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arXiv 2025年

作者： Zhang, Yuwei Jin, Zhi Xing, Ying Li, Ge Liu, Fang Zhu, Jiaxin Dou, Wensheng Wei, Jun Affiliated with Nanjing Institute of Software Technology University of Chinese Academy of Sciences Nanjing China Key Laboratory of System Software Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China School of Computer Science Wuhan University Wuhan China School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Beihang University Beijing China

Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs. Copyright © 2025, The Authors. All rights reserved.

关键词： Computer software maintenance

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CoEdge: A Collaborative Architecture for Efficient Task Offloading among Multiple Edge Service Providers

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IEEE Internet of Things Journal 2025年

作者： Xie, Xingrui Cheng, Geyao Liu, Han Luo, Lailong Ren, Bangbang Guo, Deke National University of Defense Technology National Key Laboratory of Information Systems Engineering Hunan Changsha410073 China National University of Defense Technology State Key Laboratory of Complex and Critical Software Environment College of Information and Communication Hubei Wuhan430019 China National University of Defense Technology National Key Laboratory of Information Systems Engineering National Laboratory for Parallel and Distributed Processing Hunan Changsha410073 China

Edge computing is an emerging paradigm poised to process a substantial portion of latency-sensitive and computation-intensive tasks through edge service providers (ESPs). However, these ESPs typically operate independently and locally to serve their registered users. When processing burst tasks, the ESPs have to either scale up their respective capacities by introducing additional hardware or compromise user experience by rejecting some user requests, leading to high commercial investment or service degradation. Inspired by the promise of the win-win situation for ESPs and users, we envision a novel task offloading strategy that realizes the following rationales simultaneously: 1) collaborative service, 2) rapid response, and 3) sustainable profitability, while the existing methods fail to achieve them at one shot. To this end, we report CoEdge, a collaborative architecture for efficient task offloading among multiple ESPs in the edge network, aiming at simultaneously minimizing service delay for users and enhancing service profit for ESPs. To achieve this, CoEdge employs a central optimizer to implement a two-stage strategy that determines the task scheduling and service pricing hierarchically. We then formulate these problems and prove their NP-hardness. Additionally, we also propose efficient approximate algorithms to accommodate large-scale computing scenarios with low complexity. Experimental results using real-world datasets demonstrate that our CoEdge can significantly reduce service delay by 2.87x to 4.15x for users and considerably increase service profit by 32% for ESPs. © 2014 IEEE.

关键词： NP-hard

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models

arXiv

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arXiv 2025年

作者： Zhang, Jinjin Huang, Qiuyu Liu, Junjie Guo, Xiefan Huang, Di State Key Laboratory of Complex and Critical Software Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Meituan China

In this paper, we present Diffusion-4K, a novel framework for direct ultra-high-resolution image synthesis using text-to-image diffusion models. The core advancements include: (1) Aesthetic-4K Benchmark: addressing the absence of a publicly available 4K image synthesis dataset, we construct Aesthetic-4K, a comprehensive benchmark for ultra-high-resolution image generation. We curated a high-quality 4K dataset with carefully selected images and captions generated by GPT-4o. Additionally, we introduce GLCM Score and Compression Ratio metrics to evaluate fine details, combined with holistic measures such as FID, Aesthetics and CLIPScore for a comprehensive assessment of ultra-high-resolution images. (2) Wavelet-based Fine-tuning: we propose a wavelet-based fine-tuning approach for direct training with photorealistic 4K images, applicable to various latent diffusion models, demonstrating its effectiveness in synthesizing highly detailed 4K images. Consequently, Diffusion-4K achieves impressive performance in high-quality image synthesis and text prompt adherence, especially when powered by modern large-scale diffusion models (e.g., SD3-2B and Flux-12B). Extensive experimental results from our benchmark demonstrate the superiority of Diffusion-4K in ultra-high-resolution image synthesis. Code is available at https://***/zhang0jhon/diffusion-4k. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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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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Design of High-efficiency Transmitarray with Beam Steering and Low Scattering Characteristic

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IEEE Transactions on Antennas and Propagation 2025年

作者： Shaozhi, Wang Zihao, Zheng Weiwei, Wu Yuhong, Ma Jingjian, Huang Qingzhan, Shi Naichang, Yuan National University of Defense Technology State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Hunan Changsha410000 China National University of Defense Technology College of Electronic Engineering Hefei230037 China

This article introduces a hybrid design method for high-efficiency absorbing Reconfigurable Transmitarray (ARTA). By cascading an absorbing layer with a 2-bit reconfigurable layer, the transmission unitcells can realize the frequency response characteristic of an Absorbing-Transmissive-Absorbing (A-T-A). Specifically, the frequencies with an absorption rate exceeding 80% are within the ranges of 3.3GHz-5.1GHz and 7.8GHz-9.4GHz, while the frequency range with transmission exceeding -3dB spans from 5.69GHz to 5.92GHz. Meanwhile, the transmission phase can be adjusted among four states: 0°, 90°, 180°, and 270°, enabling beam steering of ARTA. Compared to designs for non-reconfigurable transmissive arrays, our design methodology necessitates the satisfaction of absorbing performance under different phase states. For validation, A prototype sample, comprising a 12x12 array structured with 5 metallic layers and 4 dielectric layers, sandwiching an air layer in between, has been fabricated and tested. Measurement results demonstrated high radiation performance with a peak gain of 21.6 dB and aperture efficiency of 33.3% at 5.85 GHz. The BW-1 dB and BW-3 dB were 0.2 GHz and 0.6 GHz, respectively. ±60° 2D-scanning was achievable and when the beam is directed at 0°, the sidelobe levels in the E-plane and H-plane are -17.3 dB and -18 dB, respectively. Under normal incidence of an x-polarized wave, the -8 dB Monostatic RCS reduction bandwidths covered 32.1% (3.4-4.7 GHz) for lower band and 10% (8.5-9.4 GHz) for upper band. The ARTA by this hybrid design methodology represents an attractive solution for addressing electromagnetic compatibility issues and for low-detectability applications. © 1963-2012 IEEE.

关键词： Integrated circuit design

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Micro-macro Wavelet-based Gaussian Splatting for 3D Reconstruction from Unconstrained Images

arXiv

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arXiv 2025年

作者： Li, Yihui Lv, Chengxin Yang, Hongyu Huang, Di State Key Laboratory of Complex and Critical Software Environment Beijing China School of Computer Science and Engineering Beihang University China School of Artificial Intelligence Beihang University China Shanghai Artificial Intelligence Laboratory Shanghai China

3D reconstruction from unconstrained image collections presents substantial challenges due to varying appearances and transient occlusions. In this paper, we introduce Micro-macro Wavelet-based Gaussian Splatting (MW-GS), a novel approach designed to enhance 3D reconstruction by disentangling scene representations into global, refined, and intrinsic components. The proposed method features two key innovations: Micro-macro Projection, which allows Gaussian points to capture details from feature maps across multiple scales with enhanced diversity;and Wavelet-based Sampling, which leverages frequency domain information to refine feature representations and significantly improve the modeling of scene appearances. Additionally, we incorporate a Hierarchical Residual Fusion Network to seamlessly integrate these features. Extensive experiments demonstrate that MW-GS delivers state-of-the-art rendering performance, surpassing existing methods. Copyright © 2025, The Authors. All rights reserved.

关键词： 3D reconstruction

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IFKG: An Intelligent Fault Diagnosis Tool with Knowledge Graph and Generative LLM

IFKG: An Intelligent Fault Diagnosis Tool with Knowledge Gra...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Xixuan Yang Tong Jia Ying Li Gang Huang School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China National Key Laboratory of Data Space Technology and System Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

The development of effective diagnostic methodolo-gies for software system failures is of paramount importance. Traditional methods, which rely on specialized terminology and intricate reasoning, require users to have a technical background, resulting in reduced flexibility and decreased user-friendliness. With the rise of generative large language models, optimizing human-computer interaction has become a critical area of focus. Additionally, the inherent intelligence and extensive knowledge of large language models make them both easy and effective to employ for fault diagnosis assistance. We introduce IFKG, an advanced tool for diagnosing software system failures. IFKG integrates generative large language models with knowledge graphs, employing natural language interactions to implement fault detection and deliver solutions. IFKG enables users to upload descriptive problems, retrieve pertinent information from the knowledge graph, and present diagnostic results in natural language. Our accuracy assessments across diverse software system failures indicate that the IFKG provides targeted and actionable recommendations, effectively assisting users in ad-dressing a range of software system issues. The tool is available on GitHub at https://***/mako-xxlIFKG, and the demo video can be found on YouTube: https://***2vgZm2hk.

关键词： Fault diagnosis Human computer interaction Video on demand Terminology Large language models Natural languages Knowledge graphs software systems Web sites software development management

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Automatic Inverse Synthetic Aperture Radar Ship Targets Recognition using Simple Multiple Kernel Learning: Automatic ISAR Ship Targets Recognition using Simple-MKL 24

Automatic Inverse Synthetic Aperture Radar Ship Targets Reco...

引用

Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management

作者： Yuan Wu Fulin Su Xue Li Peipei Zhu Xunwei Xie National Key Laboratory of Complex Aviation System Simulation Southwest China Institute of Electronic Technology Chengdu Sichuan China School of Electronics and Information Engineering Harbin Institute of Technology Harbin Heilongjiang China

ISBN: (纸本)9798400709753

In radar automatic target recognition (RATR), inverse synthetic aperture radar (ISAR) image recognition shows its advantages. Due to the limited sample size of ISAR images, support vector machine (SVM), known for its robustness in small sample classification, is often used for ISAR image recognition. For ISAR images of different targets, the single kernel SVM algorithm might lose its robustness. Therefore, this paper applies multiple kernel learning (MKL) to ISAR ship target recognition. The process begins with the preprocessing of the ISAR images to suppress Gaussian white noise. Then, principal component analysis (PCA) is employed to extract features from the ISAR images. Finally, the Simple-MKL method is used to recognize the samples. Experiments based on simulation data indicate that the method used in this paper improves the accuracy compared to other single-kernel SVM algorithms with different kernel functions.

关键词： Inverse Synthetic Aperture Radar (ISAR)

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SlotFusion: Object-Centric Audiovisual Feature Fusion with Slot Attention for Remote Sensing Scene Recognition

SlotFusion: Object-Centric Audiovisual Feature Fusion with S...

引用

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

作者： Fangzhou Han Tianyi Yu Lamei Zhang Lingyu Si Yiqi Zhang Dept. of Information Engineering Harbin Institute of Technology Harbin China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Despite significant advancements in remote sensing multimodal learning, particularly in image-image feature fusion, the exploration of audio-image feature fusion remains insufficient. Given the complexity and redundancy of ground objects in remote sensing images, accurately aligning audio features with image features during the fusion process is a critical challenge. In this paper, we introduce an object-centric feature fusion method named SlotFusion. By employing a slot attention-based feature decoupling module and a slot-based audiovisual feature fusion module, we transform modality features with complex semantic information into a set of slot features corresponding to object units and use gated activation units to adaptively implement object-centric feature fusion. Experiments on the Audio Visual Aerial Scene Recognition dataset (ADVANCE) demonstrate that the proposed SlotFusion significantly improves remote sensing scene recognition performance, with a 7.04% increase in overall accuracy compared to previous methods, achieving state-of-the-art results.

关键词： Visualization Semantics Termination of employment Transforms Interference Signal processing Logic gates Robustness Speech processing Remote sensing

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