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Articles 1 - 16 of 16
Full-Text Articles in Social and Behavioral Sciences
Apex2s: A Two-Layer Machine Learning Model For Discovery Of Host-Pathogen Protein-Protein Interactions On Cloud-Based Multiomics Data, Huaming Chen, Jun Shen, Lei Wang, Chi-Hung Chi
Apex2s: A Two-Layer Machine Learning Model For Discovery Of Host-Pathogen Protein-Protein Interactions On Cloud-Based Multiomics Data, Huaming Chen, Jun Shen, Lei Wang, Chi-Hung Chi
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Refinement And Augmentation For Data In Micro Learning Activity With An Evolutionary Rule Generators, Geng Sun, Jiayin Lin, Tingru Cui, Jun Shen, Dongming Xu, Mahesh Kayastha
Refinement And Augmentation For Data In Micro Learning Activity With An Evolutionary Rule Generators, Geng Sun, Jiayin Lin, Tingru Cui, Jun Shen, Dongming Xu, Mahesh Kayastha
Faculty of Engineering and Information Sciences - Papers: Part B
Improving both the quantity and quality of existing data are placed at the center of research for adaptive micro open learning. To cover this research gap, our work targets on the current scarcity of both data and rules that represent open learning activities. An evolutionary rule generator is constructed, which consists of an outer loop and an inner loop. The outer loop runs a genetic algorithm (GA) to produce association rules that can be effective in the micro open learning scenario from a small amount of available data sources; while the inner loop optimizes generated candidates by taking into account …
A New Data Driven Long-Term Solar Yield Analysis Model Of Photovoltaic Power Plants, Biplob Ray, Rakibuzzaman Shah, Md Rabiul Islam, Syed Islam
A New Data Driven Long-Term Solar Yield Analysis Model Of Photovoltaic Power Plants, Biplob Ray, Rakibuzzaman Shah, Md Rabiul Islam, Syed Islam
Faculty of Engineering and Information Sciences - Papers: Part B
Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV …
On Masking And Releasing Smart Meter Data At Micro-Level: The Multiplicative Noise Approach, John Brackenbury, P. Y. O'Shaughnessy, Yan-Xia Lin
On Masking And Releasing Smart Meter Data At Micro-Level: The Multiplicative Noise Approach, John Brackenbury, P. Y. O'Shaughnessy, Yan-Xia Lin
Faculty of Engineering and Information Sciences - Papers: Part B
Smart meter electricity data presents privacy risks when malicious agents gain insights of private information, including residents’ lifestyle and daily habits. When allowing access to record-level data, we apply the multiplicative noise method to mask individual smart meter data, which simultaneously aims to minimise disclosure of a dwelling’s consumption signal to any third party and to enable accurate estimation of the sum of a cluster of households. Three testing criteria are introduced to measure the performance of multiplicative noise masking approach relevant to the smart meter data. We propose a novel ‘Twin Uniform’ noise distribution and derive relevant theoretical results. …
A Framework Towards Data Analysis On Host-Pathogen Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song
A Framework Towards Data Analysis On Host-Pathogen Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song
Faculty of Engineering and Information Sciences - Papers: Part B
With the rapid development of high-throughput technologies, systems biology is now embracing a great opportunity made possible by the increased accumulation of data available online. Biological data analytics is considered as a critical means to contribute to a better understanding on such data through extraction of the latent features, relationships and the associated mechanisms. Therefore, it is important to evaluate how to involve data analytics from both computational and biological perspectives in practice. This paper has investigated interaction relationships in the proteomics area, which provide insights of the critical molecular processes within infection mechanisms. Specifically, we focused on host–pathogen protein–protein …
Data Privacy And System Security For Banking And Financial Services Industry Based On Cloud Computing Infrastructure, Abhishek Mahalle, Jianming Yong, Xiaohui Tao, Jun Shen
Data Privacy And System Security For Banking And Financial Services Industry Based On Cloud Computing Infrastructure, Abhishek Mahalle, Jianming Yong, Xiaohui Tao, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Fast Multi-Resource Allocation With Patterns In Large Scale Cloud Data Center, Jiyuan Shi, Junzhou Luo, Fang Dong, Jiahui Jin, Jun Shen
Fast Multi-Resource Allocation With Patterns In Large Scale Cloud Data Center, Jiyuan Shi, Junzhou Luo, Fang Dong, Jiahui Jin, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
How to achieve fast and efficient resource allocation is an important optimization problem of resource management in cloud data center. On one hand, in order to ensure the user experience of resource requesting, the system has to achieve fast resource allocation to timely process resource requests; on the other hand, in order to ensure the efficiency of resource allocation, how to allocate multi-dimensional resource requests to servers needs to be optimized, such that server's resource utilization can be improved. However, most of existing approaches focus on finding out the mapping of each specific resource request to each specific server. This …
Exploring The Potential Of Big Data On The Health Care Delivery Value Chain (Cdvc): A Preliminary Literature And Research Agenda, William J. Tibben, Samuel Fosso Wamba
Exploring The Potential Of Big Data On The Health Care Delivery Value Chain (Cdvc): A Preliminary Literature And Research Agenda, William J. Tibben, Samuel Fosso Wamba
Faculty of Engineering and Information Sciences - Papers: Part B
Big data analytics (BDA) is emerging as a game changer in healthcare. While the practitioner literature has been speculating on the high potential of BDA in transforming the healthcare sector, few rigorous empirical studies have been conducted by scholars to assess the real potential of BDA. Drawing on the health care delivery value chain (CDVC) and an extensive literature review, this exploratory study aims to discuss current peer-reviewed articles dealing with BDA across the CDVC and discuss future research directions.
Data Fusion For Maas: Opportunities And Challenges, Jianqing Wu, Luping Zhou, Chen Cai, Jun Shen, S K. Lau, Jianming Yong
Data Fusion For Maas: Opportunities And Challenges, Jianqing Wu, Luping Zhou, Chen Cai, Jun Shen, S K. Lau, Jianming Yong
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Collaborative Data Analytics Towards Prediction On Pathogen-Host Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song
Collaborative Data Analytics Towards Prediction On Pathogen-Host Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song
Faculty of Engineering and Information Sciences - Papers: Part B
Nowadays more and more data are being sequenced and accumulated in system biology, which bring the data analytics researchers to a brand new era, namely 'big data', to extract the inner relationship and knowledge from the huge amount of data.
Towards Massive Data And Sparse Data In Adaptive Micro Open Educational Resource Recommendation: A Study On Semantic Knowledge Base Construction And Cold Start Problem, Geng Sun, Tingru Cui, Ghassan Beydoun, Shiping Chen, Fang Dong, Dongming Xu, Jun Shen
Towards Massive Data And Sparse Data In Adaptive Micro Open Educational Resource Recommendation: A Study On Semantic Knowledge Base Construction And Cold Start Problem, Geng Sun, Tingru Cui, Ghassan Beydoun, Shiping Chen, Fang Dong, Dongming Xu, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
Micro Learning through open educational resources (OERs) is becoming increasingly popular. However, adaptive micro learning support remains inadequate by current OER platforms. To address this, our smart system, Micro Learning as a Service (MLaaS), aims to deliver personalized OER with micro learning to satisfy their real-time needs.
Cost-Effective Big Data Mining In The Cloud: A Case Study With K-Means, Qiang He, Xiaodong Zhu, Dongwei Li, Shuliang Wang, Jun Shen, Yun Yang
Cost-Effective Big Data Mining In The Cloud: A Case Study With K-Means, Qiang He, Xiaodong Zhu, Dongwei Li, Shuliang Wang, Jun Shen, Yun Yang
Faculty of Engineering and Information Sciences - Papers: Part B
Mining big data often requires tremendous computationalresources. This has become a major obstacle to broad applicationsof big data analytics. Cloud computing allows data scientists to access computationalresources on-demand for building their big data analytics solutions in the cloud.
Metamorphic Testing For Adobe Data Analytics Software, Darryl C. Jarman, Zhiquan Zhou, Tsong Yueh Chen
Metamorphic Testing For Adobe Data Analytics Software, Darryl C. Jarman, Zhiquan Zhou, Tsong Yueh Chen
Faculty of Engineering and Information Sciences - Papers: Part B
It is challenging to test data analytics software because a test oracle might not be available. This study reports our experience of applying metamorphic testing to Adobe's data analytics software that is used for anomaly detection in a set of time series data. We make use of geometric transformations to build metamorphic relations and generate simple time series data as the source test cases. The results of this study show that metamorphic testing is highly effective for both verification and validation purposes. An investigation of the issues detected during metamorphic testing revealed three bugs in the software under test.
Towards Cost Reduction In Cloud-Based Workflow Management Through Data Replication, Fei Xie, Jun Yan, Jun Shen
Towards Cost Reduction In Cloud-Based Workflow Management Through Data Replication, Fei Xie, Jun Yan, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Text Data Mining Of Aged Care Accreditation Reports To Identify Risk Factors In Medication Management In Australian Residential Aged Care Homes, Tao Jiang, Siyu Qian, David M. Hailey, Jun Ma, Ping Yu
Text Data Mining Of Aged Care Accreditation Reports To Identify Risk Factors In Medication Management In Australian Residential Aged Care Homes, Tao Jiang, Siyu Qian, David M. Hailey, Jun Ma, Ping Yu
Faculty of Engineering and Information Sciences - Papers: Part B
This study aimed to identify risk factors in medication management in Australian residential aged care (RAC) homes. Only 18 out of 3,607 RAC homes failed aged care accreditation standard in medication management between 7th March 2011 and 25th March 2015. Text data mining methods were used to analyse the reasons for failure. This led to the identification of 21 risk indicators for an RAC home to fail in medication management. These indicators were further grouped into ten themes. They are overall medication management, medication assessment, ordering, dispensing, storage, stock and disposal, administration, incident report, monitoring, staff and resident satisfaction. The …
A Bottom-Up Data Collection Methodology For Characterising The Residential Building Stock In Australia, Clayton Mcdowell, Georgios Kokogiannakis, Paul Cooper, Michael P. Tibbs
A Bottom-Up Data Collection Methodology For Characterising The Residential Building Stock In Australia, Clayton Mcdowell, Georgios Kokogiannakis, Paul Cooper, Michael P. Tibbs
Faculty of Engineering and Information Sciences - Papers: Part B
In Australia the majority of the current residential building stock has been constructed with little regard to energy consumption or thermal comfort. With only 1-2 % of Australia's building stock being replaced each year retrofitting solutions are necessary if residential energy consumption is to be reduced. Australia's records of the characteristics of its current building stock are minimal and outdated and thus these need to be renewed to enable the evaluation of retrofit upgrade strategies. Thus this paper presents a methodology and results of a bottom-up data collection tool that captured building and occupant characteristics from 200 elderly low income …