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Feature-preserving color pencil drawings from photographs

Computational Visual Media 2023年第4期9卷 807-825页

作者： Dong Wang Guiqing Li Chengying Gao Shengwu Fu Yun Liang College of Mathematics and Informatics South China Agricultural UniversityGuangzhouChina School of Computer Science&Engineering South China University of TechnologyGuangzhouChina School of Data and Computer Science Sun Yat-sen UniversityGuangzhouChina

Color pencil drawing is well-loved due to its rich *** paper proposes an approach for generating feature-preserving color pencil drawings from *** mimic the tonal style of color pencil drawings,which are much lighter and have relatively lower saturation than photographs,we devise a lightness enhancement mapping and a saturation reduction *** lightness mapping is a monotonically decreasing derivative function,which not only increases lightness but also preserves input photograph *** saturation is usually related to lightness,so we suppress the saturation dependent on lightness to yield a harmonious ***,two extremum operators are provided to generate a foreground-aware outline map in which the colors of the generated contours and the foreground object are *** experiments show that color pencil drawings generated by our method surpass existing methods in tone capture and feature preservation.

关键词： non-photorealistic rendering pencil drawings image editing feature preservation

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EEG Sequence Dependencies Learning With Fine-grained Sleep Transition Graph for Sleep Stage Classification

EEG Sequence Dependencies Learning With Fine-grained Sleep T...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liang, Jingxin Philip, C.L. Xu, Yuebin Zhang, Tong South China University of Technology School of Computer Science&Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China

ISBN: (纸本)9798350386226

Sleep stage classification assists in evaluating sleep quality and diagnosing sleep disorders. Existing methods based on spatial-temporal graph convolutional networks (STGCNs) have shown excellent performance in recognizing sleep stages. However, current works learn transition dependencies in sleep sequence in a coarse manner and restrict the modeling of brain activity to a significantly brief period. To address the above issues, we propose a fine-grained sleep transition graph (FSTG) method to learn EEG sequence dependencies for sleep stage classification in this paper. Specifically, the novel sleep transition graph (STG) constructor (STG-C) is proposed to model sleep sequence transition dependencies in a more fine-grained manner, extracting effective auxiliary information from sleep contextual sequences for sleep staging. In the STG-C, we initially explore brain connectivity over extended sleep temporal scales, utilizing the graph fusion mechanism (GFM) to learn the multiple temporal correlations of brain channels. Furthermore, to better capture dependencies of sleep sequence, a skip-gated STGCN (SG-STGCN) is designed as the strong STG sequence learner. The proposed FSTG model achieves state-of-the-art performance in public sleep staging datasets, ISRUC-S3 and ISRUC-S1. Additionally, visualization experiments demonstrate the effectiveness and interpretability of FSTG in sleep staging classification. © 2024 IEEE.

关键词： Sleep research

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Enhancing Generalized EEG Classification with Decomposed Statistics-diverse Feature Augmentation

Enhancing Generalized EEG Classification with Decomposed Sta...

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

作者： He, Yubin Chen, C.L. Philip Chen, Bianna Zhang, Tong School of Computer Science&Engineering South China University of Technology Guangdong Guangzhou510006 China Pazhou Lab Guangdong Guangzhou510335 China

ISBN: (纸本)9798350368741

Learning a generalized EEG representation under limited data and subject variability is a long-standing challenge. Most studies utilized data augmentation to extend the distribution of training data, which may hinder the diversity of augmented samples to cover more subject variability. In this paper, we propose a decomposed statistics-diverse feature augmentation (DSFA) framework for generalized EEG learning. The wavelet-based decomposed representation module decomposes signals into approximation and detail features, thus deriving semantics from original signals into approximation features to avoid over-transformation. The statistics-diverse feature augmentation module augments features to extend beyond the original feature space by manipulating the statistics of features. Extensive experiments on three datasets demonstrate that our approach achieves state-of-the-art performance in different classification tasks. Our repository is public at https://***/1940653868/DSFA. © 2025 IEEE.

关键词： EEG Classification Feature Augmentation Model Generalization Signal Decomposition Transformer

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Evolutionary Computation in Social Propagation over Complex Networks: A Survey

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International Journal of Automation and computing 2021年第4期18卷 503-520页

作者： Tian-Fang Zhao Wei-Neng Chen Xin-Xin Ma Xiao-Kun Wu School of Computer Science&Engineering South China University of TechnologyGuangzhou 510006China School of Journalism and Communication South China University of TechnologyGuangzhou 510006China

Social propagation denotes the spread phenomena directly correlated to the human world and society, which includes but is not limited to the diffusion of human epidemics, human-made malicious viruses, fake news, social innovation, viral marketing, etc. Simulation and optimization are two major themes in social propagation, where network-based simulation helps to analyze and understand the social contagion, and problem-oriented optimization is devoted to contain or improve the infection results. Though there have been many models and optimization techniques, the matter of concern is that the increasing complexity and scales of propagation processes continuously refresh the former conclusions. Recently, evolutionary computation(EC) shows its potential in alleviating the concerns by introducing an evolving and developing perspective. With this insight, this paper intends to develop a comprehensive view of how EC takes effect in social propagation. Taxonomy is provided for classifying the propagation problems, and the applications of EC in solving these problems are reviewed. Furthermore, some open issues of social propagation and the potential applications of EC are *** paper contributes to recognizing the problems in application-oriented EC design and paves the way for the development of evolving propagation dynamics.

关键词： Evolutionary computation complex network propagation dynamics social diffusion evolution model optimization algorithm

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Attention emotion recognition via ECG signals

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Quantitative Biology 2022年第3期10卷 276-286页

作者： Aihua Mao Zihui Du Dayu Lu Jie Luo School of Computer Science&Engineering South China University of TechnologyGuangzhou 510006China School of Medicine South China University of TechnologyGuangzhou 510006China School of Fine Art and Artistic Design Guangzhou UniversityGuangzhou 510006China

Background:Physiological signal-based research has been a hot topic in affective *** works mainly focus on some strong,short-lived emotions(e.g.,joy,anger),while the attention,which is a weak and long-lasting emotion,receives less *** this paper,we present a study of attention recognition based on electrocardiogram(ECG)signals,which contain a wealth of information related to ***:The ECG dataset is derived from 10 subjects and specialized for attention *** relieve the impact of noise of baseline wondering and power-line interference,we apply wavelet threshold denoising as preprocessing and extract rich features by pan-tompkins and wavelet decomposition *** improve the generalized ability,we tested the performance of a variety of combinations of different feature selection algorithms and ***:Experiments show that the combination of generic algorithm and random forest achieve the highest correct classification rate(CCR)of 86.3%.Conclusion:This study indicates the feasibility and bright future of ECG-based attention research.

关键词： affective computing attention recognition ECG signals

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InvMetrics: Measuring Privacy Risks for Split Model-Based Customer Behavior Analysis

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 4168-4177页

作者： Deng, Ruijun Hu, Shijing Lin, Junxiong Yang, Jirui Lu, Zhihui Wu, Jie Huang, Shih-Chia Duan, Qiang Fudan University Sch. of Comp. Science and the Engineering Research Center of Cyber Security Auditing and Monitoring Ministry of Education Shanghai200433 China Fudan University School of Computer Science Shanghai200433 China Shanghai Blockchain Engineering Research Center Shanghai200433 China National Taipei University of Technology Department of Electronic Engineering Taipei City10608 Taiwan Pennsylvania State University Information Sciences and Technology Department AbingtonPA19001 United States

Mobile Edge Computing (MEC) has great potential to facilitate cheap and fast customer behavior analysis (CBA). Model splitting, widely adopted in collaborative learning of MEC, partitions the CBA models between the customer device and the edge server in a layer-wise manner to support efficient distributed learning. However, the split-model architecture (SMA) is vulnerable to data reconstruction attacks for privacy leakage in intermediate data, of which the risk measurements remain unexplored. In this paper, we propose a privacy risk measurement framework, called InvMetrics, for split model-based CBA systems, which assess the degree of privacy leakage from both the CBA owners' and the regulators' perspectives. For CBA owners, we propose a privacy metric, Distance Loss (DLoss), based on distance correlation, which is computationally efficient, and thus eligible for being deployed on the customers' devices. For third-party evaluators, we propose Uncertainty Loss (ULoss) based on entropy, which can measure privacy risk without accessing raw data. Evaluation results on three CBA datasets and one image dataset demonstrate that InvMetrics framework with DLoss and ULoss can accurately measure privacy risk and is more efficient than the state-of-the-art. © 1975-2011 IEEE.

关键词： Data privacy

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Enhancing Generalized EEG Classification with Decomposed Statistics-diverse Feature Augmentation

Enhancing Generalized EEG Classification with Decomposed Sta...

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

作者： Yubin He C.L. Philip Chen Bianna Chen Tong Zhang School of Computer Science&Engineering South China University of Technology Guangzhou China Pazhou Lab Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Adaptation models Semantics Training data Signal processing Brain modeling Data augmentation Electroencephalography Acoustics Speech processing

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EEG Sequence Dependencies Learning With Fine-grained Sleep Transition Graph for Sleep Stage Classification

EEG Sequence Dependencies Learning With Fine-grained Sleep T...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jingxin Liang C.L. Philip Yuebin Xu Tong Zhang School of Computer Science&Engineering South China University of Technology Guangzhou China Pazhou Lab Guangzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

关键词： Visualization Correlation Sleep Graph convolutional networks Biological system modeling Brain modeling Electroencephalography Data mining Bioinformatics Context modeling

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Cross-Subject EEG Emotion Recognition Using Domain Adaptive Few-Shot Learning Networks

Cross-Subject EEG Emotion Recognition Using Domain Adaptive ...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Ning, Run Philip Chen, C.L. Zhang, Tong Sch. of Computer Science Engineering South China University of Technology Guangzhou China Sch. of Computer Science Engineering South China University of Technology Pazhou Lab Guangzhou China

ISBN: (纸本)9781665401265

Due to the individual differences and nonstationary of EEG signals, it is difficult to classify EEG emotions with traditional machine methods, which assume that the training and testing set come from the same data distribution, but this assumption is usually not true in the EEG field, therefore the accuracy of emotion recognition is very poor. In this paper, a Single-Source Domain Adaptive Few-Shot Learning Networks (SDA-FSL) was proposed for cross-subject EEG emotion recognition. This is the first time that domain adaptation method with few-shot learning has been used in the field of EEG emotion recognition. A CBAM-based feature mapping module was designed to extract the common features of the two domains, and the domain adaptation module was used to align the data distribution of two domains. In addition, Prototypical Networks with instance-attention mechanism is introduced to preserve domain-specific information. The proposed method was evaluated on DEAP and SEED datasets in within-dataset and cross-dataset experiments under various N-way k-shot settings. Experimental results show that the performance of SDA-FSL outperforms other comparison methods and has superior generalization performance on cross-dataset experiments. © 2021 IEEE.

关键词： Emotion Recognition

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Towards Efficient Anomaly Detection Using Memory Broad Learning System

Towards Efficient Anomaly Detection Using Memory Broad Learn...

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IEEE International Conference on Control science and Systems engineering (CCSSE)

作者： Zhijie Zhong Kaixiang Yang Zhiwen Yu Yifan Shi C. L. Philip Chen Computer Science&Engineering South China University of Technology Guangzhou China College of Engineering Huaqiao University Quanzhou China

Anomaly detection aims to identify data points in real-world data that do not conform to expected patterns. The lack of labels, large sample size, and numerous features make it challenging to develop an effective and straightforward algorithm. This paper proposes a new approach called Memory Broad Learning System for Anomaly Detection (MemBLSAD) that can be trained end-to-end. MemBLSAD seamlessly integrates a memory module and a Double BLS-decoder module to achieve hybrid anomaly detection scores, thereby enhancing its capability. Despite the added complexity, MemBLSAD maintains the fast training ability of classic BLS and ensures an end-to-end training process. Our experiments on MNIST and CIFAR10 datasets demonstrate that MemBLSAD outperforms the comparison algorithms on the AUC measure. We also provide reliable visualizations to analyze MemBLSAD's outstanding performance and explain why it works better.

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