ORCID Profile
0000-0001-9292-1015
Current Organisations
Monash University
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VinUniversity
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Pattern Recognition and Data Mining | Artificial Intelligence and Image Processing | Applied Statistics
Expanding Knowledge in the Information and Computing Sciences |
Publisher: Elsevier BV
Date: 03-1989
Publisher: Springer Science and Business Media LLC
Date: 25-07-2009
Publisher: Elsevier BV
Date: 11-2020
Publisher: JMIR Publications Inc.
Date: 14-06-2020
Abstract: onpharmaceutical interventions (NPIs) (such as wearing masks and social distancing) have been implemented by governments around the world to slow the spread of COVID-19. To promote public adherence to these regimes, governments need to understand the public perceptions and attitudes toward NPI regimes and the factors that influence them. Twitter data offer a means to capture these insights. he objective of this study is to identify tweets about COVID-19 NPIs in six countries and compare the trends in public perceptions and attitudes toward NPIs across these countries. The aim is to identify factors that influenced public perceptions and attitudes about NPI regimes during the early phases of the COVID-19 pandemic. e analyzed 777,869 English language tweets about COVID-19 NPIs in six countries (Australia, Canada, New Zealand, Ireland, the United Kingdom, and the United States). The relationship between tweet frequencies and case numbers was assessed using a Pearson correlation analysis. Topic modeling was used to isolate tweets about NPIs. A comparative analysis of NPIs between countries was conducted. he proportion of NPI-related topics, relative to all topics, varied between countries. The New Zealand data set displayed the greatest attention to NPIs, and the US data set showed the lowest. The relationship between tweet frequencies and case numbers was statistically significant only for Australia ( i r /i =0.837, i P /i & .001) and New Zealand ( i r /i =0.747, i P /i & .001). Topic modeling produced 131 topics related to one of 22 NPIs, grouped into seven NPI categories: Personal Protection (n=15), Social Distancing (n=9), Testing and Tracing (n=10), Gathering Restrictions (n=18), Lockdown (n=42), Travel Restrictions (n=14), and Workplace Closures (n=23). While less restrictive NPIs gained widespread support, more restrictive NPIs were perceived differently across countries. Four characteristics of these regimes were seen to influence public adherence to NPIs: timeliness of implementation, NPI c aign strategies, inconsistent information, and enforcement strategies. witter offers a means to obtain timely feedback about the public response to COVID-19 NPI regimes. Insights gained from this analysis can support government decision making, implementation, and communication strategies about NPI regimes, as well as encourage further discussion about the management of NPI programs for global health events, such as the COVID-19 pandemic.
Publisher: Springer Science and Business Media LLC
Date: 30-07-2009
Publisher: Springer Science and Business Media LLC
Date: 18-05-2013
Publisher: JMIR Publications Inc.
Date: 03-09-2020
DOI: 10.2196/21419
Abstract: Nonpharmaceutical interventions (NPIs) (such as wearing masks and social distancing) have been implemented by governments around the world to slow the spread of COVID-19. To promote public adherence to these regimes, governments need to understand the public perceptions and attitudes toward NPI regimes and the factors that influence them. Twitter data offer a means to capture these insights. The objective of this study is to identify tweets about COVID-19 NPIs in six countries and compare the trends in public perceptions and attitudes toward NPIs across these countries. The aim is to identify factors that influenced public perceptions and attitudes about NPI regimes during the early phases of the COVID-19 pandemic. We analyzed 777,869 English language tweets about COVID-19 NPIs in six countries (Australia, Canada, New Zealand, Ireland, the United Kingdom, and the United States). The relationship between tweet frequencies and case numbers was assessed using a Pearson correlation analysis. Topic modeling was used to isolate tweets about NPIs. A comparative analysis of NPIs between countries was conducted. The proportion of NPI-related topics, relative to all topics, varied between countries. The New Zealand data set displayed the greatest attention to NPIs, and the US data set showed the lowest. The relationship between tweet frequencies and case numbers was statistically significant only for Australia (r=0.837, P .001) and New Zealand (r=0.747, P .001). Topic modeling produced 131 topics related to one of 22 NPIs, grouped into seven NPI categories: Personal Protection (n=15), Social Distancing (n=9), Testing and Tracing (n=10), Gathering Restrictions (n=18), Lockdown (n=42), Travel Restrictions (n=14), and Workplace Closures (n=23). While less restrictive NPIs gained widespread support, more restrictive NPIs were perceived differently across countries. Four characteristics of these regimes were seen to influence public adherence to NPIs: timeliness of implementation, NPI c aign strategies, inconsistent information, and enforcement strategies. Twitter offers a means to obtain timely feedback about the public response to COVID-19 NPI regimes. Insights gained from this analysis can support government decision making, implementation, and communication strategies about NPI regimes, as well as encourage further discussion about the management of NPI programs for global health events, such as the COVID-19 pandemic.
Publisher: ACM
Date: 15-08-2005
Publisher: ACM
Date: 29-03-2010
Publisher: Springer Science and Business Media LLC
Date: 08-05-2018
Publisher: Springer Science and Business Media LLC
Date: 03-1991
DOI: 10.1007/BF01531172
Publisher: IEEE
Date: 12-2009
DOI: 10.1109/ICDM.2009.82
Publisher: Springer Science and Business Media LLC
Date: 26-01-2017
Publisher: Springer Science and Business Media LLC
Date: 10-06-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-1996
DOI: 10.1109/69.494161
Publisher: Cambridge University Press (CUP)
Date: 2009
DOI: 10.1093/PAN/MPP006
Abstract: In this paper, we apply information theoretic measures to voting in the U.S. Senate in 2003. We assess the associations between pairs of senators and groups of senators based on the votes they cast. For pairs, we use similarity-based methods, including hierarchical clustering and multidimensional scaling. To identify groups of senators, we use principal component analysis. We also apply a discrete multinomial latent variable model that we have developed. In doing so, we identify blocs of cohesive voters within the Senate and contrast it with continuous ideal point methods. We find more nuanced blocs than simply the two-party ision. Under the bloc-voting model, the Senate can be interpreted as a weighted vote system, and we are able to estimate the empirical voting power of in idual blocs through what-if analysis.
Publisher: Association for Computational Linguistics
Date: 2021
Publisher: IEEE
Date: 2005
DOI: 10.1109/WI.2005.103
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Informa UK Limited
Date: 10-06-2016
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 12-2009
Publisher: Springer Science and Business Media LLC
Date: 22-05-2018
Publisher: Association for Computational Linguistics
Date: 2019
DOI: 10.18653/V1/P19-1396
Publisher: Springer Science and Business Media LLC
Date: 1992
Publisher: Elsevier BV
Date: 09-1988
Publisher: Springer Science and Business Media LLC
Date: 07-08-2009
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2021
Abstract: Topic modelling has been a successful technique for text analysis for almost twenty years. When topic modelling met deep neural networks, there emerged a new and increasingly popular research area, neural topic models, with nearly a hundred models developed and a wide range of applications in neural language understanding such as text generation, summarisation and language models. There is a need to summarise research developments and discuss open problems and future directions. In this paper, we provide a focused yet comprehensive overview of neural topic models for interested researchers in the AI community, so as to facilitate them to navigate and innovate in this fast-growing research area. To the best of our knowledge, ours is the first review on this specific topic.
Publisher: ACM
Date: 24-08-2014
Publisher: IEEE
Date: 11-2015
Publisher: Springer Science and Business Media LLC
Date: 1992
DOI: 10.1007/BF00994006
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11752790_1
Publisher: Springer International Publishing
Date: 2020
Publisher: Emerald
Date: 04-06-2021
Abstract: Full text of a document is a rich source of information that can be used to provide meaningful topics. The purpose of this paper is to demonstrate how to use citation context (CC) in the full text to identify the cited topics and citing topics efficiently and effectively by employing automatic text analysis algorithms. The authors present two novel topic models, Citation-Context-LDA (CC-LDA) and Citation-Context-Reference-LDA (CCRef-LDA). CC is leveraged to extract the citing text from the full text, which makes it possible to discover topics with accuracy. CC-LDA incorporates CC, citing text, and their latent relationship, while CCRef-LDA incorporates CC, citing text, their latent relationship and reference information in CC. Collapsed Gibbs s ling is used to achieve an approximate estimation. The capacity of CC-LDA to simultaneously learn cited topics and citing topics together with their links is investigated. Moreover, a topic influence measure method based on CC-LDA is proposed and applied to create links between the two-level topics. In addition, the capacity of CCRef-LDA to discover topic influential references is also investigated. The results indicate CC-LDA and CCRef-LDA achieve improved or comparable performance in terms of both perplexity and symmetric Kullback–Leibler (sKL) ergence. Moreover, CC-LDA is effective in discovering the cited topics and citing topics with topic influence, and CCRef-LDA is able to find the cited topic influential references. The automatic method provides novel knowledge for cited topics and citing topics discovery. Topic influence learnt by our model can link two-level topics and create a semantic topic network. The method can also use topic specificity as a feature to rank references.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-1994
DOI: 10.1109/72.286919
Abstract: The calculation of second derivatives is required by recent training and analysis techniques of connectionist networks, such as the elimination of superfluous weights, and the estimation of confidence intervals both for weights and network outputs. We review and develop exact and approximate algorithms for calculating second derivatives. For networks with |w| weights, simply writing the full matrix of second derivatives requires O(|w|(2)) operations. For networks of radial basis units or sigmoid units, exact calculation of the necessary intermediate terms requires of the order of 2h+2 backward/forward-propagation passes where h is the number of hidden units in the network. We also review and compare three approximations (ignoring some components of the second derivative, numerical differentiation, and scoring). The algorithms apply to arbitrary activation functions, networks, and error functions.
Publisher: IEEE
Date: 12-2010
DOI: 10.1109/ICDM.2010.51
Publisher: Springer Science and Business Media LLC
Date: 29-03-1970
Publisher: Springer Science and Business Media LLC
Date: 23-07-2010
Publisher: MDPI AG
Date: 22-11-2022
DOI: 10.3390/E24121703
Abstract: Hierarchical stochastic processes, such as the hierarchical Dirichlet process, hold an important position as a modelling tool in statistical machine learning, and are even used in deep neural networks. They allow, for instance, networks of probability vectors to be used in general statistical modelling, intrinsically supporting information sharing through the network. This paper presents a general theory of hierarchical stochastic processes and illustrates its use on the gamma process and the generalised gamma process. In general, most of the convenient properties of hierarchical Dirichlet processes extend to the broader family. The main construction for this corresponds to estimating the moments of an infinitely isible distribution based on its cumulants. Various equivalences and relationships can then be applied to networks of hierarchical processes. Ex les given demonstrate the duplication in non-parametric research, and presents plots of the Pitman–Yor distribution.
Publisher: IEEE
Date: 2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-1998
DOI: 10.1109/5254.671084
Publisher: ACM
Date: 28-07-2013
Publisher: SAE International
Date: 09-1993
DOI: 10.4271/932608
Publisher: Springer Science and Business Media LLC
Date: 12-11-2020
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 12-05-2019
Publisher: Springer Science and Business Media LLC
Date: 06-1992
DOI: 10.1007/BF01889584
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Science and Business Media LLC
Date: 06-05-2020
Publisher: Springer Science and Business Media LLC
Date: 30-03-2020
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: ACM
Date: 12-12-2006
Publisher: ACM
Date: 20-07-2008
Publisher: Association for Computing Machinery (ACM)
Date: 07-1994
Abstract: We are interested in the problem of solving a system 〈s l = t l : 1 ≤ i ≤ n, p j ≠ q j : 1 ≤ j ≤ m 〉 of equations and disequations, also known as disunification . Solutions to disunification problems are substitutions for the variables of the problem that make the two terms of each equation equal, but leave those of the disequations different. We investigate this in both algebraic and logical contexts where equality is defined by an equational theory and more generally by a definitive clause equality theory E. We show how E-disunification can be reduced to E-unification, that is, solving equations only, and give a disunification algorithm for theories given a unification algorithm. In fact, this result shows that for theories in which the solutions of all unification problems can also be represented finitely. We sketch how disunification can be applied to handle negation in logic programming with equality in a similar style to Colmerauer's logic programming with rational trees, and to represent many solutions to AC-unification problems by a few solutions to ACI-disunification problems.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2001
DOI: 10.1109/43.918210
Publisher: ACM
Date: 26-10-2008
Publisher: Association for Computing Machinery (ACM)
Date: 12-2013
Publisher: International Committee on Computational Linguistics
Date: 2020
Start Date: 07-2021
End Date: 07-2024
Amount: $450,605.00
Funder: Australian Research Council
View Funded ActivityStart Date: 04-2019
End Date: 06-2022
Amount: $315,000.00
Funder: Australian Research Council
View Funded Activity