Secure communication protocol for preserving E-tendering integrity.

This paper presents a secure communication protocol which can be used as the framework for an e-tendering scheme. This protocol is focused on securing the integrity of tendering documents and ensuring that a secure record of document generation is kept. Our protocol provides a mechanism to manage e-tendering contract evidence as a legal record in a unique and effective manner. It is the starting point of reliable record keeping. To a certain extent, it also addresses existing security problems in the traditional tendering processes.

TOPSIG : Topology Preserving Document Signatures

Performance comparisons between File Signatures and Inverted Files for text retrieval have previously shown several significant shortcomings of file signatures relative to inverted files. The inverted file approach underpins most state-of-the-art search engine algorithms, such as Language and Probabilistic models. It has been widely accepted that traditional file signatures are inferior alternatives to inverted files. This paper describes TopSig, a new approach to the construction of file signatures. Many advances in semantic hashing and dimensionality reduction have been made in recent times, but these were not so far linked to general purpose, signature file based, search engines. This paper introduces a different signature file approach that builds upon and extends these recent advances. We are able to demonstrate significant improvements in the performance of signature file based indexing and retrieval, performance that is comparable to that of state of the art inverted file based systems, including Language models and BM25. These findings suggest that file signatures offer a viable alternative to inverted files in suitable settings and positions the file signatures model in the class of Vector Space retrieval models.

Asymptotic and numerical results for a model of solvent-dependent drug diffusion through polymeric spheres

A model for drug diffusion from a spherical polymeric drug delivery device is considered. The model contains two key features. The first is that solvent diffuses into the polymer, which then transitions from a glassy to a rubbery state. The interface between the two states of polymer is modelled as a moving boundary, whose speed is governed by a kinetic law; the same moving boundary problem arises in the one-phase limit of a Stefan problem with kinetic undercooling. The second feature is that drug diffuses only through the rubbery region, with a nonlinear diffusion coefficient that depends on the concentration of solvent. We analyse the model using both formal asymptotics and numerical computation, the latter by applying a front-fixing scheme with a finite volume method. Previous results are extended and comparisons are made with linear models that work well under certain parameter regimes. Finally, a model for a multi-layered drug delivery device is suggested, which allows for more flexible control of drug release.

Sparseness vs estimating conditional probabilities : some asymptotic results

One of the nice properties of kernel classifiers such as SVMs is that they often produce sparse solutions. However, the decision functions of these classifiers cannot always be used to estimate the conditional probability of the class label. We investigate the relationship between these two properties and show that these are intimately related: sparseness does not occur when the conditional probabilities can be unambiguously estimated. We consider a family of convex loss functions and derive sharp asymptotic results for the fraction of data that becomes support vectors. This enables us to characterize the exact trade-off between sparseness and the ability to estimate conditional probabilities for these loss functions.

A boundary preserving numerical algorithm for the Wright-Fisher model with mutation

The Wright-Fisher model is an Itô stochastic differential equation that was originally introduced to model genetic drift within finite populations and has recently been used as an approximation to ion channel dynamics within cardiac and neuronal cells. While analytic solutions to this equation remain within the interval [0,1], current numerical methods are unable to preserve such boundaries in the approximation. We present a new numerical method that guarantees approximations to a form of Wright-Fisher model, which includes mutation, remain within [0,1] for all time with probability one. Strong convergence of the method is proved and numerical experiments suggest that this new scheme converges with strong order 1/2. Extending this method to a multidimensional case, numerical tests suggest that the algorithm still converges strongly with order 1/2. Finally, numerical solutions obtained using this new method are compared to those obtained using the Euler-Maruyama method where the Wiener increment is resampled to ensure solutions remain within [0,1].

Preserving the past in digital memory

The process of researching children’s literature from the past is a growing challenge as resources age and are increasingly treated as rare items, stored away within libraries and other research centres. In Australia, researchers and librarians have collaborated with the bibliographic database AustLit: The Australian Literature Resource to produce the Australian Children’s Literature Digital Resources Project (CLDR). This Project aims to address the growing demand for online access to rare children’s literature resources, and demonstrates the research potential of early Australian children’s literature by supplementing the collection with relevant critical articles. The CLDR project is designed with a specific focus and provides access to full text Australian children’s literature from European settlement to 1945. The collection demonstrates a need and desire to preserve literature treasures to prevent losing such collections in a digital age. The collection covers many themes relevant to the conference including, trauma, survival, memory, survival, hauntings, and histories. The resource provides new and exciting ways with which to research children’s literature from the past and offers a fascinating repository to scholars and professionals of ranging disciplines who are in interested in Australian children’s literature.

Structure-preserving Runge-Kutta methods for stochastic Hamiltonian equations with additive noise

There has been considerable recent work on the development of energy conserving one-step methods that are not symplectic. Here we extend these ideas to stochastic Hamiltonian problems with additive noise and show that there are classes of Runge-Kutta methods that are very effective in preserving the expectation of the Hamiltonian, but care has to be taken in how the Wiener increments are sampled at each timestep. Some numerical simulations illustrate the performance of these methods.

Threshold privacy preserving keyword searches

We consider the following problem: users of an organization wish to outsource the storage of sensitive data to a large database server. It is assumed that the server storing the data is untrusted so the data stored have to be encrypted. We further suppose that the manager of the organization has the right to access all data, but a member of the organization can not access any data alone. The member must collaborate with other members to search for the desired data. In this paper, we investigate the notion of threshold privacy preserving keyword search (TPPKS) and define its security requirements. We construct a TPPKS scheme and show the proof of security under the assumptions of intractability of discrete logarithm, decisional Diffie-Hellman and computational Diffie-Hellman problems.