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arXiv 2023年

作者： Wang, Dayang Xu, Yongshun Han, Shuo Wu, Zhan Zhou, Li Morovati, Bahareh Yu, Hengyong The Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States The Laboratory of Image Science and Technology Southeast University Nanjing210096 China The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China

Low-dose computed tomography (LDCT) offers reduced X-ray radiation exposure but at the cost of compromised image quality, characterized by increased noise and artifacts. Recently, transformer models emerged as a promising avenue to enhance LDCT image quality. However, the success of such models relies on a large amount of paired noisy and clean images, which are often scarce in clinical settings. In the fields of computer vision and natural language processing, masked autoencoders (MAE) have been recognized as an effective label-free self-pretraining method for transformers, due to their exceptional feature representation ability. However, the original pretraining and fine-tuning design fails to work in low-level vision tasks like denoising. In response to this challenge, we redesign the classical encoder-decoder learning model and facilitate a simple yet effective low-level vision MAE, referred to as LoMAE, tailored to address the LDCT denoising problem. Moreover, we introduce an MAE-GradCAM method to shed light on the latent learning mechanisms of the MAE/LoMAE. Additionally, we explore the LoMAE’s robustness and generability across a variety of noise levels. Experiments results show that the proposed LoMAE can enhance the transformer’s denoising performance and greatly relieve the dependence on the ground truth clean data. It also demonstrates remarkable robustness and generalizability over a spectrum of noise levels. © 2023, CC BY.

关键词： Image quality

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Label distribution for learning with noisy labels 29

Label distribution for learning with noisy labels

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Liu, Yun-Peng Xu, Ning Zhang, Yu Geng, Xin MOE Key Laboratory of Computer Network and Information Integration China School of Computer Science and Engineering Southeast University Nanjing210096 China

ISBN: (纸本)9780999241165

The performances of deep neural networks (DNNs) crucially rely on the quality of labeling. In some situations, labels are easily corrupted, and therefore become noisy labels. Thus, designing algorithms that deal with noisy labels is of great importance for learning robust DNNs. However, it is difficult to distinguish between noisy labels and clean labels, which becomes the bottleneck of many methods. To address the problem, this paper proposes a novel method named Label Distribution based Confidence Estimation (LDCE). LDCE estimates the confidence of the observed labels based on label distribution. Then, the boundary between clean labels and noisy labels becomes clear according to confidence scores. To verify the effectiveness of the method, LDCE is combined with the existing learning algorithm to train robust DNNs. Experiments on both synthetic and real-world datasets substantiate the superiority of the proposed algorithm against state-of-the-art methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Deep neural networks

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Graph-Based Proximal Policy Optimization Empowered Adaptive Task Scheduling Leveraging Cloud-Edge Collaboration for Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年

作者： Li, Peisong Yi, Meng Iqbal, Muddesar Wang, Xinheng Xiao, Ziren Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou215123 China Southeast University School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Prince Sultan University Renewable Energy Laboratory Communications and Networks Engineering Department College of Engineering Riyadh11586 Saudi Arabia CIP Technology ltd United Kingdom

The rapid advancement of IoT, AI, and edge computing has led to a significant increase in consumer devices and computation tasks, with new electronics incorporating these technologies to enhance services like VR/AR and autonomous driving, requiring real-time processing for safety and efficiency. However, recent research has focused on optimizing IoT task scheduling and resource allocation through various methods, yet overlooks the dynamic nature of IoT environments, fails to adapt to changing device counts and movements, and often ignores task completion rate in favor of minimizing latency and energy cost. In this context, an adaptive task scheduling and resource allocation strategy is proposed for Edge IoT systems, based on the designed Graph-based Proximal Policy Optimization (GPPO) algorithm. Firstly, the GPPO algorithm enhances PPO for adaptive task scheduling in the fluctuating MEC scenarios, adjusting for the varying number of nearby edge servers. Secondly, it accounts for consumer mobility by opting for local task execution if the consumer risks moving outside the edge server's range, ensuring result reception. Thirdly, it prioritizes task completion rate to increase the number of tasks finished within their acceptable duration. Experimental results demonstrated that the proposed method outperforms traditional methods. © 1975-2011 IEEE.

关键词： Scheduling algorithms

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Graph Contrastive Learning with Learnable Graph Augmentation

Graph Contrastive Learning with Learnable Graph Augmentation

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

作者： Xinyan Pu Ke Zhang Huazhong Shu Jean Louis Coatrieux Youyong Kong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China LTSI Universit de Rennes 1 Rennes France Centre de Recherche en Information BioMdicale Sino-Franais Rennes France

Graph contrastive learning has gained popularity due to its success in self-supervised graph representation learning. Augmented views in contrastive learning greatly determine the quality of the learned representations. Handcrafted data augmentations in previous work require tedious trial-and- errors per dataset, which is time-consuming and resource-intensive. Here, we propose a Graph Contrastive learning framework with Learnable graph Augmentation called GraphCLA. Specifically, learnable graph augmentation trains augmented views by minimizing the mutual information (MI) between the input graphs and their augmented graphs. This paper designs a kernel contrastive loss function based on an end-to-end differentiable graph kernel to learn augmented views. In addition, this paper utilizes a min-max optimization strategy to learn challenging augmented graphs and to learn discriminative representations adversarially. Finally, we compared GraphCLA with state-of-the-art self-supervised learning baselines and experimentally validate the effectiveness of GraphCLA.

关键词： Representation learning Self-supervised learning Signal processing Benchmark testing Acoustics Kernel Speech processing

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Private Protocol Traffic Identification Based on Sequence Statistical Fingerprint

Private Protocol Traffic Identification Based on Sequence St...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Junchen Li Guang Cheng Zekun Jing Haiyang Wei School of Cyber Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education Jiangsu Province Engineering Research Center of Security for Ubiquitous Network (Southeast University) Nanjing China

ISBN: (纸本)9781665435413

With more attention paid to communication security and user privacy, there has been a dramatic rise in private protocols, which brings great challenges to traditional protocol identification. The increasingly complex design of private pro-tocols has led to the loss of effectiveness of features used for traditional identification. Besides, some features are redundant and also lose sight of the particularity of private protocols, which could not be guaranteed to be the optimal identification solution. In this paper, we propose a method to obtain the Sequence Sta-tistical Fingerprint(Seq-SF) which is proprietary for each private protocol and can be used for identifying private protocols from promiscuous traffic. We extend statistical features associated with protocol transmission to make the sequence feature be integrated into it. Then, we adopt a feature selection algorithm based on mutual information ranking, which can evaluate the pertinence between features and protocols to guarantee the effectiveness and optimality of Seq-SF. The results of experiments on the real-world dataset covering 13 private protocols indicate that Seq-SF achieves an ideal performance(99.41% accuracy, 97.56% precision, 95.18% recall, and 0.20% FPR on average) and has good robustness.

关键词： Privacy Protocols Heuristic algorithms Telecommunication traffic Fingerprint recognition Robustness Classification algorithms

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PSCM: Towards Practical Encrypted Unknown Protocol Classification

PSCM: Towards Practical Encrypted Unknown Protocol Classific...

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IEEE Symposium on computers and Communications (ISCC)

作者： Hua Wu Chaoqun Cui Guang Cheng Xiaoyan Hu School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China Jiangsu Province Engineering Research Center of Security for Ubiquitous Network Nanjing China

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

network traffic classification is the basis for network management, Quality of Service and intrusion detection. As the number of Internet applications increases, the variety of unknown protocols grows, posing a significant challenge to network traffic classification. Traditional rule-based traffic classification methods are currently limited by the rise of dynamic ports and encryption protocols. Statistical methods using statistical features have good recognition of protocols with public formats. However, there is no public protocol format for unknown protocols, making it challenging to extract useful features. This paper proposes a practical Probability Statistics and Cluster Merging (PSCM) method to automatically extract encrypted unknown protocol features and map the clustering results to the actual protocols. Experimental results on real-world network traffic show that the method achieves an accuracy of 99.28% and performs well in the sampling scenarios.

关键词： Protocols Statistical analysis Merging Intrusion detection Telecommunication traffic Quality of service Probability

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A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional network

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Biomedical and Environmental Sciences 2022年第6期35卷 494-503页

作者： JI Tian Jiao CHENG Qiang ZHANG Yong ZENG Han Ri WANG Jian Xing YANG Guan Yu XU Wen Bo LIU Hong Tu NHC Key Laboratory of Medical Virology and Viral Diseases National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 100026China Academy of Cyber Science and Engineering Southeast UniversityNanjing 211189JiangsuChina Center for Biosafety Mega Science Chinese Academy of SciencesWuhan 430071HubeiChina Guangdong Center for Disease Control and Prevention Guangzhou 511430GuangdongChina Shandong Center for Disease Control and Prevention Jinan 250014ShandongChina LIST Key Laboratory of Computer Network and Information Integration(Southeast University)Ministry of EducationSoutheast UniversityNanjing 211189JiangsuChina

Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on *** achieve early intervention and to prevent outbreaks of disease,we propose a novel warning model that can accurately predict the incidence of *** We propose a spatial-temporal graph convolutional network(STGCN)that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and *** 2011-2018 data served as the training and verification set,while data from 2019 served as the prediction *** important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute *** As the first application using a STGCN for disease forecasting,we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level,especially for cities of significant *** This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance,which may significantly reduce the morbidity associated with HFMD in the future.

关键词： HFMD Early warning model STGCN Disease prediction

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A primal perspective for indefinite kernel SVM problem

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Frontiers of computer Science 2020年第2期14卷 349-363页

作者： Hui XUE Haiming XU Xiaohong CHEN Yunyun WANG School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Southeast University) MOENanjing 210096China College of Science Nanjing University of Aeronautics and AstronauticsNanjing 210016China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210046China

Indefinite kernel support vector machine(IKSVM)has recently attracted increasing attentions in machine *** IKSVM essentially is a non-convex problem,existing algorithms either change the spectrum of indefinite kernel directly but risking losing some valuable information or solve the dual form of IKSVM whereas suffering from a dual gap *** this paper,we propose a primal perspective for solving the *** is,we directly focus on the primal form of IKSVM and present a novel algorithm termed as IKSVM-DC for binary and multi-class ***,according to the characteristics of the spectrum for the indefinite kernel matrix,IKSVM-DC decomposes the primal function into the subtraction of two convex functions as a difference of convex functions(DC)*** accelerate convergence rate,IKSVM-DC combines the classical DC algorithm with a line search step along the descent direction at each ***,we construct a multi-class IKSVM model which can classify multiple classes in a unified form.A theoretical analysis is then presented to validate that IKSVM-DC can converge to a local ***,we conduct experiments on both binary and multi-class datasets and the experimental results show that IKSVM-DC is superior to other state-of-the-art IKSVM algorithms.

关键词： indefinite kernel support vector machine multi-class classification non-convex optimization

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Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Cheng, Zihan Guo, Jintao Zhang, Jian Qi, Lei Zhou, Luping Shi, Yinghuan Gao, Yang Shanghai Jiao Tong University School of Medicine Shanghai200025 China National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China State Key Laboratory for Novel Software Technology the National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China School of Computer Science and Engineering Key Lab of Computer Network and Information Integration Southeast University Jiangsu Nanjing211189 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia

To segment medical images with distribution shifts, domain generalization (DG) has emerged as a promising setting to train models on source domains that can generalize to unseen target domains. Existing DG methods are mainly based on CNN or ViT architectures. Recently, advanced state space models, represented by Mamba, have shown promising results in various supervised medical image segmentation. The success of Mamba is primarily owing to its ability to capture long-range dependencies while keeping linear complexity with input sequence length, making it a promising alternative to CNNs and ViTs. Inspired by the success, in the paper, we explore the potential of the Mamba architecture to address distribution shifts in DG for medical image segmentation. Specifically, we propose a novel Mamba-based framework, Mamba-Sea, incorporating global-to-local sequence augmentation to improve the model’s generalizability under domain shift issues. Our Mamba-Sea introduces a global augmentation mechanism designed to simulate potential variations in appearance across different sites, aiming to suppress the model’s learning of domain-specific information. At the local level, we propose a sequence-wise augmentation along input sequences, which perturbs the style of tokens within random continuous sub-sequences by modeling and resampling style statistics associated with domain shifts. To our best knowledge, Mamba-Sea is the first work to explore the generalization of Mamba for medical image segmentation, providing an advanced and promising Mamba-based architecture with strong robustness to domain shifts. Remarkably, our proposed method is the first to surpass a Dice coefficient of 90% on the Prostate dataset, which exceeds previous SOTA of 88.61%. The code is available at https://***/orange-czh/Mamba-Sea. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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