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Proceedings of the 38th International Conference on Neural information Processing Systems

作者： Yi-Bo Wang Jun-Yi Hang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China

ISBN: (纸本)9798331314385

In multi-label learning, each training instance is associated with multiple labels simultaneously. Traditional multi-label learning studies primarily focus on closed set scenario, i.e. the class label set of test data is identical to those used in training phase. Nevertheless, in numerous real-world scenarios, the environment is open and dynamic where unknown labels may emerge gradually during testing. In this paper, the problem of multi-label open set recognition (MLOSR) is investigated, which poses significant challenges in classifying and recognizing instances with unknown labels in multi-label setting. To enable open set multi-label prediction, a novel approach named SLAN is proposed by leveraging sub-labeling information enriched by structural information in the feature space. Accordingly, unknown labels are recognized by differentiating the sub-labeling information from holistic supervision. Experimental results on various datasets validate the effectiveness of the proposed approach in dealing with the MLOSR problem.

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A community detection model based on hierarchical graph clustering network with probability distribution

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Kongzhi yu Juece/Control and Decision 2025年第6期40卷 1969-1974页

作者： Xu, Sen Liu, Xuan-Qi Chen, Chao-Feng Guo, Nai-Xuan Bian, Xue-Sheng Ma, Fu-Rong Hua, Xiao-Peng Zhou, Tian School of Information Engineering Yancheng Institute of Technology Yancheng224051 China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing210000 China School of Underwater Sound Engineering Harbin Engineering University Harbin150001 China

In order to capture the hidden structure of networks and reduce the dependence of community detection models on the choice of initial parameters, this paper proposes a community detection model based on hierarchical graph clustering networks with probability distribution (HGCPD). Firstly, graph convolutional networks are utilized to learn and cache feature representations of nodes in the graph. Then, a hierarchical clustering method based on node pair similarity probability is introduced to recursively construct community structures at different levels. Finally, the problem of model hyperparameter optimization is explored, and the Bayesian optimization methods are designed to automatically adjust parameters, thereby improving the efficiency of the model. Experiments on multiple network datasets of different scales show that the HGCPD model is superior to mainstream methods in terms of the accuracy and effectiveness of community detection, and the model's interpretability is verified through visualization. © 2025 Northeast University. All rights reserved.

关键词： Normal distribution

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PSRAKA: Physically Secure and Robust Authenticated key Agreement for VANETs

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IEEE Transactions on Vehicular Technology 2024年第5期74卷 7953-7968页

作者： Liu, Gao Li, Hao Liang, Yangfan Le, Junqing Wang, Ning Mu, Nankun Liu, Zhiquan Liu, Yining Xiang, Tao Chongqing University College of Computer Science Chongqing400044 China Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Institute of Information Network and Artificial Intelligence The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China Jinan University College of Cyber Security Guangzhou510632 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325000 China

Authenticated key agreement (AKA) allows vehicles and road side units (RSUs) to mutually authenticate and establish shared session keys, which can be implemented to secure vehicular ad-hoc networks (VANETs). However, most of existing relative schemes fail to support physical security and tracing robustness, and lack a mechanism to balance pseudonym unlinkability and system efficiency. In this paper, we propose PSRAKA, a physically secure and robust authenticated key agreement scheme for VANETs, which enables anonymous authentication on vehicles through chameleon hash functions. In particular, physical security is guaranteed by integrating physical unclonable functions and fuzzy extractors to resist against physical capture attacks. Our scheme is the first to support robustness by allowing the vehicle and central authority to jointly sign or commit to the vehicle's real identity, chameleon hash value and public key for binding to ensure public verification and prevent malicious accusations during tracing. Meanwhile, it is the first to model the relationship of vehicle pseudonym unlinkability and system efficiency for balance by formulating the unlinkability based on the frequency of pseudonym updates. PSRAKA achieves anonymity, conditional privacy, robustness, key escrow freeness and physical security, and resists against most known attacks. Comprehensive performance analysis and evaluation suggest the potential usability of PSRAKA. © 1967-2012 IEEE.

关键词： Vehicular ad hoc networks

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YOLOv8-GR: Gesture Recognition by Improving of YOLOv8 Algorithm

YOLOv8-GR: Gesture Recognition by Improving of YOLOv8 Algori...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Yi Ding Naixuan Guo Haitao Meng School of Information Engineering Yancheng Institute of Technology Yancheng China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Gesture recognition has been widely used in many situations, such as human-computer interaction, virtual reality and smart home. To meet the current demands for accuracy and speed in gesture recognition, this paper proposes an improved algorithm based on YOLOv8, referred to as YOLOv8-GR. By replacing the original YOLOv8 detection head with a Dynamic Head, we integrate scale-aware, spatial-aware, and task-aware attention mechanisms within a unified framework. To enhance context information capture, the SPPF (Spatial Pyramid Pooling - Fast) module is optimized to a modified SPPF-LSKA (Large Separable Kernel Attention) module. In addition, we employ the EIoU loss function for more accurate evaluation of object detection results. To evaluate our algorithm, we integrate a gesture dataset in complex and diverse scenarios through network and field shooting annotations. Compared to the original YOLOv8, the proposed YOLOv8-GR achieves a 22.5% improvement in mAP50 and a 10.0% improvement in mAP50-95 on our dataset. We also compare YOLOv8-GR with the MobileNetv2-based SSD, which is a kind of one-stage object detection algorithm. While maintaining similar recognition accuracy, the computational cost of YOLOv8-GR is 9.9 GFLOPs, which is 15.8% of SSD, and our algorithm can be deployed on Raspberry Pi 4B.

关键词： Human computer interaction Solid modeling Accuracy Convolution Heuristic algorithms Gesture recognition Object detection Virtual reality Smart homes Kernel

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Meta-path and Meta-schema Hierarchical Aggregation for Heterogeneous Graph Neural networks

Meta-path and Meta-schema Hierarchical Aggregation for Heter...

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Smart World Congress (SWC), IEEE

作者： Ling Wu Pingping Gao Kun Guo Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Sciencen (Fuzhou University) China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

With deep learning’s advancement in recent years, heterogeneous graph neural networks (HGNNs) have drawn a lot of interest. They are a group of deep learning models specifically designed for handling heterogeneous graph data, in which nodes and edges are of various types and all nodes are assumed to have either direct or indirect associations. A metaschema, which serves as a thorough and unified blueprint for the heterogeneous graph, elegantly connects varied node types to other heterogeneous nodes via multiple heterogeneous edges with rich sematincs. However, many existing methods that utilize meta-schema to mine heterogeneous information often focus on the different meta-relationships inside the schema, limiting the meta-schema to describing only the local relational semantics. To better utilize meta-schema for efficiently integrating different heterogeneous information, we organize them into a hierarchical structure(i.e., hierarchical meta-schema), with all types of nodes distributed across different layers. The layers are interconnected through heterogeneous relationships. The target type is placed at the final layer and others are arranged hierarchically in the front according to the heterogeneous relationships defined in the meta-schema. During information aggregation, according to the hierarchical meta-schema, the information of each layer is gradually aggregated to the target type on the final layer from far to near via the attention mechanism. Moreover, metapaths regularly begin and end with the same type and help to define complex structures and semantic. Therefore, we also consider the semantic information of neighbors based on metapaths within the proposed hierarchical meta-schema to enhance the complex semantics among nodes of the same-type that may be lacking in the meta-schema. Finally, we proposed a meta-path and meta-schema hierarchical aggregation for heterogeneous graph neural network model, named HGNN-MMHA. And we may allow the meta-schema to not

关键词： Deep learning Correlation Limiting Attention mechanisms Semantics Graph neural networks Heterogeneous networks Data models

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Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges 9

Unleashing the Potential of Decentralized Federated Learning...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Cheng, Haibo Liu, Wenjian Qu, Youyang Fu, Kexue Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL's current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling. © 2024 IEEE.

关键词： Federated learning

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A Revocable Pairing-Free Certificateless Signature Scheme Based on RSA Accumulator

A Revocable Pairing-Free Certificateless Signature Scheme Ba...

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

作者： Zhuowei Shen Xiao Kou Taiyao Yang Haoqin Xu Dongbin Wang Shaobo Niu School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Intergration Minster of Education Nanjing China School of Cyberspace Security Beijing University of Post and Telecommunications Beijing China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Certificateless public key cryptography (CL-PKC) has garnered significant attention in recent years due to its ability to address the complex certificate management requirements inherent in public key infrastructure (PKI) systems and the key escrow issue associated with identity-based cryptography (IBC). However, the efficient revocation of illegitimate users in authentication schemes based on CL-PKC remains a challenge. Moreover, certain schemes still exhibit security vulnerabilities, such as susceptibility to key replacement attacks, and also require performance enhancements. In this article, we propose a novel certificateless signature scheme that incorporates the RSA accumulator. By leveraging the witness in the RSA accumulator to produce the public keys of users, the authenticity of the public key can be efficiently verified, thereby eliminating key replacement attacks. Additionally, by removing specific elements from the RSA accumulator and broadcasting the update credential to legitimate users via a public channel, our proposed scheme allows the effective revocation of malicious users. Security analysis and performance evaluations indicate that our scheme provides comprehensive security and achieves high performance in terms of computational and communication costs in both signingverification and revocation processes.

关键词： Performance evaluation Privacy Costs Identity-based encryption Authentication Broadcasting Forgery Encryption Security Digital signatures

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Optimization Research of Er-doped Magneto-refractive Photonic Crystal Fiber Based on Surface Plasmon Resonance

Optimization Research of Er-doped Magneto-refractive Photoni...

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2022 International Conference on information Processing in Ocean Science and Technology, ICIPOST 2022

作者： Yao, Siyu Wang, Dongying Yu, Yang Zhang, Zhenrong Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronic and Information Guangxi University Nanning530004 China College of Liberal Arts and Sciences National University of Defense Technology Changsha410073 China State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China

In order to meet the demand for large-scale magnetic field testing, this paper proposes a D-shaped magneto-refractive photonic crystal fiber (MRPCF) based on surface plasmon resonance (SPR) by using the erbium-doped material. By varying the diameter, location, and number of layers of the air holes, the four different Models A, B, C, and D structures are created, and the corresponding magnetic field sensing properties are analyzed. The simulation findings demonstrate that the magnetic field sensitivities of Models A, B, C, and D are 28 pm/mT, 48 pm/mT, 36 pm/mT, and 21 pm/mT, respectively when the magnetic field strength is 5 - 405 mT. Meanwhile, the figure of merit (FOM) of the four MRPCF-SPR sensors is investigated, which have FOMs of 4.8×10-4 mT-1, 6.4×10-4 mT-1, 1.9×10-4 mT-1, 0.9×10-4 mT-1. Model B has high sensitivity and large FOM. It shows that altering the structural design of the fiber can significantly improve the magneto-refractive effect of MRPCF. The proposed MRPCF has numerous uses in the areas of geological research, earthquake and tsunami monitoring, and military navigation. It can also be expected to enable quasi-distributed magnetic field sensing. © Published under licence by IOP Publishing Ltd.

关键词： Magnetos

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A Simple Semi-Supervised Joint Learning Framework for Few-shot Text Classification 5

A Simple Semi-Supervised Joint Learning Framework for Few-sh...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Lu, Shaoshuai Chen, Long Wang, Wenjing Xu, Cai Zhao, Wei Guan, Ziyu Lu, Guangyue Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450396899

The lack of labeled data is the bottleneck restricting deep text classification algorithm. State-of-the-art for most existing deep text classification methods follow the two-step transfer learning paradigm: pre-training a large model on an auxiliary task, and then fine-tuning the model on a labeled data. Their shortcoming is the high cost of training. To reduce training costs as well as alleviate the need for labeled data, we present a novel simple Semi-Supervised Joint Learning (SSJL) framework for few-shot text classification that captures the rich text semantics from large user-tagged data (referred to as weakly-labeled data) with noisy labels while also learning correct category distributions in small labeled data. We refine the contrastive loss function to better exploit inter-class contrastive patterns, making contrastive learning more applicable to the weakly-labeled setting. Besides, an appropriate temperature hyper-parameter can improve model robustness under label noise. The experimental results on four real-world datasets show that our approach outperforms the other baseline methods. Moreover, SSJL significantly boosts the deep models' performance with only 0.5% (i.e. 32 samples) of the labeled data, showing its robustness in the data sparsity scenario. © 2022 ACM.

关键词： Semantics

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Generalization analysis for multi-label learning 24

Generalization analysis for multi-label learning

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Proceedings of the 41st International Conference on Machine Learning

作者： Yi-Fan Zhang Min-Ling Zhang School of Cyber Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China and School of Computer Science and Engineering Southeast University Nanjing China

Despite great advances in algorithms for multilabel learning, research on the theoretical analysis of generalization is still in the early stage. Some recent theoretical results has investigated the generalization performance of multi-label learning under several evaluation metrics, however, how to reduce the dependency on the number of labels, explicitly introduce label correlations, and quantitatively analyze the impact of various inductive biases in the generalization analysis of multi-label learning is still a crucial and open problem. In an attempt to make up for the gap in the generalization theory of multi-label learning, we develop several novel vector-contraction inequalities, which exploit the Lipschitz continuity of loss functions, and derive generalization bounds with a weaker dependency on the number of labels than the state of the art in the case of decoupling the relationship among different components, which serves as theoretical guarantees for the generalization of multilabel learning. In addition, we derive the generalization bound for Macro-Averaged AUC and analyze its relationship with class-imbalance. The mild bounds without strong assumptions explain the good generalization ability of multi-label learning with first-order label correlations and high-order label correlations induced by norm regularizers.

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