Alan Liew - Academia.edu (original) (raw)

Papers by Alan Liew

Research paper thumbnail of An Evidential Multi-Target Domain Adaptation Method Based on Weighted Fusion for Cross-Domain Pattern Classification

IEEE transactions on neural networks and learning systems, 2023

Research paper thumbnail of Image Aesthetic Evaluation Using Parallel Deep Convolution Neural Network

Research paper thumbnail of An evidential combination method with multi-color spaces for remote sensing image scene classification

Information Fusion, May 1, 2023

Research paper thumbnail of DEFEG: Deep Ensemble with Weighted Feature Generation

Knowledge Based Systems, Sep 1, 2023

Research paper thumbnail of Linear Functionality Equivalence Attack Against Deep Neural Network Watermarks and a Defense Method by Neuron Mapping

IEEE Transactions on Information Forensics and Security, 2023

Research paper thumbnail of Synthetic Cerebral Microbleed on SWI images

This data was used and is explained in the publication listed and provided in the "Related L... more This data was used and is explained in the publication listed and provided in the "Related Links" section. The dataset comprises 75 MRI SWI with real Microbleed of the brain and the locations defined by experts of 175 microbleeds. In addition, we provide the same datasets with added 10 synthetics Microbleed lesions along with their locations for each SWI, created using a mathematical model. Our publication demonstrated that the data with synthetics could be used for training to yield superior performance when tested on real lesions

Research paper thumbnail of Deep heterogeneous ensemble

International Conference on Intelligent Information Processing, Dec 31, 2019

Research paper thumbnail of An Improved Extreme Learning Machine with Parallelized Feature Mapping Structures

Compared with deep neural network which is trained using back propagation, the extreme learning m... more Compared with deep neural network which is trained using back propagation, the extreme learning machine (ELM) learns thousands of times faster but still produces good generalization performance. To better understand the ELM, this paper studies the effect of noise on the input nodes or hidden neurons. It was found that there is no effect on the performance of ELM when small amount of noise is added to the input or the neurons in the hidden layer. Although the performance of ELM would improve with an increase in the number of neurons in the hidden layer, beyond a certain limit, this could lead to overfitting. In view of this, a parallel ELM (P-ELM) is proposed to improve the system performance. P-ELM has better robustness to noise due to the ensemble nature and is less susceptible to overfitting since each parallel hidden layer has only a moderate number of hidden neurons. Experimental results have indicated that the proposed P-ELM can achieve better classification performance than ELM without large increase in training time.

Research paper thumbnail of Watermarking Protocol for Deep Neural Network Ownership Regulation in Federated Learning

Research paper thumbnail of Calcium channels and iron metabolism: A redox catastrophe in Parkinson's disease and an innovative path to novel therapies?

Redox biology, Nov 1, 2021

Autonomously spiking dopaminergic neurons of the substantia nigra pars compacta (SNpc) are exquis... more Autonomously spiking dopaminergic neurons of the substantia nigra pars compacta (SNpc) are exquisitely specialized and suffer toxic iron-loading in Parkinson's disease (PD). However, the molecular mechanism involved remains unclear and critical to decipher for designing new PD therapeutics. The long-lasting (L-type) CaV1.3 voltage-gated calcium channel is expressed at high levels amongst nigral neurons of the SNpc, and due to its role in calcium and iron influx, could play a role in the pathogenesis of PD. Neuronal iron uptake via this route could be unregulated under the pathological setting of PD and potentiate cellular stress due to its redox activity. This Commentary will focus on the role of the CaV1.3 channels in calcium and iron uptake in the context of pharmacological targeting. Prospectively, the audacious use of artificial intelligence to design innovative CaV1.3 channel inhibitors could lead to breakthrough pharmaceuticals that attenuate calcium and iron entry to ameliorate PD pathology.

Research paper thumbnail of Fine-Grained Lip Image Segmentation using Fuzzy Logic and Graph Reasoning

IEEE Transactions on Fuzzy Systems, 2023

Research paper thumbnail of Predicting the returns of the US real estate investment trust market: evidence from the group method of data handling neural network

Financial Innovation, Jul 1, 2023

Research paper thumbnail of Weakly Supervised Video Object Segmentation

This paper proposes a novel approach of weakly supervised video object segmentation, which only n... more This paper proposes a novel approach of weakly supervised video object segmentation, which only needs one pixel to guide the segmentation. We use two deep neural networks to get the instance-level semantic segmentation masks and optical flow maps of each frame. An object probability map to the first frame in video is generated by combining the semantic masks, the optical flow maps and the guiding pixel. The object probability map propagates forward and backward and becomes more accurate to each frame. Finally, an energy minimization problem on a function that consists of unary term of object probability and pairwise terms of label smoothness potentials is solved to get the pixel-wise object segmentation mask of each frame. We evaluate our method on a benchmark dataset, and the experimental results show that the proposed approach achieves impressive performance in comparison with state-of-the-art methods.

Research paper thumbnail of Signal reconstruction from the wavelet transform modulus maxima

Research paper thumbnail of Static and Dynamic Lip Feature Analysis for Speaker Verification

Research paper thumbnail of Generative Model of Brain Microbleeds for MRI Detection of Vascular Marker of Neurodegenerative Diseases

Frontiers in Neuroscience, Dec 16, 2021

Research paper thumbnail of A Novel Online Ensemble Convolutional Neural Networks for Streaming Data

Lecture Notes in Computer Science, 2019

Research paper thumbnail of Toward adaptive BDCT feature representation based image splicing measurement in smart cities

Research paper thumbnail of Automatic Image Region Annotation by Genetic Algorithm-Based Joint Classifier and Feature Selection in Ensemble System

Lecture Notes in Computer Science, 2018

In this paper, we address the image region tagging procedure in which each image region is annota... more In this paper, we address the image region tagging procedure in which each image region is annotated by a suitable concept. Specifically, we first extract the feature vector for each segmented region. Then we propose a Genetic Algorithm (GA)-based simultaneous classifier and feature selection method working with ensemble system to learn the relationship between the low-level features and high-level concepts. The extensive experiments conducted on two public datasets namely MSRC v1 and MSRC v2 demonstrate the better performance of our method than several well-known ensemble methods, supervised machine learning methods, and sparse coding-based methods in the regions-in-image classification task.

Research paper thumbnail of Feature Extraction For Visual Speaker Authentication Against Computer-Generated Video Attacks

Recent research shows that the lip feature can achieve reliable authentication performance with a... more Recent research shows that the lip feature can achieve reliable authentication performance with a good liveness detection ability. However, with the development of sophisticated human face generation methods by the deepfake technology, the talking videos can be forged with high quality and the static lip information is not reliable in such case. Meeting with such challenge, in this paper, we propose a new deep neural network structure to extract robust lip features against human and Computer-Generated (CG) imposters. Two novel network units, i.e. the feature-level Difference block (Diffblock) and the pixel-level Dynamic Response block (DRblock), are proposed to reduce the influence of the static lip information and to represent the dynamic talking habit information. Experiments on the GRID dataset have demonstrated that the proposed network can extract discriminative and robust lip features and outperform two state-of-the-art visual speaker authentication approaches in both human imposter and CG imposter scenarios.

Research paper thumbnail of An Evidential Multi-Target Domain Adaptation Method Based on Weighted Fusion for Cross-Domain Pattern Classification

IEEE transactions on neural networks and learning systems, 2023

Research paper thumbnail of Image Aesthetic Evaluation Using Parallel Deep Convolution Neural Network

Research paper thumbnail of An evidential combination method with multi-color spaces for remote sensing image scene classification

Information Fusion, May 1, 2023

Research paper thumbnail of DEFEG: Deep Ensemble with Weighted Feature Generation

Knowledge Based Systems, Sep 1, 2023

Research paper thumbnail of Linear Functionality Equivalence Attack Against Deep Neural Network Watermarks and a Defense Method by Neuron Mapping

IEEE Transactions on Information Forensics and Security, 2023

Research paper thumbnail of Synthetic Cerebral Microbleed on SWI images

This data was used and is explained in the publication listed and provided in the "Related L... more This data was used and is explained in the publication listed and provided in the "Related Links" section. The dataset comprises 75 MRI SWI with real Microbleed of the brain and the locations defined by experts of 175 microbleeds. In addition, we provide the same datasets with added 10 synthetics Microbleed lesions along with their locations for each SWI, created using a mathematical model. Our publication demonstrated that the data with synthetics could be used for training to yield superior performance when tested on real lesions

Research paper thumbnail of Deep heterogeneous ensemble

International Conference on Intelligent Information Processing, Dec 31, 2019

Research paper thumbnail of An Improved Extreme Learning Machine with Parallelized Feature Mapping Structures

Compared with deep neural network which is trained using back propagation, the extreme learning m... more Compared with deep neural network which is trained using back propagation, the extreme learning machine (ELM) learns thousands of times faster but still produces good generalization performance. To better understand the ELM, this paper studies the effect of noise on the input nodes or hidden neurons. It was found that there is no effect on the performance of ELM when small amount of noise is added to the input or the neurons in the hidden layer. Although the performance of ELM would improve with an increase in the number of neurons in the hidden layer, beyond a certain limit, this could lead to overfitting. In view of this, a parallel ELM (P-ELM) is proposed to improve the system performance. P-ELM has better robustness to noise due to the ensemble nature and is less susceptible to overfitting since each parallel hidden layer has only a moderate number of hidden neurons. Experimental results have indicated that the proposed P-ELM can achieve better classification performance than ELM without large increase in training time.

Research paper thumbnail of Watermarking Protocol for Deep Neural Network Ownership Regulation in Federated Learning

Research paper thumbnail of Calcium channels and iron metabolism: A redox catastrophe in Parkinson's disease and an innovative path to novel therapies?

Redox biology, Nov 1, 2021

Autonomously spiking dopaminergic neurons of the substantia nigra pars compacta (SNpc) are exquis... more Autonomously spiking dopaminergic neurons of the substantia nigra pars compacta (SNpc) are exquisitely specialized and suffer toxic iron-loading in Parkinson's disease (PD). However, the molecular mechanism involved remains unclear and critical to decipher for designing new PD therapeutics. The long-lasting (L-type) CaV1.3 voltage-gated calcium channel is expressed at high levels amongst nigral neurons of the SNpc, and due to its role in calcium and iron influx, could play a role in the pathogenesis of PD. Neuronal iron uptake via this route could be unregulated under the pathological setting of PD and potentiate cellular stress due to its redox activity. This Commentary will focus on the role of the CaV1.3 channels in calcium and iron uptake in the context of pharmacological targeting. Prospectively, the audacious use of artificial intelligence to design innovative CaV1.3 channel inhibitors could lead to breakthrough pharmaceuticals that attenuate calcium and iron entry to ameliorate PD pathology.

Research paper thumbnail of Fine-Grained Lip Image Segmentation using Fuzzy Logic and Graph Reasoning

IEEE Transactions on Fuzzy Systems, 2023

Research paper thumbnail of Predicting the returns of the US real estate investment trust market: evidence from the group method of data handling neural network

Financial Innovation, Jul 1, 2023

Research paper thumbnail of Weakly Supervised Video Object Segmentation

This paper proposes a novel approach of weakly supervised video object segmentation, which only n... more This paper proposes a novel approach of weakly supervised video object segmentation, which only needs one pixel to guide the segmentation. We use two deep neural networks to get the instance-level semantic segmentation masks and optical flow maps of each frame. An object probability map to the first frame in video is generated by combining the semantic masks, the optical flow maps and the guiding pixel. The object probability map propagates forward and backward and becomes more accurate to each frame. Finally, an energy minimization problem on a function that consists of unary term of object probability and pairwise terms of label smoothness potentials is solved to get the pixel-wise object segmentation mask of each frame. We evaluate our method on a benchmark dataset, and the experimental results show that the proposed approach achieves impressive performance in comparison with state-of-the-art methods.

Research paper thumbnail of Signal reconstruction from the wavelet transform modulus maxima

Research paper thumbnail of Static and Dynamic Lip Feature Analysis for Speaker Verification

Research paper thumbnail of Generative Model of Brain Microbleeds for MRI Detection of Vascular Marker of Neurodegenerative Diseases

Frontiers in Neuroscience, Dec 16, 2021

Research paper thumbnail of A Novel Online Ensemble Convolutional Neural Networks for Streaming Data

Lecture Notes in Computer Science, 2019

Research paper thumbnail of Toward adaptive BDCT feature representation based image splicing measurement in smart cities

Research paper thumbnail of Automatic Image Region Annotation by Genetic Algorithm-Based Joint Classifier and Feature Selection in Ensemble System

Lecture Notes in Computer Science, 2018

In this paper, we address the image region tagging procedure in which each image region is annota... more In this paper, we address the image region tagging procedure in which each image region is annotated by a suitable concept. Specifically, we first extract the feature vector for each segmented region. Then we propose a Genetic Algorithm (GA)-based simultaneous classifier and feature selection method working with ensemble system to learn the relationship between the low-level features and high-level concepts. The extensive experiments conducted on two public datasets namely MSRC v1 and MSRC v2 demonstrate the better performance of our method than several well-known ensemble methods, supervised machine learning methods, and sparse coding-based methods in the regions-in-image classification task.

Research paper thumbnail of Feature Extraction For Visual Speaker Authentication Against Computer-Generated Video Attacks

Recent research shows that the lip feature can achieve reliable authentication performance with a... more Recent research shows that the lip feature can achieve reliable authentication performance with a good liveness detection ability. However, with the development of sophisticated human face generation methods by the deepfake technology, the talking videos can be forged with high quality and the static lip information is not reliable in such case. Meeting with such challenge, in this paper, we propose a new deep neural network structure to extract robust lip features against human and Computer-Generated (CG) imposters. Two novel network units, i.e. the feature-level Difference block (Diffblock) and the pixel-level Dynamic Response block (DRblock), are proposed to reduce the influence of the static lip information and to represent the dynamic talking habit information. Experiments on the GRID dataset have demonstrated that the proposed network can extract discriminative and robust lip features and outperform two state-of-the-art visual speaker authentication approaches in both human imposter and CG imposter scenarios.