检索结果-内蒙古大学图书馆

Advancing RFID Technology for Virtual Boundary Detection

IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 3407-3422页

作者： Li, Xiaoyu Liu, Jia Lin, Zihao Liu, Xuan Wang, Yanyan Zhang, Shigeng Ye, Baoliu Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Hohai Univ Coll Comp & Informat Nanjing 210024 Peoples R China Cent South Univ Sch Comp Sci & Engn Sci & Technol Parallel & Distributed Proc Lab PDL Changsha 410017 Peoples R China

A boundary is a physical or virtual line that marks the edge or limit of a specific region, which has been widely used in many applications, such as autonomous driving, virtual wall, and robotic lawn mowers. However, none of existing work can well balance the deployability and the scalability of a boundary. In this paper, we propose a brand new RFID-based virtual boundary scheme together with its detection algorithm called RF-Boundary, which has the competitive advantages of being battery-free and easy-to-maintain. We develop two technologies of phase gradient and dual-antenna AoA to address the key challenges posed by RF-boundary, in terms of lack of calibration information and multi-edge interference. Besides, we consider the presence of multipath in the real world applications, model the effect on signals in the dynamic scenarios, and demonstrate the robustness of our phase gradient-based scheme under multipath. We implement a prototype of RF-Boundary with commercial RFID systems and a mobile robot. Extensive experiments verify the feasibility as well as the good performance of RF-Boundary, with a mean detection error of only 8.6 cm.

关键词： Image edge detection Robots Radiofrequency identification Calibration Mobile computing Radio frequency Robot sensing systems Location awareness Interference Wire Boundary detection Internet of Things RFID virtual boundary

来源：评论

学校读者我要写书评

暂无评论

EmbRace: Accelerating Sparse Communication for distributed Training of Deep Neural Networks 51

EmbRace: Accelerating Sparse Communication for Distributed T...

引用

51st International Conference on parallel processing (ICPP)

作者： Li, Shengwei Lai, Zhiquan Li, Dongsheng Zhang, Yiming Ye, Xiangyu Duan, Yabo Univ Def Technol Natl Key Lab Parallel & Distributed Proc Comp Coll Changsha Peoples R China Xiamen Univ Xiamen Peoples R China

ISBN: (纸本)9781450397339

distributed data-parallel training has been widely adopted for deep neural network (DNN) models. Although current deep learning (DL) frameworks scale well for dense models like image classification models, we find that these DL frameworks have relatively low scalability for sparse models like natural language processing (NLP) models that have highly sparse embedding tables. Most existing works overlook the sparsity of model parameters thus suffering from significant but unnecessary communication overhead. In this paper, we propose EmbRace, an efficient communication framework to accelerate communications of distributed training for sparse models. EmbRace introduces Sparsity-aware Hybrid Communication, which integrates AlltoAll and model parallelism into data-parallel training, so as to reduce the communication overhead of highly sparse parameters. To effectively overlap sparse communication with both backward and forward computation, EmbRace further designs a 2D Communication Scheduling approach which optimizes the model computation procedure, relaxes the dependency of embeddings, and schedules the sparse communications of each embedding row with a priority queue. We have implemented a prototype of EmbRace based on PyTorch and Horovod, and conducted comprehensive evaluations with four representative NLP models. Experimental results show that EmbRace achieves up to 2.41x speedup compared to the state-of-the-art distributed training baselines.

关键词： distributed training deep learning sparsity of NLP models communication scheduling

来源：评论

学校读者我要写书评

暂无评论

DCSA:The Deployment Mechanism of Chained Serverless Applications in JointCloud Environment 15

DCSA:The Deployment Mechanism of Chained Serverless Applicat...

引用

15th IEEE International Conference on Joint Cloud Computing (JCC)

作者： Li, Yaojie Shi, Peichang Liu, Jianfei Li, Rui Gao, Fei Ma, Penghui Xie, Dong Yi, Guodong Natl Univ Def Technol Coll Comp Sci Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China Xiangjiang Lab Changsha 410073 Peoples R China Hunan Univ Technol & Business Sch Adv Interdisciplinary Studies Changsha 410073 Peoples R China

ISBN: (纸本)9798350387339

Serverless computing, comprised of Function as a Service (FaaS) and Backend as a Service (BaaS), has garnered widespread attention owning to its features such as maintenance-free operations, pay-per-use pricing, and automatic scalability. However, practical usage encounters several challenges: 1) The diversity of user applications makes comprehensive performance evaluation difficult, as benchmark and application tests only reflect performance under specific conditions and cannot fully capture users' actual experiences across different serverless platforms. 2) Disparities in performance and costs across different serverless platforms make it challenging to achieve optimal performance and cost efficiency through single-cloud deployment, thereby underutilizing the advantages of each platform. 3) Vendor lock-in issues restrict the migration of user applications and exacerbate dependence on a single cloud provider. To address these challenges, this paper proposes a collaborative mechanism, referred to as DCSA, which integrates FaaS and storage services to achieve automatic cross-cloud deployment of user applications while considering both performance and cost comprehensively. Firstly, we adapt the interfaces of different serverless platforms, effectively reducing the complexity of cross-cloud deployment. Secondly, we develop cost and latency models for the cross-cloud deployment of chained serverless applications and propose a deployment scheduling algorithm that simultaneously considers both latency and cost. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. Results demonstrate that our method can effectively reduce latency (up to 2.3%) and lower costs (up to 9.9%).

关键词： Cloud computing JointCloud Serverless Performance and cost Cross-cloud deployment

来源：评论

学校读者我要写书评

暂无评论

Votes-as-a-Proof (VaaP): Permissioned Blockchain Consensus Protocol Made Simple

引用

IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2022年第12期33卷 4964-4973页

作者： Fu, Xiang Wang, Huaimin Shi, Peichang Natl Univ Def Technol Coll Comp Sci Natl Key Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Comp Sci Key Lab Software Engn Complex Syst Changsha 410073 Hunan Peoples R China

With the development of Blockchain technology, permissioned Blockchains are getting more and more attention from researchers because applications based on permissioned Blockchains are more practical and easier to be carried out. This paper aims to design a dedicated consensus protocol for permissioned Blockchains. The existing consensus protocols applied to permissioned Blockchains are either derived from public Blockchains such as Proof of Work (PoW) or Proof of Stake (PoS), with full decentralization, resulting in low transaction processing efficiency;or derived from traditional Byzantine fault-tolerant (BFT) consensus protocols such as Practical BFT (PBFT) or HoneyBadgerBFT, with high communication complexity of the consensus process, resulting in low scalability. Therefore, we propose a dedicated consensus protocol for permissioned Blockchains called Votes-as-a-Proof (VaaP) with high transaction processing efficiency while ensuring high scalability. Every node in VaaP runs a simple consensus process based on voting in parallel. Faulty nodes will only deprive themselves of using consensus service. We present the comparison of VaaP and Sphinx, one of the state-of-the-art consensus protocols, analytically and experimentally (up to 500 nodes). The results indicate that VaaP outperforms Sphinx in throughput, latency and scalability.

关键词： Consensus protocol Philosophical considerations Throughput Complexity theory Scalability Safety Prototypes BFT blockchain consensus protocol

来源：评论

学校读者我要写书评

暂无评论

Topic and Reference Guided keyphrase Generation from Social Media 1

引用

15th International Conference on Knowledge Science, Engineering, and Management (KSEM)

作者： Yu, Xiubin Chen, Xingjun Huang, Zhen Dou, Yong Hu, Biao Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China Dalian Navy Acad Dalian Peoples R China

ISBN: (数字)9783031109867

ISBN: (纸本)9783031109867;9783031109850

Automatic keyphrase generation can help human efficiently understand or process critical information from massive social media posts. Seq2Seq-based generation models that can produce both present and absent keyphrases have achieved remarkable performance. However, existing models are limited by the sparseness of posts that are widely exhibited in social media language. Sparseness makes it difficult for models to obtain useful features and generate accurate keyphrases. To address this problem, we propose the Topic and Reference Guided keyphrase Generation model(TRGKG) for social media posts, which enrich scarce posts features by corpus-level topics and post-level reference knowledge. The proposed model incorporates a contextual neural topic model to exploit topics and a heterogeneous graph to capture reference knowledge from retrieved related posts. To guide the decoding process, we introduce new topic-aware hierarchical attention and copy mechanism, which directly copies appropriate words from both the source post and its references. Experiments on two public datasets demonstrate that TRGKG achieves state-of-the-art performance.

关键词： keyphrase generation Seq2Seq Neural topic model Heterogeneous graph

来源：评论

学校读者我要写书评

暂无评论

A generative adversarial network based on an efficient transformer for high-fidelity flow field reconstruction

引用

PHYSICS OF FLUIDS 2024年第7期36卷 075184-075184页

作者： Shen, Liming Deng, Liang Liu, Xuliang Wang, Yueqing Chen, Xinhai Liu, Jie Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410000 Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410000 Peoples R China China Aerodynam Res & Dev Ctr State Key Lab Aerodynam Mianyang 621000 Peoples R China China Aerodynam Res & Dev Ctr Computat Aerodynam Inst Mianyang 621000 Peoples R China

The reconstruction of high-fidelity flow fields from low-fidelity data has attracted considerable attention in fluid dynamics but poses many challenges to existing deep learning methods due to the spatiotemporal complexity of flows and the lack of standardized benchmark datasets. In this study, we generate a low- and high-fidelity dataset containing 25 600 snapshots of four representative flow dynamics simulations using eight different numerical-precision and grid-resolution configurations. Using this dataset, we develop a physics-guided transformer-based generative adversarial network (PgTransGAN) for concurrently handling numerical-precision and grid-resolution enhancement. PgTransGAN leverages a dual-discriminator-based generative adversarial network for capturing continuous spatial and temporal dynamics of flows and applies a soft-constraint approach to enforce physical consistency in the reconstructed data using gradient information. An efficient transformer model is also developed to obtain the long-term temporal dependencies and further alleviate storage constraints. We compare the performance of PgTransGAN against standard linear interpolation and solutions based solely on convolutional neural networks or generative adversarial networks, and demonstrate that our method achieves better reconstruction quality at the data, image, and physics levels with an upscaling factor of 4 or even 8 in each grid dimension.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

F3A: Fairness-Aware AI-Workloads Allocation Considering Multidimensional User Demands in JointCloud 15

F3A: Fairness-Aware AI-Workloads Allocation Considering Mult...

引用

15th IEEE International Conference on Joint Cloud Computing (JCC)

作者： Yang, Jiacheng Yi, Guodong Gao, Fei Shi, Peichang Wang, Huaimin Natl Univ Def Technol Coll Comp Sci Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China Xiangjiang Lab Changsha 410073 Peoples R China Hunan Univ Technol & Business Sch Adv Interdisciplinary Studies Changsha 410073 Peoples R China

ISBN: (纸本)9798350387339

With the rapid growth of large language models, cloud computing has become an indispensable component of the AI industry. Cloud service providers(CSPs) are establishing AI data centers to service AI workloads. In the face of this surging need for AI computing power, building a connected computing environment across various clouds and forming a JointCloud presents an attractive solution. However, scheduling AI tasks across multiple AI data centers within a JointCloud environment presents a significant challenge: how to balance users' demands while ensuring CSPs' fairness in scheduling. Existing research primarily focuses on optimizing scheduling quality with limited consideration for fairness. Therefore, this paper proposes a Fairness-Aware AI-Workloads Allocation method (F3A), a fair cross-cloud allocation technique for AI tasks. F3A utilizes Point and Token to reflect both the resource status and historical task allocations of AI data centers, enabling the consideration of users' multidimensional demands and facilitating fair task allocation across multiple centers. In order to better assess the fairness of scheduling, we also devised a fairness indicator(FI), based on the Gini coefficient to measure the fairness of task allocation. The experimental results demonstrate that F3A consistently maintains FI within 0.1 across various cluster sizes and different task quantities, representing an improvement of 76.45% compared to classical fair scheduling algorithms round-robin. F3A exhibits commendable performance in ensuring fairness in task allocation while also demonstrating effectiveness in cost reduction and enhancing user satisfaction.

关键词： JointCloud Computing Fairness Task Scheduling AI Workloads AI data center

来源：评论

学校读者我要写书评

暂无评论

PCSAGAN: a physics-constrained generative network based on self-attention for high-fidelity flow field reconstruction

引用

JOURNAL OF VISUALIZATION 2024年第4期27卷 661-676页

作者： Shen, Liming Deng, Liang Wang, Yueqing Zhang, Jian Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha Peoples R China China Aerodynam Res & Dev Ctr Computat Aerodynam Inst Mianyang Peoples R China China Aerodynam Res & Dev Ctr State Key Lab Aerodynam Mianyang Peoples R China

We propose a physics-constrained generative adversarial network, PCSAGAN, based on the self-attention mechanism for high-fidelity flow field reconstruction, which can generate high-resolution and high-precision volumes for two-dimensional time-varying datasets. PCSAGAN consists of two discriminators and one generator, capable of preserving temporal coherence and spatial characteristics. It avoids the heavy computation of multilayer convolution and obtains spatial flow evolution information by using the self-attention mechanism. A soft-constraint loss function is further employed to effectively utilize the underlying physical properties. Moreover, the dataset is generated from real-world simulations rather than artificial synthesis, which ensures generalizability to practical applications. We compare PCSAGAN against volume upscaling methods using BI, ESPCN and SSR-TVD, and demonstrate its effectiveness with several time-varying flow fields through quantitative and qualitative evaluations. Meanwhile, a re-analysis framework is introduced to enable potential later visualization and exploratory analysis of the reconstructed flow field data.

关键词： Time-varying data visualization Deep learning Super-resolution Generative adversarial network

来源：评论

学校读者我要写书评

暂无评论

Deep-to-Bottom Weights Decay: A Systemic Knowledge Review Learning Technique for Transformer Layers in Knowledge Distillation 1

引用

15th International Conference on Knowledge Science, Engineering, and Management (KSEM)

作者： Wang, Ankun Liu, Feng Huang, Zhen Hu, Minghao Li, Dongsheng Chen, Yifan Xie, Xinjia Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China Informat Res Ctr Mil Sci Beijing Peoples R China

ISBN: (数字)9783031109867

ISBN: (纸本)9783031109867;9783031109850

There are millions of parameters and huge computational power consumption behind the outstanding performance of pre-trained language models in natural language processing tasks. Knowledge distillation is considered as a compression strategy to address this problem. However, previous works have the following shortcomings: (i) distill partial transformer layers of the teacher model, which not only do not make full use of the teacher-side information. But also break the coherence of the information, (ii) neglect the difficulty differences of knowledge from deep to shallow, which corresponds to different level information of teacher model. In this paper, we introduce a deep-to-bottom weights decay review mechanism to knowledge distillation, which could fuse teacher-side information while taking each layer's difficulty level into consideration. To validate our claims, we distill a 12-layer BERT into a 6-layer model and evaluate it on the GLUE dataset. Experimental results show that our review approach is not only able to outperform other existing techniques, but also outperform the original model on partial datasets.

关键词： Knowledge distillation Pre-training language model Model compression

来源：评论

学校读者我要写书评

暂无评论

Cross Modality Knowledge Distillation for Multi-modal Aerial View Object Classification

Cross Modality Knowledge Distillation for Multi-modal Aerial...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yang, Lehan Xu, Kele Univ Sydney Camperdown NSW Australia Key Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9781665448994

In the case of bad weather or low lighting conditions, a single sensor may not be able to capture enough information for object identification. Compared with the traditional optical image, synthetic aperture radar (SAR) imaging has greater advantages, such as the ability to penetrate through fog and smoke. However, SAR images are of low resolution and contaminated by high-level speckle noise. As a result, it is of great difficulty to extract powerful and robust features from the SAR images. In this paper, we explored whether multiple imaging modalities can improve the object detection performance. Here, we propose a Cross Modality Knowledge Distillation (CMKD) paradigm, and explore two different network structures named CMKD-s and CMKD-m for the object classification task. Specifically, CMKD-s transfers the information captured by the two sensors using the online knowledge distillation, which can achieve cross-modal knowledge sharing and enhance the robustness of the aerial view object classification model. Moreover, leveraging the semi-supervised enhanced training, we proposed a novel method named CMKD-m, which strengthens the model for mutual knowledge transfer. Through quantitative comparison, we found that CMKD-s and CMKD-m outperform the method without knowledge transfer, on the NTIRE2021 SAR-EO challenge dataset.

关键词： Training Speckle Feature extraction Radar polarimetry Data models Robustness Pattern recognition

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：