Creative and literary responses to Australian middle-class experiences of financial upheaval

This thesis—comprising a novel, The Company He Keeps, and exegesis—explores the near absence of literary fiction written about the Australian Middle Class and their responses to financial stress (1980 – 2008), finding that the language of global economic reform has triumphed over political discourse and, in particular, silenced women.

Secure object tracking protocol for networked RFID systems

Networked systems have adapted Radio Frequency identification technology (RFID) to automate their business process. The Networked RFID Systems (NRS) has some unique characteristics which raise new privacy and security concerns for organizations and their NRS systems. The businesses are always having new realization of business needs using NRS. One of the most recent business realization of NRS implementation on large scale distributed systems (such as Internet of Things (IoT), supply chain) is to ensure visibility and traceability of the object throughout the chain. However, this requires assurance of security and privacy to ensure lawful business operation. In this paper, we are proposing a secure tracker protocol that will ensure not only visibility and traceability of the object but also genuineness of the object and its travel path on-site. The proposed protocol is using Physically Unclonable Function (PUF), Diffie-Hellman algorithm and simple cryptographic primitives to protect privacy of the partners, injection of fake objects, non-repudiation, and unclonability. The tag only performs a simple mathematical computation (such as combination, PUF and division) that makes the proposed protocol suitable to passive tags. To verify our security claims, we performed experiment on Security Protocol Description Language (SPDL) model of the proposed protocol using automated claim verification tool Scyther. Our experiment not only verified our claims but also helped us to eliminate possible attacks identified by Scyther.

Oxytocin enhances the appropriate use of human social cues by the domestic dog (Canis familiaris) in an object choice task

It has been postulated that the neuropeptide, oxytocin, is involved in human-dog bonding. This may explain why dogs, compared to wolves, are such good performers on object choice tasks, which test their ability to attend to, and use, human social cues in order to find hidden food treats. The objective of this study was to investigate the effect of intranasal oxytocin administration, which is known to increase social cognition in humans, on domestic dogs' ability to perform such a task. We hypothesised that dogs would perform better on the task after an intranasal treatment of oxytocin. Sixty-two (31 males and 31 females) pet dogs completed the experiment over two different testing sessions, 5-15 days apart. Intranasal oxytocin or a saline control was administered 45 min before each session. All dogs received both treatments in a pseudo-randomised, counterbalanced order. Data were collected as scores out of ten for each of the four blocks of trials in each session. Two blocks of trials were conducted using a momentary distal pointing cue and two using a gazing cue, given by the experimenter. Oxytocin enhanced performance using momentary distal pointing cues, and this enhanced level of performance was maintained over 5-15 days time in the absence of oxytocin. Oxytocin also decreased aversion to gazing cues, in that performance was below chance levels after saline administration but at chance levels after oxytocin administration.

Performance analysis for object-oriented software: a systematic mapping

Performance is a crucial attribute for most software, making performance analysis an important software engineering task. The difficulty is that modern applications are challenging to analyse for performance. Many profiling techniques used in real-world software development struggle to provide useful results when applied to large-scale object-oriented applications. There is a substantial body of research into software performance generally but currently there exists no survey of this research that would help identify approaches useful for object-oriented software. To provide such a review we performed a systematic mapping study of empirical performance analysis approaches that are applicable to object-oriented software. Using keyword searches against leading software engineering research databases and manual searches of relevant venues we identified over 5,000 related articles published since January 2000. From these we systematically selected 253 applicable articles and categorised them according to ten facets that capture the intent, implementation and evaluation of the approaches. Our mapping study results allow us to highlight the main contributions of the existing literature and identify areas where there are interesting opportunities. We also find that, despite the research including approaches specifically aimed at object-oriented software, there are significant challenges in providing actionable feedback on the performance of large-scale object-oriented applications.

Subsuming methods: finding new optimisation opportunities in object-oriented software

The majority of existing application profiling techniques ag- gregate and report performance costs by method or call- ing context. Modern large-scale object-oriented applications consist of thousands of methods with complex calling pat- terns. Consequently, when profiled, their performance costs tend to be thinly distributed across many thousands of loca- tions with few easily identifiable optimisation opportunities. However experienced performance engineers know that there are repeated patterns of method calls in the execution of an application that are induced by the libraries, design patterns and coding idioms used in the software. Automati- cally identifying and aggregating costs over these patterns of method calls allows us to identify opportunities to improve performance based on optimising these patterns. We have developed an analysis technique that is able to identify the entry point methods, which we call subsuming methods, of such patterns. Our ofiine analysis runs over previously collected runtime performance data structured in a calling context tree, such as produced by a large number of existing commercial and open source profilers. We have evaluated our approach on the DaCapo bench- mark suite, showing that our analysis significantly reduces the size and complexity of the runtime performance data set, facilitating its comprehension and interpretation. We also demonstrate, with a collection of case studies, that our analysis identifies new optimisation opportunities that can lead to significant performance improvements (from 20% to over 50% improvement in our case studies).

Visual object clustering via mixed-norm regularization

Many vision problems deal with high-dimensional data, such as motion segmentation and face clustering. However, these high-dimensional data usually lie in a low-dimensional structure. Sparse representation is a powerful principle for solving a number of clustering problems with high-dimensional data. This principle is motivated from an ideal modeling of data points according to linear algebra theory. However, real data in computer vision are unlikely to follow the ideal model perfectly. In this paper, we exploit the mixed norm regularization for sparse subspace clustering. This regularization term is a convex combination of the l1norm, which promotes sparsity at the individual level and the block norm l2/1 which promotes group sparsity. Combining these powerful regularization terms will provide a more accurate modeling, subsequently leading to a better solution for the affinity matrix used in sparse subspace clustering. This could help us achieve better performance on motion segmentation and face clustering problems. This formulation also caters for different types of data corruptions. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other state-of-arts on both motion segmentation and face clustering.

Marine object detection using background modelling and blob analysis

Monitoring marine object is important for understanding the marine ecosystem and evaluating impacts on different environmental changes. One prerequisite of monitoring is to identify targets of interest. Traditionally, the target objects are recognized by trained scientists through towed nets and human observation, which cause much cost and risk to operators and creatures. In comparison, a noninvasive way via setting up a camera and seeking objects in images is more promising. In this paper, a novel technique of object detection in images is presented, which is applicable to generic objects. A robust background modelling algorithm is proposed to extract foregrounds and then blob features are introduced to classify foregrounds. Particular marine objects, box jellyfish and sea snake, are successfully detected in our work. Experiments conducted on image datasets collected by the Australian Institute of Marine Science (AIMS) demonstrate the effectiveness of the proposed technique.

The status and function of Inanimate 'object-actors' in cyborg theatre

 This practice-led research investigates the relationships between human and technological actors in theatre with specific reference to theatrical presence.The research proposes to advance knowledge in the fields of directing and theatre making by putting forward new strategies for directing actors who perform with computer-based technologies.

Secure object tracking protocol for the internet of things

In this paper, we propose a secure object tracking protocol to ensure the visibility and traceability of an object along the travel path to support the Internet of Things (IoT). The proposed protocol is based on radio frequency identification system for global unique identification of IoT objects. For ensuring secure object tracking, lightweight cryptographic primitives and physically unclonable function are used by the proposed protocol in tags. We evaluated the proposed protocol both quantitatively and qualitatively. In our experiment, we modeled the protocol using security protocol description language (SPDL) and simulated SPDL model using automated claim verification tool Scyther. The results show that the proposed protocol is more secure and requires less computation compared to existing similar protocols.