An experiment in mixed compilation/interpretation

One of the classic forms of intermediate representation used for communication between compiler front-ends and back-ends are those based on abstract stack machines. It is possible to compile the stack machine instructions into machine code by means of an interpretive code generator, or to simulate the stack machine at runtime using an interpreter. This paper describes an approach intermediate between these two extremes. The front-end for a commercial Modula 2 compiler was ported to the "industry standard PC", and a partially compiling back-end written. The object code runs with the assistance of an interpreter, but may be linked with libraries which are fully compiled. The intent was to provide a programming environment on the PC which is identical to that of the same compilers on 32-bit UNIX machines. This objective has been met, and the compiler is available to educational institutions as free-ware. The design basis of the new compiler is described, and the performance critically evaluated.

Condition-based prognosis of machine health

Modern machines are complex and often required to operate long hours to achieve production targets. The ability to detect symptoms of failure, hence, forecasting the remaining useful life of the machine is vital to prevent catastrophic failures. This is essential to reducing maintenance cost, operation downtime and safety hazard. Recent advances in condition monitoring technologies have given rise to a number of prognosis models that attempt to forecast machinery health based on either condition data or reliability data. In practice, failure condition trending data are seldom kept by industries and data that ended with a suspension are sometimes treated as failure data. This paper presents a novel approach of incorporating historical failure data and suspended condition trending data in the prognostic model. The proposed model consists of a FFNN whose training targets are asset survival probabilities estimated using a variation of Kaplan-Meier estimator and degradation-based failure PDF estimator. The output survival probabilities collectively form an estimated survival curve. The viability of the model was tested using a set of industry vibration data.

Voter behaviour and constitutional change in Australia since 1967

Australian Constitutional referendums have been part of the Australian political system since federation. Up to the year 1999 (the time of the last referendum in Australia), constitutional change in Australia does not have a good history of acceptance. Since 1901, there have been 44 proposed constitutional changes with eight gaining the required acceptance according to section 128 of the Australian Constitution. In the modern era since 1967, there have been 20 proposals over seven referendum votes for a total of four changes. Over this same period, there have been 13 federal general elections which have realised change in government just five times. This research examines the electoral behaviour of Australian voters from 1967 to 1999 for each referendum. Party identification has long been a key indicator in general election voting. This research considers whether the dominant theory of voter behaviour in general elections (the Michigan Model) provides a plausible explanation for voting in Australian referendums. In order to explain electoral behaviour in each referendum, this research has utilised available data from the Australian Electoral Commission, the 1996 Australian Bureau of Statistics Census data, and the 1999 Australian Constitutional Referendum Study. This data has provided the necessary variables required to measure the impact of the Michigan Model of voter behaviour. Measurements have been conducted using bivariate and multivariate analyses. Each referendum provides an overview of the events at the time of the referendum as well as the =yes‘ and =no‘ cases at the time each referendum was initiated. Results from this research provide support for the Michigan Model of voter behaviour in Australian referendum voting. This research concludes that party identification, as a key variable of the Michigan Model, shows that voters continue to take their cues for voting from the political party they identify with in Australian referendums. However, the outcome of Australian referendums clearly shows that partisanship is only one of a number of contributory factors in constitutional referendums.

Creative Industries

We are experiencing a period of profound social and economic transformation. This is a shift from an industrial economy to a knowledge economy (or a “creative economy”; or an “economy of the imagination”.) This new, emerging economic system is fundamentally organised around people (not machines or buildings); and around place. We heard Richard Florida argue that creative, talented people won’t go to where the job is, but vice versa, the job will come to them. So according to Florida, where we live is becoming the primary factor in global economic development. (Incidentally, it is worth contrasting this idea with the alternative proposition - put by speakers at this Forum - of “new nomadism”, that is, that creativity is nomadic and not bound by place.)

Decomposition of aggregated load : finding induction motor fraction in real load

The main contribution of this paper is decomposition/separation of the compositie induction motors load from measurement at a system bus. In power system transmission buses load is represented by static and dynamic loads. The induction motor is considered as the main dynamic loads and in the practice for major transmission buses there will be many and various induction motors contributing. Particularly at an industrial bus most of the load is dynamic types. Rather than traing to extract models of many machines this paper seeks to identify three groups of induction motors to represent the dynamic loads. Three groups of induction motors used to characterize the load. These are the small groups (4kw to 11kw), the medium groups (15kw to 180kw) and the large groups (above 630kw). At first these groups with different percentage contribution of each group is composite. After that from the composite models, each motor percentage contribution is decomposed by using the least square algorithms. In power system commercial and the residential buses static loads percentage is higher than the dynamic loads percentage. To apply this theory to other types of buses such as residential and commerical it is good practice to represent the total load as a combination of composite motor loads, constant impedence loads and constant power loads. To validate the theory, the 24hrs of Sydney West data is decomposed according to the three groups of motor models.

Metaheuristic approaches to the hybrid flow shop scheduling problem with a cost-related criterion

In the paper, the flow-shop scheduling problem with parallel machines at each stage (machine center) is studied. For each job its release and due date as well as a processing time for its each operation are given. The scheduling criterion consists of three parts: the total weighted earliness, the total weighted tardiness and the total weighted waiting time. The criterion takes into account the costs of storing semi-manufactured products in the course of production and ready-made products as well as penalties for not meeting the deadlines stated in the conditions of the contract with customer. To solve the problem, three constructive algorithms and three metaheuristics (based one Tabu Search and Simulated Annealing techniques) are developed and experimentally analyzed. All the proposed algorithms operate on the notion of so-called operation processing order, i.e. the order of operations on each machine. We show that the problem of schedule construction on the base of a given operation processing order can be reduced to the linear programming task. We also propose some approximation algorithm for schedule construction and show the conditions of its optimality.

Improving automatic speaker verification using SVM techniques

Automatic recognition of people is an active field of research with important forensic and security applications. In these applications, it is not always possible for the subject to be in close proximity to the system. Voice represents a human behavioural trait which can be used to recognise people in such situations. Automatic Speaker Verification (ASV) is the process of verifying a persons identity through the analysis of their speech and enables recognition of a subject at a distance over a telephone channel { wired or wireless. A significant amount of research has focussed on the application of Gaussian mixture model (GMM) techniques to speaker verification systems providing state-of-the-art performance. GMM's are a type of generative classifier trained to model the probability distribution of the features used to represent a speaker. Recently introduced to the field of ASV research is the support vector machine (SVM). An SVM is a discriminative classifier requiring examples from both positive and negative classes to train a speaker model. The SVM is based on margin maximisation whereby a hyperplane attempts to separate classes in a high dimensional space. SVMs applied to the task of speaker verification have shown high potential, particularly when used to complement current GMM-based techniques in hybrid systems. This work aims to improve the performance of ASV systems using novel and innovative SVM-based techniques. Research was divided into three main themes: session variability compensation for SVMs; unsupervised model adaptation; and impostor dataset selection. The first theme investigated the differences between the GMM and SVM domains for the modelling of session variability | an aspect crucial for robust speaker verification. Techniques developed to improve the robustness of GMMbased classification were shown to bring about similar benefits to discriminative SVM classification through their integration in the hybrid GMM mean supervector SVM classifier. Further, the domains for the modelling of session variation were contrasted to find a number of common factors, however, the SVM-domain consistently provided marginally better session variation compensation. Minimal complementary information was found between the techniques due to the similarities in how they achieved their objectives. The second theme saw the proposal of a novel model for the purpose of session variation compensation in ASV systems. Continuous progressive model adaptation attempts to improve speaker models by retraining them after exploiting all encountered test utterances during normal use of the system. The introduction of the weight-based factor analysis model provided significant performance improvements of over 60% in an unsupervised scenario. SVM-based classification was then integrated into the progressive system providing further benefits in performance over the GMM counterpart. Analysis demonstrated that SVMs also hold several beneficial characteristics to the task of unsupervised model adaptation prompting further research in the area. In pursuing the final theme, an innovative background dataset selection technique was developed. This technique selects the most appropriate subset of examples from a large and diverse set of candidate impostor observations for use as the SVM background by exploiting the SVM training process. This selection was performed on a per-observation basis so as to overcome the shortcoming of the traditional heuristic-based approach to dataset selection. Results demonstrate the approach to provide performance improvements over both the use of the complete candidate dataset and the best heuristically-selected dataset whilst being only a fraction of the size. The refined dataset was also shown to generalise well to unseen corpora and be highly applicable to the selection of impostor cohorts required in alternate techniques for speaker verification.

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.

Exploiting multiple feature sets in data-driven impostor dataset selection for speaker verification

This study assesses the recently proposed data-driven background dataset refinement technique for speaker verification using alternate SVM feature sets to the GMM supervector features for which it was originally designed. The performance improvements brought about in each trialled SVM configuration demonstrate the versatility of background dataset refinement. This work also extends on the originally proposed technique to exploit support vector coefficients as an impostor suitability metric in the data-driven selection process. Using support vector coefficients improved the performance of the refined datasets in the evaluation of unseen data. Further, attempts are made to exploit the differences in impostor example suitability measures from varying features spaces to provide added robustness.

Health, endogenous time preference, and inequality : a political economy perspective

We study an overlapping-generations model in which agents' mortality risks, and consequently impatience, are endogenously determined by private and public investment in health care. Revenues allocated for public health care arc determined by a voting process. We find that the degree of substitutability between public and private health expenditures matters for macroeconomic outcomes of the model. Higher substitutability implies a “crowding-out" effect, which in turn impacts adversely on morality risks and impatience leading to lower public expenditures on health care in the political equilibrium. Consequently, higher substitutability is associated with greater polarization in wealth, and long-run distributions that are bimodal.

A political economy perspective on persistent inequality, inflation, and redistribution

In this paper we examine the dynamics of the link between inequality and inflation from a political economy perspective. We consider a simple dynamic general equilibrium model in which agents vote over the desired inflation rate in each period, and inequality is persistent. Inflation in our model is a mechanism of redistribution, and we find that the link between inequality and inflation within any period or over time depends on institutional and preference related parameters. Furthermore, we find that differences in the initial distributions of wealth can yield a diverse set of patterns for the evolution of the inflation and inequality link. Relative to existing literature, our model leads to more precise predictions about the inflation-inequality correlation. To that end, results in the extant empirical literature on the inflation and inequality link need to be interpreted with caution.

An evaluation of current and future botnet defenses

Botnets are large networks of compromised machines under the control of a bot master. These botnets constantly evolve their defences to allow the continuation of their malicious activities. The constant development of new botnet mitigation strategies and their subsequent defensive countermeasures has lead to a technological arms race, one which the bot masters have significant incentives to win. This dissertation analyzes the current and future states of the botnet arms race by introducing a taxonomy of botnet defences and a simulation framework for evaluating botnet techniques. The taxonomy covers current botnet techniques and highlights possible future techniques for further analysis under the simulation framework. This framework allows the evaluation of the effect techniques such as reputation systems and proof of work schemes have on the resources required to disable a peer-to-peer botnet. Given the increase in the resources required, our results suggest that the prospects of eliminating the botnet threat are limited.

Darjeeling, a feature-rich VM for the resource poor

The programming and retasking of sensor nodes could benefit greatly from the use of a virtual machine (VM) since byte code is compact, can be loaded on demand, and interpreted on a heterogeneous set of devices. The challenge is to ensure good programming tools and a small footprint for the virtual machine to meet the memory constraints of typical WSN platforms. To this end we propose Darjeeling, a virtual machine modelled after the Java VM and capable of executing a substantial subset of the Java language, but designed specifically to run on 8- and 16-bit microcontrollers with 2 - 10 KB of RAM. The Darjeeling VM uses a 16- rather than a 32-bit architecture, which is more efficient on the targeted platforms. Darjeeling features a novel memory organisation with strict separation of reference from non-reference types which eliminates the need for run-time type inspection in the underlying compacting garbage collector. Darjeeling uses a linked stack model that provides light-weight threads, and supports synchronisation. The VM has been implemented on three different platforms and was evaluated with micro benchmarks and a real-world application. The latter includes a pure Java implementation of the collection tree routing protocol conveniently programmed as a set of cooperating threads, and a reimplementation of an existing environmental monitoring application. The results show that Darjeeling is a viable solution for deploying large-scale heterogeneous sensor networks. Copyright 2009 ACM.

Dragline automation: experimental evaluation through productivity trial

Since 1993 we have been working on the automation of dragline excavators, the largest earthmoving machines that exist. Recently we completed a large-scale experimental program where the automation system was used for production purposes over a two week period and moved over 200,000 tonnes of overburden. This is a landmark achievement in the history of automated excavation. In this paper we briefly describe the robotic system and how it works cooperatively with the machine operator. We then describe our methodology for gauging machine performance, analyze results from the production trial and comment on the effectiveness of the system that we have created. © Springer-Verlag Berlin Heidelberg 2006.