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JDC-GCN: joint diversity and consistency graph convolutional network

Neural computing and Applications 2025年 1-17页

作者： Lin, Renjie Yao, Jie Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

In real-world scenarios, multi-view data comprises heterogeneous features, with each feature corresponding to a specific view. The objective of multi-view semi-supervised classification is to enhance classification performance by leveraging the inherent complementary and consistent information present within diverse views. Nevertheless, many existing frameworks primarily focus on assigning suitable weights to different views while neglecting the importance of consistent information. In this paper, a multi-view semi-supervised classification framework called joint diversity and consistency graph convolutional network (JDC-GCN) is proposed. Firstly, the structure of graph convolutional network is introduced to the multi-view semi-supervised classification, capable of propagating the label information over the topological structure of multi-view data. Secondly, the proposed JDC-GCN captures the complementary and consistent information from multiple views through two indispensable sub-modules, Diversity-GCN and Consistency-GCN, respectively. Finally, the attention mechanism is leveraged to dynamically adjust the weights of various views, allowing us to measure the significance of heterogeneous features and the consistent graph without introducing additional parameters. Comprehensive experiments on eight multi-view datasets are conducted to validate the effectiveness of the JDC-GCN algorithm. The results show that the proposed method exhibits superior classification performance compared to other state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Self-supervised learning

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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

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Neural computing and Applications 2025年第3期37卷 1651-1672页

作者： Xu, Junjie Chen, Yuzhong Xiao, Lingsheng Liao, Hongmiao Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding Network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural Network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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Unsupervised Domain Transfer for Object Classification in 3D Point Clouds via Hierarchical Prompt Learning

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IEEE Signal Processing Letters 2025年 32卷 1750-1754页

作者： Luo, Huan Fu, Kaiwei Fang, Lina Fuzhou University College of Computer and Data Science China Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing China Ministry of Education Key Laboratory of Spatial Data Mining & Information Sharing Fuzhou350003 China Chinese Academy of Sciences Quanzhou Institute of Equipment Manufacturing Haixi Institute Fujian Quanzhou362216 China

Traditional object classification in 3D point cloud scenes relies heavily on large-scale labeled training data, which is both time-consuming and labor-intensive to obtain. Unsupervised Domain Transfer (UDT) mitigates this challenge by transferring knowledge from a labeled source domain to an unlabeled target domain. However, existing UDT-based methods often require complex neural architectures and substantial computational resources. This letter proposes a novel UDT framework that integrates hierarchical prompt learning with a 3D foundational model. The proposed method consists of a modality alignment stage and an unsupervised transfer stage. In the modality alignment stage, cross-modal hierarchical prompts are employed to align the Visual-Language (V-L) modality in the 3D foundational model through a V-L coupling module. In the unsupervised transfer stage, cross-domain hierarchical prompts and a Target-Source (T-S) coupling module facilitate the alignment of multi-scale contextual information across domains, ensuring efficient and accurate knowledge transfer. Extensive experiments conducted on multiple datasets collected from various laser scanners demonstrate the effectiveness of our proposed approach. © 1994-2012 IEEE.

关键词： Visual languages

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A Review of Human Mesh Reconstruction: Beyond 2D Video Object Segmentation 16th

A Review of Human Mesh Reconstruction: Beyond 2D Video Obje...

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16th International Conference on Social Robotics, ICSR + InnoBiz 2024

作者： Wu, Peng Wang, Zhicheng Pan, Feiyu Li, Fangkai Hu, Hao Lu, Xiankai Guo, Yiyou School of Software Shandong University Shandong Jinan250000 China School of Mathematics and Computer Science Fujian Provincial Key Laboratory of Data-Intensive Computing Fujian University Laboratory of Intelligent Computing and Information Processing Quanzhou Normal University Quanzhou China

ISBN: (纸本)9789819611508

Video object segmentation aims to extract 2D object masks by segmenting video frames into multiple objects, which is crucial in various practical applications such as medical imaging, etc.. However, traditional video object segmentation methods produce 2D masks, which are not suitable for 3D scenarios where depth information is essential, such as in robotic grasping, virtual reality, and autonomous driving, etc.. In this paper, we present a comprehensive review of 3D human mesh reconstruction (HMR) as an extension beyond 2D video object segmentation. We begin by reviewing the mainstream video object segmentation methods, then transition from 2D video object segmentation to 3D HMR. We further categorize recent HMR methods based on key characteristics that define this research field, including the type of model input and the use of statistical models. Finally, we provide detailed information on HMR datasets and evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mesh generation

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MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation

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Computers in Biology and Medicine 2025年 186卷 109601-109601页

作者： Ke, Xiao Chen, Guanhong Liu, Hao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) the problem of model overfitting and weak generalization due to the multi-center distribution of data;(ii) the problem of interclass ambiguity caused by motion blur and overexposure to endoscopic light;and (iii) the problem of intraclass inconsistency caused by the variety of morphologies and sizes of the same type of polyps. To address these challenges, we propose a new high-precision polyp segmentation framework, MEFA-Net, which consists of three modules, including the plug-and-play Mask Enhancement Module (MEG), Separable Path Attention Enhancement Module (SPAE), and Dynamic Global Attention Pool Module (DGAP). Specifically, firstly, the MEG module regionally masks the high-energy regions of the environment and polyps through a mask, which guides the model to rely on only a small amount of information to distinguish between polyps and background features, avoiding the model from overfitting the environmental information, and improving the robustness of the model. At the same time, this module can effectively counteract the "dark corner phenomenon" in the dataset and further improve the generalization performance of the model. Next, the SPAE module can effectively alleviate the inter-class fuzzy problem by strengthening the feature expression. Then, the DGAP module solves the intra-class inconsistency problem by extracting the invariance of scale, shape and position. Finally, we propose a new evaluation metric, MultiColoScore, for comprehensively evaluating the segmentation performance of the model on five datasets with different domains. We evaluated the new method quantitatively and qualitatively on five datasets using four metrics. Experimental results show that MEFA-Net significantly improves the accuracy of polyp segmentation and outperforms current state-of-the-art algorithms. Code posted on https://***/

关键词： Endoscopy

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Enhanced Repetition Pilot Based Timing Synchronization and Channel Estimation for DCO-OFDM OWC Systems

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IEEE Wireless Communications Letters 2025年

作者： Ji, Zhihong Jiang, Yufei Zhu, Xu School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen518055 China The Guangxi Key Laboratory of Brain Inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology Shenzhen China Shenzhen Key Laboratory of Wireless AIoT Communication Shenzhen518055 China

We design pilots that include some repetition parts to allow timing synchronization and channel estimation for direct current biased optical-orthogonal frequency division multiplexing (DCO-OFDM) optical wireless communication (OWC) systems. On one hand, we propose an enhanced repetition pilot (eRP) based timing synchronization approach, robust against low correlation between real-and-positive signals and against inter-symbol interference (ISI) caused by light emitting diode (LED)'s limited bandwidth, much more than the previous works. We define a new timing metric using the ratio of auto-correlation to power, between the first and second halves of the received pilots. Maximizing the defined timing metric allows the timing offset in the cyclic prefixed (CP) area. The phase difference between a number of adjacent subcarriers is utilized to refine timing synchronization. On the other hand, we propose an eRP based channel estimation enhancement (CEE) approach and an imaginary component minimization (ICM) based channel estimation approach, utilizing pilots the same as timing synchronization. The proposed CEE approach allows the repeated parts of pilots to be averaged for channel estimation enhancement. The proposed ICM approach is divided into LED's limited bandwidth estimation and line-of-sight (LoS) channel estimation, exploring the correlation between the least-square (LS) estimated channel estimate information (CSI) and the trial LED frequency response. The proposed CEE approach provides low complexity, while the proposed ICM approach provides good performance. Simulation results show that, using a single DCO-OFDM symbol, the proposed structure provides bit error rate (BER) close to ideal case with perfect CSI and perfect timing synchronization. © 2012 IEEE.

关键词： Light emitting diodes

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Toward 3-D AAV-Ground BS CoMP-NOMA Transmission: Optimal Resource Allocation and Trajectory Design

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IEEE Internet of Things Journal 2025年第8期12卷 9671-9686页

作者： Zeng, Haiyong Zhang, Rui Zhu, Xu Wei, Zhongxiang Jiang, Yufei Sun, Sumei Zheng, Fu-Chun Cao, Bin Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips The School of Electronic and Information Engineering School of Integrated Circuits The Key Laboratory of Integrated Circuits and Microsystems The Education Department of Guangxi Zhuang Autonomous Region Guangxi Normal University Guilin541001 China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen518057 China School of Electronic and Information Engineering The Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology The Shenzhen Key Laboratory of AIoT Communication Harbin Institute of Technology Shenzhen518055 China Shanghai Institute of Intelligent Science and Technology The College of Electronic and Information Engineering Tongji University Shanghai200070 China National Mobile Communications Research Laboratory Southeast University Nanjing211102 China Department of Communications and Networks Institute for Infocomm Research Agency for Science Technology and Research Singapore Singapore

In this article, we focus on the resource allocation and autonomous aerial vehicle (AAV) 3-D trajectory design for the AAV-ground base station (GBS) coordinated multipoint nonorthogonal multiple access (CoMP-NOMA) system to maximize the sum-rate of CoMP users while maintaining users' high Quality of Service requirements. The main contributions of this article are summarized as follows: 1) with the assistance of closed-form power allocation result, a generalized joint user scheduling and power allocation (G-USPA) algorithm is proposed to derive the optimal user scheduling solution;2) by revealing the monotone increasing relationship between the sum transmit power and the transmit rates of non-CoMP users, the optimal rate of each non-CoMP users turns out to be its inherent minimum required rate, consequently, the optimal transmit rates and power allocation of all users can also be derived;and 3) moreover, considering the Line of Sight (LoS) and non-LoS factors in the air-ground channel, the 3-D trajectory of AAV is designed based on successive convex approximation to provide a flexible user-centric service. The proposed G-USPA algorithm is compatible with the AAV trajectory design, which is optimized alternatively and can lead to fast convergence. Numerical results verify that the 3-D AAV-GBS CoMP-NOMA model and the G-USPA scheme have a superior performance in terms of total system sum rate and the sum rate of CoMP users over the non-CoMP AAV assisted nonorthogonal multiple access (NOMA) systems. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Multi-bit nonvolatile memory device based on the reverse polarization under the channel of organic ferroelectric thin-film transistor

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Surface and Coatings Technology 2025年 509卷

作者： Chen, Qiusong Zhou, Yulan Sanchez, Juan E. Lin, Dong Zhu, Guodong Xiong, Zuhong School of Physics and Electronics Science & Guizhou Key Laboratory of Artificial Intelligence and Brain-inspired Computing Guizhou Education University Guiyang550018 China DEVSIM LLC PO Box 50096 AustinTX78763 United States Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing Jimei University Xiamen361021 China Department of Materials Science Fudan University No. 220 Handan Road Shanghai200433 China Chongqing Key Laboratory of Micro & Nano Structure Optoelectronics School of Physical Science and Technology Southwest University Chongqing400715 China

Ferroelectric field thin-film transistors (Fe-TFTs) are a promising candidate for multi-bit nonvolatile memory devices. Different theories and methods have been proposed to realize this kind of device. This paper introduces a novel operation procedure that utilizes reverse polarization under the organic ferroelectric thin-film transistors (OFe-TFTs) channel to achieve the multi-bit nonvolatile memory device. The potential difference between the source and the drain contact of Fe-TFT was utilized to implement a reverse distribution of the polarization under the channel of Fe-TFT. A self-designed technology computer-aided design platform of DEVSIM was used to exemplify our method of reverse polarization distribution in the bottom-gate top-contact Fe-TFTs. Then, an organic Fe-TFT using poly (vinylidene fluoride-trifluoroethylene) (PVDF-TrFE) as the dielectric and Poly [2,5-(2-octyldodecyl) -3, 6- diketopyrrolopyrrole -alt-5,5- (2,5-di (thien-2-yl) thieno [3, 2-b] thiophene)] (DPP-DTT) as the semiconductor layers was fabricated to realize a 2-bit memory device. The measured transfer characteristics and retention properties perform a good verification of our originality. © 2025 Elsevier B.V.

关键词： Ferroelectric thin films

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Detecting community structures in social networks with particle swarm optimization

Detecting community structures in social networks with parti...

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2nd CCF Internet Conference of China, ICoC 2013

作者： Chen, Yuzhong Qiu, Xiaohui Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou University China

ISBN: (纸本)9783642539589

Community detection in social networks is usually considered as an objective optimization problem. Due to the limitation of the objective function, the global optimum cannot describe the real partition well, and it is time consuming. In this paper, a novel PSO (particle swarm optimization) algorithm based on modularity optimization for community detection in social networks is proposed. Firstly, the algorithm takes similarity-based clustering to find core areas in the network, and then a modified particle swarm optimization is performed to optimize modularity in a new constructed weighted network which is compressed from the original one, and it is equivalent to optimize modularity in the original network with some restriction. Experiments are conducted in the synthetic and four real-world networks. The experimental results show that the proposed algorithm can effectively extract the intrinsic community structures of social networks. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Particle swarm optimization (PSO)

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Asymptotic solution of a wide moving jam to a class of higher-order viscous traffic flow models

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Applied Mathematics and Mechanics(English Edition) 2018年第5期39卷 609-622页

作者： Chunxiu WU College of Mathematics and Computer Science Quanzhou Normal UniversityQuanzhou 362000Fujian ProvinceChina Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou 362000Fujian ProvinceChina Key Laboratory of Intelligent Computing and Information Processing Quanzhou 362000Fujian ProvinceChina

The boundary-layer method is used to study a wide moving jam to a class of higher-order viscous models. The equations for characteristic parameters are derived to determine the asymptotic solution. The sufficient and essential conditions for the wide moving jam formation are discussed in detail, respectively, and then used to prove or disprove the existence of the wide moving jam solutions to many well-known higher-order models. It is shown that the numerical results agree with the analytical results.

关键词： higher-order traffic flow model wide moving jam boundary-layer method,weighted essentially nonoscillatory (WENO) scheme

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