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Few-shot node classification via local adaptive discriminant structure learning

Frontiers of Computer Science 2023年第2期17卷 135-143页

作者： Zhe XUE Junping DU Xin XU Xiangbin LIU Junfu WANG Feifei KOU Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and TelecommunicationsBeijing 100876China

Node classification has a wide range of application scenarios such as citation analysis and social network *** many real-world attributed networks,a large portion of classes only contain limited labeled *** of the existing node classification methods cannot be used for few-shot node *** train the model effectively and improve the robustness and reliability of the model with scarce labeled samples,in this paper,we propose a local adaptive discriminant structure learning(LADSL)method for few-shot node *** aims to properly represent the nodes in the attributed graphs and learn a metric space with a strong discriminating power by reducing the intra-class variations and *** conducted on various attributed networks datasets demonstrate that LADSL is superior to the other methods on few-shot node classification task.

关键词： few-shot learning node classification graph neural network adaptive structure learning attention strategy

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3D Hand Pose Estimation Using Semantic Dynamic Hypergraph Convolutional Networks

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Journal of Shanghai Jiaotong University (Science) 2024年 1-11页

作者： Wu, Yalei Li, Jinghua Kong, Dehui Li, Qianxing Yin, Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology Faculty of Information Technology Beijing University of Technology Beijing100124 China

Due to self-occlusion and high degree of freedom, estimating 3D hand pose from a single RGB image is a great challenging problem. Graph convolutional networks (GCNs) use graphs to describe the physical connection relationships between hand joints and improve the accuracy of 3D hand pose regression. However, GCNs cannot effectively describe the relationships between non-adjacent hand joints. Recently, hypergraph convolutional networks (HGCNs) have received much attention as they can describe multi-dimensional relationships between nodes through hyperedges;therefore, this paper proposes a framework for 3D hand pose estimation based on HGCN, which can better extract correlated relationships between adjacent and non-adjacent hand joints. To overcome the shortcomings of predefined hypergraph structures, a kind of dynamic hypergraph convolutional network is proposed, in which hyperedges are constructed dynamically based on hand joint feature similarity. To better explore the local semantic relationships between nodes, a kind of semantic dynamic hypergraph convolution is proposed. The proposed method is evaluated on publicly available benchmark datasets. Qualitative and quantitative experimental results both show that the proposed HGCN and improved methods for 3D hand pose estimation are better than GCN, and achieve state-of-the-art performance compared with existing methods. © 2024, Shanghai Jiao Tong University.

关键词： Convolution

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Wireless Charging Scheduling for Long-term Utility Optimization

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ACM Transactions on Sensor Networks 2025年第1期21卷 1-31页

作者： Xu, Jia Chen, Wenbin Dai, Haipeng Xu, Lijie Xiao, Fu Liu, Linfeng Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Wireless power transmission has been widely used to replenish energy for wireless sensor networks, where the energy consumption rate of sensor nodes is usually time varying and indefinite. However, few works have investigated the problem of long-term charging scheduling with random variable. This article designs an optimization model for the long-term scheduling of chargers to maximize the time-averaged charging utility while ensuring its time-averaged constraints of budget and response rate. The Lyapunov optimization technique is adopted to transform the stochastic optimization problem into a deterministic optimization problem, which remains NP-hard. Thus, an approximation algorithm following greedy approach is proposed to solve the deterministic optimization problem. We further provide the theoretical analysis of feasibility and performance guarantee of the proposed scheduling algorithm. The simulation results show that our algorithm outperforms three comparison algorithms by 6.53%, 20.04%, and 19.97% in terms of time-averaged charging utility, as well as by 11.25%, 4.42%, and 3.73% in terms of time-averaged response rate on average. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Inductive power transmission

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Inferring causal protein signalling networks from single-cell data based on parallel discrete artificial bee colony algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1587-1604页

作者： Jinduo Liu Jihao Zhai Junzhong Ji Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial IntelligenceFaculty of Information TechnologyBeijing University of TechnologyBeijingChina

Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells,which has attracted considerable attention within the bioinformatics ***,Bayesian network(BN)techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single-cell ***,current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells.A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony(PDABC),named ***,PDABC is a score-based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 *** experimental results on several simulated datasets,as well as a previously published multi-parameter fluorescence-activated cell sorter dataset,indicate that PDABC surpasses the existing state-of-the-art methods in terms of performance and computational efficiency.

关键词： Bayesian network causal protein signaling networks parallel discrete artificial bee colony single-cell data

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Fatigue reliability assessment of turbine blade via direct probability integral method

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Chinese Journal of Aeronautics 2025年第4期38卷 305-320页

作者： Guohai CHEN Pengfei GAO Hui LI Dixiong YANG State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentSchool of Mechanics and Aerospace EngineeringDalian University of TechnologyDalian 116024China

Fatigue analysis of engine turbine blade is an essential *** to various uncertainties during the manufacture and operation,the fatigue damage and life of turbine blade present *** this study,the randomness of structural parameters,working condition and vibration environment are considered for fatigue life predication and reliability ***,the lowcycle fatigue problem is modelled as stochastic static system with random parameters,while the high-cycle fatigue problem is considered as stochastic dynamic system under random ***,to deal with the two failure modes,the novel Direct Probability Integral Method(DPIM)is proposed,which is efficient and accurate for solving stochastic static and dynamic *** probability density functions of accumulated damage and fatigue life of turbine blade for low-cycle and high-cycle fatigue problems are achieved,***,the time–frequency hybrid method is advanced to enhance the computational efficiency for governing equation of ***,the results of typical examples demonstrate high accuracy and efficiency of the proposed method by comparison with Monte Carlo simulation and other *** is indicated that the DPIM is a unified method for predication of random fatigue life for low-cycle and highcycle fatigue *** rotational speed,density,fatigue strength coefficient,and fatigue plasticity index have a high sensitivity to fatigue reliability of engine turbine blade.

关键词： Engine turbine blade Low-cycle fatigue High-cycle fatigue Fatigue reliability Direct probability integral method

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AsyCo: an asymmetric dual-task co-training model for partial-label learning

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Science China(Information Sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

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Exploring Few-Labeled Classification Strategies for Hyperspectral Images Based on VIT and Semi-Supervised Learning 6

Exploring Few-Labeled Classification Strategies for Hyperspe...

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6th International Conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Li, Chuanxiu Pang, Shanchen Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao China

ISBN: (纸本)9798331541750

Most deep learning models require a large number of labeled samples to achieve good performance, which is often impractical in real-world scenarios. This paper explores the application of the Vision Transformer (ViT) models in few labeled samples hyperspectral image classification tasks. In response to the limited amount of hyperspectral image data, we propose an effective few labeled samples learning method aimed at improving classification accuracy and generalization capabilities. By introducing data augmentation, regularization techniques, and transfer learning strategies, we significantly enhance the performance of the ViT model in small-sample scenarios. The experimental results demonstrate that this method achieves superior classification performance compared to traditional methods on multiple hyperspectral datasets, providing a novel solution for the field of hyperspectral image analysis. © 2024 IEEE.

关键词： Self-supervised learning

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Partially-hiding functional encryption for degree-2 polynomials with fine-grained access control

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Frontiers of Computer Science 2024年第3期18卷 231-233页

作者： Haifeng QIAN Cheng LIN Qiaohan CHU Jie CHEN Shanghai Key Laboratory of Trustworthy Computing Software Engineering InstituteEast China Normal UniversityShanghai 200062China

1 Introduction Boneh et al.[1]first systematically introduce the concept of Functional Encryption(FE)which overcomes all-or-nothing limitation of traditional public key ***,FE hides a potential problem:even if the secret key is obtained legitimately,once it is released,it can be used *** leads to that the secret key may leak information *** deal with this problem and make FE more practical,Abdalla et al.[2]recently constructed inner-product FE(IPFE)with fine-grained access control achieving strong security guarantees under standard assumptions.

关键词： grain Encryption encryption

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Committed-programming reductions: formalizations,implications and relations

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Science China(Information Sciences) 2024年第10期67卷 151-171页

作者： Jiang ZHANG Yu YU Dengguo FENG Shuqin FAN Zhenfeng ZHANG State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences

In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some well-known schemes, including the full-domain hash(FDH) signature(Eurocrypt1996) and the Boneh-Franklin identity-based encryption(IBE) scheme(Crypto 2001), are provably secure under CPReds;(2) we prove that a CPRed associated with an instance-extraction algorithm implies a reduction in the quantum ROM(QROM). This unifies several recent results, including the security of the Gentry-Peikert-Vaikuntanathan IBE scheme by Zhandry(Crypto 2012) and the key encapsulation mechanism(KEM) variants using the Fujisaki-Okamoto transform by Jiang et al.(Crypto 2018) in the ***, we show that CPReds are incomparable to non-programming reductions(NPReds) and randomly-programming reductions(RPReds) formalized by Fischlin et al.(Asiacrypt 2010).

关键词： provable security random oracle model quantum random oracle model black-box reduction/separation programmability

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STAL3D: Unsupervised Domain Adaptation for 3D Object Detection via Collaborating Self-Training and Adversarial Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-12页

作者： Zhang, Yanan Zhou, Chao Huang, Di State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China

Existing 3D object detection suffers from expensive annotation costs and poor transferability to unknown data due to the domain gap, Unsupervised Domain Adaptation (UDA) aims to generalize detection models trained in labeled source domains to perform robustly on unexplored target domains, providing a promising solution for cross-domain 3D object detection. Although Self-Training (ST) based cross-domain 3D detection methods with the assistance of pseudo-labeling techniques have achieved remarkable progress, they still face the issue of low-quality pseudo-labels when there are significant domain disparities due to the absence of a process for feature distribution alignment. While Adversarial Learning (AL) based methods can effectively align the feature distributions of the source and target domains, the inability to obtain labels in the target domain forces the adoption of asymmetric optimization losses, resulting in a challenging issue of source domain bias. To overcome these limitations, we propose a novel unsupervised domain adaptation framework for 3D object detection via collaborating ST and AL, dubbed as STAL3D, unleashing the complementary advantages of pseudo labels and feature distribution alignment. Additionally, a Background Suppression Adversarial Learning (BS-AL) module and a Scale Filtering Module (SFM) are designed tailored for 3D cross-domain scenes, effectively alleviating the issues of the large proportion of background interference and source domain size bias. Our STAL3D achieves state-of-the-art performance on multiple cross-domain tasks and even surpasses the Oracle results on Waymo $\rightarrow$ KITTI and Waymo $\rightarrow$ KITTI-rain. IEEE

关键词： Feature extraction

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