ORCID Profile
0000-0001-8719-284X
Current Organisation
University of Tasmania
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Publisher: IEEE
Date: 05-2013
Publisher: Association for Computing Machinery (ACM)
Date: 18-07-2021
DOI: 10.1145/3460287
Abstract: Unprecedented attention towards blockchain technology is serving as a game-changer in fostering the development of blockchain-enabled distinctive frameworks. However, fragmentation unleashed by its underlying concepts hinders different stakeholders from effectively utilizing blockchain-supported services, resulting in the obstruction of its wide-scale adoption. To explore synergies among the isolated frameworks requires comprehensively studying inter-blockchain communication approaches. These approaches broadly come under the umbrella of Blockchain Interoperability (BI) notion, as it can facilitate a novel paradigm of an integrated blockchain ecosystem that connects state-of-the-art disparate blockchains. Currently, there is a lack of studies that comprehensively review BI, which works as a stumbling block in its development. Therefore, this article aims to articulate potential of BI by reviewing it from erse perspectives. Beginning with a glance of blockchain architecture fundamentals, this article discusses its associated platforms, taxonomy, and consensus mechanisms. Subsequently, it argues about BI’s requirement by exemplifying its potential opportunities and application areas. Concerning BI, an architecture seems to be a missing link. Hence, this article introduces a layered architecture for the effective development of protocols and methods for interoperable blockchains. Furthermore, this article proposes an in-depth BI research taxonomy and provides an insight into the state-of-the-art projects. Finally, it determines possible open challenges and future research in the domain.
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Elsevier BV
Date: 05-2021
Publisher: Informa UK Limited
Date: 12-2011
Publisher: MDPI AG
Date: 18-10-2019
DOI: 10.3390/F10100917
Abstract: In general, humans and animals often interact within the same environment at the same time. Human activities may disturb or affect some bird activities. Therefore, it is important to monitor and study the relationships between human and animal activities. This paper proposed a system able not only to automatically classify human and bird activities using bioacoustic data, but also to automatically summarize patterns of events over time. To perform automatic summarization of acoustic events, a frequency–duration graph (FDG) framework was proposed to summarize the patterns of human and bird activities. This system first performs data pre-processing work on raw bioacoustic data and then applies a support vector machine (SVM) model and a multi-layer perceptron (MLP) model to classify human and bird chirping activities before using the FDG framework to summarize results. The SVM model achieved 98% accuracy on average and the MLP model achieved 98% accuracy on average across several day-long recordings. Three case studies with real data show that the FDG framework correctly determined the patterns of human and bird activities over time and provided both statistical and graphical insight into the relationships between these two events.
Publisher: IEEE
Date: 12-2011
DOI: 10.1109/UCC.2011.24
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Publisher: Elsevier BV
Date: 06-2022
Publisher: IEEE
Date: 07-2018
Publisher: Elsevier BV
Date: 03-2022
Publisher: IEEE
Date: 05-2011
Publisher: Elsevier BV
Date: 07-2022
Publisher: Wiley
Date: 02-05-2021
DOI: 10.1002/CPE.5323
Publisher: IEEE
Date: 06-2015
Publisher: Springer Science and Business Media LLC
Date: 14-09-2008
Publisher: Wiley
Date: 15-09-2015
DOI: 10.1002/SPE.2288
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: MDPI AG
Date: 06-08-2018
Abstract: Online social network users share their information in different social sites to establish connections with in iduals with whom they want to be a friend. While users share all their information to connect to other in iduals, they need to hide the information that can bring about privacy risks for them. As user participation in social networking sites rises, the possibility of sharing information with unknown users increases, and the probability of privacy breaches for the user mounts. This work addresses the challenges of sharing information in a safe manner with unknown in iduals. Currently, there are a number of available methods for preserving privacy in order to friending (the act of adding someone as a friend), but they only consider a single source of data and are more focused on users’ security rather than privacy. Consequently, a privacy-preserving friending mechanism should be considered for information shared in multiple online social network sites. In this paper, we propose a new privacy-preserving friending method that helps users decide what to share with other in iduals with the reduced risk of being exploited or re-identified. In this regard, the first step is to calculate the sensitivity score for in iduals using Bernstein’s polynomial theorem to understand what sort of information can influence a user’s privacy. Next, a new model is applied to anonymise the data of users who participate in multiple social networks. Anonymisation helps to understand to what extent a piece of information can be shared, which allows information sharing with reduced risks in privacy. Evaluation indicates that measuring the sensitivity of information besides anonymisation provides a more accurate outcome for the purpose of friending, in a computationally efficient manner.
Publisher: IEEE
Date: 12-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: IEEE
Date: 05-2014
Publisher: Elsevier BV
Date: 05-2020
Publisher: MDPI AG
Date: 07-01-2018
DOI: 10.3390/RS10010074
Publisher: Elsevier BV
Date: 09-2021
Publisher: Springer Science and Business Media LLC
Date: 12-07-2021
Publisher: Association for Computing Machinery (ACM)
Date: 13-09-2019
DOI: 10.1145/3332301
Abstract: Interest in processing big data has increased rapidly to gain insights that can transform businesses, government policies, and research outcomes. This has led to advancement in communication, programming, and processing technologies, including cloud computing services and technologies such as Hadoop, Spark, and Storm. This trend also affects the needs of analytical applications, which are no longer monolithic but composed of several in idual analytical steps running in the form of a workflow. These big data workflows are vastly different in nature from traditional workflows. Researchers are currently facing the challenge of how to orchestrate and manage the execution of such workflows. In this article, we discuss in detail orchestration requirements of these workflows as well as the challenges in achieving these requirements. We also survey current trends and research that supports orchestration of big data workflows and identify open research challenges to guide future developments in this area.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2019
Publisher: Wiley
Date: 28-09-2013
DOI: 10.1002/SPE.2156
Publisher: Public Library of Science (PLoS)
Date: 03-08-2018
Publisher: Elsevier BV
Date: 10-2019
Publisher: Elsevier BV
Date: 06-2013
Publisher: MDPI AG
Date: 04-07-2019
DOI: 10.3390/S19132954
Abstract: Fog computing aims to support applications requiring low latency and high scalability by using resources at the edge level. In general, fog computing comprises several autonomous mobile or static devices that share their idle resources to run different services. The providers of these devices also need to be compensated based on their device usage. In any fog-based resource-allocation problem, both cost and performance need to be considered for generating an efficient resource-allocation plan. Estimating the cost of using fog devices prior to the resource allocation helps to minimize the cost and maximize the performance of the system. In the fog computing domain, recent research works have proposed various resource-allocation algorithms without considering the compensation to resource providers and the cost estimation of the fog resources. Moreover, the existing cost models in similar paradigms such as in the cloud are not suitable for fog environments as the scaling of different autonomous resources with heterogeneity and variety of offerings is much more complicated. To fill this gap, this study first proposes a micro-level compensation cost model and then proposes a new resource-allocation method based on the cost model, which benefits both providers and users. Experimental results show that the proposed algorithm ensures better resource-allocation performance and lowers application processing costs when compared to the existing best-fit algorithm.
Publisher: Springer International Publishing
Date: 2016
Publisher: Elsevier BV
Date: 2022
Publisher: MDPI AG
Date: 02-10-2021
Abstract: This study aimed to identify factors influencing student engagement in online and blended courses at one Australian regional university. It applied a data science approach to learning and teaching data gathered from the learning management system used at this university. Data were collected and analysed from 23 subjects, spanning over 5500 student enrolments and 406 lecturer and tutor roles, over a five-year period. Based on a theoretical framework adapted from Community of Inquiry (CoI) framework by Garrison et al. (2000), the data were segregated into three groups for analysis: Student Engagement, Course Content and Teacher Input. The data analysis revealed a positive correlation between Student Engagement and Teacher Input, and interestingly, a negative correlation between Student Engagement and Course Content when a certain threshold was exceeded. The findings of the study offer useful suggestions for future course design, and pedagogical approaches teachers can adopt to foster student engagement.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Elsevier BV
Date: 05-2017
Publisher: Wiley
Date: 07-01-2020
DOI: 10.1002/SPE.2787
Publisher: IEEE
Date: 12-2017
Publisher: Elsevier BV
Date: 03-2022
DOI: 10.1016/J.JBI.2022.104030
Abstract: With populations aging, the number of people with dementia worldwide is expected to triple to 152 million by 2050. Seventy percent of cases are due to Alzheimer's disease (AD) pathology and there is a 10-20 year 'pre-clinical' period before significant cognitive decline occurs. We urgently need, cost effective, objective biomarkers to detect AD, and other dementias, at an early stage. Risk factor modification could prevent 40% of cases and drug trials would have greater chances of success if participants are recruited at an earlier stage. Currently, detection of dementia is largely by pen and paper cognitive tests but these are time consuming and insensitive to the pre-clinical phase. Specialist brain scans and body fluid biomarkers can detect the earliest stages of dementia but are too invasive or expensive for widespread use. With the advancement of technology, Artificial Intelligence (AI) shows promising results in assisting with detection of early-stage dementia. This scoping review aims to summarise the current capabilities of AI-aided digital biomarkers to aid in early detection of dementia, and also discusses potential future research directions. In this scoping review, we used PubMed and IEEE Xplore to identify relevant papers. The resulting records were further filtered to retrieve articles published within five years and written in English. Duplicates were removed, titles and abstracts were screened and full texts were reviewed. After an initial yield of 1,463 records, 1,444 records were screened after removal of duplication. A further 771 records were excluded after screening titles and abstracts, and 496 were excluded after full text review. The final yield was 177 studies. Records were grouped into different artificial intelligence based tests: (a) computerized cognitive tests (b) movement tests (c) speech, conversion, and language tests and (d) computer-assisted interpretation of brain scans. In general, AI techniques enhance the performance of dementia screening tests because more features can be retrieved from a single test, there are less errors due to subjective judgements and AI shifts the automation of dementia screening to a higher level. Compared with traditional cognitive tests, AI-based computerized cognitive tests improve the discrimination sensitivity by around 4% and specificity by around 3%. In terms of speech, conversation and language tests, combining both acoustic features and linguistic features achieve the best result with accuracy around 94%. Deep learning techniques applied in brain scan analysis achieves around 92% accuracy. Movement tests and setting smart environments to capture daily life behaviours are two potential future directions that may help discriminate dementia from normal aging. AI-based smart environments and multi-modal tests are promising future directions to improve detection of dementia in the earliest stages.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: MDPI AG
Date: 14-05-2021
DOI: 10.3390/ELECTRONICS10101171
Abstract: Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 12-2016
Publisher: Elsevier BV
Date: 03-2020
Publisher: IEEE
Date: 05-2015
Publisher: Elsevier BV
Date: 05-2018
Publisher: Elsevier BV
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 10-2014
Publisher: Wiley
Date: 24-06-2011
Publisher: IEEE
Date: 11-2011
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: Elsevier BV
Date: 12-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: Elsevier BV
Date: 05-2019
Publisher: Elsevier BV
Date: 08-2019
Publisher: ACM
Date: 29-01-2019
Publisher: MDPI AG
Date: 11-06-2014
Publisher: Springer Science and Business Media LLC
Date: 18-07-2022
DOI: 10.1186/S12883-022-02772-5
Abstract: The worldwide prevalence of dementia is rapidly rising. Alzheimer’s disease (AD), accounts for 70% of cases and has a 10–20-year preclinical period, when brain pathology covertly progresses before cognitive symptoms appear. The 2020 Lancet Commission estimates that 40% of dementia cases could be prevented by modifying lifestyle/medical risk factors. To optimise dementia prevention effectiveness, there is urgent need to identify in iduals with preclinical AD for targeted risk reduction. Current preclinical AD tests are too invasive, specialist or costly for population-level assessments. We have developed a new online test, TAS Test, that assesses a range of motor-cognitive functions and has capacity to be delivered at significant scale. TAS Test combines two innovations: using hand movement analysis to detect preclinical AD, and computer-human interface technologies to enable robust ‘self-testing’ data collection. The aims are to validate TAS Test to [1] identify preclinical AD, and [2] predict risk of cognitive decline and AD dementia. Aim 1 will be addressed through a cross-sectional study of 500 cognitively healthy older adults, who will complete TAS Test items comprising measures of motor control, processing speed, attention, visuospatial ability, memory and language. TAS Test measures will be compared to a blood-based AD biomarker, phosphorylated tau 181 (p-tau181). Aim 2 will be addressed through a 5-year prospective cohort study of 10,000 older adults. Participants will complete TAS Test annually and subtests of the Cambridge Neuropsychological Test Battery (CANTAB) biennially. 300 participants will undergo in-person clinical assessments. We will use machine learning of motor-cognitive performance on TAS Test to develop an algorithm that classifies preclinical AD risk (p-tau181-defined) and determine the precision to prospectively estimate 5-year risks of cognitive decline and AD. This study will establish the precision of TAS Test to identify preclinical AD and estimate risk of cognitive decline and AD. If accurate, TAS Test will provide a low-cost, accessible enrichment strategy to pre-screen in iduals for their likelihood of AD pathology prior to more expensive tests such as blood or imaging biomarkers. This would have wide applications in public health initiatives and clinical trials. ClinicalTrials.gov Identifier: NCT05194787 , 18 January 2022. Retrospectively registered.
Publisher: IEEE
Date: 05-2012
Publisher: Elsevier BV
Date: 02-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: Elsevier BV
Date: 02-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
DOI: 10.1109/MCC.2017.55
Publisher: Springer Science and Business Media LLC
Date: 18-08-2020
Publisher: Elsevier BV
Date: 09-2020
Publisher: Elsevier BV
Date: 04-2013
Publisher: Elsevier BV
Date: 10-2022
Publisher: Elsevier BV
Date: 12-2021
Publisher: Springer International Publishing
Date: 2018
Publisher: MDPI AG
Date: 23-04-2021
DOI: 10.3390/FIRE4020020
Abstract: Rapid estimates of the risk from potential wildfires are necessary for operational management and mitigation efforts. Computational models can provide risk metrics, but are typically deterministic and may neglect uncertainties inherent in factors driving the fire. Modeling these uncertainties can more accurately predict risks associated with a particular wildfire, but requires a large number of simulations with a corresponding increase in required computational time. Surrogate models provide a means to rapidly estimate the outcome of a particular model based on implicit uncertainties within the model and are very computationally efficient. In this paper, we detail the development of a surrogate model for the growth of a wildfire based on initial meteorological conditions: temperature, relative humidity, and wind speed. Multiple simulated fires under different conditions are used to develop the surrogate model based on the relationship between the area burnt by the fire and each meteorological variable. The results from nine bio-regions in Tasmania show that the surrogate model can closely represent the change in the size of a wildfire over time. The model could be used for a rapid initial estimate of likely fire risk for operational wildfire management.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
DOI: 10.1109/TSC.2013.49
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: Elsevier BV
Date: 08-2019
Publisher: Elsevier BV
Date: 05-2021
Publisher: IEEE
Date: 12-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: IBM
Date: 09-2013
Publisher: Elsevier BV
Date: 09-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: Springer Science and Business Media LLC
Date: 11-03-2013
Publisher: IEEE
Date: 12-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2018
Publisher: IEEE
Date: 12-2008
Publisher: Informa UK Limited
Date: 28-11-2019
Publisher: Springer Science and Business Media LLC
Date: 18-05-2014
Publisher: Wiley
Date: 27-10-2020
DOI: 10.1002/SPE.2917
Publisher: Association for Computing Machinery (ACM)
Date: 12-06-2020
DOI: 10.1145/3383464
Abstract: Recent years have witnessed the booming of big data analytical applications (BDAAs). This trend provides unrivaled opportunities to reveal the latent patterns and correlations embedded in the data, and thus productive decisions may be made. This was previously a grand challenge due to the notoriously high dimensionality and scale of big data, whereas the quality of service offered by providers is the first priority. As BDAAs are routinely deployed on Clouds with great complexities and uncertainties, it is a critical task to manage the service level agreements (SLAs) so that a high quality of service can then be guaranteed. This study performs a systematic literature review of the state of the art of SLA-specific management for Cloud-hosted BDAAs. The review surveys the challenges and contemporary approaches along this direction centering on SLA. A research taxonomy is proposed to formulate the results of the systematic literature review. A new conceptual SLA model is defined and a multi-dimensional categorization scheme is proposed on its basis to apply the SLA metrics for an in-depth understanding of managing SLAs and the motivation of trends for future research.
Publisher: Elsevier BV
Date: 11-2020
Publisher: Springer Science and Business Media LLC
Date: 15-05-2021
Publisher: Elsevier BV
Date: 10-2010
Publisher: Elsevier BV
Date: 02-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: IEEE
Date: 09-2012
DOI: 10.1109/GRID.2012.16
Publisher: Elsevier BV
Date: 09-2020
Publisher: IEEE
Date: 11-2015
Publisher: Springer International Publishing
Date: 2016
Publisher: Elsevier BV
Date: 10-2020
Publisher: Association for Computing Machinery (ACM)
Date: 02-01-2021
DOI: 10.1145/3423332
Abstract: Fog computing is a promising computing paradigm in which IoT data can be processed near the edge to support time-sensitive applications. However, the availability of resources in computation devices is not stable, since they may not be exclusively dedicated to the Fog application processing in the Fog environment. This, combined with dynamic user behaviour, can affect the execution of applications. To address dynamic changes in user behaviour in resource-limited Fog devices, this article proposes a multi-criteria–based resource allocation policy with resource reservation to minimise overall delay, processing time, and SLA violations. This process considers Fog computing–related characteristics, such as device heterogeneity, resource constraints, and mobility, as well as dynamic changes in user requirements. We employ multiple objective functions to find appropriate resources for executing time-sensitive tasks in the Fog environment. Experimental results show that our proposed policy performs better than the existing one, reducing the total delay by 51%. The proposed algorithm also reduces processing time and SLA violations, which is beneficial for running time-sensitive applications in the Fog environment.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: IEEE
Date: 12-2011
DOI: 10.1109/UCC.2011.36
Publisher: Elsevier BV
Date: 09-2012
No related grants have been discovered for Saurabh Kumar Garg.