Ontology-based image annotation

With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by training Support Vectors Machines with features extracted from salient objects. After that, contextual knowledge contained in ontologies is used to obtain the �nal labels by removing the ambiguity concepts. 4) Scene semantic annotation. The scene semantic extraction phase is to get the scene type by using both mid-level concepts and domain contextual knowledge in ontologies. Domain contextual knowledge is used to create scene con�guration that describes which objects co-exist with which scene type more frequently. The scene con�guration is represented in a probabilistic graph model, and probabilistic inference is employed to calculate the scene type given an annotated image. To evaluate the proposed methods, a series of experiments have been conducted in a large set of fully annotated outdoor scene images. These include a subset of the Corel database, a subset of the LabelMe dataset, the evaluation dataset of localized semantics in images, the spatial context evaluation dataset, and the segmented and annotated IAPR TC-12 benchmark.

Blast atlas: a virtual observatory for genomes

Following the completion of the draft Human Genome in 2001, genomic sequence data is becoming available at an accelerating rate, fueled by advances in sequencing and computational technology. Meanwhile, large collections of astronomical and geospatial data have allowed the creation of virtual observatories, accessible throughout the world and requiring only commodity hardware. Through a combination of advances in data management, data mining and visualization, this infrastructure enables the development of new scientific and educational applications as diverse as galaxy classification and real-time tracking of earthquakes and volcanic plumes. In the present paper, we describe steps taken along a similar path towards a virtual observatory for genomes – an immersive three-dimensional visual navigation and query system for comparative genomic data.

The role of informal networks in providing effective work opportunities for people with an intellectual disability

Being in paid employment is socially valued, and is linked to health, financial security and time use. Issues arising from a lack of occupational choice and control, and from diminished role partnerships are particularly problematic in the lives of people with an intellectual disability. Informal support networks are shown to influence work opportunities for people without disabilities, but their impact on the work experiences of people with disability has not been thoroughly explored. The experience of 'work' and preparation for work was explored with a group of four people with an intellectual disability (the participants) and the key members of their informal support networks (network members) in New South Wales, Australia. Network members and participants were interviewed and participant observations of work and other activities were undertaken. Data analysis included open, conceptual and thematic coding. Data analysis software assisted in managing the large datasets across multiple team members. The insight and actions of network members created and sustained the employment and support opportunities that effectively matched the needs and interests of the participants. Recommendations for future research are outlined.

Virtual communities as tools to support teaching practicum

The impact of Web 2.0 and social networking tools such as virtual communities, on education has been much commented on. The challenge for teachers is to embrace these new social networking tools and apply them to new educational contexts. The increasingly digitally-abled student cohorts and the need for educational applications of Web 2.0 are challenges that overwhelm many educators. This chapter will make three important contributions. Firstly it will explore the characteristics and behaviours of digitally-abled students enrolled in higher education. An innovation of this chapter will be the appli- cation of Bourdieu’s notions of capital, particularly social, cultural and digital capital to understand these characteristics. Secondly, it will present a possible use of a commonly used virtual community, Facebook©. Finally it will offer some advice for educators who are interested in using popular social networking communities, similar to Facebook©, in their teaching and learning.

Geometric and force relationships for the development of a scaled model hydraulic excavator

Hydraulic excavators in the mining industry are widely used owing to the large payload capabilities these machines can achieve. However, there are very few optimisation studies for producing efficient hydraulic excavator backets. An efficient bucket can avoid unnecessary weight; greatly influence the payload and optimise the efficiency of hydraulic mining excavators. This paper presents a framework for the development of a scaled hydraulic excavator by examining the geometry and force relationships. A small hydraulic excavator was purchased and fitted with a broom scaled to a factor. Geometric and force relationships of the model were derived to assist computer instrumentation to retrieve necessary variable input for bucket design.

Full Circle : a collection of prototypes

This exhibition engages with one of the key issues facing the fashion textiles industry in terms of future sustainability: that of the well being of fashion industry workers in Australia and New Zealand (people). This collection formed the basis of my honours dissertation (completed in New Zealand in 2008) which examines the contribution that design can make to sustainable manufacturing; particularly design for local production and consumption. An important aspect this work is the discussion of source, the work suggests that the made in China syndrome (in reference to the current state of over-consumerism in Australia and New Zealand) could be bought to a close through design to minimize waste and maximize opportunity for ‘people’: in this case both garment workers and the SMEs that employ them. The garments reflect the possibilities of focusing on a local approach that could be put into practice by a framework of SMEs that already exist. In addition the design process is highly transferrable and could be put into practice almost anywhere with minimal set up costs and a design ethos that progresses at the same pace as the skills of workers. This collection is a physical and conceptual embodiment of a source local/make local/sell local approach. The collection is an example of design that demonstrates that this is not an unrealistic ideal and is in fact possible through the development of a sustainable industry, in the sense of people, profit and planet, through adoption of a design process model that stops the waste at the source, by making better use of the raw materials and labour involved in making fashion garments. Although the focus of this research appears to centre on people and profit, this kind of source local/make local/sell local approach also has great benefits in terms of environmental sustainability.

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.

Evangeline Series : An interpretation of Classic Gothic Lolita

This series comprises three artefacts described below: Evangeline: Classic Gothic Lolita [3 piece garment]. Evangeline 2: Classic Gothic Lolita Pullip Doll Costume [2 piece garment]. Evangeline 3: Classic Gothic Lolita Mini Pullip Doll Costume [3 piece garment]. The series was part of an exhibition curated by Kathryn Hardy Bernal entitled: "Loli-Pop: A downtown Auckland view on Japanese street fashion". The exhibition explored the connections between gothic lolita fashion and popular culture. This work reflects on the aspect of collections in respect of the work of Hardy Bernal in relation to the connection between the japanese classic gothic lolita and the doll culture surrounding the movement. The pieces are interconnected and intended to communicate these aspects through a doll like dress worn by a model (Evangeline 1], carrying a doll wearing the same dress [Evangeline 2], carrying a smaller doll again wearing the same dress [Evangeline 3]. The artefacts appeared appeared as a central piece in the exhibition which was held at the War Memorial Museum in Auckland, New Zealand (15 September - 25 November 2007).

Automatically detecting pain in video through facial action units

In a clinical setting, pain is reported either through patient self-report or via an observer. Such measures are problematic as they are: 1) subjective, and 2) give no specific timing information. Coding pain as a series of facial action units (AUs) can avoid these issues as it can be used to gain an objective measure of pain on a frame-by-frame basis. Using video data from patients with shoulder injuries, in this paper, we describe an active appearance model (AAM)-based system that can automatically detect the frames in video in which a patient is in pain. This pain data set highlights the many challenges associated with spontaneous emotion detection, particularly that of expression and head movement due to the patient's reaction to pain. In this paper, we show that the AAM can deal with these movements and can achieve significant improvements in both the AU and pain detection performance compared to the current-state-of-the-art approaches which utilize similarity-normalized appearance features only.

Practical introduction to development of sage applications

Throughout this workshop session we have looked at various configurations of Sage as well as using the Sage UI to run Sage applications (e.g. the image viewer). More advanced usage of Sage has been demonstrated using a Sage compatible version of Paraview highlighting the potential of parallel rendering. The aim of this tutorial session is to give a practical introduction to developing visual content for a tiled display using the Sage libraries. After completing this tutorial you should have the basic tools required to develop your own custom Sage applications. This tutorial is designed for software developers and intermediate programming knowledge is assumed, along with some introductory OpenGL . You will be required to write small portions of C/C++ code to complete this worksheet. However if you do not feel comfortable writing code (or have never written in C or C++), we will be on hand throughout this session so feel free to ask for some help. We have a number of machines in this lab running a VNC client to a virtual machine running Fedora 12. You should all be able to log in with the username “escience”, and password “escience10”. Some of the commands in this worksheet require you to run them as the root user, so note the password as you may need to use it a few times. If you need to access the Internet, then use the username “qpsf01”, password “escience10”

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

Optimization of container process at seaport terminals

Seaport container terminals are an important part of the logistics systems in international trades. This paper investigates the relationship between quay cranes, yard machines and container storage locations in a multi-berth and multi-ship environment. The aims are to develop a model for improving the operation efficiency of the seaports and to develop an analytical tool for yard operation planning. Due to the fact that the container transfer times are sequence-dependent and with the large number of variables involve, the proposed model cannot be solved in a reasonable time interval for realistically sized problems. For this reason, List Scheduling and Tabu Search algorithms have been developed to solve this formidable and NP-hard scheduling problem. Numerical implementations have been analysed and promising results have been achieved.

Personalized recommender system based on item taxonomy and folksonomy

Item folksonomy or tag information is popularly available on the web now. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. In this paper, we propose to combine item taxonomy and folksonomy to reduce the noise of tags and make personalized item recommendations. The experiments conducted on the dataset collected from Amazon.com demonstrated the effectiveness of the proposed approaches. The results suggested that the recommendation accuracy can be further improved if we consider the viewpoints and the vocabularies of both experts and users.