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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Zhang, Qiao Chen, Xiubo Ahmad, Haseeb Xu, Gang Yang, Yixian Guizhou Provincial Key Laboratory of Public Big Data GuiZhou University Guiyang Guizhou550025 China State Key Laboratory of Networking and Switching Technology Information Security Center Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad37610 Pakistan School of Information Science and Technology North China University of Technology Beijing100144 China

ISBN: (纸本)9783031067631

Electronic health records (EHRs) contain a large amount of private data of patients. Once these data are compromised during the process of sharing, it may threaten the patients’ privacy. In this paper, a novel blockchain-assisted electronic health record sharing scheme is proposed, which utilizes attribute-based encryption (ABE) based on the consortium blockchain to realize the privacy protection in the sharing process of EHRs. Firstly, the master key is negotiated by all consensus nodes of consortium blockchain, no one knows the specific value of the master key. Consensus nodes are acted by medical institutions, they also responsible for generating and managing users’ private keys. Secondly, with the help of powerful cloud services, pre-decryption and ciphertext retrieval assignments are outsourced to cloud services for reducing the burden of blockchain. Thirdly, this scheme is also based on searchable encryption, which allows for quick ciphertext lookup from cloud services. Data owners can generate ciphertext indexes for their private data, and data users with retrieval trapdoors can quickly retrieve ciphertexts. Finally, the security and performance of blockchain-assisted electronic health record sharing scheme are analyzed in detail, the results show that our scheme is safe and feasible. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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VMINer: Versatile Multi-view Inverse Rendering with Near-and Far-field Light Sources

VMINer: Versatile Multi-view Inverse Rendering with Near-and...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Fan Fei Jiajun Tang Ping Tan Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University Light Illusions Hong Kong University of Science and Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This paper introduces a versatile multi-view inverse rendering framework with near-and far-field light sources. Tackling the fundamental challenge of inherent ambiguity in inverse rendering, our framework adopts a lightweight yet inclusive lighting model for different near-and far-field lights, thus is able to make use of input images under varied lighting conditions available during capture. It leverages observations under each lighting to disentangle the intrinsic geometry and material from the external lighting, using both neural radiance field rendering and physically-based surface rendering on the 3D implicit fields. After training, the reconstructed scene is extracted to a textured triangle mesh for seamless integration into industrial rendering soft-ware for various applications. Quantitatively and qualitatively tested on synthetic and real-world scenes, our method shows superiority to state-of-the-art multi-view inverse rendering methods in both speed and quality.

关键词： Geometry Training Surface reconstruction Three-dimensional displays Lighting Rendering (computer graphics) Neural radiance field

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Precoding Methods for FBMC in PON with Intensity Modulation and Heterodyne Detection

Precoding Methods for FBMC in PON with Intensity Modulation ...

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2021 Asia Communications and Photonics Conference, ACP 2021

作者： Wang, Xiaowu Yin, Mingzhu Li, Zhaohui Li, Fan Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-sen University China Guangdong Provincial Key Labratory of Optoelectronic Information Processing Chips and Systems Guangzhou510275 China

In this work, various precoding methods to reduce the PAPR of FBMC signal are studied and compared by simulation in terms of PAPR reduction and transmission performance in PON with intensity modulation and heterodyne ... 详细信息

ISBN: (纸本)9781957171005

关键词： Heterodyne detection

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From Data Integrity to Global ModeI Integrity for Decentralized Federated Learning: A Blockchain-based Approach

From Data Integrity to Global ModeI Integrity for Decentrali...

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International Joint Conference on Neural Networks (IJCNN)

作者： Na Wang Yao Zhao Youyang Qu Lei Cui Bai Li Longxiang Gao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of IT Deakin University VIC Australia Zhejiang Tianheng Information Technology Company China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Decentralized Federated Learning (DFL) is extensively applied in various areas, e.g., healthcare, finance, and Internet of Things (loT), offering practical solutions for distributed intelligent applications and data collaboration. In DFL systems, participants, e.g., edge devices, organizations, or nodes, collaborate in the training of a shared global model by aggregating local models from various participants. During this process, participants need to communicate frequently with a central authority/node/server to share model parameters. Such communication is vulnerable to malicious attacks or tampering, posing a significant threat to the integrity of model training. The integrity verification method can provide an integrity guarantee for the global model of DFL. However, most of the existing integrity verification schemes are centralized and not suitable for resource-constrained DFL scenarios. Therefore, how to verify the integrity of the global model becomes an important issue in DFL. To address it, we devise a global model integrity verification method for DFL. Specifically, we generate a digital signature for each global model parameter as proof of integrity, while improving the efficiency of integrity verification by electing delegates to conduct the verification process. A series of experiments is conducted to validate the performance of the proposed method. The experimental results demonstrate that our approach not only effectively ensures the integrity of the global model but also functions well under limited resources.

关键词： Training Federated learning Data integrity Neural networks Finance Distributed databases Organizations

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Xin Xu, Feng Tao, Feifei Tong, Yao Lyu, Xin Zhong, Jianyi Kaup, André College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9798350368741

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study. © 2025 IEEE.

关键词： frequency transformer remote sensing images Semantic segmentation spectrum enhancement

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A Blockchain-Based IoT Data Secure Vickery Auction System 8th

A Blockchain-Based IoT Data Secure Vickery Auction System

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Wang, Haohui Chen, Xiubo Ahmad, Haseeb Xu, Gang Yang, Yixian Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guizhou Guiyang550025 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad37610 Pakistan School of Information Science and Technology North China University of Technology Beijing100144 China

ISBN: (纸本)9783031067631

Traditional centralized architectures for IoT data suffer from single points of failure in terms of data storage and access control, and IoT data does not fully realize its value as a digital asset. In response to the above challenges, we propose a decentralized storage and lightweight access control method for IoT data based on blockchain technology, and put forward an auction system to commercialize the IoT data. Firstly, a blockchain-based data storage method proposed using Inter Planetary File System (IPFS), which ensures the data is immutable and traceable. Secondly, in this paper, we propose a distributed lightweight Capability-Based Access Control (CapBAC) based on blockchain, in which a time dimension is added so that no additional token revocation operation is required. Thus, lightweight access control is achieved under IoT devices with limited computing power. Finally, we come up with a blockchain-based Vickery auction system to sell the data to the highest bidder in demand. This auction effectively protects the privacy of bidders and does not require third-party intermediary fees. And it can ensure fairness and non-repudiation of the auction. By analyzing the entire system process, we guarantee secure storage of IoT data. Besides, we can achieve lightweight access control and realize the value of the data based on the blockchain technology. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Access control

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SfPUEL: shape from polarization under unknown environment light 24

SfPUEL: shape from polarization under unknown environment li...

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Proceedings of the 38th International Conference on Neural information processing Systems

作者： Youwei Lyu Heng Guo Kailong Zhang Si Li Boxin Shi School of Artificial Intelligence Beijing University of Posts and Telecommunications State Key Laboratory for Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University

ISBN: (纸本)9798331314385

Shape from polarization (SfP) benefits from advancements like polarization cam eras for single-shot normal estimation, but its performance heavily relies on light conditions. This paper proposes SfPUEL, an end-to-end SfP method to jointly estimate surface normal and material under unknown environment light. To handle this challenging light condition, we design a transformer-based framework for en hancing the perception of global context features. We further propose to integrate photometric stereo (PS) priors from pretrained models to enrich extracted features for high-quality normal predictions. As metallic and dielectric materials exhibit different BRDFs, SfPUEL additionally predicts dielectric and metallic material segmentation to further boost performance. Experimental results on synthetic and our collected real-world dataset demonstrate that SfPUEL significantly outperforms existing SfP and single-shot normal estimation methods. The code and dataset is available at https://***/YouweiLyu/SfPUEL.

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Attention-based UAV Trajectory Optimization for Wireless Power Transfer-assisted IoT Systems

arXiv

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arXiv 2025年

作者： Dong, Li Jiang, Feibo Peng, Yubo School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China School of Intelligent Software and Engineering Nanjing University Suzhou215163 China

Unmanned Aerial Vehicles (UAVs) in Wireless Power Transfer (WPT)-assisted Internet of Things (IoT) systems face the following challenges: limited resources and suboptimal trajectory planning. Reinforcement learning-based trajectory planning schemes face issues of low search efficiency and learning instability when optimizing large-scale systems. To address these issues, we present an Attention-based UAV Trajectory Optimization (AUTO) framework based on the graph transformer, which consists of an Attention Trajectory Optimization Model (ATOM) and a Trajectory lEarNing Method based on Actor-critic (TENMA). In ATOM, a graph encoder is used to calculate the self-attention characteristics of all IoTDs, and a trajectory decoder is developed to optimize the number and trajectories of UAVs. TENMA then trains the ATOM using an improved Actor-Critic method, in which the real reward of the system is applied as the baseline to reduce variances in the critic network. This method is suitable for high-quality and large-scale multi-UAV trajectory planning. Finally, we develop numerous experiments, including a hardware experiment in the field case, to verify the feasibility and efficiency of the AUTO framework. Copyright © 2025, The Authors. All rights reserved.

关键词： Antennas

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Multi-Perspective Frame Element Representation for Machine Reading Comprehension 8th

Multi-Perspective Frame Element Representation for Machine ...

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8th China Conference on Knowledge Graph and Semantic Computing, CCKS 2023

作者： Guo, Shaoru Guan, Yong Li, Ru School of Computer and Information Technology Shanxi University Taiyuan China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China

ISBN: (纸本)9789819972234

Semantics understanding is a critical aspect of Machine Reading Comprehension (MRC). In recent years, researchers have started leveraging the semantic knowledge provided by FrameNet to enhance the performance of MRC systems. While significant efforts have been dedicated to Frame representation, there is a noticeable lack of research on Frame Element (FE) representation, which is equally crucial for MRC. We propose a groundbreaking approach called the Multi-Perspective Frame Elements Representation (MPFER) method. It aims to comprehensively model FEs from three distinct perspectives: FE definition, Frame (semantic scenario), and FE relation. By considering these multiple perspectives, our proposed model significantly improves the representation and understanding of FEs in MRC tasks. To validate the effectiveness of the MPFER method, we conducted extensive experiments. The results clearly demonstrate that our proposed model outperforms existing state-of-the-art methods. The superiority of our approach highlights its potential for advancing the field of MRC and showcasing the importance of properly modeling FEs for better semantic understanding. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Learning Common and Label-Specific Features for Label Distribution Learning with Correlation information

SSRN

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SSRN 2024年

作者： Wu, Yilin Lin, Yaojin Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Zhangzhou363000 China

Label distribution learning quantifies the label space for each instance and has wide applicability in different fields. However, most existing work primarily focuses on common features, ignores the significance of label-specific features. Meanwhile, label correlation is well used in these work, but they still have limitations in capturing instance correlation. Therefore, in this paper, we propose a novel approach for label distribution learning to learn common and label-specific features with correlation information i.e. instance correlation and label correlation. First, we conduct $\ell_{2,1}-$norm regularization to select common and label-specific features, simultaneously. Then, we implement an optimization function to identify instance correlation in feature space. In addition, label correlation is collected with Pearson correlation coefficient. Finally, we design an objective function that enables the joint learning of common and label-specific features while leveraging correlation information. Comprehensive experiments manifest the superiority of our proposed approach against other well-established label distribution learning algorithms. © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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