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Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1105-1121页

作者： Yue Zhou Xin Luo MengChu Zhou the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China the School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

关键词： big data analysis cryptocurrency transaction network embedding(CTNE) dynamic network network embedding network representation static network

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Reliability Analysis of Heterogeneous Sensor-Cloud Systems against Targeted Attacks

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China Communications 2022年第8期19卷 181-197页

作者： Hao Peng Zhen Qian Guangquan Xu Kejie Mao Kangtian Li Dandan Zhao Department of Computer Science and Engineering Zhejiang Normal UniversityJinhua Zhejiang 321004China Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal UniversityJinhua Zhejiang 321004China The Big Data School Qingdao Huanghai UniversityQingdao Shandong 266427China College of Intelligence and Computing Tianjin UniversityTianjin 300072China

Based on the wide application of cloud computing and wireless sensor networks in various fields,the Sensor-Cloud System(SCS)plays an indispensable role between the physical world and the network ***,due to the close connection and interdependence between the physical resource network and computing resource network,there are security problems such as cascading failures between systems in the *** this paper,we propose a model with two interdependent networks to represent a sensor-cloud ***,based on the percolation theory,we have carried out a formulaic theoretical analysis of the whole process of cascading *** the system’s subnetwork presents a steady state where there is no further collapse,we can obtain the largest remaining connected subgroup components and the penetration ***,this result is the critical maximum that the coupled SCS can *** verify the correctness of the theoretical results,we further carried out actual simulation *** results show that a scale-free network priority attack’s percolation threshold is always less than that of ER network which is priority ***,when the scale-free network is attacked first,adding the power law exponentλcan be more intuitive and more effective to improve the network’s reliability.

关键词： sensor-cloud systems targeted attacks heterogeneous networks reliability analysis system security

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big-Moe: Bypassing Isolated Gating For Generalized Multimodal Face Anti-Spoofing

Big-Moe: Bypassing Isolated Gating For Generalized Multimoda...

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

作者： Yingjie Ma Zitong Yu Xun Lin Weicheng Xie Linlin Shen College of Computer Science and Software Engineering Shenzhen University Great Bay University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbalances, as well as domain shifts. Our research introduces a Mixture of Experts (MoE) model to address these issues effectively. We identified three limitations in traditional MoE approaches to multimodal FAS: (1) Coarse-grained experts’ inability to capture nuanced spoofing indicators; (2) Gated networks’ susceptibility to input noise affecting decision-making; (3) MoE’s sensitivity to prompt tokens leading to overfitting with conventional learning methods. To mitigate these, we propose the Bypass Isolated Gating MoE (big-MoE) framework, featuring: (1) Fine-grained experts for enhanced detection of subtle spoofing cues; (2) An isolation gating mechanism to counteract input noise; (3) A novel differential convolutional prompt bypass enriching the gating network with critical local features, thereby improving perceptual capabilities. Extensive experiments on four benchmark datasets demonstrate significant generalization performance improvement in multimodal FAS task. The code is released at https://***/murInJ/big-MoE.

关键词： Convolutional codes Sensitivity Convolution Face recognition Noise Logic gates Feature extraction Security Speech processing Overfitting

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Learning with Open-world Noisy data via Class-independent Margin in Dual Representation Space

arXiv

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arXiv 2025年

作者： Pan, Linchao Gao, Can Zhou, Jie Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may occur during the training and inference stage. Such open-world noisy labels may significantly impact the performance of LNL methods. In this study, we propose a novel dual-space joint learning method to robustly handle the open-world noise. To mitigate model overfitting on closed-set and open-set noises, a dual representation space is constructed by two networks. One is a projection network that learns shared representations in the prototype space, while the other is a One-Vs-All (OVA) network that makes predictions using unique semantic representations in the class-independent space. Then, bi-level contrastive learning and consistency regularization are introduced in two spaces to enhance the detection capability for data with unknown classes. To benefit from the memorization effects across different types of samples, class-independent margin criteria are designed for sample identification, which selects clean samples, weights closed-set noise, and filters open-set noise effectively. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods and achieves an average accuracy improvement of 4.55% and an AUROC improvement of 6.17% on CIFAR80N. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Hybrid Convolutional-Transformer Neural Network for Driver Distraction Detection

Hybrid Convolutional-Transformer Neural Network for Driver D...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Penghua Li Qiyun Mou Jie Hou Yushan Tu Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications Chongqing China College of Automation Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

Distracted driving causes a significant number of traffic accidents each year, making it a crucial area of research to monitor the driver’s state and prevent accidents. Although convolutional networks are widely used for detecting driver distraction, they face several issues, such as poor generalization ability, high model capacity, and response latency. To tackle these challenges, this paper introduces a nimble and low-latency algorithm named MViTCNet, which utilizes the MobileViT module for the identification of driver distraction while operating within constrained computational resources. The MViTCNet algorithm extracts local features and constructs an original feature map, which is fused with a global feature representation processed by a Transformer to capture finer details and obtain a larger receptive field. Additionally, the classic convolutional module MBConv is employed to reduce spatial redundancy, and downsampling is used to obtain low-resolution feature maps. By stacking multiple modules to learn more complex feature representations, the network structure increases in depth and width, resulting in a better fit for complex data distributions. Experiments on an embedded hardware platform show that MViTCNet achieves an acceptable trade-off, with an accuracy of 91.04% on the StateFarm dataset and 97.80% on the LDDB dataset at a latency of 28.9±5.3 milliseconds, outperforming existing methods.

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Meta-transfer learning for predicting position-dependent milling stability across varying overhang lengths under limited experimental data

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Journal of Manufacturing Processes 2025年 149卷 43-56页

作者： Deng, Congying Wu, Qiang Miao, Jianguo Lu, Sheng Xie, Luofeng School of Integrated Circuits Chongqing University of Posts and Telecommunications Chongqing400065 China College of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China School of Mechanical Engineering Sichuan University Chengdu610065 China

Stability lobe diagram (SLD) serves as a pivotal reference in determining appropriate chatter-free milling parameters. The tool tip frequency response functions (FRFs), as crucial prerequisites for deriving theoretical SLDs, vary with the position and overhang length. The acquisition of tool tip FRFs under different machining positions and overhang lengths necessitates repeated time-consuming experiments or simulations. To guarantee the efficiency and accuracy in predicting SLDs, this paper puts forward a meta-transfer learning method utilizing a deep neural network to refine the prediction of stability limits with limited experimental data. Initially, a source overhang length is defined to perform the impact testing under adequate machining positions for acquiring tool tip dynamics and constructing a theoretical stability dataset. Fewer impact tests for a target overhang length are conducted under insufficient machining positions arranged through a proposed similarity-based sampling strategy, and a target stability dataset is constructed using limited tool tip FRFs. Secondly, the source dataset is updated by domain adaptation on the modal parameters with a multi-fidelity modeling. Subsequently, meta-learning is carried out on multiple meta-tasks randomly determined from the updated source dataset. The ultimately trained deep neural network during the meta-learning phase undergoes fine-tuning with the target dataset for predicting the stability limits at various machining positions. Finally, a three-axis vertical machining center is taken to conduct a detailed experimental validation. Comparative analyses indicate that the proposed methodology outperforms others in accurately predicting stability limits, even with a constrained number of labeled samples. © 2025 The Society of Manufacturing Engineers

关键词： Milling (machining)

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Deformable registration framework for glioma images with absent correspondence based on auxiliary-image-aided intensity-consistency constraint

Deformable registration framework for glioma images with abs...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tang, Kun Wang, Lihui Yang, Menglong Xu, Jingwen Cheng, Xinyu Zhang, Jian Zhu, Yuemin Wei, Hongjiang Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

Considering the tumor aggressive nature and the significant changes in anatomical structure, aligning the preoperative and follow up scans of glioma patients remains a challenge due to the presence of regions with absent correspondence. To address this challenge, this work proposed a novel bidirectional unsupervised deformable image registration framework for image pairs with missing correspondence based on an auxiliary-image-aided intensity-consistency constraint (ICC) strategy. Specifically, for any fixed and moving image pairs, we introduced an auxiliary image and warped it directly to fixed/moving image or warped it twice through a transition of moving/fixed image. By comparing the difference between these warped images, the weighting maps to identify and exclude regions with absent correspondence between fixed and moving image pairs can be generated. To verify the effectiveness of the proposed framework, we combined it with several deep learning-based registration models and tested it on BraTS-Reg challenge dataset, the results demonstrated that the proposed ICC strategy can improve the registration performance for all the models, with the improvement of average target registration error (TRE) and success rate (SR) being up to 44.9% and 66.7%, respectively. Comparing against the best existing forward-backward consistency strategy for dealing with missing correspondence registration, our auxiliary-image-aided ICC strategy can also decrease average TRE by 2.9%, demonstrating the superiority of the proposed framework. The present work is not limited to the glioma images, it can be used to address the registration problems for any image pairs with absent correspondence or inconsistent intensity. © 2024 IEEE.

关键词： Image registration

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Gb-Dbscan: A Fast Granular-Ball Based Dbscan Clustering Algorithm

SSRN

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SSRN 2023年

作者： Cheng, Dongdong Zhang, Cheng Li, Ya Xia, Shuyin Wang, Guoyin Huang, Jinlong Zhang, Sulan Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing408100 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

DBSCAN algorithm identifies high-density connected areas as clusters, so that it has advantages in discovering arbitrary-shaped clusters. However, it has difficulty in adjusting parameters and its time complexity is O(n2). Granular-ball (GB) is a coarse grained representation of data. It is on the basis of the assumption that an object and its local neighbors have similar distribution and they have high possibility of belonging to the same class. Inspired by the idea of granular-ball, we introduce it into unsupervised learning and use it to improve the efficiency of DBSCAN, called GB-DBSCAN. Firstly, we use k-nearest neighbor (KNN) to generate granular-balls, which is a bottom-up strategy, and describe granular-balls according to their centers and radius. Then, the granular-balls are divided into Core-GB and Non-Core-GB according to their density. After that, the Core-GBs are merged into clusters according to the idea of DBSCAN and the Non-Core-GBs are assigned to appropriate clusters. Since the granular-balls' number is much smaller than the size of the objects in a data, the running time of DBSCAN is greatly reduced. By comparing with state-of-the-art clustering algorithms, the proposed algorithm can get similar or better clustering result in much less running time. © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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Extracting High-order Connectivity of EEG-based Dynamic Functional Connectivity Networks for Diagnosis of Major Depressive Disorder 24

Extracting High-order Connectivity of EEG-based Dynamic Func...

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4th Asia-Pacific Artificial Intelligence and big data Forum, AIBDF 2024

作者： Zhao, Feng Fan, Wenxuan Han, Zhongwei Chen, Hongyu School of Computer Science and Technology Shandong Technology and Business University Shandong Yantai China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Shandong Yantai China Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China Information Engineering College Yantai Institute of Technology Shandong Yantai China Shandong Technology and Business University Shandong Yantai China

ISBN: (纸本)9798400710865

Functional connectivity networks (FCNs), particularly those utilizing the phase lag index (PLI) method, have been instrumental in elucidating the pathological features of Major Depressive Disorder (MDD) by assessing the synchrony and lag of electroencephalogram (EEG) signal waveforms. Currently, the more prevalent methods are traditional FCNs that quantify phase synchronization as scalar values and dynamic FCNs (D-FCNs) that employ sliding window approaches to capture the instantaneous variations in functional connectivity. However, these two methods merely capture the low-level functional connections between paired regions of interest (ROIs) from both the static and dynamic perspectives, and fail to reflect the high-level functional connections involving multiple ROIs. Moreover, the feature dimension of the dynamic FCN is excessively high, and there exists a phase mismatch issue between different samples, which renders it impossible to be directly applied to the classification task, and no better solution is available at present. To mitigate these issues, we proposed a comprehensive framework that includes segmenting EEG sequences based on PLI to construct dynamic FCNs;employing seventh-order central moments for feature extraction to form low-order FCNs (Lo-FCNs);and establishing high-order FCNs (Ho-FCNs) based on Pearson correlation theory derived from Lo-FCNs. We conducted classification experiments across four distinct EEG frequency bands using our proposed framework while analyzing relevant features through graph theory and brain topological mapping techniques. The experiments showed that the proposed framework has a high classification accuracy rate in the classification experiments of MDD and NC, and proved the importance of high-order features in classification experiments, indicating that they may serve as physiological markers for MDD diagnosis. © 2024 Copyright held by the owner/author(s).

关键词： Electroencephalography

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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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