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

6,705 篇 会议
116 册 图书
70 篇 期刊文献

馆藏范围

6,891 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,560 篇 工学
- 3,366 篇 计算机科学与技术...
- 1,736 篇 软件工程
- 1,054 篇 信息与通信工程
- 673 篇 电气工程
- 228 篇 控制科学与工程
- 179 篇 电子科学与技术（可...
- 143 篇 机械工程
- 97 篇 动力工程及工程热...
- 97 篇 生物工程
- 94 篇 测绘科学与技术
- 42 篇 生物医学工程（可授...
- 40 篇 仪器科学与技术
- 39 篇 光学工程
- 34 篇 建筑学
- 29 篇 土木工程
- 28 篇 网络空间安全
- 27 篇 安全科学与工程
771 篇 理学
- 568 篇 数学
- 112 篇 生物学
- 102 篇 统计学（可授理学、...
- 100 篇 物理学
- 75 篇 系统科学
- 37 篇 化学
511 篇 管理学
- 383 篇 管理科学与工程(可...
- 197 篇 工商管理
- 155 篇 图书情报与档案管...
82 篇 医学
- 80 篇 临床医学
- 25 篇 基础医学(可授医学...
42 篇 经济学
- 42 篇 应用经济学
35 篇 法学
- 25 篇 社会学
12 篇 农学
10 篇 文学
8 篇 教育学
8 篇 军事学
1 篇 艺术学

主题

894 篇 distributed comp...
801 篇 computer network...
513 篇 concurrent compu...
450 篇 wireless sensor ...
377 篇 parallel process...
354 篇 computer science
352 篇 routing
344 篇 neural networks
343 篇 computational mo...
304 篇 computer archite...
260 篇 application soft...
257 篇 protocols
255 篇 network topology
253 篇 peer to peer com...
244 篇 bandwidth
235 篇 grid computing
232 篇 software enginee...
193 篇 costs
179 篇 artificial intel...
175 篇 intelligent netw...

机构

28 篇 institute of com...
24 篇 shandong provinc...
20 篇 key laboratory o...
15 篇 shandong fundame...
14 篇 department of co...
13 篇 university of ch...
12 篇 department of co...
11 篇 department of co...
11 篇 department of co...
11 篇 institute of com...
11 篇 national laborat...
11 篇 beijing universi...
10 篇 college of compu...
10 篇 institute of inf...
10 篇 school of cyber ...
10 篇 department of co...
10 篇 school of comput...
9 篇 shandong enginee...
9 篇 department of co...
9 篇 school of comput...

作者

18 篇 jiannong cao
15 篇 m. takizawa
15 篇 li dongsheng
15 篇 eyas el-qawasmeh
14 篇 shietung peng
14 篇 shen hong
14 篇 rothermel kurt
14 篇 hong shen
13 篇 lemin li
12 篇 hai jin
11 篇 dongsheng li
11 篇 wei zhang
10 篇 koibuchi michihi...
10 篇 jack dongarra
10 篇 t. enokido
10 篇 wang yang
10 篇 stephen l. scott
10 篇 j. duato
10 篇 m. ould-khaoua
9 篇 duerr frank

语言

6,711 篇 英文
168 篇 其他
17 篇 中文

检索条件"任意字段=IASTED International Conference on Parallel and Distributed Computing and Networks"

共 6891 条记录，以下是331-340 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：

Accelerate Model parallel Deep Learning Training Using Effective Graph Traversal Order in Device Placement 22nd

Accelerate Model Parallel Deep Learning Training Using Effec...

引用

22nd IFIP WG 6.1 international conference on distributed Applications and Interoperable Systems (DAIS) Held as Part of the 17th international Federated conference on distributed computing Techniques (DisCoTec)

作者： Wang, Tianze Payberah, Amir H. Hagos, Desta Haileselassie Vlassov, Vladimir KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9783031160929;9783031160912

Modern neural networks require long training to reach decent performance on massive datasets. One common approach to speed up training is model parallelization, where large neural networks are split across multiple devices. However, different device placements of the same neural network lead to different training times. Most of the existing device placement solutions treat the problem as sequential decisionmaking by traversing neural network graphs and assigning their neurons to different devices. This work studies the impact of neural network graph traversal orders on device placement. In particular, we empirically study how different graph traversal orders of neural networks lead to different device placements, which in turn affects the training time of the neural network. Our experiment results show that the best graph traversal order depends on the type of neural networks and their computation graphs features. In this work, we also provide recommendations on choosing effective graph traversal orders in device placement for various neural network families to improve the training time in model parallelization.

关键词： Device Placement Model parallelization Deep Learning Graph Traversal Order

来源：评论

学校读者我要写书评

暂无评论

Modified LSTM Based Intrusion Detection System for networks

Modified LSTM Based Intrusion Detection System for Networks

引用

international conference on parallel distributed and Grid computing (PDGC)

作者： R Allen Joseph Abhishek K Anna Sanjay Kumar T Department of Information Technology Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The proliferation of wireless networks in the past decade is seriously coupled with security threats and intrusion incidents, raising greater demands for robust and efficient intrusion detection systems. A new approach is present in this paper, to intrusion detection in wireless networks based on the computational paradigm Echo State networks. ESN is one type of reservoir computing model. The inherent advantages of training efficiency and dynamic responses for temporal patterns make ESN especially fit for the task of detecting network traffic anomalies. In the following, we design our proposed ESN-based IDS to identify and classify various types of network intrusions with high accuracy and low computational overhead. This system will be robust and adaptive against different attack scenarios by training with the entire dataset of normal and malicious network activities. We show the efficiency of our technique through extensive simulations and real experimentation for a wide range of intrusion detection such as exploit as well as unauthorized access attempts. The results explicitly show that ESN-based IDS outperforms detection accuracy, false positive rate, and time response by traditional machine learning methods.

关键词： Training Accuracy Wireless networks Computational modeling Intrusion detection Reservoir computing Echo state networks Threat assessment Time factors Computer crime

来源：评论

学校读者我要写书评

暂无评论

A Nonlinear Interval Programming Model based on Fuzzy Neural networks

A Nonlinear Interval Programming Model based on Fuzzy Neural...

引用

distributed computing and Optimization Techniques (ICDCOT), international conference on

作者： Jijin Zhu Qinqin Wu Chan Luo College of Mechanical and Electrical Engineering Guangdong University of Science and Technology Dongguan China Mathematics Teaching and Research Office Dongguan Guangming Primary School Dongguan China

Compared to traditional deterministic problem research methods, interval analysis methods have a larger computational load and longer computational time due to the presence of uncertainty. Therefore, for models with complex structures or large scales, there is a lot of uncertain information, which requires a significant amount of computation and reduces the efficiency of the algorithm. The current optimization algorithms also have such shortcomings. In order to improve algorithm efficiency, this article focuses on researching parallel computing methods for interval optimization, designing efficient parallel algorithms, decomposing optimization problems into multiple subtasks that can be parallelized, and analyzing the feasibility and effectiveness of these algorithms in detail. The results indicate that multiple artificial neural network models are used to establish mapping relationships between design variables and uncertain variables, corresponding objective function values, and constraint function values. The objective function and each constraint function are trained with one network each, and a heuristic algorithm is selected as the inner and outer layer optimization solver.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DIPPM: A Deep Learning Inference Performance Predictive Model Using Graph Neural networks 29th

DIPPM: A Deep Learning Inference Performance Predictive Mode...

引用

29th international conference on parallel and distributed computing (Euro-Par)

作者： Selvam, Karthick Panner Brorsson, Mats Univ Luxembourg SnT Kirchberg Luxembourg

ISBN: (纸本)9783031396977;9783031396984

Deep Learning (DL) has developed to become a corner-stone in many everyday applications that we are now relying on. However, making sure that the DL model uses the underlying hardware efficiently takes a lot of effort. Knowledge about inference characteristics can help to find the right match so that enough resources are given to the model, but not too much. We have developed a DL Inference Performance Predictive Model (DIPPM) that predicts the inference latency, energy, and memory usage of a given input DL model on the NVIDIA A100 GPU. We also devised an algorithm to suggest the appropriate A100 Multi-Instance GPU profile from the output of DIPPM. We developed a methodology to convert DL models expressed in multiple frameworks to a generalized graph structure that is used in DIPPM. It means DIPPM can parse input DL models from various frameworks. Our DIPPM can be used not only helps to find suitable hardware configurations but also helps to perform rapid design-space exploration for the inference performance of a model. We constructed a graph multi-regression dataset consisting of 10,508 different DL models to train and evaluate the performance of DIPPM, and reached a resulting Mean Absolute Percentage Error (MAPE) as low as 1.9%.

关键词： Performance Prediction Multi Instance GPU Deep Learning Inference

来源：评论

学校读者我要写书评

暂无评论

Elastic Deep Learning Using Knowledge Distillation with Heterogeneous computing Resources 27th

Elastic Deep Learning Using Knowledge Distillation with Hete...

引用

27th international European conference on parallel and distributed computing (Euro-Par)

作者： Dong, Daxiang Liu, Ji Wang, Xi Gong, Weibao Qin, An Li, Xingjian Yu, Dianhai Valduriez, Patrick Dou, Dejing Baidu Beijing Peoples R China Univ Montpellier INRIA CNRS LIRMM Montpellier France

ISBN: (纸本)9783031061561;9783031061554

In deep neural networks, using more layers and parameters generally improves the accuracy of the models, which get bigger. Such big models have high computational complexity and big memory requirements, which exceed the capacity of small devices for inference. Knowledge distillation is an efficient approach to compress a large deep model (a teacher model) to a compact model (a student model). Existing online knowledge distillation methods typically exploit an extra data storage layer to store the knowledge or deploy the teacher model and the student model at the same computing resource, thus hurting elasticity and fault-tolerance. In this paper, we propose an elastic deep learning framework, EDL-Dist, for large scale knowledge distillation to efficiently train the student model while exploiting elastic computing resources. The advantages of EDL-Dist are three-fold. First, it decouples the inference and the training process to use heterogeneous computing resources. Second, it can exploit dynamically available computing resources. Third, it supports fault-tolerance during the training and inference processes within knowledge distillation. Our experimental validation, based on industrial-strength implementation and real datasets, shows that the throughput of EDL-Dist is up to 181% faster than the baseline method (online knowledge distillation).

关键词： Knowledge distillation distributed computing Deep neural network

来源：评论

学校读者我要写书评

暂无评论

Automated Detection of Manipulated Images Using Deep Learning and Computer Vision Techniques

Automated Detection of Manipulated Images Using Deep Learnin...

引用

international conference on parallel distributed and Grid computing (PDGC)

作者： Atika Gupta Priya Matta School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Tula’s Institute Dehradun India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In the current era, where image manipulation techniques have developed at a faster pace, detection of altered images using the conventional methods has become increasingly challenging. This article presents a framework for automatically detecting the altered images using deep learning and computer vision techniques. The system focuses on providing methods to detect various image manipulations such as copy-move, inpainting and image-slicing. With the help of integrating convolutional neural networks (CNN) with anomaly detection methods and image preprocessing, the proposed framework can successfully find visual image anomalies that show potential image manipulation. The experiment conducted shows that the framework could effectively detect image alterations using the state-of-the-art computer vision and deep learning algorithms.

关键词： Deep learning Computer vision Visualization Accuracy Computational modeling Splicing Computer architecture Data augmentation Robustness Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Deployment of Sensor Node in Heterogeneous Network

Deployment of Sensor Node in Heterogeneous Network

引用

international conference on parallel distributed and Grid computing (PDGC)

作者： Sandeep Sandeep Rishi Pal Singh CSE Department GJUS&T Hisar India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The use of sensor nodes in heterogeneous networks marks a significant progress in the field of wireless sensor networks (WSNs), hence enhancing the adaptability and efficiency of the networks in various applications. This approach combines several sensor nodes into one network to offer thorough and strong data collecting. Every node has unique power needs, communication methods, and set of capabilities. Among the several significant applications—such as in smart cities, industrial automation, environmental monitoring, and healthcare—the main advantages are enhanced data accuracy, error resilience, and situational adaptation to new conditions. The areas of worry include assuring perfect compatibility, effective energy management, and safe data transport. Future research and development activities based on advanced technologies such 5G, machine learning, and artificial intelligence will mostly focus on enhancing the efficiency and efficacy of network deployment and operation. This will enable intelligent decision-making and real-time monitoring in environments limited resources.

关键词： Wireless sensor networks Smart cities Heuristic algorithms Surveillance Scalability Heterogeneous networks Real-time systems Security Research and development Resilience

来源：评论

学校读者我要写书评

暂无评论

Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

引用

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

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China 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

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

来源：评论

学校读者我要写书评

暂无评论

A distributed Ensemble of Diverse Deep Learning Models for Predicting COVID-19 Cases 20

A Distributed Ensemble of Diverse Deep Learning Models for P...

引用

20th international conference on Smart Technologies, EUROCON 2023

作者： Hassan, Mohammad Mehedi Alrakhami, Mabrook S. Elhendi, Ahmed Zohier Alqahtani, Salman A. King Saud University Information Systems Department Ccis Riadh 11543 Saudi Arabia King Saud University Science Technology and Innovation Dept. Riadh 11543 Saudi Arabia King Saud University Computer Engineering Department Ccis Riadh 11543 Saudi Arabia

ISBN: (纸本)9781665463973

The outbreak of the COVID-19 pandemic has resulted in a significant impact on global health and economy. Forecasting the spread of COVID-19 cases is essential for policymakers to make informed decisions and allocate resources accordingly. Deep learning has shown promising results in predicting the spread of infectious diseases, including COVID-19. However, selecting the most appropriate model for a given forecasting task can be challenging, as no single model is guaranteed to perform well for all scenarios. In this paper, we propose a distributed ensemble approach that combines the predictions of different deep learning models, including Autoregressive Integrated Moving Average (Auto-ARIMA), Convolutional Neural networks (CNN) and Long Short-Term Memory (LSTM), to improve the accuracy of COVID-19 case forecasts. Specifically, we train each deep learning model on a subset of the data and aggregate their predictions to obtain an ensemble forecast. To achieve a distributed ensemble, we use an edge computing framework to train the deep learning models on multiple nodes in a parallel and distributed manner. We evaluate our approach on publicly available COVID-19 datasets, and our results show that the proposed ensemble model outperforms the individual models in terms of forecasting accuracy. Furthermore, our distributed ensemble approach significantly reduces the training and prediction time compared to training each model separately. © 2023 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Energy-Aware KNN for EEG Classification: A Case Study in Heterogeneous Platforms 17th

Energy-Aware KNN for EEG Classification: A Case Study in Het...

引用

17th international Work-conference on Artificial Neural networks (IWANN)

作者： Jose Escobar, Juan Rodriguez, Francisco Kiziltepe, Rukiye Savran Prieto, Beatriz Kimovski, Dragi Ortiz, Andres Damas, Miguel Univ Granada CITIC Dept Software Engn Granada Spain Univ Granada CITIC Dept Comp Engn Automat & Robot Granada Spain Karadeniz Tech Univ Sofware Engn Trabzon Turkiye Univ Klagenfurt Inst Informat Technol Klagenfurt Austria Univ Malaga Dept Commun Engn Malaga Spain

ISBN: (纸本)9783031430848;9783031430855

The growing energy consumption caused by IT is forcing application developers to consider energy efficiency as one of the fundamental design parameters. This parameter acquires great relevance in HPC systems when running artificial neural networks and Machine Learning applications. Thus, this article shows an example of how to estimate and consider energy consumption in a real case of EEG classification. An efficient and distributed implementation of the KNN algorithm that uses mRMR as a feature selection technique to reduce the dimensionality of the dataset is proposed. The performance of three different workload distributions is analyzed to identify which one is more suitable according to the experimental conditions. The proposed approach outperforms the classification results obtained by previous works. It achieves an accuracy rate of 88.8% and a speedup of 74.53 when running on a multi-node heterogeneous cluster, consuming only 13.38% of the energy of the sequential version.

关键词： parallel and distributed programming Heterogeneous clusters Energy-aware computing EEG classification KNN mRMR

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500页 << < 30 31 32 33 34 35 36 37 38 39 > >>

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：