Capital music : personal expression with a public display of song choice

Using information and communication technology devices in public urban places can help to create a personalised space. Looking at a mobile phone screen or listening to music on an MP3 player is a common practice avoiding direct contact with others e.g. whilst using public transport. However, such devices can also be utilised to explore how to build new meaningful connections with the urban space and the collocated people within. We present findings of work-in-progress on Capital Music, a mobile application enabling urban dwellers to listen to music songs as usual, but also allowing them to announce song titles and discover songs currently being listened to by other people in the vicinity. We study the ways that this tool can change or even enhance people’s experience of public urban spaces. Our first user study also found changes in choosing different songs. Anonymous social interactions based on users’ music selection are implemented in the first iteration of the prototype that we studied.

QoS-oriented sesource allocation and scheduling of multiple composite web services in a hybrid cloud using a random-key genetic algorithm

In cloud computing resource allocation and scheduling of multiple composite web services is an important challenge. This is especially so in a hybrid cloud where there may be some free resources available from private clouds but some fee-paying resources from public clouds. Meeting this challenge involves two classical computational problems. One is assigning resources to each of the tasks in the composite web service. The other is scheduling the allocated resources when each resource may be used by more than one task and may be needed at different points of time. In addition, we must consider Quality-of-Service issues, such as execution time and running costs. Existing approaches to resource allocation and scheduling in public clouds and grid computing are not applicable to this new problem. This paper presents a random-key genetic algorithm that solves new resource allocation and scheduling problem. Experimental results demonstrate the effectiveness and scalability of the algorithm.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.

Virtual diasporas and the dilemma of multiple belongings in cyberspace

One of the most celebrated qualities of the Internet is its enabling of simultaneity and multiplicity. By allowing users to open as many windows into the world as they (and their computers) can withstand, the Internet is understood to have brought places and cultures together on a scale and in a manner unprecedented. Yet, while the Internet has enabled many to reconnect with cultures and places long distanced and/or lost, it has also led to the belief that these reconnections are established with little correspondent cost to existent ties of belonging. In this paper, I focus on the dilemma multiple belongings engender for the ties of national belonging and question the sanguinity of multiple belongings as practised online. In particular, I use Lefebvre's notion of lived space to unpack the problems and contradictions of what has been called 'Greater China' for the ethnic Chinese minority in nations like Malaysia, Singapore and Australia.

Immunogenicity and protective efficacy of an anti-Streptococcus pyogenes vaccine candidate in multiple animal species

Streptococcus pyogenes, also known as Group A Streptococcus (GAS) has been associated with a range of diseases from the mild pharyngitis and pyoderma to more severe invasive infections such as streptococcal toxic shock. GAS also causes a number of non-suppurative post-infectious diseases such as rheumatic fever, rheumatic heart disease and glomerulonephritis. The large extent of GAS disease burden necessitates the need for a prophylactic vaccine that could target the diverse GAS emm types circulating globally. Anti-GAS vaccine strategies have focused primarily on the GAS M-protein, an extracellular virulence factor anchored to GAS cell wall. As opposed to the hypervariable N-terminal region, the C-terminal portion of the protein is highly conserved among different GAS emm types and is the focus of a leading GAS vaccine candidate, J8-DT/alum. The vaccine candidate J8-DT/alum was shown to be immunogenic in mice, rabbits and the non-human primates, hamadryas baboons. Similar responses to J8-DT/alum were observed after subcutaneous and intramuscular immunization with J8-DT/alum, in mice and in rabbits. Further assessment of parameters that may influence the immunogenicity of J8-DT demonstrated that the immune responses were identical in male and female mice and the use of alum as an adjuvant in the vaccine formulation significantly increased its immunogenicity, resulting in a long-lived serum IgG response. Contrary to the previous findings, the data in this thesis indicates that a primary immunization with J8-DT/alum (50ƒÊg) followed by a single boost is sufficient to generate a robust immune response in mice. As expected, the IgG response to J8- DT/alum was a Th2 type response consisting predominantly of the isotype IgG1 accompanied by lower levels of IgG2a. Intramuscular vaccination of rabbits with J8-DT/alum demonstrated that an increase in the dose of J8-DT/alum up to 500ƒÊg does not have an impact on the serum IgG titers achieved. Similar to the immune response in mice, immunization with J8-DT/alum in baboons also established that a 60ƒÊg dose compared to either 30ƒÊg or 120ƒÊg was sufficient to generate a robust immune response. Interestingly, mucosal infection of naive baboons with a M1 GAS strain did not induce a J8-specific serum IgG response. As J8-DT/alum mediated protection has been previously reported to be due to the J8- specific antibody formed, the efficacy of J8-DT antibodies was determined in vitro and in vivo. In vitro opsonization and in vivo passive transfer confirmed the protective potential of J8-DT antibodies. A reduction in the bacterial burden after challenge with a bioluminescent M49 GAS strain in mice that were passively administered J8-DT IgG established that protection due to J8-DT was mediated by antibodies. The GAS burden in infected mice was monitored using bioluminescent imaging in addition to traditional CFU assays. Bioluminescent GAS strains including the ‘rheumatogenic’ M1 GAS could not be generated due to limitations with transformation of GAS, however, a M49 GAS strain was utilized during BLI. The M49 serotype is traditionally a ‘nephritogenic’ serotype associated with post-streptococcal glomerulonephritis. Anti- J8-DT antibodies now have been shown to be protective against multiple GAS strains such as M49 and M1. This study evaluated the immunogenicity of J8-DT/alum in different species of experimental animals in preparation for phase I human clinical trials and provided the ground work for the development of a rapid non-invasive assay for evaluation of vaccine candidates.

A process for the management of physical infrastructure

Physical infrastructure assets are important components of our society and our economy. They are usually designed to last for many years, are expected to be heavily used during their lifetime, carry considerable load, and are exposed to the natural environment. They are also normally major structures, and therefore present a heavy investment, requiring constant management over their life cycle to ensure that they perform as required by their owners and users. Given a complex and varied infrastructure life cycle, constraints on available resources, and continuing requirements for effectiveness and efficiency, good management of infrastructure is important. While there is often no one best management approach, the choice of options is improved by better identification and analysis of the issues, by the ability to prioritise objectives, and by a scientific approach to the analysis process. The abilities to better understand the effect of inputs in the infrastructure life cycle on results, to minimise uncertainty, and to better evaluate the effect of decisions in a complex environment, are important in allocating scarce resources and making sound decisions. Through the development of an infrastructure management modelling and analysis methodology, this thesis provides a process that assists the infrastructure manager in the analysis, prioritisation and decision making process. This is achieved through the use of practical, relatively simple tools, integrated in a modular flexible framework that aims to provide an understanding of the interactions and issues in the infrastructure management process. The methodology uses a combination of flowcharting and analysis techniques. It first charts the infrastructure management process and its underlying infrastructure life cycle through the time interaction diagram, a graphical flowcharting methodology that is an extension of methodologies for modelling data flows in information systems. This process divides the infrastructure management process over time into self contained modules that are based on a particular set of activities, the information flows between which are defined by the interfaces and relationships between them. The modular approach also permits more detailed analysis, or aggregation, as the case may be. It also forms the basis of ext~nding the infrastructure modelling and analysis process to infrastructure networks, through using individual infrastructure assets and their related projects as the basis of the network analysis process. It is recognised that the infrastructure manager is required to meet, and balance, a number of different objectives, and therefore a number of high level outcome goals for the infrastructure management process have been developed, based on common purpose or measurement scales. These goals form the basis of classifYing the larger set of multiple objectives for analysis purposes. A two stage approach that rationalises then weights objectives, using a paired comparison process, ensures that the objectives required to be met are both kept to the minimum number required and are fairly weighted. Qualitative variables are incorporated into the weighting and scoring process, utility functions being proposed where there is risk, or a trade-off situation applies. Variability is considered important in the infrastructure life cycle, the approach used being based on analytical principles but incorporating randomness in variables where required. The modular design of the process permits alternative processes to be used within particular modules, if this is considered a more appropriate way of analysis, provided boundary conditions and requirements for linkages to other modules, are met. Development and use of the methodology has highlighted a number of infrastructure life cycle issues, including data and information aspects, and consequences of change over the life cycle, as well as variability and the other matters discussed above. It has also highlighted the requirement to use judgment where required, and for organisations that own and manage infrastructure to retain intellectual knowledge regarding that infrastructure. It is considered that the methodology discussed in this thesis, which to the author's knowledge has not been developed elsewhere, may be used for the analysis of alternatives, planning, prioritisation of a number of projects, and identification of the principal issues in the infrastructure life cycle.

Reducing high levels of communication apprehension among primary school students

This study focused on a group of primary school teachers as they implemented a variety of intervention actions within their class programs aimed towards supporting the reduction of high levels of communication apprehension (CA) among students.Six teachers and nine students, located across three primary schools, four year levels,and six classes, participated in this study. For reasons of confidentiality the schools,principals, parents, teachers, teacher assistants, and students who were involved in this study were given fictitious names. The following research question was explored in this study: What intervention actions can primary school teachers implement within their class programs that support the reduction of high CA levels among students? Throughout this study the term CA referred to "an individual's level of fear or anxiety associated with either real or anticipated (oral) communication with another person or persons" (McCroskey, 1984, p. 13). The sources of CA were explained with reference to McCroskey's state-trait continuum. The distinctions between high and appropriate levels of CA were determined conceptually and empirically. The education system within which this study was conducted promoted the philosophy of inclusion and the practices of inclusive schooling. Teachers employed in this system were encouraged to create class programs inclusive of and successful for all students. Consequently the conceptual framework within which this study was conducted was based around the notion of inclusion. Action research and case study research were the methodologies used in the study. Case studies described teachers' action research as they responded to the challenge of executing intervention actions within their class programs directed towards supporting the reduction of high CA levels among students. Consequently the teachers and not the researcher were the central characters in each of the case studies. Three principal data collection instruments were used in this study: Personal Report of Communication Fear (PRCF) scale, semistructured interviews, and dialogue journals. The PRCF scale was the screening tool used to identify a pool of students eligible for the study. Data relevant to the students involved in the study were gathered during semistructured interviews and throughout the dialogue journaling process. Dialogue journaling provided the opportunity for regular contact between teachers and the researcher, a sequence to teacher and student intervention behaviours, and a permanent record of teacher and student growth and development. The majority of teachers involved in this study endeavoured to develop class programs inclusive of all students.These teachers acknowledged the importance of modifying aspects of their class programs in response to the diverse and often multiple needs of individual students with high levels of CA. Numerous conclusions were drawn regarding practical ways that the teachers in this study supported the reduction of high CA levels among students. What this study has shown is that teachers can incorporate intervention actions within their class programs aimed towards supporting students lower their high levels of CA. Whilst no teacher developed an identical approach to intervention, similarities and differences were evident among teachers regarding their selection, interpretation, and implementation of intervention actions. Actions that teachers enacted within their class programs emerged from numerous fields of research including CA, inclusion, social skills, behaviour teaching, co-operative learning, and quality schools. Each teacher's knowledge of and familiarity with these research fields influenced their preference for and commitment to particular intervention actions. Additional factors including each teacher's paradigm of inclusion and exclusion contributed towards their choice of intervention actions. Possible implications of these conclusions were noted with reference to teachers,school administrators, support personnel, system personnel, teacher educators, parents, and researchers.