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

898 篇 distributed comp...
802 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
341 篇 computational mo...
304 篇 computer archite...
260 篇 application soft...
255 篇 network topology
253 篇 peer to peer com...
253 篇 protocols
244 篇 bandwidth
235 篇 grid computing
232 篇 software enginee...
189 篇 costs
177 篇 intelligent netw...
176 篇 artificial intel...

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

16 篇 jiannong cao
15 篇 m. takizawa
15 篇 li dongsheng
15 篇 shen hong
15 篇 hong shen
14 篇 shietung peng
14 篇 eyas el-qawasmeh
13 篇 lemin li
13 篇 rothermel kurt
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441-450 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：

Accelerating distributed ML Training via Selective Synchronization

Accelerating Distributed ML Training via Selective Synchroni...

引用

IEEE international conference on Cluster computing

作者： Sahil Tyagi Martin Swany Indiana University Bloomington USA

In distributed training, deep neural networks (DNNs) are launched over multiple workers concurrently and aggregate their local updates on each step in bulk-synchronous parallel (BSP) training. However, BSP does not linearly scale-out due to high communication cost of aggregation. To mitigate this overhead, alternatives like Federated Averaging (FedAvg) and Stale-Synchronous parallel (SSP) either reduce synchronization frequency or eliminate it altogether, usually at the cost of lower final accuracy. In this paper, we present SelSync, a practical, low-overhead method for DNN training that dynamically chooses to incur or avoid communication at each step either by calling the aggregation op or applying local updates based on their significance. We propose various optimizations as part of SelSync to improve convergence in the context of semi-synchronous training. Our system converges to the same or better accuracy than BSP while reducing training time by up to 14×.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Anomaly Detection in Microservices Architecture Using Graph Neural networks 33

Anomaly Detection in Microservices Architecture Using Graph ...

引用

33rd Euromicro international conference on parallel, distributed, and Network-Based Processing, PDP 2025

作者： Osswald, Matthias Schonenberger, Timothy Cantali, Gokcan Soussi, Wissem Gur, Gurkan InIT WinterthurCH-8401 Switzerland University of Zürich UZH Communication Systems Group CSG ZürichCH-8050 Switzerland

ISBN: (纸本)9798331524937

The rise of cloud computing has transformed application development and deployment, with Kubernetes emerging as a key platform for managing containerized applications. This paper explores the use of Graph Neural networks (GNNs) to detect anomalies in Kubernetes clusters and proposes GNN-based Anomaly Detection in Kubernetes (GATAKU), which is instrumental in maintaining security and performance. Our work involves integrating Cilium for detailed network monitoring and data collection, setting up a Kubernetes cluster with k3s and Traefik, and simulating attack scenarios to generate realistic data. Data preprocessing and feature engineering prepare this data for the GNN training. We present the GATAKU model's performance, highlighting metrics such as accuracy, precision, recall, and F 1 -score and compare it to baseline ML models, namely Support Vector Machine (SVM) and Random Forest (RF). Moreover, we discuss these findings and emerging challenges, including handling high-dimensional data, and explore practical implications for cybersecurity. © 2025 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

PCM Enabled Low-Power Photonic Accelerator for Inference and Training on Edge Devices

PCM Enabled Low-Power Photonic Accelerator for Inference and...

引用

1st international conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Curry, Juliana Louri, Ahmed Karanth, Avinash Bunescu, Razvan George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA Ohio Univ Sch Elect Engn & Comp Sci Athens OH 45701 USA Univ N Carolina Dept Comp Sci Charlotte NC USA

ISBN: (纸本)9798350364613;9798350364606

The convergence of edge computing and artificial intelligence requires that inference is performed on-device to provide rapid response with low latency and high accuracy without transferring large amounts of data to the cloud. However, power and size limitations make it challenging for electrical accelerators to support both inference and training for large neural network models. To this end, we propose Trident, a low-power photonic accelerator that combines the benefits of phase change material (PCM) and photonics to implement both inference and training in one unified architecture. Emerging silicon photonics has the potential to exploit the parallelism of neural network models, reduce power consumption and provide high bandwidth density via wavelength division multiplexing, making photonics an ideal candidate for on-device training and inference. As PCM is recoilfigurable and non-volatile, we utilize it for two distinct purposes: (i) to maintain resonant wavelength without expensive electrical or thermal heaters, and (ii) to implement non-linear activation function, which eliminates the need to move data between memory and compute units. This multi-purpose use of PCM is shown to lead to significant reduction in energy consumption and execution time. Compared to photonic accelerators DEAP-CNN, CrossLight, and PIXEL, Trident improves energy efficiency by up to 43% and latency by up to 150% on average. Compared to electronic edge AI accelerators Google Coral which utilizes the Google Edge TPU and Bearkey TB96-AL Trident improves energy efficiency by 11% and 93% respectively. While NVIDIA AGX Xavier is more energy efficient, the reduced data movement and GST activation of Trident reduce latency by 107% on average compared to the NVIDIA accelerator. When compared to the Google Coral and the Bearkey TB96-AI, Trident reduces latency by 1413% and 595% on average.

关键词： neural networks photonic accelerators phase change material training inference

来源：评论

学校读者我要写书评

暂无评论

On Scaling Neuronal Network Simulations Using distributed computing

On Scaling Neuronal Network Simulations Using Distributed Co...

引用

High Performance computing, Networking, Storage and Analysis, SC-W: Workshops of the international conference for

作者： Vladimir Omelyusik Khawar Shehzad Tyler Banks Praveen Rao Satish S. Nair University of Missouri

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

We investigated the computational capabilities of FABRIC, a nationwide research infrastructure with nearly 40 sites, for scaling neuroscience simulations. From the hardware standpoint, single-site characterization showed that FABRIC is a promising alternative to conventional neuroscience setups, particularly due to the availability of powerful graphics processing units (GPUs). While multi-site simulations are affected by network latency, it becomes less critical for larger networks. From the software perspective, we found that in the popular CoreNEURON library, cell distribution strategy (for parallel execution) does not affect the simulation time for biologically realistic networks, while other cases can be addressed with a minimum k-cut graph partitioning algorithm. Overall, scalability experiments revealed that FABRIC can be used to simulate networks of up to twenty-five thousand cells, with the limiting issue being GPU memory.

关键词： Neuroscience Distribution strategy Computational modeling Biological system modeling Software algorithms Graphics processing units Fabrics Hardware Software Partitioning algorithms

来源：评论

学校读者我要写书评

暂无评论

SPM-GCN: An adaptive reordering algorithm for sparse LU factorization via GCN

SPM-GCN: An adaptive reordering algorithm for sparse LU fact...

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Min Tian Huazeng Liu Junjie Wang Qi Liu Wei Du Zanjun Zhang Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan Key Laboratory of High Performance Computing Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shaanxi Key Laboratory of Large Scale Electromagnetic Computing Xidian University Xi’an China

Sparse LU factorization is a critical kernel in scientific computing and engineering applications. A better nonzero pattern of sparse matrixes can accelerate LU factorization by reordering. Traditionally it’s difficult to predict which non-zero pattern is optimal for a sparse matrix. In this paper, we proposed a graph convolutional neural network (GCN) for adaptively selecting the optimal reordering algorithm from five candidate reordering algorithms in the Matrix preprocessing step, referred to as SPM-GCN. After using our SPM-GCN, the average numerical factorization time outperformed the other five algorithms, and the average numerical factorization time was reduced by 17.3% compared with the default method of swSuperLU.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Should I Mine or Should I Break: On the Worthiness of Brute-Forcing Cryptocurrency Addresses

Should I Mine or Should I Break: On the Worthiness of Brute-...

引用

20th IEEE international conference on Pervasive computing and Communications (IEEE PerCom)

作者： Brighente, Alessandro Camaioni, Martina Conti, Mauro Olivastri, Emilio Univ Padua Dept Math Padua Italy Univ Padua HIT Res Ctr Padua Italy

ISBN: (纸本)9781665416474

Cryptocurrencies are digital assets that can be used as a medium of exchange and are stored in a ledger in the form of a cryptography-secured computer database. Most cryptocurrencies are deployed in decentralized networks such as the blockchain, where they can be transferred from an address to another. However, anyone having access to the private key of an address can fully operate and steal the full balance of such address. Although brute-force attacks consume significant resources in terms of time and power, breaking one of the richest cryptocurrency addresses leads to a huge reward in terms of hard money. Therefore, an attacker may be incentivized to run this attack. In this paper, we run a statistical study of cryptocurrencies distribution over the richest addresses and assess the worthiness of a brute-force attack towards their public key. We consider an attacker investing a certain amount of hard money (i.e., a currency that is made up of or directly backed by a valuable commodity such as gold) to run the attack and propose a statistical characterization of the average gained reward. To further assess the worthiness of a brute-forcing attack, we compare the average reward with that of other probabilistic reward systems, such as mining and scratch cards. Results show that the average reward of the proposed attack is six orders of magnitude lower than that of mining and 10 orders of magnitude lower than that of scratching cards for a given initial investment.

关键词： Bitcoin blockchain distributed ledger technology crypto-wallet bruteforce attack

来源：评论

学校读者我要写书评

暂无评论

Cloud-Driven AI: Enhancing Machine Learning Performance Through distributed computing

Cloud-Driven AI: Enhancing Machine Learning Performance Thro...

引用

international conference on ICT in Business Industry & Government (ICTBIG)

作者： Kunal Dixit Himani Parul Bhardwaj Ritu Sachdeva School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

The exponential data volume and complexity of Machine Learning (ML) algorithms has resulted in an increase in computational limitations, which affects artificial intelligence [AI]. This research seeks to establish whether cloud-based distributed computing can effectively address these limitations and improve the performance of ML. Our approach is a new architecture that deploys the computing load over a cloud framework in order to distribute and process in parallel some of the computational majorities of learning algorithms. To further enhance the application’s efficiency of processing the circuit data, a combination of adaptive resource allocation techniques, data partitioning strategies, and a new synchronization protocol has been proposed and implemented. This framework was assessed with deep neural networks, random forests, gradient-boosting machines to different datasets, and complex tasks. Focusing on the obtained results, it becomes possible to state that the proposed methods yield up to 87% improvement in the training speed and 5–12% increase in the accuracy of the models compared to the traditional implementations in single nodes. Moreover, our approach proved its capability of scalability did not degrade the performance and achieved near-linear speedup throughout the distributed nodes of up to 1000. These results propose that cloud-based AI can significantly reduce time to on-ramp for training numerous models on larger datasets. In light of this, distributed computing in the cloud offers a way to meet the computational demands of today’s AI applications while opening the door to new advancements in ML throughout different domains.

关键词： Training Machine learning algorithms Protocols Scalability Government Partitioning algorithms Synchronization Resource management distributed computing Random forests

来源：评论

学校读者我要写书评

暂无评论

A Review of Dynamic RRA Techniques on 5G And Beyond Mobile networks

A Review of Dynamic RRA Techniques on 5G And Beyond Mobile N...

引用

ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed computing (SNPD)

作者： Boikobo Nokane Bassey Isong Moshe Masonta Computer Science Dept. & UDSC North-West University Mafikeng and Council for Scientific and Industrial Research (CSIR) South Africa Computer Science Dept. & UDSC North-West University Mafikeng South Africa Council for Scientific and Industrial Research (CSIR) Pretoria South Africa

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

The 5G mobile network aims to enhance wireless communication by providing faster and reliable connectivity. Open radio access network (RAN) architecture, which offers flexibility and innovation in Radio Resource Allocation (RRA), is central for optimal network performance. However, traditional RRA methods fall short of meeting the complex demands of 5G due to scalability issues. Incorporating machine learning (ML) techniques into open RAN can enhance adaptability and intelligence, ensuring that 5G networks meet high performance and service quality standards. This paper presents a comprehensive review of ML-based and traditional RRA methods in meeting the evolving demands of wireless networks. Literature from relevant articles were selected and analysed to highlight the techniques used, trends, strengths, and limitations. The findings reveal the potential and transformative impact of ML on the future of wireless communications, particularly in achieving the key performance indicators and quality of service expected from 5G and beyond networks. It also shows that research in ML-based RRA methods is at its infancy stage and more research is needed to advance the technology.

关键词： Technological innovation 5G mobile communication Reviews Wireless networks Scalability Software algorithms Open RAN

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Teacher-Student Framework for Real-time Video Inference in Multi-User Heterogeneous MEC networks

An Adaptive Teacher-Student Framework for Real-time Video In...

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Yuan Lu Shuxin Ge Weixu Wang Xiaobo Zhou Tie Qiu Tianjin Key Laboratory of Advanced Networking College of Intelligence and Computing Tianjin University Tianjin China

Teacher-student learning has emerged as a promising framework for real-time video inference on mobile devices in multi-access edge computing (MEC) networks, where heavyweight teacher models are deployed on edge servers, and lightweight student models distilled from teacher models are deployed on mobile devices. To deal with data drift and maintain the inference accuracy, the student model has to be updated periodically with the help of the teacher model through a training process. Different training configurations, such as training epochs and frozen layers, lead to different accuracy improvements with different resource requirements. However, in multi-user heterogeneous MEC networks, due to resource heterogeneity and limited computing resources of edge servers, it is quite challenging to update all the student models simultaneously to achieve high inference accuracy. To address this problem, in this paper, we propose an adaptive teacher-student framework in multi-user heterogeneous MEC networks. The key idea is to adaptively make optimal updating decisions (i.e., offloading decision and configuration selection decision) for each user, where the available resources of edge servers and network conditions are taken into account. First, we model the teacher-student collaborative video inference problem as an optimization problem with the aim of maximizing the average inference accuracy. Then, we propose an evolutionary deep reinforcement learning algorithm, CEM-MASAC, to solve this problem. Finally, trace-driven simulations employing real-world bandwidth traces demonstrate the superiority of our algorithm compared to the baseline methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

international conference Automatics and Informatics, ICAI 2022 - Proceedings

International Conference Automatics and Informatics, ICAI 20...

引用

2022 international conference Automatics and Informatics, ICAI 2022

ISBN: (纸本)9781665476256

The proceedings contain 74 papers. The topics discussed include: overview of industrial communication in process automation;parallel distributed compensation for the control of processes in anaerobic organic wastewater treatment;multi-objective optimization of urban design using a genetic algorithm;study of received signal strength indicator values of Bluetooth low energy in test environment and simulation;a methodology for estimation of software architectural complexity in publish-subscribe systems;an approach for computing the average moisture content in batches of wood materials subjected to thermal treatment;intelligent logistics at harvest time in grain production;biosignal measurements for neurophysiological tests aimed to determine new beverage responses;energy management system based on wireless sensor networks and power line communications;and android application prototype to study text reading speed and comprehension.

关键词：

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 << < 41 42 43 44 45 46 47 48 49 50 > >>

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：