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Distributed Truss Computation in Dynamic Graphs

Tsinghua Science and Technology 2023年第5期28卷 873-887页

作者： Ziwei Mo Qi Luo Dongxiao Yu Hao Sheng Jiguo Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266200China State Key Laboratory of Software Development Environment School of Computer Science and Engineering and the Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Big Data Institute Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

关键词： distributed algorithm dynamic graph graph mining cohesive subgraph k-truss

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A Length-Adaptive Non-Dominated Sorting Genetic Algorithm for Bi-Objective High-Dimensional Feature Selection

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1834-1844页

作者： Yanlu Gong Junhai Zhou Quanwang Wu MengChu Zhou Junhao Wen College of Computer Science Chongqing UniversityChongqing 400044China IEEE Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA College of Big Data and Software Engineering Chongqing UniversityChongqing 400044China

As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected *** computing(EC)is promising for FS owing to its powerful search ***,in traditional EC-based methods,feature subsets are represented via a length-fixed individual *** is ineffective for high-dimensional data,because it results in a huge search space and prohibitive training *** work proposes a length-adaptive non-dominated sorting genetic algorithm(LA-NSGA)with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective highdimensional *** LA-NSGA,an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths,and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space ***,a dominance-based local search method is employed for further *** experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.

关键词： Bi-objective optimization feature selection(FS) genetic algorithm high-dimensional data length-adaptive

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C2BN: Cross-Modality and Cross-Scale Balance Network for Multi-Modal 3D Object Detection 48

C2BN: Cross-Modality and Cross-Scale Balance Network for Mul...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Ding, Bonan Xie, Jin Nie, Jing Chongqing University School of Big Data and Software Engineering China Chongqing University School of Microelectronics and Communication Engineering China

ISBN: (纸本)9781728163277

Multi-modal 3D object detection that classifies and locates objects in 3D space by combining point-clouds captured by lidars and RGB images captured by cameras, serves as the basis for autonomous driving. Most of the existing methods aggregate features from point-clouds and images by plain element-wise additions or multiplications. Although these methods improve detection accuracy, such simple operations have difficulties in balancing both modalities. Further, the multi-level features from images also suffer from imbalance problems in receptive fields. To address the above problems, we propose two novel networks: cross-modality balance network (CMN) and cross-scale balance network (CSN). CMN utilizes cross-modality attention mechanisms to balance the importance and receptive field of two modalities. CSN employs cross-scale attention mechanisms to reduce the imbalance in multi-level features. Experiments are performed on the challenging benchmark: KITTI. The experimental results show consistent improvements in different 3D object detection frameworks, which verifies the effectiveness and generality of our proposed networks. © 2023 IEEE.

关键词： 3D Object Detection multi-modal learning neural networks

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Towards Artist Recognition Based on Material Rendering. A Case Study for Recognition of Rembrandt and Van Dyck

Towards Artist Recognition Based on Material Rendering. A Ca...

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IS and T International Symposium on Electronic Imaging 2024: Computer Vision and Image Analysis of Art 2024

作者： Huang, Jing Elgammal, Ahmed Department of Big Data and Software Engineering Chongqing University Chongqing China Department of Computer Science Rutgers University NJ United States

Can artists be recognized from the way they render certain materials, such as fabric, skin, or hair? In this paper, we study this problem with a focus on recognizing works by Rembrandt, Van Dyck, and other Dutch and Flemish artists from the same era. This paper proposes a novel material-based approach based on Swin Transformer and Cascade Mask R-CNN to address artist recognition task. We report the performance on a dataset of 644 images. Additionally, the model’s robustness to image variations is studied. © 2024, Society for Imaging Science and Technology.

关键词： Rendering (computer graphics)

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A study of data-driven fault diagnosis and early warning systems for power batteries in new energy vehicles 24

A study of data-driven fault diagnosis and early warning sys...

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8th International Conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Li, Qiang Ma, Xinqiang Yan, Meng School of Big Data and Software Engineering Chongqing University Chongqing China School of Mathematics and Artificial Intelligence Chongqing University of Arts and Sciences Chongqing China

ISBN: (纸本)9798400710094

This study addresses the prevalent issues with new energy vehicle batteries, including failure and other complications. It focuses on lithium-ion batteries in pure electric vehicles and proposes a diagnostic approach based on SDO and NARX. The study also examines the methodology employed in fault judgement and early warning. The findings offer novel insights into the diagnosis of faults in new energy vehicles and provide technical guidance for ensuring the safe operation of these vehicles. © 2024 Copyright held by the owner/author(s).

关键词： Fault diagnosis and early warning

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SparseCoder: Identifier-Aware Sparse Transformer for File-Level Code Summarization 31

SparseCoder: Identifier-Aware Sparse Transformer for File-Le...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Wang, Yanlin Huang, Yanxian Guo, Daya Zhang, Hongyu Zheng, Zibin School of Software Engineering Sun Yat-sen University China School of Computer Science and Technology Sun Yat-sen University China School of Big Data and Software Engineering Chongqing University China

ISBN: (纸本)9798350330663

Code summarization aims to generate natural language descriptions of source code, facilitating programmers to understand and maintain it rapidly. While previous code summarization efforts have predominantly focused on method-level, this paper studies file-level code summarization, which can assist programmers in understanding and maintaining large source code projects. Unlike method-level code summarization, file-level code summarization typically involves long source code within a single file, which makes it challenging for Transformer-based models to understand the code semantics for the maximum input length of these models is difficult to set to a large number that can handle long code input well, due to the quadratic scaling of computational complexity with the input sequence length. To address this challenge, we propose SparseCoder, an identifier-aware sparse transformer for effectively handling long code sequences. Specifically, the SparseCoder employs a sliding window mechanism for self-attention to model short-term dependencies and leverages the structure message of code to capture long-term dependencies among source code identifiers by introducing two types of sparse attention patterns named global and identifier attention. To evaluate the performance of SparseCoder, we construct a new dataset FILE-CS for file-level code summarization in Python. Experimental results show that our SparseCoder model achieves state-of-the-art performance compared with other pre-trained models, including full self-attention and sparse models. Additionally, our model has low memory overhead and achieves comparable performance with models using full self-attention mechanism. Furthermore, we verify the generality of SparseCoder on other code understanding tasks, i.e., code clone detection and code search, and results show that our model outperforms baseline models in both tasks, demonstrating that our model can generate better code representations for various downstream tasks. Our

关键词： Semantics

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RLDR: Reinforcement Learning-Based Fast data Recovery in Cloud-of-Clouds Storage Systems

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IEEE Transactions on Cloud Computing 2025年第02期13卷 526-543页

作者： Shen, Jiajie Wu, Bochun Wang, Maoyi Zou, Sai Cui, Laizhong Ni, Wei Fudan University Shanghai200433 China Guizhou University College of Big Data and Information Engineering Guiyang550025 China Shenzhen University College of Computer Science and Software Engieering Shenzhen518060 China Data61 Commonwealth Scientific and Industrial Research Organization NSW2122 Australia

Cloud-of-clouds storage systems are widely used in online applications, where user data are encrypted, encoded, and stored in multiple clouds. When some cloud nodes fail, the storage systems can reconstruct the lost data and store it in the substitute nodes. It is a challenge to reduce the latency of data recovery to ensure data reliability. In this paper, we adopt a Reinforcement Learning-based data Recovery (RLDR) approach to reduce the regeneration time. By employing the Monte-Carlo method, our approach can construct the tree-topology-based regeneration process, a.k.a. regeneration tree, to effectively reduce the regeneration time. Through rigorous analysis, we apply the information flow graph to optimize the inter-cloud traffic for a given regeneration tree. To verify the merit of RLDR, We conduct extensive experiments on real-world traces. Experiments demonstrate that RLDR can significantly accelerate the regeneration process. Specifically, RLDR can reduce the regeneration time by up to 92% and increase the throughput by up to twelve-fold, compared to the prior art. © 2013 IEEE.

关键词： Cloud storage

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Design of cylindrical conformal transmitted metasurface for orbital angular momentum vortex wave generation

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Chinese Physics B 2022年第4期31卷 234-238页

作者： Ben Fu Shi-Xing Yu Na Kou Zhao Ding Zheng-Ping Zhang Key Laboratory of Micro-Nano-Electronics and Software Technology of Guizhou Province College of Big Data and Information EngineeringGuizhou UniversityGuiyang 550025China

We propose a cylindrical conformal transmitted metasurface for orbital angular momentum vortex wave *** for calculating the phase distributions of cylindrical conformal transmitted metasurface is presented.A prototype of the proposed conformal transmitted metasurface is designed,fabricated and *** results shows that the proposed conformal transmitted metasurface can effectively generate vortex waves,which verifies the effectiveness of our *** proposed method may pave the way of vortex wave generation with cylindrical conformal devices.

关键词： orbital angular momentum cylindrical conformal transmitted metasurface

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NCD: Normal-Guided Chamfer Distance Loss for Watertight Mesh Reconstruction from Unoriented Point Clouds

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Computer Graphics Forum 2025年第0期

作者： Li, Jiaxin Tan, Jiawei Ou, Zhilong Wang, Hongxing School of Big Data and Software Engineering Chongqing University Chongqing China

As a widely used loss function in learnable watertight mesh reconstruction from unoriented point clouds, Chamfer Distance (CD) efficiently quantifies the alignment between the sampled point cloud from the reconstructed mesh and its corresponding input point cloud. Occasionally, to enhance reconstruction fidelity, CD incorporates a normal consistency term, albeit at the cost of efficiency. In this context, normal estimation for unoriented point clouds requires computationally intensive matrix decomposition or specialized pre-trained models, whereas deriving normals for mesh-sampled points can be readily achieved using the cross product of mesh vertices. However, the reconstruction models employing CD and its variants typically rely solely on the spatial coordinates of the points, which omits normal information in favor of efficiency and deployability. To tackle this challenge, we propose a novel loss function for watertight mesh reconstruction from unoriented point clouds, termed Normal-guided Chamfer Distance (NCD). Building upon CD, NCD introduces a normal-steered weighting mechanism based on the angle between the normal at each mesh-sampled point and the vector to its corresponding input point, offering several advantages: (i) it leverages readily available mesh-sampled point normals to weight coordinate-based Euclidean distances, thus extending the capability of CD;(ii) it eliminates the need for normal estimation from input unoriented point clouds;(iii) it incurs a negligible increase in computational complexity compared to CD. We employ NCD as the training loss for point-to-mesh reconstruction with multiple models and initial watertight meshes on benchmark datasets, demonstrating its superiority over state-of-the-art CD variants. © 2025 Eurographics - The European Association for Computer Graphics and John Wiley & Sons Ltd.

关键词： Mesh generation

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Dual-Enhanced High-Order Self-Learning Tensor Singular Value Decomposition for Robust Principal Component Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3564-3578页

作者： Xu, Honghui Fang, Chuangjie Wang, Renfang Chen, Shengyong Zheng, Jianwei Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Zhejiang Wanli University College of Big Data and Software Engineering Ningbo315200 China Tianjin University of Technology College of Computer Sciences and Engineering Tianjin300384 China

Recently, tensor singular value decomposition (TSVD) within high-order (Ho) algebra framework has shed new light on tensor robust principal component analysis (TRPCA) problem. However, HoTSVD lacks flexibility in handling the hidden correlations along different modes of large-scale multidimensional data. Moreover, the utilization of fixed or data-independent transformations in HoTSVD may result in suboptimality. For a relief, we propose a dual-enhanced self-learning TSVD along all modes to address computational flaws and learn a lossless transformation that induces a lower average-rank tensor. Specifically, we multiply the learnable semiorthogonal matrices obtained through Tucker compression with the original tensor along all modes, thus obtaining a core tensor with more inherent low rankness. Building upon this foundation, a new TNN is introduced by generalizing HoTSVD to Mode-k TSVD, followed by the facilitation to the core tensor, achieving dual-enhancement. Moreover, a reweighting scheme is imposed on the Mode-k HoTSVD to learn the global low-rank correlation and provide an efficient numerical solution. Finally, an alternating direction method of multipliers (ADMM)-based algorithm is developed as a solver. Experimental results on several types of multidimensional visual data, including light field images (LFI) and color videos, demonstrate the superiority of the proposal over previous state-of-the-art methods. © 2020 IEEE.

关键词： Discrete Fourier transforms

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