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arXiv 2025年

作者： Garg, Arpit Nguyen, Cuong Felix, Rafael Liu, Yuyuan Do, Thanh-Toan Carneiro, Gustavo Australian Institute for Machine Learning University of Adelaide Australia Centre for Vision Speech and Signal Processing University of Surrey United Kingdom Department of Engineering Science University of Oxford United Kingdom Department of Data Science and AI Monash University Australia

Robust training with noisy labels is a critical challenge in image classification, offering the potential to reduce reliance on costly clean-label datasets. Real-world datasets often contain a mix of in-distribution (ID) and out-of-distribution (OOD) instance-dependent label noise, a challenge that is rarely addressed simultaneously by existing methods and is further compounded by the lack of comprehensive benchmarking datasets. Furthermore, even though current noisy-label learning approaches attempt to find noisy-label samples during training, these methods do not aim to estimate ID and OOD noise rates to promote their effectiveness in the selection of such noisy-label samples, and they are often represented by inefficient multi-stage learning algorithms. We propose the Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise (AEON) approach to address these research gaps. AEON is an efficient one-stage noisy-label learning methodology that dynamically estimates instance-dependent ID and OOD label noise rates to enhance robustness to complex noise settings. Additionally, we introduce a new benchmark reflecting real-world ID and OOD noise scenarios. Experiments demonstrate that AEON achieves state-of-the-art performance on both synthetic and real-world datasets*. © 2025, CC BY-NC-ND.

关键词： Contrastive Learning

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Bursaphelenchus xylophilus disaster monitoring based on deep learning and Remotely sensed images 2

Bursaphelenchus xylophilus disaster monitoring based on deep...

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2nd International Conference on Computer Science, Electronic Information engineering and Intelligent Control Technology, CEI 2022

作者： Li, Bingzheng Huang, Fenghua Digital China Research Institute Fuzhou350108 China Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing Yango University Fuzhou350015 China Fujian University Engineering Research Center of Spatial Data Mining and Application Yango University Fuzhou350015 China

ISBN: (数字)9781665476164

ISBN: (纸本)9781665476164

With the increasingly severe epidemic of forest diseases and insect disasters in China, the monitoring of forest diseases and insect pests has attracted more and more attention from the academic community. This paper first introduces the changes in the spectral characteristics and identification methods of Bursaphelenchus xylophilus disease. Secondly, the research status, research hotspots, and development trends of Bursaphelenchus xylophilus disease monitoring based on hyperspectral remote sensing are elaborated. It then discusses the development of deep learning and its research status in pest identification in combination with hyperspectral remote sensing and introduces three of the most popular methods of current Bursaphelenchus xylophilus disaster monitoring: You Only Look Once (YOLO), Faster Region-CNN (Faster R-CNN), and Single Shot MultiBox Detector (SSD). Finally, the main work and problems of the existing literature research are summarized to guide future work in this field. © 2022 IEEE.

关键词： Disasters

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Tiny noise, big mistakes: adversarial perturbations induce errors in brain–computer interface spellers

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National Science Review 2021年第4期8卷 78-90页

作者： Xiao Zhang Dongrui Wu Lieyun Ding Hanbin Luo Chin-Teng Lin Tzyy-Ping Jung Ricardo Chavarriaga Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology School of Civil Engineering and Mechanics Huazhong University of Science and Technology Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Swartz Center for Computational Neuroscience Institute for Neural ComputationUniversity of California San Diego Center for Advanced Neurological Engineering Institute of Engineering in Medicine University of California San Diego ZHAW Data Lab Zürich University of Applied Sciences

An electroencephalogram(EEG)-based brain–computer interface(BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g. amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a *** studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e. they can be severely attacked by adversarial perturbations,which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.

关键词： electroencephalogram brain-computer interfaces BCI spellers adversarial examples

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Sequence-Aware Online Container Scheduling with Reinforcement Learning in Parked Vehicle Edge Computing

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IEEE Transactions on Vehicular Technology 2025年

作者： Wu, Jianqiu Guo, Jianxiong Tang, Zhiqing Luo, Chuanwen Wang, Tian Jia, Weijia Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science Zhuhai519087 China Beijing Normal University Advanced Institute of Natural Sciences Zhuhai519087 China Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Zhuhai519087 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China

Intelligent vehicles, often parked for long periods, are ideally suited to serve as computational nodes to expand the Mobile Edge Computing (MEC) infrastructure, with containerization significantly enhancing the system's load balancing, self-healing, resource isolation, and security. However, fluctuations in task demand and frequent container image downloads during peak hours create high loads on containerized nodes, as multiple mobile devices offload tasks simultaneously, leading to significant processing delays. Many existing studies make the simplified assumption of predefined patterns of task arrivals, which overlooks this issue and makes suboptimal decisions. In this paper, we consider a Parked Vehicles (PVs)-extended MEC scenario, where multiple devices request services on PVs functioning as edge servers, all controlled by a central base station. Task arrivals follow observed patterns based on long-term trends, such as peak and off-peak periods, resembling realistic arrival patterns rather than predefined ones. To optimize task offloading by identifying these patterns, we propose the Sequence-Aware Task Scheduling (SATS) algorithm, which is a policy gradient-based deep reinforcement learning approach that integrates Transformer and LSTM architectures to capture patterns in time-series task arrivals and relationships between nodes in a collaborative and containerized environment, thereby enhancing the efficiency of online task scheduling. The primary objective of SATS is to optimize the task offloading policy and minimize delay and energy consumption for all devices and PVs. Extensive numerical comparisons against baselines demonstrate the effectiveness and advantages of our algorithm. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Chaotic Neural Network with Legendre Function Self-feedback and Applications

Chaotic Neural Network with Legendre Function Self-feedback ...

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作者： Zhang, Yu Wei, SiFan Wang, Yu Xu, Yaoqun School of Computer and Information Engineering Harbin University of Commerce Harbin150028 China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin150028 China Heilongjiang Cultural Big Data Theory Application Research Center Harbin150028 China Institute of System Engineering Harbin University of Commerce Harbin150028 China

A new transient chaotic neuron model which introduces Legendre function into the self-feedback term is constructed. The dynamic characteristics of the single neuron are analyzed by the time evolution graph and the inverted bifurcation graph of the largest Lyapunov exponent. The network parameters are set for settling the combinatorial optimization problem. The effectiveness of this model has been verified by the simulation results in nonlinear function optimization, traveling salesman problem (TSP). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traveling salesman problem

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Performance Bounds and Optimization for CSI-Ratio based Bi-static Doppler Sensing in ISAC Systems

arXiv

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arXiv 2024年

作者： Hu, Yanmo Wu, Kai Andrew Zhang, J. Deng, Weibo Jay Guo, Y. Key Laboratory of Marine Environmental Monitoring and Information Processing Ministry of Industry and Information Technology China School of Electronic and Information Engineering Harbin Institute of Technology China Global Big Data Technologies Centre The University of Technology Sydney Australia

Bi-static sensing is crucial for exploring the potential of networked sensing capabilities in integrated sensing and communications (ISAC). However, it suffers from the challenging clock asynchronism issue. CSI ratio-based sensing is an effective means to address the issue. Its performance bounds, particular for Doppler sensing, have not been fully understood yet. This work endeavors to fill the research gap. Focusing on a single dynamic path in high-SNR scenarios, we derive the closed-form CRB. Then, through analyzing the mutual interference between dynamic and static paths, we simplify the CRB results by deriving close approximations, further unveiling new insights of the impact of numerous physical parameters on Doppler sensing. Moreover, utilizing the new CRB and analyses, we propose novel waveform optimization strategies for noise- and interference-limited sensing scenarios, which are also empowered by closed-form and efficient solutions. Extensive simulation results are provided to validate the preciseness of the derived CRB results and analyses, with the aid of the maximum-likelihood estimator. The results also demonstrate the substantial enhanced Doppler sensing accuracy and the sensing capabilities for low-speed target achieved by the proposed waveform design. © 2024, CC BY.

关键词： Signal to noise ratio

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching 39

DAMPER: A Dual-Stage Medical Report Generation Framework wit...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Diagnosis

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Joint Node Selection and Resource Allocation Optimization for Cooperative Sensing with a Shared Wireless Backhaul

arXiv

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arXiv 2024年

作者： Chen, Mingxin Zhao, Ming-Min Liu, An Li, Min Shi, Qingjiang College of Information Science and Electronic Engineering Zhejiang University Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou310027 China School of Software Engineering Tongji University Shanghai201804 China Shenzhen Research Institute of Big Data Shenzhen518172 China

In this paper, we consider a cooperative sensing framework in the context of future multi-functional network with both communication and sensing ability, where one base station (BS) serves as a sensing transmitter and several nearby BSs serve as sensing receivers. Each receiver receives the sensing signal reflected by the target and communicates with the fusion center (FC) through a wireless multiple access channel (MAC) for cooperative target localization. To improve the localization performance, we present a hybrid information-signal domain cooperative sensing (HISDCS) design, where each sensing receiver transmits both the estimated time delay/effective reflecting coefficient and the received sensing signal sampled around the estimated time delay to the FC. Then, we propose to minimize the number of channel uses by utilizing an efficient Karhunen-Loéve transformation (KLT) encoding scheme for signal quantization and proper node selection, under the Cramér-Rao lower bound (CRLB) constraint and the capacity limits of MAC. A novel matrix-inequality constrained successive convex approximation (MCSCA) algorithm is proposed to optimize the wireless backhaul resource allocation, together with a greedy strategy for node selection. Despite the high non-convexness of the considered problem, we prove that the proposed MCSCA algorithm is able to converge to the set of Karush-Kuhn-Tucker (KKT) solutions of a relaxed problem obtained by relaxing the discrete variables. Besides, a low-complexity quantization bit reallocation algorithm is designed, which does not perform explicit node selection, and is able to harvest most of the performance gain brought by HISDCS. Finally, numerical simulations are presented to show that the proposed HISDCS design is able to significantly outperform the baseline schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Dynamic guidance strategy and path optimization method for crowd evacuation in large space public buildings based Multi-agent modeling and simulation

Dynamic guidance strategy and path optimization method for c...

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第35届中国控制与决策会议

作者： Yuling Hu You Lv Jian Tang Institute of electrical and information engineering Beijing University of Civil Engineering and Architecture Beijing key laboratory of Intelligent processing for building big data Beijing University of Civil Engineering and Architecture AVIC International Construction Investment Co.Ltd Beijing China

At present,the proportion of stampede accidents in large space public buildings is relatively high,and it is easy to cause secondary casualties in *** evacuation based on guiders can effectively avoid trampling and improve crowd evacuation *** has been a vital evacuation strategy in recent *** improve personnel safety and efficiency in evacuation processes,a real-time dynamic guidance strategy is proposed,and a path optimization method cooperated with a dynamic guidance strategy is also put forward for obstacle avoidance.A real stadium is taken as a case to study and verify the validity of the method based on Multi-agent modeling and simulation *** the future,it should be considered to apply in emergency management and control in reality.

关键词： Evacuation Dynamic guidance strategy Multiagent modeling and simulation Obstacle avoidance Optimization method

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Online Container Scheduling for Low-Latency IoT Services in Edge Cluster Upgrade: A Reinforcement Learning Approach

Online Container Scheduling for Low-Latency IoT Services in ...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Cui, Hanshuai Tang, Zhiqing Lou, Jiong Jia, Weijia Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai519087 China Beijing Normal University School of Artificial Intelligence Beijing100875 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China BNU-HKBU United International College Guangdong Key Lab of Ai and Multi-Modal Data Processing Zhuhai519087 China

ISBN: (纸本)9798350345384

In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent up-grades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers and upgrade edge clusters to minimize the impact on running tasks. In this paper, we propose a low-latency container scheduling algorithm for edge cluster upgrades. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm to address this problem, considering the unique characteristics of MEC. 3) Experimental results demonstrate that our algorithm reduces total task latency by approximately 27% compared to baseline algorithms. © 2023 IEEE.

关键词： Reinforcement learning

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