检索结果-内蒙古大学图书馆

15th ieee Vehicular networking conference, VNC 2024

作者： Andronovici, Darinela Turcanu, Ion Bigge, Jannusch Sommer, Christoph Luxembourg Luxembourg TU Dresden Faculty of Computer Science Germany

ISBN: (纸本)9798350362701

We tackle the challenges of accurately replicating modern automotive network architectures, particularly those reliant on automotive Ethernet and Time-Sensitive networking (TSN), in simulation environments. We describe an open-source TSN simulation model tailored to match a real-world TSN testbed built using off-The-shelf hardware. We address key challenges such as hardware and software imperfections as well as varying data traffic patterns from automotive sensors. By incorporating real data traces from LIDAR and camera sensors, we improve simulation accuracy. Our findings underscore the importance of accounting for hardware and software nuances to ensure faithful simulation results, thus advancing the reliability of automotive network simulations. © 2024 ieee.

关键词： network architecture

来源：评论

学校读者我要写书评

暂无评论

Rethinking the Up-Sampling Operations in CNN-based Generative network for Generalizable Deepfake Detection

Rethinking the Up-Sampling Operations in CNN-based Generativ...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Tan, Chuangchuang Liu, Huan Zhao, Yao Wei, Shikui Gu, Guanghua Liu, Ping Wei, Yunchao Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao Hebei Peoples R China Hebei Key Lab Informat Transmiss & Signal Proc Qinhuangdao Hebei Peoples R China Univ Nevada CSE Dept Reno NV 89557 USA

ISBN: (纸本)9798350353006

Recently, the proliferation of highly realistic synthetic images, facilitated through a variety of GANs and Diffusions, has significantly heightened the susceptibility to misuse. While the primary focus of deepfake detection has traditionally centered on the design of detection algorithms, an investigative inquiry into the generator architectures has remained conspicuously absent in recent years. This paper contributes to this lacuna by rethinking the architectures of CNN-based generator, thereby establishing a generalized representation of synthetic artifacts. Our findings illuminate that the up-sampling operator can, beyond frequency-based artifacts, produce generalized forgery artifacts. In particular, the local interdependence among image pixels caused by upsampling operators is significantly demonstrated in synthetic images generated by GAN or diffusion. Building upon this observation, we introduce the concept of Neighboring Pixel Relationships(NPR) as a means to capture and characterize the generalized structural artifacts stemming from up-sampling operations. A comprehensive analysis is conducted on an open-world dataset, comprising samples generated by 28 distinct generative models. This analysis culminates in the establishment of a novel state-of-the-art performance, showcasing a remarkable 12.8% improvement over existing methods. The code is available at https://***/chuangchuangtan/NPRDeepfakeDetection.

关键词： Deepfake Detection Neighboring Pixel Relationships Up-Sampling Operations

来源：评论

学校读者我要写书评

暂无评论

OneOS: Distributed Operating System for the Edge-to-Cloud Continuum

引用

ieee TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第6期36卷 1175-1192页

作者： Jung, Kumseok Gascon-Samson, Julien Gopalakrishnan, Sathish Pattabiraman, Karthik Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada ETS Montreal Montreal PQ H3C 1K3 Canada

Application developers often need to employ a combination of software such as communication middleware and cloud-based services to deal with the challenges of heterogeneity and network dynamism in the edge-to-cloud continuum. Consequently, developers write extra glue code peripheral to the application's core business logic, to provide interoperability between interacting software frameworks. Each software framework comes with its own framework-specific API, and as technology evolves, the developer must keep up with the changing APIs by updating the glue code in their application. Thus, framework-specific APIs hinder interoperability and cause technology fragmentation. We propose a design of a middleware-based distributed operating system (OS) called OneOS to realize a computing paradigm that alleviates such interoperability challenges. OneOS provides a single system image of the distributed computing platform, and transparently provides interoperability between software components through the standard POSIX API. Using OneOS's domain-specific language, users can compose complex distributed applications from legacy POSIX programs. OneOS tolerates failures by adopting a distributed checkpoint-restore algorithm. We evaluate the performance of OneOS against an open-source IoT Platform, ThingsJS, using an IoT stream processing benchmark suite, and a video processing application. OneOS executes the programs about 3x faster than ThingsJS, reduces the code size by about 22%, and recovers the state of failed applications within 1 s upon detecting their failure.

关键词： Codes Cloud computing Logic Pipelines Costs Streaming media Standards programming Domain specific languages Interoperability N-tier architectures edge computing cloud computing development frameworks and environments domain specific languages

来源：评论

学校读者我要写书评

暂无评论

Analysis and Optimization of Beyond Diagonal-RIS Assisted RSMA Multi-User MIMO network

Analysis and Optimization of Beyond Diagonal-RIS Assisted RS...

引用

2025 ieee Wireless Communications and networking conference, WCNC 2025

作者： Agham, Kunal Singh, Yatindra Nath Peruru, Subrahmanya Swamy Iit Kanpur Department of Electrical Engineering Kanpur208016 India

ISBN: (纸本)9798350368369

The beyond-diagonal reconfigurable intelligent surface (BD-RIS) represents the next advancement in reconfigurable intelligent surfaces (RIS), offering enhanced flexibility by supporting multiple modes and architectures. This study examines the spectral efficiency (SE) of a single-cell RSMA-based multi-user MIMO system assisted by two sector BD-RIS. The scattering matrix of BD-RIS and the power allocation between common and private precoders primarily determine the SE. We employ a two-step optimization process to determine these parameters. First, the scattering matrix is optimized by maximizing the total channel gain of the user equipment (UEs). Using the optimized scattering matrix, the power allocation for the common and private streams is then calculated by maximizing the SE of the system through a fractional programming technique, which gives a closed-form solution. The SE is computed using the optimized parameters and is numerically plotted to evaluate the performance of the system. The system is extensively analyzed across various precoding techniques, numbers of UEs, and BD-RIS elements, and is also compared to space division multiple access (SDMA). Numerical results show that BD-RIS outperforms both the traditional RIS and the scenario without RIS. © 2025 ieee.

关键词： Reconfigurable architectures

来源：评论

学校读者我要写书评

暂无评论

Mapping Spiking Neural networks to Heterogeneous Crossbar architectures using Integer Linear programming

Mapping Spiking Neural Networks to Heterogeneous Crossbar Ar...

引用

2025 Design, Automation and Test in Europe conference, DATE 2025

作者： Pohl, Devin Young, Aaron Asifuzzaman, Kazi Miniskar, Narasinga Rao Vetter, Jeffrey S. Georgia Institute of Technology United States Oak Ridge National Lab United States

ISBN: (纸本)9783982674100

Advances in novel hardware devices and architectures allow Spiking Neural network (SNN) evaluation using ultra-low power, mixed-signal, memristor crossbar arrays. As individual network sizes quickly scale beyond the dimensional capabilities of single crossbars, networks must be mapped onto multiple crossbars. Crossbar sizes within modern Memristor Crossbar architectures (MCAs) are determined predominately not by device technology but by network topology;more, smaller crossbars consume less area thanks to the high structural sparsity found in larger, brain-inspired SNNs. Motivated by continuing increases in SNN sparsity due to improvements in training methods, we propose utilizing heterogeneous crossbar sizes to further reduce area consumption. This approach was previously unachievable as prior compiler studies only explored solutions targeting homogeneous MCAs. Our work improves on the state-of-the-art by providing Integer Linear programming (ILP) formulations supporting arbitrarily heterogeneous architectures. By modeling axonal interactions between neurons, our methods produce better mappings while removing inhibitive a priori knowledge requirements. We first show a 16.7-27.6% reduction in area consumption for square-crossbar homogeneous architectures. Then, we demonstrate 66.9-72.7% further reduction when using a reasonable configuration of heterogeneous crossbar dimensions. Next, we present a new optimization formulation capable of minimizing the number of inter-crossbar routes. When applied to solutions already near-optimal in area, an 11.9-26.4% routing reduction is observed without impacting area consumption. Finally, we present a profile-guided optimization capable of minimizing the number of runtime spikes between crossbars. Compared to the best-area-then-route optimized solutions, we observe a further 0.5-14.8% inter-crossbar spike reduction while requiring 1-3 orders of magnitude less solver time. © 2025 EDAA.

关键词： Linear programming

来源：评论

学校读者我要写书评

暂无评论

Maximizing Aggregation Throughput for Distributed Training with Constrained In-network Computing

Maximizing Aggregation Throughput for Distributed Training w...

引用

ieee International conference on Communications (ieee ICC)

作者： Luo, Long Yang, Shulin Wu, Hao Yu, Hongfang Lei, Bo Gao, Shuai Univ Elect Sci & Technol China Chengdu Peoples R China China Telecom Corp Ltd Res Inst Beijing Peoples R China Beijing Jiaotong Univ Beijing Peoples R China

ISBN: (纸本)9781538674628

Distributed training (DT) has become an important and popular practice for collaborative training of high-quality machine learning (ML) models. The communication efficiency of gradient aggregation has been shown to be the primary performance bottleneck for distributed training today. Advanced programmable switches with in-network computing capabilities provide a promising direction for improving the communication efficiency of DT by offloading some gradient aggregations from the host to switches in the network. In this paper, we propose SPAR to optimize the performance of gradient aggregation under constrained in-network computing capabilities. To improve the aggregation throughput, SPAR jointly optimizes the deployment of in-network aggregation switches and the routing of aggregation requests from workers. We formulate this joint optimization problem as an integer nonlinear programming problem and design an efficient greedy algorithm to compute solutions quickly. The experimental results show that SPAR significantly outperforms the other state-of-the-art solutions based on in-network aggregation, improving aggregation throughput by up to 3x.

关键词： Integer programming

来源：评论

学校读者我要写书评

暂无评论

AUXSEGCOUNT: AUXILIARY SEG-ATTENTION BASED network FOR WHEAT EARS COUNTING IN FIELD CONDITIONS

AUXSEGCOUNT: AUXILIARY SEG-ATTENTION BASED NETWORK FOR WHEAT...

引用

ieee International conference on Multimedia and Expo (ICME)

作者： Zhang, Jie Xiong, Hao Zhang, Hecang Zhou, Meng Liu, Dong Liu, Zhonghua Shen, Hualei Henan Normal Univ Coll Comp & Informat Engn Xinxiang Peoples R China Key Lab Artificial Intelligence & Personalized Le Xinxiang Peoples R China Big Data Engn Lab Teaching Resources & Educ Qual Xinxiang Peoples R China Macquarie Univ Ctr Hlth Informat Australian Inst Hlth Innovat Sydney NSW Australia Henan Acad Agr Sci Inst Agr Informat Technol Zhengzhou Peoples R China Zhejiang Ocean Univ Sch Informat Engn Zhoushan Peoples R China

ISBN: (纸本)9798350390155;9798350390162

Accurate wheat ears counting is crucial to the wheat yield estimation. Existing counting methods explore various architectures using density maps for training, and a few works also incorporate an auxiliary network. However, the small wheat ear with background noises makes its detection hard, and the auxiliary network methods tend to ignore interactions with its main network. To mitigate these issues, we propose a novel framework AuxSegCount which includes a segmentation auxiliary network and a main network for wheat ears counting. Unlike density map, the segmentation mask provides more local contexts of wheat ears. We therefore utilise it and introduce the segmentation attention module (SAM) that aims to capture local features around wheat ears. To promote the interactions, we further present the BiAttention Fusion Module (BiFM) that exploits both global information and local contexts into the main network. The experimental results on two datasets show the superiority of our method.

关键词： Wheat ears counting auxiliary network feature fusion segmentation attention

来源：评论

学校读者我要写书评

暂无评论

Application of Ensembles of Neural networks for Deepfake Recognition

Application of Ensembles of Neural Networks for Deepfake Rec...

引用

2023 ieee Ural-Siberian conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023

作者： Yadryshnikova, Maria Latipova, Alina South Ural State University System Programming Dept. Chelyabinsk Russia

ISBN: (纸本)9798350336054

At the moment, the creation of deepfakes for various activities is widespread: sometimes for the sake of humor, and sometimes for malicious purposes. In the second case, such deepfakes can potentially harm a person. Neural networks trained to recognize deepfakes can become an actual tool for combating malicious fakes. This article proposes neural network architectures which can be used in the task of recognizing photo and video deepfakes, describes the datasets that can help in training neural networks for the task, and determines what pre-processing is needed for different datasets. Particular attention is paid to possible options for combining the results of several trained models. The architectures of neural networks Inception-Resnet and Xception were chosen as the basis, the conceptual features of these architectures were described, and their practical testing was carried out in the problem of classifying deepfakes. A method of ensembling models is chosen for better results. © 2023 ieee.

关键词： network architecture

来源：评论

学校读者我要写书评

暂无评论

Deep Neural networks architectures from the Perspective of Manifold Learning 6

Deep Neural Networks Architectures from the Perspective of M...

引用

6th ieee International conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Magai, German Higher School of Economics Department of Big Data and Information Retrieval Moscow Russia

ISBN: (纸本)9798350325485

Despite significant advances in the field of deep learning in applications to various areas, an explanation of the learning process of neural network models remains an important open question. The purpose of this paper is a comprehensive comparison and description of neural network architectures in terms of geometry and topology. We focus on the internal representation of neural networks and on the dynamics of changes in the topology and geometry of a data manifold on different layers. In this paper, we use the concepts of topological data analysis (TDA) and persistent homological fractal dimension. We present a wide range of experiments with various datasets and configurations of convolutional neural network (CNNs) architectures and transformers in CV and NLP tasks. Our work is a contribution to the development of the important field of explainable and interpretable AI within the framework of geometrical deep learning. © 2023 ieee.

关键词： Topology

来源：评论

学校读者我要写书评

暂无评论

A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen networks 59

A Deep Reinforcement Learning Approach for Adaptive Traffic ...

引用

59th Annual ieee International conference on Communications (ieee ICC)

作者： Abrol, Akshita Mohan, Purnima Murali Truong-Huu, Tram Singapore Inst Technol SIT Singapore Singapore ASTAR Singapore Singapore

ISBN: (纸本)9781728190549

Next-gen networks require significant evolution of management to enable automation and adaptively adjust network configuration based on traffic dynamics. The advent of software-defined networking (SDN) and programmable switches enables flexibility and programmability. However, traditional techniques that decide traffic policies are usually based on hand-crafted programming optimization and heuristic algorithms. These techniques make non-realistic assumptions, e.g., considering static network load and topology, to obtain tractable solutions, which are inadequate for next-gen networks. In this paper, we design and develop a deep reinforcement learning (DRL) approach for adaptive traffic routing. We design a deep graph convolutional neural network (DGCNN) integrated into the DRL framework to learn the traffic behavior from not only the network topology but also link and node attributes. We adopt the Deep Q-Learning technique to train the DGCNN model in the DRL framework without the need for a labeled training dataset, enabling the framework to quickly adapt to traffic dynamics. The model leverages q-value estimates to select the routing path for every traffic flow request, balancing exploration and exploitation. We perform extensive experiments with various traffic patterns and compare the performance of the proposed approach with the open Shortest Path First (OSPF) protocol. The experimental results show the effectiveness and adaptiveness of the proposed framework by increasing the network throughput by up to 7.8% and reducing the traffic delay by up to 16.1% compared to OSPF.

关键词： Reinforcement Learning Deep Graph Convolution Neural networks Adaptive Traffic Routing Deep Q-network Next generation networking

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：