Person-independent facial expression detection using constrained local models

In automatic facial expression detection, very accurate registration is desired which can be achieved via a deformable model approach where a dense mesh of 60-70 points on the face is used, such as an active appearance model (AAM). However, for applications where manually labeling frames is prohibitive, AAMs do not work well as they do not generalize well to unseen subjects. As such, a more coarse approach is taken for person-independent facial expression detection, where just a couple of key features (such as face and eyes) are tracked using a Viola-Jones type approach. The tracked image is normally post-processed to encode for shift and illumination invariance using a linear bank of filters. Recently, it was shown that this preprocessing step is of no benefit when close to ideal registration has been obtained. In this paper, we present a system based on the Constrained Local Model (CLM) which is a generic or person-independent face alignment algorithm which gains high accuracy. We show these results against the LBP feature extraction on the CK+ and GEMEP datasets.

Design Led Innovation : exploring the synthesis of needs, technologies and business models

The term Design is used to describe a wide range of activities. Like the term innovation, it is often used to describe both an activity and an outcome. Many products and services are often described as being designed, as they describe a conscious process of linking form and function. Alternatively, the many and varied processes of design are often used to describe a cost centre of an organisation to demonstrate a particular competency. However design is often not used to describe the ‘value’ it provides to an organisation and more importantly the ‘value’ it provides to both existing and future customers. Design Led Innovation bridges this gap. Design Led Innovation is a process of creating a sustainable competitive advantage, by radically changing the customer value proposition. A conceptual model has been developed to assist organisations apply and embed design in a company’s vision, strategy, culture, leadership and development processes.

Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia

Background: It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia. ----- ----- Methods: Data on weather variables, notified cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of cryptosporidiosis in Queensland, Australia. ----- ----- Results: The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of cryptosporidiosis. When SEIFA was less than 892 the RR of cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models. ----- ----- Conclusions: The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of cryptosporidiosis in Queensland, Australia.

Distinguishing forced versus discretionary replacements in new product diffusion models

In many product categories of durable goods such as TV, PC, and DVD player, the largest component of sales is generated by consumers replacing existing units. Aggregate sales models proposed by diffusion of innovation researchers for the replacement component of sales have incorporated several different replacement distributions such as Rayleigh, Weibull, Truncated Normal and Gamma. Although these alternative replacement distributions have been tested using both time series sales data and individual-level actuarial “life-tables” of replacement ages, there is no census on which distributions are more appropriate to model replacement behaviour. In the current study we are motivated to develop a new “modified gamma” distribution by two reasons. First we recognise that replacements have two fundamentally different drivers – those forced by failure and early, discretionary replacements. The replacement distribution for each of these drivers is expected to be quite different. Second, we observed a poor fit of other distributions to out empirical data. We conducted a survey of 8,077 households to empirically examine models of replacement sales for six electronic consumer durables – TVs, VCRs, DVD players, digital cameras, personal and notebook computers. This data allows us to construct individual-level “life-tables” for replacement ages. We demonstrate the new modified gamma model fits the empirical data better than existing models for all six products using both a primary and a hold-out sample.

Multivariate Markov Process Models for the transmission of methicillin-resistant Staphylococcus Aureus in a hospital ward

Methicillin-resistant Staphylococcus Aureus (MRSA) is a pathogen that continues to be of major concern in hospitals. We develop models and computational schemes based on observed weekly incidence data to estimate MRSA transmission parameters. We extend the deterministic model of McBryde, Pettitt, and McElwain (2007, Journal of Theoretical Biology 245, 470–481) involving an underlying population of MRSA colonized patients and health-care workers that describes, among other processes, transmission between uncolonized patients and colonized health-care workers and vice versa. We develop new bivariate and trivariate Markov models to include incidence so that estimated transmission rates can be based directly on new colonizations rather than indirectly on prevalence. Imperfect sensitivity of pathogen detection is modeled using a hidden Markov process. The advantages of our approach include (i) a discrete valued assumption for the number of colonized health-care workers, (ii) two transmission parameters can be incorporated into the likelihood, (iii) the likelihood depends on the number of new cases to improve precision of inference, (iv) individual patient records are not required, and (v) the possibility of imperfect detection of colonization is incorporated. We compare our approach with that used by McBryde et al. (2007) based on an approximation that eliminates the health-care workers from the model, uses Markov chain Monte Carlo and individual patient data. We apply these models to MRSA colonization data collected in a small intensive care unit at the Princess Alexandra Hospital, Brisbane, Australia.

An analytical method to solve a general class of nonlinear reactive transport models

Three recent papers published in Chemical Engineering Journal studied the solution of a model of diffusion and nonlinear reaction using three different methods. Two of these studies obtained series solutions using specialized mathematical methods, known as the Adomian decomposition method and the homotopy analysis method. Subsequently it was shown that the solution of the same particular model could be written in terms of a transcendental function called Gauss’ hypergeometric function. These three previous approaches focused on one particular reactive transport model. This particular model ignored advective transport and considered one specific reaction term only. Here we generalize these previous approaches and develop an exact analytical solution for a general class of steady state reactive transport models that incorporate (i) combined advective and diffusive transport, and (ii) any sufficiently differentiable reaction term R(C). The new solution is a convergent Maclaurin series. The Maclaurin series solution can be derived without any specialized mathematical methods nor does it necessarily involve the computation of any transcendental function. Applying the Maclaurin series solution to certain case studies shows that the previously published solutions are particular cases of the more general solution outlined here. We also demonstrate the accuracy of the Maclaurin series solution by comparing with numerical solutions for particular cases.

Clone detection in repositories of business process models

As organizations reach to higher levels of business process management maturity, they often find themselves maintaining repositories of hundreds or even thousands of process models, representing valuable knowledge about their operations. Over time, process model repositories tend to accumulate duplicate fragments (also called clones) as new process models are created or extended by copying and merging fragments from other models. This calls for methods to detect clones in process models, so that these clones can be refactored as separate subprocesses in order to improve maintainability. This paper presents an indexing structure to support the fast detection of clones in large process model repositories. The proposed index is based on a novel combination of a method for process model decomposition (specifically the Refined Process Structure Tree), with established graph canonization and string matching techniques. Experiments show that the algorithm scales to repositories with hundreds of models. The experimental results also show that a significant number of non-trivial clones can be found in process model repositories taken from industrial practice.

Discovering characteristics of stochastic collections of process models

Process models in organizational collections are typically modeled by the same team and using the same conventions. As such, these models share many characteristic features like size range, type and frequency of errors. In most cases merely small samples of these collections are available due to e.g. the sensitive information they contain. Because of their sizes, these samples may not provide an accurate representation of the characteristics of the originating collection. This paper deals with the problem of constructing collections of process models, in the form of Petri nets, from small samples of a collection for accurate estimations of the characteristics of this collection. Given a small sample of process models drawn from a real-life collection, we mine a set of generation parameters that we use to generate arbitrary-large collections that feature the same characteristics of the original collection. In this way we can estimate the characteristics of the original collection on the generated collections.We extensively evaluate the quality of our technique on various sample datasets drawn from both research and industry.

Automated error correction of business process models

As order dependencies between process tasks can get complex, it is easy to make mistakes in process model design, especially behavioral ones such as deadlocks. Notions such as soundness formalize behavioral errors and tools exist that can identify such errors. However these tools do not provide assistance with the correction of the process models. Error correction can be very challenging as the intentions of the process modeler are not known and there may be many ways in which an error can be corrected. We present a novel technique for automatic error correction in process models based on simulated annealing. Via this technique a number of process model alternatives are identified that resolve one or more errors in the original model. The technique is implemented and validated on a sample of industrial process models. The tests show that at least one sound solution can be found for each input model and that the response times are short.

Hierarchical Bayesian models for estimating the extent of plant pest invasions

Plant biosecurity requires statistical tools to interpret field surveillance data in order to manage pest incursions that threaten crop production and trade. Ultimately, management decisions need to be based on the probability that an area is infested or free of a pest. Current informal approaches to delimiting pest extent rely upon expert ecological interpretation of presence / absence data over space and time. Hierarchical Bayesian models provide a cohesive statistical framework that can formally integrate the available information on both pest ecology and data. The overarching method involves constructing an observation model for the surveillance data, conditional on the hidden extent of the pest and uncertain detection sensitivity. The extent of the pest is then modelled as a dynamic invasion process that includes uncertainty in ecological parameters. Modelling approaches to assimilate this information are explored through case studies on spiralling whitefly, Aleurodicus dispersus and red banded mango caterpillar, Deanolis sublimbalis. Markov chain Monte Carlo simulation is used to estimate the probable extent of pests, given the observation and process model conditioned by surveillance data. Statistical methods, based on time-to-event models, are developed to apply hierarchical Bayesian models to early detection programs and to demonstrate area freedom from pests. The value of early detection surveillance programs is demonstrated through an application to interpret surveillance data for exotic plant pests with uncertain spread rates. The model suggests that typical early detection programs provide a moderate reduction in the probability of an area being infested but a dramatic reduction in the expected area of incursions at a given time. Estimates of spiralling whitefly extent are examined at local, district and state-wide scales. The local model estimates the rate of natural spread and the influence of host architecture, host suitability and inspector efficiency. These parameter estimates can support the development of robust surveillance programs. Hierarchical Bayesian models for the human-mediated spread of spiralling whitefly are developed for the colonisation of discrete cells connected by a modified gravity model. By estimating dispersal parameters, the model can be used to predict the extent of the pest over time. An extended model predicts the climate restricted distribution of the pest in Queensland. These novel human-mediated movement models are well suited to demonstrating area freedom at coarse spatio-temporal scales. At finer scales, and in the presence of ecological complexity, exploratory models are developed to investigate the capacity for surveillance information to estimate the extent of red banded mango caterpillar. It is apparent that excessive uncertainty about observation and ecological parameters can impose limits on inference at the scales required for effective management of response programs. The thesis contributes novel statistical approaches to estimating the extent of pests and develops applications to assist decision-making across a range of plant biosecurity surveillance activities. Hierarchical Bayesian modelling is demonstrated as both a useful analytical tool for estimating pest extent and a natural investigative paradigm for developing and focussing biosecurity programs.

The influence of platform walking on BRT station bus dwell time estimation

The common approach to estimate bus dwell time at a BRT station platform is to apply the traditional dwell time methodology derived for suburban bus stops. Current dwell time models are sensitive towards bus type, fare collection policy along with the number of boarding and alighting passengers. However, they fall short in accounting for the effects of passenger/s walking on a relatively longer BRT station platform. Analysis presented in this paper shows that the average walking time of a passenger at BRT platform is 10 times more than that of bus stop. The requirement of walking to the bus entry door at the BRT station platform may lead to the bus experiencing a higher dwell time. This paper presents a theory for a BRT network which explains the loss of station capacity during peak period operation. It also highlights shortcomings of present available bus dwell time models suggested for the analysis of BRT operation.

Configurable multi-perspective business process models

A configurable process model provides a consolidated view of a family of business processes. It promotes the reuse of proven practices by providing analysts with a generic modelling artifact from which to derive individual process models. Unfortunately, the scope of existing notations for configurable process modelling is restricted, thus hindering their applicability. Specifically, these notations focus on capturing tasks and control-flow dependencies, neglecting equally important ingredients of business processes such as data and resources. This research fills this gap by proposing a configurable process modelling notation incorporating features for capturing resources, data and physical objects involved in the performance of tasks. The proposal has been implemented in a toolset that assists analysts during the configuration phase and guarantees the correctness of the resulting process models. The approach has been validated by means of a case study from the film industry.

Essays on irrigation development, farm production and unaccounted costs : theory and empirical evidence

A review of the literature related to issues involved in irrigation induced agricultural development (IIAD) reveals that: (1) the magnitude, sensitivity and distribution of social welfare of IIAD is not fully analysed; (2) the impacts of excessive pesticide use on farmers’ health are not adequately explained; (3) no analysis estimates the relationship between farm level efficiency and overuse of agro-chemical inputs under imperfect markets; and (4) the method of incorporating groundwater extraction costs is misleading. This PhD thesis investigates these issues by using primary data, along with secondary data from Sri Lanka. The overall findings of the thesis can be summarised as follows. First, the thesis demonstrates that Sri Lanka has gained a positive welfare change as a result of introducing new irrigation technology. The change in the consumer surplus is Rs.48,236 million, while the change in the producer surplus is Rs. 14,274 millions between 1970 and 2006. The results also show that the long run benefits and costs of IIAD depend critically on the magnitude of the expansion of the irrigated area, as well as the competition faced by traditional farmers (agricultural crowding out effects). The traditional sector’s ability to compete with the modern sector depends on productivity improvements, reducing production costs and future structural changes (spillover effects). Second, the thesis findings on pesticides used for agriculture show that, on average, a farmer incurs a cost of approximately Rs. 590 to 800 per month during a typical cultivation period due to exposure to pesticides. It is shown that the value of average loss in earnings per farmer for the ‘hospitalised’ sample is Rs. 475 per month, while it is approximately Rs. 345 per month for the ‘general’ farmers group during a typical cultivation season. However, the average willingness to pay (WTP) to avoid exposure to pesticides is approximately Rs. 950 and Rs. 620 for ‘hospitalised’ and ‘general’ farmers’ samples respectively. The estimated percentage contribution for WTP due to health costs, lost earnings, mitigating expenditure, and disutility are 29, 50, 5 and 16 per cent respectively for hospitalised farmers, while they are 32, 55, 8 and 5 per cent respectively for ‘general’ farmers. It is also shown that given market imperfections for most agricultural inputs, farmers are overusing pesticides with the expectation of higher future returns. This has led to an increase in inefficiency in farming practices which is not understood by the farmers. Third, it is found that various groundwater depletion studies in the economics literature have provided misleading optimal water extraction quantity levels. This is due to a failure to incorporate all production costs in the relevant models. It is only by incorporating quality changes to quantity deterioration, that it is possible to derive socially optimal levels. Empirical results clearly show that the benefits per hectare per month considering both the avoidance costs of deepening agro-wells by five feet from the existing average, as well as the avoidance costs of maintaining the water salinity level at 1.8 (mmhos/Cm), is approximately Rs. 4,350 for farmers in the Anuradhapura district and Rs. 5,600 for farmers in the Matale district.

Corrected mean-field models for spatially-dependent advection-diffusion-reaction phenomena

In the exclusion-process literature, mean-field models are often derived by assuming that the occupancy status of lattice sites is independent. Although this assumption is questionable, it is the foundation of many mean-field models. In this work we develop methods to relax the independence assumption for a range of discrete exclusion process-based mechanisms motivated by applications from cell biology. Previous investigations that focussed on relaxing the independence assumption have been limited to studying initially-uniform populations and ignored any spatial variations. By ignoring spatial variations these previous studies were greatly simplified due to translational invariance of the lattice. These previous corrected mean-field models could not be applied to many important problems in cell biology such as invasion waves of cells that are characterised by moving fronts. Here we propose generalised methods that relax the independence assumption for spatially inhomogeneous problems, leading to corrected mean-field descriptions of a range of exclusion process-based models that incorporate (i) unbiased motility, (ii) biased motility, and (iii) unbiased motility with agent birth and death processes. The corrected mean-field models derived here are applicable to spatially variable processes including invasion wave type problems. We show that there can be large deviations between simulation data and traditional mean-field models based on invoking the independence assumption. Furthermore, we show that the corrected mean-field models give an improved match to the simulation data in all cases considered.

Microscopic modelling of freeway operations

Freeways are divided roadways designed to facilitate the uninterrupted movement of motor vehicles. However, many freeways now experience demand flows in excess of capacity, leading to recurrent congestion. The Highway Capacity Manual (TRB, 1994) uses empirical macroscopic relationships between speed, flow and density to quantify freeway operations and performance. Capacity may be predicted as the maximum uncongested flow achievable. Although they are effective tools for design and analysis, macroscopic models lack an understanding of the nature of processes taking place in the system. Szwed and Smith (1972, 1974) and Makigami and Matsuo (1990) have shown that microscopic modelling is also applicable to freeway operations. Such models facilitate an understanding of the processes whilst providing for the assessment of performance, through measures of capacity and delay. However, these models are limited to only a few circumstances. The aim of this study was to produce more comprehensive and practical microscopic models. These models were required to accurately portray the mechanisms of freeway operations at the specific locations under consideration. The models needed to be able to be calibrated using data acquired at these locations. The output of the models needed to be able to be validated with data acquired at these sites. Therefore, the outputs should be truly descriptive of the performance of the facility. A theoretical basis needed to underlie the form of these models, rather than empiricism, which is the case for the macroscopic models currently used. And the models needed to be adaptable to variable operating conditions, so that they may be applied, where possible, to other similar systems and facilities. It was not possible to produce a stand-alone model which is applicable to all facilities and locations, in this single study, however the scene has been set for the application of the models to a much broader range of operating conditions. Opportunities for further development of the models were identified, and procedures provided for the calibration and validation of the models to a wide range of conditions. The models developed, do however, have limitations in their applicability. Only uncongested operations were studied and represented. Driver behaviour in Brisbane was applied to the models. Different mechanisms are likely in other locations due to variability in road rules and driving cultures. Not all manoeuvres evident were modelled. Some unusual manoeuvres were considered unwarranted to model. However the models developed contain the principal processes of freeway operations, merging and lane changing. Gap acceptance theory was applied to these critical operations to assess freeway performance. Gap acceptance theory was found to be applicable to merging, however the major stream, the kerb lane traffic, exercises only a limited priority over the minor stream, the on-ramp traffic. Theory was established to account for this activity. Kerb lane drivers were also found to change to the median lane where possible, to assist coincident mergers. The net limited priority model accounts for this by predicting a reduced major stream flow rate, which excludes lane changers. Cowan's M3 model as calibrated for both streams. On-ramp and total upstream flow are required as input. Relationships between proportion of headways greater than 1 s and flow differed for on-ramps where traffic leaves signalised intersections and unsignalised intersections. Constant departure onramp metering was also modelled. Minimum follow-on times of 1 to 1.2 s were calibrated. Critical gaps were shown to lie between the minimum follow-on time, and the sum of the minimum follow-on time and the 1 s minimum headway. Limited priority capacity and other boundary relationships were established by Troutbeck (1995). The minimum average minor stream delay and corresponding proportion of drivers delayed were quantified theoretically in this study. A simulation model was constructed to predict intermediate minor and major stream delays across all minor and major stream flows. Pseudo-empirical relationships were established to predict average delays. Major stream average delays are limited to 0.5 s, insignificant compared with minor stream delay, which reach infinity at capacity. Minor stream delays were shown to be less when unsignalised intersections are located upstream of on-ramps than signalised intersections, and less still when ramp metering is installed. Smaller delays correspond to improved merge area performance. A more tangible performance measure, the distribution of distances required to merge, was established by including design speeds. This distribution can be measured to validate the model. Merging probabilities can be predicted for given taper lengths, a most useful performance measure. This model was also shown to be applicable to lane changing. Tolerable limits to merging probabilities require calibration. From these, practical capacities can be estimated. Further calibration is required of traffic inputs, critical gap and minimum follow-on time, for both merging and lane changing. A general relationship to predict proportion of drivers delayed requires development. These models can then be used to complement existing macroscopic models to assess performance, and provide further insight into the nature of operations.