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

作者： Zhou, Chenyang Monghjaya Wu, Licheng School of Chinese Ethnic Minority Languages and Literatures Minzu University of China Beijing100081 China College of Computer Science Inner Mongolia University Hohhot010021 China School of Information Engineering Minzu University of China Beijing100081 China Key Laboratory of Ethnic Language Intelligent Analysis and Security Management of MOE Minzu University of China Beijing100081 China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot010021 China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot010021 China

ISBN: (纸本)9798350368741

The traditional Mongolian script poses significant challenges for text rendering due to its vertical orientation, complex glyph variations, and context-dependent shapes, compounded by limited linguistic resources. This paper introduces the first method specifically designed to address the out-of-vocabulary (OOV) word problem in traditional Mongolian script conversion. We present a novel dataset and propose MonTransformer, the first Transformer-based model tailored for converting Unicode phonetic sequences into glyph sequences. MonTransformer utilizes dynamic optimization and contrastive learning to effectively manage OOV cases and improve sequence generation accuracy, transforming Unicode inputs into visually accurate glyph representations. Our approach achieves state-of-the-art performance, maintaining the script's visual fidelity and advancing efforts in digital preservation for complex scripts. © 2025 IEEE.

关键词： Contrastive learning Dynamic optimization Out-of-vocabulary (OOV) Sequence generation Traditional Mongolian script

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Dynamic Strip Convolution and Adaptive Morphology Perception Plugin for Medical Anatomy Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Hu, Guyue Kang, Yukun Zhao, Gangming Jin, Zhe Li, Chenglong Tang, Jin Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Hefei230601 China Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei230601 China The University of Hong Kong Department of Computer Science Hong Kong Anhui University School of Artificial Intelligence Hefei230601 China

Medical anatomy segmentation is essential for computer-aided diagnosis and lesion localization in medical images. For example, segmenting individual ribs benefits localizing the lung lesions and providing vital medical measurements (such as rib spacing) for generating medical reports. Existing methods segment shape-different anatomies (such as striped ribs, bulky lungs, and angular scapula) with the same network architecture, the morphology heterogeneity is heavily overlooked. Although some shape-aware operators like deformable convolution and dynamic snake convolution have been introduced to cater to specific object morphology, they still struggle with orientation-varying strip structures, such as 24 ribs and 2 clavicles. In this paper, we propose a novel convolution plugin (DSC-AMP) for medical anatomy segmentation, which is comprised of a dynamic strip convolution (DSC) operator and an adaptive morphology perception (AMP) strategy. Specifically, the dynamic strip convolution customizes gradually varying directions and offsets for each local region, achieving dynamic striped receptive fields. Additionally, the adaptive morphology perception strategy incorporates insights from various shape-aware convolutional kernels, enabling the model to discern and integrate crucial representations corresponding to heterogeneous anatomies. Extensive experiments on two large-scale datasets demonstrate the effectiveness and superiority of the proposed approach for tackling heterogeneous medical anatomy segmentation. © 2025 IEEE. All rights reserved.

关键词： Diagnosis

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Sparse and Dense: Learning Confusion Representation Network for 3D Action Recognition

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IEEE Internet of Things Journal 2025年

作者： Hou, Xinran Geng, Pei Li, Tianchen Li, Yalin Li, Yan Lyu, Lei School of Information Science and Engineering Shandong Normal University Jinan250358 China School of Information Engineering Shandong Management University Jinan250100 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

In the field of the Internet of Medical Things (IoMT), the demand for Human Action Recognition (HAR) is growing. Due to the limitations of portability and privacy of traditional sensors, many endeavors have made significant progress in 3D skeleton-based action recognition. However, existing methods ignore potential higher-order semantic information between joints and fail to perceive rapidly changing dynamic details, resulting in frequent confusion of actions with similar motion trajectories. To alleviate this issue, we propose a novel learning confusion representation network (LCR-Net). Specifically, a progressive feature enhancement module is first designed to utilize self-attention to gradually aggregate lower-order features to higher-order features, emphasizing the relative movement between body parts. Second, we design the enhanced spatiotemporal convolution to explore the potential spatio-temporal dependencies between joints by adding a mask matrix and an attention fusion mechanism. To further perceive the spatiotemporal relationships in subtle changes, we divide the interactive sparse-dense pathways at different spatio-temporal resolutions and enhance the complementary information between the two pathways through feature interaction. Finally, the frequency excitation learning module is proposed to efficiently learn the importance of different frequencies by cross-channel modeling, promoting the compactness of actions within classes and the separability of confusion actions. In addition, the lightweight LCR-Net+ is achieved through model compression optimization to meet the deployment requirements of IoT systems. Comprehensive experiments conducted on three public datasets (NTU-RGB+ D60&120, NW-UCLA) demonstrate the superior performance of our model. © 2025 IEEE. All rights reserved.

关键词： Joints (anatomy)

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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Delay-Aware Digital Twin Synchronization in Mobile Edge Networks With Semantic Communications

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IEEE Transactions on Vehicular technology 2025年

作者： Li, Bin Cai, Haichen Liu, Lei Fei, Zesong Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Ministry of Education Guilin University of Electronic Technology Key Laboratory of Cognitive Radio and Information Processing Guilin541004 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China

The synchronization of digital twins (DT) serves as the cornerstone for effective operation of the DT framework. However, the limitations of channel capacity can greatly affect the data transmission efficiency of wireless communication. Unlike traditional communication methods, semantic communication transmits the intended meanings of physical objects instead of raw data, effectively saving bandwidth resource and reducing DT synchronization latency. Hence, we are committed to integrating semantic communication into the DT synchronization framework within the mobile edge computing system, aiming to enhance the DT synchronization efficiency of user devices (UDs). Our goal is to minimize the average DT synchronization latency of all UDs by jointly optimizing the synchronization strategy, transmission power of UDs, and computational resource allocation for both UDs and base station. The formulated problem involves sequential decision-making across multiple coherent time slots. Furthermore, the mobility of UDs introduces uncertainties into the decision-making process. To solve this challenging optimization problem efficiently, we propose a soft actor-critic-based deep reinforcement learning algorithm to optimize synchronization strategy and resource allocation. Numerical results demonstrate that our proposed algorithm can reduce synchronization latency by up to 13.2% and improve synchronization efficiency compared to other benchmark schemes. © 1967-2012 IEEE.

关键词： Resource allocation

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An Efficient Phase Noise Elimination Method to Facilitate Cost-effective 100/200G Coherent PON

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Journal of Lightwave technology 2025年

作者： Chen, Yifan Wang, Chen Yu, Jianjun Long, Jianyu Sang, Bohan Li, Fan Sun Yat-Sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Opto-Electronic Information Processing Chips and Systems Guangzhou 510275 China Fudan University State Key Laboratory of ASIC and System Shanghai 200433 China

In the field of passive optical networks (PONs), coherent sinking has emerged as a significant topic, particularly in the context of cost and complexity improvements. We experimentally demonstrate a cost-effective 100-/200-Gbits/s/λ coherent PON system employing dual-polarization quadrature phase shift keying (DP-QPSK) and 16-ary quadrature amplitude modulation (DP-16QAM). To achieve cost reductions in coherent PON implementations, we employ low-cost distributed feedback lasers (DFBs) as the sole active light sources, replacing the much more expensive external cavity lasers (ECLs) at both the optical line terminal (OLT) and the optical network units (ONUs). To effectively address the performance penalty associated with phase noise (PN) of DFBs characterized by MHz-class linewidths, a straightforward carrier phase recovery (CPR) is proposed via embedding a one-tap carrier phase estimator (CPE) with gradient-descent algorithm into a 4×4 multiple-input multiple-output (MIMO) equalizer following the least-mean square algorithm (LMS). This approach simplifies the structure and enables more precise cancellation of PN. We conduct a transmission performance comparison at 32 Gbaud between our proposed scheme and the blind phase searching algorithm (BPS) for single-carrier (SC) signals, as well as between our method and the frequency-domain pilot-tone carrier phase recovery (FPT-CPR) for two-sub-band digital subcarrier multiplexing (DScM) signals. Experimental results demonstrate the corresponding power budgets of 38.1, 30.8, 36.5, and 29.1 dB for SC-QPSK/16QAM and DScM-QPSK/16QAM at the 20% soft-decision forward error correction (SD-FEC) threshold of 2×10^-2. Furthermore, complexity analysis of these phase noise estimation (PNE) methods reveals the superiority of our proposed scheme in balancing performance and computational complexity. © 1983-2012 IEEE.

关键词： 100/200G PON distributed feedback laser laser phase noise low-cost coherent PON phase noise estimation

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Homophily Induced Contrastive Attributed Graph Clustering

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Chen, Man-Sheng Lai, Pei-Yuan Liao, De-Zhang Wang, Chang-Dong Lai, Jian-Huang Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China South China Technology Commercialization Center Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China

Attributed graph clustering, aiming to discover the underlying graph structure and partition the graph nodes into several disjoint categories, is a basic task in graph data analysis. Although recent efforts over graph contrastive clustering have achieved decent performances, most of them get accustomed to construct the positive neighbor set by the generated pseudo clustering information, directly ignoring the ready-made neighbor nodes and the underlying semantics of edges in a graph. How to well deal with the graph neighbor-specific information to facilitate the performance of graph contrastive clustering is still a challenging problem. Therefore, in this work, we propose a novel Homophily Induced Contrastive Attributed Graph Clustering (HomoCAGC) method, where the power of homophily is exploited in facilitating the performance of contrastive attributed graph clustering, while the pseudo homophily in a graph is also explored and distinguished. Especially, the node feature as reliable information guidance is used to compute the underlying feature-oriented pair-wise node similarity, based on which the positive node pairs in contrastive regularizer are adjusted for better node representation characterization. According to the refined node representations, a triplet self-supervised clustering objective is well-designed to ensure the output embedding is cluster-oriented, and suitable for the downstream clustering task. Extensive experiments on seven benchmarks are conducted to demonstrate the effectiveness of HomoCAGC. © 1991-2012 IEEE.

关键词： Attributed graph clustering contrastive learning graph homophily self-supervised clustering

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NOMA Assisted Semi-Grant-Free Transmission for UAV Networks: A Multi-User Scheduling Approach

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IEEE Transactions on Communications 2025年

作者： Huang, Junlin Lu, Huabing Tang, Jie Zhao, Nan Shi, Zhaoyuan Wang, Xianbin Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Nanchang University School of Information Engineering Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications Nanchang330031 China South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China Anqing Normal University School of Computer and Information Anqing246052 China Western University Department of Electrical and Computer Engineering LondonN6A 5B9 Canada

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission, which enables grant-based users to share their spectrum with grant-free (GF) users, becomes an effective solution to the challenge of massive connections. However, this improvement is limited as most of the existing schemes can only admit one GF user. In this paper, we propose a novel NOMA-assisted SGF transmission scheme to support the access of multiple GF users in unmanned aerial vehicle networks. Moreover, in order to further enhance the advantages of the multi-user SGF scheme, two SGF schemes with power matching are devised to further improve the performance by employing the benefits of distributed contention. For ease of performance evaluation, the theoretical expressions of achievable sum rate and average age of information of the three SGF schemes are derived by applying order statistics. We also investigate the high signal-to- noise approximation expressions of sum rate for these schemes to give more insight. Finally, simulation results are provided to demonstrate the performance improvement of three proposed SGF schemes and validate the correctness of the theoretical analysis expressions. © 1972-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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End-to-end robust and transparent image watermarking method for screen-shooting scenarios

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Journal of Electronic Imaging 2025年第2期34卷

作者： Yang, Nana Chen, Yi Wang, Hongxia Shen, Guowei Guo, Chun Cui, Yunhe Liu, Yong Wu, Hanzhou Guizhou University College of Computer Science and Technology State Key Laboratory of Public Big Data Guiyang China Guizhou University Guizhou Provincial Characteristic Key Laboratory of Software Engineering and Information Security Guiyang China Sichuan University School of Cyber Science and Engineering Chengdu China Hubei University of Automotive Technology School of Electrical and Information Engineering Shiyan China Shanghai University School of Communication and Information Engineering Shanghai China

Screen-shooting robust image watermarking techniques are the primary means for copyright protection and leakage traceability of screen content. However, existing image watermarking methods generate watermarked images with visible artifacts and are not robust enough against attacks. Therefore, we propose an end-to-end robust and transparent image watermarking method for protecting copyright in screen-shooting scenarios. In this method, we introduce scaling distortion to combine with color distortion, moiré distortion, Gaussian noise, and JPEG compression to design a new noise layer. In the noise layer, we propose two ways to simulate real moiré distortion. We also propose a training strategy adding five distortions in stages during end-to-end adversarial training. Both the noise layer and training strategy can help improve robustness. In addition, we propose an edge-aware loss function to guide the encoder to focus on modifying the edge regions of the cover image, thereby enhancing transparency. The experimental results show that, under different shooting conditions, the watermark extraction accuracy exceeds 97%, indicating the high robustness of our proposed method. In terms of transparency, the peak signal-to-noise ratio reaches 38.51 dB, which is 1.34 dB higher than the state-of-the-art deep learning-based screen-shooting watermarking method, PIMoG. © 2025 SPIE and IS&T.

关键词： edge-aware loss image watermarking noise layer screen-shooting training strategy

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Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification

arXiv

引用

arXiv 2025年

作者： Yang, Wennuo Wu, Shiling Zhou, Yuzhi Luo, Cheng He, Xilin Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China HBUG Lab University of Exeter United Kingdom

Multivariate Time Series Classification (MTSC) enables the analysis if complex temporal data, and thus serves as a cornerstone in various real-world applications, ranging from healthcare to finance. Since the relationship among variables in MTS usually contain crucial cues, a large number of graph-based MTSC approaches have been proposed, as the graph topology and edges can explicitly represent relationships among variables (channels), where not only various MTS graph representation learning strategies but also different Graph Neural Networks (GNNs) have been explored. Despite such progresses, there is no comprehensive study that fairly benchmarks and investigates the performances of existing widely-used graph representation learning strategies/GNN classifiers in the application of different MTSC tasks. In this paper, we present the first benchmark which systematically investigates the effectiveness of the widely-used three node feature definition strategies, four edge feature learning strategies and five GNN architecture, resulting in 60 different variants for graph-based MTSC. These variants are developed and evaluated with a standardized data pipeline and training/validation/testing strategy on 26 widely-used suspensor MTSC datasets. Our experiments highlight that node features significantly influence MTSC performance, while the visualization of edge features illustrates why adaptive edge learning outperforms other edge feature learning methods. The code of the proposed benchmark is publicly available at https://***/CVI-yangwn/*** Codes 68T10 © 2025, CC BY.

关键词： Graph neural networks

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