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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Guo, Zhipeng Yu, Zhiyong Huang, Fangwan Tu, Chunyu Fuzhou University College of Computer and Data Science Fuzhou China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9798350312270

How to schedule public resources to maximize the coverage of crowds has always been a hot topic. There are mainly two types of research. One is the scheduling of static resources, whose coverage efficiency is lower because the resources cannot be moved;the other is the dynamic scheduling of movable public resources, but the existing methods require additional devices to collect crowd volume data. To address this issue, this article introduces a new category of mobile agents that can sense and execute simultaneously. This means that an agent can simultaneously perform crowd volume data collection tasks and crowds coverage tasks in its area. Taking these multi-ability agents as public resources, this article proposes a scheduling method called GE-STMC (Greedy Execute - Spatial and Temporal Monte-Carlo dropout). The method considers the spatiotemporal correlation between areas and evaluates the sensing value and the executing value of areas comprehensively to allocate the agents accordingly. Through the experimental on the real data of Beijing Happy Valley, this method can get 16% larger crowds volume coverage than other baseline methods with the same number of agents. © 2023 IEEE.

关键词： Mobile agents

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Determination of Continuous Dielectric Properties of Clothing Materials Using Hybrid Method

Determination of Continuous Dielectric Properties of Clothin...

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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Chen, Manni Chung, Kwok L. Huang, Zhining Wen, Jiating Li, Yingsong Huizhou University School of Computer Science and Engineering Guangdong516007 China Huizhou University Glorious Sun Guangdong School of Fashion Guangdong516007 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing China

ISBN: (纸本)9798350336740

This paper presents a hybrid method for determination of continuous dielectric properties of clothing materials. The dielectric constant and loss tangent of three types of materials are firstly investigated using open-ended coaxial probe from 0.5 to 20 GHz. The average measured values are input into the CST simulation of two CPW lines to obtain the two-port S-parameters. Finally, more reasonable yet reliable results are obtained by using two retrieval algorithms (TRACE and ABCD) for validation and comparative purposes. © 2023 IEEE.

关键词： Dielectric properties

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A Privacy-friendly sequential progressive framework for segmented decision making

A Privacy-friendly sequential progressive framework for segm...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Liu, Peng Yu, Zhiyong Huang, Fangwan Fuzhou University College of Computer and Data Science Fuzhou China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9798350312270

To achieve progressive and accurate decision-making for long-term time series data while meeting the needs of privacy-friendly and early, this paper proposes a universal framework for sequential progressive decision-making (SPD). This framework first segments the data and sets up multiple columns of neural networks according to the number of segments. Each column can make segmented decisions based on the inputs for the period. Additionally, without sharing the original data, the framework leverages lateral hidden layer connections between preceding and succeeding columns to obtain useful features for subsequent decision-making, gradually improving accuracy while avoiding the risk of data leakage. SPD has the advantages of privacy friendliness, column model diversification, prior knowledge reuse, and easy scalability, making it an effective framework for continuous decision-making. The effectiveness of that was validated using various network models in handwritten digit recognition and electrocardiogram classification tasks. The obtained experimental results reveal that SPD not only enables early decision-making while ensuring accuracy but also achieves accuracy levels comparable to or even surpassing those obtained using complete data, with the added benefit of privacy protection. © 2023 IEEE.

关键词： Decision making

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Representation Purification for End-to-End Speech Translation 31

Representation Purification for End-to-End Speech Translatio...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Chengwei Zhou, Yue Zhao, Rui Chen, Yidong Shi, Xiaodong School of Informatics Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen China

ISBN: (纸本)9798891761964

Speech-to-text translation (ST) is a cross-modal task that involves converting spoken language into text in a different language. Previous research primarily focused on enhancing speech translation by facilitating knowledge transfer from machine translation, exploring various methods to bridge the gap between speech and text modalities. Despite substantial progress made, factors in speech that are not relevant to translation content, such as timbre and rhythm, often limit the efficiency of knowledge transfer. In this paper, we conceptualize speech representation as a combination of content-agnostic and content-relevant factors. We examine the impact of content-agnostic factors on translation performance through preliminary experiments and observe a significant performance deterioration when content-agnostic perturbations are introduced to speech signals. To address this issue, we propose a Speech Representation Purification with Supervision Enhancement (SRPSE) framework, which excludes the content-agnostic components within speech representations to mitigate their negative impact on ST. Experiments on MuST-C and CoVoST-2 datasets demonstrate that SRPSE significantly improves translation performance across all translation directions in three settings and achieves preeminent performance under a transcript-free setting. © 2025 Association for Computational Linguistics.

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DeepPointMap2: Accurate and Robust LiDAR-Visual SLAM with Neural Descriptors 24

DeepPointMap2: Accurate and Robust LiDAR-Visual SLAM with Ne...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Xiaze Ding, Ziheng Jing, Qi Cheng, Ying Ding, Wenchao Feng, Rui Fudan University Shanghai China The School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China The Academy for Engineering and Technology Fudan University China The Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

ISBN: (纸本)9798400706868

Simultaneous Localization and Mapping (SLAM) plays a pivotal role in autonomous driving and robotics. Existing methods often rely on hand-craft feature extraction and cross-modal fusion techniques, resulting in limited feature representation capability and reduced robustness. To address this challenge, we introduce DeepPointMap2, a novel learning-based LiDAR-Visual SLAM architecture that leverages neural descriptors to tackle multiple SLAM sub-tasks in a unified manner. Our approach employs neural networks to extract multi-modal tokens, which are then adaptively fused by the Visual-Point Fusion Module to generate sparse 3D neural descriptors, ensuring precise and robust performance. As a pioneering work, our method achieves state-of-the-art localization performance among various Visual-, LiDAR-, and Visual-LiDAR-based methods in widely-used benchmarks, as shown in the experiment results. Furthermore, the approach proves to be robust in scenarios involving camera failure and LiDAR obstruction. © 2024 ACM.

关键词： SLAM robotics

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COMPLEX-VALUED CROSS-DOMAIN FEW-SHOT LEARNING NETWORK FOR POLSAR IMAGE CLASSIFICATION

COMPLEX-VALUED CROSS-DOMAIN FEW-SHOT LEARNING NETWORK FOR PO...

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IET International Radar Conference 2023, IRC 2023

作者： Cao, Yice Zhu, Dayi Wu, Zhenhua Chen, Jie Huang, Zhixiang Yang, Lixia Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University Hefei230601 China 38th Research Institute China Electronics Technology Group Corporation Hefei230088 China

ISBN: (纸本)9781839539954

Gathering reliable labeled samples for polarimetric synthetic aperture (PolSAR) image classification is laborious. Moreover, applying a trained classifier to new domains often leads to noticeable performance degradation due to domain disparities. Therefore, this paper proposes the novel complex-valued cross-domain (CD) few-shot learning classification (CCFSLC) method for PolSAR images to address these issues. Firstly, the transferrable knowledge learning module (TKLM) with a complex-valued feature encoder (CVFE) is trained using source data with sufficient labeled samples. Then, the deep few-shot learning module (DFSLM), constructed using the pre-trained CVFE, is trained by episodes in both source and target domains, with only minimal target labeled samples. Meanwhile, the adversarial domain adaptation module (ADAM) is employed to eliminate domain shift. The proposed CCFSLC mainly focuses on exploring discriminative information from raw PolSAR data, while reducing the domain gap to recognize novel categories in unseen domains with only a few annotated samples. Experiments on typical PolSAR datasets validate the effectiveness of the proposed method. © The Institution of Engineering & Technology 2023.

关键词： Polarimeters

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Hybrid-RIS-Aided Vehicular Wireless Sensing and Communication: Transmission Protocol and Performance Enhancement

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IEEE Transactions on Vehicular Technology 2025年

作者： Cui, Jun Cao, Xueyan Wang, Shubin Wu, Xiaolong Ge, Gerile Inner Mongolia University Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal Processing Hohhot010021 China

The reconfigurable intelligent surface (RIS) enhances vehicle-to-everything (V2X) integrated sensing and communication (ISAC) by enabling more precise sensing and efficient communication. However, achieving low complexity and low overhead V2X ISAC remains challenging in time-varying channel conditions. To overcome this, we propose a distributed hybrid RIS (HRIS)-aided V2X ISAC system featuring a predesigned transmission protocol that improves ISAC performance while minimizing channel estimation overhead and algorithmic complexity. In this system, the first sub-block of each coherence time block encompasses location sensing, beamforming and ISAC, along with a location prediction phase. At the same time, subsequent sub-blocks focus on the latter two phases. During the initial phase, two-dimensional multiple signal classificationbased adaptive location sensing is performed by sensor elements in the distributed HRISs to obtain location information without requiring channel estimation. This information is then used to design optimal beams for enhanced ISAC performance in the subsequent phase. Finally, an extended Kalman filter-based location prediction method generates optimal beams for the following sub-blocks. Simulation results confirm the effectiveness of the proposed scheme, demonstrating its superiority over existing methods in terms of ISAC performance, computational complexity, overhead, and angle estimation accuracy. © 1967-2012 IEEE.

关键词： Extended Kalman filters

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An Optimization Method Based on Deep Learning for Electromagnetic Inverse Scattering Problems

An Optimization Method Based on Deep Learning for Electromag...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Wangchao Hu Guoda Xie Jinpeng Dai Guilin Hou Xiang Li Zhixiang Huang The Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China Education of Anhui Province Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Key Laboratory of Electromagnetic Environmental Sensing Anhui Province Anhui University Hefei China Anhui University Hefei China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

In this paper, a deep learning optimization method combining U-net model and cycle generative adversarial network (Cycle-GAN) is proposed to efficiently solve the electromagnetic inverse scattering (EMIS) problems. Firstly, a U-net model is used to obtain a rough preliminary reconstruction. Through down-sampling and up-sampling processes, the basic features of the object are extracted and reconstructed from the measured scattered field, providing a coarse dielectric parameter distribution. Then, a Cycle-GAN model further refines the output of the U-net. The Cycle-GAN model, through its adversarial training mechanism involving a generator and a discriminator, enhances the details of the preliminary reconstruction, resulting in a finely detailed dielectric parameter distribution his method improves the problem of rough results obtained by using U-net method alone, and can effectively solve and optimize EMIS problems.

关键词： Deep learning Inverse problems Computational modeling Electromagnetic interference Electromagnetic scattering Optimization methods Feature extraction Dielectric measurement Electromagnetics Image reconstruction

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Attacks Against Cross-Chain Systems and Defense Approaches:A Contemporary Survey

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1647-1667页

作者： Li Duan Yangyang Sun Wei Ni Weiping Ding Jiqiang Liu Wei Wang IEEE the Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong UniversityBeijing 100044China the Guangxi Key Laboratory of Cryptography and Information Security Guilin 541004China the Communications and Signal Processing Team Data61 Business UnitCommonwealth Scientific and Industrial Research Organisation(CSIRO)Sydney NSW 2109Australia the School of Information Science and Technology Nantong UniversityNantong 226019China

The blockchain cross-chain is a significant technology for inter-chain interconnection and value transfer among different blockchain ***-chain overcomes the“information island”problem of the closed blockchain network and is increasingly applied to multiple critical areas such as finance and the internet of things(IoT).Blockchain can be divided into three main categories of blockchain networks:public blockchains,private blockchains,and consortium ***,there are differences in block structures,consensus mechanisms,and complex working mechanisms among heterogeneous *** fragility of the cross-chain system itself makes the cross-chain system face some potential security and privacy *** paper discusses security defects on the cross-chain implementation mechanism,and discusses the impact of the structural features of blockchain networks on cross-chain *** terms of cross-chain intercommunication,a cross-chain attack can be divided into a multi-chain combination attack,native chain attack,and inter-chain attack *** various security threats and attack paths faced by the cross-chain system are *** last,the corresponding security defense methods of cross-chain security threats and future research directions for cross-chain applications are put forward.

关键词： Blockchain cross-chain defense distributed private key control hash-locking notary security threats sidechain/relay

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A Blockchain-Based Framework for Secure Storage and Sharing of Resumes

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Computers, Materials & Continua 2022年第9期72卷 5395-5413页

作者： Huanrong Tang Changlin Hu Tianming Liu Jianquan Ouyang Key Laboratory of Intelligent Computing and Information Processing Ministry of EducationComputer Science College of Xiangtan UniversityXiangtanHunan ProvinceChina Department of Computer Science The University of GeorgiaAthensGeorgiaUSA

In response to problems in the centralized storage of personal resumes on third-party recruitment platforms,such as inadequate privacy protection,inability of individuals to accurately authorize downloads,and inability to determine who downloaded the resume and when,this study proposes a blockchain-based framework for secure storage and sharing of *** can employ an authorized access mechanism to protect their privacy *** proposed framework uses smart contracts,interplanetary file system,symmetric encryption,and digital signatures to protect,verify,and share *** keys are split and stored in multiple depositories through secret-sharing technology to improve the security of these *** key escrow incentives are implemented using smart contracts to automatically verify the correctness of keys and encourage the active participation of honest key escrow *** framework combines blockchain and searchable symmetric encryption technology to realize multikeyword search using inverted indexing and Bloom filters and verify search results on the *** search service fees are charged through *** after the search results are verified can the search service provider obtain the search fee,thus ensuring fair and efficient search for encrypted *** framework is decentralized,secure,and tamper-evident,and achieves controlled sharing while protecting personal privacy and information security.

关键词： Resume blockchain interplanetary file system secret sharing searchable encryption

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