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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Jiongge Dong, Hang Tian, Long He, Xiongpeng Sun, Huimin Liu, Yuan Pilot School of Software Engineering Xidian University Xi'an China Key Lab. of Radar Signal Processing Xidian University Xi'an China Electronic Engineering Research Institute Xi'an China Nanyang Technological University Singapore

ISBN: (纸本)9798350368741

Micro-Doppler Analysis (MDA) of rigid-body targets is crucial for various practical downstream tasks such as target imaging and recognition. Radar echoes from micro-moving targets typically represent non-stationary signals and are often described using the parameterized Time-Varying Auto Regressive (TVAR) model. Sparse Bayesian Learning (SBL) is commonly employed to estimate time-invariant coefficients, thereby achieving high-resolution micro-Doppler time-frequency distributions. Despite its effectiveness, SBL-based optimization methods often face inefficiency due to the computational burden of inverse operation. To address this challenge and enhance the efficiency of MDA based on TVAR model, we propose a Sparse Bayesian Network (SBN) that unfolds a fast Mean Field SBL (MF) using a deep variational autoencoding framework. This method retains the optimization effectiveness of SBL while incorporating the inference efficiency of Deep Neural Networks (DNNs). Our proposed method demonstrates strong generalization capabilities, performing well on both simulated and measured radar echoes. © 2025 IEEE.

关键词： Micro-Doppler Analysis (MDA) Rigid-body Target Sparse Bayesian Network (SBN)

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ESA: Example Sieve Approach for Multi-Positive and Unlab.led Learning 25

ESA: Example Sieve Approach for Multi-Positive and Unlabeled...

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18th ACM International Conference on Web Search and Data Mining, WSDM 2025

作者： Li, Zhongnian Wei, Meng Ying, Peng Xu, Xinzheng School of Computer Science and Technology China University of Mining and Technology Xuzhou China Mine Digitization Engineering Research Center of the Ministry of Education Xuzhou China State Key Lab. for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9798400713293

Learning from Multi-Positive and Unlab.led (MPU) data has gradually attracted significant attention from practical applications. Unfortunately, the risk of MPU also suffer from the shift of minimum risk, particularly when the models are very flexible as shown in Fig.1. In this paper, to alleviate the shifting of minimum risk problem, we propose an Example Sieve Approach (ESA) to select examples for training a multi-class classifier. Specifically, we sieve out some examples by utilizing the Certain Loss (CL) value of each example in the training stage and analyze the consistency of the proposed risk estimator. Besides, we show that the estimation error of proposed ESA obtains the optimal parametric convergence rate. Extensive experiments on various real-world datasets show the proposed approach outperforms previous methods. Source code is availab.e at https://***/WilsonMqz/ESA. © 2025 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Palmprint Anti-spoofing via Frequency Enhancement and Selection 18th

Palmprint Anti-spoofing via Frequency Enhancement and Select...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Ren, Yani Shao, Huikai Zhong, Dexing School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China State Key Lab. for Novel Software Technology Nanjing University Jiangsu NanJing210000 China Xi’an Yizhanghui Technology Co. Shannxi Xi’an712000 China Pazhou Lab Guangzhou510335 China Research Institute of Xi’an Jiaotong University Zhejiang311215 China

ISBN: (纸本)9789819610679

Palmprint recognition has attracted considerable interest from researchers as a convenient and secure biometric identification technology. The majority of research on palmprint recognition focuses on enhancing recognition accuracy and speed. However, the security aspects of palmprint recognition are rarely considered. This paper proposed a palmprint anti-spoofing method, called Frequency Enhancement and Selection (FES), based on frequency-domain features. Firstly, the palmprint images are transformed using the discrete cosine transform to obtain frequency-domain features. These features are then enhanced by the frequency-domain enhancement module. Finally, the frequency channels for classification are filtered based on the gate module. In this paper, experiments are conducted on a palmprint anti-spoofing database, and the experimental results demonstrate that our method can obtain the anti-spoofing accuracy of 99.81% and can outperform other methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Frequency domain analysis

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Sparse Bayesian Network for Fast Micro-Doppler Analysis

Sparse Bayesian Network for Fast Micro-Doppler Analysis

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

作者： Jiongge Zhang Hang Dong Long Tian Xiongpeng He Huimin Sun Yuan Liu Pilot School of Software Engineering Xidian University Key Lab. of Radar Signal Processing Xidian University Pilot School of Software Engineering Xidian University Xi’an China Electronic Engineering Research Institute Xi’an China Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Time-frequency analysis Target recognition Radar measurements Computational modeling Optimization methods Radar Radar imaging Signal processing Bayes methods Speech processing

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Fairness-Aware Incentive Mechanism for Multi-Server Federated Learning in Edge-Enabled Wireless Networks with Differential Privacy

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IEEE Transactions on Mobile Computing 2025年

作者： Yang, Yu Peng, Kai Wang, Shangguang Xu, Xiaolong Xiao, Peiyun Leung, Victor C.M Huaqiao University College of Engineering Quanzhou362021 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanjing University of Information Science and Technology School of Software Nanjing China Nanjing University State Key Lab. for Novel Software Technology Nanjing210093 China Shenzhen University College of the Computer Science and Software Engineering Shenzhen518060 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

As a distributed machine learning method, federated learning (FL) can collab.ratively train a global model with multiple devices without sharing the original data, thus protecting certain privacy. However, due to the strong heterogeneity of edge nodes (ENs) participating in FL, the quality of data uploaded to the parameter server (PS) varies significantly. Without an appropriate incentive mechanism, low-quality contributors may receive disproportionately high rewards, while high-quality contributors may lack sufficient motivation, leading to inefficient participation and suboptimal global model performance. Consequently, it is critical to develop an effective incentive mechanism to promote fairness for the FL process. To address the issues of existing FL incentive mechanisms lacking privacy protection performance analysis, we propose a fairness-aware incentive mechanism for multi-server FL in edge-enabled wireless differential privacy (DP) networks. Specifically, the wireless channel noise is used to provide DP protection for the local model gradients uploaded by ENs. Next, the interaction between the PSs and ENs is modeled as a Stackelberg game. Furthermore, we solve the Stackelberg game process using backward induction and theoretically propose optimal strategies for both the PSs and ENs. Finally, extensive numerical simulations using real datasets demonstrate the superior performance of our theoretical analysis of the proposed scheme. © 2002-2012 IEEE.

关键词： Federated learning

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Cross-level Multi-instance Distillation for Self-supervised Fine-grained Visual Categorization

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IEEE Transactions on Image Processing 2025年 34卷 2954-2969页

作者： Bi, Qi Ji, Wei Yi, Jingjun Zhan, Haolan Xia, Gui-Song University of Amsterdam 1098XH Netherlands Yale University New Haven United States Wuhan University Wuhan 430072 China Monash University Australia Wuhan University School of Artificial Intelligence National Engineering Research Center for Multimedia Software School of Computer Science State Key Lab. LIESMARS Wuhan 430072 China

High-quality annotation of fine-grained visual categories demands great expert knowledge, which is taxing and time consuming. Alternatively, learning fine-grained visual representation from enormous unlab.led images (e.g., species, brands) by self-supervised learning becomes a feasible solution. However, recent investigations find that existing self-supervised learning methods are less qualified to represent fine-grained categories. The bottleneck lies in that the pre-trained class-agnostic representation is built from every patch-wise embedding, while finegrained categories are only determined by several key patches of an image. In this paper, we propose a Cross-level Multiinstance Distillation (CMD) framework to tackle this challenge. Our key idea is to consider the importance of each image patch in determining the fine-grained representation by multiple instance learning. To comprehensively learn the relation between informative patches and fine-grained semantics, the multi-instance knowledge distillation is implemented on both the region/image crop pairs from the teacher and student net, and the regionimage crops inside the teacher / student net, which we term as intra-level multi-instance distillation and inter-level multi-instance distillation. Extensive experiments on several commonly used datasets, including CUB-200-2011, Stanford Cars and FGVC Aircraft, demonstrate that the proposed method outperforms the contemporary methods by up to 10.14% and existing state-of-the-art self-supervised learning approaches by up to 19.78% on both top-1 accuracy and Rank-1 retrieval metric. © 1992-2012 IEEE.

关键词： Fine-grained Visual Categorization Knowledge Distillation Multiple Instance Learning Self-supervised Learning

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Extended UML with Role Modeling

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Wuhan University Journal of Natural Sciences 2001年第Z1期6卷 175-182页

作者： He Ke\|qing 1, Jiang Hong 1, He Fei 2, Ying Shi 3 1 School of Computer Science,Wuhan University,Wuhan 430072,China 2.Japan Advanced Institute of Science and Technogy,923\|1292,Japan 3.stae key lab. of software engineering, Wuhan University,Wuhan School of Computer Science Wuhan University Wuhan China Japan Advanced Institute of Science and Technogy Japan Stae Key Lab. of Software Engineering Wuhan University Wuhan China

UML is widely accepted and applied by the international software industry. UML is a powerful language for Object oriented modeling, designing, and implementing software systems, but its Use Case method for requirement analysis and modeling software patterns has some explicit drawbacks. For more complete UML, this paper proposes the Role Use Case modeling and its glyphs, and provides an instance of requirement analysis using Role Use Case method. Uses the Role Model to modeling software pattern at knowledge level. This paper also extends the UML Meta Model and accentuates “RM before UML's class Modeling”.

关键词： UML role role modeling software pattern use case

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Artificial bee colony with dynamic Cauchy mutation for numerical optimization

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Journal of Information and Computational Science 2011年第15期8卷 3371-3376页

作者： Yang, Xiaoji Huang, Zhiguo State Key Lab. of Software Engineering Wuhan University Wuhan 430079 China

In this paper, an improved Artificial Bee Colony (ABC) optimization algorithm, which is called ABC with dynamic Cauchy mutation (CMABC), is proposed for numerical optimization. The proposed CMABC employs a dynamic Cauchy mutation which automatically adjust the step size of mutation during the evolutionary process. To verify the performance of CMABC, eight well-known benchmark optimization functions are selected in the experiments. Simulation results show that our approach achieves better results than the original ABC and Particle Swarm Optimization (PSO) on a majority of test functions. © 2009 by Binary Information Press.

关键词： Swarm intelligence

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Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks

arXiv

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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collab.rative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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Domain-oriented semantic knowledge extraction

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Journal of Computational Information Systems 2012年第10期8卷 4331-4337页

作者： Xiao, Kui Li, Bing Tan, Xiaohu State Key Lab. of Software Engineering Wuhan University Wuhan 430072 China School of Computer and Software Wuhan Vocational College of Software and Engineering Wuhan 430205 China

Semantic knowledge extraction task is groundwork of ontology building. As one of the most important public knowledge bases, Wikipedia has a lot of comparative advantages in the research field. In this paper, we propose a new method for extracting domain-oriented semantic knowledge from Wikipedia. During the process, every category in domain is assigned a weight, so that we can calculate the score of articles. Besides, practical experience in storing and utilizing big data of Wikipedia are detailed in this paper too. © 2011 by Binary Information Press.

关键词： Big data

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