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arXiv 2025年

作者： Wan, Xixi Zheng, Aihua Wang, Zi Jiang, Bo Tang, Jin Ma, Jixin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China School of Biomedical Engineering Anhui Medical University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom

Multi-modal data provides abundant and diverse object information, crucial for effective modal interactions in Re-Identification (ReID) tasks. However, existing approaches often overlook the quality variations in local features and fail to fully leverage the complementary information across modalities, particularly in the case of low-quality features. In this paper, we propose to address this issue by leveraging a novel graph reasoning model, termed the Modality-aware Graph Reasoning Network (MGRNet). Specifically, we first construct modality-aware graphs to enhance the extraction of fine-grained local details by effectively capturing and modeling the relationships between patches. Subsequently, the selective graph nodes swap operation is employed to alleviate the adverse effects of low-quality local features by considering both local and global information, enhancing the representation of discriminative information. Finally, the swapped modality-aware graphs are fed into the local-aware graph reasoning module, which propagates multimodal information to yield a reliable feature representation. Another advantage of the proposed graph reasoning approach is its ability to reconstruct missing modal information by exploiting inherent structural relationships, thereby minimizing disparities between different modalities. Experimental results on four benchmarks (RGBNT201, Market1501-MM, RGBNT100, MSVR310) indicate that the proposed method achieves state-of-the-art performance in multi-modal object ReID. The code for our method will be available upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Modal analysis

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ATHENA: An In-vehicle CAN Intrusion Detection Framework Based on Physical Characteristics of Vehicle Systems

arXiv

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arXiv 2025年

作者： Wang, Kai Sun, Zhen Wang, Bailing Fan, Qilin Li, Ming Zhang, Hongke School of Computer Science and Technology Harbin Institute of Technology Weihai China Shandong Key Laboratory of Industrial Network Security China School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing China Jinan Key Laboratory of Distributed Databases Shandong Inspur Database Technology Co. Ltd Jinan China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

With the growing interconnection between In-Vehicle Networks (IVNs) and external environments, intelligent vehicles are increasingly vulnerable to sophisticated external network attacks. This paper proposes ATHENA, the first IVN intrusion detection framework that adopts a vehicle-cloud integrated architecture to achieve better security performance for the resource-constrained vehicular environment. Specifically, in the cloud with sufficient resources, ATHENA uses the clustering method of multi-distribution mixture model combined with deep data mining technology to generate the raw Payload Rule Bank of IVN CAN messages, and then improves the rule quality with the help of exploitation on the first-principled physical knowledge of the vehicle system, after which the payload rules are periodically sent to the vehicle terminal. At the vehicle terminal, a simple LSTM component is used to generate the Time Rule Bank representing the long-term time series dependencies and the periodic characteristics of CAN messages, but not for any detection tasks as in traditional usage scenarios, where only the generated time rules are the candidates for further IVN intrusion detection tasks. Based on both the payload and time rules generated from cloud and vehicle terminal, ATHENA can achieve efficient intrusion detection capability by simple rule-base matching operations, rather than using complex black-box reasoning of resource-intensive neural network models, which is in fact only used for rule logic generation phase instead of the actual intrusion detection phase in our framework. Comparative experimental results on the ROAD dataset, which is current the most outstanding real-world in-vehicle CAN dataset covering new instances of sophisticated and stealthy masquerade attacks, demonstrate ATHENA significantly outperforms the state-of-the-art IVN intrusion detection methods in detecting complex attacks. We make the code available at https://***/wangkai-tech23/ATHENA. Copyright

关键词： Network intrusion

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A Hierarchical Semantic Distillation Framework for Open-Vocabulary Object Detection

arXiv

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arXiv 2025年

作者： Fu, Shenghao Yan, Junkai Yang, Qize Wei, Xihan Xie, Xiaohua Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Peng Cheng Laboratory China Tongyi Lab Alibaba Group China School of Computer Science and Engineering Sun Yat-sen University Guangdong Guangzhou China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangdong Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education Guangdong Guangzhou China

Open-vocabulary object detection (OVD) aims to detect objects beyond the training annotations, where detectors are usually aligned to a pre-trained vision-language model, e.g., CLIP, to inherit its generalizable recognition ability so that detectors can recognize new or novel objects. However, previous works directly align the feature space with CLIP and fail to learn the semantic knowledge effectively. In this work, we propose a hierarchical semantic distillation framework named HD-OVD to construct a comprehensive distillation process, which exploits generalizable knowledge from the CLIP model in three aspects. In the first hierarchy of HD-OVD, the detector learns fine-grained instance-wise semantics from the CLIP image encoder by modeling relations among single objects in the visual space. Besides, we introduce text space novel-class-aware classification to help the detector assimilate the highly generalizable classwise semantics from the CLIP text encoder, representing the second hierarchy. Lastly, abundant image-wise semantics containing multi-object and their contexts are also distilled by an image-wise contrastive distillation. Benefiting from the elaborated semantic distillation in triple hierarchies, our HD-OVD inherits generalizable recognition ability from CLIP in instance, class, and image levels. Thus, we boost the novel AP on the OV-COCO dataset to 46.4% with a ResNet50 backbone, which outperforms others by a clear margin. We also conduct extensive ablation studies to analyze how each component works. Code is available at https://***/iSEE-laboratory/HD-OVD. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection

arXiv

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arXiv 2025年

作者： Li, Yong Liu, Menglin Cui, Zhen Ding, Yi Zong, Yuan Zheng, Wenming Shan, Shiguang Guan, Cuntai School of Computer Science and Engineering The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing210096 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Artificial Intelligence Beijing Normal University Beijing100875 China Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Key Laboratory of Child Development and Learning Science of Ministry of Education School of Biological Science and Medical Engineering Nanjing210096 China

Despite the impressive performance of current vision-based facial action unit (AU) detection approaches, they are heavily susceptible to the variations across different domains and the cross-domain AU detection methods are under-explored. In response to this challenge, we propose a decoupled doubly contrastive adaptation (D2CA) approach to learn a purified AU representation that is semantically aligned for the source and target domains. Specifically, we decompose latent representations into AU-relevant and AU-irrelevant components, with the objective of exclusively facilitating adaptation within the AU-relevant subspace. To achieve the feature decoupling, D2CA is trained to disentangle AU and domain factors by assessing the quality of synthesized faces in cross-domain scenarios when either AU or domain attributes are modified. To further strengthen feature decoupling, particularly in scenarios with limited AU data diversity, D2CA employs a doubly contrastive learning mechanism comprising image and feature-level contrastive learning to ensure the quality of synthesized faces and mitigate feature ambiguities. This new framework leads to an automatically learned, dedicated separation of AU-relevant and domain-relevant factors, and it enables intuitive, scale-specific control of the cross-domain facial image synthesis. Extensive experiments demonstrate the efficacy of D2CA in successfully decoupling AU and domain factors, yielding visually pleasing cross-domain synthesized facial images. Meanwhile, D2CA consistently outperforms state-of-the-art cross-domain AU detection approaches, achieving an average F1 score improvement of 6%-14% across various cross-domain scenarios. © 2025, CC BY-NC-SA.

关键词： Contrastive Learning

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Model-based kinetics of heavy metal enrichment in plants affected by hydrodynamics

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Journal of Contaminant Hydrology 2025年 273卷 104610页

作者： Lou, Sha Zhang, Zhirui Liu, Shuguang Yang, Zhongyuan Chen, Shizhe Zou, Yuwen Fedorova, Irina Viktorovna Department of Hydraulic Engineering College of Civil Engineering Tongji University Shanghai200092 China Key Laboratory of Yangtze River Water Environment Ministry of Education Tongji University Shanghai200092 China State Key Laboratory of Disaster Reduction in Civil Engineering Tongji University Shanghai200092 China Project Management Office of China National Scientific Seafloor Observatory Tongji University Shanghai200092 China Institute of Earth Sciences Saint Petersburg State University 7-9 Universitetskaya Embankment St Petersburg199034 Russia

Heavy metal contamination threatens aquatic ecosystems by degrading water quality, biodiversity, and ecological stability. However, the role of hydrodynamics in modulating metal distribution and biogeochemical cycling, particularly in water-sediment-plant systems, remains unclear. Therefore, circular flume experiments were integrated with multiphase kinetic modeling to unravel cadmium (Cd) kinetics in water-sediment-plant systems under flow conditions. Affected by hydrodynamics, the distribution of Cd surged within 2 h and the Cd distribution coefficients showed a desorption peak within 8 h from the start of the experiment followed by reabsorption, and the hydrodynamic strength was negatively correlated with the resorption degree. Plant enrichment of heavy metals under hydrodynamics was observed initially, reaching a peak at the end of the experiment with phyto-sediment distribution coefficients of 0.22–0.25. The pseudo-second-order kinetic model effectively simulated the desorption of Cd from water, with r2 > 0.97. However, the Plant Enrichment Factor (PEF) failed to accurately describe the plant enrichment kinetics of the water-sediment-plant system under hydrodynamic conditions, with the r2 of cumulative Cd uptake kinetic of plants based on PEF lower than 0.77. Consequently, the nonlinear relationship of Cd in a multi-medium distribution was identified using the Artificial Neural Network (ANN), and a modified PEF was proposed, with r2 > 0.96. From the modified PEF, the competitive advantage of the phytobiological action was emphasized as the experiment proceeded. The modified model proposed in this study explains the physical process of Cd enrichment by plants in the water-sediment-plant system under hydrodynamics, providing a feasible tool for the study of pollutant transport in aquatic environments, and the results can contribute to theoretical support for optimizing wetland ecosystem protection strategies. © 2025 Elsevier B.V.

关键词： Aquatic ecosystems

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NEURAL NETWORK-ENHANCED hr-ADAPTIVE FINITE ELEMENT ALGORITHM FOR PARABOLIC EQUATIONS

arXiv

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arXiv 2025年

作者： Hao, Jiaxiong Huang, Yunqing Yi, Nianyu Yin, Peimeng Hunan Key Laboratory for Computation and Simulation in Science and Engineering School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China National Center for Applied Mathematics in Hunan Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University Hunan Xiangtan411105 China Department of Mathematical Sciences University of Texas at El Paso El PasoTX79968 United States

In this paper, we present a novel enhancement to the conventional hr-adaptive finite element methods for parabolic equations, integrating traditional h-adaptive and r-adaptive methods via neural networks. A major challenge in hr-adaptive finite element methods lies in projecting the previous step’s finite element solution onto the updated mesh. This projection depends on the new mesh and must be recomputed for each adaptive iteration. To address this, we introduce a neural network to construct a mesh-free surrogate of the previous step finite element solution. Since the neural network is mesh-free, it only requires training once per time step, with its parameters initialized using the optimizer from the previous time step. This approach effectively overcomes the interpolation challenges associated with non-nested meshes in computation, making node insertion and movement more convenient and efficient. The new algorithm also emphasizes SIZING and GENERATE, allowing each refinement to roughly double the number of mesh nodes of the previous iteration and then redistribute them to form a new mesh that effectively captures the singularities. It significantly reduces the time required for repeated refinement and achieves the desired accuracy in no more than seven space-adaptive iterations per time step. Numerical experiments confirm the efficiency of the proposed algorithm in capturing dynamic changes of *** Codes 92B20, 65M60, 35K57 © 2025, CC BY.

关键词： Interpolation

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Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding

arXiv

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arXiv 2025年

作者： Hu, Jiliang Li, Zuchao Shen, Mengjia Ai, Haojun Li, Sheng Zhang, Jun Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China National Institute of Information and Communications Technology Japan

Spoken language understanding (SLU) is a structure prediction task in the field of speech. Recently, many works on SLU that treat it as a sequence-to-sequence task have achieved great success. However, This method is not suitable for simultaneous speech recognition and understanding. In this paper, we propose a joint speech recognition and structure learning framework (JSRSL), an end-to-end SLU model based on span, which can accurately transcribe speech and extract structured content simultaneously. We conduct experiments on name entity recognition and intent classification using the Chinese dataset AISHELL-NER and the English dataset SLURP. The results show that our proposed method not only outperforms the traditional sequence-to-sequence method in both transcription and extraction capabilities but also achieves state-of-the-art performance on the two datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Speech recognition

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Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding

Joint Automatic Speech Recognition And Structure Learning Fo...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiliang Hu Zuchao Li Mengjia Shen Haojun Ai Sheng Li Jun Zhang Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China National Institute of Information and Communications Technology Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Accuracy Intent recognition Speech recognition Signal processing Acoustics Speech processing Automatic speech recognition

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FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation

arXiv

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arXiv 2025年

作者： Hou, Qingshan Wang, Meng Cao, Peng Ke, Zou Liu, Xiaoli Fu, Huazhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Alberta Machine Intelligence Institute University of Alberta Edmonton Canada Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore

Recent advancements in ophthalmology foundation models such as RetFound have demonstrated remarkable diagnostic capabilities but require massive datasets for effective pre-training, creating significant barriers for development and deployment. To address this critical challenge, we propose FundusGAN, a novel hierarchical feature-aware generative framework specifically designed for high-fidelity fundus image synthesis. Our approach leverages a Feature Pyramid Network within its encoder to comprehensively extract multi-scale information, capturing both large anatomical structures and subtle pathological features. The framework incorporates a modified StyleGAN-based generator with dilated convolutions and strategic upsampling adjustments to preserve critical retinal structures while enhancing pathological detail representation. Comprehensive evaluations on the DDR, DRIVE, and IDRiD datasets demonstrate that FundusGAN consistently outperforms state-of-the-art methods across multiple metrics (SSIM: 0.8863, FID: 54.2, KID: 0.0436 on DDR). Furthermore, disease classification experiments reveal that augmenting training data with FundusGAN-generated images significantly improves diagnostic accuracy across multiple CNN architectures (up to 6.49% improvement with ResNet50). These results establish FundusGAN as a valuable foundation model component that effectively addresses data scarcity challenges in ophthalmological AI research, enabling more robust and generalizable diagnostic systems while reducing dependency on large-scale clinical data collection. Copyright © 2025, The Authors. All rights reserved.

关键词： Ophthalmology

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Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

引用

IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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