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MixSA: Training-free Reference-based Sketch Extraction via Mixture-of-Self-Attention

IEEE Transactions on Visualization and Computer Graphics 2024年 PP卷 PP页

作者： Yang, Rui Wu, Xiaojun He, Shengfeng Shaanxi Normal University School of Computer Sciences Xi'an China Shaanxi Normal University School of Computer Science The Key Laboratory of Intelligent Computing and Service Technology for Folk Song The Ministry of Culture and Tourism Xi'an710119 China Singapore Management University School of Computing and Information Systems Singapore Singapore

Current sketch extraction methods either require extensive training or fail to capture a wide range of artistic styles, limiting their practical applicability and versatility. We introduce Mixture-of-Self-Attention (MixSA), a training-free sketch extraction method that leverages strong diffusion priors for enhanced sketch perception. At its core, MixSA employs a mixture-of-self-attention technique, which manipulates self-attention layers by substituting the keys and values with those from reference sketches. This allows for the seamless integration of brushstroke elements into initial outline images, offering precise control over texture density and enabling interpolation between styles to create novel, unseen styles. By aligning brushstroke styles with the texture and contours of colored images, particularly in late decoder layers handling local textures, MixSA addresses the common issue of color averaging by adjusting initial outlines. Evaluated with various perceptual metrics, MixSA demonstrates superior performance in sketch quality, flexibility, and applicability. This approach not only overcomes the limitations of existing methods but also empowers users to generate diverse, high-fidelity sketches that more accurately reflect a wide range of artistic expressions. © 1995-2012 IEEE.

关键词： Image texture

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Complex Industrial Process Monitoring using Improved Dynamic Latent Variables - Neighborhood Preserving Embedding

Complex Industrial Process Monitoring using Improved Dynamic...

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Chinese Automation Congress (CAC)

作者： Hancheng Wang Peng Li Mingxi Ai Jiande Wu Chuangyan Yang Qingmin Xu School of Information Science and Engineering name of organization Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In complex industrial process monitoring, prevalent challenges include high dimensionality, extensive redundancies, and dynamic characteristics. In this paper, we propose an Improved Dynamic Latent Variable-Neighborhood Preserving Embedding (DLV -NPE) algorithm specifically designed to tackle these issues. The methodology begins with the implementation of a variance-covariance feature selection method to minimize redundancies among monitoring variables. This is followed using a dynamic latent variable algorithm to extract dynamic regression properties from the selected variables. Subsequently, the NPE algorithm is employed to preserve the residuals of high-dimensional dynamic latent variables and the high-dimensional residuals of selected variables, thereby preserving the integrity of the low-dimensional manifold structure. Finally, Bayesian inference is applied to develop comprehensive monitoring indicators. The efficacy of the DL V -NPE algorithm is validated through its application to the Tennessee Eastman (TE) process, where it demonstrates superior fault detection capabilities compared to other algorithms.

关键词： Process monitoring Manifolds Fault diagnosis Statistical analysis Heuristic algorithms Fault detection Termination of employment Feature extraction Inference algorithms Bayes methods

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RIGID: Recurrent GAN Inversion and Editing of Real Face Videos

RIGID: Recurrent GAN Inversion and Editing of Real Face Vide...

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International Conference on Computer Vision (ICCV)

作者： Yangyang Xu Shengfeng He Kwan-Yee K. Wong Ping Luo Deapartment of Computer Science The University of Hong Kong School of Computing and Information Systems Singapore Management University Shanghai AI Laboratory

GAN inversion is indispensable for applying the powerful editability of GAN to real images. However, existing methods invert video frames individually often leading to undesired inconsistent results over time. In this paper, we propose a unified recurrent framework, named Recurrent vIdeo GAN Inversion and eDiting (RIGID), to explicitly and simultaneously enforce temporally coherent GAN inversion and facial editing of real videos. Our approach models the temporal relations between current and previous frames from three aspects. To enable a faithful real video reconstruction, we first maximize the inversion fidelity and consistency by learning a temporal compensated latent code. Second, we observe incoherent noises lie in the high-frequency domain that can be disentangled from the latent space. Third, to remove the inconsistency after attribute manipulation, we propose an in-between frame composition constraint such that the arbitrary frame must be a direct composite of its neighboring frames. Our unified framework learns the inherent coherence between input frames in an end-to-end manner, and therefore it is agnostic to a specific attribute and can be applied to arbitrary editing of the same video without re-training. Extensive experiments demonstrate that RIGID outperforms state-of-the-art methods qualitatively and quantitatively in both inversion and editing tasks. The deliverables can be found in https://***/RIGID.

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Relevance Search over Knowledge Graph with User-entity Interaction Data

Relevance Search over Knowledge Graph with User-entity Inter...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Jianyu Li Kun Yue School of Information Science and Engineering Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

Relevance search is to find a list of entities in a knowledge graph (KG), which are associative to a query entity. However, many entities are not linked in KG but are actually associated by user interactions. To this end, we propose a joint weighting function to evaluate the entity associations from both KG and user-entity interaction data simultaneously. Upon the subgraph extracted from KG w.r.t the query entity, we obtain the associative entities by calculating their association degrees. Experimental results show that the effectiveness of our method outperforms other competitors.

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Structural Entropy Based Graph Node Classification

Structural Entropy Based Graph Node Classification

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Jun Zhang Kun Yue School of Information Science and Engineering Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

Node classification is to predict the labels of the unlabeled nodes in a graph, which is useful for various applications of social network and biological information analysis. To measure the uncertainty of structural information, we propose the structural entropy theory based method for graph node classification. First, we calculate the structural entropy of different graph structures, since the smaller the structural entropy, the less the uncertainty of the structural relationship in the graph. Then, we use the minimal structural entropy to determine the uncertainty of graphical structures. Finally, the nodes with similar structural relationships in the graph are classified. Experimental results show that our method outperforms some state-of-the-art competitors.

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Time-independent Spiking Neuron via Membrane Potential Estimation for Efficient Spiking Neural Networks

Time-independent Spiking Neuron via Membrane Potential Estim...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hanqi Chen Lixing Yu Shaojie Zhan Penghui Yao Jiankun Shao Yunnan Key Laboratory of Intelligent Systems and Computing School of Information Science and Engineering Yunnan University China State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The computational inefficiency of spiking neural networks (SNNs) is primarily due to the sequential updates of membrane potential, which becomes more pronounced during extended encoding periods compared to artificial neural networks (ANNs). This highlights the need to parallelize SNN computations effectively to leverage available hardware parallelism. To address this, we propose Membrane Potential Estimation Parallel Spiking Neurons (MPE-PSN), a parallel computation method for spiking neurons that enhances computational efficiency by enabling parallel processing while preserving the intrinsic dynamic characteristics of SNNs. Our approach exhibits promise for enhancing computational efficiency, particularly under conditions of elevated neuron density. Empirical experiments demonstrate that our method achieves state-of-the-art (SOTA) accuracy and efficiency on neuromorphic datasets. Codes are available at https://***/chrazqee/MPE-PSN.

关键词： Neuromorphics Computational modeling Neurons Membrane potentials Estimation Spiking neural networks Parallel processing Signal processing Computational efficiency Speech processing

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and cloud computing System 14

HealthEdge: A Machine Learning-Based Smart Healthcare Framew...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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Path Planning and information Protection of Mobile Robots Based on Deceptive Reinforcement Learning 23rd

Path Planning and Information Protection of Mobile Robots Ba...

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23rd IFToMM China International Conference on Mechanism and Machine Science and Engineering, IFToMM CCMMS 2022

作者： Xu, Qingfeng Shi, Yingnan Wang, Junchao Miller, Tim Xu, Hangding Wang, Tianmu Lin, Hongbin Liu, Xin-Jun Nie, Zhenguo Ths State Key Laboratory of Tribology Department of Mechanical Engineering Tsinghua University Beijing100084 China Beijing Key Laboratory of Precision/Ultra-Precision Manufacturing Equipments and Control Tsinghua University Beijing100084 China School of Computing and Information Systems Melbourne School of Engineering University of Melbourne Melbourne3000 Australia School of Mathematics and Information Science Guangzhou University Guangzhou510006 China

ISBN: (纸本)9789811993978

Mobile robots have been widely used in military and other fields. By observing the path of the mobile robot, it is easy to infer the destination of its path. However, in some special situations, the user of the mobile robot does not want to reveal its purpose information, such as carrying out military attacks, sneak reconnaissance missions, etc. In order to protect the mobile robot's path information from being leaked, we combine the reinforcement learning path planning method to propose a computationally efficient deceptive reinforcement learning mobile robot path planning method. The path planned by this method balances the path cost and the target deception weight, making it difficult for observers observing the mobile robot path to predict the real target of the mobile robot, while ensuring a lower path cost for the mobile robot. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

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Data Engineering and AI-Powered Skin Cancer Identification for Healthcare Applications 28th

Data Engineering and AI-Powered Skin Cancer Identification f...

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Animal and Agriculture Department Hartpury University Gloucester United Kingdom Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol United Kingdom Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

Skin cancer diagnosis, a critical task in the medical domain, can be revolutionized through the application of advanced deep-learning techniques. This work investigates the efficacy of Convolutional Neural Networks (CNNs) in the automated classification of skin cancer. The process begins with a comprehensive explanation of key CNN layers: Conv2D, MaxPool2D, Dropout, and Dense. The Conv2D layers employ learnable filters that transform localized image segments, while MaxPool2D contributes to downsampling, effectively reducing computational cost and overfitting risk. Integrating these layers enables the network to capture local and global characteristics, which is crucial for accurate classification. Adding Dropout layers enhances generalization and mitigates overfitting by introducing randomness during training. ReLU activation functions infuse non-linearity, and the Flatten layer facilitates the transition to fully connected layers. The proposed CNN architecture is meticulously designed considering filter counts, kernel sizes, and pooling dimensions. The trained model demonstrates promising performance by utilizing the HAM10000 dataset, encompassing diverse skin lesion images across seven classes. The CNN model's parameters and architecture are systematically presented, offering insights into its design rationale. The model undergoes optimization with the Adam optimizer and annealing techniques to facilitate convergence. The model's effectiveness is evaluated on validation and test datasets, demonstrating an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. Data augmentation strategies are introduced to enhance model generalization further. The results underscore CNN's potential as a robust tool for automating skin cancer diagnosis, aligning with the broader trend of leveraging deep learning for medical image analysis © 2024 The Authors.

关键词： Convolutional neural networks

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An Improved PI Controller for Stiction Compensation of Control Valves in Process Industry

An Improved PI Controller for Stiction Compensation of Contr...

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第43届中国控制会议

作者： Dong Yang Xun Lang Jiande Wu Yumin Lang Lei Xie Hongye Su Department of Electronic Engineering School of Information Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University State Key Laboratory of Industrial Control Technology Zhejiang University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the process industry, limit cycles are mostly induced by stiction-related nonlinearity in control valves, severely deteriorating control loop performance. Such problem highlights the importance of compensating for oscillations due to valve stiction. To this end, this work introduces an improved PI controller(IPIC) for stiction compensation, which retains the advantages of the traditional PI controller with fewer adjustment parameters. The enhancement involves nonlinear variation of the integral gain based on instantaneous error. Additionally, a differential evolution algorithm is utilized for controller *** to state-of-the-art methods in setpoint tracking, disturbance rejection, and robustness to parameter uncertainties, the proposed approach exhibits superior performance.

关键词： Stiction Limit Cycle Nonlinear PI controller Differential Evolution Algorithm

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