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18th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Zeng, Ruolan Liu, Zhanghui Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819996360

Community detection is widely used in network analysis, which seeks to divide network nodes into distinct communities based on the topology structure and attribute information of the network. Due to its interpretability, nonnegative matrix factorization becomes an essential method for community detection. However, it decomposes the adjacency matrix and attribute matrix separately, which do not tightly incorporate topology and attributes. And in the problem of division inconsistency based on topology and attributes caused by the mismatch between the topology similarity and attribute similarity of paired nodes, it ignores the difference in the matching degree of each attribute and each node. In this paper, we propose a nonnegative matrix factorization algorithm for community detection (MTACD) based on the matching degree between topology and attribute. First, we employ an attribute embedding mechanism to enhance the node-attribute relationship. Second, we design an attribute matching degree and a node topology-and-attribute matching degree in order to resolve the mismatch between topology and attribute similarity. Experiments on both real-world and synthetic networks demonstrate the effectiveness of our algorithm. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Topology

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Hybrid particle swarm optimization with adaptive learning strategy

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Soft Computing 2024年第17-18期28卷 9759-9784页

作者： Wang, Lanyu Tian, Dongping Gou, Xiaorui Shi, Zhongzhi School of Mathematics and Information Sciences Baoji University of Arts and Science Shaanxi Baoji721007 China Institute of Computer Software Baoji University of Arts and Sciences Shaanxi Baoji721007 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Particle swarm optimization (PSO) is a meta-heuristic algorithm that can be used to solve different real-world optimization problems. However, PSO suffers from some intrinsic drawbacks, including falling into local optimal and slow convergence in the latter evolution process. To deal with these issues, a hybrid PSO with adaptive learning strategy (abbreviated as HPSO-ALS) is proposed in this paper. To start with, the chaotic opposition-learning method is employed to generate the uniformly distributed particles so as to enhance the quality of the initial swarm. Next, a self-logical mapping-based nonlinear decreasing inertia weight is formulated to trade off the global search and local search abilities. Meanwhile, different adaptive position updating strategies are leveraged to achieve better convergence according to different evolution status. In addition, inspired by the cask effect, the global worst particle (gworst) is introduced into the velocity updating to make full use of the potential information of the particles. Finally, extensive experiments on sixteen benchmark functions and CEC’13 test suite have verified the promising performance of HPSO-ALS proposed in this paper. Alternatively, HPSO-ALS is also applied to the classical traveling salesman problem (TSP) and the experimental results have further validated the effectiveness and efficiency of our proposal. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Traveling salesman problem

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Contrastive Subspace Clustering with Dissimilarity Regularization

Contrastive Subspace Clustering with Dissimilarity Regulariz...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Chen, Yu-Jie Wu, Wen-Hui Ou-Yang, Le College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798350303780

Subspace Clustering (SC) aims at clustering the data according to its underlying subspaces. However, existing subspace clustering methods fail to make full use of available information. Inspired by advances that contrastive learning has achieved in unsupervised representation learning, we propose a method, namely contrastive subspace clustering with dissimilarity regularization (CSCDR), incorporating contrastive learning into subspace clustering. Specifically, we first construct another view of data through augmentation. Then, the deep subspace clustering networks (DSC-Nets) are used to learn the self-expressive coefficients of data and its augmentation, which further interact with each other by contrastive learning. Moreover, a dissimilarity matrix is formulated based on the latent feature of data to further calibrate the noisy connections in the self-expressive coefficient. And we also utilize the relevant similarity matrix contributing to the multi-positive contrasitve learning, which ameliorates the cluster-collapse problem of representation. Experimental results on several data sets demonstrate the efficacy of the proposed method. © 2023 IEEE.

关键词： Matrix algebra

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Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh

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Energy Engineering 2025年第1期122卷 243-264页

作者： Liyao Yang Hongyan Ma Yingda Zhang Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Institute of Distributed Energy Storage Safety Big Data Beijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China

Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management ***,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is *** address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and ***,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance ***,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution *** Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction ***,the proposed models are validated using NASA and CALCE lithium-ion battery *** results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.

关键词： State of health remaining useful life variational modal decomposition random forest twin support vector machine convolutional optimization algorithm

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Relaxing Distillation Constraints for Improved New Class Learning in Continual Semantic Segmentation

Relaxing Distillation Constraints for Improved New Class Lea...

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

作者： Ti, Zheng Hao, Xuze Chen, Renhai College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9798350368741

Continual Semantic Segmentation (CSS) aims to continuously learn new classes while mitigating catastrophic forgetting. Existing CSS methods primarily address this challenge through knowledge distillation. While they focus on maintaining stability for old classes, this emphasis often restricts plasticity in learning new ones. In this work, we propose two plug-and-play methods to better balance stability and plasticity. First, for feature map distillation, we replace pixel-level constraints with statistical information extraction within a window, improving the learning of new classes while preserving structural integrity to overcome forgetting. Second, we use relative ranks of predictions to replace exact probabilistic values to loosen the constraints in the logit distribution of old classes. Extensive experiments demonstrate that our methods outperform state-of-the-art methods when integrating into several baselines across various CSS scenarios. © 2025 IEEE.

关键词： Continual Learning Knowledge Distillation Semantic Segmentation

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ArtDiff: Artwork Generation via Conditional Diffusion Models 23

ArtDiff: Artwork Generation via Conditional Diffusion Models

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15th International Conference on Digital Image processing, ICDIP 2023

作者： Liu, Wenhui Liu, Bin Lin, Xiao Li, Bo He, Zhifen Wang, Kang School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Jiangxi Nanchang China Department of Supercomputing Management Jiangxi Science and Technology Infrastructure Center Jiangxi Nanchang China

ISBN: (纸本)9798400708237

Artwork Generation is an important research area of computer vision. Recently, kinds of generative models have achieved great success in natural image generation. However, artwork generation has rarely been studied due to the unfixed structure of artworks. Combined with prevailing diffusion models, we propose a simple yet effective framework, named as ArtDiff, for artwork generation. Given a name of an artist, we can generate diverse and novel artworks which reflecting the style of the artist. To the best of our knowledge, ArtDiff is the first diffusion model that generate artworks with the guidance of artist's name. Experimental results demonstrate that ArtDiff is able to recognize the artist's preference and generate artworks with reasonable structures and fine-grained details. © 2023 ACM.

关键词： Diffusion

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Deep Reinforcement Learning based intelligent Routing for Multi-service Traffic SDN 5

Deep Reinforcement Learning based Intelligent Routing for Mu...

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5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023

作者： Wang, Lingjiao Li, Jiachen Xiang Tan University Key Laboratory of Intelligent Computing and Information Processing Ministry of Education China Xiang Tan University School of Automation and Electronic Information China

ISBN: (纸本)9798350357738

The development of network has imposed higher requirements on QoS (Quality of Service) for routing, and the emergence of traffic classification techniques, software defined networking (SDN), and programmable network devices has made it possible to quickly identify user needs and route for service traffic requirements. Deep reinforcement learning routing algorithms for existing QoS compliant routing algorithms perform poorly in terms of generalizability. A deep graph reinforcement learning intelligent routing algorithm DDGL applied to SDN scenarios is proposed, which uses GNN to extract the link feature information of SDN data plane for deep reinforcement learning routing decision making, and comprehensively takes into account the performance metrics such as latency, packet loss, throughput, and bandwidth, to improve the algorithm generalization ability and enhance the SDN routing performance. © 2023 IEEE.

关键词： Reinforcement learning

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Remote Sensing Images Semantic Segmentation Method Based on Improved Nested UNet

Remote Sensing Images Semantic Segmentation Method Based on ...

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2022 International Conference on Geographic information and Remote Sensing Technology, GIRST 2022

作者： Li, Zhongyu Liu, Yang Kuang, Yin Wang, Huajun Liu, Cheng College of Geophysics Chengdu University of Technology Chengdu610059 China College of Computer Science Chengdu Normal University Chengdu611130 China Key Laboratory of interior Layout optimization and Security Institutions of Higher Education of Sichuan Province Sichuan Chengdu611130 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu610106 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong643000 China

ISBN: (纸本)9781510662186

With the development of remote sensing technology, remote sensing images of buildings are of great significance in urban planning, disaster response, and other directions. When we use a neural network containing batch normalization layers for semantic segmentation, the neural network is sensitive to batch size and has low segmentation accuracy for occluded and dense buildings. This paper proposes a method for building segmentation in remote sensing images based on Nested UNet (UNet++) deep neural network. First, the UNet++ network is used to extract features, and the Group Normalization (GN) method is used instead of Batch Normalization (BN) to alleviate the model's sensitivity to batch size. Then the weighted combination of Cross-Entropy Loss (CELoss) and DiceLoss is used as the loss function to improve the feature extraction ability of the neural network for unbalanced buildings. Finally, experiments are carried out on the WHUBuilding dataset. The experimental results show that the improved model (UNet++-GN) improves Mean Intersection over Union (MIoU) and Mean Pixel Accuracy (Macc) by 12.16% and 2.92%, respectively, compared with the original model (UNet++-BN). © 2023 SPIE.

关键词： Feature extraction

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Accelerating the HS-type Richardson Iteration Method with Anderson Mixing

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Acta Mathematica Sinica,English Series 2022年第11期38卷 2069-2089页

作者： Zhi Zhi LI Huai ZHANG Le OU-YANG Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049P.R.China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Securityand Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ)College of Electronics and Information EngineeringShenzhen UniversityShenzhen 518060P.R.China

The Accelerated Hermitian/skew-Hermitian type Richardson(AHSR)iteration methods are presented for solving non-Hermitian positive definite linear systems with three schemes,by using Anderson *** upper bounds of spectral radii of iteration matrices are studied,and then the convergence theories of the AHSR iteration methods are ***,the optimal iteration parameters are provided,which can be computed *** addition,the application to the model convection-diffusion equation is depicted and numerical experiments are conducted to exhibit the effectiveness and confirm the theoretical analysis of the AHSR iteration methods.

关键词： Anderson mixing Hermitian/skew-Hermitian splitting the Richardson iteration convergence analyses optimal parameters the model convection-diffusion equation

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A novel torus-based optical networks-on-chip exploiting double path communication algorithm 2

A novel torus-based optical networks-on-chip exploiting doub...

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2nd International Conference on Electronic information Engineering, Big Data, and Computer Technology, EIBDCT 2023

作者： Dai, Li Su, Ye Xie, Yiyuan School of Electronic and Information Engineering Southwest University Chongqing China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China

ISBN: (纸本)9781510664968

Optical networks-on-chip (ONoCs) using optical media to interconnect cores, have the advantages of high bandwidth, low power consumption and low latency. At present, mesh, torus and fat-tree are the most widely used topologies in ONoCs. Torus structure has the advantages of higher connectivity due to its annular structure. However, the traditional torus structure still has the problem of high end-to-end (ETE) delay. This paper proposes a new double-path ONoCs based on torus topology, named DPtorus. The DPtorus consists of two layers: one layer is a torus network divided into unit clusters, the other layer is an all-pass optical router. The dual-layer network is connected with each other by optical vias, so that there are two communication pathes between the source node and the target node in the network can be selected. The One is inter-cluster communication and the other is non-cluster communication. According to the comparison of the number of hop of the two communication paths, the communication path with fewer hops between the nodes is preferably selected to reduce the communication cost of the data packet. The network simulation results show that the DPtorus reduces latency and improves throughput compared to the torus network, resulting in better performance and enhanced reliability of network communications for the DPtorus. © 2023 SPIE.

关键词： Topology

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