Data-driven background dataset selection for SVM-based speaker verification

The recently proposed data-driven background dataset refinement technique provides a means of selecting an informative background for support vector machine (SVM)-based speaker verification systems. This paper investigates the characteristics of the impostor examples in such highly-informative background datasets. Data-driven dataset refinement individually evaluates the suitability of candidate impostor examples for the SVM background prior to selecting the highest-ranking examples as a refined background dataset. Further, the characteristics of the refined dataset were analysed to investigate the desired traits of an informative SVM background. The most informative examples of the refined dataset were found to consist of large amounts of active speech and distinctive language characteristics. The data-driven refinement technique was shown to filter the set of candidate impostor examples to produce a more disperse representation of the impostor population in the SVM kernel space, thereby reducing the number of redundant and less-informative examples in the background dataset. Furthermore, data-driven refinement was shown to provide performance gains when applied to the difficult task of refining a small candidate dataset that was mis-matched to the evaluation conditions.

Evolutionary algorithms for sustainable building design

This approach to sustainable design explores the possibility of creating an architectural design process which can iteratively produce optimised and sustainable design solutions. Driven by an evolution process based on genetic algorithms, the system allows the designer to “design the building design generator” rather than to “designs the building”. The design concept is abstracted into a digital design schema, which allows transfer of the human creative vision into the rational language of a computer. The schema is then elaborated into the use of genetic algorithms to evolve innovative, performative and sustainable design solutions. The prioritisation of the project’s constraints and the subsequent design solutions synthesised during design generation are expected to resolve most of the major conflicts in the evaluation and optimisation phases. Mosques are used as the example building typology to ground the research activity. The spatial organisations of various mosque typologies are graphically represented by adjacency constraints between spaces. Each configuration is represented by a planar graph which is then translated into a non-orthogonal dual graph and fed into the genetic algorithm system with fixed constraints and expected performance criteria set to govern evolution. The resultant Hierarchical Evolutionary Algorithmic Design System is developed by linking the evaluation process with environmental assessment tools to rank the candidate designs. The proposed system generates the concept, the seed, and the schema, and has environmental performance as one of the main criteria in driving optimisation.

Political theory and theoretical physics

This paper explains, somewhat along a Simmelian line, that political theory may produce practical and universal theories like those developed in theoretical physics. The reasoning behind this paper is to show that the Element of Democracy Theory may be true by way of comparing it to Einstein’s Special Relativity – specifically concerning the parameters of symmetry, unification, simplicity, and utility. These parameters are what make a theory in physics as meeting them not only fits with current knowledge, but also produces paths towards testing (application). As the Element of Democracy Theory meets these same parameters, it could settle the debate concerning the definition of democracy. This will be shown firstly by discussing why no one has yet achieved a universal definition of democracy; secondly by explaining the parameters chosen (as in why these and not others confirm or scuttle theories); and thirdly by comparing how Special Relativity and the Element of Democracy match the parameters.

Migrant and refugee children, their families, and early childhood education

One of the effects of globalization is the increasing movement of people around the globe. Transnational migration brings demographic changes that produce challenges for education and social services. While there is a growing body of literature about educational concerns associated with migrant and refugee children, young migrant children are not often included in this research because it concentrates on secondary and primary schooling. In this chapter we review the literature that relates to young migrant and refugee children, their families and early childhood education. More specifically, we synthesize the state of knowledge relating to curriculum, parents and teacher education. Following the analysis of recent research, the chapter concludes with some suggestions for further research, policy makers and practitioners.

A comparative analysis of the victim policies across the Anglo-speaking world

The study of criminal victimisation has developed to such an extent that victimology is now regarded as a central component to the study of crime and criminology. This focus of concern has been matched by the growth and development of support services for the victim of crime alongside increasing political concern with similar issues. The central purpose of this book is to bring together leading scholars to produce an authoritative handbook on victims and victimology that provides a comprehensive review of these developments, reflecting contemporary academic, policy, and political debates on the nature, extent and impact of criminal victimisation and policy responses to it.

Responding to sustainability challenge and cost implications in highway construction projects

Highway construction often requires a significant capital input; therefore it often causes serious financial implications for developers, owners and operators. The recent industry-wide focus on sustainability has added a new dimension to the evaluation of highway projects, particularly on the economical scale of ‘going green’. Comprehensive analysis of the whole-of-life highway development that responds to sustainability challenges is one of the primary concerns for stakeholders. Principles of engineering economics and life cycle costing have been used to determine the incremental capacity investments for highway projects. However, the consideration of costs and issues associated with sustainability is still very limited in current studies on highway projects. Previous studies have identified that highway project investments are primarily concerned with direct market costs that can be quantified through life cycle costing analysis (LCCA). But they tend to ignore costs that are difficult to calculate, as those related to environmental and social elements. On a more positive note, these studies proved that the inclusion of such costs is an essential part of the overall development investment and a primary concern for decision making by the stakeholders. This paper discusses a research attempt to identify and categorise sustainability cost elements for highway projects. Through questionnaire survey, a set of sustainability cost elements on highway projects has been proposed. These cost elements are incorporated into the extension of some of the existing Life Cycle Costing Analysis (LCCA) models in order to produce a holistic financial picture of the highway project. It is expected that a new LCCA model will be established to serve as a suitable tool for decision making for highway project stakeholders.

Incorporating sustainability measures in life-cycle financial decision making for highway construction

Public awareness and the nature of highway construction works demand that sustainability measures are first on the development agenda. However, in the current economic climate, individual volition and enthusiasm for such high capital investments do not present as strong cases for decision making as the financial pictures of pursuing sustainability. Some stakeholders consider sustainability to be extra work that costs additional money. Though, stakeholders realised its importance in infrastructure development. They are keen to identify the available alternatives and financial implications on a lifecycle basis. Highway infrastructure development is a complex rocess which requires expertise and tools to evaluate investment options, such as environmentally sustainable features for road and highway development. Life-cycle cost analysis (LCCA) is a valuable approach for investment decision making for construction works. However, LCCA applications in highway development are still limited. Current models, for example focus on economic issues alone and do not deal with sustainability factors, which are more difficult to quantify and encapsulate in estimation modules. This paper reports the research which identifies sustainability related factors in highway construction projects, in quantitative and qualitative forms of a multi-criteria analysis. These factors are then incorporated into past and proven LCCA models to produce a new long term decision support model. The research via questionnaire, model building, analytical hierarchy processes (AHP) and case studies have identified, evaluated and then processed highway sustainability related cost elements. These cost elements need to be verified by industry before being integrated for further development of the model. Then the Australian construction industry will have a practical tool to evaluate investment decisions which provide an optimum balance between financial viability and sustainability deliverables.

Spatial cognition for robots

The paper discusses robot navigation from biological inspiration. The authors sought to build a model of the rodent brain that is suitable for practical robot navigation. The core model, dubbed RatSLAM, has been demonstrated to have exactly the same advantages described earlier: it can build, maintain, and use maps simultaneously over extended periods of time and can construct maps of large and complex areas from very weak geometric information. The work contrasts with other efforts to embody models of rat brains in robots. The article describes the key elements of the known biology of the rat brain in relation to navigation and how the RatSLAM model captures the ideas from biology in a fashion suitable for implementation on a robotic platform. The paper then outline RatSLAM's performance in two difficult robot navigation challenges, demonstrating how a cognitive robotics approach to navigation can produce results that rival other state of the art approaches in robotics.

Persistent navigation and mapping using a biologically inspired SLAM system

The challenge of persistent navigation and mapping is to develop an autonomous robot system that can simultaneously localize, map and navigate over the lifetime of the robot with little or no human intervention. Most solutions to the simultaneous localization and mapping (SLAM) problem aim to produce highly accurate maps of areas that are assumed to be static. In contrast, solutions for persistent navigation and mapping must produce reliable goal-directed navigation outcomes in an environment that is assumed to be in constant flux. We investigate the persistent navigation and mapping problem in the context of an autonomous robot that performs mock deliveries in a working office environment over a two-week period. The solution was based on the biologically inspired visual SLAM system, RatSLAM. RatSLAM performed SLAM continuously while interacting with global and local navigation systems, and a task selection module that selected between exploration, delivery, and recharging modes. The robot performed 1,143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), traveled a total distance of more than 40 km over 37 hours of active operation, and recharged autonomously a total of 23 times.

Classifying an opponent’s behaviour in robot soccer

This paper illustrates the prediction of opponent behaviour in a competitive, highly dynamic, multi-agent and partially observable environment, namely RoboCup small size league robot soccer. The performance is illustrated in the context of the highly successful robot soccer team, the RoboRoos. The project is broken into three tasks; classification of behaviours, modelling and prediction of behaviours and integration of the predictions into the existing planning system. A probabilistic approach is taken to dealing with the uncertainty in the observations and with representing the uncertainty in the prediction of the behaviours. Results are shown for a classification system using a Naïve Bayesian Network that determines the opponent’s current behaviour. These results are compared to an expert designed fuzzy behaviour classification system. The paper illustrates how the modelling system will use the information from behaviour classification to produce probability distributions that model the manner with which the opponents perform their behaviours. These probability distributions are show to match well with the existing multi-agent planning system (MAPS) that forms the core of the RoboRoos system.

A Helicopter named Dolly : behavioural cloning for autonomous helicopter control

This paper considers the pros and cons of using Behavioural cloning for the development of low-level helicopter automation modules. Over the course of this project several Behavioural cloning approaches have been investigated. The results of the most effective Behavioural cloning approach are then compared to PID modules designed for the same aircraft. The comparison takes into consideration development time, reliability, and control performance. It has been found that Behavioural cloning techniques employing local approximators and a wide state-space coverage during training can produce stabilising control modules in less time than tuning PID controllers. However, performance and reliabity deficits have been found to exist with the Behavioural Cloning, attributable largely to the time variant nature of the dynamics due to the operating environment, and the pilot actions being poor for teaching. The final conclusion drawn here is that tuning PID modules remains superior to behavioural cloning for low-level helicopter automation.

Experience mapping : producing spatially continuous environment representations using RatSLAM

RatSLAM is a system for vision-based Simultaneous Localisation and Mapping (SLAM) inspired by models of the rodent hippocampus. The system can produce stable representations of large complex environments during robot experiments in both indoor and outdoor environments. These representations are both topological and metric in nature, and can involve multiple representations of the same place as well as discontinuities. In this paper we describe a new technique known as experience mapping that can be used online with the RatSLAM system to produce world representations known as experience maps. These maps group together multiple place representations and are spatially continuous. A number of experiments have been conducted in simulation and a real world office environment. These experiments demonstrate the high degree to which experience maps are representative of the spatial arrangement of the environment.

Effect of representation size and visual ambiguity on RatSLAM system performance

RatSLAM is a vision-based SLAM system based on extended models of the rodent hippocampus. RatSLAM creates environment representations that can be processed by the experience mapping algorithm to produce maps suitable for goal recall. The experience mapping algorithm also allows RatSLAM to map environments many times larger than could be achieved with a one to one correspondence between the map and environment, by reusing the RatSLAM maps to represent multiple sections of the environment. This paper describes experiments investigating the effects of the environment-representation size ratio and visual ambiguity on mapping and goal navigation performance. The experiments demonstrate that system performance is weakly dependent on either parameter in isolation, but strongly dependent on their joint values.

Biologically inspired visual landmark processing for simultaneous localization and mapping

This paper illustrates a method for finding useful visual landmarks for performing simultaneous localization and mapping (SLAM). The method is based loosely on biological principles, using layers of filtering and pooling to create learned templates that correspond to different views of the environment. Rather than using a set of landmarks and reporting range and bearing to the landmark, this system maps views to poses. The challenge is to produce a system that produces the same view for small changes in robot pose, but provides different views for larger changes in pose. The method has been developed to interface with the RatSLAM system, a biologically inspired method of SLAM. The paper describes the method of learning and recalling visual landmarks in detail, and shows the performance of the visual system in real robot tests.

Mediatisation and institutions of public memory : digital storytelling and the apology

Institutions of public memory are increasingly undertaking co-creative media initiatives in which community members create content with the support of institutional expertise and resources. This paper discusses one such initiative: the State Library of Queensland’s ‘Responses to the Apology’, which used a collaborative digital storytelling methodology to co-produce seven short videos capturing individual responses to Prime Minister Kevin Rudd’s 2008 ‘Apology to Australia’s Indigenous Peoples’. In examining this program, we are interested not only in the juxtaposition of ‘ordinary’ responses to an ‘official’ event, but also in how the production and display of these stories might also demonstrate a larger mediatisation of public memory.