ORCID Profile
0000-0002-3466-9218
Current Organisation
Queensland University of Technology
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 09-2016
Publisher: IEEE
Date: 05-2010
Publisher: No publisher found
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: MDPI AG
Date: 02-2023
DOI: 10.3390/S23031561
Abstract: Industrial Control Systems (ICSs) were initially designed to be operated in an isolated network. However, recently, ICSs have been increasingly connected to the Internet to expand their capability, such as remote management. This interconnectivity of ICSs exposes them to cyber-attacks. At the same time, cyber-attacks in ICS networks are different compared to traditional Information Technology (IT) networks. Cyber attacks on ICSs usually involve a sequence of actions and a multitude of devices. However, current anomaly detection systems only focus on local analysis, which misses the correlation between devices and the progress of attacks over time. As a consequence, they lack an effective way to detect attacks at an entire network scale and predict possible future actions of an attack, which is of significant interest to security analysts to identify the weaknesses of their network and prevent similar attacks in the future. To address these two key issues, this paper presents a system-wide anomaly detection solution using recurrent neural networks combined with correlation analysis techniques. The proposed solution has a two-layer analysis. The first layer targets attack detection, and the second layer analyses the detected attack to predict the next possible attack actions. The main contribution of this paper is the proof of the concept implementation using two real-world ICS datasets, SWaT and Power System Attack. Moreover, we show that the proposed solution effectively detects anomalies and attacks on the scale of the entire ICS network.
Publisher: Wiley
Date: 09-10-2017
DOI: 10.1111/EPI.13907
Abstract: Epilepsy being one of the most prevalent neurological disorders, affecting approximately 50 million people worldwide, and with almost 30-40% of patients experiencing partial epilepsy being nonresponsive to medication, epilepsy surgery is widely accepted as an effective therapeutic option. Presurgical evaluation has advanced significantly using noninvasive techniques based on video monitoring, neuroimaging, and electrophysiological and neuropsychological tests however, certain clinical settings call for invasive intracranial recordings such as stereoelectroencephalography (SEEG), aiming to accurately map the eloquent brain networks involved during a seizure. Most of the current presurgical evaluation procedures focus on semiautomatic techniques, where surgery diagnosis relies immensely on neurologists' experience and their time-consuming subjective interpretation of semiology or the manifestations of epilepsy and their correlation with the brain's electrical activity. Because surgery misdiagnosis reaches a rate of 30%, and more than one-third of all epilepsies are poorly understood, there is an evident keen interest in improving diagnostic precision using computer-based methodologies that in the past few years have shown near-human performance. Among them, deep learning has excelled in many biological and medical applications, but has advanced insufficiently in epilepsy evaluation and automated understanding of neural bases of semiology. In this paper, we systematically review the automatic applications in epilepsy for human motion analysis, brain electrical activity, and the anatomoelectroclinical correlation to attribute anatomical localization of the epileptogenic network to distinctive epilepsy patterns. Notably, recent advances in deep learning techniques will be investigated in the contexts of epilepsy to address the challenges exhibited by traditional machine learning techniques. Finally, we discuss and propose future research on epilepsy surgery assessment that can jointly learn across visually observed semiologic patterns and recorded brain electrical activity.
Publisher: Springer Science and Business Media LLC
Date: 30-06-2022
DOI: 10.1038/S41598-022-14380-X
Abstract: This work addresses hand mesh recovery from a single RGB image. In contrast to most of the existing approaches where parametric hand models are employed as the prior, we show that the hand mesh can be learned directly from the input image. We propose a new type of GAN called Im2Mesh GAN to learn the mesh through end-to-end adversarial training. By interpreting the mesh as a graph, our model is able to capture the topological relationship among the mesh vertices. We also introduce a 3D surface descriptor into the GAN architecture to further capture the associated 3D features. We conduct experiments with the proposed Im2Mesh GAN architecture in two settings: one where we can reap the benefits of coupled groundtruth data availability of the images and the corresponding meshes and the other which combats the more challenging problem of mesh estimation without the corresponding groundtruth. Through extensive evaluations we demonstrate that even without using any hand priors the proposed method performs on par or better than the state-of-the-art.
Publisher: IEEE
Date: 12-2019
Publisher: Elsevier BV
Date: 10-2018
Publisher: IEEE
Date: 09-2015
Publisher: IEEE
Date: 07-2018
Publisher: IEEE
Date: 11-2015
Publisher: Elsevier BV
Date: 2021
Publisher: IEEE
Date: 09-2017
Publisher: IEEE
Date: 11-2010
Publisher: IEEE
Date: 09-2017
Publisher: Elsevier BV
Date: 06-2018
Publisher: Elsevier BV
Date: 12-2018
Publisher: Elsevier BV
Date: 09-2018
Publisher: IEEE
Date: 07-2023
Publisher: IEEE
Date: 04-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Publisher: IEEE
Date: 03-2017
DOI: 10.1109/WACV.2017.14
Publisher: IEEE
Date: 10-2018
Publisher: Elsevier BV
Date: 02-2019
DOI: 10.1016/J.SEIZURE.2018.12.017
Abstract: The recent explosion of artificial intelligence techniques in video analytics has highlighted the clinical relevance in capturing and quantifying semiology during epileptic seizures however, we lack an automated anomaly identification system for aberrant behaviors. In this paper, we describe a novel system that is trained with known clinical manifestations from patients with mesial temporal and extra-temporal lobe epilepsy and presents aberrant semiology to physicians. We propose a simple end-to-end-architecture based on convolutional and recurrent neural networks to extract spatiotemporal representations and to create motion capture libraries from 119 seizures of 28 patients. The cosine similarity distance between a test representation and the libraries from five aberrant seizures separate to the main dataset is subsequently used to identify test seizures with unusual patterns that do not conform to known behavior. Cross-validation evaluations are performed to validate the quantification of motion features and to demonstrate the robustness of the motion capture libraries for identifying epilepsy types. The system to identify unusual epileptic seizures successfully detects out of the five seizures categorized as aberrant cases. The proposed approach is capable of modeling clinical manifestations of known behaviors in natural clinical settings, and effectively identify aberrant seizures using a simple strategy based on motion capture libraries of spatiotemporal representations and similarities between hidden states. Detecting anomalies is essential to alert clinicians to the occurrence of unusual events, and we show how this can be achieved using pre-learned database of semiology stored in health records.
Publisher: Elsevier BV
Date: 05-2019
Publisher: IEEE
Date: 07-2019
Publisher: Cambridge University Press (CUP)
Date: 07-07-2011
DOI: 10.1017/S0033291711001073
Abstract: It is not known whether first-episode psychosis is characterized by the same prefrontal cortex functional imaging abnormalities as chronic schizophrenia. Thirty patients with a first episode of non-affective functional psychosis and 28 healthy controls underwent functional magnetic resonance imaging (fMRI) during performance of the n-back working memory task. Voxel-based analyses of brain activations and deactivations were carried out and compared between groups. The connectivity of regions of significant difference between the patients and controls was also examined. The first-episode patients did not show significant prefrontal hypo- or hyperactivation compared to controls. However, they showed failure of deactivation in the medial frontal cortex. This area showed high levels of connectivity with the posterior cingulate gyrus recuneus and parts of the parietal cortex bilaterally. Failure of deactivation was significantly greater in first-episode patients who had or went on to acquire a DSM-IV diagnosis of schizophrenia than in those who did not, and in those who met RDC criteria for schizophrenia compared to those who did not. First-episode psychosis is not characterized by hypo- or hyperfrontality but instead by a failure of deactivation in the medial frontal cortex. The location and connectivity of this area suggest that it is part of the default mode network. The failure of deactivation seems to be particularly marked in first-episode patients who have, or progress to, schizophrenia.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2011
Publisher: ACM
Date: 30-01-2023
Publisher: IEEE
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2015
Publisher: Springer Science and Business Media LLC
Date: 04-11-2021
Publisher: Elsevier BV
Date: 10-2013
Publisher: Elsevier BV
Date: 05-2018
DOI: 10.1016/J.YEBEH.2018.02.010
Abstract: Semiology observation and characterization play a major role in the presurgical evaluation of epilepsy. However, the interpretation of patient movements has subjective and intrinsic challenges. In this paper, we develop approaches to attempt to automatically extract and classify semiological patterns from facial expressions. We address limitations of existing computer-based analytical approaches of epilepsy monitoring, where facial movements have largely been ignored. This is an area that has seen limited advances in the literature. Inspired by recent advances in deep learning, we propose two deep learning models, landmark-based and region-based, to quantitatively identify changes in facial semiology in patients with mesial temporal lobe epilepsy (MTLE) from spontaneous expressions during phase I monitoring. A dataset has been collected from the Mater Advanced Epilepsy Unit (Brisbane, Australia) and is used to evaluate our proposed approach. Our experiments show that a landmark-based approach achieves promising results in analyzing facial semiology, where movements can be effectively marked and tracked when there is a frontal face on visualization. However, the region-based counterpart with spatiotemporal features achieves more accurate results when confronted with extreme head positions. A multifold cross-validation of the region-based approach exhibited an average test accuracy of 95.19% and an average AUC of 0.98 of the ROC curve. Conversely, a leave-one-subject-out cross-validation scheme for the same approach reveals a reduction in accuracy for the model as it is affected by data limitations and achieves an average test accuracy of 50.85%. Overall, the proposed deep learning models have shown promise in quantifying ictal facial movements in patients with MTLE. In turn, this may serve to enhance the automated presurgical epilepsy evaluation by allowing for standardization, mitigating bias, and assessing key features. The computer-aided diagnosis may help to support clinical decision-making and prevent erroneous localization and surgery.
Publisher: IEEE
Date: 03-2017
Publisher: IEEE
Date: 08-2018
Publisher: Elsevier BV
Date: 09-2023
Publisher: Springer Science and Business Media LLC
Date: 22-05-2018
Publisher: IEEE
Date: 07-2019
Publisher: No publisher found
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 10-02-2022
DOI: 10.1007/S10462-022-10142-3
Abstract: An efficient store layout presents merchandise to attract customer attention and encourages customers to walk down more aisles which exposes them to more merchandise, which has been shown to be positively correlated with the sales. It is one of the most effective in-store marketing tactics which can directly influence customer decisions to boost store sales and profitability. The recent development of Artificial Intelligence techniques, especially with its sub-fields in Computer Vision and Deep Learning, has enabled retail stores to take advantage of existing CCTV infrastructure to extract in-store customer and business insights. This research aims to conduct a comprehensive review on existing approaches in store layout design and modern AI techniques that can be utilized in the layout design task. Based on this review, we propose an AI-powered store layout design framework. This framework applies advanced AI and data analysis techniques on top of existing CCTV video surveillance infrastructure to understand, predict and suggest a better store layout.
Publisher: IEEE
Date: 06-2012
Publisher: Wiley
Date: 2022
DOI: 10.1002/EJHF.2351
Abstract: The heart failure epidemic is growing and its prevention, in order to reduce associated hospital readmission rates and its clinical and economic burden, is a key issue in modern cardiovascular medicine. The present position paper aims to provide practical evidence-based information to support the implementation of effective preventive measures. After reviewing the most common risk factors, an overview of the population attributable risks in different continents is presented, to identify potentially effective opportunities for prevention and to inform preventive strategies. Finally, potential interventions that have been proposed and have been shown to be effective in preventing heart failure are listed.
Publisher: Elsevier BV
Date: 09-2020
Publisher: Elsevier BV
Date: 09-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: MDPI AG
Date: 15-02-2023
DOI: 10.3390/S23042195
Abstract: There exists a growing interest from the clinical practice research communities in the development of methods to automate HEp-2 stained cells classification procedure from histopathological images. Challenges faced by these methods include variations in cell densities and cell patterns, overfitting of features, large-scale data volume and stained cells. In this paper, a multi-class multilayer perceptron technique is adapted by adding a new hidden layer to calculate the variation in the mean, scale, kurtosis and skewness of higher order spectra features of the cell shape information. The adapted technique is then jointly trained and the probability of classification calculated using a Softmax activation function. This method is proposed to address overfitting, stained and large-scale data volume problems, and classify HEp-2 staining cells into six classes. An extensive experimental analysis is studied to verify the results of the proposed method. The technique has been trained and tested on the dataset from ICPR-2014 and ICPR-2016 competitions using the Task-1. The experimental results have shown that the proposed model achieved higher accuracy of 90.3% (with data augmentation) than of 87.5% (with no data augmentation). In addition, the proposed framework is compared with existing methods, as well as, the results of methods using in ICPR2014 and ICPR2016 competitions.The results demonstrate that our proposed method effectively outperforms recent methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: ACM Press
Date: 2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Elsevier BV
Date: 12-2017
Publisher: MDPI AG
Date: 23-09-2022
DOI: 10.3390/EN15196998
Abstract: Increasingly people, especially those residing in urban areas with the urban heat island effect, are getting exposed to extreme heat due to ongoing global warming. A number of methods have been developed, so far, to assess urban heat vulnerability in different locations across the world concentrating on erse aspects of these methods. While there is growing literature, thorough review studies that compare, contrast, and help understand the prospects and constraints of urban heat vulnerability assessment methods are scarce. This paper aims to bridge this gap in the literature. A systematic literature review with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach is utilized as the methodological approach. PRISMA is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses. The results are analyzed in three aspects—i.e., indicators and data, modelling approaches, and validation approaches. The main findings disclose that: (a) Three types of indicators are commonly used—i.e., demographic properties and socioeconomic status, health conditions and medical resources, and natural and built environmental factors (b) Heat vulnerability indexing models, equal weighting method, and principal component analysis are commonly used in modelling and weighting approaches (c) Statistical regressions and correlation coefficients between heat vulnerability results and adverse health outcomes are commonly used in validation approaches, but the performance varies across studies. This study informs urban policy and generates directions for prospective research and more accurate vulnerability assessment method development.
No related grants have been discovered for Kien Nguyen.