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18th International Conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Ou, Pingjie Chen, Ningjiang Huang, Zizhan School of Computer and Electronic Information Guangxi University Guangxi Nanning530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Guangxi University Guangxi Nanning530004 China

ISBN: (纸本)9783031714696

Due to the limited resources of edge networks, the heterogeneity of user content requests, high-cost caching from direct resource hits, and redundancy in resource retention time hinder system performance. Traditional caching methods based on learning and popularity tend to encounter data skew when handling vehicle requests, leading to untimely cache data updates. To address these issues, this paper proposes an efficient edge cooperative caching strategy with recommendation awareness. Firstly, a time series prediction model based on a multi-dense multilayer perceptron is designed. This model segments historical request information into sub-sequence level slivers to enhance positional correlations between information, predicting user requests through historical time series and covariates. Secondly, to optimize cache utilization, comprehensively considers user mobility and contribution to enable recommendation awareness. Additionally, an optimized lifecycle is introduced to design a cache replacement strategy. Simulation experiments demonstrate that, compared to caching schemes based on content popularity and deep reinforcement learning, the proposed method achieves lower caching costs and higher cache hit rates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep reinforcement learning

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MDGCN-Lt:Fair Web API Classification with Sparse and Heterogeneous Data Based on Deep GCN

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1294-1314页

作者： Boyuan Yan Yankun Zhang Wenwen Gong Haoyang Wan Wenwei Wang Weiyi Zhong Caixia Bu Department of School of Computer Science Qufu Normal UniversityRizhao 276500China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261000China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China Department of Univalsoft Joint Stock Co. Ltd.Weifang 261000China

Developers integrate web Application Programming Interfaces(APIs)into edge applications,enabling data expansion to the edge computing area for comprehensive coverage of devices in that *** develop edge applications,developers search API categories to select APIs that meet specific ***,the accurate classification of APIs becomes critically ***,existing approaches,as evident on platforms like ***,face significant ***,sparsity in API data reduces classification accuracy in works focusing on single-dimensional API ***,the multidimensional and heterogeneous structure of web APIs adds complexity to data mining tasks,requiring sophisticated techniques for effective integration and analysis of diverse data ***,the long-tailed distribution of API data introduces biases,compromising the fairness of classification *** these challenges,we propose MDGCN-Lt,an API classification approach offering flexibility in using multi-dimensional heterogeneous *** tackles data sparsity through deep graph convolutional networks,exploring high-order feature interactions among API ***-Lt employs a loss function with logit adjustment,enhancing efficiency in handling long-tail data *** results affirm our approach's superiority over existing methods.

关键词： bidirectional encoder representations from transformers deep graph convolutional networks logit adjustment web application programming interface classification application programming interface correlation graph

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SRBF-Gaussian: Streaming-Optimized 3D Gaussian Splatting 32

SRBF-Gaussian: Streaming-Optimized 3D Gaussian Splatting

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32nd IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2025

作者： Zhang, Dayou Liang, Zhicheng Cao, Zijian Wang, Dan Wang, Fangxin The Hong Kong Polytechnic University Department of Computing Hong Kong The Chinese University of HongKong School of Science and Engineering Shenzhen China Shenzhen Future Network of Intelligence Institute China The Guangdong Provincial Key Laboratory of Future Networks of Intelligence China

ISBN: (纸本)9798331536459

3D Gaussian Splatting (3DGS) has emerged as a groundbreaking 3D scene representation technique, offering unprecedented visual quality and rendering efficiency. However, the substantial data volume of 3DGS scenes poses significant challenges for streaming applications. Existing research on 3DGS has primarily focused on compression and rendering efficiency, neglecting the specific requirements of streaming transmission. Moreover, the Spherical Harmonics color representation in 3DGS complicates viewport-based transmission partitioning. Achieving hierarchical Gaussian streaming without noticeable quality degradation also remains a significant *** address these challenges, we propose SRBF-Gaussian, a new paradigm that revolutionizes the traditional 3DGS format. Our approach introduces viewport-dependent color encoding based on Spherical Radial Basis Functions (SRBFs) and HSL color space, enabling selective transmission of viewport-relevant color data. This reduces data transmission while maintaining visual quality. We implement adaptive Gaussian pruning and transmission, optimized for current viewports and network conditions. Additionally, we develop coherent multi-level Gaussian representations for smooth transitions between quality levels. Our system incorporates user-behavior-aware streaming strategies to anticipate and pre-fetch relevant data. In cloud VR scenarios, our approach demonstrates substantial improvements, achieving a 5.63% - 14.17% increase in PSNR, a 7.61% - 59.16% reduction in latency, and a 10.45% - 30.12% improvement in overall Quality of Experience (QoE). © 2025 IEEE.

关键词： Volume rendering

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SMDPG: Identifying Metabolite-Disease Associations via Optimized Negative Sampling and Sparse Graph Convolutional network

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 672-683页

作者： Yiran Huang Qiulong Pu Wei Lan Cuiyu Huang Cheng Zhong School of Computer Electronics and Information Guangxi University Nanning China Guangxi Universities Key Laboratory of Parallel and Distributed Computing and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China College of Chemistry Nankai University Tianjin China

Identifying disease-associated metabolites could provide critical clues for the diagnosis and treatment of diseases. Although computational approaches have been proposed to predict disease-associated metabolites by training models using positive and negative samples, few efforts have paid attention to optimize the reliability of negative samples, which could possibly improve the prediction accuracy of model. In this work, we propose a novel method called SMDPG to leverage optimized negative sampling and sparse graph convolutional network to predict metabolite-disease associations. In SMDPG, we first build a metabolite-disease bipartite graph based on less similar metabolites and diseases and propose a negative sampling method to select reliable negative metabolite-disease samples from the bipartite graph to reduce the effect of noisy samples. Then a homogeneous metabolite-disease pair graph is constructed based on the selected negative samples and known metabolite-disease associations. Further, an edge sparseness operation is designed to simplify the connection of the homogeneous graph. Finally, the simplified homogeneous graph is fed into the graph convolutional network to predict metabolite-disease associations. Experimental results show that SMDPG can more accurately predicts metabolite-disease associations as compared to the existing methods. Moreover, the study case demonstrates that SMDPG is an effective framework for identifying potential metabolite-disease associations.

关键词： Diseases Feature extraction Semantics Databases Reliability Graph convolutional networks Training Sparse matrices Predictive models Bipartite graph

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Riemannian Complex Hermit Positive Definite Convolution network for Polarimetric SAR Image Classification

arXiv

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arXiv 2025年

作者： Shi, Junfei Nie, Mengmeng Li, Yuke Jin, Haiyan Lin, Weisi Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China College of Computing and Data Science Nanyang Technological University 639798 Singapore

Deep learning can learn high-level semantic features in Euclidean space effectively for PolSAR images, while they need to covert the complex covariance matrix into a feature vector or complex-valued vector as the network input. However, the complex covariance matrices are essentially a complex Hermit positive definite (HPD) matrix endowed in Riemannian manifold rather than Euclidean space. The matrix’s real and imagery parts are with the same significance, as the imagery part represents the phase information. The matrix vectorization will destroy the geometric structure and manifold characteristics of complex covariance matrices. To learn complex HPD matrices directly, we propose a Riemannian complex HPD convolution network(HPD CNN) for PolSAR images. This method consists of a complex HPD unfolding network(HPDnet) and a CV-3DCNN enhanced network. The proposed complex HPDnet defines the HPD mapping, rectifying and the logEig layers to learn geometric features of complex matrices. In addition, a fast eigenvalue decomposition method is designed to reduce computation burden. Finally, a Riemannian-to-Euclidean enhanced network is defined to enhance contextual information for classification. Experimental results on two real PolSSAR datasets demonstrate the proposed method can achieve superior performance than the state-of-the-art methods especially in heterogeneous regions. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolution

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network intrusion detection based on feature fusion of attack dimension

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Journal of Supercomputing 2025年第6期81卷 1-33页

作者： Sun, Xiaolong Gu, Zhengyao Zhang, Hao Gu, Jason Liu, Yanhua Dong, Chen Ye, Junwei College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Chinese Ministry of Education Fuzhou350116 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3J 1Z1 Canada

network traffic anomaly detection involves the rapid identification of intrusions within a network through the detection, analysis, and classification of network traffic data. The variety of cyberattacks encompasses diverse attack principles. Employing an indiscriminate feature selection strategy may lead to the neglect of key features highly correlated with specific attack types. This oversight could diminish the recognition rate for that category, thereby impacting the overall performance of the detection model. To address this issue, this paper proposes a network traffic anomaly detection model based on the fusion of attack-dimensional features. Firstly, construct binary classification datasets independently for each attack class and perform individual feature selection to extract positively correlated features for each class. The features are then fused by employing a combination methods. Subsequently, based on the fused sub-datasets, base classifiers are trained. Finally, an ensemble learning approach is introduced to integrate the predictions of individual classifiers, enhancing the robustness of the model. The proposed approach, validated on NSL-KDD and UNSW-NB15 benchmark datasets, outperforms the latest methods in the field by achieving a 2% and 7% increase in precision on weighted averages. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Feature Selection

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Spatial-Temporal Resource Utilization for Partially Connected Multi-AUV Broadcast Scheduling

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IEEE Wireless Communications Letters 2025年第5期14卷 1321-1325页

作者： He, Hongyi Du, Jun Jiang, Chunxiao Wang, Jintao Song, Jian Niyato, Dusit Tsinghua University Beijing National Research Center for Information Science and Technology and State Key laboratory of Space Network and Communications Department of Electronic Engineering Beijing100084 China Tsinghua University Department of Electronic Engineering and State Key laboratory of Space Network and Communications Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China Nanyang Technological University College of Computing and Data Science Singapore

The long propagation delays in underwater acoustic channels can be leveraged to improve communication efficiency using spatial-temporal reuse techniques. This work focuses on a system of partially connected AUVs that continuously broadcast packets. A central controller gathers the location information of the AUVs and designs a broadcast sequence exploiting propagation delays to minimize the broadcast cycle. Since finding optimal solutions is time-consuming, we propose a scheduling algorithm that uses heuristics based on partial connections to reduce computational complexity. Additionally, we allow collision tolerance to shorten the broadcast cycle further and investigate the trade-off of the shorter broadcast cycle and collision probability, resulting in a scheduling algorithm exhibiting receiving-alignment properties. Simulations demonstrate the effectiveness of the proposed algorithm in reducing broadcast cycles while maintaining low computational complexity. © 2012 IEEE.

关键词： Scheduling algorithms

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Task Offloading in Internet of Vehicles: A DRL-based Approach with Representation Learning for DAG Scheduling

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IEEE Transactions on Mobile computing 2025年第6期24卷 5045-5060页

作者： Deng, Xiaoheng Yang, Haoyu Zhang, Jingjing Gui, Jinsong Lin, Siyu Wang, Xin Min, Geyong Central South University School of Electronic Information Changsha410075 China Central South University Shenzhen Research Institute China Beijing Jiaotong University School of Electronic and Information Engineering The State Key Laboratory of Rail Traffic Control and Safety Beijing100044 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

The rapid evolution of the Internet-of-Vehicles (IoV) has amplified the need for mobile computing resources, driving the shift toward offloading tasks to edge servers or vehicles with idle resources to optimize computational efficiency. To this end, an approach based on Deep Reinforcement Learning (DRL) is presented in this paper, termed DVTP, which integrates Variational Graph Attention networks (VGAT) and Transformer models to optimize Directed Acyclic Graph (DAG) task scheduling in vehicular networks. DVTP effectively captures both the spatiotemporal information and task dependencies, enabling more accurate and efficient task offloading decisions. Extensive simulation experiments demonstrate that DVTP outperforms traditional methods in reducing task completion times across various multi-vehicle and multi-edge server scenarios, showcasing its potential for real-world IoV applications. © 2025 IEEE.

关键词： Deep reinforcement learning

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DCGSD: Low-Light Image Enhancement with Dual-Conditional Guidance Sparse Diffusion Model

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Jin, Haiyan Wang, Jing Zuo, Fengyuan Su, Haonan Xiao, Zhaolin Wang, Bin Zhang, Yuanlin Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Department of Computer Science and Engineering NO. 5 South Jinhua Road Shaanxi Xi'an710048 China

When restoring low-light images, most methods largely overlook the ambiguity due to dark noise and lack discrimination for region and shape representations, resulting in invalid feature enhancement. In this work, we propose a physically explainable and prior guidance model for low-light image enhancement, termed Dual-Conditional Guidance Sparse Diffusion (DCGSD). Specifically, we introduce an elaborately designed Luminance Structure Guidance Head, which can be easily plugged into the existing diffusion model to emphasize the value of the luminance and structural representation. Furthermore, for reliable noise analysis, we provide a novel Sparse Attention Enhancement Module that is adaptively empowered to exploit the most useful region-to-region dependencies. This dynamic selection makes the diffusion process from dense to sparse, thus improving the efficiency of the reasoning noise distributions. To avoid noise amplification, we further present a Skip Calibration Module, which can be used to refine the local neighborhood that contains noisy and structural information. Extensive experiments have been performed to verify the superiority of the proposed method. DCGSD shows that leveraging dual-conditional guidance can support the diffusion model to produce sharper and more realistic results. © 1991-2012 IEEE.

关键词： Feature extraction

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A Novel Riemannian Sparse Representation Learning network for Polarimetric SAR Image Classification

arXiv

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arXiv 2025年

作者： Shi, Junfei Nie, Mengmeng Lin, Weisi Jin, Haiyan Li, Junhuai Wang, Rui The Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China The Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an710048 China the School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China

Deep learning is an effective end-to-end method for Polarimetric Synthetic Aperture Radar(PolSAR) image classification, but it lacks the guidance of related mathematical principle and is essentially a black-box model. In addition, existing deep models learn features in Euclidean space, where PolSAR complex matrix is commonly converted into a complex-valued vector as the network input, distorting matrix structure and channel relationship. However, the complex covariance matrix is Hermitian positive definite (HPD), and resides on a Riemannian manifold instead of a Euclidean one. Existing methods cannot measure the geometric distance of HPD matrices and easily cause some misclassifications due to inappropriate Euclidean measures. To address these issues, we propose a novel Riemannian Sparse Representation Learning network (SRSR_CNN) for PolSAR images. Firstly, a superpixel-based Riemannian Sparse Representation (SRSR) model is designed to learn the sparse features with Riemannian metric. Then, the optimization procedure of the SRSR model is inferred and further unfolded into an SRSRnet, which can automatically learn the sparse coefficients and dictionary atoms. Furthermore, to learn contextual high-level features, a CNN-enhanced module is added to improve classification performance. The proposed network is a Sparse Representation (SR) guided deep learning model, which can directly utilize the covariance matrix as the network input, and utilize Riemannian metric to learn geometric structure and sparse features of complex matrices in Riemannian space. Experiments on three real PolSAR datasets demonstrate that the proposed method surpasses state-of-the-art techniques in ensuring accurate edge details and correct region homogeneity for classification. Copyright © 2025, The Authors. All rights reserved.

关键词： Covariance matrix

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