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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Guo, Xue Qu, Hui Sun, Hanrui He, Long Zhang, Can Technology Innovation Center of Marine Information MNR Tianjin300171 China National Marine Data and Information Service Tianjin300171 China Marine academy of Zhejiang Province Hangzhou310012 China

ISBN: (纸本)9781510688018

With the development of marine information in China, the application of spatial basic geographic information in the marine field is increasing. System integration and specialized applications based on marine spatial geographic information are becoming increasingly common. This article establishes a spatiotemporal database for marine geographic information applications. It produces and releases basic geographic base map data services for vector, terrain, and remote sensing images, as well as marine 3D sphere model services. Application of microservice architecture and container technology, platform portal customized integration technology, sea and land integrated data distribution integration technology to build marine map service platform and realize the base map sharing application, special data query and analysis, spatial processing analysis, online mapping and microservice API sharing, and other functions. The platform improves the utilization efficiency of marine spatial geographic information resources. © 2025 SPIE.

关键词： Marine applications

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Two efficient beamforming methods for hybrid IRS-aided AF relay wireless networks

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Science China(information Sciences) 2025年第4期68卷 363-379页

作者： Xuehui WANG Qingbo LI Wen ZHU Feng SHU Mengxing HUANG Fuhui ZHOU Riqing CHEN Cunhua PAN Yongpeng WU Jiangzhou WANG School of Mathematics and Statistics Hainan Normal University Key Laboratory of Data Science and Smart Education Ministry of Education Hainan Normal University School of Information and Communication Engineering Hainan University Collaborative Innovation Center of Information Technology Hainan University School of Electronic and Optical Engineering Nanjing University of Science and Technology College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Digital Fujian Institute of Big Data for Agriculture Fujian Agriculture and Forestry University National Mobile Communications Research Laboratory Southeast University Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University School of Engineering University of Kent

Owing to its ability to mitigate the double-fading effect by amplifying the reflected signal, the active intelligent reflecting surface(IRS) has garnered significant attention. In this paper, an amplify-and-forward(AF) relay network assisted by a hybrid IRS consisting of both passive and active units is developed. A signal-to-noise ratio(SNR) maximization problem is formulated, where the AF relay beamforming matrix and the hybrid IRS reflecting coefficient matrices for two-time slots need to be optimized. To address the SNR maximization problem, this paper proposes both a high-performance(HP) method and a low-complexity(LC) method. The HP method is based on the semidefinite relaxation and fractional programming(SDR-FP)algorithm, with rank-1 solutions obtained through Gaussian randomization. For the LC method, the amplification coefficient of each active IRS element is assumed to be equal. The SNR maximization problem is then addressed using the whitening filter,generalized power iteration, and generalized Rayleigh-Ritz(WF-GPI-GRR) approach. Simulation results show that compared with the benchmarks, such as the passive IRS-aided AF relay network, the proposed HP-SDR-FP and WF-GPI-GRR methods achieve significant rate improvements. In particular, the HP-SDR-FP and WF-GPI-GRR methods yield more than a 135.0%rate gain when the transmit power Ps of the source is 10 dBm. Furthermore, the proposed HP-SDR-FP method outperforms the WF-GPI-GRR method in terms of rate performance.

关键词： double-fading intelligent reflecting surface active elements passive elements hybrid IRS AF relay

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Federated Dynamic Client Selection for Fairness Guarantee in Heterogeneous Edge Computing

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Journal of Computer Science & technology 2024年第1期39卷 139-158页

作者：毛莺池沈莉娟吴俊平萍吴杰 Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjing 211100China College of Computer and Information Hohai UniversityNanjing 211100China Center of Networked Computing Temple UniversityPhiladelphiaPA 19122-6096U.S.A

Federated learning has emerged as a distributed learning paradigm by training at each client and aggregat-ing at a parameter *** heterogeneity hinders stragglers from responding to the server in time with huge com-munication *** client grouping in federated learning can solve the straggler problem,the stochastic selection strategy in client grouping neglects the impact of data distribution within each ***,current client grouping ap-proaches make clients suffer unfair participation,leading to biased performances for different *** order to guaran-tee the fairness of client participation and mitigate biased local performances,we propose a federated dynamic client selec-tion method based on data representativity(FedSDR).FedSDR clusters clients into groups correlated with their own lo-cal computational *** estimate the significance of client datasets,we design a novel data representativity evalua-tion scheme based on local data ***,the two most representative clients in each group are selected to optimize the global ***,the DYNAMIC-SELECT algorithm updates local computational efficiency and data representativity states to regroup clients after periodic average *** on real datasets show that FedS-DR improves client participation by 27.4%,37.9%,and 23.3%compared with FedAvg,TiFL,and FedSS,respectively,tak-ing fairness into account in federated *** addition,FedSDR surpasses FedAvg,FedGS,and FedMS by 21.32%,20.4%,and 6.90%,respectively,in local test accuracy variance,balancing the performance bias of the global model across clients.

关键词： federated learning fairness computational efficiency data distribution client selection client grouping

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Shared Encoding and Representation of Tool-Mediated and Direct Touch Via Superposition Mechanisms

Shared Encoding and Representation of Tool-Mediated and Dire...

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2025 IEEE Symposium on Computational Intelligence in Artificial Life and Cooperative Intelligent Systems, ALIFE-CIS 2025

作者： Kasashima, Haruna Noguchi, Wataru Tamura, Yasumasa Yamamoto, Masahito Iizuka, Hiroyuki Graduate School of Information Science and Technology Hokkaido University Japan Hokkaido University Education and Research Center for Mathematical and Data Science Japan Hokkaido University Faculty of Information Science and Technology Japan Hokkaido University Center for Human Nature Artificial Intelligence and Neuroscience Japan

ISBN: (纸本)9798331508371

This study proposes a multimodal predictive neural network model that simulates tool-mediated object perception, addressing the neurologically observed phenomenon where subjective experiences during tool use are similar to direct tactile perception. To explain this phenomenon, we apply a superposition mechanism that processes experiences from both tool use and non-tool use scenarios indistinguishably. Our model processes visual, tactile, and proprioceptive inputs from a simulated robotic arm using an encoder-decoder architecture with a shared Long Short-Term Memory (LSTM) module. Trained on long-term sensorimotor data collected from robot movements during tool use and non-tool use, the network demonstrates the ability to predict visual and tactile sensations even when certain sensory inputs are masked. Notably, our results reveal that tactile stimuli in similar situations are processed uniformly, regardless of tool use. When the model's internal state during tool use is employed to simulate non-tool experiences, it predicts as if the tool were an extension of the arm itself. This suggests that the model has incorporated the tool as an integral part of its body representation, mirroring the phenomenon of tool embodiment observed in humans. © 2025 IEEE.

关键词： Robotic arms

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Using analytic hierarchy process to evaluate deep learning for infrared target recognition

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Multimedia Tools and Applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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A Weighted Gridding Method for the Long-Range Lightning Location Network Accuracy Evaluation 37

A Weighted Gridding Method for the Long-Range Lightning Loca...

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37th International Conference on Lightning Protection, ICLP 2024

作者： Song, Lin Li, Jie Zhang, Qilin Li, Qingda Yang, Jun Qingdao Ecological and Agricultural Meteorological Center Qingdao China Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9783800764174

Location accuracy is an important index to evaluate the performance of the very low frequency long-range lightning location network (VLF-LLN). However, it is impossible for us to evaluate the location accuracy of any area of the location network objectively and fairly by relying on real lightning observation data. Based on two-year lightning data of a newly built long-range lightning location network, we propose a weighted gridding method for evaluating the location network accuracy, which is different from the Monte Carlo method. We complete the following research and the results suggest the conclusions: 1) taking the location results of 11-station synchronization as a reference, it is found that the different numbers and configurations of stations have much more effect on the location results outside the network than those inside, and the deviation distance decreases with the increase of station numbers. 2) The location results of the 11-station and 13-station synchronizations were assumed to be the reference stroke points, the results of 5-to 10-station synchronizations were relocated, and the probability of deviation distance less than 1 km, 3 km, 5 km and 7 km were calculated. It is found that the proportion of the deviation distance is similar for these references, when the station numbers exceed 7-8, the median location results approximately reach a constant value, while the distribution of data samples is relatively scattered. 3) the location deviation of a large lightning samples is analyzed to statistically evaluate the location accuracy of any grid of the network by weighted gridding method, and present the percentage of deviation distance less than specific location accuracy for each grid of the network. © 2024 IEEE, © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

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Robust twin-field quantum key distribution through sending or not sending

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National Science Review 2023年第4期10卷 76-85页

作者： Cong Jiang Zong-Wen Yu Xiao-Long Hu Xiang-Bin Wang Jinan Institute of Quantum Technology State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Data Communication Science and Technology Research Institute School of Physics State Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-sen University Shanghai Branch CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shenzhen Institute for Quantum Science and Engineering and Physics Department Southern University of Science and Technology Frontier Science Center for Quantum Information

The sending-or-not-sending(SNS) protocol is one of the most major variants of the twin-field(TF)quantum key distribution(QKD) protocol and has been realized in a 511-km field fiber, the farthest field experiment to date. In practice, however, all decoy-state methods have unavoidable source errors, and the source errors may be non-random, which compromises the security condition of the existing TF-QKD protocols. In this study, we present a general approach for efficiently calculating the SNS protocol's secure key rate with source errors, by establishing the equivalent protocols through virtual atenuation and the tagged model. This makes the first result for TF QKD in practice where source intensity cannot be controlled exactly. Our method can be combined with the two-way classical communication method such as active odd-parity pairing to further improve the key rate. The numerical results show that if the intensity error is within a few percent, the key rate and secure distance only decrease marginally. The key rate of the recent SNS experiment in the 511-km field fiber is still positive using our method presented here, even if there is a ±9.5% intensity fluctuation. This shows that the SNS protocol is robust against source errors.

关键词： quantum information quantum key distribution sending or not sending twin field source error

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A Novel Metaverse-as-a-Service Architecture from an Operator View

A Novel Metaverse-as-a-Service Architecture from an Operator...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Ahsani, Vesal Rahimi, Ali Letafati, Mehdi Khalaj, Babak Hossein Sharif University of Technology Department of Electrical Engineering and Sharif Center for Information Systems and Data Science Tehran Iran

ISBN: (纸本)9798350333336

In this research paper, the authors present a comprehensive overview of three key elements of Metaverse-as-a-service (MaaS) platforms: privacy and security, edge computing, and blockchain technology. The study begins by examining security concerns for wireless access to the Metaverse. It then delves into privacy and security issues within the Metaverse from data-centric, learning-centric, and human-centric perspectives. The authors propose novel and less-explored methods to aid mobile network operators and Metaverse service providers in creating secure and private MaaS platforms across various layers of the Metaverse, from access to social interactions. The article also explores the potential of edge computing to enhance various aspects of the Metaverse and the challenges associated with its implementation. Furthermore, the study examines the 10 main challenges of MaaS platforms and how blockchain technology can address them. Finally, the authors present future visions and recommendations, such as content-centric security and zero-trust Metaverse, as well as unresolved challenges in blockchain technology to provide insights for network designers in the Metaverse era. © 2023 IEEE.

关键词： Edge computing

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Active Large Language Model-Based Knowledge Distillation for Session-Based Recommendation 39

Active Large Language Model-Based Knowledge Distillation for...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Du, Yingpeng Sun, Zhu Wang, Ziyan Chua, Haoyan Zhang, Jie Ong, Yew-Soon College of Computing and Data Science Nanyang Technological University Singapore Information Systems Technology and Design Singapore University of Technology and Design Singapore A*STAR Center for Frontier AI Research Singapore

ISBN: (纸本)157735897X

Large language models (LLMs) provide a promising way for accurate session-based recommendation (SBR), but they demand substantial computational time and memory. Knowledge distillation (KD)-based methods can alleviate these issues by transferring the knowledge to a small student, which trains a student based on the predictions of a cumbersome teacher. However, these methods encounter difficulties for LLM-based KD in SBR. 1) It is expensive to make LLMs predict for all instances in KD. 2) LLMs may make ineffective predictions for some instances in KD, e.g., incorrect predictions for hard instances or similar predictions as existing recommenders for easy instances. In this paper, we propose an active LLM-based KD method in SBR, contributing to sustainable AI. To efficiently distill knowledge from LLMs with limited cost, we propose to extract a small proportion of instances predicted by LLMs. Meanwhile, for a more effective distillation, we propose an active learning strategy to extract instances that are as effective as possible for KD from a theoretical view. Specifically, we first formulate gains based on potential effects (e.g., effective, similar, and incorrect predictions by LLMs) and difficulties (e.g., easy or hard to fit) of instances for KD. Then, we propose to maximize the minimal gains of distillation to find the optimal selection policy for active learning, which can largely avoid extracting ineffective instances in KD. Experiments on real-world datasets show that our method significantly outperforms state-of-the-art methods for SBR. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Active learning

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DBKEM-AACS: a distributed key escrow model in blockchain with anonymous authentication and committee selection

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Science China(information Sciences) 2023年第3期66卷 289-290页

作者： Axin XIANG Hongfeng GAO Youliang TIAN Liang WAN College of Computer Science and Technology Guizhou University Institute of Cryptography and Data Security Guizhou University Network and Information Management Center Guizhou University State Key Laboratory of Public Big Data Guizhou University Institute of Computer Software and Theory Guizhou University

Dear editor, Key security is of great practical significance and demand to guarantee the security of digital assets in the blockchain system. At present, users prefer to escrow their assets on centralized institutions, but this phenomenon has gone against the unique characteristics of decentralization and anonymity in the blockchain. Among them, the suspense incidents of assets lock or lost are enough to prove that the security of escrowed keys on the exchanges is questionable.

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