A statistical framework for natural feature representation

This paper presents a robust stochastic framework for the incorporation of visual observations into conventional estimation, data fusion, navigation and control algorithms. The representation combines Isomap, a non-linear dimensionality reduction algorithm, with expectation maximization, a statistical learning scheme. The joint probability distribution of this representation is computed offline based on existing training data. The training phase of the algorithm results in a nonlinear and non-Gaussian likelihood model of natural features conditioned on the underlying visual states. This generative model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The instantiated likelihoods are expressed as a Gaussian mixture model and are conveniently integrated within existing non-linear filtering algorithms. Example applications based on real visual data from heterogenous, unstructured environments demonstrate the versatility of the generative models.

Rich probabilistic representations for bearing only decentralised data fusion

The aim of this paper is to demonstrate the validity of using Gaussian mixture models (GMM) for representing probabilistic distributions in a decentralised data fusion (DDF) framework. GMMs are a powerful and compact stochastic representation allowing efficient communication of feature properties in large scale decentralised sensor networks. It will be shown that GMMs provide a basis for analytical solutions to the update and prediction operations for general Bayesian filtering. Furthermore, a variant on the Covariance Intersect algorithm for Gaussian mixtures will be presented ensuring a conservative update for the fusion of correlated information between two nodes in the network. In addition, purely visual sensory data will be used to show that decentralised data fusion and tracking of non-Gaussian states observed by multiple autonomous vehicles is feasible.

Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data

In this paper, we apply the incremental EM method to Bayesian Network Classifiers to learn and interpret hyperspectral sensor data in robotic planetary missions. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. Many spacecraft carry spectroscopic equipment as wavelengths outside the visible light in the electromagnetic spectrum give much greater information about an object. The algorithm used is an extension to the standard Expectation Maximisation (EM). The incremental method allows us to learn and interpret the data as they become available. Two Bayesian network classifiers were tested: the Naive Bayes, and the Tree-Augmented-Naive Bayes structures. Our preliminary experiments show that incremental learning with unlabelled data can improve the accuracy of the classifier.

Towards a relational contracting framework in the Australian construction industry : an initial framework

The Australian construction industry is characterized as being a competitive and risky business environment due to lack of cooperation, insufficient trust, ineffective communication and adversarial relationships which are likely lead to poor project performance. Relational contracting (RC) is advocated by literature as an innovative approach to improve the procurement process in the construction industry. Various studies have collectively added to the current knowledge of known RC norms, but there seem to be little effort on investigating the determinants of RC and its impact on project outcomes. In such circumstances, there is lack of evidence and explanation on the manner on how these issues lead to different performance. Simultaneously, the New Engineering Contract (NEC) that embraced the concept of RC is seen as a modern way of contracting and also considered as one of the best approaches to the perennial problem of improving adversarial relationships within the industry. The reality of practice of RC in Australia is investigated through the lens of the NEC. A synthesis of literature views on the concept, processes and tools of RC is first conducted to develop the framework of RC. A case study approach is proposed for an in-depth analysis to explore the critical issues addressed by RC in relation to project performance. Understanding the realities of RC will assist stakeholders in the construction industry with their investment in RC.

Definition of an airworthiness certification framework for civil unmanned aircraft systems

The development of effective safety regulations for unmanned aircraft systems (UAS) is an issue of paramount concern for industry. The development of this framework is a prerequisite for greater UAS access to civil airspace and, subsequently, the continued growth of the UAS industry. The direct use of the existing conventionally piloted aircraft (CPA) airworthiness certification framework for the regulation of UAS has a number of limitations. The objective of this paper is to present one possible approach for the structuring of airworthiness regulations for civilian UAS. The proposed approach facilitates a more systematic, objective and justifiable method for managing the spectrum of risk associated with the diversity of UAS and their potential operations. A risk matrix is used to guide the development of an airworthiness certification matrix (ACM). The ACM provides a structured categorisation that facilitates the future tailoring of regulations proportionate to the levels of risk associated with the operation of the UAS. As a result, an objective and traceable link may be established between mandated regulations and the overarching objective for an equivalent level of safety to CPA. The ACM also facilitates the systematic consideration of a range of technical and operational mitigation strategies. For these reasons, the ACM is proposed as a suitable method for the structuring of an airworthiness certification framework for civil or commercially operated UAS (i.e., the UAS equivalent in function to the Part 21 regulations for civil CPA) and for the further structuring of requirements on the operation of UAS in un-segregated airspace.

Bayesian network for risk of diarrhea associated with the use of recycled water

Estimating potential health risks associated with recycled (reused) water is highly complex given the multiple factors affecting water quality. We take a conceptual model, which represents the factors and pathways by which recycled water may pose a risk of contracting gastroenteritis, convert the conceptual model to a Bayesian net, and quantify the model using one expert’s opinion. This allows us to make various predictions as to the risks posed under various scenarios. Bayesian nets provide an additional way of modeling the determinants of recycled water quality and elucidating their relative influence on a given disease outcome. The important contribution to Bayesian net methodology is that all model predictions, whether risk or relative risk estimates, are expressed as credible intervals.

Incorporating parameter uncertainty into quantitative microbial risk assessment (QMRA)

Modern statistical models and computational methods can now incorporate uncertainty of the parameters used in Quantitative Microbial Risk Assessments (QMRA). Many QMRAs use Monte Carlo methods, but work from fixed estimates for means, variances and other parameters. We illustrate the ease of estimating all parameters contemporaneously with the risk assessment, incorporating all the parameter uncertainty arising from the experiments from which these parameters are estimated. A Bayesian approach is adopted, using Markov Chain Monte Carlo Gibbs sampling (MCMC) via the freely available software, WinBUGS. The method and its ease of implementation are illustrated by a case study that involves incorporating three disparate datasets into an MCMC framework. The probabilities of infection when the uncertainty associated with parameter estimation is incorporated into a QMRA are shown to be considerably more variable over various dose ranges than the analogous probabilities obtained when constants from the literature are simply ‘plugged’ in as is done in most QMRAs. Neglecting these sources of uncertainty may lead to erroneous decisions for public health and risk management.

Approximate Bayesian computation using indirect inference

We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms by using indirect inference. ABC methods are useful for posterior inference in the presence of an intractable likelihood function. In the indirect inference approach to ABC the parameters of an auxiliary model fitted to the data become the summary statistics. Although applicable to any ABC technique, we embed this approach within a sequential Monte Carlo algorithm that is completely adaptive and requires very little tuning. This methodological development was motivated by an application involving data on macroparasite population evolution modelled by a trivariate stochastic process for which there is no tractable likelihood function. The auxiliary model here is based on a beta–binomial distribution. The main objective of the analysis is to determine which parameters of the stochastic model are estimable from the observed data on mature parasite worms.

An evaluation framework for sustaining the impact of educational development

Notwithstanding significant efforts by international aid agencies, aid ineffectiveness became apparent in 1990s as the impact of continued development intervention did not endure the expected outcomes. Conventional monitoring and evaluation by those agencies is critiqued for focusing on measuring project outcomes and giving little attention to aspects of sustainability. As a result, devising a rigorous evaluation framework for educational development has been sought in light of recent paradigm shifts in international development. This paper reports on a case study of an Egyptian educational development project highlighting the importance of transforming the evaluation procedures to process evaluation so as to enhance project impact and longevity. This requires building evaluation capacity of the aid recipient country.

Review of regulatory framework for environmental information

If Australian scientists are to fully and actively participate in international scientific collaborations utilising online technologies, policies and laws must support the data access and reuse objectives of these projects. To date Australia lacks a comprehensive policy and regulatory framework for environmental information and data generally. Instead there exists a series of unconnected Acts that adopt historically-based, sector-specific approaches to the collection, use and reuse of environmental information. This paper sets out the findings of an analysis of a representative sample of Australian statutes relating to environmental management and protection to determine the extent to which they meet best practice criteria for access to and reuse of environmental information established in international initiatives. It identifies issues that need to be addressed in the legislation governing environmental information to ensure that Australian scientists are able to fully engage in international research collaborations.

A framework for reasoning about inherent parallelism in modern object-oriented languages

With the emergence of multi-core processors into the mainstream, parallel programming is no longer the specialized domain it once was. There is a growing need for systems to allow programmers to more easily reason about data dependencies and inherent parallelism in general purpose programs. Many of these programs are written in popular imperative programming languages like Java and C]. In this thesis I present a system for reasoning about side-effects of evaluation in an abstract and composable manner that is suitable for use by both programmers and automated tools such as compilers. The goal of developing such a system is to both facilitate the automatic exploitation of the inherent parallelism present in imperative programs and to allow programmers to reason about dependencies which may be limiting the parallelism available for exploitation in their applications. Previous work on languages and type systems for parallel computing has tended to focus on providing the programmer with tools to facilitate the manual parallelization of programs; programmers must decide when and where it is safe to employ parallelism without the assistance of the compiler or other automated tools. None of the existing systems combine abstraction and composition with parallelization and correctness checking to produce a framework which helps both programmers and automated tools to reason about inherent parallelism. In this work I present a system for abstractly reasoning about side-effects and data dependencies in modern, imperative, object-oriented languages using a type and effect system based on ideas from Ownership Types. I have developed sufficient conditions for the safe, automated detection and exploitation of a number task, data and loop parallelism patterns in terms of ownership relationships. To validate my work, I have applied my ideas to the C] version 3.0 language to produce a language extension called Zal. I have implemented a compiler for the Zal language as an extension of the GPC] research compiler as a proof of concept of my system. I have used it to parallelize a number of real-world applications to demonstrate the feasibility of my proposed approach. In addition to this empirical validation, I present an argument for the correctness of the type system and language semantics I have proposed as well as sketches of proofs for the correctness of the sufficient conditions for parallelization proposed.

Wellness in higher education : a transformative framework for health related disciplines

The perceived benefits of Wellness Education in University environments are substantiated by a number of studies in relation to the place, impact and purpose of Wellness curricula. Many authors recommend that Wellness curriculum design must include personal experiences, reflective practice and active self-managed learning approaches in order to legitimise the adoption of Wellness as a personal lifestyle approach. Wellness Education provides opportunities to engage in learning self-regulation skills both within and beyond the context of the Wellness construct. Learner success is optimised by creating authentic opportunities to develop and practice self regulation strategies that facilitate making meaning of life's experiences. Such opportunities include provision of options for self determined outcomes and are scaffolded according to learner needs; thus, configuring a learner-centred curriculum in Wellness Education would potentially benefit by overlaying principles from the domains of Self Determination Theory, Self Regulated Learning and Transformative Education Theory to highlight authentic, transformative learning as a lifelong approach to Wellness.

Development of a workplace wellness promotion pilot framework : a case study of the Blue Care Staff Wellness Program

Workplace wellness initiatives are currently unreflective of the multidimensional and holistic nature of the wellness construct. There exists an opportunity for promoters of health to move toward models of workplace wellness promotion that more fully appreciate the interconnected nature of health dimensions and promote them even-handedly. The Blue Care Staff Wellness Program framework was developed in response to a recognised need for consistent and wellness-focused constructs for workplace wellness promotion and dissemination. The framework promotes and supports the individual and organisational wellness of the Blue Care employee population by providing a comprehensive and sustainable employee wellness program. This has been achieved by the adoption of consistent wellness principles to guide the framework conception and theory based development. The use of the framework in a pilot program will provide insight into the frameworks effectiveness in promoting a comprehensive workplace wellness program, and go further to establish the relationship between wellness and productivity in the workplace.

Improving accuracy and intelligibility of decisions

Intelligible and accurate risk-based decision-making requires a complex balance of information from different sources, appropriate statistical analysis of this information and consequent intelligent inference and decisions made on the basis of these analyses. Importantly, this requires an explicit acknowledgement of uncertainty in the inputs and outputs of the statistical model. The aim of this paper is to progress a discussion of these issues in the context of several motivating problems related to the wider scope of agricultural production. These problems include biosecurity surveillance design, pest incursion, environmental monitoring and import risk assessment. The information to be integrated includes observational and experimental data, remotely sensed data and expert information. We describe our efforts in addressing these problems using Bayesian models and Bayesian networks. These approaches provide a coherent and transparent framework for modelling complex systems, combining the different information sources, and allowing for uncertainty in inputs and outputs. While the theory underlying Bayesian modelling has a long and well established history, its application is only now becoming more possible for complex problems, due to increased availability of methodological and computational tools. Of course, there are still hurdles and constraints, which we also address through sharing our endeavours and experiences.

Strategic management of technology in construction : a framework and case study

This case study analyzes a firm's technology strategy for its fit or match with the requirements of the industry environment in which it operates. Understanding the relationships between market characteristics and technology strategies can assist managers in making complex and difficult decisions regarding their use of technology to improve competitive performance. Using the technology strategy framework, managers can map their own capabilities for comparison with the more appropriate or superior approach to technology in that industry environment. Alternatively, firms seeking to transition from one industry niche or environment to another could identify and move to acquire the required capabilities. The dynamics of industry competition, both domestic and international, emphasize the need for improved management of the strategic fit between technical capabilities and industry environment.