Probability density estimation with tunable kernels using orthogonal forward regression

A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and diagonal covariance matrix, by minimizing a leave-one-out test criterion. The kernel mixing weights of the constructed sparse density estimate are finally updated using the multiplicative nonnegative quadratic programming algorithm to ensure the nonnegative and unity constraints, and this weight-updating process additionally has the desired ability to further reduce the model size. The proposed tunable-kernel model has advantages, in terms of model generalization capability and model sparsity, over the standard fixed-kernel model that restricts kernel centers to the training data points and employs a single common kernel variance for every kernel. On the other hand, it does not optimize all the model parameters together and thus avoids the problems of high-dimensional ill-conditioned nonlinear optimization associated with the conventional finite mixture model. Several examples are included to demonstrate the ability of the proposed novel tunable-kernel model to effectively construct a very compact density estimate accurately.

Radial basis function classifier construction using particle swarm optimisation aided orthogonal forward regression

We develop a particle swarm optimisation (PSO) aided orthogonal forward regression (OFR) approach for constructing radial basis function (RBF) classifiers with tunable nodes. At each stage of the OFR construction process, the centre vector and diagonal covariance matrix of one RBF node is determined efficiently by minimising the leave-one-out (LOO) misclassification rate (MR) using a PSO algorithm. Compared with the state-of-the-art regularisation assisted orthogonal least square algorithm based on the LOO MR for selecting fixednode RBF classifiers, the proposed PSO aided OFR algorithm for constructing tunable-node RBF classifiers offers significant advantages in terms of better generalisation performance and smaller model size as well as imposes lower computational complexity in classifier construction process. Moreover, the proposed algorithm does not have any hyperparameter that requires costly tuning based on cross validation.

Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. 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 structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

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