The legal framework for Australia's carbon pricing mechanism : a critique

As part of the Australian Government’s Clean Energy Plan, the Government has attempted to harness the legal innovation of the tradeable emissions unit, within a capped carbon trading system, to reduce greenhouse gas emissions. Such an approach promises to send a price signal to the market which will influence emitting behaviours and reduce our emissions in a cost-effective manner. However, if the carbon trading scheme is to successfully achieve cost-effective emissions reductions then the carbon market must be supported by an appropriate legal framework. This paper will consider the key features of the Australian Carbon Pricing Mechanism, including the Carbon Farming Initiative, and critique whether it has all the hallmarks of an effective legal framework to reduce Australia’s net greenhouse gas emissions. The likely future of the trading scheme, following the 2013 elections, will also be addressed.

Delegation framework for federated systems

It is not uncommon for enterprises today to be faced with the demand to integrate and incor- porate many different and possibly heterogeneous systems which are generally independently designed and developed, to allow seamless access. In effect, the integration of these systems results in one large whole system that must be able, at the same time, to maintain the local autonomy and to continue working as an independent entity. This problem has introduced a new distributed architecture called federated systems. The most challenging issue in federated systems is to find answers for the question of how to efficiently cooperate while preserving their autonomous characteristic, especially the security autonomy. This thesis intends to address this issue. The thesis reviews the evolution of the concept of federated systems and discusses the organisational characteristics as well as remaining security issues with the existing approaches. The thesis examines how delegation can be used as means to achieve better security, especially authorisation while maintaining autonomy for the participating member of the federation. A delegation taxonomy is proposed as one of the main contributions. The major contribution of this thesis is to study and design a mechanism to support dele- gation within and between multiple security domains with constraint management capability. A novel delegation framework is proposed including two modules: Delegation Constraint Man- agement module and Policy Management module. The first module is designed to effectively create, track and manage delegation constraints, especially for delegation processes which require re-delegation (indirect delegation). The first module employs two algorithms to trace the root authority of a delegation constraint chain and to prevent the potential conflict when creating a delegation constraint chain if necessary. The first module is designed for conflict prevention not conflict resolution. The second module is designed to support the first module via the policy comparison capability. The major function of this module is to provide the delegation framework the capability to compare policies and constraints (written under the format of a policy). The module is an extension of Lin et al.'s work on policy filtering and policy analysis. Throughout the thesis, some case studies are used as examples to illustrate the discussed concepts. These two modules are designed to capture one of the most important aspects of the delegation process: the relationships between the delegation transactions and the involved constraints, which are not very well addressed by the existing approaches. This contribution is significant because the relationships provide information to keep track and en- force the involved delegation constraints and, therefore, play a vital role in maintaining and enforcing security for transactions across multiple security domains.

Discovering the best feature extraction and selection algorithms for spontaneous facial expression recognition

Feature extraction and selection are critical processes in developing facial expression recognition (FER) systems. While many algorithms have been proposed for these processes, direct comparison between texture, geometry and their fusion, as well as between multiple selection algorithms has not been found for spontaneous FER. This paper addresses this issue by proposing a unified framework for a comparative study on the widely used texture (LBP, Gabor and SIFT) and geometric (FAP) features, using Adaboost, mRMR and SVM feature selection algorithms. Our experiments on the Feedtum and NVIE databases demonstrate the benefits of fusing geometric and texture features, where SIFT+FAP shows the best performance, while mRMR outperforms Adaboost and SVM. In terms of computational time, LBP and Gabor perform better than SIFT. The optimal combination of SIFT+FAP+mRMR also exhibits a state-of-the-art performance.

An information accountability framework for shared eHealth policies

Privacy issues have hindered the evolution of e-health since its emergence. Patients demand better solutions for the protection of private information. Health professionals demand open access to patient health records. Existing e-health systems find it difficult to fulfill these competing requirements. In this paper, we present an information accountability framework (IAF) for e-health systems. The IAF is intended to address privacy issues and their competing concerns related to e-health. Capabilities of the IAF adhere to information accountability principles and e-health requirements. Policy representation and policy reasoning are key capabilities introduced in the IAF. We investigate how these capabilities are feasible using Semantic Web technologies. We discuss with the use of a case scenario, how we can represent the different types of policies in the IAF using the Open Digital Rights Language (ODRL).

Service delivery framework : an architectural strategy for next-generation service delivery in business network

The next-generation of service-oriented architecture (SOA) needs to scale for flexible service consumption, beyond organizational and application boundaries, into communities, ecosystems and business networks. In wider and, ultimately, global settings, new capabilities are needed so that business partners can efficiently and reliably enable, adapt and expose services. Those services can then be discovered, ordered, consumed, metered and paid for, through new applications and opportunities, driven by third-parties in the global “village”. This trend is already underway, in different ways, through different early adopter market segments. This paper proposes an architectural strategy for the provisioning and delivery of services in communities, ecosystems and business networks – a Service Delivery Framework (SDF). The SDF is intended to support multiple industries and deployments where a SOA platform is needed for collaborating partners and diverse consumers. Specifically, it is envisaged that the SDF allows providers to publish their services into network directories so that they can be repurposed, traded and consumed, and leveraging network utilities like B2B gateways and cloud hosting. To support these different facets of service delivery, the SDF extends the conventional service provider, service broker and service consumer of the Web Services Architecture to include service gateway, service hoster, service aggregator and service channel maker.

Feature-domain super-resolution framework for gabor-based face and iris recognition

The low resolution of images has been one of the major limitations in recognising humans from a distance using their biometric traits, such as face and iris. Superresolution has been employed to improve the resolution and the recognition performance simultaneously, however the majority of techniques employed operate in the pixel domain, such that the biometric feature vectors are extracted from a super-resolved input image. Feature-domain superresolution has been proposed for face and iris, and is shown to further improve recognition performance by capitalising on direct super-resolving the features which are used for recognition. However, current feature-domain superresolution approaches are limited to simple linear features such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which are not the most discriminant features for biometrics. Gabor-based features have been shown to be one of the most discriminant features for biometrics including face and iris. This paper proposes a framework to conduct super-resolution in the non-linear Gabor feature domain to further improve the recognition performance of biometric systems. Experiments have confirmed the validity of the proposed approach, demonstrating superior performance to existing linear approaches for both face and iris biometrics.

An integrated approach for precise road reconstruction from aerial imagery and LiDAR data

Accurate and detailed road models play an important role in a number of geospatial applications, such as infrastructure planning, traffic monitoring, and driver assistance systems. In this thesis, an integrated approach for the automatic extraction of precise road features from high resolution aerial images and LiDAR point clouds is presented. A framework of road information modeling has been proposed, for rural and urban scenarios respectively, and an integrated system has been developed to deal with road feature extraction using image and LiDAR analysis. For road extraction in rural regions, a hierarchical image analysis is first performed to maximize the exploitation of road characteristics in different resolutions. The rough locations and directions of roads are provided by the road centerlines detected in low resolution images, both of which can be further employed to facilitate the road information generation in high resolution images. The histogram thresholding method is then chosen to classify road details in high resolution images, where color space transformation is used for data preparation. After the road surface detection, anisotropic Gaussian and Gabor filters are employed to enhance road pavement markings while constraining other ground objects, such as vegetation and houses. Afterwards, pavement markings are obtained from the filtered image using the Otsu's clustering method. The final road model is generated by superimposing the lane markings on the road surfaces, where the digital terrain model (DTM) produced by LiDAR data can also be combined to obtain the 3D road model. As the extraction of roads in urban areas is greatly affected by buildings, shadows, vehicles, and parking lots, we combine high resolution aerial images and dense LiDAR data to fully exploit the precise spectral and horizontal spatial resolution of aerial images and the accurate vertical information provided by airborne LiDAR. Objectoriented image analysis methods are employed to process the feature classiffcation and road detection in aerial images. In this process, we first utilize an adaptive mean shift (MS) segmentation algorithm to segment the original images into meaningful object-oriented clusters. Then the support vector machine (SVM) algorithm is further applied on the MS segmented image to extract road objects. Road surface detected in LiDAR intensity images is taken as a mask to remove the effects of shadows and trees. In addition, normalized DSM (nDSM) obtained from LiDAR is employed to filter out other above-ground objects, such as buildings and vehicles. The proposed road extraction approaches are tested using rural and urban datasets respectively. The rural road extraction method is performed using pan-sharpened aerial images of the Bruce Highway, Gympie, Queensland. The road extraction algorithm for urban regions is tested using the datasets of Bundaberg, which combine aerial imagery and LiDAR data. Quantitative evaluation of the extracted road information for both datasets has been carried out. The experiments and the evaluation results using Gympie datasets show that more than 96% of the road surfaces and over 90% of the lane markings are accurately reconstructed, and the false alarm rates for road surfaces and lane markings are below 3% and 2% respectively. For the urban test sites of Bundaberg, more than 93% of the road surface is correctly reconstructed, and the mis-detection rate is below 10%.

A software framework for risk-aware business process management

With the large diffusion of Business Process Managemen (BPM) automation suites, the possibility of managing process-related risks arises. This paper introduces an innovative framework for process-related risk management and describes a working implementation realized by extending the YAWL system. The framework covers three aspects of risk management: risk monitoring, risk prevention, and risk mitigation. Risk monitoring functionality is provided using a sensor-based architecture, where sensors are defined at design time and used at run-time for monitoring purposes. Risk prevention functionality is provided in the form of suggestions about what should be executed, by who, and how, through the use of decision trees. Finally, risk mitigation functionality is provided as a sequence of remedial actions (e.g. reallocating, skipping, rolling back of a work item) that should be executed to restore the process to a normal situation.

The Influence of work motivation on project success : towards a framework

The purpose of this paper is to suggest a framework to study the influence of 'Work motivation' on 'Project success' from a team members' perspective. Results from a literature review of 1345 articles on ' Project success' and 1063 articles of 'Work motivation' appearing in peer-reviewed journals between 1985 and 2005 are presented. We then propose a framework to study the impact of 'Work motivation' on 'Project success' by incorporating the key constructs pertaining to these variables derived from the literature review.

SOA for business networks - service delivery framework

The next generation of SOA needs to scale for flexible service consumption, beyond organizational boundaries and current B2B applications, into communities, eco-systems, and business networks. In the wider and, ultimately, global settings, new capabilities are needed so that business partners can efficiently and reliably enable, adapt, and expose services where they can be discovered, ordered, consumed, metered, and paid for, through new applications and opportunities, driven by third parties in the global "village". This trend is already underway, in different ways, through various early adopter market segments. For the small medium enterprises segment, Google, Intuit-Microsoft, and others have launched appstores, through which an open-ended array of hosted applications are sourced from the development community and procured as maketplace commondities. In the corporate sector, the marketplace model and business network hubs are being put in place on top of connectivity and network orchestration investments for capitalizing services as tradable assets, seen in banking/finance (e.g. American Express Intelligent Marketplace), logistics (e.g., the E2open hub), and the public sector (e.g., UK DirectGov whole-of-government citizen services delivery).

A requirements framework for semantic business process modelling

The emergence of semantic technologies to deal with the underlying meaning of things, instead of a purely syntactical representation, has led to new developments in various fields, including business process modeling. Inspired by artificial intelligence research, technologies for semantic Web services have been proposed and extended to process modeling. However, the applicablility of semantic Web services for semantic business processes is limited because business processes encompass wider requirements of business than Web services. In particular, processes are concerned with the composition of tasks, that is, in which order activities are carried out, regardless of their implementation details; resources assigned to carry out tasks, such as machinery, people, and goods; data exchange; and security and compliance concerns.

Youth alcohol and drug good practice guide 1 : a framework for youth alcohol and other drug practice

This Guide outlines a framework for working with young people whose AOD use creates significant vulnerability to current or future harm. The target audience is practitioners who work with young people who have problematic AOD use and the managers of these practitioners. Areas of content include the elements of a framework for AOD practice, an appreciation of the developmental, social and institutional location of young people, key concepts and understandings regarding good youth centered context responsive practice, and key policy constructs and directions.

Enterprise information security policy assessment : an extended framework for metrics development utilising the goal-question-metric approach

Effective enterprise information security policy management requires review and assessment activities to ensure information security policies are aligned with business goals and objectives. As security policy management involves the elements of policy development process and the security policy as output, the context for security policy assessment requires goal-based metrics for these two elements. However, the current security management assessment methods only provide checklist types of assessment that are predefined by industry best practices and do not allow for developing specific goal-based metrics. Utilizing theories drawn from literature, this paper proposes the Enterprise Information Security Policy Assessment approach that expands on the Goal-Question-Metric (GQM) approach. The proposed assessment approach is then applied in a case scenario example to illustrate a practical application. It is shown that the proposed framework addresses the requirement for developing assessment metrics and allows for the concurrent undertaking of process-based and product-based assessment. Recommendations for further research activities include the conduct of empirical research to validate the propositions and the practical application of the proposed assessment approach in case studies to provide opportunities to introduce further enhancements to the approach.

Development of distributed fuzzy systems with a runtime-adaptable mobile components framework

A distributed fuzzy system is a real-time fuzzy system in which the input, output and computation may be located on different networked computing nodes. The ability for a distributed software application, such as a distributed fuzzy system, to adapt to changes in the computing network at runtime can provide real-time performance improvement and fault-tolerance. This paper introduces an Adaptable Mobile Component Framework (AMCF) that provides a distributed dataflow-based platform with a fine-grained level of runtime reconfigurability. The execution location of small fragments (possibly as little as few machine-code instructions) of an AMCF application can be moved between different computing nodes at runtime. A case study is included that demonstrates the applicability of the AMCF to a distributed fuzzy system scenario involving multiple physical agents (such as autonomous robots). Using the AMCF, fuzzy systems can now be developed such that they can be distributed automatically across multiple computing nodes and are adaptable to runtime changes in the networked computing environment. This provides the opportunity to improve the performance of fuzzy systems deployed in scenarios where the computing environment is resource-constrained and volatile, such as multiple autonomous robots, smart environments and sensor networks.

A classification framework for design-build variants from an operational perspective

Design-build (DB) is a generic form of construction procurement, and, rather than simply representing a single system, it has evolved in practice into a variety of forms, each of which is similar to, and yet different from each other. Although the importance of selecting an appropriate DB variant has been widely accepted, difficulties occur in practice due to the multiplicity of terms and concepts used. What is needed is some kind of taxonomy or framework within which the individual variants can be placed and their relative attributes identified and understood. Through a comprehensive literature review and content analysis, this paper establishes a systematic classification framework for DB variants based on their operational attributes. In addition to providing much needed support for decision-making, this classification framework provides client/owners with perspectives to understand and examine different categories of DB variants from an operational perspective.