We present three different privacy preserving protocols for computing reputation. They vary in strength in terms of preserving privacy, however, a common thread in all three protocols is that they are fully decentralized and efficient. Our protocols that are resilient against semi-honest adversaries and non-disruptive malicious adversaries have linear and loglinear communication complexity resp...

Minimum Error Rate Training (MERT) is a method for training the parameters of a loglinear model. One advantage of this method of training is that it can use the large number of hypotheses encoded in a translation lattice as training data. We demonstrate that the MERT line optimisation can be modelled as computing the shortest distance in a weighted finite-state transducer using a tropical polyn...

The purpose of this project is to show observations taken from the Iterative Proportional Scaling algorithm when used with data taken from Hierarchical LogLinear Models. We show with empirical proof that the difference between decomposable and non-decomposable graph models used is not exceedingly different. For the purpose of this project we looked at the properties of the two, three, and four ...

This paper presents the modules in the statistical package ViSta related to categorical data analysis. These modules are: visualization of frequency data with mosaic and bar plots, correspondence analysis, multiple correspondence analysis and loglinear analysis. All these methods are implemented in ViSta with a big emphasis on plots and graphical representations of data, as well as interactivit...