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

作者： Qiao, Xiaoyu Li, Weisheng Xiao, Bin Huang, Yuping Yang, Lijian Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Ministry of Education Chongqing China

ISBN: (纸本)9798350368741

Model-based networks have shown convincing performance in MRI reconstruction. However, the unrolled cascades within the networks are constrained to solely obtain information from the preceding counterpart, resulting in potential error accumulation. Moreover, the linear structure fails to address the challenge of recovering fine-grained details. To tackle these problems, we propose to redesign the information flow in model-based networks. Our method features a large U-shaped network, where the nodes are built with unrolled cascades and U-Net-based regularizers. We design an input-level integration module to help the cascades acquire information from adjacent and skip-connected counterparts, building robust mappings to the target. We further design a coarse-to-fine feature-level integration module, aiming at guiding the network to progressively recover fine details. Intermediate reconstructions produced by subnetworks of different scales are integrated, enabling the extraction of complementary information to enhance the final performance. Compared with cutting-edge methods on different datasets, our method exhibits superior performances. © 2025 IEEE.

关键词： compressed sensing deep learning model-based MRI reconstruction U-Net

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Learning Preconditioners in Gates-controlled Deep Unfolding Networks based on Quasi-Newton Methods For Accelerated MRI Reconstruction

Learning Preconditioners in Gates-controlled Deep Unfolding ...

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

ISBN: (纸本)9798350368741

Deep unfolding networks (DUNs) have made significant progress in MRI reconstruction, successfully tackling the problem of prolonged imaging time. However, the ill-conditioned nature of MRI reconstruction often causes slow convergence in iterative optimization, potentially compromising the performance of DUNs. In this study we propose a preconditioned and gates-controlled DUN (PGDUN) to address these challenges. Our approach starts with optimizing the step size of proximal gradient descent (PGD) through a preconditioner. To improve flexibility and adaptability, we relax the constrains on quasi-newton-based optimization procedure. We design ConvLSTM-based modules, where the gate units automatically preserve necessary long- and short-term information, facilitating the learning of optimized variables and their combinations. Furthermore, we design gate units to modulate the features fed to regularizers across different iterations, boosting their robustness against potential accumulated errors. Evaluations using PSNR and SSIM metrics reveal that our approach outperforms existing state-of-the-art methods, achieving superior reconstruction results across various sequences. © 2025 IEEE.

关键词： Compressed Sensing LSTM MRI reconstruction Preconditioner Quasi-Newton

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Enhancing graph representation learning via type-aware decoupling and node influence allocation

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Applied Intelligence 2025年第6期55卷 1-14页

作者： Zhu, Guochang Hu, Jun Liu, Li Zhang, Qinghua Wang, Guoyin School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongwen Road Chongqing Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongwen Road Chongqing Chongqing400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing400065 China

The traditional graph representation methods can fit the information of graph with low-dimensional vectors, but they cannot interpret their composition, resulting in insufficient security. Graph decoupling, as a method of graph representation, can analyze the latent factors composing the graph representation vectors. However, in current graph decoupling methods, the number of factors is a hyperparameter, and enforce uniform decoupling vector dimensions which leads to information loss or redundancy. To address these issues, we propose a type-aware graph decoupling based on influence called Variational Graph Decoupling Auto-Encoder (VGDAE). It uses node labels as interpretable and objectively existing natural semantics for decoupling and allocates embedding space based on node influence, addressing the issues of manually setting the number of factors in traditional graph decoupling and the mismatch between node information size and embedding space. On the Cora, Citeseer, and fb-CMU datasets, VGDAE shows the impact of different node classes as decoupling targets on classification tasks. Furthermore, we perform visualization of the representations, VGDAE exhibits performance improvements of 2% in classification tasks and 12% in clustering tasks when compared with baseline models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Graph embeddings

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A study of MySQL protocol-based database proxy approval system for fortress machine

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International Journal of data Science 2025年第1期10卷 96-117页

作者： Zhang, Xian Luo, Xinhui Yin, Dong Qu, Taiguo Li, Hao Huaihua University Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Huaihua Tourism School Hunan Huaihua418099 China

With the increase of enterprise informatisation, database security and compliance operation management have become increasingly important. Therefore, it is essential to design an efficient database proxy approval system. In this paper, we develop a database proxy approval system based on the MySQL protocol for fortress machines, which provides a real-time customised configuration scheme for high-risk commands, designs a real-time approval process for six types of high-risk commands, and creates a simple and efficient matching algorithm for high-risk commands. We designed a large number of experiments to test the system’s connection success rate, operation stability, response time, CPU resource consumption, matching algorithm performance, and other aspects. The experimental results show that this database proxy approval system has good configuration flexibility, high accuracy, and good time performance. This system has a wide range of applications in electric power, finance, petroleum, and other fields. © 2025 Inderscience Enterprises Ltd.

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Rethinking-based Code Summarization with Chain of Comments 31

Rethinking-based Code Summarization with Chain of Comments

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31st International Conference on Computational Linguistics, COLING 2025

作者： Cao, Liuwen He, Hongkui Huang, Hailin Wang, Jiexin Cai, Yi School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China

ISBN: (纸本)9798891761964

Automatic code summarization aims to generate concise natural language descriptions (summary) for source code, which can free software developers from the heavy burden of manual commenting and software maintenance. Existing methods focus on learning a direct mapping from pure code to summaries, overlooking the significant heterogeneity gap between code and summary. Moreover, existing methods lack a human-like re-check process to evaluate whether the generated summaries match well with the code. To address these two limitations, we introduce RBCoSum, a novel framework that incorporates the generated Chain Of Comments (COC) as auxiliary intermediate information for the model to bridge the gap between code and summaries. Also, we propose a rethinking process where a learned ranker trained on our constructed ranking dataset scores the extent of matching between the generated summary and the code, selecting the highest-scoring summary to achieve a re-check process. We conduct extensive experiments to evaluate our approach and compare it with other automatic code summarization models as well as multiple code Large Language Models (LLMs). The experimental results show that RBCoSum is effective and outperforms baselines by a large margin. The human evaluation also proves the summaries generated with RBCoSum are more natural, informative, useful, and truthful. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement

arXiv

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arXiv 2025年

作者： Fang, Runnan Wang, Xiaobin Liang, Yuan Qiao, Shuofei Wu, Jialong Xi, Zekun Zhang, Ningyu Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Zhejiang University China Alibaba Group China Zhejiang Key Laboratory of Big Data Intelligent Computing China

In the interaction between agents and their environments, agents expand their capabilities by planning and executing actions. However, LLM-based agents face substantial challenges when deployed in novel environments or required to navigate unconventional action spaces. To empower agents to autonomously explore environments, optimize workflows, and enhance their understanding of actions, we propose SynWorld, a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search (MCTS) exploration to effectively refine their action knowledge in the current environment. Our experiments demonstrate that SynWorld is an effective and general approach to learning action knowledge in new environments.1 Copyright © 2025, The Authors. All rights reserved.

关键词： Virtual reality

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Trajectory optimization for UAV-enabled relaying with reinforcement learning

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Digital Communications and Networks 2025年第1期11卷 200-209页

作者： Chiya Zhang Xinjie Li Chunlong He Xingquan Li Dongping Lin School of Electronic and Information Engineering Harbin Institute of TechnologyShenzhenChina Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhenChina Peng Cheng Laboratory(PCL) ShenzhenChina National Mobile Communications Research Laboratory Southeast UniversityNanjingChina Shenzhen Institute of Information Technology ShenzhenChina Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen UniversityShenzhenChina

In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users to a Base Station(BS).We maximize the total transmitted data from the users to the BS,by optimizing the user communication scheduling and association along with the power allocation and the trajectory of the *** solve this non-convex optimization problem,we propose the traditional Convex Optimization(CO)and the Reinforcement Learning(RL)-based ***,we apply the block coordinate descent and successive convex approximation techniques in the CO approach,while applying the soft actor-critic algorithm in the RL *** simulation results show that both approaches can solve the proposed optimization problem and obtain good ***,the RL approach establishes emergency communications more rapidly than the CO approach once the training process has been completed.

关键词： Unmanned aerial vehicle Emergency communications Trajectory optimization Convex optimization Reinforcement learning

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Nonlinear Interference-Aware Routing,Wavelength and Power Allocation in C+L+S Multi-Band Optical Networks

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China Communications 2025年第4期22卷 129-142页

作者： Zhang Xu Xie Wang Feng Chuan Zeng Hankun Zhou Shanshan Zhang Fan Gong Xiaoxue School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Intelligent Communications and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China

Multi-band optical networks are a potential technology for increasing network ***,the strong interference and non-uniformity between wavelengths in multi-band optical networks have become a bottleneck restricting the transmission capacity of multi-band optical *** overcome these challenges,it is particularly important to implement optical power optimization targeting wavelength ***,based on the generalized Gaussian noise model,we first formulate an optimization model for the problems of routing,modulation format,wavelength,and power allocation in C+L+S multi-band optical *** objective function is to maximize the average link capacity of the network while ensuring that the Optical Signal-to-Noise(OSNR)threshold of the service request is not ***,we propose a NonLinear Interferenceaware(NLI-aware)routing,modulation format,wavelength,and power allocation ***,we conduct simulations under different test *** simulation results indicate that our algorithm can effectively reduce the blocking probability by 23.5%and improve the average link capacity by 3.78%in C+L+S multi-band optical networks.

关键词： multiband optical communications multiband optical networks power allocation wavelength assignment

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A label-guided contrastive capsule network for few-shot text classification

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Neural computing and Applications 2025年 1-26页

作者： Xu, Yang Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China

Few-shot text classification involves transferring knowledge from a limited dataset to perform classification tasks in unseen domains. Existing metric-based meta-learning models, such as prototypical networks, have shown to be effective. However, these models fail to separate embeddings of similar classes, resulting in performance stagnation. Meanwhile, traditional text classification recently has succeeded with the support of extensive prior knowledge from large language models. However, in few-shot scenarios, it is challenging to generate discriminative sentence embeddings using pre-trained models, often leading to overfitting issues. Furthermore, random support set sampling adversely affects prototype quality, resulting in inaccurate category representations. In this paper, we propose the label-guided contrastive capsule network (LGCCN) to address these issues. LGCCN introduces label information as external knowledge for each category, comprising a label-guided contrastive clustering module (LGCCM) and a label-guided prototype enhancement module (LGPEM). LGCCM employs label information to guide a contrastive clustering process, using labels as anchors to enhance the process, yielding discriminative sentence embeddings from the pre-trained model. LGPEM iteratively generates a robust prototype with label guidance, significantly reducing the impact of random support set sampling. The proposed LGCCN was thoroughly evaluated on seven benchmark datasets from diverse application domains. Experimental results demonstrate that LGCCN outperforms the state-of-the-art (SOTA) method by 9.7% in 1-shot tasks and 1.1% in 5-shot tasks on news datasets. Additionally, on intent recognition datasets, LGCCN surpasses the SOTA with a 3.3% improvement in 1-shot tasks and a 1.4% improvement in 5-shot tasks, highlighting its superior performance in few-shot text classification. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Contrastive Learning

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Lightweight Dual Grouped Large-Kernel Convolutions for Salient Object Detection Network 31st

Lightweight Dual Grouped Large-Kernel Convolutions for Sali...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Liu, Jiajie Zhang, Zhibin Engineering Research Center of Ecological Big Data Ministry of Education Beijing China Key Laboratory of Wireless Networks and Mobile Computing Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9789819620609

Most existing Salient Object Detection (SOD) methods focus on achieving better performance, often resulting in models with a large number of parameters. However, there is limited research on lightweight models in this field. To address this gap, our goal is to maintain performance while reducing the number of network parameters. Thanks to the development of large-kernel convolutions in recent years, we have improved the U2Net as the base network by adding our lightweight dual large-kernel fusion module. Our module better utilizes the depth information of U2Net and, due to the large receptive field of large-kernel convolutions, better captures the relationships between image elements. This allows our network to remain lightweight while maintaining performance. We designed a large-kernel (DLK) fusion module and a lightweight dual grouped large-kernel Unet network (DGLKUNET). Our network uses SRUS (an improved Residual U-blocks module, RUS) to construct DGLKUNET that predicts image contours and labels. Compared to the base RUS network (U2Net), our network reduces the number of parameters by up to 70% and the computational cost by 40% while maintaining performance. Evaluation results on five datasets demonstrate the performance of our lightweight network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object recognition

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