Models and empirical data for the production of referring expressions

Article Accepted Date: 29 May 2014 Acknowledgements The authors gratefully acknowledge the support of the Cognitive Science Society for the organisation of the Workshop on Production of Referring Expressions: Bridging the Gap between Cognitive and Computational Approaches to Reference, from which this special issue originated. Funding Emiel Krahmer and Albert Gatt thank The Netherlands Organisation for Scientific Research (NWO) for VICI grant Bridging the Gap between Computational Linguistics and Psycholinguistics: The Case of Referring Expressions (grant number 277-70-007).

Production of Referring Expressions for an Unknown Audience : a Computational Model of Communal Common Ground

The research reported in this article is based on the Ph.D. project of Dr. RK, which was funded by the Scottish Informatics and Computer Science Alliance (SICSA). KvD acknowledges support from the EPSRC under the RefNet grant (EP/J019615/1).

Sentiment-Topic Modelling in Text Mining

Peer reviewed

Spacetime foam induced collective bundling of intense fields

This research is supported by the Carnegie Trust for the Universities of Scotland (TO) and by the EPSRC GG-Top Project and the Cruickshank Trust (CW).

Parallel and Streaming Truth Discovery in Large-Scale Quantitative Crowdsourcing

ACKNOWLEDGEMENTS This research is based upon work supported in part by the U.S. ARL and U.K. Ministry of Defense under Agreement Number W911NF-06-3-0001, and by the NSF under award CNS-1213140. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views or represent the official policies of the NSF, the U.S. ARL, the U.S. Government, the U.K. Ministry of Defense or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.

Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming

In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain-expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labelled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel Genetic Programming based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation, and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation, and has significantly outperformed, or achieved a comparable performance to, the baseline methods.

Numerical simulation of the mass loss process in pyrolizing char materials

Abstract not available

Statistical software usage by student and consultant statisticians in the UK - A comparative study

Abstract not available

[Review] Assessment: Problems, Developments and Statistical Issues; a Volume of Expert Contributions. Edited by H. Goldstein and T. Lewis. ISBN 0 471 95668 6. Wiley, Chichester, 1996. viii+274 pp., £45.

This is a book on general issues to do with assessment, not a statistics book, although it touches on statistical issues. Although it is intended for an international readership to include teachers, educationalists, policy makers and personnel managers and would be of interest to such groups it has been written mainly from the viewpoint of developed countries and is not obviously essential reading or an essential purchase for anyone. Short biographies of the 15 contributors are given at the start of the book and references from all the chapters are brought together at the end. The order of chapters appears to be arbitrary to some extent and there is little cross-referencing from one chapter to another.

A heuristic algorithm for the two-machine re-entrant shop scheduling

Abstract not available

The Pocket Statistician: A Practical Guide to Quality Improvement by Shirley Coleman, Tony Greenfield, Roger Jones, Clare Morris and Ian Puzey. Arnold . 1996. 252 pages. £12.99 [Book review]

Abstract not available

Impressions of ICOTS5

Abstract not available

The Plastic Coupled Map Lattice: a Novel Image‐processing Paradigm

Coupled map lattices (CML) can describe many relaxation and optimization algorithms currently used in image processing. We recently introduced the ‘‘plastic‐CML’’ as a paradigm to extract (segment) objects in an image. Here, the image is applied by a set of forces to a metal sheet which is allowed to undergo plastic deformation parallel to the applied forces. In this paper we present an analysis of our ‘‘plastic‐CML’’ in one and two dimensions, deriving the nature and stability of its stationary solutions. We also detail how to use the CML in image processing, how to set the system parameters and present examples of it at work. We conclude that the plastic‐CML is able to segment images with large amounts of noise and large dynamic range of pixel values, and is suitable for a very large scale integration(VLSI) implementation.