Catalogue of manuscripts from the Hayes Collection in the University of Queensland Library

Index to correspondence and manuscript material on literary and historical matters, mostly in Queensland and New South Wales, Australia held in the Fryer Library, University of Queensland - UQFL2. Authors and subjects include J.H.M. Abbott, Archer Family, E. Armitage, R. Bedford, H.S. Bloxome, E.J. Brady, 'Broadside', F. Broomfield, A.H. Chisholm, C.B. Christesen, R.H. Croll, Z. Cross, F.W.S. Cumbrae-Stewart, E. Dark, D. Deamer, C.J. Dennis, J. Devaney, E.M. England, P. Fitzgerald, R.D. Fitzgerald, Dame Mary Gilmore, C. Gittins, A.L. Gordon (criticism), P. Grano, M. Haley, W.A. Horn, R.G. Howarth, J. Howlett Ross, E.H. Lane, H. Lane, F.J. McAuley, D. McConnel, G. McCrae, K. (S) Mackenzie, P. Miles, J.K. Moir, C.P. Mountford, A. Muir, D.A. O'Brien, J.H. O'Dwyer, W.H. Ogilvie, M. Potter, T. Playford, H. Power, Queensland Authors' and Artists' Association, I. Southall, W. Sowden, A.G. Stephens, P.R. Stephensen, H. Tyron, A.J. Vogan, B. Vrepont, T. Welsby, H.R. White and Duke of Windsor. Also personal papers of Father Hayes, relating to his activities as parish priest.

Measurement Function Design for Visual Tracking Applications

Extracting human postural information from video sequences has proved a difficult research question. The most successful approaches to date have been based on particle filtering, whereby the underlying probability distribution is approximated by a set of particles. The shape of the underlying observational probability distribution plays a significant role in determining the success, both accuracy and efficiency, of any visual tracker. In this paper we compare approaches used by other authors and present a cost path approach which is commonly used in image segmentation problems, however is currently not widely used in tracking applications.

Lambda-fold 3-perfect 9-cycle systems

The spectrum for the decomposition of lambda K-v into 3-perfect 9-cycles is found for all lambda > 1. (The case lambda = 1 was dealt with in an earlier paper by the authors and Lindner.) The necessary conditions for the existence of a suitable decomposition turn out to be sufficient.

Authorship Attribution with Support Vector Machines

In this paper we explore the use of text-mining methods for the identification of the author of a text. We apply the support vector machine (SVM) to this problem, as it is able to cope with half a million of inputs it requires no feature selection and can process the frequency vector of all words of a text. We performed a number of experiments with texts from a German newspaper. With nearly perfect reliability the SVM was able to reject other authors and detected the target author in 60–80% of the cases. In a second experiment, we ignored nouns, verbs and adjectives and replaced them by grammatical tags and bigrams. This resulted in slightly reduced performance. Author detection with SVMs on full word forms was remarkably robust even if the author wrote about different topics.