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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Kangkang Lu Meiyu Liang Zhe Xue Xiaowen Cao Mengran Yin Zehua Zhao Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665456579

Due to low storage cost and fast query speed, deep hashing methods are widely used in cross-modal retrieval. However, the “heterogeneous gap” between multi-modal data is still a challenge. Moreover, a major difficulty in deep hashing lies in the discrete constraints imposed on the network output. Existing solutions usually use relaxation techniques, but this inevitably produces quantization error, leading to sub-optimal hash code. In this paper, we propose Adversarial Guided Gradient Estimation Hashing (AGEH). Firstly, in order to bridge the heterogeneous gap between different modal data, a cross-modal adversarial feature learning network is constructed to learn cross-modal semantic associations. Secondly, to solve the discrete optimization problem of hash code, we propose a hashing optimization strategy based on gradient estimation for sign function, which strictly uses sign function to maintain discrete constraints in forward propagation, while in back propagation, the gradient is directly transmitted to the previous layer and thus avoid quantization error. Extensive experiments conducted on two cross-modal benchmark datasets show that our proposed AGEH outperforms several state-of-the-art methods.

关键词： Representation learning Bridges Backpropagation Training Codes Quantization (signal) Semantics

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CESFusion: Cross-Frequency Enhanced Spatial—Spectral Fusion Network for Hyperspectral and Multispectral Image Fusion

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Haozheng Yang, Yanhong Li, Chaoyang Lu, Yanjie Zhang, Guodao Chen, Shengyong Tianjin University of Technology School of Computer Science and Engineering Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China School of Computer Science and Engineering Tianjin300384 China Hangzhou Dianzi University Institute of Intelligent Media Computing Hangzhou310018 China

The fusion of hyperspectral and multispectral images (MSIs) involves integrating a high spectral resolution hyperspectral image (HSI) and a high spatial resolution MSI to generate an HSI with high-resolution HSI (HR-HSI). Existing HSI-MSI fusion methods primarily focus on information fusion within the spatial domain;however, few solutions have explored the employment of frequency analysis to enhance spatial resolution, limiting their capability for global perception. In this article, we propose an efficient and novel paradigm for HSI-MSI fusion through the cross-frequency enhanced spatial-spectral fusion network, named CESFusion, exploring the complementary fusion of information between the spatial and frequency domains. Specifically, we first present the cross-frequency fusion module (CFFM) to perform global analysis through the Fourier transform (FT) and effectively integrate and enhance the frequency domain information from both HSI and MSI. Subsequently, we propose the spectral modeling module (SpeMM) based on a state space model (SMM) to capture long-range spectral dependencies with linear complexity, and integrate it with the spatial residual block-based module (SRM) for joint spatial-spectral feature extraction. Finally, to enable sufficient interaction between the spatial and frequency domains, we adopt the cross-domain interaction module (CDIM), capturing and integrating complementary information from both domains. Moreover, a frequency-based loss function is purposely designed to further improve the restoration of global information. Extensive experiments conducted on both synthetic and real datasets demonstrate the superiority of our CESFusion, as evidenced by both quantitative and qualitative evaluation results. © 1980-2012 IEEE.

关键词： Image fusion

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How to Design the Emergency of Takeover Warning and Takeover Scene to Make Drivers have Better Takeover Performance?

How to Design the Emergency of Takeover Warning and Takeover...

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Conference on Vehicle Control and Intelligence (CVCI)

作者： Zhihao Wang Haolin Chen Qiuyang Tang Xiaohua Zhao Zhenlong Li Qiang Fu State Key Laboratory of Intelligent Vehicle Safety Technology Electronic Communication and Software Evaluation Research Center China Automotive Engineering Research institute Co. Ltd. Chongqing China Beijing Key Laboratory of Traffic Engineering Beijing University of Technology Beijing China Electronic Communication and Software Evaluation Research Center China Automotive Engineering Research institute Co. Ltd. Chongqing China

ISBN: (数字)9798331504892

ISBN: (纸本)9798331504908

This study aims to explore the driver's takeover performance in different emergency takeover scenes and takeover warnings, and to respond to how to design the emergency degree of takeover warning. First, we designed 4 takeover events based on takeover scenes (accident scene - emergency, ramp scene - non-emergency) and takeover request time variables (5s- emergency, l0s- non-emergency). 42 drivers participated in the driving simulation experiment, and 8 evaluation indicators were obtained from four dimensions: control behavior, eye tracking, heart rate and vehicle performance. The results show that the driver's takeover performance is globally optimal in non-emergency scenes and non-emergency warnings. In non-emergency situations, drivers can reduce stress behavior and maintain good driving status. After receiving the non-emergency takeover warning, the driver's control and psychological performance are better in the emergency scene. This study can provide a reference for a comprehensive evaluation of takeover performance in different takeover scenes and warnings, and can also be applied to the human-machine interaction takeover warning design of automated vehicles.

关键词： Heart rate Psychology Human factors Gaze tracking Vehicles Accidents

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Learning Nonconvex Tensor Representation with Generalized Reweighted Sparse Regularization for Hyperspectral Anomaly Detection

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 14718-14737页

作者： Yang, Jing Zhao, Jianbin Chen, Lu Geng, Haijun Zhang, Pei Shanxi University School of Automation and Software Engineering Taiyuan 030031 China Shanxi University Institute of Big Data Science and Industry Key Laboratory of Evolutionary Science Intelligence of Shanxi Province Taiyuan 030006 China Yan'an University Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yanan 716000 China

Tensor representation can sensitively perceive the inherent prior structure of hyperspectral images, showing broad prospects in hyperspectral anomaly detection (HAD). However, current models are still limited in effectively representing background and anomaly components therein. First, the depiction of background priors relies on multiple separate regularizers with lenient convex surrogate measures, leading to suboptimal background representation. Second, treating all entries in anomaly component equally diminishes the ability to identify anomaly targets. To address these challenges, we propose a novel tensor representation model termed as NTCTV-GRS, which is characterized by two innovative regularizers. Particularly, for background component, a nonconvex tensor correlated total variation (NTCTV) regularizer is developed. It effectively integrates low-rankness and smoothness priors of the background in one unified regularizer. More importantly, it fully considers the physical difference of singular values through a rigorous nonconvex surrogate, ensuring robust background representation. For anomaly component, a generalized reweighted sparse (GRS) regularizer is formulated by assigning the optimal weight distribution for all entries, which more effectively detects and pinpoints anomaly targets. Comparative experiments on five public datasets are extensively conducted and verify the superiority of the proposed method over existing methods in terms of diverse evaluation metrics. © 2008-2012 IEEE.

关键词： generalized reweighted sparse regularization Hyperspectral anomaly detection nonconvex optimization nonconvex tensor correlated total variation tensor representation

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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation 15th

RobNODDI: Robust NODDI Parameter Estimation with Adaptive S...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Temporal-Assisted Dynamic Beampattern Optimization in Integrated Sensing and Communication Systems

arXiv

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arXiv 2024年

作者： Zhou, Shengcai Xiang, Luping Yang, Kun University of Electronic Science and Technology of China Quzhou324003 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China School of Intelligent Software and Engineering Nanjing University Suzhou Campus Suzhou215163 China

In this paper, an integrated sensing and communication (ISAC) system is investigated. Initially, we introduce a design criterion wherein sensing data acquired from the preceding time slot is employed for instantaneous optimal beamforming in the succeeding time slot, aiming to enhance the communication rate. Subsequently, the development of optimal beamforming is addressed, and a high-caliber suboptimal resolution is derived utilizing successive convex approximation (SCA) techniques combined with the iterative rank minimization (IRM) methodology. Our evaluations, grounded on numerical analyses, reveal that the communication rate of the introduced beamforming strategy surpasses that of conventional omnidirectional sensing and pilot based approaches. Copyright © 2024, The Authors. All rights reserved.

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Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model

arXiv

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arXiv 2025年

作者： Chen, Jiali Hei, Xusen Xue, Yuqi Wu, Zihan Xie, Jiayuan Cai, Yi Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China School of Software Engineering South China University of Technology China The Hong Kong Polytechnic University Hong Kong

Chinese literary classics hold significant cultural and educational value, offering deep insights into morality, history, and human nature. These works often include classical Chinese and complex narratives, making them difficult for children to read. To bridge this gap, we introduce a child-friendly literary adaptation (CLA) task to adapt the Chinese literary classic into engaging and accessible text for children. However, recent large language models (LLMs) overlook children’s reading preferences (i.e., vivid character portrayals, concise narrative structures, and appropriate readability), which poses challenges in CLA. In this paper, we propose a method called InstructChild, which augments the LLM with these preferences for adaptation. Specifically, we first obtain the characters’ personalities and narrative structure as additional information for fine-grained instruction tuning. Then, we devise a readability metric as the reward to align the LLM with the children’s reading level. Finally, a lookahead decoding strategy is applied to improve the readability of the generated text during inference. To support the evaluation of CLA task, we construct the Classic4Children dataset, which comprises both the original and child-friendly versions of the Four Great Classical Novels of Chinese literature. Experimental results show that our InstructChild significantly improves automatic and human evaluation performance. Copyright © 2025, The Authors. All rights reserved.

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Deep Reinforcement Learning for Active RIS-Assisted Full-Duplex Integrated Sensing and Communication Systems

Deep Reinforcement Learning for Active RIS-Assisted Full-Dup...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Bingxin Zhang Kang Zheng Chao Tong Kun Yang Kang Yan State Key Laboratory of Novel Software Technology Nanjing University Nanjing China School of Intelligent Software and Engineering Nanjing University (Suzhou Campus) Suzhou China China Mobile Zijin Innovation Institute Co. Ltd. Nanjing China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Compared to traditional passive reconfigurable intelligent surfaces (RISs), active RISs can actively amplify incident signals, enhancing signal strength, reducing path-loss, and extending coverage. In this paper, we investigate an active RIS-assisted full-duplex integrated sensing and communication (ISAC) system. By jointly designing the power allocation factor for communication user equipment (UE) and the reflecting coefficients of the active RIS, the long-term average sensing performance is maximized while satisfying the signal-to-interference-plus-noise ratio (SINR) constraints for all UEs. In this paper, we explore a deep reinforcement learning (DRL) algorithm to address the complex non-convex optimization problem, aiming to maximize long-term gains in system sensing performance. Simulation results indicate that the soft actor-critic (SAC) algorithm achieves superior performance compared to two benchmark algorithms, and that active RIS demonstrates enhanced capabilities over traditional RIS.

关键词： Smart cities Simulation Full-duplex system Interference Reconfigurable intelligent surfaces Integrated sensing and communication Deep reinforcement learning Resource management Security Signal to noise ratio

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Blockchain-Based Efficiently Privacy-Preserving Federated Learning Framework Using Shamir Secret Sharing

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Fu, Xiya Xiong, Ling Li, Fagen Yang, Xingchun Xiong, Naixue School of Computer and Software Engineering Xihua University Chengdu China University of Electronic Science and Technology of China Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Chengdu China College of Intelligence and Computing Tianjin University Tianjin China

Federated learning, an emerging distributed learning paradigm, offers significant advantages and holds promise for addressing trust issues, breaking down data silos, and enabling active data sharing in the realm of consumer electronics. However, traditional federated learning faces critical security and privacy challenges in this domain, including single-point attacks, inference attacks, and poisoning attacks. To address these pressing security concerns, this paper proposes a decentralized federated learning framework focusing on security, reliability, and efficiency, tailored to meet the sustainability demands of consumer electronics. The proposed framework is founded upon masking and secret sharing techniques, establishing an emphatic privacy-preserving federated learning framework that ensures the security of gradient data and robustness against participant dropouts. Additionally, we actively motivate high-quality participants to collaborate by incorporating an incentive mechanism. Building upon the enhancement of existing federated learning approaches reliant on masking techniques, the method outlined in this paper significantly reduces communication overhead while preserving accuracy. Empirical research results comprehensively substantiate the superiority of this approach. Furthermore, compared to prevalent blockchain-based federated learning methods, our approach makes noteworthy strides in accuracy and efficiency. IEEE

关键词： Blockchain

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Adaptive Neighborhoods Locality Preserving Projections

Adaptive Neighborhoods Locality Preserving Projections

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Jiayi Xu Wendong Huang Yang Zhao Jihong Pei Xuan Yang Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University College of Electronics and Information Engineering Shenzhen China Shenzhen University College of Electronics and Information Engineering Shenzhen China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

In contemporary society, the analysis and utilisation of big data is of paramount importance. However, traditional feature extraction methods frequently encounter the curse of dimensionality when dealing with high-dimensional data, which is no longer able to adapt to the current needs of practical applications. The manifold learning method, which is capable of effectively extracting features of high-dimensional data, ne-cessitates the resolution of the problem of how to choose an appropriate local neighbourhood. Consequently, this paper proposes an Adaptive neighborhood Locality Preserving Projection (LAPP) based on the Locality Preserving Projection (LPP). TThis method combines two neighborhood selection method. To achieve an adaptive radius selection for local neighborhoods of sample points based on the distance characteristics between sample points. The LAPP proposed in this paper markedly reduces the dimensionality reduction and enhances the computational efficiency while maintaining a certain degree of accuracy.

关键词： Manifolds Accuracy High dimensional data Signal processing Big Data Feature extraction Computational efficiency Manifold learning Data mining

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