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IEEE International Conference on Systems, Man and Cybernetics

作者： Gaofei Qiao Zhibin Zhang Liqiang He Key Laboratory of Wireless Networks and Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia University Hohhot China Geomechanica Inc Canada

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the real world, due to various challenging lighting conditions such as low light, underexposure, and overexposure, captured images often exhibit undesirable appearances. Given that images with different exposure levels require different correction processes, a single neural network struggles to produce satisfactory results. We propose a coarse-to-fine exposure correction model for learning exposure consistency representation to address underexposure and overexposure issues. Building upon the bilateral activation mechanism, we introduce the Fourier transform to capture global information and fuse it with locally extracted information through convolution to achieve superior feature representation. Additionally, we employ Laplacian pyramids to decompose the source image into different spatial frequency bands, then the image details are enhanced by denoising high-frequency layers. Experimental results on the MSEC and SICE datasets demonstrate the superiority of our proposed method over current state-of-the-art approaches. Our code will be made available on GitHub.

关键词： Representation learning Laplace equations Fourier transforms Image color analysis Plants (biology) Noise reduction Neural networks Lighting Feature extraction Software development management

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Single-View Scene Point Cloud Human Grasp Generation

Single-View Scene Point Cloud Human Grasp Generation

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yan-Kang Wang Chengyi Xing Yi-Lin Wei Xiao-Ming Wu Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China Stanford University Stanford CA Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this work, we explore a novel task of generating human grasps based on single-view scene point clouds, which more accurately mirrors the typical real-world situation of observing objects from a single viewpoint. Due to the incompleteness of object point clouds and the presence of numerous scene points, the generated hand is prone to penetrating into the invisible parts of the object and the model is easily affected by scene points. Thus, we introduce S2HGrasp, a framework composed of two key modules: the Global Perception module that globally perceives partial object point clouds, and the DiffuGrasp module designed to generate high-quality human grasps based on complex inputs that include scene points. Additionally, we introduce S2HGD dataset, which comprises approximately 99,000 single-object single-view scene point clouds of 1,668 unique objects, each annotated with one human grasp. Our extensive experiments demonstrate that S2HGrasp can not only generate natural human grasps regardless of scene points, but also effectively prevent penetration between the hand and invisible parts of the object. Moreover, our model showcases strong generalization capability when applied to unseen objects. Our code and dataset are available at https://***/iSEE-laboratory/S2HGrasp.

关键词： Point cloud compression Computer vision Codes Computational modeling Pattern recognition Mirrors Task analysis

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FDSC-YOLOv8:Advancements in Automated Crack Identification for Enhanced Safety in Underground engineering

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Computer Modeling in engineering & Sciences 2024年第9期140卷 3035-3049页

作者： Rui Wang Zhihui Liu Hongdi Liu Baozhong Su Chuanyi Ma Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan250353China School of Qilu Transportation Shandong UniversityJinan250100China Geotechnical and Structural Engineering Center Shandong UniversityJinan250061China Shandong Lairong High-Speed Railway Co. Ltd.Weihai264200China Shandong High-Speed Group Co. Ltd.Jinan250014China

In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel ***,the dim and dusty environment inherent to under-ground engineering poses considerable challenges to crack *** paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8(FDSC-YOLOv8)specifically designed for underground engineering structural ***,to improve the extraction of multi-layer convolutional features,the fixed convolutional module is replaced with a deformable convolutional ***,the model’s receptive field is enhanced by introducing a multi-branch convolutional module,improving the extraction of shallow features for small ***,the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak ***,the Convolutional Block Attention Module(CBAM)module is employed to achieve better target *** FDSC-YOLOv8s algorithm’s mAP50 and mAP50-95 reach 96.5%and 66.4%,according to the testing data.

关键词： Crack segmentation improved YOLOv8 engineering applications feature extraction

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TEMSA:Text enhanced modal representation learning for multimodal sentiment analysis

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Computer Vision and Image Understanding 2025年 258卷

作者： Chen, Jingwen Song, Shuxiang Tan, Yumei Xia, Haiying The Key Lab of Education Blockchain and Intelligent Technology Ministry of Education China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China Education Department of Guangxi Zhuang Autonomous Region Guilin China

Multimodal sentiment analysis aims to identify human emotions by leveraging multimodal information, including language, visual, and audio data. Most existing models focus on extracting common features across modalities or simply integrating heterogeneous multimodal data. However, such approaches often overlook the unique representation advantages of individual modalities, as they treat all modalities equally and use bidirectional information transfer mechanisms. This can lead to information redundancy and feature conflicts. To address this challenge, we propose a Text-Enhanced Modal Representation Learning Model (TEMSA), which builds robust and unified multimodal representations through the design of text-guided pairwise cross-modal mapping modules. Specifically, TEMSA employs a text-guided multi-head cross-attention mechanism to embed linguistic information into the emotion-related representation learning of non-linguistic modalities, thereby enhancing the representations of visual and audio modalities. In addition to preserving consistent information through cross-modal mapping, TEMSA also incorporates text-guided reconstruction modules, which leverage text-enhanced non-linguistic modal features to decouple modality-specific representations from non-linguistic modalities. This dual representation learning framework captures inter-modal consistent information through cross-modal mapping, and extracts modal difference information through intra-modal decoupling, thus improving the understanding of cross-modal affective associations. The experimental results on the CMU-MOSI, CMU-MOSEI, and CH-SIMS datasets demonstrate that TEMSA achieves superior performance, highlighting the critical role of text-guided cross-modal and intra-modal representation learning in multimodal sentiment analysis. © 2025 Elsevier Inc.

关键词： Emotion Recognition

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Temsa:Text Enhanced Modal Representation Learning for Multimodal Sentiment Analysis

SSRN

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SSRN 2025年

Multimodal sentiment analysis aims to identify human emotions by leveraging multimodal information, including language, visual, and audio data. Different modalities contribute unequally to sentiment analysis, with text being more effective than non-linguistic modalities in multimodal sentiment analysis (MSA) due to its intuitive emotional expressiveness. A major challenge lies in leveraging the strengths of text to create fused representations that capture rich emotional content while preserving both inter-modal and intra-modal relationships. To address this challenge, we propose a Text-Enhanced Modal Representation Learning Model (TEMSA), which builds robust and unified multimodal representations through the design of text-guided pairwise cross-modal mapping modules. Specifically, TEMSA employs a text-guided multi-head cross-attention mechanism to embed linguistic information into the emotion-related representation learning of non-linguistic modalities, thereby enhancing the representations of visual and audio modalities. In addition to preserving consistent information through cross-modal mapping, TEMSA also incorporates text-guided reconstruction modules, which leverage text-enhanced non-linguistic modal features to decouple modality-specific representations from non-linguistic modalities. This dual representation learning framework captures inter-modal consistent information through cross-modal mapping, and extracts modal difference information through intra-modal decoupling, thus improving the understanding of cross-modal affective associations. The experimental results on the CMU-MOSI, CMU-MOSEI, and CH-SIMS datasets demonstrate that TEMSA achieves state-of-the-art performance, highlighting the critical role of text-guided cross-modal and intra-modal representation learning in multimodal sentiment analysis. © 2025, The Authors. All rights reserved.

关键词： Linguistics

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Why not Just Look for Answers? Using a More Direct Way for API Recommendation

Why not Just Look for Answers? Using a More Direct Way for A...

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Asia-Pacific Conference on Software engineering

作者： Changxin Liu Ling Xu Wenhan Mu Rui Qin Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

Automated API recommendation technology can help developers quickly find target APIs that meet their require-ments. Some retrieval-based API recommendation approaches, such as BIKER and CLEAR, adopt an indirect API recommen-dation strategy. Although these approaches have shown good performance, we observed two limitations in these approaches: (1) These approaches heavily relies on the coverage of the collected question titles proposed by developers; (2) These approaches ignore the API source code information. To overcome the two limitations, we propose an approach named AnsAPIRec (Answer- directed API Recommendation), which leverages a pre-trained model and joint-attention mechanism in this paper. In addition to the fully qualified name of the API, AnsAPIRec extracts the corresponding source code from the JDK as an additional feature. AnsAPIRec adopts a direct API recommendation strategy, which directly calculates the similarity between user queries and API features, i.e., API fully qualified name and API source code, to find the target API, alleviating the reliance on question titles. To further enhance AnsAPIRec's ability to understand the semantics of the inputs, we propose a joint-attention mechanism to learn the interdependent representations between the queries and API features. This deep semantic fusion mechanism enables AnsAPIRec to perform well in understanding user intent and API functionality. We reused the APIBench-Q dataset, containing 5885 queries for training and 653 queries for testing. The experimental result shows that AnsAPIRec achieved an MRR of 0.473, significantly outperforming the baseline approaches, BIKER and CLEAR.

关键词： Training Source coding Semantics Feature extraction Testing Software engineering

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Connected Autonomous Vehicle Control Considering Human Driving Aggressive Lane Change from A Cyber-Physical Perspective

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Zhe Zhao, Min Sun, Dihua Wang, Liuping Lin, Jingdong Cheng, Senlin School of Engineering MelbourneVIC3000 Australia Chongqing University School of Automation Chongqing 400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing 400044 China

The aggressive lane-changing behavior of humandriven vehicles (HVs) under mixed traffic has a negative impact on connected autonomous vehicles (CAVs). This behavior is frequently observed in the vicinity of signalized intersection. From the perspective of cyber-physical systems (CPS), the negative impact on driving safety can be eliminated through the control algorithm at the CPS micro-scale level;and the negative impact on traffic efficiency can be suppressed through the CAV decision-making strategy at the CPS meso-scale level. This paper introduces a two-stage method aimed at safeguarding the right of way for CAVs and optimizing traffic efficiency in the scenario. Additionally, A CAV safety controller is developed to effectively handle the challenges posed by HVs violating game rules through continued aggressive lane changes. Simulation experiments demonstrate that compared to conventional game algorithms, the proposed method enhances traffic flow, increases average speeds, and diminishes traffic congestion in this scenario. The designed controller can ensure the safety of CAV driving in the target lane. © 1967-2012 IEEE.

关键词： Traffic congestion

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The grad-div conforming virtual element method for the quad-div problem in three dimensions

arXiv

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arXiv 2024年

作者： Dong, Xiaojing Han, Yibing Huang, Yunqing Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Er huan Road Hunan Xiangtan411105 China

We propose a new stable variational formulation for the quad-div problem in three dimensions and prove its well-posedness. Using this weak form, we develop and analyze the H(grad-div)-conforming virtual element method of arbitrary approximation orders on polyhedral meshes. Three families of H(grad-div)conforming virtual elements are constructed based on the structure of a de Rham sub-complex with enhanced smoothness, resulting in an exact discrete virtual element complex. In the lowest-order case, the simplest element has only one degree of freedom at each vertex and face, respectively. We rigorously prove the interpolation error estimates, the stability of discrete bilinear forms, the well-posedness of discrete formulation and the optimal error estimates. Some numerical examples are shown to verify the theoretical results. © 2024, CC BY-NC-ND.

关键词： Mesh generation

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Energy-Efficient Anti-Jamming Metaverse Resource Allocation Based on Reinforcement Learning

Energy-Efficient Anti-Jamming Metaverse Resource Allocation ...

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IEEE Conference on Wireless Communications and Networking

作者： Yiwen Zhan Jinming Zhang Zhiping Lin Liang Xiao Dept. of Information and Communication Engineering Xiamen University Xiamen China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Metaverse system facilitated by Extended Reality (XR) requires extensive computing and communication resources to provide seamless services for users and has to resist jamming attacks. In this paper, we propose a reinforcement learning based Metaverse resource allocation scheme against jamming, which optimizes the rendering mode, transmit channel, and power to fulfill the quality of experience (QoE) requirements for Metaverse services. This scheme incorporates both the background resolution, determined by the linear model of visual acuity decline, and the background correlation into the state formulation and rendering process, with both factors influencing the data size of the rendering task. Based on the required resolution, the background resolution, the data size of foreground and background, the background correlation, and the radio channel gain, the 2-level hierarchical architecture evaluates the expected utility of the rendering policy in the first level, and the transmission policy in the second level, and estimates the risk value depending on the latency to mitigate the risk of user experience disruption. Simulation results show that our proposed scheme reduces the service latency, conserves energy consumption, and enhances the QoE of Metaverse users.

关键词： Visualization Correlation Metaverse Reinforcement learning Rendering (computer graphics) Energy efficiency Resource management

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Reinforcement Learning Based Jamming Detection for Reliable Wireless Communications

Reinforcement Learning Based Jamming Detection for Reliable ...

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IEEE Conference on Vehicular Technology (VTC)

作者： Chen Wang Yifan Chen Zhiping Lin Qiaoxin Chen Liang Xiao Department of Information and Communication Engineering Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University P.R. China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

Both the radio spectrum features such as power spectral density (PSD) and the communication performance such as packet loss rate (PLR) can be exploited to detect jamming attacks, with the resulting detection results used to enhance the reliability of wireless communications. In this paper, we propose a reinforcement learning (RL)-based jamming detection scheme based on the channel energy, the received signal strength indi-cator of each packet, the channel gains, PLR and transmission latency of mobile devices, in which the test threshold and the number of PSD bins are optimized by access point to enhance the utility as a weighted function of the detection speed and accuracy. The detection results are exploited for mobile devices to choose the transmit power and channel to reduce the PLR and transmission latency. Experimental results based on the universal software radio peripheral and Raspberry Pi to detect four jamming types including constant, sweeping, random and smart jamming show that our proposed schemes improve the detection accuracy and speed, as well as the communication performance.

关键词： Wireless communication Vehicular and wireless technologies Accuracy Channel estimation Packet loss Reinforcement learning Mobile handsets

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