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2nd International Conference on Artificial Intelligence and Computer Information Technology, AICIT 2023

作者： Li, Yumiao Li, Peng Shen, Ziqi Li, Wenhui Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an710119 China School of Computer Science Shaanxi Normal University Xi'an710119 China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an710062 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi'an710119 China Xi'an Key Laboratory of Cultural Tourism Resources Development and Utilization Xi'an710062 China

ISBN: (纸本)9798350302950

With the development of image processing technology and the improvement of people's living standards, there is a wide demand for makeup services in society. In this paper, we focus on style transfer and its application in automatic makeup generation, and propose a facial feature fusion recovery technique that utilizes an adaptive spatial feature fusion attention mechanism to solve the problem of inconsistent clarity between the reference image and the source image. By using the moving least squares method to calculate similarity on each sample image, the reference image set achieving the optimal makeup transfer effect can be efficiently selected. At the same time, the latent vector can be manipulated to achieve on-demand hybrid makeup generation and de-makeup image generation. By assigning different weights, the proportion of makeup in specific reference image in the final makeup effect can be controlled, and a new mixed makeup style can be obtained. The experiment shows that the proposed model can achieve state-of-the-art makeup transfer effect, and can also generate suitable makeup effects for low resolution input images. © 2023 IEEE.

关键词： Image enhancement

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FedGR: Genetic Algorithm and Relay Strategy Based Federated Learning

FedGR: Genetic Algorithm and Relay Strategy Based Federated ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yifei Zeng Kai Zhao Fangchao Yu Bo Zeng Zhi Pang Lina Wang Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Federated learning (FL) is a privacy-preserving distributed machine learning approach that enables multiple parties to collaboratively train machine learning models without sharing local data. However, compared with the models trained on independent and identically distributed (IID) data, existing methods still face significant degradation in model performance when running on non-IID data. To solve this problem, we propose a federated learning framework based on genetic algorithm and relay strategy in this paper. The framework groups clients according to their local data distribution using genetic algorithm. After obtaining the optimal grouping result, a relay strategy is used to train and aggregate models within each group. Extensive experiments on three benchmark datasets show that FedGR significantly outperforms other state-of-the-art federated learning algorithms on various image classification tasks. The source code is available at https://***/zyfhylyh/FedGR.

关键词： Federated learning Computational modeling Source coding Distributed databases Benchmark testing Data models Relays

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Revisiting Spatio-Temporal Forecasting: Feature Propagation Carry More Weights Than How They Do 27

Revisiting Spatio-Temporal Forecasting: Feature Propagation ...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Fang, Shen Liu, Hongyan Yu, Chengcheng Xie, Tian Hua, Wei Research Center for Intelligent Manufacturing Computing Zhejiang Lab Hangzhou311121 China Urban Mobility Institute Tongji University Shanghai200092 China Tongji University Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University Shanghai201804 China Research Center for Space Computing System Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9798331505929

Spatio-temporal forecasting, a prominent research focus for understanding the dynamics of data flow in various domains, has recently extended its significance to traffic prediction as a notable application. Considering topology of data flow, existing methods mainly utilize Graph Convolutional Networks (GCNs), where graph construction is the basic concern and generally determines how data features are propagated. However, through extensive investigations and theoretical evidence, it is revealed that graph construction strategy, which is usually regarded as the key step to success, has actually provided very little benefit, while the presence of feature propagation itself on spatiotemporal domain is more significant, i.e., feature propagation carry more weights than how they do. Thus, by making slight refinements of a feature normalization method, we propose a Spatio-Temporal Propagation (STP) module, which does not require intervention of a specific graph structure, yet simple and effective. Various experiments on public datasets verify that the proposed STP module is an on-the-shelf tool that can be accessed to the end of current models or even replace GCNs as an alternative for capturing spatiotemporal features, while achieving better predictions. All the source codes are open accessed on GitHub. © 2024 IEEE.

关键词： Spatio-temporal data

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Towards Efficient and Diverse Generative Model for Unconditional Human Motion Synthesis 24

Towards Efficient and Diverse Generative Model for Unconditi...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yu, Hua Liu, Weiming Bai, Jiapeng Gui, Xu Hou, Yaqing Ong, Yewsoon Zhang, Qiang Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education School of Computer Science and Technology Dalian University of Technology Dalian China College of Computer Science Zhejiang University Hangzhou China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China College of Computing and Data Science Nanyang Technological University Singapore Singapore Yonsei University Seoul Korea Republic of

ISBN: (纸本)9798400706868

Recent generative methods have revolutionized the way of human motion synthesis, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DMs). These methods have gained significant attention in human motion fields. However, there are still challenges in unconditionally generating highly diverse human motions from a given distribution. To enhance the diversity of synthesized human motions, previous methods usually employ deep neural networks (DNNs) to train a transport map that transforms Gaussian noise distribution into real human motion distribution. According to Figalli's regularity theory, the optimal transport map computed by DNNs frequently exhibits discontinuities. This is due to the inherent limitation of DNNs in representing only continuous maps. Consequently, the generated human motions tend to heavily concentrate on densely populated regions of the data distribution, resulting in mode collapse or mode mixture. To address the issues, we propose an efficient method called MOOT for unconditional human motion synthesis. First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space. Next, we employ convex optimization to match the noise distribution with the latent space distribution of human motions through the Optimal Transport (OT) map. Then, we combine the extended OT map with the generator of reconstruction network to generate new human motions. Thereby overcoming the issues of mode collapse and mode mixture. MOOT generates a latent code distribution that is well-behaved and highly structured, providing a strong motion prior for various applications in the field of human motion. Through qualitative and quantitative experiments, MOOT achieves state-of-the-art results surpassing the latest methods, validating its superiority in unconditional human motion generation. © 2024 ACM.

关键词： Generative adversarial networks

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Preprocessed GMRES for fast solution of linear equations

arXiv

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arXiv 2024年

作者： Zhang, Juan Luo, Yiyi Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University China

The article mainly introduces preprocessing algorithms for solving linear equation systems. This algorithm uses three algorithms as inner iterations, namely RPCG algorithm, ADI algorithm, and Kaczmarz algorithm. Then, it uses BA-GMRES as an outer iteration to solve the linear equation system. These three algorithms can indirectly generate preprocessing matrices, which are used for solving equation systems. In addition, we provide corresponding convergence analysis and numerical examples. Through numerical examples, we demonstrate the effectiveness and feasibility of these preprocessing methods. Furthermore, in the Kaczmarz algorithm, we introduce both constant step size and adaptive step size, and extend the parameter range of the Kaczmarz algorithm to α ∈ (0, ∞). We also study the solution rate of linear equation systems using different step sizes. Numerical examples show that both constant step size and adaptive step size have higher solution efficiency than the solving algorithm without preprocessing. © 2024, CC BY.

关键词： Linear equations

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A preconditioned iteration method for solving saddle point problems

arXiv

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arXiv 2024年

This paper introduces a preconditioned method designed to comprehensively address the saddle point system with the aim of improving convergence efficiency. In the preprocessor construction phase, a technical approach for solving the approximate inverse matrix of sparse matrices is presented. The effectiveness of the proposed method is demonstrated through numerical examples, emphasizing its efficacy in approximating the inverse matrix. Furthermore, the preprocessing technology includes a low-rank processing step, effectively reducing algorithmic complexity. Numerical experiments validate the effectiveness and feasibility of PSLR-GMRES in solving the saddle point system. © 2024, CC BY.

关键词： Iterative methods

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A Sentiment Classification Model Based on Attention TIG-CNN-BiGRU

A Sentiment Classification Model Based on Attention TIG-CNN-...

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2023 International Conference on Culture-Oriented Science and Technology, CoST 2023

作者： Li, Peng Liang, Tianmian An, Ning Zhang, Lichen Wu, Xiaojun Wang, Xiaoming Lu, Junling Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an710119 China School of Computer Science Shaanxi Normal University Xi'an710119 China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an710062 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi'an710119 China Xi'An Key Laboratory of Cultural Tourism Resources Development and Utilization Xi'an710062 China

ISBN: (纸本)9798350357998

The uniqueness of folk song expression forms and complex semantic relationships, and the existing models are not good at recognizing the emotion category of folk song lyrics, so an attention TIG-CNN-BiGRU based emotion classification model is proposed. That is, we first propose a text vector representation based on TF-IDF feature weighted pretrained Glove word embedding to extract important features by assigning different weights, and then combine CNN, BiGRU and attention mechanism to extract local features using multi-scale convolutional kernel CNN, BiGRU to capture semantic dependencies of long sequences and attention mechanism to assign high weights to key features to make the model have better lyrics recognition ability. Experiments comparing the model with various baseline methods on the lyrics dataset show that the model outperforms other lyrics sentiment classification models in terms of sentiment classification accuracy and various other metrics. © 2023 IEEE.

关键词： Deep neural networks

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Synchronization of Hyper-Lorenz System Based on DNA Strand Displacement

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2022年第3期19卷 1897-1908页

作者： Zou, Chengye Zhang, Qiang Wei, Xiaopeng School of Mathematics and Statistics of Anyang Normal University Anyang455000 China Dalian University Ministry of Education Key Laboratory of Advanced and Intelligent Computing Dalian116622 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China

Lorenz system is depicted by chemical reaction equations of an ideal formal chemical reaction network, and a series of reversible reactions are added into chemical reaction network in order to construct a cluster of hyper-Lorenz system. DNA as a universal substrate for chemical dynamics can approximate arbitrary dynamical characteristics of ideal formal chemical reaction network through auxiliary DNA strands and displacement reactions. Based on Lyapunov's stableness theory, a novel synchronization strategy is proposed. A 6-dimensional hyper-Lorenz system is taken as examples for simulation and shows that DNA strands displacement reactions can implement the synchronization of ideal formal chemical reaction networks. Numerical simulations indicate that synchronization based on DNA strand displacement is robust to the detection of DNA strand concentration, control of reaction rate, and noise. © 2004-2012 IEEE.

关键词： Substrates

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Physiochemical and biological characteristics of fouling on landfill leachate treatment systems surface

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Journal of Environmental Sciences 2024年第1期135卷 59-71页

作者： Zhourui Gong Shu Yang Rui Zhang Yangqing Wang Xiaoqing Wu Liyan Song School of resources and environmental engineering Anhui UniversityHefei 230601China Anhui Shengjin Lake Wetland Ecology National Long-term Scientific Research Base Dongzhi 247230China Key Laboratory of Three Gorges Reservoir Region’s Eco-Environment Ministry of EducationChongqing UniversityChongqing 400045China Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714China Xing Lu Huan Jing Co.LTD. Luzhou 646000China

Fouling of landfill leachate,a biofilm formation process on the surface of the collection system,migration pipeline and treatment system causes low efficiency of leachate transportation and treatment and increases cost for maintenance of those *** addition,landfill leachate fouling might accumulate pathogens and antibiotic resistance genes(ARGs),posing threats to the *** of the landfill leachate fouling and its associated environmental behavior is essential for the management of *** this study,physicochemical and biological properties of landfill leachate fouling and the possible accumulation capacity of pathogens and ARGs were investigated in nitrification(aerobic condition)and denitrification(anaerobic condition)process during landfill leachate biological treatment,*** show that microbial(bacterial,archaeal,eukaryotic,and viral)community structure and function(carbon fixation,methanogenesis,nitrification and denitrification)differed in fouling under aerobic and anaerobic conditions,driven by the supplemental leachate water *** fouling had a higher abundance of nitrification and denitrification functional genes,while anaerobic fouling harbored a higher abundance of carbon fixation and methanogenesis *** forms of leachate fouling had a higher abundance of pathogens and ARGs than the associated leachate,suggesting the accumulation capacity of fouling on biotic ***,aerobic fouling harbored three orders of magnitude higher multidrug resistance genes mex D than its associated *** finding provides fundamental knowledge on the biological properties of leachate fouling and suggests that leachate fouling might harbor significant pathogens and ARGs.

关键词： Landfill leachate fouling Biofilm Microbiome Carbon and nitrogen cycle Pathogens Antibiotic resistance genes

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Electrokinetic mixing of two fluids with equivalent conductivity

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Chinese Journal of Chemical engineering 2022年第2期35卷 256-260页

作者： Fang Yang Wei Zhao Guiren Wang Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education School of Life SciencesJilin UniversityChangchun 130012China Institute of Photonics and Photon-technology International Scientific and Technological Cooperation Base of Photoelectric Technology and Functional Materials and ApplicationNorthwest UniversityXi’an 710127China Department of Mechanical Engineering University of South CarolinaColumbiaUSA

Electrokinetic(EK)micromixers have been widely studied in the past decade for biochemical applications,biological and chemical analysis,***,almost all EK mixers require different electrical conductivity between the two fluids to be mixed,which has greatly limited their wide applications,in cases where the two streams to be mixed have equivalent electrical *** we show that mixing enhancement between two fluids with identical conductivity can be achieved in an EK micromixer with conductive sidewalls,where the electric field is in transverse direction of the *** results revealed that the mixing became stronger with increased conductivity *** mixing method provides a novel and convenient strategy for mixing two liquids with the same or similar electrical conductivity in microfluidic systems,and could potentially serves as a powerful tool for sample preparation in applications such as liquid biopsy,and environmental monitoring,etc.

关键词： Microfluidics Microchannels Mixing Electrokinetics Micromixer

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