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Faculty of Engineering and Information Sciences - Papers: Part A

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Full-Text Articles in Social and Behavioral Sciences

Identity-Based Remote Data Integrity Checking With Perfect Data Privacy Preserving For Cloud Storage, Yong Yu, Man Ho Au, Giuseppe Ateniese, Xinyi Huang, Willy Susilo, Yuanshun Dai, Geyong Min Jan 2017

Identity-Based Remote Data Integrity Checking With Perfect Data Privacy Preserving For Cloud Storage, Yong Yu, Man Ho Au, Giuseppe Ateniese, Xinyi Huang, Willy Susilo, Yuanshun Dai, Geyong Min

Faculty of Engineering and Information Sciences - Papers: Part A

Remote data integrity checking (RDIC) enables a data storage server, say a cloud server, to prove to a verifier that it is actually storing a data owner's data honestly. To date, a number of RDIC protocols have been proposed in the literature. However, most of the constructions suffer from the issue of requiring complex key management. That is, they rely on the expensive public key infrastructure (PKI), which might hinder the deployment of RDIC in practice. In this paper, we propose a new construction of identity-based (ID-based) RDIC protocol by making use of key-homomorphic cryptographic primitive to reduce the ...


Sharing Social Network Data: Differentially Private Estimation Of Exponential Family Random-Graph Models, Vishesh Karwa, Pavel N. Krivitsky, Aleksandra B. Slavkovic Jan 2017

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 ...


Two-Factor Data Security Protection Mechanism For Cloud Storage System, Joseph K. Liu, Kaitai Liang, Willy Susilo, Jianghua Liu, Yang Xiang Jan 2016

Two-Factor Data Security Protection Mechanism For Cloud Storage System, Joseph K. Liu, Kaitai Liang, Willy Susilo, Jianghua Liu, Yang Xiang

Faculty of Engineering and Information Sciences - Papers: Part A

In this paper, we propose a two-factor data security protection mechanism with factor revocability for cloud storage system. Our system allows a sender to send an encrypted message to a receiver through a cloud storage server. The sender only needs to know the identity of the receiver but no other information (such as its public key or its certificate). The receiver needs to possess two things in order to decrypt the ciphertext. The first thing is his/her secret key stored in the computer. The second thing is a unique personal security device which connects to the computer. It is ...


A Data-Driven Predictive Model For Residential Mobility In Australia - A Generalised Linear Mixed Model For Repeated Measured Binary Data, Mohammad-Reza Namazi-Rad, Payam Mokhtarian, Nagesh Shukla, Albert Munoz Jan 2016

A Data-Driven Predictive Model For Residential Mobility In Australia - A Generalised Linear Mixed Model For Repeated Measured Binary Data, Mohammad-Reza Namazi-Rad, Payam Mokhtarian, Nagesh Shukla, Albert Munoz

Faculty of Engineering and Information Sciences - Papers: Part A

Household relocation modelling is an integral part of the Government planning process as residential movements influence the demand for community facilities and services. This study will address the problem of modelling residential relocation choice by estimating a logit-link class model. The proposed model estimates the probability of an event which triggers household relocation. The attributes considered in this study are: requirement for bedrooms, employment status, income status, household characteristics, and tenure (i.e. duration living at the current location). Accurate prediction of household relocations for population units should rely on real world observations. In this study, a longitudinal survey data ...


Public Cloud Data Auditing With Practical Key Update And Zero Knowledge Privacy, Yong Yu, Yannan Li, Man Ho Au, Willy Susilo, Kim-Kwang Raymond Choo, Xinpeng Zhang Jan 2016

Public Cloud Data Auditing With Practical Key Update And Zero Knowledge Privacy, Yong Yu, Yannan Li, Man Ho Au, Willy Susilo, Kim-Kwang Raymond Choo, Xinpeng Zhang

Faculty of Engineering and Information Sciences - Papers: Part A

Data integrity is extremely important for cloud based storage services, where cloud users no longer have physical possession of their outsourced files. A number of data auditing mechanisms have been proposed to solve this problem. However, how to update a cloud user's private auditing key (as well as the authenticators those keys are associated with) without the user's re-possession of the data remains an open problem. In this paper, we propose a key-updating and authenticator-evolving mechanism with zero-knowledge privacy of the stored files for secure cloud data auditing, which incorporates zero knowledge proof systems, proxy re-signatures and homomorphic ...


Towards Data Analytics Of Pathogen-Host Protein-Protein Interaction: A Survey, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song Jan 2016

Towards Data Analytics Of Pathogen-Host Protein-Protein Interaction: A Survey, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song

Faculty of Engineering and Information Sciences - Papers: Part A

"Big Data" is immersed in many disciplines, including computer vision, economics, online resources, bioinformatics and so on. Increasing researches are conducted on data mining and machine learning for uncovering and predicting related domain knowledge. Protein-protein interaction is one of the main areas in bioinformatics as it is the basis of the biological functions. However, most pathogen-host protein-protein interactions, which would be able to reveal much more infectious mechanisms between pathogen and host, are still up for further investigation. Considering a decent feature representation of pathogen-host protein-protein interactions (PHPPI), currently there is not a well structured database for research purposes, not ...


Predictive Inference For Big, Spatial, Non-Gaussian Data: Modis Cloud Data And Its Change-Of-Support, Aritra Sengupta, Noel A. Cressie, Brian H. Kahn, Richard Frey Jan 2016

Predictive Inference For Big, Spatial, Non-Gaussian Data: Modis Cloud Data And Its Change-Of-Support, Aritra Sengupta, Noel A. Cressie, Brian H. Kahn, Richard Frey

Faculty of Engineering and Information Sciences - Papers: Part A

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non-Gaussian in distribution. To overcome computational challenges, we use the reduced-rank spatial random effects (SRE) model in a statistical analysis of cloud-mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models' future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel-scale clear-sky-probability process, is needed to ...


Recent Advances In Security And Privacy In Big Data, Yong Yu, Yi Mu, Giuseppe Ateniese Jan 2015

Recent Advances In Security And Privacy In Big Data, Yong Yu, Yi Mu, Giuseppe Ateniese

Faculty of Engineering and Information Sciences - Papers: Part A

Big data has become an important topic in science, engineering, medicine, healthcare, finance, business and ultimately society itself. Big data refers to the massive amount of digital information stored or transmitted in computer systems. Approximately, 2.5 quintillion bytes of data are created every day. Almost 90% of data in the world today are created in the last two years alone. Security and privacy issues becomes more critical due to large volumes and variety, due to data hosted in large-scale cloud infrastructures, diversity of data sources and formats, streaming nature of data acquisition and high volume inter-cloud migration. In large-scale ...


Searchable Atribute-Based Mechanism With Efficiient Data Sharing For Secure Cloud Storage, Kaitai Liang, Willy Susilo Jan 2015

Searchable Atribute-Based Mechanism With Efficiient Data Sharing For Secure Cloud Storage, Kaitai Liang, Willy Susilo

Faculty of Engineering and Information Sciences - Papers: Part A

To date, the growth of electronic personal data leads to a trend that data owners prefer to remotely outsource their data to clouds for the enjoyment of the high-quality retrieval and storage service without worrying the burden of local data management and maintenance. However, secure share and search for the outsourced data is a formidable task, which may easily incur the leakage of sensitive personal information. Efficient data sharing and searching with security is of critical importance. This paper, for the first time, proposes a searchable attribute-based proxy re-encryption system. When compared to existing systems only supporting either searchable attribute-based ...


Tour-Based Travel Mode Choice Estimation Based On Data Mining And Fuzzy Techniques, Nagesh Shukla, Jun Ma, Rohan Wickramasuriya, Nam N. Huynh, Pascal Perez Jan 2015

Tour-Based Travel Mode Choice Estimation Based On Data Mining And Fuzzy Techniques, Nagesh Shukla, Jun Ma, Rohan Wickramasuriya, Nam N. Huynh, Pascal Perez

Faculty of Engineering and Information Sciences - Papers: Part A

No abstract provided.


Vivambc: Estimating Viral Sequence Variation In Complex Populations From Illumina Deep-Sequencing Data Using Model-Based Clustering, Bie M. P Verbist, Lieven Clement, Joke Reumers, Kim Thys, Alexander Vapirev, Willem Talloen, Yves Wetzels, Joris Meys, Jeroen Aerssens, Luc Bijnens, Olivier Thas Jan 2015

Vivambc: Estimating Viral Sequence Variation In Complex Populations From Illumina Deep-Sequencing Data Using Model-Based Clustering, Bie M. P Verbist, Lieven Clement, Joke Reumers, Kim Thys, Alexander Vapirev, Willem Talloen, Yves Wetzels, Joris Meys, Jeroen Aerssens, Luc Bijnens, Olivier Thas

Faculty of Engineering and Information Sciences - Papers: Part A

Background: Deep-sequencing allows for an in-depth characterization of sequence variation in complex populations. However, technology associated errors may impede a powerful assessment of low-frequency mutations. Fortunately, base calls are complemented with quality scores which are derived from a quadruplet of intensities, one channel for each nucleotide type for Illumina sequencing. The highest intensity of the four channels determines the base that is called. Mismatch bases can often be corrected by the second best base, i.e. the base with the second highest intensity in the quadruplet. A virus variant model-based clustering method, ViVaMBC, is presented that explores quality scores and ...


Arcgis V.10 Landslide Susceptibility Data Mining Add-In Tool Integrating Data Mining And Gis Techniques To Model Landslide Susceptibility, Darshika Palamakumbure, David Stirling, Phillip N. Flentje, Robin N. Chowdhury Jan 2015

Arcgis V.10 Landslide Susceptibility Data Mining Add-In Tool Integrating Data Mining And Gis Techniques To Model Landslide Susceptibility, Darshika Palamakumbure, David Stirling, Phillip N. Flentje, Robin N. Chowdhury

Faculty of Engineering and Information Sciences - Papers: Part A

Landslide susceptibility modeling is an essential early step towards managing landslide risk. A minimum of $4.8 million is lost due to landslide related damages every year in Illawara region of Australia. At present, Data mining and knowledge discovery techniques are becoming popular in building landslide susceptibility models due to their enhanced predictive performances. Until now, the lack of tools to undertake data extraction and making the predictions have limited the applicability of this novel technique in landslide model building. This paper discusses the development of the LSDM (Landslide Susceptibility Data Mining) toolbar which was designed to utilize machine learning ...


Linear Regression With Nested Errors Using Probability-Linked Data, Klairung Samart, Raymond Chambers Jan 2014

Linear Regression With Nested Errors Using Probability-Linked Data, Klairung Samart, Raymond Chambers

Faculty of Engineering and Information Sciences - Papers: Part A

Probabilistic matching of records is widely used to create linked data sets for use in health science, epidemiological, economic, demographic and sociological research. Clearly, this type of matching can lead to linkage errors, which in turn can lead to bias and increased variability when standard statistical estimation techniques are used with the linked data. In this paper we develop unbiased regression parameter estimates to be used when fitting a linear model with nested errors to probabilistically linked data. Since estimation of variance components is typically an important objective when fitting such a model, we also develop appropriate modifications to standard ...


Multi-Phase Ant Colony System For Multi-Party Data-Intensive Service Provision, Lijuan Wang, Jun Shen Jan 2014

Multi-Phase Ant Colony System For Multi-Party Data-Intensive Service Provision, Lijuan Wang, Jun Shen

Faculty of Engineering and Information Sciences - Papers: Part A

The rapid proliferation of enormous sources of digital data has led to greater dependence on data-intensive services. Each service may actually request or create a large amount of data sets. To compose these services will be more challenging. Issues such as autonomy, scalability, adaptability, and robustness, become difficult to resolve. In order to automate the process of reaching an agreement among service composers, service providers, and data providers, an ant-inspired negotiation mechanism is considered in this paper. We exploit a group of agents automatically negotiating to establish agreeable service contracts. Twostage negotiation procedures are used in our data-intensive service provision ...


Multi-Objective Ant Colony System For Data-Intensive Service Provision, Lijuan Wang, Jun Shen, Junzhou Luo Jan 2014

Multi-Objective Ant Colony System For Data-Intensive Service Provision, Lijuan Wang, Jun Shen, Junzhou Luo

Faculty of Engineering and Information Sciences - Papers: Part A

Data-intensive services have become one of the most challenging applications in cloud computing. The classical service composition problem will face new challenges as the services and correspondent data grow. A typical environment is the large scale scientific project AMS, which we are processing huge amount of data streams. In this paper, we will resolve service composition problem by considering the multi-objective data-intensive features. We propose to apply ant colony optimization algorithms and implemented them with simulated workflows in different scenarios. To evaluate the proposed algorithm, we compared it with a multi-objective genetic algorithm with respect to five performance metrics


A Review Of Data Quality Assessment Methods For Public Health Information Systems, Hong Chen, David Hailey, Ning Wang, Ping Yu Jan 2014

A Review Of Data Quality Assessment Methods For Public Health Information Systems, Hong Chen, David Hailey, Ning Wang, Ping Yu

Faculty of Engineering and Information Sciences - Papers: Part A

High quality data and effective data quality assessment are required for accurately evaluating the impact of public health interventions and measuring public health outcomes. Data, data use, and data collection process, as the three dimensions of data quality, all need to be assessed for overall data quality assessment. We reviewed current data quality assessment methods. The relevant study was identified in major databases and well-known institutional websites. We found the dimension of data was most frequently assessed. Completeness, accuracy, and timeliness were the three most-used attributes among a total of 49 attributes of data quality. The major quantitative assessment methods ...


Attribute-Based Data Transfer With Filtering Scheme In Cloud Computing, Jinguang Han, Willy Susilo, Yi Mu, Jun Yan Jan 2014

Attribute-Based Data Transfer With Filtering Scheme In Cloud Computing, Jinguang Han, Willy Susilo, Yi Mu, Jun Yan

Faculty of Engineering and Information Sciences - Papers: Part A

Data transfer is a transmission of data over a point-to-point or point-to-multipoint communication channel. To protect the confidentiality of the transferred data, public-key cryptography has been introduced in data transfer schemes (DTSs). Data transfer is a transmission of data over a point-to-point or point-to-multipoint communication channel. To protect the confidentiality of the transferred data, public-key cryptography has been introduced in data transfer schemes (DTSs). Unfortunately, there exist some drawbacks in the current DTSs. First, the sender must know who the real receivers are. This is undesirable in a system where the number of the users is very large, such as ...


Identity-Based Secure Distributed Data Storage Schemes, Jinguang Han, Willy Susilo, Yi Mu Jan 2014

Identity-Based Secure Distributed Data Storage Schemes, Jinguang Han, Willy Susilo, Yi Mu

Faculty of Engineering and Information Sciences - Papers: Part A

Secure distributed data storage can shift the burden of maintaining a large number of files from the owner to proxy servers. Proxy servers can convert encrypted files for the owner to encrypted files for the receiver without the necessity of knowing the content of the original files. In practice, the original files will be removed by the owner for the sake of space efficiency. Hence, the issues on confidentiality and integrity of the outsourced data must be addressed carefully. In this paper, we propose two identity-based secure distributed data storage (IBSDDS) schemes. Our schemes can capture the following properties: (1 ...


Impacts Of Pheromone Modification Strategies In Ant Colony For Data-Intensive Service Provision, Lijuan Wang, Jun Shen, Junzhou Luo Jan 2014

Impacts Of Pheromone Modification Strategies In Ant Colony For Data-Intensive Service Provision, Lijuan Wang, Jun Shen, Junzhou Luo

Faculty of Engineering and Information Sciences - Papers: Part A

In the provision of dynamic data-intensive services, the cost and response time of data sets as well as the states of services may change over time. An ant colony system for this problem is studied in this paper. Specifically, we consider changing the QoS attributes of services and replacing a certain number of services with new ones at different frequencies. In order to adapt the ant colony system to handle the dynamic scenarios, several pheromone modification strategies in reaction to changes of the optimization scenarios are investigated. The aim of the strategies is to find a balance between preserving enough ...


Ant-Inspired Multi-Phase And Multi-Party Negotiations In The Data-Intensive Service Provision, Lijuan Wang, Jun Shen, Qingguo Zhou, Ghassan Beydoun Jan 2014

Ant-Inspired Multi-Phase And Multi-Party Negotiations In The Data-Intensive Service Provision, Lijuan Wang, Jun Shen, Qingguo Zhou, Ghassan Beydoun

Faculty of Engineering and Information Sciences - Papers: Part A

The rapid proliferation of enormous sources of digital data and the development of cloud computing have led to greater dependence on data-intensive services. Each service may actually request or create a large amount of data sets. To compose these services will be more challenging. Issues of autonomy, scalability, adaptability, and robustness, become difficult to resolve. In order to automate the process of reaching an agreement in data-intensive service provision, the ant-inspired negotiation mechanism is considered in this paper. There are twostage negotiation procedures in our model, which will provide effective and efficient service selection for service composers. We also present ...


An Adaptively Cca-Secure Ciphertext-Policy Attribute-Based Proxy Re-Encryption For Cloud Data Sharing, Kaitai Liang, Man Ho Au, Willy Susilo, Duncan Wong, Guomin Yang, Yong Yu Jan 2014

An Adaptively Cca-Secure Ciphertext-Policy Attribute-Based Proxy Re-Encryption For Cloud Data Sharing, Kaitai Liang, Man Ho Au, Willy Susilo, Duncan Wong, Guomin Yang, Yong Yu

Faculty of Engineering and Information Sciences - Papers: Part A

A Ciphertext-Policy Attribute-Based Proxy Re-Encryption (CP-ABPRE) employs the PRE technology in the attribute-based en- cryption cryptographic setting, in which the proxy is allowed to convert an encryption under an access policy to another encryption under a new access policy. CP-ABPRE is applicable to many real world applications, such as network data sharing. The existing CP-ABPRE systems, how- ever, leave how to achieve adaptive CCA security as an interesting open problem. This paper, for the rst time, proposes a new CP-ABPRE to tackle the problem by integrating the dual system encryption technology with selective proof technique. The new scheme supports any ...


A Comparative Analysis Of Multichannel Data Acquisition Systems For Quality Assurance In External Beam Radiation Therapy, Iolanda Fuduli, Claudiu Porumb, Anthony Espinoza, Abdullah Aldosari, Martin Carolan, Michael Lf Lerch, Peter E. Metcalfe, Anatoly Rosenfeld, Marco Petasecca Jan 2014

A Comparative Analysis Of Multichannel Data Acquisition Systems For Quality Assurance In External Beam Radiation Therapy, Iolanda Fuduli, Claudiu Porumb, Anthony Espinoza, Abdullah Aldosari, Martin Carolan, Michael Lf Lerch, Peter E. Metcalfe, Anatoly Rosenfeld, Marco Petasecca

Faculty of Engineering and Information Sciences - Papers: Part A

The paper presents a comparative study performed by the Centre of Medical Radiation Physics (CMRP) on three multichannel Data Acquisition Systems (DAQ) based on different analogue front-ends to suit a wide range of radiotherapy applications. The three front-ends are: a charge-to-frequency converter developed by INFN Torino, an electrometer and a charge-to-digital converter (both commercial devices from Texas Instruments). For the first two (named DAQ A and B), the CMRP has designed the read-out systems whilst the third one (DAQ C) comes with its own evaluation board. For the purpose of the characterization DAQ A and DAQ B have been equipped ...


New Insight To Preserve Online Survey Accuracy And Privacy In Big Data Era, Joseph K. Liu, Man Ho Au, Xinyi Huang, Willy Susilo, Jianying Zhou, Yong Yu Jan 2014

New Insight To Preserve Online Survey Accuracy And Privacy In Big Data Era, Joseph K. Liu, Man Ho Au, Xinyi Huang, Willy Susilo, Jianying Zhou, Yong Yu

Faculty of Engineering and Information Sciences - Papers: Part A

An online survey system provides a convenient way for people to conduct surveys. It removes the necessity of human resources to hold paper surveys or telephone interviews and hence reduces the cost significantly. Nevertheless, accuracy and privacy remain as the major obstacles that need additional attention. To conduct an accurate survey, privacy maybe lost, and vice versa. In this paper, we provide new insight to preserve these two seeming contradictory issues in online survey systems especially suitable in big data era. We propose a secure system, which is shown to be efficient and practical by simulation data. Our analysis further ...


An Improved Genetic Algorithm For Cost-Effective Data-Intensive Service Composition, Lijuan Wang, Jun Shen, Junzhou Luo, Fang Dong Jan 2014

An Improved Genetic Algorithm For Cost-Effective Data-Intensive Service Composition, Lijuan Wang, Jun Shen, Junzhou Luo, Fang Dong

Faculty of Engineering and Information Sciences - Papers: Part A

The explosion of digital data and the dependence on data-intensive services have been recognized as the most significant characteristics of IT trends in the current decade. Designing workflow of data-intensive services requires data analysis from multiple sources to get required composite services. Composing such services requires effective transfer of large data. Thus many new challenges are posed to control the cost and revenue of the whole composition. This paper addresses the data-intensive service composition and presents an innovative data-intensive service selection algorithm based on a modified genetic algorithm. The performance of this new algorithm is also tested by simulations and ...


Enhanced Privacy Of A Remote Data Integrity-Checking Protocol For Secure Cloud Storage, Yong Yu, Man Ho Au, Yi Mu, S Tang, J Ren, Willy Susilo, Liju Dong Jan 2014

Enhanced Privacy Of A Remote Data Integrity-Checking Protocol For Secure Cloud Storage, Yong Yu, Man Ho Au, Yi Mu, S Tang, J Ren, Willy Susilo, Liju Dong

Faculty of Engineering and Information Sciences - Papers: Part A

Remote data integrity checking (RDIC) enables a server to prove to an auditor the integrity of a stored file. It is a useful technology for remote storage such as cloud storage. The auditor could be a party other than the data owner; hence, an RDIC proof is based usually on publicly available information. To capture the need of data privacy against an untrusted auditor, Hao et al. formally defined "privacy against third party verifiers" as one of the security requirements and proposed a protocol satisfying this definition. However, we observe that all existing protocols with public verifiability supporting data update ...


Learning Diagnostic Diagrams In Transport-Based Data-Collection Systems, Vu Tran, Peter W. Eklund, Christopher David Cook Jan 2014

Learning Diagnostic Diagrams In Transport-Based Data-Collection Systems, Vu Tran, Peter W. Eklund, Christopher David Cook

Faculty of Engineering and Information Sciences - Papers: Part A

Insights about service improvement in a transit network can be gained by studying transit service reliability. In this paper, a general procedure for constructing a transit service reliability diagnostic (Tsrd) diagram based on a Bayesian network is proposed to automatically build a behavioural model from Automatic Vehicle Location (AVL) and Automatic Passenger Counters (APC) data. Our purpose is to discover the variability of transit service attributes and their effects on traveller behaviour. A Tsrd diagram describes and helps to analyse factors affecting public transport by combining domain knowledge with statistical data.


A Structure Optimization Algorithm Of Neural Networks For Large-Scale Data Sets, Jie Yang, Jun Ma, Matthew J. Berryman, Pascal Perez Jan 2014

A Structure Optimization Algorithm Of Neural Networks For Large-Scale Data Sets, Jie Yang, Jun Ma, Matthew J. Berryman, Pascal Perez

Faculty of Engineering and Information Sciences - Papers: Part A

Over the past several decades, neural networks have evolved into powerful computation systems, which are able to learn complex nonlinear input-output relationship from data. However, the structure optimization problem of neural network is a big challenge for processing huge-volumed, diversified and uncertain data. This paper focuses on this problem and introduces a network pruning algorithm based on sparse representation, termed SRP. The proposed approach starts with a large network, then selects important hidden neurons from the original structure using a forward selection criterion that minimizes the residual output error. Furthermore, the presented algorithm has no constraints on the network type ...


Roles Of Social Media In Open Data Environments: A Case Study Of The 2014 Indonesian Presidential Election Voting Results, Uuf Brajawidagda, Akemi T. Chatfield Jan 2014

Roles Of Social Media In Open Data Environments: A Case Study Of The 2014 Indonesian Presidential Election Voting Results, Uuf Brajawidagda, Akemi T. Chatfield

Faculty of Engineering and Information Sciences - Papers: Part A

Open data initiatives are critical to open government policies which promote transparency, citizen engagement and collaboration. However, they face challenges in realizing their potential benefits through citizens' active engagement. Despite the sharp rise of social media use by governments or quasi-governmental organizations to engage citizens in transforming public service quality and offers, very little has been written on enabling roles of social media in influencing the outcome of open data initiatives. This research examines the potential enabling roles of social media in motivating and having citizens' engagement easier in open data environments. Specifically, we present social media use in supporting ...


Data Scientists As Game Changers In Big Data Environments, Akemi T. Chatfield, Vivian N. Shlemoon, Wilbur Redublado, Faizur Rahman Jan 2014

Data Scientists As Game Changers In Big Data Environments, Akemi T. Chatfield, Vivian N. Shlemoon, Wilbur Redublado, Faizur Rahman

Faculty of Engineering and Information Sciences - Papers: Part A

The potential power of big data to generate insights and create new forms of value in the ways which transform organizations and society has been observed by big data-driven organizations and big data experts. Despite the recent sensational declaration of a data scientist as "the sexiest job of the 21st century", however, there has been the lack of rigorous studies of what a data scientist is, and what job skill requirements this hottest job title may need. In order to address this gap, we systematically examine relevant source material to extract definitions and categorize them with a classification scheme developed ...


A Scalable Unsupervised Feature Merging Approach To Efficient Dimensionality Reduction Of High-Dimensional Visual Data, Lingqiao Liu, Lei Wang Jan 2013

A Scalable Unsupervised Feature Merging Approach To Efficient Dimensionality Reduction Of High-Dimensional Visual Data, Lingqiao Liu, Lei Wang

Faculty of Engineering and Information Sciences - Papers: Part A

To achieve a good trade-off between recognition accuracy and computational efficiency, it is often needed to reduce high-dimensional visual data to medium-dimensional ones. For this task, even applying a simple full-matrix-based linear projection causes significant computation and memory use. When the number of visual data is large, how to efficiently learn such a projection could even become a problem. The recent feature merging approach offers an efficient way to reduce the dimensionality, which only requires a single scan of features to perform reduction. However, existing merging algorithms do not scale well with high-dimensional data, especially in the unsupervised case. To ...