ORCID Profile
0000-0002-5912-293X
Current Organisations
University of Tasmania
,
Deakin University
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Publisher: IEEE
Date: 07-2015
Publisher: Springer Science and Business Media LLC
Date: 06-03-2019
Publisher: IEEE
Date: 03-2018
Publisher: IEEE
Date: 07-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: Springer Science and Business Media LLC
Date: 12-07-2021
Publisher: Springer Science and Business Media LLC
Date: 12-11-2019
Publisher: Springer International Publishing
Date: 2022
Publisher: Wiley
Date: 30-07-2023
DOI: 10.1002/ALZ.13401
Abstract: Finding low‐cost methods to detect early‐stage Alzheimer's disease (AD) is a research priority for neuroprotective drug development. Presymptomatic Alzheimer's is associated with gait impairment but hand motor tests, which are more accessible, have hardly been investigated. This study evaluated how home‐based Tasmanian (TAS) Test keyboard tapping tests predict episodic memory performance. 1169 community participants (65.8 ± 7.4 years old 73% female) without cognitive symptoms completed online single‐key and alternate‐key tapping tests and episodic memory, working memory, and executive function cognitive tests. All single‐key ( R 2 adj = 8.8%, ΔAIC = 5.2) and alternate‐key ( R 2 adj = 9.1%, ΔAIC = 8.8) motor features predicted episodic memory performance relative to demographic and mood confounders only ( R 2 adj = 8.1%). No tapping features improved estimation of working memory. Brief self‐administered online hand movement tests predict asymptomatic episodic memory impairment. This provides a potential low‐cost home‐based method for stratification of enriched cohorts. We devised two brief online keyboard tapping tests to assess hand motor function. 1169 cognitively asymptomatic adults completed motor‐ and cognitive tests online. Impaired hand motor function predicted reduced episodic memory performance. This brief self‐administered test may aid stratification of community cohorts.
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 10-12-2022
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2021
Abstract: This paper introduces Compositional Neural Logic Programming (CNLP), a framework that integrates neural networks and logic programming for symbolic and sub-symbolic reasoning. We adopt the idea of compositional neural networks to represent first-order logic predicates and rules. A voting backward-forward chaining algorithm is proposed for inference with both symbolic and sub-symbolic variables in an argument-retrieval style. The framework is highly flexible in that it can be constructed incrementally with new knowledge, and it also supports batch reasoning in certain cases. In the experiments, we demonstrate the advantages of CNLP in discriminative tasks and generative tasks.
Publisher: Elsevier BV
Date: 2024
Publisher: IEEE
Date: 13-12-2020
Publisher: IEEE
Date: 07-2016
Publisher: ACM
Date: 03-07-2014
Publisher: Institution of Engineering and Technology (IET)
Date: 09-04-2021
DOI: 10.1049/CVI2.12028
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Hindawi Limited
Date: 12-11-2020
DOI: 10.1155/2020/8875910
Abstract: Taking time to identify expected products and waiting for the checkout in a retail store are common scenes we all encounter in our daily lives. The realization of automatic product recognition has great significance for both economic and social progress because it is more reliable than manual operation and time-saving. Product recognition via images is a challenging task in the field of computer vision. It receives increasing consideration due to the great application prospect, such as automatic checkout, stock tracking, planogram compliance, and visually impaired assistance. In recent years, deep learning enjoys a flourishing evolution with tremendous achievements in image classification and object detection. This article aims to present a comprehensive literature review of recent research on deep learning-based retail product recognition. More specifically, this paper reviews the key challenges of deep learning for retail product recognition and discusses potential techniques that can be helpful for the research of the topic. Next, we provide the details of public datasets which could be used for deep learning. Finally, we conclude the current progress and point new perspectives to the research of related fields.
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 17-08-2016
Publisher: IEEE
Date: 07-2014
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
DOI: 10.24963/KR.2020/90
Abstract: Neural-symbolic systems combine the strengths of neural networks and symbolic formalisms. In this paper, we introduce a neural-symbolic system which combines restricted Boltzmann machines and probabilistic semi-abstract argumentation. We propose to train networks on argument labellings explaining the data, so that any s led data outcome is associated with an argument labelling. Argument labellings are integrated as constraints within restricted Boltzmann machines, so that the neural networks are used to learn probabilistic dependencies amongst argument labels. Given a dataset and an argumentation graph as prior knowledge, for every ex le/case K in the dataset, we use a so-called K-maxconsistent labelling of the graph, and an explanation of case K refers to a K-maxconsistent labelling of the given argumentation graph. The abilities of the proposed system to predict correct labellings were evaluated and compared with standard machine learning techniques. Experiments revealed that such argumentation Boltzmann machines can outperform other classification models, especially in noisy settings.
Publisher: Elsevier BV
Date: 07-2022
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 09-2022
DOI: 10.1016/J.JNS.2022.120336
Abstract: Across the world, Essential Tremor (ET) is the most common tremor diagnosis but up to half of these diagnoses are inaccurate. The misdiagnosis rate is particularly high in late-onset ET, when tremor begins after the age of 60 years. Currently, ET is reported to affect 5.5% of those over 65 years old and 21.7% aged over 95 but there is emerging evidence that late-onset ET has associations with dementia, mortality and more rapid progression. With ageing populations, and a range of new surgical treatments for ET, there is urgent need to clarify whether the clinical manifestations of late-onset ET are the same as for earlier-onset ET. This scoping review used MEDLINE, EMBASE and CINAHL as the information sources of published peer-reviewed research articles between 2011 and 2021. Analysis was done by narrative synthesis. 14 relevant papers were retrieved from studies conducted in Denmark, India, Italy, Germany, Spain and the US and, together, they comprised 7684 participants in total. Compared to older adults with earlier-onset ET, there is evidence that late-onset ET is associated with higher risk of cognitive impairment and dementia, higher mortality rate, faster rate of progression, lack of family history, altered cortical electrical activity, prolonged pupillary responses, and less propensity to demonstrate characteristic alcohol sensitivity. There is evidence that late-onset ET has different clinical manifestations to earlier-onset ET in particular there is higher risk of dementia and mortality. The prognosis is important for clinicians to consider when selecting candidates for deep brain stimulation surgery and also for advanced care planning.
Publisher: Springer Science and Business Media LLC
Date: 09-06-2020
Publisher: Elsevier BV
Date: 03-2024
Publisher: Springer International Publishing
Date: 2015
Publisher: Oxford University Press (OUP)
Date: 07-2022
Abstract: Essential tremor (ET) is the most common cause of tremor in older adults. However, it is increasingly recognised that 30–50% of ET cases are misdiagnosed. Late-onset ET, when tremor begins after the age of 60, is particularly likely to be misdiagnosed and there is mounting evidence that it may be a distinct clinical entity, perhaps better termed ‘ageing-related tremor’. Compared with older adults with early-onset ET, late-onset ET is associated with weak grip strength, cognitive decline, dementia and mortality. This raises questions around whether late-onset ET is a pre-cognitive biomarker of dementia and whether modification of dementia risk factors may be particularly important in this group. On the other hand, it is possible that the clinical manifestations of late-onset ET simply reflect markers of healthy ageing, or frailty, superimposed on typical ET. These issues are important to clarify, especially in the era of specialist neurosurgical treatments for ET being increasingly offered to older adults, and these may not be suitable in people at high risk of cognitive decline. There is a pressing need for clinicians to understand late-onset ET, but this is challenging when there are so few publications specifically focussed on this subject and no specific features to guide prognosis. More rigorous clinical follow-up and precise phenotyping of the clinical manifestations of late-onset ET using accessible computer technologies may help us delineate whether late-onset ET is a separate clinical entity and aid prognostication.
Publisher: Incoma Ltd., Shoumen, Bulgaria
Date: 22-10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer International Publishing
Date: 2018
Publisher: Institution of Engineering and Technology (IET)
Date: 07-2014
DOI: 10.1049/EL.2014.1207
Publisher: Elsevier BV
Date: 05-2021
Publisher: Springer International Publishing
Date: 21-08-2020
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Son Tran.