ORCID Profile
0000-0002-6639-6824
Current Organisation
Deakin University
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Publisher: Elsevier BV
Date: 05-2023
Publisher: Elsevier BV
Date: 11-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: ACM
Date: 29-01-2018
Publisher: MDPI AG
Date: 25-02-2023
Abstract: DNA (Deoxyribonucleic Acid) N4-methylcytosine (4mC), a kind of epigenetic modification of DNA, is important for modifying gene functions, such as protein interactions, conformation, and stability in DNA, as well as for the control of gene expression throughout cell development and genomic imprinting. This simply plays a crucial role in the restriction–modification system. To further understand the function and regulation mechanism of 4mC, it is essential to precisely locate the 4mC site and detect its chromosomal distribution. This research aims to design an efficient and high-throughput discriminative intelligent computational system using the natural language processing method “word2vec” and a multi-configured 1D convolution neural network (1D CNN) to predict 4mC sites. In this article, we propose a grid search-based multi-layer dynamic ensemble system (GS-MLDS) that can enhance existing knowledge of each level. Each layer uses a grid search-based weight searching approach to find the optimal accuracy while minimizing computation time and additional layers. We have used eight publicly available benchmark datasets collected from different sources to test the proposed model’s efficiency. Accuracy results in test operations were obtained as follows: 0.978, 0.954, 0.944, 0.961, 0.950, 0.973, 0.948, 0.952, 0.961, and 0.980. The proposed model has also been compared to 16 distinct models, indicating that it can accurately predict 4mC.
Publisher: Wiley
Date: 09-03-2015
DOI: 10.1111/COIN.12063
Publisher: MDPI AG
Date: 14-09-2023
Publisher: MDPI AG
Date: 15-10-2023
DOI: 10.3390/S23208472
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Elsevier BV
Date: 11-2021
Publisher: Elsevier BV
Date: 10-2024
Publisher: IEEE
Date: 11-2018
Publisher: IGI Global
Date: 2014
DOI: 10.4018/978-1-4666-6248-3.CH010
Abstract: Information and Communication Technologies (ICTs) have been seen as pioneering tools for the promotion of the better delivery of government programmes and services, enabling the empowerment of citizens through greater access to information, delivery of more efficient government management processes, better transparency and accountability, and the mitigation of corruption risks. Based on a literature survey of previous research conducted on ICT systems implemented in various countries, this chapter discusses the potential of different ICT tools that have the capacity to help to promote public participation for the purpose of reducing corruption. The chapter specifically reviews the different ICT tools and platforms and their roles as potential weapons in fighting corruption. This chapter also evaluates different ICT tools, including e-government and public e-procurement. Finally, the authors develop a theoretical research model that depicts the anti-corruption capabilities of ICT tools, which in turn, has implications for academics, policy makers, and politicians.
Publisher: Informa UK Limited
Date: 03-11-2011
DOI: 10.1080/13803395.2010.509714
Abstract: In iduals with hemiplegia have difficulty planning movements, which may stem from deficits in motor imagery ability. We explored motor imagery ability in three groups of 21 children, aged 8-12 years: children with hemiplegia children with developmental coordination disorder (DCD) and a comparison group. They completed two tasks requiring laterality judgments of body parts--hand and whole-body rotation. Accuracy in both was reduced for the motor-impaired groups, and response time was atypical for the whole-body task. This suggests that motor imagery deficits exist in children with hemiplegia and DCD, supporting previous findings that planning deficits in hemiplegia may result from deficits in motor imagery.
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer International Publishing
Date: 2014
Publisher: Elsevier BV
Date: 10-2021
Publisher: MDPI AG
Date: 27-02-2022
DOI: 10.3390/LAND11030351
Abstract: Obtaining accurate, precise and timely spatial information on the distribution and dynamics of urban green space is crucial in understanding livability of the cities and urban dwellers. Inspired from the importance of spatial information in planning urban lives, and availability of state-of-the-art remote sensing data and technologies in open access forms, in this work, we develop a simple three-level hierarchical mapping of urban green space with multiple usability to various stakeholders. We utilize the established Normalized Difference Vegetation Index (NDVI) threshold on Sentinel-2A Earth Observation image data to classify the urban vegetation of each Victorian Local Government Area (LGA). Firstly, we categorize each LGA region into two broad classes as vegetation and non-vegetation secondly, we further categorize the vegetation regions of each LGA into two sub-classes as shrub (including grassland) and trees thirdly, for both shrub and trees classes, we further classify them as stressed and healthy. We not only map the urban vegetation in hierarchy but also develop Urban Green Space Index (UGSI) and Per Capita Green Space (PCGS) for the Victorian Local Government Areas (LGAs) to provide insights on the association of demography with urban green infrastructure using urban spatial analytics. To show the efficacy of the applied method, we evaluate our results using a Google Earth Engine (GEE) platform across different NDVI threshold ranges. The evaluation result shows that our method produces excellent performance metrics such as mean precision, recall, f-score and accuracy. In addition to this, we also prepare a recent Sentinel-2A dataset and derived products of urban green space coverage of the Victorian LGAs that are useful for multiple stakeholders ranging from bushfire modellers to bio ersity conservationists in contributing to sustainable and resilient urban lives.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-04-2023
DOI: 10.36227/TECHRXIV.20494851
Abstract: Omnidirectional Videos (or 360° videos) are widely used in Virtual Reality (VR) to facilitate immersive and interactive viewing experiences. However, the limited spatial resolution in 360° videos does not allow for each degree of view to be represented with adequate pixels, limiting the visual quality offered in the immersive experience. Deep learning Video Super-Resolution (VSR) techniques used for conventional videos could provide a promising software-based solution however, these techniques do not tackle the distortion present in equirectangular projections of 360° video signals. An additional obstacle is the limited 360° video datasets to study. To address these issues, this paper creates a novel 360° Video Dataset (360VDS) with a study of the extensibility of conventional VSR models to 360° videos. This paper further proposes a novel deep learning model for 360° Video Super-Resolution (360° VSR), called Spherical Signal Super-resolution with a Proportioned Optimisation (S3PO). S3PO adopts recurrent modelling with an attention mechanism, unbound from conventional VSR techniques like alignment. With a purpose-built feature extractor and a novel loss function addressing spherical distortion, S3PO outperforms most state-of-the-art conventional VSR models and 360° specific super-resolution models on 360° video datasets. A step-wise ablation study is presented to understand and demonstrate the impact of the chosen architectural subcomponents, targeted training and optimisation.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 29-08-2016
Publisher: IEEE
Date: 12-2014
DOI: 10.1109/ICDM.2014.33
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-04-2023
DOI: 10.36227/TECHRXIV.20494851.V2
Abstract: Omnidirectional Videos (or 360° videos) are widely used in Virtual Reality (VR) to facilitate immersive and interactive viewing experiences. However, the limited spatial resolution in 360° videos does not allow for each degree of view to be represented with adequate pixels, limiting the visual quality offered in the immersive experience. Deep learning Video Super-Resolution (VSR) techniques used for conventional videos could provide a promising software-based solution however, these techniques do not tackle the distortion present in equirectangular projections of 360° video signals. An additional obstacle is the limited 360° video datasets to study. To address these issues, this paper creates a novel 360° Video Dataset (360VDS) with a study of the extensibility of conventional VSR models to 360° videos. This paper further proposes a novel deep learning model for 360° Video Super-Resolution (360° VSR), called Spherical Signal Super-resolution with a Proportioned Optimisation (S3PO). S3PO adopts recurrent modelling with an attention mechanism, unbound from conventional VSR techniques like alignment. With a purpose-built feature extractor and a novel loss function addressing spherical distortion, S3PO outperforms most state-of-the-art conventional VSR models and 360° specific super-resolution models on 360° video datasets. A step-wise ablation study is presented to understand and demonstrate the impact of the chosen architectural subcomponents, targeted training and optimisation.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 14-08-2023
DOI: 10.36227/TECHRXIV.23896986.V1
Abstract: Video super-resolution (VSR) is a prominent research topic in low-level computer vision, where deep learning technologies have played a significant role. The rapid progress in deep learning and its applications in VSR has led to a proliferation of tools and techniques in the literature. However, the usage of these methods is often not adequately explained, and decisions are primarily driven by quantitative improvements. Given the significance of VSR's potential influence across multiple domains, it is imperative to conduct a comprehensive analysis of the elements and deep learning methodologies employed in VSR research. This methodical analysis will facilitate the informed development of models tailored to specific application needs. In this paper, we present a comprehensive overview of deep learning-based video super-resolution models, investigating each component and discussing its implications. Furthermore, we provide a synopsis of key components and technologies employed by state-of-the-art and earlier VSR models. By elucidating the underlying methodologies and categorising them systematically, we identified trends, requirements, and challenges in the domain. As a first-of-its-kind comprehensive overview of deep learning-based VSR models, this work also establishes a multi-level taxonomy to guide current and future VSR research, enhancing the maturation and interpretation of VSR practices for various practical applications.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-04-2023
DOI: 10.36227/TECHRXIV.22591426.V1
Abstract: This paper presents a novel approach to video super-resolution (VSR) by focusing on the selection of input frames, a process critical to VSR. VSR methods typically rely on deep learning techniques, those that are able to learn features from a large dataset of low-resolution (LR) and corresponding high-resolution (HR) videos and generate high-quality HR frames from any new LR input frames using the learned features. However, these methods often use as input the immediate neighbouring frames to a given target frame without considering the importance and dynamics of the frames across the temporal dimension of a video. This work aims to address the limitations of the conventional sliding-window mechanisms by developing input frame selection algorithms. By dynamically selecting the most representative neighbouring frames based on content-aware selection measures, our proposed algorithms enable VSR models to extract more informative and accurate features that are better aligned with the target frame, leading to improved performance and higher-quality HR frames. Through an empirical study, we demonstrate that the proposed dynamic content-aware selection mechanism improves super-resolution results without any additional architectural overhead, offering a counter-intuitive yet effective alternative to the long-established trend of increasing architectural complexity to improve VSR results.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 23-08-2022
DOI: 10.36227/TECHRXIV.20494851.V1
Abstract: Omnidirectional Videos (or 360° videos) are widely used in Virtual Reality (VR) to facilitate immersive and interactive viewing experiences. However, the limited spatial resolution in 360° videos does not allow for each degree of view to be represented with adequate pixels, limiting the visual quality offered in the immersive experience. Deep learning Video Super-Resolution (VSR) techniques used for conventional videos could provide a promising software-based solution however, these techniques do not tackle the distortion present in equirecentagular projections of 360° video signals. An additional obstacle is the limited 360° video datasets to study. To address these issues, this paper creates a novel 360° Video Dataset (360VDS) with a study of the extensibility of conventional VSR models to 360° videos. This paper further proposes a novel deep learning model for 360° Video Super-Resolution (360° VSR), called Spherical Signal Super-resolution with Proportioned Optimisation (S3PO). S3PO adopts recurrent modelling with attention mechanism, unbound from conventional VSR techniques like alignment. With a purpose built feature extractor and a novel loss function addressing spherical distortion, S3PO outperforms most state-of-the-art conventional VSR models and 360° specific super-resolution models on 360° video datasets. A step-wise ablation study is presented to understand and demonstrate the impact of the chosen architectural sub-components, targeted training and optimisation.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 14-08-2023
DOI: 10.36227/TECHRXIV.23896986
Abstract: Video super-resolution (VSR) is a prominent research topic in low-level computer vision, where deep learning technologies have played a significant role. The rapid progress in deep learning and its applications in VSR has led to a proliferation of tools and techniques in the literature. However, the usage of these methods is often not adequately explained, and decisions are primarily driven by quantitative improvements. Given the significance of VSR's potential influence across multiple domains, it is imperative to conduct a comprehensive analysis of the elements and deep learning methodologies employed in VSR research. This methodical analysis will facilitate the informed development of models tailored to specific application needs. In this paper, we present a comprehensive overview of deep learning-based video super-resolution models, investigating each component and discussing its implications. Furthermore, we provide a synopsis of key components and technologies employed by state-of-the-art and earlier VSR models. By elucidating the underlying methodologies and categorising them systematically, we identified trends, requirements, and challenges in the domain. As a first-of-its-kind comprehensive overview of deep learning-based VSR models, this work also establishes a multi-level taxonomy to guide current and future VSR research, enhancing the maturation and interpretation of VSR practices for various practical applications.
Publisher: IEEE
Date: 11-2018
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer International Publishing
Date: 2018
Publisher: MDPI AG
Date: 26-06-2023
DOI: 10.20944/PREPRINTS202306.1738.V1
Abstract: The use of visual signals in horticulture has attracted significant attention and encompassed a wide range of data types such as 2D images, videos, hyperspectral images, and 3D point clouds. These visual signals have proven to be valuable in developing cutting-edge computer vision systems for various applications in horticulture, enabling plant growth monitoring, pest and disease detection, quality and yield estimation, and automated harvesting. However, unlike other sectors, developing deep learning computer vision systems for horticulture encounters unique challenges due to the limited availability of high-quality training and evaluation datasets necessary for deep learning models. This paper investigates the current status of vision systems and available data in order to identify the high-quality data requirements specific to horticultural applications. We analyse the impact of the quality of visual signals on the information content and features that can be extracted from these signals. To address the identified data quality requirements, we explore the usage of a deep learning-based super-resolution model for generative quality enhancement of visual signals. Furthermore, we discuss how these can be applied to meet the growing requirements around data quality for learning-based vision systems. We also present a detailed analysis of the competitive quality generated by the proposed solution compared to cost-intensive hardware-based alternatives. This work aims to guide the development of efficient computer vision models in horticulture by overcoming existing data challenges and paving a pathway forward for contemporary data acquisition.
Publisher: American Chemical Society (ACS)
Date: 14-11-2017
DOI: 10.1021/ACS.JPROTEOME.7B00212
Abstract: The release of damage-associated molecular patterns (DAMPs) by airway epithelial cells is believed to play a crucial role in the initiation and development of chronic airway conditions such as asthma and chronic obstructive pulmonary disease (COPD). Intriguingly, the classic DAMP high-mobility group box-1 (HMGB1) is detected in the culture supernatant of airway epithelial cells under basal conditions, indicating a role for HMGB1 in the regulation of epithelial cellular and immune homeostasis. To gain contextual insight into the potential role of HMGB1 in airway epithelial cell homeostasis, we used the orthogonal and complementary methods of high-resolution clear native electrophoresis, immunoprecipitation, and pull-downs coupled to liquid chromatography-tandem mass spectrometry (LC-MS/MS) to profile HMGB1 and its binding partners in the culture supernatant of unstimulated airway epithelial cells. We found that HMGB1 presents exclusively as a protein complex under basal conditions. Moreover, protein network analysis performed on 185 binding proteins revealed 14 that directly associate with HMGB1: amyloid precursor protein, F-actin-capping protein subunit alpha-1 (CAPZA1), glyceraldehyde-3 phosphate dehydrogenase (GAPDH), ubiquitin, several members of the heat shock protein family (HSPA8, HSP90B1, HSP90AA1), XRCC5 and XRCC6, high mobility group A1 (HMGA1), histone 3 (H3F3B), the FACT (facilitates chromatin transcription) complex constituents SUPT1H and SSRP1, and heterogeneous ribonucleoprotein K (HNRNPK). These studies provide a new understanding of the extracellular functions of HMGB1 in cellular and immune homeostasis at the airway mucosal surface and could have implications for therapeutic targeting.
Publisher: IGI Global
Date: 2017
DOI: 10.4018/978-1-5225-2203-4.CH003
Abstract: The use of Information and Communication Technologies (ICTs) plays a significant role in the economic, technological and social progression of a country. Corruption in government agencies and institutions is a serious problem in many countries in the world, especially in under-developed and developing countries. The use of ICT tools such as e-governance can help to reduce corruption. In this chapter, the authors discussed the application of e-government principles to mitigate corruption. Based on the available literature, this study identified some potential elements of e-government, which are currently practised around the world and how they are interrelated to fight against corruption. Finally, the authors present an evidence-based e-government anti-corruption framework.
Publisher: IEEE
Date: 11-2018
Publisher: Springer Science and Business Media LLC
Date: 30-10-2020
Publisher: Springer Science and Business Media LLC
Date: 09-02-2013
Publisher: IGI Global
Date: 2015
DOI: 10.4018/978-1-4666-8195-8.CH018
Abstract: Information and Communication Technologies (ICTs) have been seen as pioneering tools for the promotion of the better delivery of government programmes and services, enabling the empowerment of citizens through greater access to information, delivery of more efficient government management processes, better transparency and accountability, and the mitigation of corruption risks. Based on a literature survey of previous research conducted on ICT systems implemented in various countries, this chapter discusses the potential of different ICT tools that have the capacity to help to promote public participation for the purpose of reducing corruption. The chapter specifically reviews the different ICT tools and platforms and their roles as potential weapons in fighting corruption. This chapter also evaluates different ICT tools, including e-government and public e-procurement. Finally, the authors develop a theoretical research model that depicts the anti-corruption capabilities of ICT tools, which in turn, has implications for academics, policy makers, and politicians.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IGI Global
Date: 2015
DOI: 10.4018/978-1-4666-8358-7.CH119
Abstract: Information and Communication Technologies (ICTs) have been seen as pioneering tools for the promotion of the better delivery of government programmes and services, enabling the empowerment of citizens through greater access to information, delivery of more efficient government management processes, better transparency and accountability, and the mitigation of corruption risks. Based on a literature survey of previous research conducted on ICT systems implemented in various countries, this chapter discusses the potential of different ICT tools that have the capacity to help to promote public participation for the purpose of reducing corruption. The chapter specifically reviews the different ICT tools and platforms and their roles as potential weapons in fighting corruption. This chapter also evaluates different ICT tools, including e-government and public e-procurement. Finally, the authors develop a theoretical research model that depicts the anti-corruption capabilities of ICT tools, which in turn, has implications for academics, policy makers, and politicians.
Publisher: Elsevier BV
Date: 09-2022
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 04-04-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 17-05-2023
DOI: 10.36227/TECHRXIV.21500235
Abstract: Recurrent Neural Networks (RNN) are widespread for Video Super-Resolution (VSR) because of their proven ability to learn spatiotemporal inter-dependencies across the temporal dimension. Despite RNN’s ability to propagate memory across longer sequences of frames, vanishing gradient and error accumulation remain major obstacles to unidirectional RNNs in VSR. Several bi-directional recurrent models are suggested in the literature to alleviate this issue however, these models are only applicable to offline use cases due to heavy demands for computational resources and the number of frames required per input. This paper proposes a novel unidirectional recurrent model for VSR, namely “Replenished Recurrency with Dual-Duct” (R2D2), that can be used in an online application setting. R2D2 incorporates a recurrent architecture with a sliding-window-based local alignment resulting in a recurrent hybrid architecture. It also uses a dual-duct residual network for concurrent and mutual refinement of local features along with global memory for full utilisation of the information available at each timest . With novel modelling and sophisticated optimisation, R2D2 demonstrates competitive performance and efficiency despite the lack of information available at each time-st compared to its offline (bi-directional) counterparts. Ablation analysis confirms the additive benefits of the proposed sub-components of R2D2 over baseline RNN models.The PyTorch-based code for the R2D2 model will be released at R2D2 GitRepo.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-04-2023
DOI: 10.36227/TECHRXIV.22591426
Abstract: This paper presents a novel approach to video super-resolution (VSR) by focusing on the selection of input frames, a process critical to VSR. VSR methods typically rely on deep learning techniques, those that are able to learn features from a large dataset of low-resolution (LR) and corresponding high-resolution (HR) videos and generate high-quality HR frames from any new LR input frames using the learned features. However, these methods often use as input the immediate neighbouring frames to a given target frame without considering the importance and dynamics of the frames across the temporal dimension of a video. This work aims to address the limitations of the conventional sliding-window mechanisms by developing input frame selection algorithms. By dynamically selecting the most representative neighbouring frames based on content-aware selection measures, our proposed algorithms enable VSR models to extract more informative and accurate features that are better aligned with the target frame, leading to improved performance and higher-quality HR frames. Through an empirical study, we demonstrate that the proposed dynamic content-aware selection mechanism improves super-resolution results without any additional architectural overhead, offering a counter-intuitive yet effective alternative to the long-established trend of increasing architectural complexity to improve VSR results.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-11-2022
DOI: 10.36227/TECHRXIV.21500235.V1
Abstract: This is an original research article entitled “Online Video Super-Resolution using Unidirectional Recurrent Model”. Considering the critical constraints around video frames and resource availability in an online setting, this paper presents a new unidirectional video super-resolution (VSR) model with a recurrent architecture specifically designed for online applications. Many recent works in the video super-resolution domain focus on improving the super-resolution quality at the cost of computationally intense and input-heavy bidirectional modelling. To alleviate these drawbacks, we propose the Replenished Recurrency with Dual-Duct (R2D2) model which adopts unidirectional architecture to fully utilise local features and global memory available at each timest . The two variants – R2D2 and R2D2-lite presented in the paper generate state-of-the-art super-resolution quality at significantly optimised efficiency. This is believed an important step forward in real-world applications-inspired research in the video super-resolution domain.
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 17-05-2023
DOI: 10.36227/TECHRXIV.21500235.V2
Abstract: Recurrent Neural Networks (RNN) are widespread for Video Super-Resolution (VSR) because of their proven ability to learn spatiotemporal inter-dependencies across the temporal dimension. Despite RNN’s ability to propagate memory across longer sequences of frames, vanishing gradient and error accumulation remain major obstacles to unidirectional RNNs in VSR. Several bi-directional recurrent models are suggested in the literature to alleviate this issue however, these models are only applicable to offline use cases due to heavy demands for computational resources and the number of frames required per input. This paper proposes a novel unidirectional recurrent model for VSR, namely “Replenished Recurrency with Dual-Duct” (R2D2), that can be used in an online application setting. R2D2 incorporates a recurrent architecture with a sliding-window-based local alignment resulting in a recurrent hybrid architecture. It also uses a dual-duct residual network for concurrent and mutual refinement of local features along with global memory for full utilisation of the information available at each timest . With novel modelling and sophisticated optimisation, R2D2 demonstrates competitive performance and efficiency despite the lack of information available at each time-st compared to its offline (bi-directional) counterparts. Ablation analysis confirms the additive benefits of the proposed sub-components of R2D2 over baseline RNN models.The PyTorch-based code for the R2D2 model will be released at R2D2 GitRepo.
Publisher: Springer International Publishing
Date: 2021
No related grants have been discovered for Sunil Aryal.