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Cloud-based storage and computing for remote sensing big data:a technical review

International Journal of Digital Earth 2022年第1期15卷 1417-1445页

作者： Chen Xu Xiaoping Du Xiangtao Fan Gregory Giuliani Zhongyang Hu Wei Wang Jie Liu Teng Wang Zhenzhen Yan Junjie Zhu Tianyang Jiang Huadong Guo Key Lab of Digital Earth Sciences Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China International Research Center of Big Data for Sustainable Development Goals BeijingPeople’s Republic of China College of Resources and Environment University of Chinese Academy of SciencesBeijingPeople’s Republic of China Institute for Environmental Sciences/GRID-Geneva University of GenevaGenevaSwitzerland Institute for Marine and Atmospheric research Utrecht (IMAU) Utrecht UniversityUtrechtThe Netherlands State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijingPeople’s Republic of China Tianyi Cloud People’s Republic of China Telecom Co.LtdGuangzhouPeople’s Republic of China Department of Computer Science and Technology Tsinghua UniversityBeijingPeople's Republic of China

The rapid growth of remote sensing big data(RSBD)has attracted considerable attention from both academia and *** the progress of computer technologies,conventional computing implementations have become technically inefficient for processing *** computing is effective in activating and mining large-scale heterogeneous data and has been widely applied to RSBD over the past *** study performs a technical review of cloud-based RSBD storage and computing from an interdisciplinary viewpoint of remote sensing and computer ***,we elaborate on four critical technical challenges resulting from the scale expansion of RSBD applications,*** storage,metadata management,data homogeneity,and computing ***,we introduce state-of-the-art cloud-based data management technologies for RSBD *** unit for manipulating remote sensing data has evolved due to the scale expansion and use of novel technologies,which we name the RSBD data *** data models are suggested,***,ARD,data cubes,and composite ***,we summarize recent research on the application of various cloud-based parallel computing technologies to RSBD computing ***,we categorize the architectures of mainstream RSBD *** research provides a comprehensive review of the fundamental issues of RSBD for computing experts and remote sensing researchers.

关键词： Remote sensing big data cloud computing data cube analysis ready data parallel computing data model

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Language Person Search with Pair-Based Weighting Loss 1

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27th International Conference on MultiMedia Modeling, MMM 2021

作者： Zhang, Peng Ouyang, Deqiang Jiang, Chunlin Shao, Jie Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang550025 China Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan Artificial Intelligence Research Institute Yibin644000 China

ISBN: (数字)9783030678326

ISBN: (纸本)9783030678319

Language person search, which means retrieving specific person images with natural language description, is becoming a research hotspot in the area of person re-identification. Compared with person re-identification which belongs to image retrieval task, language person search poses challenges due to heterogeneous semantic gap between different modal data of image and text. To solve this problem, most existing methods employ softmax-based classification loss in order to embed the visual and textual features into a common latent space. However, pair-based loss, as a successful approach of metric learning, is hardly mentioned in this task. Recently, pair-based weighting loss for deep metric learning has shown great potential in improving the performance of many retrieval-related tasks. In this paper, to better correlate person image with given language description, we introduce pair-based weighting loss which encourages model to assign appropriate weights to different image-text pairs. We have conducted extensive experiments on the dataset CUHK-PEDES and the experimental results validated the effectiveness of our proposed method. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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Privacy-Preserving Federated Learning Framework with Resisting Source Inference Attacks in Smart Healthcare

SSRN

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SSRN 2024年

作者： Chenghe, Dong Xu, Guangquan Zhang, Jianhong Bai, Hongpeng Hao, Peng College of Intelligence and Computing Tianjin University Tianjin300354 China School of Big Data Qingdao Huanghai University Qingdao China Tianjin300350 China Department of Electrical and Computer Engineering North China University of Technology Beijing100144 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

Federated Learning (FL) is a distributed machine learning technology that is extensive applications in smart healthcare, since it allows multiple medical institutions to jointly train medical diagnostic models without sharing their local sensitive dataset with others. Despite its benefits, FL is susceptible to source inference attacks and model poisoning. To avoid these issues, we propose a federated learning framework for smart healthcare that preserves privacy and employs certificateless ring signatures to counteract potential attacks. During the collaborative training process of the FL model, certificateless ring signatures are used to obscure the origin of parameter updates, ensuring the anonymity of the parameter sources. This approach has the advantage of being computationally efficient due to no complicated pairing operations. Furthermore, compared with the previous certificateless ring signature schemes, our proposed scheme is more effective in reducing the success rate of source inference attacks, and is efficient in terms of computation and communication overhead. © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Machine Reading Comprehension of High-Tech Industry Policies: A New dataset and Chinese Pre-Trained Language Model

Machine Reading Comprehension of High-Tech Industry Policies...

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2021 IEEE Conference on Telecommunications, Optics and computer science, TOCS 2021

作者： Zeng, Changchang Li, Shaobo Chen, Bin Chengdu Institute of Computer Application in Chinese Academy of Sciences University of Chinese Academy of Sciences Chengdu China Guizhou University State Key Laboratory of Public Big Data Guiyang China Chongqing Research Institute of HIT Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9781665424981

Machine reading comprehension (MRC) is a challenging research hotspot in the field of Artificial Intelligence (AI). It can be applied to many scenarios, such as intelligent question answering, intelligent document retrieval, and so on. In this article, we focus on the machine reading comprehension of high-Tech industry policy texts in China. First, we create a cloze style machine reading comprehension dataset of Chinese high-Tech industrial policies. Next, we propose a new pre-Training objective named multi-segment ordering discriminator, and we also use domain-specific dictionary to improve the MLM pre-Training process. Finally, on our dataset, we trained a new pre-Trained language model for machine reading comprehension of Chinese industrial policies. Experiment results show that our pre-Trained language model surpasses existing models such as BERT and RoBERTa in the new dataset. © 2021 IEEE.

关键词： Computational linguistics

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Attention Field Size Alters Patterns of Population Receptive Fields in the Early Visual Cortex

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Neuroscience Bulletin 2022年第2期38卷 205-208页

作者： Bo Liu Xiaochun Wang Le Wang Qiaojun Qu Wei Zhang Bin Wang Jie Xiang Ting Yan Tianyi Yan Hui Zhang College of Medical Imaging Shanxi Medical UniversityTaiyuan 030001China Department of Radiology First Clinical Medical CollegeShanxi Medical UniversityTaiyuan 030001China College of Information and Computer Taiyuan University of TechnologyTaiyuan 030001China Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province Shanxi Medical UniversityTaiyuan 030001China Translational Medicine Research Center Shanxi Medical UniversityTaiyuan 030001China School of Life Science Beijing Institute of TechnologyBeijing 100081China Ministry of Industry and Information Technology Key Laboratory of Convergence Medical Engineering System and Healthcare TechnologyBeijing Institute of TechnologyBeijing 100081China

Dear Editor,Visual spatial attention is a cognitive process by which prior information about the relevance of spatial locations is used to improve perceptual performance[1].Visual attention is spread across the attention field(AF),a range within visual *** AF reshapes the distribution of activity throughout the visual pathway,including the early visual cortex(EVC)and all areas along the dorsal and ventral visual cortical pathways[2,3].Importantly,the effect of attention depends not only on the spatial position of the AF but also on its size[4].

关键词： Visual Cortex visual

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Multi-Objective Genetic Programming Assisted Stochastic Deep Reinforcement Learning for Dynamic Knowledge Integration in Transportation Networks

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Xue, Xingsi Mao, Guojun Kumari, Saru Liu, Zhonghua Yango University College of Artificial Intelligence Fujian Fuzhou350118 China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350011 China Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350011 China Chaudhary Charan Singh University at Meerut Department of Mathematics Meerut250001 India Shengjing Hospital of China Medical University Computer Center Liaoning Shenyang110004 China

Transportation Networks (TNs) play a critical role in economic and social systems, yet the dynamic nature and inherent heterogeneity of TN data pose challenges for Dynamic Knowledge Integration (DKI). Traditional approaches for matching entities from different knowledge bases often struggle with the complexity and diversity of TN data, which varies across systems and sources such as traffic sensors and GPS devices. To address this issue, this paper proposes a novel Multi-Objective Genetic Programming assisted Stochastic Deep Reinforcement Learning (MOGP-SDRL) for DKI in TNs. Unlike existing methods, the proposed framework combines SDRL and MOGP to achieve superior efficiency, accuracy and adaptability in handling heterogeneous TN data. First, a novel SDRL framework is designed to automate and optimize the selection of Similarity Features (SFs) for entity matching. This framework incorporates a probabilistic action selection mechanism, which enhances exploration during the SF selection process. Second, a novel MOGP is presented to construct high-quality, diverse SFs by exploring non-dominated feature ensembles, enhancing both accuracy and adaptability in matching results, leading to more accurate and adaptable matching results compared to conventional methods. Lastly, new approximate evaluation metrics are developed to assess alignment quality without relying on predefined entity alignments, guiding the optimization process. Experimental evaluations on OAEI's knowledge graph (KG) dataset and five pairs of real-world TN dataset demonstrate the effectiveness of the MOGP-SDRL framework, which consistently produces high-quality matching results and achieves significant improvements in both accuracy and robustness over existing approaches. © 2000-2011 IEEE.

关键词： Deep reinforcement learning

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AdaFuse: Adaptive Medical Image Fusion Based on Spatial-Frequential Cross Attention

arXiv

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arXiv 2023年

作者： Gu, Xianming Wang, Lihui Deng, Zeyu Cao, Ying Huang, Xingyu Zhu, Yue-Min Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 Lyon69621 France

Multi-modal medical image fusion is essential for the precise clinical diagnosis and surgical navigation since it can merge the complementary information in multi-modalities into a single image. The quality of the fused image depends on the extracted single modality features as well as the fusion rules for multi-modal information. Existing deep learning-based fusion methods can fully exploit the semantic features of each modality, they cannot distinguish the effective low and high frequency information of each modality and fuse them adaptively. To address this issue, we propose AdaFuse, in which multimodal image information is fused adaptively through frequency-guided attention mechanism based on Fourier transform. Specifically, we propose the cross-attention fusion (CAF) block, which adaptively fuses features of two modalities in the spatial and frequency domains by exchanging key and query values, and then calculates the cross-attention scores between the spatial and frequency features to further guide the spatial-frequential information fusion. The CAF block enhances the high-frequency features of the different modalities so that the details in the fused images can be retained. Moreover, we design a novel loss function composed of structure loss and content loss to preserve both low and high frequency information. Extensive comparison experiments on several datasets demonstrate that the proposed method outperforms state-of-the-art methods in terms of both visual quality and quantitative metrics. The ablation experiments also validate the effectiveness of the proposed loss and fusion strategy. © 2023, CC BY.

关键词： Deep learning

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Space-Air-Ground Integrated Network (SAGIN) for 6G: Requirements, Architecture and Challenges

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China Communications 2022年第2期19卷 90-108页

作者： Huanxi Cui Jun Zhang Yuhui Geng Zhenyu Xiao Tao Sun Ning Zhang Jiajia Liu Qihui Wu Xianbin Cao School of Electronic and Information Engineering Beijing Key Laboratory for Network-Based Cooperative Air application ManagementBeihang UniversityBeijing 100191China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China China Mobile Research Institute Beijing 100053China Department of Electrical and Computer Engineering University of WindsorONCanadaN9B 3P4 National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology the School of CybersecurityNorthwestern Polytechnical UniversityXi’anShaanxi710072China College of Electronics and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210007China

As the fifth-generation(5G)mobile communication network may not meet the requirements of emerging technologies and applications,including ubiquitous coverage,industrial internet of things(IIoT),ubiquitous artificial intelligence(AI),digital twins(DT),etc.,this paper aims to explore a novel space-air-ground integrated network(SAGIN)architecture to support these new requirements for the sixth-generation(6G)mobile communication network in a flexible,low-latency and efficient ***,we first review the evolution of the mobile communication network,followed by the application and technology requirements of *** the current 5G non-terrestrial network(NTN)architecture in supporting the new requirements is deeply *** that,we proposes a new flexible,low-latency and flat SAGIN architecture,and presents corresponding use ***,the future research directions are discussed.

关键词： 6G AI DT SAGIN NTN Network architecture

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PPO-ACT: Proximal policy optimization with adversarial curriculum transfer for spatial public goods games

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Chaos, Solitons & Fractals 2025年 199卷

作者： Zhaoqilin Yang Chanchan Li Xin Wang Youliang Tian State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang 550025 Guizhou China Institute of Cryptography and Data Security Guizhou University Guiyang 550025 Guizhou China State Key Laboratory of Public Big Data College of Mathematics and Statistics Guizhou University Guiyang 550025 Guizhou China School of Mathematics and Statistics Beijing Jiaotong University Beijing 100044 Beijing China State Key Laboratory of Public Big Data College of Big Data and Information Engineering Guizhou University Guiyang 550025 Guizhou China

This study investigates cooperation evolution mechanisms in the spatial public goods game. A novel deep reinforcement learning framework, Proximal Policy Optimization with Adversarial Curriculum Transfer (PPO-ACT), is proposed to model agent strategy optimization in dynamic environments. Traditional evolutionary game models often exhibit limitations in modeling long-term decision-making processes. Imitation-based rules (e.g., Fermi) lack strategic foresight, while tabular methods (e.g., Q-learning) fail to capture spatial–temporal correlations. Deep reinforcement learning effectively addresses these limitation by bridging policy gradient methods with evolutionary game theory. Our study pioneers the application of proximal policy optimization’s continuous strategy optimization capability to public goods games through a two-stage adversarial curriculum transfer training paradigm. The experimental results show that PPO-ACT performs better in critical enhancement factor regimes. Compared to conventional standard proximal policy optimization methods, Q-learning and Fermi update rules, achieve earlier cooperation phase transitions and maintain stable cooperative equilibria. This framework exhibits better robustness when handling challenging scenarios like all-defector initial conditions. Systematic comparisons reveal the unique advantage of policy gradient methods in population-scale cooperation, i.e., achieving spatiotemporal payoff coordination through value function propagation. Our work provides a new computational framework for studying cooperation emergence in complex systems, algorithmically validating the punishment promotes cooperation hypothesis while offering methodological insights for multi-agent system strategy design.

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Achieving trustworthiness, Transferable, Cross-domain Dynamic Accumulator Authentication in Industrial Internet of Things 13

Achieving trustworthiness, Transferable, Cross-domain Dynami...

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13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020

作者： Wang, Linjie Tian, Youliang Xiong, Jinbo State Key Laboratory of Public Big Data College of Computer Science Technology Guizhou University Guiyang550025 China School of Data Science Tongren University Tongren554300 China College of Mathematics and Informatics Fujian Normal University Fuzhou350007 China

ISBN: (纸本)9781631902710

The authentication problem is one of the most significant challenges in the industrial Internet of Things control system applications. To authenticate the relationships between devices is an effective solution to tackle authentication issue. However, the previous studies of Internet of Things focused on connecting sensors and setting the sharing condition from either private or public in the sense that the relationship authentication issue between devices is not well solved. In this paper, we propose CDAA, a Cross-domain Dynamic Accumulator Authentication with a general undirected graph representing the relationship between devices needed authentication for industrial Internet of Things supporting trustworthiness cross-domain, cryptographic accumulator, and transferability. Specifically, a dynamically updatable cross-domain authentication scheme is proposed based on the cryptographic accumulator and a standard digital signature scheme as the underlay of the CDAA. Finally, we give the analysis and comparison and the results show that the proposed scheme can address the authentication issue. The effectiveness and feasibility of proposed scheme are presented and analyzed through a comparison with traditional systems in practical application. Copyright © 2020 EAI.

关键词： Internet of things

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