Evolution in software systems: foundations of the SPE classification scheme

The SPE taxonomy of evolving software systems, first proposed by Lehman in 1980, is re-examined in this work. The primary concepts of software evolution are related to generic theories of evolution, particularly Dawkins' concept of a replicator, to the hermeneutic tradition in philosophy and to Kuhn's concept of paradigm. These concepts provide the foundations that are needed for understanding the phenomenon of software evolution and for refining the definitions of the SPE categories. In particular, this work argues that a software system should be defined as of type P if its controlling stakeholders have made a strategic decision that the system must comply with a single paradigm in its representation of domain knowledge. The proposed refinement of SPE is expected to provide a more productive basis for developing testable hypotheses and models about possible differences in the evolution of E- and P-type systems than is provided by the original scheme. Copyright (C) 2005 John Wiley & Sons, Ltd.

Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms

This work compares and contrasts results of classifying time-domain ECG signals with pathological conditions taken from the MITBIH arrhythmia database. Linear discriminant analysis and a multi-layer perceptron were used as classifiers. The neural network was trained by two different methods, namely back-propagation and a genetic algorithm. Converting the time-domain signal into the wavelet domain reduced the dimensionality of the problem at least 10-fold. This was achieved using wavelets from the db6 family as well as using adaptive wavelets generated using two different strategies. The wavelet transforms used in this study were limited to two decomposition levels. A neural network with evolved weights proved to be the best classifier with a maximum of 99.6% accuracy when optimised wavelet-transform ECG data wits presented to its input and 95.9% accuracy when the signals presented to its input were decomposed using db6 wavelets. The linear discriminant analysis achieved a maximum classification accuracy of 95.7% when presented with optimised and 95.5% with db6 wavelet coefficients. It is shown that the much simpler signal representation of a few wavelet coefficients obtained through an optimised discrete wavelet transform facilitates the classification of non-stationary time-variant signals task considerably. In addition, the results indicate that wavelet optimisation may improve the classification ability of a neural network. (c) 2005 Elsevier B.V. All rights reserved.

Ontology supported competency system

Traditionally representation of competencies has been very difficult using computer-based techniques. This paper introduces competencies, how they are represented, and the related concept of competency frameworks and the difficulties in using traditional ontology techniques to formalise them. A “vaguely” formalised framework has been developed within the EU project TRACE and is presented. The framework can be used to represent different competencies and competency frameworks. Through a case study using an example from the IT sector, it is shown how these can be used by individuals and organisations to specify their individual competency needs. Furthermore it is described how these representations are used for comparisons between different specifications applying ontologies and ontology toolsets. The end result is a comparison that is not binary, but tertiary, providing “definite matches”, possible / partial matches, and “no matches” using a “traffic light” analogy.

Perspectives on community representation within the Extractive Industries Transparency Initiative: experiences from south-east Madagascar

This article critically examines the nature and quality of governance in community representation and civil society engagement in the context of trans-national large-scale mining, drawing on experiences in the Anosy Region of south-east Madagascar. An exploration of functional relationships between government, mining business and civil society stakeholders reveals an equivocal legitimacy of certain civil society representatives, created by state manipulation, which contributes to community disempowerment. The appointment of local government officials, rather than election, creates a hierarchy of upward dependencies and a culture where the majority of officials express similar views and political alliances. As a consequence, community resistance is suppressed. Voluntary mechanisms such as Corporate Social Responsibility (CSR) and the Extractive Industries Transparency Initiative (EITI) advocate community stakeholder engagement in decision making processes as a measure to achieve public accountability. In many developing countries, where there is a lack of transparency and high levels of corruption, the value of this engagement, however, is debatable. Findings from this study indicate that the power relationships which exist between stakeholders in the highly lucrative mining industry override efforts to achieve "good governance" through voluntary community engagement. The continuing challenge lies in identifying where the responsibility sits in order to address this power struggle to achieve fair representation.

Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.

PMCRI: a parallel modular classification rule induction framework

In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction of Decision Trees (TDIDT) algorithm is a very widely used technology to predict the classification of newly recorded data. However alternative technologies have been derived that often produce better rules but do not scale well on large datasets. Such an alternative to TDIDT is the PrismTCS algorithm. PrismTCS performs particularly well on noisy data but does not scale well on large datasets. In this paper we introduce Prism and investigate its scaling behaviour. We describe how we improved the scalability of the serial version of Prism and investigate its limitations. We then describe our work to overcome these limitations by developing a framework to parallelise algorithms of the Prism family and similar algorithms. We also present the scale up results of a first prototype implementation.

Language and thought in bilinguals: the case of grammatical number and nonverbal classification preferences

Recent research shows that speakers of languages with obligatory plural marking (English) preferentially categorize objects based on common shape, whereas speakers of nonplural-marking classifier languages (Yucatec and Japanese) preferentially categorize objects based on common material. The current study extends that investigation to the domain of bilingualism. Japanese and English monolinguals, and Japanese–English bilinguals were asked to match novel objects based on either common shape or color. Results showed that English monolinguals selected shape significantly more than Japanese monolinguals, whereas the bilinguals shifted their cognitive preferences as a function of their second language proficiency. The implications of these findings for conceptual representation and cognitive processing in bilinguals are discussed.

Searching for happiness across cultures

Three experiments examined the cultural relativity of emotion recognition using the visual search task. Caucasian-English and Japanese participants were required to search for an angry or happy discrepant face target against an array of competing distractor faces. Both cultural groups performed the task with displays that consisted of Caucasian and Japanese faces in order to investigate the effects of racial congruence on emotion detection performance. Under high perceptual load conditions, both cultural groups detected the happy face more efficiently than the angry face. When perceptual load was reduced such that target detection could be achieved by feature-matching, the English group continued to show a happiness advantage in search performance that was more strongly pronounced for other race faces. Japanese participants showed search time equivalence for happy and angry targets. Experiment 3 encouraged participants to adopt a perceptual based strategy for target detection by removing the term 'emotion' from the instructions. Whilst this manipulation did not alter the happiness advantage displayed by our English group, it reinstated it for our Japanese group, who showed a detection advantage for happiness only for other race faces. The results demonstrate cultural and linguistic modifiers on the perceptual saliency of the emotional signal and provide new converging evidence from cognitive psychology for the interactionist perspective on emotional expression recognition.

Terrorist transgressions: gendered representations of the terrorist in visual culture

The aim of Terrorist Transgressions is to analyse the myths inscribed in images of the terrorist and identify how agency is attributed to representation through invocations and inversions of gender stereotypes. In modern discourses on the terrorist the horror experienced in Western societies was the appearance of a new sense of the vulnerability of the body politic, and therefore of the modern self with its direct dependency on security and property. The terrorist has been constructed as the epitome of transgression against economic resources and moral, physical and political boundaries. Although terrorism has been the focus of intense academic activity, cultural representations of the terrorist have received less attention. Yet terrorism is dependent on spectacle and the topic is subject to forceful exposure in popular media. While the terrorist is predominantly aligned with masculinity, women have been active in terrorist organisations since the late 19th century and in suicidal terrorist attacks since the 1980s. Such attacks have confounded constructions of femininity and masculinity, with profound implications for the gendering of violence and horror. The publication arises from an AHRC networking grant, 2011-12, with Birkbeck, and includes collaboration with the army at Sandhurst RMA. The project relates to a wider investigation into feminism, violence and contemporary art.

Postmodernism, culture and feminism: the aesthetics of space and performance in Rock Follies and Rock Follies of '77

This article discusses the aesthetic and spatial representational strategies of the popular studio-based musical television drama serials Rock Follies and Rock Follies of ’77. It analyses how the texts’ themes relating to women and the entertainment industry are mediated through their postmodern ironic mode and representation of fantastic spaces. Rock Follies’ distinctive stylised aesthetic and mode of caricature are analysed with reference to the visual intentions and ‘voice’ of the writer, Howard Schuman. Through considering the programmes’ various spatial strategies, the article draws attention to the importance of visual and performance style in their postmodern discourse on culture, fantasy, gender and subjectivity. Analysis of the spaces of musical performance, characters’ domestic environments and simulated entertainment spaces reveals how a dialectic is established between the escapist imaginative pleasures of fantasy and the manipulative and exploitative practices of the culture industry. The shift from the optimism of the first series, when the LittleLadies first form, to the darker mood of the second series, in which they are increasingly divided by industry pressures, is traced through changes in the aesthetics of space and characterisation. As a space of artifice, performance and electronic visual manipulation that facilitates the texts’ reflexive representation of culture and feminised fantasy, the studio’s unique aesthetic strengths emerge through this case study.

Nationalised large-scale mining, trade unions and community representation: perspectives from Northern Madagascar

This article critically explores the nature and purpose of relationships and inter-dependencies between stakeholders in the context of a parastatal chromite mining company in the Betsiboka Region of Northern Madagascar. An examination of the institutional arrangements at the interface between the mining company and local communities identified power hierarchies and dependencies in the context of a dominant paternalistic environment. The interactions, inter alia, limited social cohesion and intensified the fragility and weakness of community representation, which was further influenced by ethnic hierarchies between the varied community groups; namely, indigenous communities and migrants to the area from different ethnic groups. Moreover, dependencies and nepotism, which may exist at all institutional levels, can create civil society stakeholder representatives who are unrepresentative of the society they are intended to represent. Similarly, a lack of horizontal and vertical trust and reciprocity inherent in Malagasy society engenders a culture of low expectations regarding transparency and accountability, which further catalyses a cycle of nepotism and elite rent-seeking behaviour. On the other hand, leaders retain power with minimal vertical delegation or decentralisation of authority among levels of government and limit opportunities to benefit the elite, perpetuating rent-seeking behaviour within the privileged minority. Within the union movement, pluralism and the associated politicisation of individual unions restricts solidarity, which impacts on the movement’s capacity to act as a cohesive body of opinion and opposition. Nevertheless, the unions’ drive to improve their social capital has increased expectations of transparency and accountability, resulting in demands for greater engagement in decision-making processes.

Unified framework for construction of rule based classification systems

Automatic generation of classification rules has been an increasingly popular technique in commercial applications such as Big Data analytics, rule based expert systems and decision making systems. However, a principal problem that arises with most methods for generation of classification rules is the overfit-ting of training data. When Big Data is dealt with, this may result in the generation of a large number of complex rules. This may not only increase computational cost but also lower the accuracy in predicting further unseen instances. This has led to the necessity of developing pruning methods for the simplification of rules. In addition, classification rules are used further to make predictions after the completion of their generation. As efficiency is concerned, it is expected to find the first rule that fires as soon as possible by searching through a rule set. Thus a suit-able structure is required to represent the rule set effectively. In this chapter, the authors introduce a unified framework for construction of rule based classification systems consisting of three operations on Big Data: rule generation, rule simplification and rule representation. The authors also review some existing methods and techniques used for each of the three operations and highlight their limitations. They introduce some novel methods and techniques developed by them recently. These methods and techniques are also discussed in comparison to existing ones with respect to efficient processing of Big Data.

Smart-phone based electrocardiogram wavelet decomposition and neural network classification

This paper discusses ECG classification after parametrizing the ECG waveforms in the wavelet domain. The aim of the work is to develop an accurate classification algorithm that can be used to diagnose cardiac beat abnormalities detected using a mobile platform such as smart-phones. Continuous time recurrent neural network classifiers are considered for this task. Records from the European ST-T Database are decomposed in the wavelet domain using discrete wavelet transform (DWT) filter banks and the resulting DWT coefficients are filtered and used as inputs for training the neural network classifier. Advantages of the proposed methodology are the reduced memory requirement for the signals which is of relevance to mobile applications as well as an improvement in the ability of the neural network in its generalization ability due to the more parsimonious representation of the signal to its inputs.