Peter C. R. Lane

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Quantitative predictions for complex scientific theories are often obtained by running simulations on computational models. In order for a theory to meet with widespread acceptance, it is important that the model be reproducible and comprehensible by independent researchers. However, the complexity of computational models can make the task of replication(More)
In this paper, we describe the attention mechanisms in CHREST, a computational architecture of human visual expertise. CHREST organ-ises information acquired by direct experience from the world in the form of chunks. These chunks are searched for, and verified, by a unique set of heuristics, comprising the attention mechanism. We explain how the attention(More)
(Received 00 Month 200x; In final form 00 Month 200x) Computer implementations of theoretical concepts play an ever-increasing role in the development and application of scientific ideas. As the scale of such implementations increases from relatively small models and empirical setups to overarching frameworks from which many kinds of results may be(More)
Text-based approaches to the analysis of software evolution are attractive because of the fine-grained, token-level comparisons they can generate. The use of such approaches has, however, been constrained by the lack of an efficient implementation. In this paper we demonstrate the ability of Ferret, which uses n-grams of 3 tokens, to characterise the(More)
Locating documents carrying positive or negative favourability is an important application within media analysis. This article presents some empirical results on the challenges facing a machine-learning approach to this kind of opinion mining. Some of the challenges include: the often considerable imbalance in the distribution of positive and negative(More)
We apply machine-learning techniques to help automate the process of mining the version history of software projects. Analysis of version histories is important in the study of software evolution. One of the associated problems is tracing program elements which have changed or moved as the result of file restructuring. As an initial application, we have(More)
When applying a machine-learning approach to develop classifiers in a new domain, an important question is what measurements to take and how they will be used to construct informative features. This paper develops a novel set of machine-learning classifiers for the domain of classifying files taken from software projects; the target classifications are(More)