The Role of Global and Feature Based Information in Gender Classification of Faces: A Comparison of Human Performance and Computational Models

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Gender classification of face images: the role of global and feature-based information

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Teaching mathematics with audiograph

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Reflections on academic leadership

This article introduces some reflections on the aspect of teaching and learning that is often called ‘academic leadership’. Programme tutors, curriculum developers and those with special responsibility for academic quality, for example, often have to deal with teaching and learning issues which transcend individual groups of students, and cannot be located within the classroom. We argue that reflection on practices in these areas is very important, but presents specific challenges that are relatively unexplored.Peer reviewe

Applying software metrics to formal specifications : a cognitive approach.

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.---- Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. DOI : 10.1109/METRIC.1998.731248It is generally accepted that failure to reason correctly during the early stages of software development causes developers to make incorrect decisions which can lead to the introduction of faults or anomalies in systems. Most key development decisions are usually made at the early system speci cation stage of a software pro- ject and developers do not receive feedback on their ac- curacy until near its completion. Software metrics are generally aimed at the coding or testing stages of devel- opment, however, when the repercussions of erroneous work have already been incurred. This paper presents a tentative model for predicting those parts of formal speci cations which are most likely to admit erroneous inferences, in order that potential sources of human er- ror may be reduced. The empirical data populating the model was generated during a series of cognitive experi- ments aimed at identifying linguistic properties of the Z notation which are prone to admit non-logical reasoning errors and biases in trained users

The naming of systems and software evolvability

Software systems are unlike most entities whose--existence, persistence, development, and integrity as single individuals are presupposed by ordinary acts of naming. This paper broaches the issue of how naming practices in software evolution may significantly impact software maintenance and--evolvability. We explore how naming in the realm of software is unlike naming of other types of phenomena to which we apply usual human naming practices. Such naming practices have been--naively generalized to the realm of software. In the software realm, naming practices have been co-opted for political roles in reification as well as in the mobilization of commitment and resources.Final Published versio

Self-Organising Map Representations Of Greyscale Images Reflect Human Similarity Judgements

Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.In this study we assessed a Kohonen network's ability to represent visual similarity between grayscale pictures and whether these representations were associated with human ratings af perceived similarity. We trained a Kohonen network (SOM) with 370 standardized grayscale pictures deriving from 70 basic level object categories (e.g. dog, apple, chair, etc.) and measured, for each category, the average euclidean distance of the SOM output patterns to provide an index of the visual similarity between exemplars of the same basic level category. We then asked human subjects to provide visual similarity ratings for the same categories of stimuli and compared these with the measures extracted from the SOM. The significant correlation between the SOM and human measures suggests that a SOM may he a useful way 01 modeling certain stages of human visual categorization. Interestingly, the human ratings showed category-specific differences in the level of similarity ascribed to living and nonliving things. However, this pattern was not reflected in the SOM representations of the same stimuli. This has important implications for theories of object recognition and, specifically, our understanding af category-specific naming impairments.Final Published versio

Constructive biology and approaches to temporal grounding in post-reactive robotics

Constructive Biology ( as opposed to descriptive biology) means understanding biological mechanisms through building systems that exhibit life-like properties. Applications include learning engineering tricks from biological systems, as well as the validation in biological modelling. In Particular, biological systems (unlike reactive robots) in the course of development and experience become temporally grounded. Researchers attempting to transcend mere reactivity have been inspired by the drives, motivations, homeostasis, hormonal control, and emotions of animals. In order to contextualize and modulate behavior, these ideas have been introduced into robotics and synthetic agents, while further flexibility is achieved by introducing learning.Peer reviewe

Segmenting hand written text using supervised classification techniques

Recent work on extracting features of gaps in handwritten text allows a classification into inter-word and intraword classes using suitable classification techniques. In this paper, we apply 5 different supervised classification algorithms from the machine learning field on both the original dataset and a dataset with the best features selected using mutual information. The classifiers are compared by employing McNemar's test. We find that SVMs and MLPs outperform the other classifiers and that preprocessing to select features works well