Relative entropy rate based multiple hidden Markov Model Approximation

This paper proposes a novel relative entropy rate (RER) based approach for multiple HMM (MHMM) approximation of a class of discrete-time uncertain processes. Under different uncertainty assumptions, the model design problem is posed either as a min-max optimisation problem or stochastic minimisation problem on the RER between joint laws describing the state and output processes (rather than the more usual RER between output processes). A suitable filter is proposed for which performance results are established which bound conditional mean estimation performance and show that estimation performance improves as the RER is reduced. These filter consistency and convergence bounds are the first results characterising multiple HMM approximation performance and suggest that joint RER concepts provide a useful model selection criteria. The proposed model design process and MHMM filter are demonstrated on an important image processing dim-target detection problem.

Extending the advanced forensic format to accommodate multiple data sources, logical evidence, arbitrary information and forensic workflow

Forensic analysis requires the acquisition and management of many different types of evidence, including individual disk drives, RAID sets, network packets, memory images, and extracted files. Often the same evidence is reviewed by several different tools or examiners in different locations. We propose a backwards-compatible redesign of the Advanced Forensic Formatdan open, extensible file format for storing and sharing of evidence, arbitrary case related information and analysis results among different tools. The new specification, termed AFF4, is designed to be simple to implement, built upon the well supported ZIP file format specification. Furthermore, the AFF4 implementation has downward comparability with existing AFF files.

Losing their marbles : syntax-free programming for assessing problem-solving skills

Novice programmers have difficulty developing an algorithmic solution while simultaneously obeying the syntactic constraints of the target programming language. To see how students fare in algorithmic problem solving when not burdened by syntax, we conducted an experiment in which a large class of beginning programmers were required to write a solution to a computational problem in structured English, as if instructing a child, without reference to program code at all. The students produced an unexpectedly wide range of correct, and attempted, solutions, some of which had not occurred to their teachers. We also found that many common programming errors were evident in the natural language algorithms, including failure to ensure loop termination, hardwiring of solutions, failure to properly initialise the computation, and use of unnecessary temporary variables, suggesting that these mistakes are caused by inexperience at thinking algorithmically, rather than difficulties in expressing solutions as program code.

Further evidence of a relationship between explaining, tracing and writing skills in introductory programming

This paper reports on a replication of earlier studies into a possible hierarchy of programming skills. In this study, the students from whom data was collected were at a university that had not provided data for earlier studies. Also, the students were taught the programming language Python, which had not been used in earlier studies. Thus this study serves as a test of whether the findings in the earlier studies were specific to certain institutions, student cohorts, and programming languages. Also, we used a non–parametric approach to the analysis, rather than the linear approach of earlier studies. Our results are consistent with the earlier studies. We found that students who cannot trace code usually cannot explain code, and also that students who tend to perform reasonably well at code writing tasks have also usually acquired the ability to both trace code and explain code.

The impact of pain on the quality of life of people with multiple sclerosis: A community survey

The purpose of this study was to examine the impact of pain on functioning across multiple quality of life (QOL) domains among individuals with multiple sclerosis (MS). A total of 219 people were recruited from a regional MS society membership database to serve as the community-based study sample. All participants completed a questionnaire containing items about their demographic and clinical characteristics, validated measures of QOL and MS-related disability, and a question on whether or not they had experienced clinically significant pain in the preceding 2 weeks. Respondents who reported pain then completed an in-person structured pain interview assessing pain characteristics (intensity, quality, location, extent, and duration). Comparisons between participants with and without MS-related pain demonstrated that pain prevalence and intensity were strongly correlated with QOL: physical health, psychological health, level of independence, and global QOL were more likely to be impaired among people with MS when pain was present, and the extent of impairment was associated with the intensity of pain. Moreover, these relationships remained significant even after statistically controlling for multiple demographic and clinical covariates associated with self-reported QOL. These findings suggest that for people with MS, pain is an important source of distress and disability beyond that caused by neurologic impairments.

Development of the benefit finding in multiple sclerosis (MS) caregiving scale: A longitudinal study of relations between benefit finding and adjustment

Benefit finding is a meaning making construct that has been shown to be related to adjustment in people with MS and their carers. This study investigated the dimensions, stability and potency of benefit finding in predicting adjustment over a 12 month interval using a newly developed Benefit Finding in Multiple Sclerosis Scale (BFiMSS). Usable data from 388 persons with MS and 232 carers was obtained from questionnaires completed at Time 1 and 12 months later (Time 2). Factor analysis of the BFiMSS revealed seven psychometrically sound factors: Compassion/Empathy, Spiritual Growth, Mindfulness, Family Relations Growth, Life Style Gains, Personal Growth, New Opportunities. BFiMSS total and factors showed satisfactory internal and retest reliability coefficients, and convergent, criterion and external validity. Results of regression analyses indicated that the Time 1 BFiMSS factors accounted for significant amounts of variance in each of the Time 2 adjustment outcomes (positive states of mind, positive affect, anxiety, depression) after controlling for Time 1 adjustment, and relevant demographic and illness variables. Findings delineate the dimensional structure of benefit finding in MS, the differential links between benefit finding dimensions and adjustment and the temporal unfolding of benefit finding in chronic illness.

Crowd Counting Using Multiple Local Features

In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.

Seeing is understanding : the effect of visualisation in understanding programming concepts

How and why visualisations support learning was the subject of this qualitative instrumental collective case study. Five computer programming languages (PHP, Visual Basic, Alice, GameMaker, and RoboLab) supporting differing degrees of visualisation were used as cases to explore the effectiveness of software visualisation to develop fundamental computer programming concepts (sequence, iteration, selection, and modularity). Cognitive theories of visual and auditory processing, cognitive load, and mental models provided a framework in which student cognitive development was tracked and measured by thirty-one 15-17 year old students drawn from a Queensland metropolitan secondary private girls’ school, as active participants in the research. Seventeen findings in three sections increase our understanding of the effects of visualisation on the learning process. The study extended the use of mental model theory to track the learning process, and demonstrated application of student research based metacognitive analysis on individual and peer cognitive development as a means to support research and as an approach to teaching. The findings also forward an explanation for failures in previous software visualisation studies, in particular the study has demonstrated that for the cases examined, where complex concepts are being developed, the mixing of auditory (or text) and visual elements can result in excessive cognitive load and impede learning. This finding provides a framework for selecting the most appropriate instructional programming language based on the cognitive complexity of the concepts under study.

Engaging students in programming

Poor student engagement and high failure rates in first year units were addressed at the Queensland University of Technology (QUT) with a course restructure involving a fresh approach to introducing programming. Students’ first taste of programming in the new course focused less on the language and syntax, and more on problem solving and design, and the role of programming in relation to other technologies they are likely to encounter in their studies. In effect, several technologies that have historically been compartmentalised and taught in isolation have been brought together as a breadth-first introduction to IT. Incorporating databases and Web development technologies into what used to be a purely programming unit gave students a very short introduction to each technology, with programming acting as the glue between each of them. As a result, students not only had a clearer understanding of the application of programming in the real world, but were able to determine their preference or otherwise for each of the technologies introduced, which will help them when the time comes for choosing a course major. Students engaged well in an intensely collaborative learning environment for this unit which was designed to both support the needs of students and meet industry expectations. Attrition from the unit was low, with computer laboratory practical attendance rates for the first time remaining high throughout semester, and the failure rate falling to a single figure percentage.

Monitoring the Performance of Complex Projects from Multiple Perspectives over Multiple Time Frames

When complex projects go wrong they can go horribly wrong with severe financial consequences. We are undertaking research to develop leading performance indicators for complex projects, metrics to provide early warning of potential difficulties. The assessment of success of complex projects can be made by a range of stakeholders over different time scales, against different levels of project results: the project’s outputs at the end of the project; the project’s outcomes in the months following project completion; and the project’s impact in the years following completion. We aim to identify leading performance indicators, which may include both success criteria and success factors, and which can be measured by the project team during project delivery to forecast success as assessed by key stakeholders in the days, months and years following the project. The hope is the leading performance indicators will act as alarm bells to show if a project is diverting from plan so early corrective action can be taken. It may be that different combinations of the leading performance indicators will be appropriate depending on the nature of project complexity. In this paper we develop a new model of project success, whereby success is assessed by different stakeholders over different time frames against different levels of project results. We then relate this to measurements that can be taken during project delivery. A methodology is described to evaluate the early parts of this model. Its implications and limitations are described. This paper describes work in progress.

Prediction of psychological adjustment to multiple sclerosis

This study examined the utility of self-efficacy as a predictor of social activity and mood control in multiple sclerosis (MS). Seventy-one subjects with MS were recruited from people attending an MS centre or from a mailing list and were examined on two occasions that were two months apart. Clinic patients were more disabled than patients who completed assessments by post, but they were of higher socioeconomic status and were less dysphoric. We attempted to predict self-reported performance of mood control and social activity at two months, from self-efficacy or performance on these tasks at pretest. Demographic variables, disorder status, disability, self-esteem and depression were also allowed to compete for entry into multiple regressions. Substantial stability in mood, performance and disability was observed over the two months. In both mood control and social activity, past performance was the strongest predictor of later performance, but self-efficacy also contributed significantly to the prediction. The disability level entered a prediction of socila activity, but no other variables predicted either type of performance. A secondary analysis predicting self-esteem at two months also included self-efficacy for social activity, illustrating the contribution of perceived capability to later assessments of self-worth. The study provided support for self-efficacy as a predictor of later behavioural outcomes and self-esteem in multiple sclerosis.

Evaluation of multiple sewage-associated bacteroides PCR markers for sewage pollution tracking

The host specificity of the five published sewage-associated Bacteroides markers (i.e., HF183, BacHum, HuBac, BacH and Human-Bac) was evaluated in Southeast Queensland, Australia by testing fecal DNA samples (n = 186) from 11 animal species including human fecal samples collected via influent to a sewage treatment plant (STP). All human fecal samples (n = 50) were positive for all five markers indicating 100% sensitivity of these markers. The overall specificity of the HF183 markers to differentiate between humans and animals was 99%. The specificities of the BacHum and BacH markers were > 94%, suggesting that these markers are suitable for sewage pollution in environmental waters in Australia. The BacHum (i.e., 63% specificity) and Human-Bac (i.e., 79% specificity) markers performed poorly in distinguishing between the sources of human and animal fecal samples. It is recommended that the specificity of the sewage-associated markers must be rigorously tested prior to its application to identify the sources of fecal pollution in environmental waters.

Getting Excited About Software Programming

Invited one hour presentation at Microsoft Tech Ed 2009 about getting students interested in games programming at QUT.