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.

Testing for the non-separability of bi-ambiguous compounds

Separability is a concept that is very difficult to define, and yet much of our scientific method is implicitly based upon the assumption that systems can sensibly be reduced to a set of interacting components. This paper examines the notion of separability in the creation of bi-ambiguous compounds that is based upon the CHSH and CH inequalities. It reports results of an experiment showing that violations of the CHSH and CH inequality can occur in human conceptual combination.

Performance optimization for parallel processing on a multiple-CPU server

Symmetric multi-processor (SMP) systems, or multiple-CPU servers, are suitable for implementing parallel algorithms because they employ dedicated communication devices to enhance the inter-processor communication bandwidth, so that a better performance can be obtained. However, the cost for a multiple-CPU server is high and therefore, the server is usually shared among many users. The work-load due to other users will certainly affect the performance of the parallel programs so it is desirable to derive a method to optimize parallel programs under different loading conditions. In this paper, we present a simple method, which can be applied in SPMD type parallel programs, to improve the speedup by controlling the number of threads within the programs.

Using the semantic grid to build bridges between museums and indigenous communities

In this paper we describe a Semantic Grid application designed to enable museums and indigenous communities in distributed locations, to collaboratively discuss, describe and annotate digital objects and documents in museums that originally belonged to or are of cultural or historical significance to indigenous groups. By extending and refining an existing application, Vannotea, we enable users on access grid nodes to collaboratively attach descriptive, rights and tribal care metadata and annotations to digital images, video or 3D representations. The aim is to deploy the software within museums to enable the traditional owners to describe and contextualize museum content in their own words and from their own perspectives. This sharing and exchange of knowledge will hopefully revitalize cultures eroded through colonization and globalization and repair and strengthen relationships between museums and indigenous communities.

Semantic search and inferencing in health informatics

Consider a person searching electronic health records, a search for the term ‘cracked skull’ should return documents that contain the term ‘cranium fracture’. A information retrieval systems is required that matches concepts, not just keywords. Further more, determining relevance of a query to a document requires inference – its not simply matching concepts. For example a document containing ‘dialysis machine’ should align with a query for ‘kidney disease’. Collectively we describe this problem as the ‘semantic gap’ – the difference between the raw medical data and the way a human interprets it. This paper presents an approach to semantic search of health records by combining two previous approaches: an ontological approach using the SNOMED CT medical ontology; and a distributional approach using semantic space vector space models. Our approach will be applied to a specific problem in health informatics: the matching of electronic patient records to clinical trials.

A virtual test assessment tool for second year signal processing students

The development and use of a virtual assessment tool for a signal processing unit is described. It allows students to take a test from anywhere using a web browser to connect to the university server that hosts the test. While student responses are of the multiple choice type, they have to work out problems to arrive at the answer to be entered. CGI programming is used to verify student identification information and record their scores as well as provide immediate feedback after the test is complete. The tool has been used at QUT for the past 3 years and student feedback is discussed. The virtual assessment tool is an efficient alternative to marking written assignment reports that can often take more hours than actual lecture hall contact from a lecturer or tutor. It is especially attractive for very large classes that are now the norm at many universities in the first two years.

Digital processing of diffusion-tensor images of avascular tissues

Diffusion is the process that leads to the mixing of substances as a result of spontaneous and random thermal motion of individual atoms and molecules. It was first detected by the English botanist Robert Brown in 1827, and the phenomenon became known as ‘Brownian motion’. More specifically, the motion observed by Brown was translational diffusion – thermal motion resulting in random variations of the position of a molecule. This type of motion was given a correct theoretical interpretation in 1905 by Albert Einstein, who derived the relationship between temperature, the viscosity of the medium, the size of the diffusing molecule, and its diffusion coefficient. It is translational diffusion that is indirectly observed in MR diffusion-tensor imaging (DTI). The relationship obtained by Einstein provides the physical basis for using translational diffusion to probe the microscopic environment surrounding the molecule.