Random Indexing K-tree

Random Indexing K-tree is the combination of two algorithms suited for large scale document clustering.

Interventions for preventing and managing radiation-induced skin reactions in cancer patients (Protocol)

To assess the effects of any interventions which aim to prevent or manage radiation-induced skin reactions in people with cancer.

Clustering with random indexing K-tree and XML structure

This paper describes the approach taken to the clustering task at INEX 2009 by a group at the Queensland University of Technology. The Random Indexing (RI) K-tree has been used with a representation that is based on the semantic markup available in the INEX 2009 Wikipedia collection. The RI K-tree is a scalable approach to clustering large document collections. This approach has produced quality clustering when evaluated using two different methodologies.

Metadata aggregation : a critical component of research infrastructure for the future

The development of research data management infrastructure and services and making research data more discoverable and accessible to the research community is a key priority at the national, state and individual university level. This paper will discuss and reflect upon a collaborative project between Griffith University and the Queensland University of Technology to commission a Metadata Hub or Metadata Aggregation service based upon open source software components. It will describe the role that metadata aggregation services play in modern research infrastructure and argue that this role is a critical one.

Towards improved speech recognition for resource poor languages

In recent times, the improved levels of accuracy obtained by Automatic Speech Recognition (ASR) technology has made it viable for use in a number of commercial products. Unfortunately, these types of applications are limited to only a few of the world’s languages, primarily because ASR development is reliant on the availability of large amounts of language specific resources. This motivates the need for techniques which reduce this language-specific, resource dependency. Ideally, these approaches should generalise across languages, thereby providing scope for rapid creation of ASR capabilities for resource poor languages. Cross Lingual ASR emerges as a means for addressing this need. Underpinning this approach is the observation that sound production is largely influenced by the physiological construction of the vocal tract, and accordingly, is human, and not language specific. As a result, a common inventory of sounds exists across languages; a property which is exploitable, as sounds from a resource poor, target language can be recognised using models trained on resource rich, source languages. One of the initial impediments to the commercial uptake of ASR technology was its fragility in more challenging environments, such as conversational telephone speech. Subsequent improvements in these environments has gained consumer confidence. Pragmatically, if cross lingual techniques are to considered a viable alternative when resources are limited, they need to perform under the same types of conditions. Accordingly, this thesis evaluates cross lingual techniques using two speech environments; clean read speech and conversational telephone speech. Languages used in evaluations are German, Mandarin, Japanese and Spanish. Results highlight that previously proposed approaches provide respectable results for simpler environments such as read speech, but degrade significantly when in the more taxing conversational environment. Two separate approaches for addressing this degradation are proposed. The first is based on deriving better target language lexical representation, in terms of the source language model set. The second, and ultimately more successful approach, focuses on improving the classification accuracy of context-dependent (CD) models, by catering for the adverse influence of languages specific phonotactic properties. Whilst the primary research goal in this thesis is directed towards improving cross lingual techniques, the catalyst for investigating its use was based on expressed interest from several organisations for an Indonesian ASR capability. In Indonesia alone, there are over 200 million speakers of some Malay variant, provides further impetus and commercial justification for speech related research on this language. Unfortunately, at the beginning of the candidature, limited research had been conducted on the Indonesian language in the field of speech science, and virtually no resources existed. This thesis details the investigative and development work dedicated towards obtaining an ASR system with a 10000 word recognition vocabulary for the Indonesian language.

Calculating shades of meaning in semantic spaces

This thesis introduces the problem of conceptual ambiguity, or Shades of Meaning (SoM) that can exist around a term or entity. As an example consider President Ronald Reagan the ex-president of the USA, there are many aspects to him that are captured in text; the Russian missile deal, the Iran-contra deal and others. Simply finding documents with the word “Reagan” in them is going to return results that cover many different shades of meaning related to "Reagan". Instead it may be desirable to retrieve results around a specific shade of meaning of "Reagan", e.g., all documents relating to the Iran-contra scandal. This thesis investigates computational methods for identifying shades of meaning around a word, or concept. This problem is related to word sense ambiguity, but is more subtle and based less on the particular syntactic structures associated with or around an instance of the term and more with the semantic contexts around it. A particularly noteworthy difference from typical word sense disambiguation is that shades of a concept are not known in advance. It is up to the algorithm itself to ascertain these subtleties. It is the key hypothesis of this thesis that reducing the number of dimensions in the representation of concepts is a key part of reducing sparseness and thus also crucial in discovering their SoMwithin a given corpus.

Workflows, processes and technical solutions for seeding the research data commons

Queensland University of Technology (QUT) completed an Australian National Data Service (ANDS) funded “Seeding the Commons Project” to contribute metadata to Research Data Australia. The project employed two Research Data Librarians from October 2009 through to July 2010. Technical support for the project was provided by QUT’s High Performance Computing and Research Support Specialists. ---------- The project identified and described QUT’s category 1 (ARC / NHMRC) research datasets. Metadata for the research datasets was stored in QUT’s Research Data Repository (Architecta Mediaflux). Metadata which was suitable for inclusion in Research Data Australia was made available to the Australian Research Data Commons (ARDC) in RIF-CS format. ---------- Several workflows and processes were developed during the project. 195 data interviews took place in connection with 424 separate research activities which resulted in the identification of 492 datasets. ---------- The project had a high level of technical support from QUT High Performance Computing and Research Support Specialists who developed the Research Data Librarian interface to the data repository that enabled manual entry of interview data and dataset metadata, creation of relationships between repository objects. The Research Data Librarians mapped the QUT metadata repository fields to RIF-CS and an application was created by the HPC and Research Support Specialists to generate RIF-CS files for harvest by the Australian Research Data Commons (ARDC). ---------- This poster will focus on the workflows and processes established for the project including: ---------- • Interview processes and instruments • Data Ingest from existing systems (including mapping to RIF-CS) • Data entry and the Data Librarian interface to Mediaflux • Verification processes • Mapping and creation of RIF-CS for the ARDC

Semantic oscillations : encoding context and structure in complex valued holographic vectors

In computational linguistics, information retrieval and applied cognition, words and concepts are often represented as vectors in high dimensional spaces computed from a corpus of text. These high dimensional spaces are often referred to as Semantic Spaces. We describe a novel and efficient approach to computing these semantic spaces via the use of complex valued vector representations. We report on the practical implementation of the proposed method and some associated experiments. We also briefly discuss how the proposed system relates to previous theoretical work in Information Retrieval and Quantum Mechanics and how the notions of probability, logic and geometry are integrated within a single Hilbert space representation. In this sense the proposed system has more general application and gives rise to a variety of opportunities for future research.

Building an Australian user community for VIVO

The Australian National Data Service (ANDS) was established in 2008 and aims to: influence national policy in the area of data management in the Australian research community; inform best practice for the curation of data, and, transform the disparate collections of research data around Australia into a cohesive collection of research resources One high profile ANDS activity is to establish the population of Research Data Australia, a set of web pages describing data collections produced by or relevant to Australian researchers. It is designed to promote visibility of research data collections in search engines, in order to encourage their re-use. As part of activities associated with the Australian National Data Service, an increasing number of Australian Universities are choosing to implement VIVO, not as a platform to profile information about researchers, but as a 'metadata store' platform to profile information about institutional research data sets, both locally and as part of a national data commons. To date, the University of Melbourne, Griffith University, the Queensland University of Technology, and the University of Western Australia have all chosen to implement VIVO, with interest from other Universities growing.

Getting research data ‘out there’ : collaborative solutions to identifying, describing and making research data more visible

At QUT research data refers to information that is generated or collected to be used as primary sources in the production of original research results, and which would be required to validate or replicate research findings (Callan, De Vine, & Baker, 2010). Making publicly funded research data discoverable by the broader research community and the public is a key aim of the Australian National Data Service (ANDS). Queensland University of Technology (QUT) has been innovating in this space by undertaking mutually dependant technical and content (metadata) focused projects funded by ANDS. Research Data Librarians identified and described datasets generated from Category 1 funded research at QUT, by interviewing researchers, collecting metadata and fashioning metadata records for upload to the Australian Research Data commons (ARDC) and exposure through the Research Data Australia interface. In parallel to this project, a Research Data Management Service and Metadata hub project were being undertaken by QUT High Performance Computing & Research Support specialists. These projects will collectively store and aggregate QUT’s metadata and research data from multiple repositories and administration systems and contribute metadata directly by OAI-PMH compliant feed to RDA. The pioneering nature of the work has resulted in a collaborative project dynamic where good data management practices and the discoverability and sharing of research data were the shared drivers for all activity. Each project’s development and progress was dependent on feedback from the other. The metadata structure evolved in tandem with the development of the repository and the development of the repository interface responded to meet the needs of the data interview process. The project environment was one of bottom-up collaborative approaches to process and system development which matched top-down strategic alliances crossing organisational boundaries in order to provide the deliverables required by ANDS. This paper showcases the work undertaken at QUT, focusing on the Seeding the Commons project as a case study, and illustrates how the data management projects are interconnected. It describes the processes and systems being established to make QUT research data more visible and the nature of the collaborations between organisational areas required to achieve this. The paper concludes with the Seeding the Commons project outcomes and the contribution this project made to getting more research data ‘out there’.

Bushfire sustainability : Strategies for reconciling bushfire and biodiversity with daily life

This exhibition and catalogue provides a visual record of student work exhibited at the Australian Institute of Architects offices in Brisbane from November 15 to 29, 2010. The exhibition features the final design outcomes of the inaugural Bushfire Sustainability unit conducted at QUT in semester two, 2010. The core objective of this unit was to develop our students’ skills in collaborative practice in design, research and presentation. The theme of ‘bushfire sustainability’ was chosen because living sustainably in bushfire prone landscapes presents a number of problems, the nature of which might only be resolved via multidisciplinary collaboration among the design disciplines. The students involved represent the disciplines of Interior Design, Landscape Architecture, Industrial Design, Architecture and Sustainability – all from within the School of Design at QUT. 55 students, mostly in their third year of study, worked in teams of five (one from each discipline) to design one of a number of homes in highly bushfire prone sites in either Western Australia or SE Queensland. This year level and the interdisciplinary mix are perhaps the best placed to resolve these problems: being unrestrained from the burdens of professional practice and technical overload they retain the potential for innovative, lateral thinking across the range of spatial scales and philosophical perspectives associated with inhabitation of bushfire prone landscapes. It is envisaged that, through the ‘vehicle’ of this design research, that the students’ work will contribute to understandings of how creative design disciplines might respond to this significant national problem, which hitherto has been attended to primarily by engineering and the sciences.