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

10,916 篇 会议
230 篇 期刊文献
173 册 图书
3 篇 学位论文

馆藏范围

11,322 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

6,679 篇 工学
- 6,108 篇 计算机科学与技术...
- 2,915 篇 软件工程
- 1,181 篇 电气工程
- 1,067 篇 信息与通信工程
- 482 篇 电子科学与技术（可...
- 358 篇 控制科学与工程
- 176 篇 仪器科学与技术
- 152 篇 机械工程
- 117 篇 动力工程及工程热...
- 113 篇 生物医学工程（可授...
- 101 篇 生物工程
- 87 篇 光学工程
- 87 篇 建筑学
- 69 篇 土木工程
- 68 篇 网络空间安全
- 65 篇 化学工程与技术
- 61 篇 材料科学与工程（可...
- 58 篇 安全科学与工程
1,468 篇 理学
- 976 篇 数学
- 311 篇 物理学
- 156 篇 系统科学
- 140 篇 统计学（可授理学、...
- 136 篇 生物学
- 83 篇 化学
898 篇 管理学
- 648 篇 管理科学与工程(可...
- 307 篇 图书情报与档案管...
- 290 篇 工商管理
145 篇 医学
- 120 篇 临床医学
- 62 篇 基础医学(可授医学...
62 篇 经济学
- 62 篇 应用经济学
56 篇 法学
34 篇 农学
26 篇 教育学
18 篇 文学
11 篇 军事学

主题

1,464 篇 parallel process...
584 篇 computer archite...
553 篇 distributed comp...
534 篇 application soft...
531 篇 computational mo...
528 篇 concurrent compu...
342 篇 hardware
319 篇 graphics process...
309 篇 scalability
307 篇 parallel program...
306 篇 graphics process...
304 篇 computer science
285 篇 runtime
263 篇 big data
262 篇 optimization
250 篇 parallel process...
226 篇 throughput
225 篇 processor schedu...
214 篇 resource managem...
209 篇 bandwidth

机构

31 篇 national laborat...
23 篇 science and tech...
21 篇 department of co...
19 篇 national laborat...
18 篇 university of ch...
17 篇 school of comput...
17 篇 institute of com...
16 篇 school of comput...
15 篇 college of compu...
15 篇 georgia inst tec...
15 篇 barcelona superc...
15 篇 intel corporatio...
14 篇 school of comput...
14 篇 chinese acad sci...
14 篇 department of co...
13 篇 tsinghua univ de...
13 篇 ibm thomas j. wa...
13 篇 cent s univ sch ...
13 篇 college of compu...
13 篇 ohio state univ ...

作者

21 篇 jack dongarra
16 篇 badia rosa m.
16 篇 liu jie
15 篇 wang guojun
15 篇 yijie wang
15 篇 a. choudhary
14 篇 anon
14 篇 kurt rothermel
13 篇 jun wang
13 篇 koldehofe boris
13 篇 mencagli gabriel...
13 篇 prodan radu
13 篇 wang yijie
12 篇 fahringer thomas
12 篇 dongsheng li
12 篇 zomaya albert y.
12 篇 navaux philippe ...
12 篇 yong dou
11 篇 fernandes luiz g...
11 篇 guangwen yang

语言

11,155 篇 英文
133 篇 其他
36 篇 中文
2 篇 俄文
2 篇 土耳其文
1 篇 德文

检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

共 11322 条记录，以下是1561-1570 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：

distributed DETECTION OF SPARSE SIGNALS WITH 1-BIT DATA IN TWO-LEVEL TWO-DEGREE TREE-STRUCTURED SENSOR NETWORKS

DISTRIBUTED DETECTION OF SPARSE SIGNALS WITH 1-BIT DATA IN T...

引用

IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Li, Chengxi Li, Gang Varshney, Pramod K. Tsinghua Univ Dept Elect Engn Beijing Peoples R China Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY USA

ISBN: (纸本)9781509066315

In this paper, we present a new detector for the detection of sparse stochastic signals using 1-bit data in two-level two-degree tree-structured sensor networks (2L-2D TSNs). Related prior work mostly concentrates on parallel sensor networks (PSNs). However, PSNs may sometime become impractical in many applications including the case where some sensors are beyond the communication range of the fusion center (FC). Therefore, we design the proposed detector for 2L-2D TSNs where information is transmitted hierarchically. To satisfy severe resource constraints, each local sensor performs 1-bit quantization before transmission to the FC. The FC fuses the received 1-bit data employing the locally most powerful test (LMPT). It is shown theoretically and numerically that, compared with the LMPT detector with Q sensors that transmit analog measurements in 2L-2D TSNs, the proposed 1-bit LMPT detector that uses quantization thresholds derived in this paper asymptotically requires 1.74Q sensors to compensate for the performance loss induced by local quantization.

关键词： distributed detection Locally most powerful tests Sparse signals Tree-structured sensor networks 1-bit quantization

来源：评论

学校读者我要写书评

暂无评论

Transparent Autonomicity for OpenMP applications 25th

Transparent Autonomicity for OpenMP Applications

引用

25th international conference on parallel and distributed Computing (Euro-Par)

作者： De Sensi, Daniele Danelutto, Marco Univ Pisa Comp Sci Dept Pisa Italy

ISBN: (纸本)9783030483401;9783030483395

One of the key needs of an autonomic computing system is the ability to monitor the application performance with minimal intrusiveness and performance overhead. Several solutions have been proposed, differing in terms of effort required by the application programmers to add autonomic capabilities to their applications. In this work we extend the NORNIR autonomic framework, allowing it to transparently monitor OpenMP applications thanks to the novel OpenMP Tools (OMPT) API. By using this interface, we are able to transparently transfer performance monitoring information from the application to the NORNIR framework. This does not require any manual intervention by the programmer, which can seamlessly control an already existing application, enforcing any performance and/or power consumption requirement. We evaluate our approach on some real applications from the PARSEC and NAS benchmarks, showing that our solution introduces a negligible performance overhead, while being able to correctly control applications' performance and power consumption.

关键词： Power-aware computing Autonomic computing OpenMP Power capping

来源：评论

学校读者我要写书评

暂无评论

Cloud-scale VM Deflation for Running Interactive applications On Transient Servers 20

Cloud-scale VM Deflation for Running Interactive Application...

引用

29th international conference on High-Performance parallel and distributed Computing (HPDC)

作者： Fuerst, Alexander Ali-Eldin, Ahmed Shenoy, Prashant Sharma, Prateek Indiana Univ Bloomington IN 47405 USA Univ Massachusetts Amherst Amherst MA USA Chalmers Univ Technol Stockholm Sweden

ISBN: (纸本)9781450370523

Transient computing has become popular in public cloud environments for running delay-insensitive batch and data processing applications at low cost. Since transient cloud servers can be revoked at any time by the cloud provider, they are considered unsuitable for running interactive application such as web services. In this paper, we present VM deflation as an alternative mechanism to server preemption for reclaiming resources from transient cloud servers under resource pressure. Using real traces from top-tier cloud providers, we show the feasibility of using VM deflation as a resource reclamation mechanism for interactive applications in public clouds. We show how current hypervisor mechanisms can be used to implement VM deflation and present cluster deflation policies for resource management of transient and on-demand cloud VMs. Experimental evaluation of our deflation system on a Linux cluster shows that microservice-based applications can be deflated by up to 50% with negligible performance overhead. Our cluster-level deflation policies allow overcommitment levels as high as 50%, with less than a 1% decrease in application throughput, and can enable cloud platforms to increase revenue by 30%.

关键词： deflation data center cloud computing transient computing

来源：评论

学校读者我要写书评

暂无评论

Selective Velocity distributed Indexing for Continuously Moving Objects Model 19th

Selective Velocity Distributed Indexing for Continuously Mov...

引用

19th international conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Bareche, Imene Xia, Ying Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing Peoples R China

ISBN: (纸本)9783030389611;9783030389604

The widespread of GPS embedded devices has lead to a ubiquitous location dependent services, based on the generated real-time location data. This introduced the notion of continuous querying, and with the aid of advanced indexing techniques several complex query types could be supported. However the efficient querying and manipulation of such highly dynamic data is not trivial, processing factors of crucial importance should be carefully thought out such as accuracy and scalability. In this study we focus on Continuous KNN (CKNN) queries processing, one of the most well-know spatio-temporal queries over large scale of continuously moving objects. In this paper we provide an overview of CKNN queries and related challenges, as well as an outline of proposed works in the literature and their limitations, before getting to our contribution proposal. We propose a novel indexing approach model for CKNN querying, namely VS-TIMO. The proposed structure is based on a selective velocity partitioning method, since we have different objects with varying speeds. Our structure base unit is a comprised of a non overlapping R-tree and a two dimensions grid. In order to enhance performances, we design a compact multi-layer index structure on a distributed setting, and propose a CKNN search algorithm for accurate results using a candidate cells identification process. We provide a comprehensive vision of our indexing model and the adopted querying technique.

关键词： Continuous KNN querying Moving objects indexing distributed spatio-temporal indices Velocity partitioning based index parallel processing

来源：评论

学校读者我要写书评

暂无评论

FALKON: Large-Scale Content-based Video Retrieval Utilizing Deep-features and distributed In-Memory Computing

FALKON: Large-Scale Content-based Video Retrieval Utilizing ...

引用

IEEE international conference on Big Data and Smart Computing (BigComp)

作者： Khan, Muhammad Numan Alam, Aftab Lee, Young-Koo Kyung Hee Univ Dept Comp Engn Gyeonggi Do South Korea

ISBN: (纸本)9781728160344

In the boom of the big-data era, social media, 5g, High-speed Internet, and High-Tech Vision equipment have resulted in an enormous amount of video data production at an alarming speed. A fast and efficient content-based video retrieval system is not only desirable but essential in many domains with widespread applications. Conventional video retrieval techniques are inadequate to fulfill the needs of the day and keep pace with the rate of video production due to three core challenges: the sheer volume of the videos being produced, the complexity of video data, and redundancy in the video data. Due to these challenges, video operations & processing demand an enormous computing power and resources to process effectively. In this paper, we propose FALKON, a content-based video retrieval system harnessing the power of big-data technologies, deep-learning and distributed in-memory computation. First, we perform structural analysis on the videos, then spatial and temporal features are computed and indexed. We apply various optimization techniques to accelerate video processing as well as accuracy. We introduce VidRDD as a basic unit for distributed in-memory video computation. Furthermore, we introduce Video Query Maps as a relevance feedback mechanism to make the proposed system more reliable, user-friendly, and to improve the retrieval results. We implement FALKON on Hadoop, Hbase, Spark, and OpenCV. We achieve an average accuracy of 97.3%. Our evaluation results show that FALKON performs very well in terms of efficiency, scalability, computation time, and precision.

关键词： CBVR Big-data Deep-Learning Spark

来源：评论

学校读者我要写书评

暂无评论

D-SmartML: A distributed Automated Machine Learning Framework 40

D-SmartML: A Distributed Automated Machine Learning Framewor...

引用

40th IEEE international conference on distributed Computing Systems (ICDCS)

作者： Abd Elrahman, Ahmed El Helw, Mohamed Elshawi, Radwa Sakr, Sherif Nile Univ Giza Egypt Univ Tartu Tartu Estonia

ISBN: (纸本)9781728170022

Nowadays, machine learning is playing a crucial role in harnessing the value of massive data amount currently produced every day. The process of building a high-quality machine learning model is an iterative, complex and time-consuming process that requires solid knowledge about the various machine learning algorithms in addition to having a good experience with effectively tuning their hyper-parameters. With the booming demand for machine learning applications, it has been recognized that the number of knowledgeable data scientists can not scale with the growing data volumes and application needs in our digital world. Therefore, recently, several automated machine learning (AutoML) frameworks have been developed by automating the process of Combined Algorithm Selection and Hyper-parameter tuning (CASH). However, a main limitation of these frameworks is that they have been built on top of centralized machine learning libraries (e.g. scikit-learn) that can only work on a single node and thus they are not scalable to process and handle large data volumes. To tackle this challenge, we demonstrate D-SmartML, a distributed AutoML framework on top of Apache Spark, a distributed data processing framework. Our framework is equipped with a meta learning mechanism for automated algorithm selection and supports three different automated hyper-parameter tuning techniques: distributed grid search, distributed random search and distributed hyperband optimization. We will demonstrate the scalability of our framework on handling large datasets. In addition, we will show how our framework outperforms the-state-of-the-art framework for distributed AutoML optimization, TransmogrifAI.

关键词： Large dataset

来源：评论

学校读者我要写书评

暂无评论

SFTA: Spiking Neural Networks Vulnerable to Spiking Feature Transferable Attack

SFTA: Spiking Neural Networks Vulnerable to Spiking Feature ...

引用

IEEE international Symposium on parallel and distributed processing with applications and IEEE international conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xuanwei Lin Chen Dong Ximeng Liu College of Computer and Data Science Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University) Key Lab of Information Security of Network Systems (Fuzhou University)

Many recent works have shown that spiking neural networks (SNNs) are vulnerable to well-crafted adversarial attacks in the white-box setting, which may lead to misclassification of the trained model. However, the information about the model is in a black-box setting and is usually unknown to the attacker, e.g., the parameters and structure of the model. So it is more challenging to counter the attack in the black-box setting, and there is still little research on this aspect in SNNs. Therefore, this work describes a transfer-based black box adversarial attack against spike feature space representations, providing insights for robust spike features in SNNs models. The attack is explicitly designed to be transferable, and more robust feature gradients are obtained by accumulating the feature gradients of the intermediate layers. Then we can successfully attack the black-box model by suppressing robust positive features and enhancing negative features. Extensive experimental evaluation demonstrates the proposed method’s effectiveness and superior performance on static and neuromorphic datasets. i.e., The attack success is 89.72% and 90% on the static MNIST and CIFAR10 datasets, and 85.97% and 69.09% on the neuromorphic N-MNIST and CIFARO-DVS datasets.

关键词： Correlation Neuromorphics Neural networks Closed box Ubiquitous computing Optimization Glass box

来源：评论

学校读者我要写书评

暂无评论

8th EAI international conference on Advancement of Science and Technology, ICAST 2020

8th EAI International Conference on Advancement of Science a...

引用

8th EAI international conference on Advancement of Science and Technology, ICAST 2020

ISBN: (纸本)9783030806170

The proceedings contain 74 papers. The special focus in this conference is on Advancement of Science and Technology. The topics include: Earliest-Arrival Route: A Global Optimized Communication for Networked Control Systems;Impulsive Noise Mitigation Using Hybrid Nonlinear Preprocessor and Turbo Code in OFDM-PLC System;comparative Analysis of Kalman Filtering and Machine Learning Based Cardiovascular Signal processing Algorithms;modeling and Analysis of Three-Phase Inverter for Induction Motor Drive for Three-Wheel Electric Vehicle Application;performance Analysis of Multicarrier Modulation techniques for Next Generation Networks;synchronous Generator Excitation Loss Detection Based on Reactive Power Flow Limit;estimation of Synchrophasor Parameters in the Presence of 3rd & 5th Harmonics and White Gaussian Noise;Performance Analysis of Vertical Sectorization in Sub-6-GHz Frequency Bands for 4G Mobile Network Under Realistic Deployment Scenario;power Loss Reduction and Voltage Profile Improvement of Radial Distribution System Through Simultaneous Network Reconfiguration and distributed Generation Integration;A Comprehensive Review and Evaluation of Classical MPPT techniques for a Photovoltaic System;antiviral Activity of Aloe pirottae A. Berger Root Extracts Against Influenza A and B Viruses, Picornaviruses and Dengue Virus: An Endemic Plant Species of Ethiopia;dynamic Performance Evaluation of Type-3 Compensator for Switch-Mode Dc-Dc Converter;inset-Feed Rectangular Microstrip Patch Antenna Array Performance Enhancement for 5G Mobile applications;blockchain Based Green Coffee Supply Chain Management to Improve Traceability and Transparency (Case Study on Sidama Coffee);wireless Local Area Network Intrusion Detection System Using Deep Belief Networks;Direct Adaptive Fuzzy PI Strategy for a Smooth MPPT of Variable Speed Wind Turbines.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cost-aware & Fault-tolerant Geo-distributed Edge Computing for Low-latency Stream processing

Cost-aware & Fault-tolerant Geo-distributed Edge Computing f...

引用

international conference on Collaborative Computing: Networking, applications and Worksharing (CollaborateCom)

作者： Jinlai Xu Balaji Palanisamy School of Computing and Information University of Pittsburgh Pittsburgh PA USA

ISBN: (纸本)9781665416269

The number of Internet-of-Things (IoT) devices is rapidly increasing with the growth of IoT applications in various domains. As IoT applications have a strong demand for low latency and high throughput computing, stream processing using edge computing resources is a promising approach to support low latency processing of large-scale data. Edge-based stream processing extends the capability of cloud-based stream processing by processing the data streams near the edge of the network. In this vision paper, we discuss a distributed stream processing framework that optimizes the performance of stream processing applications through a careful allocation of geo-distributed computing and network resources available in edge computing environments. The framework includes key optimizations in both the platform layer and the infrastructure layer. While the platform layer is responsible for converting the user program into a stream processing physical plan and optimizing the physical plan and operator placement, the infrastructure layer is responsible for provisioning geo-distributed resources to the platform layer. The framework optimizes the performance of stream query processing at the platform layer through its careful consideration of data locality and resource constraints during physical plan generation and operator placement and by incorporating resilience to deal with failures. The framework also includes techniques to dynamically determine the level of parallelism to adapt to changing workload conditions. At the infrastructure layer, the framework includes a novel model for allocating computing resources in edge and geo-distributed cloud computing environments by carefully considering latency and cost. End users benefit from the platform through reduced cost and improved user experience in terms of response time and latency.

关键词： Cloud computing Costs Throughput User experience Internet of Things Time factors Resource management

来源：评论

学校读者我要写书评

暂无评论

Building and Evaluating Federated Models for Edge Computing 16

Building and Evaluating Federated Models for Edge Computing

引用

16th international conference on Network and Service Management (CNSM) / 2nd international Workshop on Analytics for Service and Application Management (AnServApp) / 1st international Workshop on the Future Evolution of Internet Protocols (IPFuture)

作者： Amannejad, Yasaman Mt Royal Univ Dept Math & Comp Calgary AB Canada

ISBN: (纸本)9783903176317

Today's state-of-the-art machine learning (ML) techniques, such as deep learning (DL) networks are typically trained using cloud platforms, leveraging elastic scalability of the cloud. For such processing, data from various sources need to be transferred to a cloud server. While this works well for some application domains, it is not suitable for all applications due to concerns about latency, connectivity, and privacy. For example, sharing life logging photos and videos from cellphones and wearable devices can cause privacy concerns for users, and transferring the unstructured data can burden the communication network. With the increase of such applications, federated learning (FL) is proposed as a distributed ML solution for learning on edge devices, such as cellphones and wearable devices. In FL, clients collaboratively train a model on their device without sharing their data. Each client trains a local model with their data and shares the model parameters with a FL server to aggregate and build a global model. Shifting from traditional ML techniques to federated solutions requires comparing these two approaches. Moreover, users need to study the performance of FL models to decide if federation is feasible for their learning task. In this paper, we propose an automated solution to compare centrally trained DL models with federated solutions. The tool allows users to easily analyze the accuracy of federated models for their learning task and study the effect of the federated parameters. We show the features of our tool building central and federated DL models from an input model structure for recognizing images in the MNIST benchmark dataset.

关键词： distributed Machine Learning Federated Learning Edge Computing

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 << < 153 154 155 156 157 158 159 160 161 162 > >>

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：