A web-based approach to data imputation

In this paper, we present WebPut, a prototype system that adopts a novel web-based approach to the data imputation problem. Towards this, Webput utilizes the available information in an incomplete database in conjunction with the data consistency principle. Moreover, WebPut extends effective Information Extraction (IE) methods for the purpose of formulating web search queries that are capable of effectively retrieving missing values with high accuracy. WebPut employs a confidence-based scheme that efficiently leverages our suite of data imputation queries to automatically select the most effective imputation query for each missing value. A greedy iterative algorithm is proposed to schedule the imputation order of the different missing values in a database, and in turn the issuing of their corresponding imputation queries, for improving the accuracy and efficiency of WebPut. Moreover, several optimization techniques are also proposed to reduce the cost of estimating the confidence of imputation queries at both the tuple-level and the database-level. Experiments based on several real-world data collections demonstrate not only the effectiveness of WebPut compared to existing approaches, but also the efficiency of our proposed algorithms and optimization techniques.

An evidence-based brochure to educate contact lens wearers about safe contact lens wear

Contact lenses are a successful and popular means to correct refractive error and are worn by just under 700,000 Australians1 and approximately 125 million people worldwide. The most serious complication of contact lens wear is microbial keratitis, a potentially sight-threatening corneal infection most often caused by bacteria. Gram-negative bacteria, in particular pseudomonas species, account for the majority of severe bacterial infections. Pathogens such as fungi or amoebae, which feature less often, are associated with significant morbidity. These unusual pathogens have come into the spotlight in recent times with an apparent association with specific lens cleaning solutions...

Creating synergy in teacher education through robotics-based STEM activities

Robotics has created opportunities for educators to teach concepts across Science, Technology, Engineering, and Mathematics (STEM). This is one of the reasons robotics is becoming increasingly common in primary and secondary classrooms in Australia. To enable pre-service teachers to design engaging STEM activities that incorporate these technologies, robotics is part of the teaching program in the primary education degree at Queensland University of Technology (QUT). A number of pre-service teachers also choose to extend their abilities by implementing robotics activities on field studies, in schools on a voluntary basis, and in outreach activities such as the Robotics@QUT project. The Robotics@QUT project is a support network developed to build professional knowledge and capacity of classroom teachers in schools from a low SES area, engaging in robotics-based STEM activities. Professional Development (PD) workshops are provided to teachers in order to build their knowledge and confidence in implementing robotics activities in their classrooms, loan kits are provided, and pre-service teacher visits arranged to provide the teachers with on-going support. A key feature of the project is the partnerships developed between the teachers and the pre-service teachers involved in the project. The purpose of this study was to ascertain how the teachers in the project perceived the value of the PD workshops and the pre-service teachers’ involvement and what the benefits of the involvement in the project were for the pre-service teachers. Seventeen teachers completed a five-point (1-5) likert scale questionnaire regarding their involvement in the Robotics@QUT project. Teachers’ responses on the value of the project and the pre-service teacher support highlighted the benefits of the partnerships formed and provided insights into the value of the support provided by the pre-service teachers. This paper also describes one pre-service teacher’s experience with the project and the perceived benefits from being involved.

The Implications of Activity Based Funding for Emergency Departments : A Comprehensive Literature Review

Executive Summary Emergency Departments (EDs) locally, nationally and internationally are becoming increasingly busy. Within this context, it can be challenging to deliver a health service that is safe, of high quality and cost-effective. Whilst various models are described within the literature that aim to measure ED ‘work’ or ‘activity’, they are often not linked to a measure of costs to provide such activity. It is important for hospital and ED managers to understand and apply this link so that optimal staffing and financial resourcing can be justifiably sought. This research is timely given that Australia has moved towards a national Activity Based Funding (ABF) model for ED activity. ABF is believed to increase transparency of care and fairness (i.e. equal work receives equal pay). ABF involves a person-, performance- or activity-based payment system, and thus a move away from historical “block payment” models that do not incentivise efficiency and quality. The aim of the Statewide Workforce and Activity-Based Funding Modelling Project in Queensland Emergency Departments (SWAMPED) is to identify and describe best practice Emergency Department (ED) workforce models within the current context of ED funding that operates under an ABF model. The study is comprised of five distinct phases. This monograph (Phase 1) comprises a systematic review of the literature that was completed in June 2013. The remaining phases include a detailed survey of Queensland hospital EDs’ resource levels, activity and operational models of care, development of new resource models, development of a user-friendly modelling interface for ED mangers, and production of a final report that identifies policy implications. The anticipated deliverable outcome of this research is the development of an ABF based Emergency Workforce Modelling Tool that will enable ED managers to profile both their workforce and operational models of care. Additionally, the tool will assist with the ability to more accurately inform adequate staffing numbers required in the future, inform planning of expected expenditures and be used for standardisation and benchmarking across similar EDs. Summary of the Findings Within the remit of this review of the literature, the main findings include: 1. EDs are becoming busier and more congested Rising demand, barriers to ED throughput and transitions of care all contribute to ED congestion. In addition requests by organisational managers and the community require continued broadening of the scope of services required of the ED and further increases in demand. As the population live longer with more lifestyle diseases their propensity to require ED care continues to grow. 2. Various models of care within EDs exist Models often vary to account for site specific characteritics to suit staffing profile, ED geographical location (e.g. metropolitan or rural site), and patient demographic profile (e.g. paediatrics, older persons, ethnicity). Existing and new models implemented within EDs often depend on the target outcome requiring change. Generally this is focussed on addressing issues at the input, throughput or output areas of the ED. Even with models targeting similar demographic or illness, the structure and process elements underpinning the model can vary, which can impact on outcomes and variance to the patient and carer experience between and within EDs. Major models of care to manage throughput inefficiencies include: A. Workforce Models of Care focus on the appropriate level of staffing for a given workload to provide prompt, timely and clinically effective patient care within an emergency care setting. The studies reviewed suggest that the early involvement of senior medical decision maker and/or specialised nursing roles such as Emergency Nurse Practitioners and Clinical Initiatives Nurse, primary contact or extended scope Allied Health Practitioners can facilitate patient flow and improve key indicators such as length of stay and reducing the number of those who did not wait to be seen amongst others. B. Operational Models of Care within EDs focus on mechanisms for streaming (e.g. fast-tracking) or otherwise grouping patient care based on acuity and complexity to assist with minimising any throughput inefficiencies. While studies support the positive impact of these models in general, it appears that they are most effective when they are adequately resourced. 3. Various methods of measuring ED activity exist Measuring ED activity requires careful consideration of models of care and staffing profile. Measuring activity requires the ability to account for factors including: patient census, acuity, LOS, intensity of intervention, department skill-mix plus an adjustment for non-patient care time. 4. Gaps in the literature Continued ED growth calls for new and innovative care delivery models that are safe, clinically effective and cost effective. New roles and stand-alone service delivery models are often evaluated in isolation without considering the global and economic impact on staffing profiles. Whilst various models of accounting for and measuring health care activity exist, costing studies and cost effectiveness studies are lacking for EDs making accurate and reliable assessments of care models difficult. There is a necessity to further understand, refine and account for measures of ED complexity that define a workload upon which resources and appropriate staffing determinations can be made into the future. There is also a need for continued monitoring and comprehensive evaluation of newly implemented workforce modelling tools. This research acknowledges those gaps and aims to: • Undertake a comprehensive and integrated whole of department workforce profiling exercise relative to resources in the context of ABF. • Inform workforce requirements based on traditional quantitative markers (e.g. volume and acuity) combined with qualitative elements of ED models of care; • Develop a comprehensive and validated workforce calculation tool that can be used to better inform or at least guide workforce requirements in a more transparent manner.

Load profile based determination of distribution feeder configuration by dynamic programming

This paper presents a new method to determine feeder reconfiguration scheme considering variable load profile. The objective function consists of system losses, reliability costs and also switching costs. In order to achieve an optimal solution the proposed method compares these costs dynamically and determines when and how it is reasonable to have a switching operation. The proposed method divides a year into several equal time periods, then using particle swarm optimization (PSO), optimal candidate configurations for each period are obtained. System losses and customer interruption cost of each configuration during each period is also calculated. Then, considering switching cost from a configuration to another one, dynamic programming algorithm (DPA) is used to determine the annual reconfiguration scheme. Several test systems were used to validate the proposed method. The obtained results denote that to have an optimum solution it is necessary to compare operation costs dynamically.

A novel hybrid evolutionary algorithm based on ACO and SA for distribution feeder reconfiguration with regard to DGs

This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Ant Colony Optimization (ACO) and Simulated Annealing (SA), called ACO-SA, for distribution feeder reconfiguration (DFR) considering Distributed Generators (DGs). Due to private ownership of DGs, a cost based compensation method is used to encourage DGs in active and reactive power generation. The objective function is summation of electrical energy generated by DGs and substation bus (main bus) in the next day. The approach is tested on a real distribution feeder. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for solving DFR problem.

Distribution harmonic state estimation based on a hybrid PSO and SA algorithm considering parameters uncertainty

This paper presents a new algorithm based on a Hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) called PSO-SA to estimate harmonic state variables in distribution networks. The proposed algorithm performs estimation for both amplitude and phase of each harmonic currents injection by minimizing the error between the measured values from Phasor Measurement Units (PMUs) and the values computed from the estimated parameters during the estimation process. The proposed algorithm can take into account the uncertainty of the harmonic pseudo measurement and the tolerance in the line impedances of the network as well as uncertainty of the Distributed Generators (DGs) such as Wind Turbines (WT). The main feature of proposed PSO-SA algorithm is to reach quickly around the global optimum by PSO with enabling a mutation function and then to find that optimum by SA searching algorithm. Simulation results on IEEE 34 bus radial and a realistic 70-bus radial test networks are presented to demonstrate the speed and accuracy of proposed Distribution Harmonic State Estimation (DHSE) algorithm is extremely effective and efficient in comparison with the conventional algorithms such as Weight Least Square (WLS), Genetic Algorithm (GA), original PSO and Honey Bees Mating Optimization (HBMO) algorithm.

A novel process of group-oriented question reduction for rule-based recommendation websites

Several websites utilise a rule-base recommendation system, which generates choices based on a series of questionnaires, for recommending products to users. This approach has a high risk of customer attrition and the bottleneck is the questionnaire set. If the questioning process is too long, complex or tedious; users are most likely to quit the questionnaire before a product is recommended to them. If the questioning process is short; the user intensions cannot be gathered. The commonly used feature selection methods do not provide a satisfactory solution. We propose a novel process combining clustering, decisions tree and association rule mining for a group-oriented question reduction process. The question set is reduced according to common properties that are shared by a specific group of users. When applied on a real-world website, the proposed combined method outperforms the methods where the reduction of question is done only by using association rule mining or only by observing distribution within the group.

Does counting still count? Revisiting the security of counting based user authentication protocols against statistical attacks

At NDSS 2012, Yan et al. analyzed the security of several challenge-response type user authentication protocols against passive observers, and proposed a generic counting based statistical attack to recover the secret of some counting based protocols given a number of observed authentication sessions. Roughly speaking, the attack is based on the fact that secret (pass) objects appear in challenges with a different probability from non-secret (decoy) objects when the responses are taken into account. Although they mentioned that a protocol susceptible to this attack should minimize this difference, they did not give details as to how this can be achieved barring a few suggestions. In this paper, we attempt to fill this gap by generalizing the attack with a much more comprehensive theoretical analysis. Our treatment is more quantitative which enables us to describe a method to theoretically estimate a lower bound on the number of sessions a protocol can be safely used against the attack. Our results include 1) two proposed fixes to make counting protocols practically safe against the attack at the cost of usability, 2) the observation that the attack can be used on non-counting based protocols too as long as challenge generation is contrived, 3) and two main design principles for user authentication protocols which can be considered as extensions of the principles from Yan et al. This detailed theoretical treatment can be used as a guideline during the design of counting based protocols to determine their susceptibility to this attack. The Foxtail protocol, one of the protocols analyzed by Yan et al., is used as a representative to illustrate our theoretical and experimental results.

Binary image steganographic techniques classification based on multi-class steganalysis

In this paper, we propose a new multi-class steganalysis for binary image. The proposed method can identify the type of steganographic technique used by examining on the given binary image. In addition, our proposed method is also capable of differentiating an image with hidden message from the one without hidden message. In order to do that, we will extract some features from the binary image. The feature extraction method used is a combination of the method extended from our previous work and some new methods proposed in this paper. Based on the extracted feature sets, we construct our multi-class steganalysis from the SVM classifier. We also present the empirical works to demonstrate that the proposed method can effectively identify five different types of steganography.

Predicting freeway pavement construction cost using a back-propagation neural network : a case study in Henan, China

The objective of this research was to develop a model to estimate future freeway pavement construction costs in Henan Province, China. A comprehensive set of factors contributing to the cost of freeway pavement construction were included in the model formulation. These factors comprehensively reflect the characteristics of region and topography and altitude variation, the cost of labour, material, and equipment, and time-related variables such as index numbers of labour prices, material prices and equipment prices. An Artificial Neural Network model using the Back-Propagation learning algorithm was developed to estimate the cost of freeway pavement construction. A total of 88 valid freeway cases were obtained from freeway construction projects let by the Henan Transportation Department during the period 1994−2007. Data from a random selection of 81 freeway cases were used to train the Neural Network model and the remaining data were used to test the performance of the Neural Network model. The tested model was used to predict freeway pavement construction costs in 2010 based on predictions of input values. In addition, this paper provides a suggested correction for the prediction of the value for the future freeway pavement construction costs. Since the change in future freeway pavement construction cost is affected by many factors, the predictions obtained by the proposed method, and therefore the model, will need to be tested once actual data are obtained.

Bone regeneration based on tissue engineering conceptions – a 21st century perspective

The role of Bone Tissue Engineering in the field of Regenerative Medicine has been the topic of substantial research over the past two decades. Technological advances have improved orthopaedic implants and surgical techniques for bone reconstruction. However, improvements in surgical techniques to reconstruct bone have been limited by the paucity of autologous materials available and donor site morbidity. Recent advances in the development of biomaterials have provided attractive alternatives to bone grafting expanding the surgical options for restoring the form and function of injured bone. Specifically, novel bioactive (second generation) biomaterials have been developed that are characterised by controlled action and reaction to the host tissue environment, whilst exhibiting controlled chemical breakdown and resorption with an ultimate replacement by regenerating tissue. Future generations of biomaterials (third generation) are designed to be not only osteo- conductive but also osteoinductive, i.e. to stimulate regeneration of host tissues by combining tissue engineer- ing and in situ tissue regeneration methods with a focus on novel applications. These techniques will lead to novel possibilities for tissue regeneration and repair. At present, tissue engineered constructs that may find future use as bone grafts for complex skeletal defects, whether from post-traumatic, degenerative, neoplastic or congenital/developmental “origin” require osseous reconstruction to ensure structural and functional integrity. Engineering functional bone using combinations of cells, scaffolds and bioactive factors is a promising strategy and a particular feature for future development in the area of hybrid materials which are able to exhibit suitable biomimetic and mechanical properties. This review will discuss the state of the art in this field and what we can expect from future generations of bone regeneration concepts.

Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.

Performance-based structural fire design and testing of structures

Fire incident in buildings is common in Hong Kong and this could lead to heavy casualties due to its high population density, so the fire safety design of the framed structure is an important research topic. This paper describes a computer tool for determination of capacity of structural safety against various fire scenarios and the well-accepted second-order direct plastic analysis is adopted for simulation of material yielding and buckling. A computer method is developed to predict structural behaviour of bare steel framed structures at elevated temperatures but the work can be applied to structures made of other materials. These effects of thermal expansion and material degradation due to heating are required to be considered in order to capture the actual behavior of the structure under fire. Degradation of material strength with increasing temperature is included by a set of temperature-stress-strain curves according to BS5950 Part 8 mainly, which implicitly allows for creep deformation. Several numerical and experimental verifications of framed structures are presented and compared against solutions by other researchers. The proposed method allows us to adopt the truly performance-based structural fire analysis and design with significant saving in cost and time.

Random Gabor based templates for facial expression recognition in images with facial occlusion

Robust facial expression recognition (FER) under occluded face conditions is challenging. It requires robust algorithms of feature extraction and investigations into the effects of different types of occlusion on the recognition performance to gain insight. Previous FER studies in this area have been limited. They have spanned recovery strategies for loss of local texture information and testing limited to only a few types of occlusion and predominantly a matched train-test strategy. This paper proposes a robust approach that employs a Monte Carlo algorithm to extract a set of Gabor based part-face templates from gallery images and converts these templates into template match distance features. The resulting feature vectors are robust to occlusion because occluded parts are covered by some but not all of the random templates. The method is evaluated using facial images with occluded regions around the eyes and the mouth, randomly placed occlusion patches of different sizes, and near-realistic occlusion of eyes with clear and solid glasses. Both matched and mis-matched train and test strategies are adopted to analyze the effects of such occlusion. Overall recognition performance and the performance for each facial expression are investigated. Experimental results on the Cohn-Kanade and JAFFE databases demonstrate the high robustness and fast processing speed of our approach, and provide useful insight into the effects of occlusion on FER. The results on the parameter sensitivity demonstrate a certain level of robustness of the approach to changes in the orientation and scale of Gabor filters, the size of templates, and occlusions ratios. Performance comparisons with previous approaches show that the proposed method is more robust to occlusion with lower reductions in accuracy from occlusion of eyes or mouth.