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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Qian, Feifei Bai, Lu Cui, Lixin Li, Ming Lyu, Ziyu Du, Hangyuan Hancock, Edwin School of Artificial Intelligence Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing Normal University Beijing China School of Information Central University of Finance and Economics Beijing China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Zhejiang Institute of Optoelectronics Jinhua China School of Cyber Science and Technology Sun Yat-Sen University Shenzhen China School of Computer and Information Technology Shanxi University Taiyuan China Department of Computer Science University of York York United Kingdom

ISBN: (纸本)157735897X

Graph-based representations are powerful tools for analyzing structured data. In this paper, we propose a novel model to learn Deep Hierarchical Attention-Based Kernelized Representations (DHAKR) for graph classification. To this end, we commence by learning an assignment matrix to hierarchically map the substructure invariants into a set of composite invariants, resulting in hierarchical kernelized representations for graphs. Moreover, we introduce the feature-channel attention mechanism to capture the interdependencies between different substructure invariants that will be converged into the composite invariants, addressing the shortcoming of discarding the importance of different substructures arising in most existing R-convolution graph kernels. We show that the proposed DHAKR model can adaptively compute the kernel-based similarity between graphs, identifying the common structural patterns over all graphs. Experiments demonstrate the effectiveness of the proposed DHAKR model. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Deep learning

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Multi-GNSS simulation and its positioning performance for static and dynamic users 6

Multi-GNSS simulation and its positioning performance for st...

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6th International Symposium on Autonomous Systems, ISAS 2023

作者： Li, Kun Gao, Mengzhi Zheng, Shuaiyong Wang, Xuanwen Jin, Xiaoqin Yang, Peng Yang, Jianlei Lu, Jing Yao, Gaole Tianjin University of Technology School of Integrated Circuit Science and Engineering Tianjin China Nankai University College of Artificial Intelligence Tianjin China Tianjin University of Technology School of Computer Science and Engineering Tianjin China The 54th Research Institute of China Electronics Technology Group Corporation State Key Laboratory of Satellite Navigation System and Equipment Technology Shijiazhuang China Land Satellite Remote Sensing Application Center Ministry of Natural Resources Beijing China Tianjin AtoN Division The Navigation Guarantee Centre of North China Sea Tianjin China

ISBN: (纸本)9798350316155

Global navigation satellite system plays an important role in military and civil fields, providing global positioning, navigation and timing services in real time. Unfortunately, the services provided by single constellation or double constellations cannot meet the requirements of high-level navigation, and there are still some problems such as low positioning accuracy and low number of visible satellites at specific circumstances to be solved. In this paper, multi-GNSSs are constructed and their global positioning performance is analyzed with respect to static and dynamic users. Results show that with more constellations, the position dilution of precision is decreased, and the number of visible satellites and the positioning accuracy are improved, which reveals that compared with single constellation or double constellations, multi-GNSSs can obviously improve the positioning performance. The methodology proposed can serve as a technical reference for national integrated PNT systems. © 2023 IEEE.

关键词： Navigation

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Blockchain-Based Identity Authentication Oriented to Multi-Cluster UAV Networking

arXiv

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arXiv 2023年

作者： Dong, Zesong Tong, Wei Zhang, Zhiwei Li, Jian Yang, Weidong Shen, Yulong The Hangzhou Institute of Technology Xidian University Hangzhou China The School of Computer Science and Technology Xidian University Xi’an China The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Xi’an China Shaanxi Key Laboratory of Network and System Security Xi’an China

Unmanned Aerial Vehicle (UAV) networking is increasingly used in field environments such as power inspection, agricultural plant protection, and emergency rescue. To guarantee UAV networking security, UAV identity authentication attracts wide attention, especially in the field environment without perfect infrastructure. Some blockchain-based UAV identity authentication solutions are proposed to establish decentralized and trusted authentication systems without relying on infrastructure. However, these solutions do not support disconnected UAV reconnection or even disband a cluster directly after its head UAV disconnection, which compromises cluster robustness and task result integrity. In this paper, we propose a blockchain-based identity authentication solution oriented to multi-cluster UAV networking with a UAV disconnection mechanism and a task result backup mechanism. Specifically, we build a blockchain maintained by head UAVs of all clusters, managing identity information to guarantee the security of decentralized identity management. The UAV disconnection mechanism permits a verified distributed UAV reconnection to ensure the robustness of the UAV cluster, and on this basis, the task result backup mechanism ensures the integrity of the task results stored in a cluster even any UAV disconnection. Finally, extensive experimental results prove the superiority of our solutions in terms of robustness, integrity, delay, and energy consumption. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Prediction of Ionospheric TEC based on BLS-LSTM-GRU Hybrid model

Prediction of Ionospheric TEC based on BLS-LSTM-GRU Hybrid m...

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第十四届中国卫星导航年会

作者： Dengao Li Xinfang Zhang Jumin Zhao Fanming Wu Ran Feng Jinhua Zhao Danyang Shi College of Data Science Taiyuan University of Technology Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Intelligent Perception Engineering Technology Center of Shanxi Shanxi Province Engineering Technology Research Center of Spatial Information Network College of Information and Computer Taiyuan University of Technology

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content(TEC)in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network(LSTM)and the fast training speed of gated recurrent unit network(GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour(HD)are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error(RMSE)of the prediction results of this BLSLSTM-GRU model is decreased by 23.07%,17.48% and 9.46%compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy.

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A Dual-View Fusion Network for Automatic Spinal keypoint Detection in Biplane X-ray Images

A Dual-View Fusion Network for Automatic Spinal Keypoint Det...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Dandan Zhou Lijun Guo Rong Zhang Xiuchao He Qiang Wang Jianhua Wang The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Department of Radiology The Affiliated Hospital of Medicine School of Ningbo University Ningbo China

Accurate keypoint detection in medical images of the spine is critical for the assessment, diagnosis, treatment planning, and clinical investigation of spinal deformities. However, due to severe occlusions of spinal structures in lateral X-ray images, accurate keypoint detection can be hardly achieved in lateral X-ray images based on single-view information. Thus, methods based on both the anterior-posterior (AP) and lateral (LAT) X-ray image views have been proposed to alleviate occlusion problems and achieve better keypoint detection performance. Although some progress has been made with these dual-view methods, they do not effectively exploit a priori knowledge of the spine and hence cannot adequately account for the structural correlation of the vertebrae across views. In this paper, a new dual-view fusion network (DVFNet) framework is proposed for keypoint detection in spinal X-ray images. This framework obtains structural correlations between AP and LAT views of the spine based on a priori spine knowledge represented by high-level semantic features. Meanwhile, the proposed framework combines local and global features extracted respectively by a local subnetwork and a global subnetwork. On the one hand, the local subnetwork is constructed as an enhanced codec structure based on both the AP and LAT views. This subnetwork is trained to output local features that contain both joint semantic features of the two views and independent fine-grained features of each individual view. This scheme leads to accurate keypoint estimation locally. On the other hand, the global subnetwork utilizes a self-attention mechanism to extract view-specific global features based on either the AP view or the LAT view in order to eliminate ambiguity, and reduce confusion on keypoint locations. Further, we propose a weighted feature fusion (WFF) module for adaptive fusion of the local and global features. We evaluated the DVFNet model on a private dataset and found that our proposed metho

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Blockchain-Based Identity Authentication Oriented to Multi-Cluster UAV Networking

Blockchain-Based Identity Authentication Oriented to Multi-C...

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IEEE International Conference on Blockchain (Blockchain)

作者： Zesong Dong Wei Tong Zhiwei Zhang Jian Li Weidong Yang Yulong Shen Hangzhou Institute of Technology Xidian University Hangzhou China Shaanxi Key Laboratory of Network and System Security Xi’an China Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Xi’an China School of Computer Science and Technology Xidian University Xi’an China

Unmanned Aerial Vehicle (UAV) networking is increasingly used in field environments such as power inspection, agricultural plant protection, and emergency rescue. To guarantee UAV networking security, UAV identity authentication attracts wide attention, especially in the field environment without perfect infrastructure. Some blockchain-based UAV identity authentication solutions are proposed to establish decentralized and trusted authentication systems without relying on infrastructure. However, these solutions do not support disconnected UAV reconnection or even disband a cluster directly after its head UAV disconnection, which compromises cluster robustness and task result integrity. In this paper, we propose a blockchain based identity authentication solution oriented to multi-cluster UAV networking with a UAV disconnection mechanism and a task result backup mechanism. Specifically, we build a blockchain maintained by head UAVs of all clusters, managing identity information to guarantee the security of decentralized identity management. The UAV disconnection mechanism permits a verified distributed UAV reconnection to ensure the robustness of the UAV cluster, and on this basis, the task result backup mechanism ensures the integrity of the task results stored in a cluster even any UAV disconnection. Finally, extensive experimental results prove the superiority of our solutions in terms of robustness, integrity, delay, and energy consumption.

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Augmenting Short Enrollment Speech via Synthesis for Target Speaker Extraction

Augmenting Short Enrollment Speech via Synthesis for Target ...

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

作者： Zikang Huang Jingru Lin Meng Ge Yu Jiang Xiaobao Wang Longbiao Wang Jianwu Dang Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China Department of Electrical and Computer and Engineering National University of Singapore Huiyan Technology (Tianjin) Co. Ltd. Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

A high-quality enrollment speech is crucial to target speaker extraction (TSE), since it provides essential cues for identifying the target speaker in the mixture. However, real applications usually only permit a short enrollment speech, e.g. a wakeup word for a mobile device, that provides limited cues. To address this issue, we propose an enrollment augmentation strategy that allows us to enrich the limited enrollment speech with massive text data through speech synthesis. By doing so, the extended enrollment speech contains enhanced speaker timbre and phonetic content which leads to better extraction quality. Furthermore, we propose a training data augmentation strategy to improve the model’s robustness and generalization in short enrollment speech scenarios. Experiments on Libri2Mix demonstrate that our proposed strategies bring a significant improvement in extreme scenarios where only 0.5s and 1-word enrollment speech is provided. We also release our code at https://***/HuangZikang-TJU/Aug4TSE.

关键词： Training data Speech enhancement Phonetics Signal processing Speech Robustness Mobile handsets Data models Timbre Speech synthesis

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Privacy-Preserving Federated Learning Framework with Resisting Source Inference Attacks in Smart Healthcare

SSRN

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SSRN 2024年

作者： Chenghe, Dong Xu, Guangquan Zhang, Jianhong Bai, Hongpeng Hao, Peng College of Intelligence and Computing Tianjin University Tianjin300354 China School of Big Data Qingdao Huanghai University Qingdao China Tianjin300350 China Department of Electrical and Computer Engineering North China University of Technology Beijing100144 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

Federated Learning (FL) is a distributed machine learning technology that is extensive applications in smart healthcare, since it allows multiple medical institutions to jointly train medical diagnostic models without sharing their local sensitive dataset with others. Despite its benefits, FL is susceptible to source inference attacks and model poisoning. To avoid these issues, we propose a federated learning framework for smart healthcare that preserves privacy and employs certificateless ring signatures to counteract potential attacks. During the collaborative training process of the FL model, certificateless ring signatures are used to obscure the origin of parameter updates, ensuring the anonymity of the parameter sources. This approach has the advantage of being computationally efficient due to no complicated pairing operations. Furthermore, compared with the previous certificateless ring signature schemes, our proposed scheme is more effective in reducing the success rate of source inference attacks, and is efficient in terms of computation and communication overhead. © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Towards robust data augmentation for sequence recommendation

Towards robust data augmentation for sequence recommendation

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Yan Chen Sunhang Zhao Xinyuan Wang Zhengxuan Jin Zekai Lin Bin Wang The Third Research Institute of the Ministry of Public Security Shanghai China College of Letters and Science University of California Santa Barbara (UCSB) Computer Science and Technology Zhejiang Gongshang University China Binjiang Institute of Zhejiang University China School of Computer and Artificial Intelligence Zhengzhou University China Zhejiang Key Laboratory of Multi-Dimensional Perception Technology Application and Cybersecurity China

ISBN: (数字)9798331506896

ISBN: (纸本)9798331506902

This paper presents RAS-Rec, a novel and robust approach to sequence recommendation that leverages the power of data augmentation techniques. RAS-Rec proposes data augmentation as a means to enhance the representations of users and items in sequence recommendation. By generating synthetic training samples through various transformations applied to the original data, RAS-Rec increases the diversity and quantity of the training set. The augmented sequence data addresses the limitations of existing methods and improves the robustness and generalization capabilities of sequence recommendation models. Additionally, a contrastive learning scheme is designed to train and further enhance the representations in RAS-Rec. Extensive experiments validate the effectiveness of the proposed approach, demonstrating its superiority over existing methods in terms of recommendation performance. The results highlight the value of data augmentation and contrastive learning in sequence recommendation tasks. The findings of this study contribute to the advancement of robust sequence recommendation techniques.

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Cryptocurrency Transaction Anomaly Detection Based on Chebyshev Graph Neural Network 1st

Cryptocurrency Transaction Anomaly Detection Based on Cheby...

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1st Conference on Blockchain and Web3.0 technology Innovation and application, BWTAC 2024

作者： Li, Chao Zhu, Xiangkai Li, Jike Xie, Nengfu Zeng, Qingtian School of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Key Laboratory of Agricultural Blockchain Application Ministry of Agriculture and Rural Affairs Agricultural Information Institute Chinese Academy of Agricultural Sciences Beijing China School of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9789819794119

Recent research has focused on effectively combining blockchain with graph neural networks, utilizing the efficient computing methods of graph neural networks to promote the application and development of blockchain technology in more fields. However, traditional GNN methods only focus on the neighborhood information around the account, ignoring global topology and high-order neighborhood information. Additionally, the importance of different fraudulent accounts with the same neighborhood cannot be distinguished during the aggregation process. To address these above challenges, we propose a research model for anomaly detection in cryptocurrency transactions based on Chebyshev graph neural networks. Specifically, we use the Chebyshev kernel function to obtain high-order neighborhood information and differentiate the importance of accounts with the same neighborhood through the Laplace operator. We demonstrate through lemma that unreasonable coefficients lead to Chebyshev networks being inferior to other methods and penalize the learned outlier coefficients to mitigate the impact on higher-order neighborhood approximations. Subsequently, we aggregate multi-layer neighbor information through attention mechanisms to achieve comprehensive information integration. The effectiveness of the model has been verified through multiple experiments on the Elliptical dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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