Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 7 of 7
Full-Text Articles in Engineering
A Comparison Study For Supervised Machine Learning Models In Cancer Classification, Huaming Chen, Hong Zhao, Lei Wang, Jiangning Song, Jun Shen
A Comparison Study For Supervised Machine Learning Models In Cancer Classification, Huaming Chen, Hong Zhao, Lei Wang, Jiangning Song, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Threat Models For Analyzing Plausible Deniability Of Deniable File Systems, Michal Kedziora, Yang-Wai Chow, Willy Susilo
Threat Models For Analyzing Plausible Deniability Of Deniable File Systems, Michal Kedziora, Yang-Wai Chow, Willy Susilo
Faculty of Engineering and Information Sciences - Papers: Part B
Plausible deniability is a property of Deniable File System (DFS), which are encrypted using a Plausibly Deniable Encryption (PDE) scheme, where one cannot prove the existence of a hidden file system within it. This paper investigates widely used security models that are commonly employed for analyzing DFSs. We contend that these models are no longer adequate considering the changing technological landscape that now encompass platforms like mobile and cloud computing as a part of everyday life. This necessitates a shift in digital forensic analysis paradigms, as new forensic models are required to detect and analyze DFSs. As such, it is …
Sharing Social Network Data: Differentially Private Estimation Of Exponential Family Random-Graph Models, Vishesh Karwa, Pavel N. Krivitsky, Aleksandra B. Slavkovic
Sharing Social Network Data: Differentially Private Estimation Of Exponential Family Random-Graph Models, Vishesh Karwa, Pavel N. Krivitsky, Aleksandra B. Slavkovic
Faculty of Engineering and Information Sciences - Papers: Part A
Motivated by a real life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyse synthetic graphs to protect privacy of individual relationships captured by the social network while maintaining the validity of statistical results. A case-study using a version of the Enron e-mail corpus data set demonstrates the application and usefulness of the proposed techniques in solving the challenging problem of maintaining privacy and supporting open access to network data to ensure reproducibility of existing studies and discovering new scientific insights that can be obtained by analysing such data. …
Using Contrastive Divergence To Seed Monte Carlo Mle For Exponential-Family Random Graph Models, Pavel N. Krivitsky
Using Contrastive Divergence To Seed Monte Carlo Mle For Exponential-Family Random Graph Models, Pavel N. Krivitsky
Faculty of Engineering and Information Sciences - Papers: Part A
Exponential-family models for dependent data have applications in a wide variety of areas, but the dependence often results in an intractable likelihood, requiring either analytic approximation or MCMC-based techniques to fit, the latter requiring an initial parameter configuration to seed their simulations. A poor initial configuration can lead to slow convergence or outright failure. The approximate techniques that could be used to find them tend not to be as general as the simulation-based and require implementation separate from that of the MLE-finding algorithm. Contrastive divergence is a more recent simulation-based approximation technique that uses a series of abridged MCMC runs …
Optimizing Wearable Assistive Devices With Neuromuscular Models And Optimal Control, Manish Sreenivasa, Matthew Millard, Paul Manns, Katja Mombaur
Optimizing Wearable Assistive Devices With Neuromuscular Models And Optimal Control, Manish Sreenivasa, Matthew Millard, Paul Manns, Katja Mombaur
Faculty of Engineering and Information Sciences - Papers: Part B
The coupling of human movement dynamics with the function and design of wearable assistive devices is vital to better understand the interaction between the two. Advanced neuromuscular models and optimal control formulations provide the possibility to study and improve this interaction. In addition, optimal control can also be used to generate predictive simulations that generate novel movements for the human model under varying optimization criterion.
Linear Regression Models For Prediction Of Annual Heating And Cooling Demand In Representative Australian Residential Dwellings, Navid Aghdaei, Georgios Kokogiannakis, Daniel J. Daly, Timothy J. Mccarthy
Linear Regression Models For Prediction Of Annual Heating And Cooling Demand In Representative Australian Residential Dwellings, Navid Aghdaei, Georgios Kokogiannakis, Daniel J. Daly, Timothy J. Mccarthy
Faculty of Engineering and Information Sciences - Papers: Part B
This paper presents the development methodology of linear regression models that were developed for the prediction of annual thermal loads in representative residential buildings across three major climates in New South Wales, Australia, and the assessment of the impact of building envelope upgrades. A differential sensitivity analysis was undertaken for sixteen building envelope parameters, with six parameters being identified as significant. These six parameters were then explored using EnergyPlus simulation, and a number of linear regression models developed from the simulation outputs. Random values for design parameters were generated, and the results of EnergyPlus simulations using these parameters were used …
Impact Of Trucking Network Flow On Preferred Biorefinery Locations In The Southern United States, Timothy M. Young, Lee D. Han, James H. Perdue, Stephanie R. Hargrove, Frank M. Guess, Xia Huang, Chung-Hao Chen
Impact Of Trucking Network Flow On Preferred Biorefinery Locations In The Southern United States, Timothy M. Young, Lee D. Han, James H. Perdue, Stephanie R. Hargrove, Frank M. Guess, Xia Huang, Chung-Hao Chen
Electrical & Computer Engineering Faculty Publications
The impact of the trucking transportation network flow was modeled for the southern United States. The study addresses a gap in existing research by applying a Bayesian logistic regression and Geographic Information System (GIS) geospatial analysis to predict biorefinery site locations. A one-way trucking cost assuming a 128.8 km (80-mile) haul distance was estimated by the Biomass Site Assessment model. The "median family income," "timberland annual growth-to-removal ratio," and "transportation delays" were significant in determining mill location. Transportation delays that directly impacted the costs of trucking are presented. A logistic model with Bayesian inference was used to identify preferred site …