A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems

This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier's structure and the parameters of RBF kernels are determined using a PSO algorithm based on the criterion of minimising the leave-one-out misclassification rate. The experimental results obtained on a simulated imbalanced data set and three real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

Anxious adults vs cool children: children's view on smoking and addiction

Tobacco addiction represents a major public health problem, and most addicted smokers take up the habit during adolescence. We need to know why. With the aim of gaining a better understanding of the meanings smoking and tobacco addiction hold for young people, 85 focused interviews were conducted with adolescent children from economically deprived areas of Northern Ireland. Through adopting a qualitative approach within the community rather than the school context, the adolescent children were given the opportunity to freely express their views in confidence. Children seem to differentiate conceptually between child smoking and adult smoking. Whereas adults smoke to cope with life and are thus perceived by children as lacking control over their consumption, child smoking is motivated by attempts to achieve the status of cool and hard, and to gain group membership. Adults have personal reasons for smoking, while child smoking is profoundly social. Adults are perceived as dependent on nicotine, and addiction is at the core of the children's understanding of adult smoking. Child smoking, on the other hand, is seen as oriented around social relations so that addiction is less relevant. These ideas leave young people vulnerable to nicotine addiction. It is clearly important that health promotion efforts seek to understand and take into account the actions of children within the context of their own world-view to secure their health

Reflective practice among MATESOL graduates in the UAE: theoretical construct or ongoing benefit?

Many teacher training programs, including the MATESOL program at the American University of Sharjah (AUS) in United Arab Emirates, encourage their trainees to reflect on their practice. However, whether or not reflection becomes a part of the trainees’ practice once they leave these programs is a thought-provoking question, which formed the core of the current study. The study was qualitative in nature, using interviewing as its method of data collection. The researcher conducted semi-structured interviews with four AUS MATESOL program graduates, and investigated their perceptions of and engagement with reflective practice. The findings of the study indicate that the participants have generally developed an understanding of and appreciation for reflection and reflective practice, are aware of its values, and use different forms of reflection in order to reflect on their practice. However, some of them hold some uncertainties and misconceptions about reflective practice and its different aspects.

Cloud based global positioning system as a safety monitor for dementia patients

The aim of using GPS for Alzheimer's Patients is to give carers and families of those affected by Alzheimer's Disease, as well as all the other dementia related conditions, a service that can, via SMS text message, notify them should their loved one leave their home. Through a custom website, it enables the carer to remotely manage a contour boundary that is specifically assigned to the patient as well as the telephone numbers of the carers. The technique makes liberal use of such as Google Maps.

Policy making without legislating: the self-regulation of commercial property leasing

The terms of a commercial property lease covers aspects such as rent, alterations to premises and the ability to leave; consequently they have a significant impact on cash flow and the ability of a business to develop. In contrast to the heavily-legislated residential sector, commercial landlords and tenants in the UK are largely free to negotiate the terms of their contract. Yet, since the property crash of 1989/90, successive governments have taken an interest in commercial leasing; in particular there is a desire to see landlords being more flexible. UK Government policy in this area has been pursued through industry self-regulation rather than legislation; since 1995 there have been three industry codes of practice on leasing. These codes are sanctioned by government and monitored by them. Yet, 15 years after the first code was launched, many in the industry see the whole code concept as ineffective and unlikely to ever achieve changes to certain aspects of landlord behaviour. This paper is the first step in considering the lease codes in the wider context of industry self-regulation. The aim of the paper is twofold: First a framework is created using the literature on industry self-regulation from various countries and industries which suggests key criteria to explain the effectiveness (or ineffectiveness) of self-regulation. This is then applied to the UK lease codes based on research carried out by the authors for the UK Government to monitor the success of all three codes. The outcome is a clearer understanding of the possibilities and limitations of using a voluntary solution to achieve policy aims within the property industry.

Europe’s biocidal products directive: benefits and costs in urban pest management

The European Commission’s Biocidal Products Directive (Council Directive 98/8 EC), known as the BPD, is the largest regulatory exercise ever to affect the urban pest control industry. Although focussed in the European Union its impact is global because any company selling pest control products in the EU must follow its principles. All active substances, belonging to 23 different biocidal product types, come within the Directive’s scope of regulatory control. This will eventually involve re-registration of all existing products, as well as affecting any new product that comes to the market. Some active substances, such as the rodenticides and insecticides, are already highly regulated in Europe but others, such as embalming fluids, masonry preservatives, disinfectants and repellents/attractants will come under intensive regulatory scrutiny for the first time. One of the purposes of the Directive is to offer enhanced protection for human health and the environment. The potential benefit for suppliers of pest control products is mutual recognition of regulatory product dossiers across 25 Member States of the European Union. This process, requiring harmonisation of all regulatory decision-making processes, should reduce duplicated effort and, potentially, allow manufacturers speedier access to European markets. However, the cost to industry is enormous, both in terms of the regulatory resources required to assemble BPD dossiers and the development budgets required to conduct studies to meet its new standards. The cost to regulatory authorities is also tremendous, in terms of the need to upgrade staff capabilities to meet new challenges and the volume of the work expected by the Commission when they are appointed the Rapporteur Member State (RMS) for an active substance. Users of pest control products will pay a price too. The increased regulatory costs of maintaining products in the European market are likely to be passed on, at least in part, to users. Furthermore, where the costs of meeting new regulatory requirements cannot be recouped from product sales, many well-known products may leave the market. For example, it seems that in future few rodenticides that are not anticoagulants will be available within the EU. An understanding of the BPD is essential to those who intend to place urban pest control products on the European market and may be useful to those considering the harmonisation of regulatory processes elsewhere. This paper reviews the operation of the first stages of the BPD for rodenticides, examines the potential benefits and costs of the legislation to the urban pest control industry and looks forward to the next stages of implementation involving all insecticides used in urban pest management.

‘That’s definite discrimination’: practice under the umbrella of inclusion

Inclusive practice is well embedded across society and has developed over time. However, although policy and public view have moved forward, the way organisations address the agenda for inclusion often represents a superficial interpretation of this concept. Qualitative data were gathered using new ethnography to explore the experiences of a library-based reading group for visually impaired readers. The voices of the individuals shed light on the individual and collective experience of reading. These insights challenge the traditional views of distinct provision that are designed to address targets for inclusion of individuals with disabilities. We argue for a clearer focus on the unintentional consequences of practice in the name of inclusion that leave individuals feeling marginalised. This paper suggests the alternative focus on social justice as offering a discourse that focuses on society and away from the individual.

An elastic net orthogonal forward regression algorithm

In this paper we propose an efficient two-level model identification method for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularization parameters in the elastic net are optimized using a particle swarm optimization (PSO) algorithm at the upper level by minimizing the leave one out (LOO) mean square error (LOOMSE). Illustrative examples are included to demonstrate the effectiveness of the new approaches.

Evaluating the legacies of state-building: success, failure, and the role of responsibility

What impact do international state-building missions have on the domestic politics of states they seek to build, and how can we measure this impact with confidence? This article seeks to address these questions and challenge some existing approaches that often appear to assume that state-builders leave lasting legacies rather than demonstrating such influence with the use of carefully chosen empirical evidence. Too often, domestic conditions that follow in the wake of international state-building are assumed to follow as a result of international intervention, usually due to insufficient attention to the causal processes that link international actions to domestic outcomes. The article calls for greater appreciation of the methodological challenges to establishing causal inferences regarding the legacies of state-building and identifies three qualitative methodological strategies—process tracing, counterfactual analysis, and the use of control cases—that can be used to improve confidence in causal claims about state-building legacies. The article concludes with a case study of international state-building in East Timor, highlighting several flaws of existing evaluations of the United Nations' role in East Timor and identifying the critical role that domestic actors play even in the context of authoritative international intervention

eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.

From projects into operations: lessons for data handover

Data from civil engineering projects can inform the operation of built infrastructure. This paper captures lessons for such data handover, from projects into operations, through interviews with leading clients and their supply chain. Clients are found to value receiving accurate and complete data. They recognise opportunities to use high quality information in decision-making about capital and operational expenditure; as well as in ensuring compliance with regulatory requirements. Providing this value to clients is a motivation for information management in projects. However, data handover is difficult as key people leave before project completion; and different data formats and structures are used in project delivery and operations. Lessons learnt from leading practice include defining data requirements at the outset, getting operations teams involved early, shaping the evolution of interoperable systems and standards, developing handover processes to check data rather than documentation, and fostering skills to use and update project data in operations

Music and Metaphor

Peter Kivy’s contour theory provides a promising explanation of the way we describe instrumental music as expressive of emotions. I argue that if, unlike Kivy, we emphasise the metaphorical character of such descriptions, the contour theory, as a strategy for unpacking such metaphors, can be defended convincingly against common objections. This approach is more satisfactory than those of Scruton and Peacocke, who make much of metaphorical experiences, but leave the underlying metaphors unexplained. Moreover, it gives the contour theory a wider scope than Kivy intended, for even very specific narrative descriptions of music in non-musical terms are perfectly legitimate as long as they are presented, and justified, as metaphors, that is, as mere comparisons, rather than as interpretative claims about the music’s actual contents.

Benchmarking energy use in licensed restaurants and pubs

Commercial kitchens often leave a large carbon footprint. A new dataset of energy performance metrics from a leading industrial partner is presented. Categorising these types of buildings is challenging. Electricity use has been analysed using data from automated meter readings (AMR) for the purpose of benchmarking and discussed in terms of factors such as size and food output. From the analysed results, consumption is found to be almost double previous sector estimates of 6480 million kWh per year. Recommendations are made to further improve the current benchmarks in order to attain robust, reliable and transparent figures, such as the introduction of normalised performance indicators to include kitchen size (m2) and kWh per thousand-pound turnover.

Energy benchmarking in commercial kitchens

Commercial kitchens are one of the most profligate users of gas, water and electricity in the UK and can leave a large carbon footprint. It is estimated that the total energy consumption of Britain’s catering industry is in excess of 21,600 million kWh per year. In order to facilitate appropriate energy reduction within licensed restaurants, energy use must be translated into a form that can be compared between kitchens to enable operators to assess how they are improving and to allow rapid identification of facilities which require action. A review of relevant literature is presented and current benchmarking methods are discussed in order to assist in the development and categorisation of benchmarking energy reduction in commercial kitchens. Energy use within UK industry leading brands is discussed for the purpose of benchmarking in terms of factors such as size and output.

Elastic-net prefiltering for two-class classification

A two-stage linear-in-the-parameter model construction algorithm is proposed aimed at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage which constructs a sparse linear-in-the-parameter classifier. The prefiltering stage is a two-level process aimed at maximizing a model's generalization capability, in which a new elastic-net model identification algorithm using singular value decomposition is employed at the lower level, and then, two regularization parameters are optimized using a particle-swarm-optimization algorithm at the upper level by minimizing the leave-one-out (LOO) misclassification rate. It is shown that the LOO misclassification rate based on the resultant prefiltered signal can be analytically computed without splitting the data set, and the associated computational cost is minimal due to orthogonality. The second stage of sparse classifier construction is based on orthogonal forward regression with the D-optimality algorithm. Extensive simulations of this approach for noisy data sets illustrate the competitiveness of this approach to classification of noisy data problems.