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

4,280 篇 会议
47 篇 期刊文献
3 册 图书

馆藏范围

4,330 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,431 篇 工学
- 2,167 篇 计算机科学与技术...
- 1,080 篇 软件工程
- 607 篇 电气工程
- 586 篇 信息与通信工程
- 185 篇 仪器科学与技术
- 138 篇 控制科学与工程
- 120 篇 电子科学与技术（可...
- 64 篇 生物工程
- 59 篇 机械工程
- 43 篇 动力工程及工程热...
- 38 篇 交通运输工程
- 31 篇 生物医学工程（可授...
- 27 篇 安全科学与工程
- 26 篇 网络空间安全
- 24 篇 光学工程
- 23 篇 土木工程
- 22 篇 材料科学与工程（可...
- 20 篇 建筑学
- 19 篇 航空宇航科学与技...
418 篇 理学
- 253 篇 数学
- 86 篇 物理学
- 72 篇 生物学
- 51 篇 系统科学
- 42 篇 统计学（可授理学、...
261 篇 管理学
- 179 篇 管理科学与工程(可...
- 97 篇 图书情报与档案管...
- 65 篇 工商管理
35 篇 医学
- 30 篇 临床医学
33 篇 法学
- 19 篇 社会学
18 篇 经济学
- 18 篇 应用经济学
11 篇 农学
7 篇 教育学
7 篇 文学
2 篇 军事学
1 篇 艺术学

主题

543 篇 parallel process...
259 篇 computer archite...
238 篇 computational mo...
207 篇 distributed comp...
186 篇 concurrent compu...
179 篇 computer network...
164 篇 network topology
157 篇 hardware
157 篇 graphics process...
148 篇 routing
130 篇 bandwidth
123 篇 application soft...
120 篇 neural networks
120 篇 protocols
107 篇 scalability
104 篇 multicore proces...
103 篇 throughput
102 篇 algorithm design...
102 篇 kernel
101 篇 computer science

机构

15 篇 institute of inf...
14 篇 institute of com...
14 篇 science and tech...
13 篇 college of compu...
13 篇 school of cyber ...
13 篇 shandong fundame...
12 篇 school of comput...
10 篇 science and tech...
9 篇 univ chinese aca...
9 篇 university of ch...
9 篇 natl univ def te...
9 篇 natl univ def te...
8 篇 shenyang aerospa...
8 篇 beijing univ pos...
8 篇 nanjing univ aer...
8 篇 beijing universi...
7 篇 shanghai jiao to...
7 篇 college of compu...
7 篇 chinese acad sci...
7 篇 national key lab...

作者

17 篇 bagherzadeh nade...
16 篇 kotenko igor
14 篇 j. duato
12 篇 wang yijie
11 篇 daneshtalab maso...
10 篇 ebrahimi masoume...
10 篇 yijie wang
10 篇 nader bagherzade...
10 篇 igor kotenko
10 篇 lexi xu
9 篇 li dongsheng
9 篇 marco aldinucci
9 篇 matsutani hiroki
8 篇 hiroki matsutani
8 篇 masoumeh ebrahim...
8 篇 plosila juha
8 篇 marco danelutto
8 篇 wang huaimin
7 篇 tenhunen hannu
7 篇 xinzhou cheng

语言

4,282 篇 英文
39 篇 其他
11 篇 中文
1 篇 乌克兰文

检索条件"任意字段=Euromicro Conference on Parallel, Distributed and Network-Based Processing"

共 4330 条记录，以下是521-530 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：

Raw Ultrasound-based Phonetic Segments Classification Via Mask Modeling 48

Raw Ultrasound-Based Phonetic Segments Classification Via Ma...

引用

48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： You, Kang Liu, Bo Xu, Kele Xiong, Yunsheng Xu, Qisheng Feng, Ming Csapó, Tamás Gábor Zhu, Boqing Tongji University Shanghai China National University of Defense Technology Changsha China National Key Lab of Parallel and Distributed Processing Changsha China Budapest University of Technology and Economics Budapest Hungary

ISBN: (纸本)9781728163277

Ultrasound tongue imaging is widely used in clinical linguistics and phonetics. Recently, deep neural networks, especially convolutional neural networks, have been widely used in the interpretation and analysis of ultrasound tongue images (UTI). Despite achieving satisfactory performance, deep models rely on a large amount of manually labeled data, which is often difficult to obtain in practical settings. To address this issue, this paper focuses on how to utilize a large amount of unlabeled UTI data to improve the performance of UTI classification task. Specifically, we explore self-supervised learning with masking modeling strategy. By predicting the masked part, our pre-trained model enables the neural network to infer contextual information. Then, we fine-tune the pre-trained model with a small amount of labeled data. Compared with the previous competing algorithms, our method can improve the classification accuracy by an average of 13.33% in four different scenarios. © 2023 IEEE.

关键词： Mask modeling Masked auto-encoder Self-supervised learning Ultrasound tongue imaging.

来源：评论

学校读者我要写书评

暂无评论

Towards a Broadcast Time-Lock based Token Exchange Protocol 27th

Towards a Broadcast Time-Lock Based Token Exchange Protocol

引用

27th International European conference on parallel and distributed Computing (Euro-Par)

作者： Barbara, Fadi Murru, Nadir Schifanella, Claudio Univ Turin Dept Comp Sci Turin Italy Univ Trento Dept Math Trento Italy

ISBN: (纸本)9783031061561;9783031061554

Many proposals for token exchange mechanisms between multiple parties have centralization points. This prevents a completely trustless and secure exchange between those parties. The main issue lies in the fact that communications in projects using a blockchain are asynchronous: classical result asserts that in an asynchronous system a secure exchange of secrets is impossible, unless there is a trusted third party. In this paper, we propose our preliminary results in the creation of our Broadcast Time-Lock Exchange (BTLE) protocol. The core of BTLE is the introduction of synchronicity in communications through the use of time-lock puzzles. This makes it possible to exchange secrets between two parties while eliminating the need for a trusted third party.

关键词： Locks (fasteners)

来源：评论

学校读者我要写书评

暂无评论

Research on Feature Extraction Algorithm of Graph Data based on Graph Neural network

Research on Feature Extraction Algorithm of Graph Data Based...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE International conference on

作者： Shuang Qiu Hairong Hao Zhou Dong School of Information Engineering Eurasia University Xi'an China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

This paper proposes an optimization model that integrates Hadoop, Graph Convolutional networks (GCN), and Ant Colony Optimization (ACO) to enhance GNN performance in large-scale networks. Hadoop is utilized for distributed storage and processing of large graph datasets, dividing them into subgraphs across multiple computing nodes. The MapReduce framework accelerates the training and inference of GCN through parallel computing. GCN is employed to extract node features by performing convolution on the graph's adjacency matrix. ACO is then applied for global hyperparameter tuning and network structure optimization. By exploring the parameter space, ACO identifies the optimal configuration for GCN. This model leverages Hadoop's distributed computing power, GCN's effective feature extraction, and ACO's optimization capabilities. Experimental results demonstrate improved efficiency and performance on large-scale network datasets compared to traditional methods.

关键词： Training Graph convolutional networks Convolution Optimization models Computational modeling parallel processing Feature extraction distributed computing Optimization Tuning

来源：评论

学校读者我要写书评

暂无评论

A Two-Stage Multimodal Emotion Recognition Model based on Graph Contrastive Learning 29

A Two-Stage Multimodal Emotion Recognition Model Based on Gr...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Ai, Wei Zhang, Fuchen Meng, Tao Shou, Yuntao Shao, Hongen Li, Keqin Central South University of Forestry and Technology School of Computer and Information Engineering ChangSha China State University of New York Department of Computer Science New PaltzNY2561 United States

ISBN: (纸本)9798350330717

In terms of human-computer interaction, it is becoming more and more important to correctly understand the user's emotional state in a conversation, so the task of multimodal emotion recognition (MER) started to receive more attention. However, existing emotion classification methods usually perform classification only once. Sentences are likely to be misclassified in a single round of classification. Previous work usually ignores the similarities and differences between different morphological features in the fusion process. To address the above issues, we propose a two-stage emotion recognition model based on graph contrastive learning (TS-GCL). First, we encode the original dataset with different preprocessing modalities. Second, a graph contrastive learning (GCL) strategy is introduced for these three modal data with other structures to learn similarities and differences within and between modalities. Finally, we use MLP twice to achieve the final emotion classification. This staged classification method can help the model to better focus on different levels of emotional information, thereby improving the performance of the model. Extensive experiments show that TS-GCL has superior performance on IEMOCAP and MELD datasets compared with previous methods. © 2023 IEEE.

关键词： graph contrastive learning graph convolutional network multimodal emotion recognition two-stage classification

来源：评论

学校读者我要写书评

暂无评论

A Time Series Clustering Method for network Big Data

A Time Series Clustering Method for Network Big Data

引用

International conference on parallel and distributed Systems (ICPADS)

作者： Yujia Zhu Geyong Min Yulei Wu Haozhe Wang Department of Computer Science Falcuty of Environment Science and Economy University of Exeter Exeter United Kingdom

In the era of big data, network data increase rapidly in a distributed manner, giving birth to the network big data. network big data with the extra features such as distributed and decentralized data collection and storage, distributed and parallel data processing, more complex and evolving relationships among data, and heterogeneous data representation, pose opportunities together with challenges to the traditional network analysis algorithms. A hopeful solution is combining machine learning techniques with network big data analysis. In this paper, we proposed a novel time series clustering method which effectively combines machine learning techniques with network big data analysis for fault diagnosis task based on network logs. Verification experimental results on classic HDFS dataset demonstrate the outstanding performance of the proposed method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Nature Inspired Meta-Heuristic Algorithms based Load Balancing in Fog Computing Environment 7

Nature Inspired Meta-Heuristic Algorithms based Load Balanci...

引用

7th International conference on parallel, distributed and Grid Computing, PDGC 2022

作者： Kashyap, Vijaita Ahuja, Rakesh Kumar, Ashok Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Lovely Professional University School of Computer Application Punjab Phagwara India

ISBN: (纸本)9781665454018

LB is simply the methodical distribution of load among many servers. To expedite the handling of customer requests, the fog server manages the substantial data on the cloud server. Data requirements are increasing, and Fog computing (FC) has made data more readily available. Despite the fact that FC overcomes many issues based on user requirements, other challenges still necessitate further effort. The challenge that FC confronts is Load Balancing(LB), which causes an increase in network layer traffic. The cloud layer has previously offered several LB solutions, but development on the fog layer is currently underway. Inadequate LB can lower service quality by delaying response time, processing time, security, and other concerns. This paper describes nature-based meta-heuristic algorithms that are based on LB and are used to the problem of overloaded data on the network. This review has explored some of the unresolved issues of the researchers problems and also a brief review on the advantages and disadvantages of the existing literature's. Some of the challenges has also been discussed. Also, when it comes to LB, writers have concentrated on bandwidth, response time, latency, cost, security, and execution time. © 2022 IEEE.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Research and Implementation of network Defense and Non-repeated Substring Pattern Matching Algorithm

Research and Implementation of Network Defense and Non-repea...

引用

2022 International conference on Signal processing and Communication Security, ICSPCS 2022

作者： Duan, Xiaorong Dong, Chenni Ma, Xin Information and Communication Branch State Grid Shanxi Electric Power Company Production Scheduling Building No. 3 Xieyuan Road Jinyuan District Shanxi Taiyuan030000 China

ISBN: (纸本)9781510660151

Cloud computing network and next-generation network is now widely used, which brings more security threats. based on the process and the network tuples communication to construct defense system, the key technologies of defense under IPv4/IPv6 dual stack network are researched. It presents the NRLS_Sunday based on improvement of the longest unique substring and Sunday algorithm, to avoid excessive repetitive character comparison and improve the matching efficiency of a single pattern string. Compared with BM, Sunday algorithm, the experiment shows the improved algorithm optimizes its time complexity. Under high-speed network, packet data contents is carried on rapid detection and analysis. based on intelligent traffic restrictions for process and network, the intrusion detection and defense is conducted for network. In this paper, parallel packet detection method is studied under the high speed. Application web collaboration, and the unified arrangements and issued policies is applied in a complex network environment, proposing and realizing the effective method of defense in the complex network environment. © 2022 SPIE.

关键词： network security

来源：评论

学校读者我要写书评

暂无评论

LBoDSN: An In-network Load Balancing Mechanism for Lossless Data Center networks based on Direct Switch Notification 20th

LBoDSN: An In-Network Load Balancing Mechanism for Lossless...

引用

20th IFIP WG 10.3 International conference on network and parallel Computing, NPC 2024

作者： Shi, Qingyu Jiang, Fangxue Huang, Huang Li, Xiaocui Li, Chuang Cao, Wenzhi Liu, Limei School of Advanced Interdisciplinary Studies Hunan University of Technology and Business Changsha China Xiangjiang Laboratory Changsha China

ISBN: (纸本)9789819628292

With the extensive deployment of Remote Direct Memory Access (RDMA) in lossless data center networks (DCNs), enhancing the RDMA load balancing performance for distributed AI training and HPC applications becomes particularly critical. However, existing load balancing schemes either suffer difficulty in responding to congestion in sub-RTT time, or cannot accurately detect and reroute flows that are on the verge of creating path congestion for Priority-based Flow Control (PFC) enabled lossless DCNs. In this paper, we propose LBoDSN, an in-network load balancing mechanism for lossless DCNs based on direct swtich notification, to address above challenges. LBoDSN monitors ingress queue length evolution at destination switches to predict the triggering time of PFC pause, and accurately identifies congested flows based on the congestion contribution level before PFC pause, then further proactively sends the flow congestion notification (FCN) to source switches for fast rerouting. The FCN contains a flow ID that identifies the congested flow, and a path ID that identifies the target path to which the congested flow will be rerouted. And after rerouting, the Congestion Notification Packet (CNP) of the old path is selectively discarded at source switches to improve transmission performance, while out-of-order packets are reordered at the destination switch. The experimental results show that under realistic workloads, LBoDSN achieves 9–64% and 20–80% better than CONGA for the average and tail Flow Completion Times (FCTs), respectively. Compared with ConWeave, LBoDSN achieves around 8% better performance for the average and tail FCTs while significantly reducing switch queue usage for reordering. © IFIP International Federation for Information processing 2025.

关键词： Time switches

来源：评论

学校读者我要写书评

暂无评论

distributed Task-based Training of Tree Models 38

Distributed Task-Based Training of Tree Models

引用

38th IEEE International conference on Data Engineering (ICDE)

作者： Yan, Da Chowdhury, Md Mashiur Rahman Guo, Guimu Kahlil, Jalal Jiang, Zhe Prasad, Sushil Univ Alabama Birmingham Dept Comp Sci Birmingham AL 35294 USA Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA Univ Texas San Antonio Dept Comp Sci San Antonio TX USA

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Decision trees and tree ensembles are popular supervised learning models on tabular data. Two recent research trends on tree models stand out: (1) bigger and deeper models with many trees, and (2) scalable distributed training frameworks. However, existing implementations on distributed systems are IObound leaving CPU cores underutilized. They also only find best node-splitting conditions approximately due to row-based data partitioning scheme. In this paper, we target the exact training of tree models by effectively utilizing the available CPU cores. The resulting system called TreeServer adopts a column-based data partitioning scheme to minimize communication, and a nodecentric task-based engine to fully explore the CPU parallelism. Experiments show that TreeServer is up to 10x faster than models in Spark MLlib. We also showcase TreeServer's high training throughput by using it to build big "deep forest" models.

关键词： Training Supervised learning Forestry parallel processing Throughput Market research Data models

来源：评论

学校读者我要写书评

暂无评论

QDPformer: Quantum-Driven Workload Prediction Model based on Transformer 20th

QDPformer: Quantum-Driven Workload Prediction Model Based on...

引用

20th IFIP WG 10.3 International conference on network and parallel Computing, NPC 2024

作者： Cui, Zixuan Huang, Shaoyuan Zhang, Cheng Li, Xudong Wang, Xiaofei Qiu, Chao Niyato, Dusit Tianjin University Tianjin China Tianjin University of Finance and Economics Tianjin China Software Technology Co. Ltd. Suzhou China Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819628292

Workload prediction is critical to ensuring quality of service in distributed edge cloud platforms (ECP). However, this task faces multiple challenges due to the inherent uncertainties within ECP. On the one hand, ECP aggregates heterogeneous infrastructure resources, whose static attributes are difficult to effectively represent and utilize. On the other hand, the dynamic switching of applications deployed on edge servers leads to diverse workload patterns. These uncertainties make server workload prediction in ECP challenging. Inspired by quantum theory in modeling uncertainty, we make an important attempt to design a Quantum-Driven workload Prediction model based on Transformer (QDPformer). Specifically, we introduce fundamental concepts such as quantum states and density matrices to model various uncertain features in ECP. Additionally, we present an improved quantum attention mechanism based on quantum measurement and quantum evolution that enables QDPformer to simulate dynamic changes in servers, significantly enhancing the model’s predictive performance. Experimental results on real ECP datasets show that QDPformer achieves up to a 24.52% reduction in mean squared error (MSE) compared with the baseline models. © IFIP International Federation for Information processing 2025.

关键词： Quality of service

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433页 << < 49 50 51 52 53 54 55 56 57 58 > >>

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：