A case study of utilising concept knowledge in a topic specific document collection

The use of ‘topic’ concepts has shown improved search performance, given a query, by bringing together relevant documents which use different terms to describe a higher level concept. In this paper, we propose a method for discovering and utilizing concepts in indexing and search for a domain specific document collection being utilized in industry. This approach differs from others in that we only collect focused concepts to build the concept space and that instead of turning a user’s query into a concept based query, we experiment with different techniques of combining the original query with a concept query. We apply the proposed approach to a real-world document collection and the results show that in this scenario the use of concept knowledge at index and search can improve the relevancy of results.

Experiential brand deployment : improving tourism brand evaluations

This article examines the use of an experiential branding process to help leisure resort businesses evaluate their brand. We integrate experiential marketing and the quality function development approach in combination to help understand the brand from the perspectives of both the consumer and firm, to help resort service businesses build their experience-oriented competitive brands. The value of this study is that it provides a real-world brand framework, especially those resorts with limited resources. Much is spoken about the influence of the brand and why it is important, but little is known about decisions related to developing a brand, especially for firms that have limited resources such as resort tourism operators. Tourism operators tend to be small-to-medium enterprises that do not necessarily have the capacity to do everything suggested. Therefore, we explore how firms assess the critical elements of their brand by using an integrated approach. For example, the study finds that, first, by using the quality function development method resorts can identify the most critical brand elements, and second, we identify the associated strengths of each brand element and confirm the identified resort’s critical brand elements for investment. Results show the potential strategies to create a more holistic set of experiences.

Pre-processing for approximate Bayesian computation in image analysis

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.

Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey

We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set.We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

Using upper bounds on attainable discrimination to select discrete valued features

Selection of features that will permit accurate pattern classification is a difficult task. However, if a particular data set is represented by discrete valued features, it becomes possible to determine empirically the contribution that each feature makes to the discrimination between classes. This paper extends the discrimination bound method so that both the maximum and average discrimination expected on unseen test data can be estimated. These estimation techniques are the basis of a backwards elimination algorithm that can be use to rank features in order of their discriminative power. Two problems are used to demonstrate this feature selection process: classification of the Mushroom Database, and a real-world, pregnancy related medical risk prediction task - assessment of risk of perinatal death.

A data mining based method for discovery of web services and their compositions

Due to the availability of huge number of web services, finding an appropriate Web service according to the requirements of a service consumer is still a challenge. Moreover, sometimes a single web service is unable to fully satisfy the requirements of the service consumer. In such cases, combinations of multiple inter-related web services can be utilised. This paper proposes a method that first utilises a semantic kernel model to find related services and then models these related Web services as nodes of a graph. An all-pair shortest-path algorithm is applied to find the best compositions of Web services that are semantically related to the service consumer requirement. The recommendation of individual and composite Web services composition for a service request is finally made. Empirical evaluation confirms that the proposed method significantly improves the accuracy of service discovery in comparison to traditional keyword-based discovery methods.

Detecting rare events using Kullback-Leibler divergence

One main challenge in developing a system for visual surveillance event detection is the annotation of target events in the training data. By making use of the assumption that events with security interest are often rare compared to regular behaviours, this paper presents a novel approach by using Kullback-Leibler (KL) divergence for rare event detection in a weakly supervised learning setting, where only clip-level annotation is available. It will be shown that this approach outperforms state-of-the-art methods on a popular real-world dataset, while preserving real time performance.

Teaching and Assessing Reflective Writing in a Large Undergraduate Core Substantive Law Unit

The focus of higher education has shifted towards building students’ skills and self-awareness for future employment, in addition to developing substantive discipline knowledge. This means that there is an increasing need for embedding approaches to teaching and learning which provide a context for skills development and opportunities for students to prepare for the transition from legal education to professional practice. This chapter reports on a large (500-600 students) core undergraduate Equity law unit in an Australian University. ePortfolio has been embedded in Equity as a means of enabling students to document their reflections on their skill development in that unit. Students are taught, practice and are assessed on their teamwork and letter writing skills in the context of writing a letter of advice to a fictional client in response to a real world problem. Following submission of the team letter, students are asked to reflect on their skill development and document their reflections in ePortfolio. A scaffolded approach to teaching reflective writing is adopted using a blended model of delivery which combines face to face lectures and online resources, including an online module, facts sheets designed to guide students through the process of reflection by following the TARL model of reflection, and exemplars of reflective writing. Although students have engaged in the process of reflective writing in Equity for some years, in semester one 2011 assessment criteria were developed and the ePortfolio reflections were summatively assessed for the first time. The model of teaching and assessing reflective practice was evaluated in a range of ways by seeking feedback from students and academic staff responsible for implementing the model and asking them to reflect on their experiences. This chapter describes why skill development and reflective writing were embedded in the undergraduate law unit Equity; identify the teaching and learning approaches which were implemented to teach reflective writing to online and internal Equity students; explain the assessment processes; analyse the empirical evidence from evaluations; document the lessons learnt and discuss planned future improvements to the teaching and assessment strategies.

Consumer Behaviour : Buying, Having, Being (3e)

Consumer behaviour is more than buying things; it also embraces the study of how having (or not having) things affects our lives and how possessions influence the way we feel about ourselves and each other - our state of being. The 3rd edition of Consumer Behaviour is presented in a contemporary framework based around the buying, having and being model and in an Australasian context. Students will be engaged and excited by the most current research, real-world examples, global coverage, managerial applications and ethical examples to cover all facets of consumer behaviour. With new coverage of Personality and incorporating real consumer data, Consumer Behaviour is fresh, relevant and up-to-date . It provides students with the best possible introduction to this fascinating discipline.

Towards coordinated vision-based docking using an Autonomous Surface Vehicle

This paper describes the development and experimental evaluation of a novel vision-based Autonomous Surface Vehicle with the purpose of performing coordinated docking manoeuvres with a target, such as an Autonomous Underwater Vehicle, on the water’s surface. The system architecture integrates two small processor units; the first performs vehicle control and implements a virtual force obstacle avoidance and docking strategy, with the second performing vision-based target segmentation and tracking. Furthermore, the architecture utilises wireless sensor network technology allowing the vehicle to be observed by, and even integrated within an ad-hoc sensor network. The system performance is demonstrated through real-world experiments.

Automated sensory data alignment for environmental and epidermal change monitoring

In this paper we present research adapting a state of the art condition-invariant robotic place recognition algorithm to the role of automated inter- and intra-image alignment of sensor observations of environmental and skin change over time. The approach involves inverting the typical criteria placed upon navigation algorithms in robotics; we exploit rather than attempt to fix the limited camera viewpoint invariance of such algorithms, showing that approximate viewpoint repetition is realistic in a wide range of environments and medical applications. We demonstrate the algorithms automatically aligning challenging visual data from a range of real-world applications: ecological monitoring of environmental change, aerial observation of natural disasters including flooding, tsunamis and bushfires and tracking wound recovery and sun damage over time and present a prototype active guidance system for enforcing viewpoint repetition. We hope to provide an interesting case study for how traditional research criteria in robotics can be inverted to provide useful outcomes in applied situations.

Teaching dispute resolution in the first year for law student well-being

At the QUT Law School, the most recent curriculum review responded to an increasing demand from the profession for law graduates to be equipped with dispute resolution knowledge, skills and attitudes. From 2015, a compulsory dispute resolution subject will be a critical part of an intentionally designed core first year curriculum. It is important for the Law School at QUT that no graduate of the new curriculum will leave our institution without real world dispute resolution knowledge and skills. This initiative is also grounded in evidenced-based research about the benefits for student well-being that derive from the subject content and pedagogy of dispute resolution. This paper explains why teaching dispute resolution in the first year of the law degree is an important strategy for promoting the well-being of law students.

Corner detection in images under different noise levels

Corner detection has shown its great importance in many computer vision tasks. However, in real-world applications, noise in the image strongly affects the performance of corner detectors. Few corner detectors have been designed to be robust to heavy noise by now, partly because the noise could be reduced by a denoising procedure. In this paper, we present a corner detector that could find discriminative corners in images contaminated by noise of different levels, without any denoising procedure. Candidate corners (i.e., features) are firstly detected by a modified SUSAN approach, and then false corners in noise are rejected based on their local characteristics. Features in flat regions are removed based on their intensity centroid, and features on edge structures are removed using the Harris response. The detector is self-adaptive to noise since the image signal-to-noise ratio (SNR) is automatically estimated to choose an appropriate threshold for refining features. Experimental results show that our detector has better performance at locating discriminative corners in images with strong noise than other widely used corner or keypoint detectors.

Tech Ed students strut their stuff: information literacy and a practical assignment

This reports a study that seeks to explore the experience of students majoring in technology and design in an undergraduate education degree. It examines their experiences in finding and using information for a practical assignment. In mapping the variation of the students' experience, the study uses a qualitative, interpretive approach to analyse the data, which was collected via one-to-one interviews. The analysis yielded five themes through which technology education students find and use information: interaction with others; experience (past and new); formal educational learning; the real world; and incidental occurrences. The intentions and strategies that form the students' approaches to finding and using information are discussed. So too are the implications for teaching practice.

Locally weighted learning methods for non-rigid robot control

This thesis develops a novel approach to robot control that learns to account for a robot's dynamic complexities while executing various control tasks using inspiration from biological sensorimotor control and machine learning. A robot that can learn its own control system can account for complex situations and adapt to changes in control conditions to maximise its performance and reliability in the real world. This research has developed two novel learning methods, with the aim of solving issues with learning control of non-rigid robots that incorporate additional dynamic complexities. The new learning control system was evaluated on a real three degree-of-freedom elastic joint robot arm with a number of experiments: initially validating the learning method and testing its ability to generalise to new tasks, then evaluating the system during a learning control task requiring continuous online model adaptation.