Proceedings of the Third Computing Women Congress (CWC 2008): Student papers

The Third Computing Women Congress was held at the University of Waikato, Hamilton, New Zealand from February 11th to 13th, 2008. The Computing Women Congress (CWC) is a Summer University for women in Computer Science. It is a meeting-place for female students, academics and professionals who study or work in Information Technology. CWC provides a forum to learn about and share the latest ideas of computing related topics in a supportive environment. CWC provides an open, explorative learning and teaching environment. Experimentation with new styles of learning is encouraged, with an emphasis on hands-on experience and engaging participatory techniques

Continuous Typist Verification using Machine Learning

A keyboard is a simple input device. Its function is to send keystroke information to the computer (or other device) to which it is attached. Normally this information is employed solely to produce text, but it can also be utilized as part of an authentication system. Typist verification exploits a typist’s patterns to check whether they are who they say they are, even after standard authentication schemes have confirmed their identity. This thesis investigates whether typists behave in a sufficiently unique yet consistent manner to enable an effective level of verification based on their typing patterns. Typist verification depends on more than the typist’s behaviour. The quality of the patterns and the algorithms used to compare them also determine how accurately verification is performed. This thesis sheds light on all technical aspects of the problem, including data collection, feature identification and extraction, and sample classification. A dataset has been collected that is comparable in size, timing accuracy and content to others in the field, with one important exception: it is derive

One-class Classification by Combining Density and Class Probability Estimation

Abstract. One-class classification has important applications such as outlier and novelty detection. It is commonly tackled using density estimation techniques or by adapting a standard classification algorithm to the problem of carving out a decision boundary that describes the location of the target data. In this paper we investigate a simple method for one-class classification that combines the application of a density estimator, used to form a reference distribution, with the induction of a standard model for class probability estimation. In this method, the reference distribution is used to generate artificial data that is employed to form a second, artificial class. In conjunction with the target class, this artificial class is the basis for a standard two-class learning problem. We explain how the density function of the reference distribution can be combined with the class probability estimates obtained in this way to form an adjusted estimate of the density function of the target class. Using UCI datasets, and data from a typist recognition problem, we show that the combined model, consisting of both a density estimator and a class probability estimator, can improve on using either component technique alone when used for one-class classification. We also compare the method to one-class classification using support vector machines.

One-Class Classification by Combining Density and Class Probability Estimation

One-class classification has important applications such as outlier and novelty detection. It is commonly tackled using density estimation techniques or by adapting a standard classification algorithm to the problem of carving out a decision boundary that describes the location of the target data. In this paper we investigate a simple method for one-class classification that combines the application of a density estimator, used to form a reference distribution, with the induction of a standard model for class probability estimation. In this method, the reference distribution is used to generate artificial data that is employed to form a second, artificial class. In conjunction with the target class, this artificial class is the basis for a standard two-class learning problem. We explain how the density function of the reference distribution can be combined with the class probability estimates obtained in this way to form an adjusted estimate of the density function of the target class. Using UCI datasets, and data from a typist recognition problem, we show that the combined model, consisting of both a density estimator and a class probability estimator, can improve on using either component technique alone when used for one-class classification. We also compare the method to one-class classification using support vector machines