangfeng
5 篇 xiangyu bai
5 篇 wu jingchen
5 篇 roy nirmalya
5 篇 edenhofer sarah
5 篇 rothermel kurt
5 篇 yan zheng

语言

2,920 篇 英文
40 篇 中文
23 篇 其他
1 篇 俄文

检索条件"任意字段=8th International Conference on Distributed Computing and Networking"

共 2976 条记录，以下是71-80 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Lightweight Fog Attention Graph Convolutional Network for Secure Data Aggregation Mechanism to Fog based Cloud Assisted IoT Network 8

Lightweight Fog Attention Graph Convolutional Network for Se...

引用

8th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Akilesh Kumar, S. Hariharan, P. Sukhel Ahamed, N. Gokilavani, S. Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

Ever Increasing demand of Cloud computing paradigm has resulted in widespread development and deployment of multiple fog nodes to cloud assisted internet of things networks as it is highly capable of providing the users with on demand and low latency services through mobile collaborative devices in the edge of multiple clouds. Mobile Collaborative Devices is used for fog computing with integrated storage, computing and communication capabilities. However it offers improved efficiency and increased flexibility. Despite of multiple advantageous of fog based cloud assisted internet of things, security of the user data and their privacy can be compromised during data aggregation. In addition, it faces huge challenges in effectively aggregating the data and transmitting to the cloud server on establishing the strong security. In order to enhance the security of the user data and to preserve the privacy of the user information along establishing a secure data aggregation and transmission, a new light weight convolutional attention network is proposed in this article. It establishes secure data aggregation and data transmission to the distributed cloud data centers efficiently. Fog attention graph convolutional network leverages neighbour fog data efficiently and securely from dataset and represents in form of graph. Attention coefficient is assigned to each fog data represented in graph structure and attention score is calculated to each update of the nodes in graph. Specifically softmax function employs ID3 decision tree classifier and K anonymization mechanism to aggregates fog data on basis of updates of the attributes of the fog nodes in terms of the attention score and transmits aggregated data to the distributed cloud server. Further proposed model enhance the security of the data aggregation and transmission with index function through hash mechanism. Experimental analysis is carried out on the Fog assisted IoT medical (FIoMT) dataset. Dataset composed of patient h

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

SSDAEE: Semi-Supervised Data-Augmented Event Extraction for Transfer Learning 8

SSDAEE: Semi-Supervised Data-Augmented Event Extraction for ...

引用

8th international conference on Data Science in Cyberspace, DSC 2023

作者： Shen, Yueyue Song, Feifan Wu, Jingchen Wu, Xu School of Cyberspace Security Beijing University of Posts and Telecommunications Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Economics and Finance Changchun Finance College Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Jilin China School of Computer Science Beijing University of Posts and Telecommunications Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9798350331035

Although the general event extraction model has achieved good results in the standard data sets, there is a gap in the extraction results in low-resource environments. the general event extraction model still has room for improvement in the performance of special fields. therefore, this paper proposes a semi-supervised reading comprehension event extraction model adapted to transfer learning, based on improved UDA for data enhancement, called SSDAEE, which can increase the accuracy of reading comprehension event type recognition. SSDAEE has a good performance on the public opinion data set. It can not only enhance a small number of supervised data in low-resource fields with unsupervised data but also improve the generalization ability of the model. Experiments show that when only 5% unsupervised data is used, the F1 score of SSDAEE on the public opinion data set is increased by 9% compared with the fully supervised model, and by 2% compared with other semi-supervised models. © 2023 IEEE.

关键词： data augmentation event extraction semi-supervised transfer learning

来源：评论

学校读者我要写书评

暂无评论

A Novel Cluster Control Method for Effectively Improving the Hosting Photovoltaic Capacity of Distribution Networks 8

A Novel Cluster Control Method for Effectively Improving the...

引用

8th IEEE international conference on Energy Internet, ICEI 2024

作者： Li, Haobin Li, Yiquan Zhao, Ruifeng Guo, Ruifeng Chen, Zhiwei Pan, Kaiyan Wu, Haiyong Guangdong Power Grid Co. Ltd Electric Power Dispatching and Control Center Guangzhou China Dongfang Electronics Cooperation Yantai China

ISBN: (纸本)9798331523558

To handle the problem of not considering the photovoltaic hosting capacity for cluster control in distribution networks, this paper proposes an effective cluster control strategy to improve the distributed photovoltaic (PV) hosting capacity. Firstly, based on a "cloud-cluster-end"architecture, a cloud optimization model aiming at minimizing PV curtailment and the voltage deviation of dominant nodes is established to improve PV accommodation. Secondly, a cluster optimization model aiming at minimizing power loss is established to implement coordinated optimization between cloud and clusters. Different from previous studies, the PV outputs from the cloud optimization are used as the constraints for the cluster optimization. Finally, the proposed model is converted from the nonlinear programming into a linear programing problem using the second-order cone relaxation technique combined with the polygon interior approximation method (PIAM). the accuracy and efficiency of the proposed method compared to existing methods are verified through validation on the actual IEEE151-bus system. © 2024 IEEE.

关键词： Cloud computing architecture

来源：评论

学校读者我要写书评

暂无评论

Cloud computing and its Application in the Future Fire Control System of Ground Combat Platform 8

Cloud Computing and its Application in The Future Fire Contr...

引用

8th international conference on Big Data and Information Analytics, BigDIA 2022

作者： Wang, Quandong Xiao, Bo Niu, Zhiyi Center for Strategic Evaluation Consultation Academy of Military Sciences Beijing China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

this paper first discusses the concept, architecture and technical advantages of cloud computing. Secondly, focusing on the operational requirements of future information warfare, the development trend of existing fire control platforms is analyzed and the concept of cloud fire control is proposed. Taking the ground combat platform as the object, the basic principle, system structure and composition of the cloud fire control of the ground combat platform are analyzed and designed in depth. Finally, the key technologies of distributed dynamic networking, mobile cooperative awareness, and multi-platform collaborative attack and decision-making that need to be solved in cloud fire control system are pointed out, which can provide reference for the development of cloud fire control system for future ground combat platforms. © 2022 IEEE.

关键词： Fire control systems

来源：评论

学校读者我要写书评

暂无评论

DDoS Attacks Detection in Multi-Controller Based Software Defined Network 8

DDoS Attacks Detection in Multi-Controller Based Software De...

引用

8th international conference on Web Research, ICWR 2022

作者： Valizadeh, Parisa Taghinezhad-Niar, Ahmad University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9781665466264

With the rapid growth of computer devices, network communication faced different challenges from network management to traffic engineering. Software-Defined networking (SDN) is a well-known solution for optimizing these communications. SDN is a new networking architecture to simplify network management that separates the control plane from the data plane. the central controller is the major advantage of SDN;however, it has security vulnerabilities such as being unreachable in distributed Denial-of-Service attacks (DDoS). Consequently, it is very important to protect SDN from DDoS attacks. In this paper, we proposed an algorithm for DDoS attack detection and reducing its impact in SDN architecture with multiple distributed controllers. We presented two methods 1) the entropy of destination IP addresses and 2) Packet window initiation rate for early detection of DDoS. We used Mininet and floodlight to simulate our algorithm in different scenarios. the result shows that our algorithm outperforms other works in various network configurations and multi-victim attacks. © 2022 IEEE.

关键词： Software defined networking

来源：评论

学校读者我要写书评

暂无评论

Bias-Resilient Elephant Flow Detection in distributed SDNs through Federated Learning 8th

Bias-Resilient Elephant Flow Detection in Distributed SDNs ...

引用

8th international conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Boussaoud, Kaoutar Bellouch, Mohamed Ayache, Meryeme En-Nouaary, Abdeslam Rabat Morocco

ISBN: (纸本)9789819702091

this paper presents an innovative approach based on Federated Learning (FL) for detecting Elephant/Mouse Flows within distributed Software-Defined networking (SDN) environments. In such networks, multiple domains are overseen by individual SDN controllers. Traditional centralized Machine Learning (ML) methods for detecting these flows involve training separate local models on data specific to each domain. this can lead to biased local models due to their dependence on context during data preparation. Consequently, these local models may struggle to adapt to different contexts and may not effectively handle the complexities of modern networks. To address these challenges, the proposed approach leverages the power of FL by fostering collaboration among controllers from various domains. this collaborative process aims to alleviate issues of local bias and, in turn, introduce greater adaptability and generalization to the detection process. through experiments, we demonstrated the impact of data heterogeneity on the performance of local models and how the FL approach effectively mitigates these issues. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software defined networking

来源：评论

学校读者我要写书评

暂无评论

Research on Model Optimization Technology of Federated Learning 8

Research on Model Optimization Technology of Federated Learn...

引用

IEEE 8th international conference on Big Data Analytics (ICBDA)

作者： Dai, Hang Hong, Yusheng Univ Sci & Technol China Sch Software Engn Suzhou Peoples R China

ISBN: (纸本)9798350310764

Federated learning is a new distributed machine learning model training method, which can protect user privacy while enabling a large number of edge devices to train a shared model based on their local data sets, and enable participants to benefit from each other. However, federated learning also faces some serious challenges, such as resource constraints, uneven data distribution, edge dynamics, and so on. Moreover, the existing federated learning methods may lead to long training time, consume a lot of bandwidth and other resources, and the heterogeneity of edge devices further worsens the inherent synchronization barrier problem of federated learning. In response to the above problems, this paper studies the existing model optimization technologies of federated learning, including node selection mechanism, local data enhancement, model compression, local update, gradient compression and other model optimization technologies, and summarizes and analyzes them.

关键词： Federated learning Edge computing Model optimization

来源：评论

学校读者我要写书评

暂无评论

Intelligent System for distributed Deep Learning Training Traffic Optimization 24

Intelligent System for Distributed Deep Learning Training Tr...

引用

24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Lu, Weipeng Wu, Shuang Ou, Junhui Niu, Bing He, Jin Song, Kevin Zheng, Xinwei Ustc Hefei China Vmware Beijing China

ISBN: (纸本)9798350319934

distributed training crossing multiple computing nodes and accelerators has been the mainstream solution for large model training. Precedent work on distributed deep learning (DDL) training acceleration has focused on improving the efficiency of communication and GPU resource scheduling, with little attention given to network bandwidth usage. However, due to cluster GPU fragmentation, more than one training job is frequently assigned to the same node to reduce waiting time. the competition for network bandwidth among various distributed training jobs becomes a problem that cannot be overlooked, and this competition results in a considerable reduction in training speed. We propose a system deployed in cluster nodes that includes traffic monitoring, classification, and prediction of DDL training status and implement a priority-based traffic scheduling policy that considers DDL training features. We demonstrate that this policy is beneficial compared to Linux's default policy, which can increase training speed by approximately 18%. Additionally, we increase the training speed by approximately 20% in the case of continuous background traffic by increasing the bandwidth priority during DDL training. © 2022 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

BTQoS: A Tenant Relationship-Aware QoS Framework for Multi-tenant distributed Storage System 8th

BTQoS: A Tenant Relationship-Aware QoS Framework for Multi-t...

引用

8th international Joint conference on Web and Big Data and Web-Age Information Management (APWeb-WAIM)

作者： Lu, Yixuan Qu, Yun Zhu, Dongjie Zhang, Rui Du, Haiwen Harbin Inst Technol Weihai Peoples R China

ISBN: (纸本)9789819772407;9789819772414

Quality of Service (QoS) is widely applied in multi-tenant distributed systems to meet tenants' Service Level Objective (SLO). Previous work ignored the relationship between tenants, making it challenging to achieve hierarchical resource management of tenants. this work proposes BTQoS, a tenant relationship-aware QoS framework for multi-tenant distributed storage systems. BTQoS introduces behavior and behavior tree abstractions to manage tenants' SLOs and relationships. BTQoS uses a two-level scheduling scheme to implement SLO enforcement, resource adaptation, and skew avoidance under dynamic workloads. In addition, BTQoS's two-level scheduler decouples tenant resource allocation and cluster resource adaptation, allowing the SLOs supported by BTQoS to be expanded with low development costs. We implement and evaluate BTQoS in HDFS. the experimental results show that BTQoS can hierarchically manage and enforce tenants' SLOs at low overheads and provide skew avoidance.

关键词： QoS Cloud computing distributed storage system

来源：评论

学校读者我要写书评

暂无评论

Benchmarking OpenStack for edge computing applications 8

Benchmarking OpenStack for edge computing applications

引用

8th IEEE/ACIS international conference on Big Data, Cloud computing, and Data Science, BCD 2023

作者： Dollinger, Jean-Francois Bouhouch, Kaouther Bouzarkouna, Imen CESI LINEACT Strasbourg France CESI LINEACT Nanterre France

ISBN: (纸本)9798350373615

In this paper, we focus on the identification and evaluation of performance factors of OpenStack-based edge computing platforms. Such infrastructure relies on the deployment of additional computing resources close to the data source, to alleviate low throughput, latencies and network congestion. While cloud data centres offer numerous compute-intensive processing units, the edge layer leverages heterogeneous, dynamic and performance-constrained computing devices. the edge avoids the systematic use of low Quality of Service (QoS) of Wide Area Network (WAN) links to perform computations. Many edge platforms incorporate cloud software environment provisioning and elasticity capabilities. In this regard, we investigate the performance of the state-of-the-Art OpenStack framework to set up an edge computing infrastructure. To achieve our goal, we first evaluate the impact of the deployment type, whether bare metal, containerised, virtualized or emulated on compute-intensive kernels. Within the same deployment scenarios, we examine the OpenStack virtual network infrastructure latency and throughput. We also cover the duration of several OpenStack primitives involved in the deployment of applications. Finally, we execute an MPI (Message Passing Interface) distributed application and highlight the performance impact of our infrastructure. Our study provides a comprehensive analysis of parameters influencing performance in an edge computing infrastructure based on OpenStack. © 2023 IEEE.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共298页 << < 4 5 6 7 8 9 10 11 12 13 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：