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6th International Conference on Security and Privacy in New Computing Environments, SPNCE 2023

作者： Ali, Sheraz Mamoon, Saqib Usman, Areeba Abidin, Zain ul Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan250022 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China University of Sargodha Sargodha Pakistan Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Software Engineering University of Jinan Jinan250022 China

ISBN: (纸本)9783031736988

In this study, we introduce a novel Hybrid Federated Learning (HybridFL) approach aimed at enhancing privacy and accuracy in collaborative machine learning scenarios. Our methodology integrates Differential Privacy (DP) and secret sharing techniques to address inference risks during training and protect against information leakage in the output model. Drawing inspiration from recent advances, we present a HybridFL framework that combines the strengths of Homomorphic Encryption (HE) and Multi-Party Computation (MPC) to achieve secure computation without the computational overhead of pure HE methods. Our contributions include a privacy-preserving design for Federated Learning (FL) that ensures local data privacy through secret sharing while leveraging DP mechanisms for noise addition. The system offers resilience against unreliable participants and is evaluated using various machine learning models, including Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), and linear regression. Furthermore, we address potential external threats by deploying predictive model outputs as robust services against inference attacks. Experimental results demonstrate improved accuracy and convergence speed, establishing the viability of HybridFL as an effective solution for collaborative machine learning with enhanced privacy guarantees. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Federated learning

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M2-Net: Multi-Task Learning-Based Multi-Band Signal Recognition Network

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IEEE Internet of Things Journal 2025年

作者： Zhang, Xingjian Wang, Pengxu Ma, Yuan Jiao, Jian Wu, Shaohua Zhang, Qinyu Harbin Institute of Technology Shenzhen Guangdong Provincial Key Laboratory of ACNT Shenzhen518055 China Pengcheng Laboratory Shenzhen518055 China Guangdong Provincial Key Laboratory of ACNT Shenzhen518055 China Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration Guangdong Key Laboratory of Intelligent Information Processing the College of Electronic and Information Engineering Shenzhen518060 China

Traditional signal recognition requires the design of multiple different deep neural networks to handle different signal recognition tasks, which not only fails to take into account the correlation among different subtasks, but also leads to large model size and higher computational complexity. In this work, we propose a multi-task learning-based multi-band signal recognition network (M2-Net) to simultaneously recognize the location of occupied frequency bands, modulation types, and signal types. The proposed M2-Net consists of two main parts: shared feature extraction network (SFEN) and multi-task classification header (MCH). In SFEN, a plug-and-play multi-task feature extraction convolution and an adaptive threshold denoising module are introduced to provide better shared feature extraction and denoising performance. In MCH, the shared features obtained from SFEN are further processed for different recognition tasks. Furthermore, during the multi-task model training, homoscedastic uncertainty is introduced as a task-dependent weight to adaptively balance the training loss of different tasks. To evaluate the recognition performance of the proposed method, we construct a multi-band signal dataset and compare M2-Net with several state-of-the-art models in signal recognition field. Experiment results show that the proposed M2-Net has significant performance improvements in terms of recognition accuracy and model complexity, especially under low signal-to-noise ratio conditions. © 2025 IEEE.

关键词： Deep neural networks

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Identity and Modality Attributes Driven Multimodal Fusion Networks for Emotion Recognition in Conversations

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IEEE Transactions on Multimedia 2025年

作者： Shi, Wuzhen Chen, Xuping Yao, Biyun Wen, Yang Sheng, Bin Shenzhen University Guangdong Province Engineering Laboratory for Digital Creative Technology Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

Emotion recognition in conversations (ERC) is a crucial aspect of human-computer interaction and plays an important role in various domains, including healthcare, entertainment, and education. Since the conversation data in the form of multimodal sequences is well suited to be constructed into graphs, the methods based on graph convolutional network (GCN) show incomparable advantages. However, existing methods attempt to model the highly uncertain emotional relationships between different speakers, which is not an easy task and may even introduce interference information. Therefore, we propose an identity and modality attributes driven multimodality fusion network (dubbed IMDNet) for emotion recognition in conversations. Specifically, we construct a speaker-centric graph that only connects nodes of the same speaker within modalities to each other, reducing the interference between the emotions of different speakers. We also introduce the attribute embedding mechanism, which facilitates the correct calculation of correlations between nodes for better multimodal feature fusion. Considering that the emotional correlation between utterances will decrease over time, we present an utterance distance attention to make the fusion network pay more attention to the adjacent utterances. Furthermore, we explore the solution to the data imbalance problem suitable for conversation scenarios. Given the presence of possible anomalous samples in the dataset, we opt for the BoundaryFocalLoss. Experiments on the IEMOCAP and MELD datasets show that our IMDNet outperforms the state-of-the-art methods. © 1999-2012 IEEE.

关键词： Convolution

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M²-Net: Multitask-Learning-Based Multiband Signal Recognition Network

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IEEE Internet of Things Journal 2025年第11期12卷 16543-16558页

作者： Xingjian Zhang Pengxu Wang Yuan Ma Jian Jiao Shaohua Wu Qinyu Zhang Guangdong Provincial Key Laboratory of ACNT Harbin Institute of Technology (Shenzhen) Shenzhen China Department of Boardband Communications Pengcheng Laboratory Shenzhen China State Key Laboratory of Radio Frequency Heterogeneous Integration the Guangdong Key Laboratory of Intelligent Information Processing and the College of Electronic and Information Engineering Shenzhen University Shenzhen China

Traditional signal recognition requires the design of multiple different deep neural networks to handle different signal recognition tasks, which not only fails to take into account the correlation among different subtasks, but also leads to large model size and higher computational complexity. In this work, we propose a multitask-learning-based multiband signal recognition network $(\text {M}^{2}\text {-Net})$ to simultaneously recognize the location of occupied frequency bands, modulation types, and signal types. The proposed $\text {M}^{2}\text {-Net}$ consists of two main parts: 1) shared feature extraction network (SFEN) and 2) multitask classification header (MCH). In SFEN, a plug-and-play multitask feature extraction convolution and an adaptive threshold denoising module are introduced to provide better shared feature extraction and denoising performance. In MCH, the shared features obtained from SFEN are further processed for different recognition tasks. Furthermore, during the multitask model training, homoscedastic uncertainty is introduced as a task-dependent weight to adaptively balance the training loss of different tasks. To evaluate the recognition performance of the proposed method, we construct a multiband signal dataset and compare $\text {M}^{2}\text {-Net}$ with several state-of-the-art models in signal recognition field. Experiment results show that the proposed $\text {M}^{2}\text {-Net}$ has significant performance improvements in terms of recognition accuracy and model complexity, especially under low signal-to-noise ratio conditions.

关键词： Sensors Training Feature extraction Multitasking Wireless communication Accuracy Wireless sensor networks Pattern classification Narrowband Computational modeling

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Bayesian Synthetic Aperture processing for Direction Finding With a Deformed Towed Array

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Yang, Jie Yang, Yixin Liao, Bin Northwestern Polytechnical University Key Laboratory of Ocean Acoustics and Sensing Shaanxi Key Laboratory of Underwater Information Technology Shaanxi Xi'an710072 China Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration Guangdong Provincial Key Laboratory of Intelligent Information Processing Guangdong Shenzhen518060 China

This paper considers direction finding with a passive towed sonar array that is deformed during platform maneuver. Since uncertainties in sensor location can severely degrade the direction finding performance, joint estimation of source directions-of-arrival (DOAs) and sensor positions is desired. This motivates us to present a synthetic aperture algorithm here that has improved performance compared to conventional schemes regarding phase coherent limitations on the incoming signals. With certain prior knowledge of source-array geometry, the maximum likelihood estimates of DOAs and sensor positions are found from maximizing the model evidence of these parameters and the observed data. The resulting computationally expensive optimization problem is simplified by utilizing the variational Bayesian Expectation-Maximization (EM) framework. By representing the array observation as a hierarchical binary mask formulation with several latent variables, whose estimates are iteratively updated by approximating the posteriors thereof analytically, we gain the benefit of selecting the model order automatically. This methodology, while retaining the advantage of synthesizing an aperture with improved angular resolution, is robust to perturbations in the array manifold. Additionally, several open-form Cramér-Rao bound (CRB) expressions are derived as indicators of limits on array and signal parameter estimates. Numerical simulations illustrate that the proposed technique is statistically efficient in both deterministic and stochastic source scenarios. © 1965-2011 IEEE.

关键词： Cramer-Rao bounds

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KAC-Unet: A Medical Image Segmentation With the Adaptive Group Strategy and Kolmogorov-Arnold Network

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Lin, Shiying Hu, Rong Li, Zuoyong Lin, Qinghua Zeng, Kun Wu, Xiang Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Fuzhou350121 China Fuzhou University Affiliated Provincial Hospital Provincial Clinical Medical College Fujian Medical University Department of Urology Fuzhou350001 China

In the field of deep learning-based medical image segmentation, convolutional neural networks (CNNs) extract image features by combining linear convolutional layers with nonlinear activation functions. However, excessive stacking of linear layers in the network limits the model’s ability to capture fine-grained details. In addition, the feature distribution imbalance caused by the traditional fixed grouping strategy (FGS) can affect the deep model’s capacity to perceive the overall structure of the image. To address these challenges, we propose a medical image segmentation framework, called Kolmogorov-Arnold Network with the adaptive group strategy and contextual Transformer based on Unet (KAC-Unet). First, we propose the adaptive group strategy (AGS) to balance the grouping of different input channels, alleviating the performance degradation caused by differences in group information. Then, we propose the Shift Tokenized Kolmogorov-Arnold Network (KAN) Block to capture complex features in medical images through flexible nonlinear transformations and shift operations. Extensive experiments are conducted on three medical image segmentation datasets. The results demonstrate the effectiveness and superiority of our proposed method compared with state-of-the-art algorithms. © 1963-2012 IEEE.

关键词： Convolutional neural networks

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Data-Driven Digital Adaptive Timing Mismatch Calibration for Sub-Nyquist Wideband Sampling

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Ma, Yuan Zheng, Yiling Zhang, Xingjian Liao, Bin Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China Pengcheng Laboratory Shenzhen518055 China Shenzhen University College of Electronic and Information Engineering Shenzhen518060 China Harbin Institute of Technology Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen518060 China Fudan University Institute of Space Internet Shanghai China

The employment of sub-Nyquist sampling enables the adaptation to the ever-increasing bandwidth demands of wireless communication systems. Multicoset sampling is a nonuniform sub-Nyquist sampling technique that can be realized by using a multi-channel architecture. Nevertheless, the performance of multicoset sampling is significantly affected by timing mismatches among sub-ADCs. Traditional analog calibration scheme mitigates timing mismatch errors by adjusting the sampling clock in the front-end, but its performance is suboptimal due to the difficulty in selecting an appropriate initial step size and the sequential calibration process. In this paper, a data-driven digital adaptive timing mismatch calibration approach is proposed that uses real-time measurement data only to calculate calibration parameters without changing hardware circuits, making it effective for tracking variations in mismatches among multiple channels. Based on the nonlinear relationship between the timing mismatches and the normalized mean squared error of the output spectrum, the proposed data-driven calibration scheme jointly computes calibration parameters among multiple channels in parallel through a series of dynamic linearization data models with a time-varying pseudo-partial derivative parameter estimation procedure, and then adaptively adjusts the sampling matrix in the reconstruction to correct unwanted distortions in the output spectrum. To further improve the calibration performance, particle swarm optimization algorithm is exploited to generate parameter vectors used in the proposed data-driven calibration scheme. Numerical analyses indicate that the proposed data-driven digital calibration scheme can eliminate timing mismatches with fewer calibration iterations and enhanced accuracy. © 2015 IEEE.

关键词： Particle swarm optimization (PSO)

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Analysis of Schema Formation in Genetic Algorithms: A Review 16th

Analysis of Schema Formation in Genetic Algorithms: A Review

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Zhang, Tiancong Chu, Shu-Chuan Weng, Shaowei Dao, Thi-Kien Nguyen, Trong-The School of Computing and Information Science Fuzhou Institute of Technology Fuzhou350506 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qing-Dao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9789819615346

Genetic algorithms (GAs) are a powerful class of optimization techniques inspired by the principles of natural selection and genetics. One of the theoretical cornerstones of GAs is schema theory, which provides a framework for understanding how building blocks of solutions, known as schemas, are propagated and combined through generations. This review paper aims to provide a comprehensive theoretical analysis of schema formation in genetic algorithms. It delves into the foundational principles of schema theory, including the schema theorem and its implications for the efficiency and behavior of GAs. The paper also explores advanced topics such as schema disruption mechanisms, the impact of multimodal and epistatic fitness landscapes, and the integration of schema theory with hybrid approaches. Additionally, practical implications and real-world applications of schema theory are discussed through case studies and best practices. By elucidating the mechanisms of schema formation and their influence on genetic algorithm performance, this review seeks to offer valuable insights and directions for future research. Ultimately, this theoretical exploration aims to bridge the gap between abstract concepts and practical application, contributing to the advancement of genetic algorithms as robust optimization tools. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic algorithms

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BEVUDA++: Geometric-Aware Unsupervised Domain Adaptation for Multi-View 3D Object Detection

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IEEE Transactions on Circuits and Systems for Video technology 2025年第5期35卷 5109-5122页

作者： Zhang, Rongyu Liu, Jiaming Li, Xiaoqi Chi, Xiaowei Wang, Dan Du, Li Du, Yuan Zhang, Shanghang Nanjing University School of Electronic Science and Engineering Nanjing210093 China The Hong Kong Polytechnic University Department of Computing Hung Hom Hong Kong Peking University National Key Laboratory for Multimedia Information Processing School of Computer Science Beijing100871 China The Hong Kong University of Science and Technology Academy of Interdisciplinary Studies Hung Hom Hong Kong

Vision-centric Bird’s Eye View (BEV) perception holds considerable promise for autonomous driving. Recent studies have prioritized efficiency or accuracy enhancements, yet the issue of domain shift has been overlooked, leading to substantial performance degradation upon transfer. We identify major domain gaps in real-world cross-domain scenarios and initiate the first effort to address the Domain Adaptation (DA) challenge in multi-view 3D object detection for BEV perception. Given the complexity of BEV perception approaches with their multiple components, domain shift accumulation across multi-geometric spaces (e.g., 2D, 3D Voxel, BEV) poses a significant challenge for BEV domain adaptation. In this paper, we introduce an innovative geometric-aware teacher-student framework, BEVUDA++, to diminish this issue, comprising a Reliable Depth Teacher (RDT) and a Geometric Consistent Student (GCS) model. Specifically, RDT effectively blends target LiDAR with dependable depth predictions to generate depth-aware information based on uncertainty estimation, enhancing the extraction of Voxel and BEV features that are essential for understanding the target domain. To collaboratively reduce the domain shift, GCS maps features from multiple spaces into a unified geometric embedding space, thereby narrowing the gap in data distribution between the two domains. Additionally, we introduce a novel Uncertainty-guided Exponential Moving Average (UEMA) to further reduce error accumulation due to domain shifts informed by previously obtained uncertainty guidance. To demonstrate the superiority of our proposed method, we execute comprehensive experiments in four cross-domain scenarios, securing state-of-the-art performance in BEV 3D object detection tasks, e.g., 12.9% NDS and 9.5% mAP enhancement on Day-Night adaptation. © 1991-2012 IEEE.

关键词： Teaching

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LSU-NET: Lightweight Automatic Organs Segmentation Network for Medical Images

LSU-NET: Lightweight Automatic Organs Segmentation Network f...

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

作者： Yujie Ding Shenghua Teng Zuoyong Li Xiao Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.

关键词： Training Image segmentation Convolution Dynamic scheduling Stability analysis Resource management Speech processing Standards Monitoring Biomedical imaging

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