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International Conference on computers, Communications, and Systems (ICCCS)

作者： Hong Qian Wei Wu Qi Zhu Fei Wang Jinwei Zhao Yan Zheng School of Computer Science and Technology University of Science and Technology of China Hefei China System Software Research Department Jiangnan Institute of Computing Technology Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs.

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DAM-Net: Global Flood Detection from SAR Imagery Using Differential Attention Metric-Based Vision Transformers

arXiv

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arXiv 2023年

作者： Saleh, Tamer Weng, Xingxing Holail, Shimaa Hao, Chen Xia, Gui-Song Wuhan University Wuhan430079 China National Engineering Research Center for Multi-media Software School of Computer Science Institute of Artificial Intelligence Wuhan University Wuhan430072 China Geomatics Engineering Department Faculty of Engineering at Shoubra Benha University Cairo1196 Egypt School of Computer Science Wuhan University Wuhan430072 China

The detection of flooded areas using high-resolution synthetic aperture radar (SAR) imagery is a critical task with applications in crisis and disaster management, as well as environmental resource planning. However, the complex nature of SAR images presents a challenge that often leads to an overestimation of the flood extent. To address this issue, we propose a novel differential attention metric-based network (DAM-Net) in this study. The DAM-Net comprises two key components: a weight-sharing Siamese backbone to obtain multi-scale change features of multi-temporal images and tokens containing high-level semantic information of water-body changes, and a temporal differential fusion (TDF) module that integrates semantic tokens and change features to generate flood maps with reduced speckle noise. Specifically, the backbone is split into multiple stages. In each stage, we design three modules, namely, temporal-wise feature extraction (TWFE), cross-temporal change attention (CTCA), and temporal-aware change enhancement (TACE), to effectively extract the change features. In TACE of the last stage, we introduce a class token to record high-level semantic information of water-body changes via the attention mechanism. Another challenge faced by data-driven deep learning algorithms is the limited availability of flood detection datasets. To overcome this, we have created the S1GFloods open-source dataset, a global-scale high-resolution Sentinel-1 SAR image pairs dataset covering 46 global flood events between 2015 and 2022. The experiments on the S1GFloods dataset using the proposed DAM-Net showed top results compared to state-of-the-art methods in terms of overall accuracy, F1-score, and IoU, which reached 97.8%, 96.5%, and 93.2%, respectively. Our dataset and code will be available online at https://***/Tamer-Saleh/S1GFlood-Detection. © 2023, CC BY.

关键词： Floods

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Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis

Efficient Region-Aware Neural Radiance Fields for High-Fidel...

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International Conference on computer Vision (ICCV)

作者： Jiahe Li Jiawei Zhang Xiao Bai Jun Zhou Lin Gu School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University School of Information and Communication Technology Griffith University RIKEN AIP The University of Tokyo

This paper presents ER-NeRF, a novel conditional Neural Radiance Fields (NeRF) based architecture for talking portrait synthesis that can concurrently achieve fast convergence, real-time rendering, and state-of-the-art performance with small model size. Our idea is to explicitly exploit the unequal contribution of spatial regions to guide talking portrait modeling. Specifically, to improve the accuracy of dynamic head reconstruction, a compact and expressive NeRF-based Tri-Plane Hash Representation is introduced by pruning empty spatial regions with three planar hash encoders. For speech audio, we propose a Region Attention Module to generate region-aware condition feature via an attention mechanism. Different from existing methods that utilize an MLP-based encoder to learn the cross-modal relation implicitly, the attention mechanism builds an explicit connection between audio features and spatial regions to capture the priors of local motions. Moreover, a direct and fast Adaptive Pose Encoding is introduced to optimize the head-torso separation problem by mapping the complex transformation of the head pose into spatial coordinates. Extensive experiments demonstrate that our method renders better high-fidelity and audio-lips synchronized talking portrait videos, with realistic details and high efficiency compared to previous methods. Code is available at https://***/Fictionarry/ER-NeRF.

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Dual-Functional Radar-Communication Based on Frequency Modulated Continuous Wave Exploiting Constraint Frequency Hopping

SSRN

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SSRN 2023年

作者： Xu, Rui Wen, Ruiming Gang, Li Chu, Chu Wen, Guangjun School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China School of Infomation and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China College of Computer Science Sichuan Normal University Sichuan Chengdu610101 China

This paper proposes a dual-functional radar- communication (DFRC) scheme based on the constrained frequency hopping (C-FH) chirp waveform. For communication function, the FH sequence mapping and up/down chirp are used to embed the data. We give explicit rules for traversal-FH sequence to adjust the number of configurable frequency points for each chip, and optimize the communication symbol error rate (SER) at the receiver. For radar signal processing, after coherent processing and sampling, we proceed time-domain extension and range-bin phase compensation (TE-RBPC) according to the FH value, achieving the same range profile as unmodulated frequency modulated continuous wave without influence from different FH sequences. We analyze the deterioration effects of non-traversal FH sequences, to verify the significance of traversal rule in TE-RBPC for C-FH-chirp waveform. The simulation results show that the proposed CFH-chirp waveform reduces out-of-band power leakage by 10 dB compared with phase shift keying scheme and provides 5 dB improvement of signal noise ratio under the same SER compared with unconstrained FH-chirp scheme. Compared with the scheme without compensation, the main lobe width of range profile in TE-RBPC scheme is reduced to 1/16 and the sidelobe amplitude is reduced by about 39 dB. © 2023, The Authors. All rights reserved.

关键词： Frequency hopping

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A Pose and Shape-Aware Cross-Skeleton Motion Retargeting Framework

A Pose and Shape-Aware Cross-Skeleton Motion Retargeting Fra...

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IEEE International Conference on Big Data

作者： Chengyu Liu Bo Zhang Wendong Wang Beijing Univ. of Posts and Telecom Beijing China State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications

Motion retargeting technology plays a vital role in fields such as computer animation, virtual reality, and gaming industries. Users can save a lot of costs in art design and animation production. However, the current motion retargeting methods still has many strict requirements. For example, some methods require the source and target skeletons need to have the same number of joints or share the same topology. In addition, we find many methods currently use the Mixamo dataset as the training set and test set, and use joint position errors optimize retargeting results, but ignore the source-target differences at the shape geometry level. This may result in interpenetration or loss of contact. Therefore, we introduce a novel framework and use evaluation metrics such as position error related to skeleton and interpenetration ratio related to shape, which makes the retargeting result more realistic.

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HyCU: Hybrid Consistent Update for software Defined Network

HyCU: Hybrid Consistent Update for Software Defined Network

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Xudong Mou Jie Sun Yingying Zhong Tianyu Wo School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Gusu Laboratory of Materials Suzhou Jiangsu China SKLSDE School of Software Beihang University Beijing China

software Defined Network (SDN) enables network operators to achieve the customization of network services, which tends to be more dynamic and fine-grained. However, the distributed nature of rule updating in SDN brings consistency problems, i.e., packets travel according to different versions of rules. It leads to the issues of blackholes, loops, congestion, and deadlock in the data plane, which may further affect the service quality of the application plane. With the emergence of new computing paradigms such as edge computing and fog computing, the heterogeneity of network devices and links, as well as the diversity of network application requirements have become increasingly prominent. Traditional update methods ignore these key factors when modeling, so they cannot cope with the increasingly complex network environment, resulting in delays or packet loss rates that do not meet service requirements. This paper proposes HyCU, which takes device performance as a constraint and optimizes updates based on flow service requirements. We conduct experiments under different scenarios and constraints over two real-world topologies with real-time running flows, demonstrating the effectiveness of HyCU.

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A Novel Method for Network Traffic Prediction Using Residual Mogrifier GRU

A Novel Method for Network Traffic Prediction Using Residual...

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2021 IEEE World Congress on Services, SERVICES 2021

作者： Tian, Ji-Yu Qin, Jing Chen, Li-Ming Fang, Hui Wang, Zu-Min Dalian University College Of Information Engineering Dalian China Dalian University School Of Software Engineering Dalian China Ulster University School Of Computing Belfast United Kingdom Loughborough University School Of Computer Science Loughborough United Kingdom

ISBN: (纸本)9781665427197

Network traffic prediction is essential for network management and resource scheduling within Web information systems. However, existing prediction methods have difficulty fitting mutation values in traffic time-series data and are still inadequate in terms of precision. Here we describe a method for prediction using multimodal web traffic data. The method creates multi-dimensional time series on request traffic, response traffic, and abnormal code traffic, and uses the rich information contained in the different sequences in the preceding time window to make inferences about the traffic scale in subsequent time windows. In addition, we propose an improved algorithm based on the Gated Recurrent Unit (GRU) to reduce the prediction error. The algorithm introduces the residual structure into a stacked multi-layer recurrent network structure and uses the Mogrifier structure to interact the information before it is fed to the gating unit. The experimental results show that the improved method leads to a further reduction in the error between the predicted and true values, providing high usability in the field of network traffic prediction. © 2021 IEEE.

关键词： Forecasting

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A Blockchain-Enabled Federated Learning System with Edge Computing for Vehicular Networks

A Blockchain-Enabled Federated Learning System with Edge Com...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： He, Ying Huang, Ke Zhang, Guangzheng Li, Jianqiang Chen, Jianyong Leung, Victor C. M. Shenzhen University College of Computer Science and Software Engineering China University of California Jacobs School of Engineering San Diego United States Department of Electrical and Computer Eng. The University of British Columbia VancouverBC Canada

ISBN: (纸本)9781665423908

Machine learning (ML) algorithms are essential components in autonomous driving. In most existing connected and autonomous vehicles (CAVs), a large amount of driving data collected from multiple vehicles are sent to a central server for unified training. However, data privacy and security are not well protected during the data sharing process. Moreover, the centralized architecture has some inherent issues, such as single point of failure, overload requests, intolerable delay, etc. In this article, we propose Bift: a fully decentralized machine learning system combined with federated learning and blockchain to provide a privacy-preserving ML process for CAVs. Bift enables distributed CAVs to train machine learning models locally using their own driving data and then upload the local models to the nearest mobile edge computing node (MECN) to get a better global model. More importantly, Bift provides a consensus algorithm named PoFL to resist possible adversaries. We evaluate the performance of Bift and demonstrate that Bift is scalable and robust, and can defend against malicious attacks. © 2021 IEEE.

关键词： Blockchain

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Enhancing Perception of Key Changes in Remote Sensing Image Change Captioning

arXiv

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arXiv 2024年

作者： Yang, Cong Li, Zuchao Jiao, Hongzan Gao, Zhi Zhang, Lefei The National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China The Hubei Luojia Laboratory Wuhan430079 China The School of Urban Design Wuhan University Wuhan430072 China The School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China

Recently, while significant progress has been made in remote sensing image change captioning, existing methods fail to filter out areas unrelated to actual changes, making models susceptible to irrelevant features. In this article, we propose a novel multimodal framework for remote sensing image change captioning, guided by Key Change Features and Instruction-tuned (KCFI). This framework aims to fully leverage the intrinsic knowledge of large language models through visual instructions and enhance the effectiveness and accuracy of change features using pixel-level change detection tasks. Specifically, KCFI includes a ViTs encoder for extracting bi-temporal remote sensing image features, a key feature perceiver for identifying critical change areas, a pixel-level change detection decoder to constrain key change features, and an instruction-tuned decoder based on a large language model. Moreover, to ensure that change description and change detection tasks are jointly optimized, we employ a dynamic weight-averaging strategy to balance the losses between the two tasks. We also explore various feature combinations for visual fine-tuning instructions and demonstrate that using only key change features to guide the large language model is the optimal choice. To validate the effectiveness of our approach, we compare it against several state-of-the-art change captioning methods on the LEVIR-CC dataset, achieving the best performance. Our code will be available at https://***/yangcong356/***. Copyright © 2024, The Authors. All rights reserved.

关键词： Pixels

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Personalized Federated Learning System Based on Permissioned Blockchain

Personalized Federated Learning System Based on Permissioned...

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2021 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2021

作者： Yuan, Bo Qiu, Wangjie Beihang University School of Computer Science and Engineering State Key Laboratory of Software Development Environment Beijing100191 China Beihang University State Key Laboratory of Software Development Environment Beijing100191 China

ISBN: (纸本)9781665428101

Federated learning ensures the privacy of data generated by large-scale IoT devices. Existing federated learning frameworks, based on centralized model coordinators, still face serious security challenges such as single point of failure and lack of privacy. In this paper, we propose a personalized federated learning system based on permissioned blockchain, which is divided into four layers of architecture are IoT device layer, network layer, edge computing layer, blockchain layer, and application layer, using permissioned blockchain as a federated learning server. And a permission blockchain-based personalized federation learning algorithm is proposed, which can achieve privacy protection and resistance to poisoning attacks with high accuracy. The experimental results revealed that the system has high privacy protection and anti-poisoning attack capability and can be deployed in edge computing situations. © 2021 IEEE.

关键词： Internet of things

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