Sample size determination for kernel regression estimation using sequential fixed-width confidence bands

We consider a random design model based on independent and identically distributed pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1−. Here, d(> 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths. The sample size was optimized using the purely and two-stage sequential procedures together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confi dence bands based on the local linear estimator have the better performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.

The structural properties of sexual fantasies for sexual offenders : a preliminary model

While the phenomenon of sexual fantasy has been researched extensively, little contemporary inquiry has investigated the structural properties of sexual fantasy within the context of sexual offending. In this study, a qualitative analysis was used to develop a descriptive model of the phenomena of sexual fantasy during the offence process. Twenty-four adult males convicted of sexual offences provided detailed retrospective descriptions of their thoughts, emotions and behaviours—before, during and after their offences. A data-driven approach to model development, known as Grounded Theory, was undertaken to analyse the interview transcripts. A model was developed to elucidate the structural properties of sexual fantasy in the process of sexual offending, as well as the physiological and psychological variables associated with it. The Sexual Fantasy Structural Properties Model (SFSPM) comprises eight categories that describe various properties of sexual fantasy across the offence process. These categories are: origin, context, trigger, perceptual modality, clarity, motion, intensity and emotion. The strengths of the SFSPM are discussed and its clinical implications are reviewed. Finally, the limitations of the study are presented and future research directions discussed.

Geographical Information Systems (GIS) and their role in sustainable planning : a case study from a Local Government Area (LGA) in Australia

A case study is used to demonstrate the application of Geographical Information Systems (GIS) to inform sustainable development. The suitability of the landscape to support tourism accommodation in a Local Government Area (LGA) is modelled by integrating existing datasets, including conservation areas, residential zones, major roads and known locations of tourism operators into a logistic regression framework. By using a data-driven approach an indication of the relative importance of each explanatory variable can be accounted for, therefore informing planners of the importance of different assets. In a region where tourism is reliant upon natural features, this use of information systems in conjunction with quantitative statistical modelling can value-add to existing datasets. The provision of this kind of knowledge is important as it would otherwise not factor into the decision-making process had the datasets been considered independently of each other – a concept that applies to both the public and private sectors.

Eventscapes : visualizing events over time with emotive facets

The scale and dynamicity of social media, and interaction between traditional news sources and online communities, has created challenges to information retrieval approaches. Users may have no clear information need or be unable to express it in the appropriate idiom, requiring instead to be oriented in an unfamiliar domain, to explore and learn. We present a novel data-driven visualization, termed Eventscape, that combines time, visual media, mood, and controversy. Formative evaluation highlights the value of emotive facets for rapid evaluation of mixed news and social media topics, and a role for such visualizations as pre-cursors to deeper search. Copyright 2011 ACM.

Semi-supervised classification based on subspace sparse representation

Graph plays an important role in graph-based semi-supervised classification. However, due to noisy and redundant features in high-dimensional data, it is not a trivial job to construct a well-structured graph on high-dimensional samples. In this paper, we take advantage of sparse representation in random subspaces for graph construction and propose a method called Semi-Supervised Classification based on Subspace Sparse Representation, SSC-SSR in short. SSC-SSR first generates several random subspaces from the original space and then seeks sparse representation coefficients in these subspaces. Next, it trains semi-supervised linear classifiers on graphs that are constructed by these coefficients. Finally, it combines these classifiers into an ensemble classifier by minimizing a linear regression problem. Unlike traditional graph-based semi-supervised classification methods, the graphs of SSC-SSR are data-driven instead of man-made in advance. Empirical study on face images classification tasks demonstrates that SSC-SSR not only has superior recognition performance with respect to competitive methods, but also has wide ranges of effective input parameters.

Prediction interval-based modelling of polymerization reactor: a new modelling strategy for chemical reactors

Precise and reliable modelling of polymerization reactor is challenging due to its complex reaction mechanism and non-linear nature. Researchers often make several assumptions when deriving theories and developing models for polymerization reactor. Therefore, traditional available models suffer from high prediction error. In contrast, data-driven modelling techniques provide a powerful framework to describe the dynamic behaviour of polymerization reactor. However, the traditional NN prediction performance is significantly dropped in the presence of polymerization process disturbances. Besides, uncertainty effects caused by disturbances present in reactor operation can be properly quantified through construction of prediction intervals (PIs) for model outputs. In this study, we propose and apply a PI-based neural network (PI-NN) model for the free radical polymerization system. This strategy avoids assumptions made in traditional modelling techniques for polymerization reactor system. Lower upper bound estimation (LUBE) method is used to develop PI-NN model for uncertainty quantification. To further improve the quality of model, a new method is proposed for aggregation of upper and lower bounds of PIs obtained from individual PI-NN models. Simulation results reveal that combined PI-NN performance is superior to those individual PI-NN models in terms of PI quality. Besides, constructed PIs are able to properly quantify effects of uncertainties in reactor operation, where these can be later used as part of the control process. © 2014 Taiwan Institute of Chemical Engineers.

Bootstrapping mobile app development

Modern IDEs provide limited support for developers when starting a new data-driven mobile app. App developers are currently required to write copious amounts of boilerplate code, scripts, organise complex directories, and author actual functionality. Although this scenario is ripe for automation, current tools are yet to address it adequately. In this paper we present RAPPT, a tool that generates the scaffolding of a mobile app based on a high level description specified in a Domain Specific Language (DSL). We demonstrate the feasibility of our approach by an example case study and feedback from a professional development team. Demo at: https://www.youtube.com/watch?v=ffquVgBYpLM.

Approximation of centroid end-points and switch points for replacing type reduction algorithms

Despite several years of research, type reduction (TR) operation in interval type-2 fuzzy logic system (IT2FLS) cannot perform as fast as a type-1 defuzzifier. In particular, widely used Karnik-Mendel (KM) TR algorithm is computationally much more demanding than alternative TR approaches. In this work, a data driven framework is proposed to quickly, yet accurately, estimate the output of the KM TR algorithm using simple regression models. Comprehensive simulation performed in this study shows that the centroid end-points of KM algorithm can be approximated with a mean absolute percentage error as low as 0.4%. Also, switch point prediction accuracy can be as high as 100%. In conjunction with the fact that simple regression model can be trained with data generated using exhaustive defuzzification method, this work shows the potential of proposed method to provide highly accurate, yet extremely fast, TR approximation method. Speed of the proposed method should theoretically outperform all available TR methods while keeping the uncertainty information intact in the process.

Linear approximation of Karnik-Mendel type reduction algorithm

Karnik-Mendel (KM) algorithm is the most used and researched type reduction (TR) algorithm in literature. This algorithm is iterative in nature and despite consistent long term effort, no general closed form formula has been found to replace this computationally expensive algorithm. In this research work, we demonstrate that the outcome of KM algorithm can be approximated by simple linear regression techniques. Since most of the applications will have a fixed range of inputs with small scale variations, it is possible to handle those complexities in design phase and build a fuzzy logic system (FLS) with low run time computational burden. This objective can be well served by the application of regression techniques. This work presents an overview of feasibility of regression techniques for design of data-driven type reducers while keeping the uncertainty bound in FLS intact. Simulation results demonstrates the approximation error is less than 2%. Thus our work preserve the essence of Karnik-Mendel algorithm and serves the requirement of low
computational complexities.

Switch point finding using polynomial regression for fuzzy type reduction algorithms

Karnik-Mendel (KM) algorithm is the most widely used type reduction (TR) method in literature for the design of interval type-2 fuzzy logic systems (IT2FLS). Its iterative nature for finding left and right switch points is its Achilles heel. Despite a decade of research, none of the alternative TR methods offer uncertainty measures equivalent to KM algorithm. This paper takes a data-driven approach to tackle the computational burden of this algorithm while keeping its key features. We propose a regression method to approximate left and right switch points found by KM algorithm. Approximator only uses the firing intervals, rnles centroids, and FLS strnctural features as inputs. Once training is done, it can precisely approximate the left and right switch points through basic vector multiplications. Comprehensive simulation results demonstrate that the approximation accuracy for a wide variety of FLSs is 100%. Flexibility, ease of implementation, and speed are other features of the proposed method.

A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting

Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial-Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial-temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. urthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. inally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naïve Bayes (NB), Random orest (R), and C4.. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.9% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 4% and 6.% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.

A cost/benefit approach to performance analysis

Most performance engineering approaches focus on understanding the use of runtime resources. However such approaches do not quantify the value being provided in return for the consumption of these resources. Without such a measure it is not possible to compare the e ciency of these components (that is whether the runtime cost is reasonable given the bene t being provided). We have created an empirical approach that measures the value being provided by a code path in terms of the visible data it generates for the rest of the application. Combining this with traditional performance cost data, creates an e ciency measure for every code path in the application. We have evaluated our approach using the DaCapo benchmark suite, demonstrating our analysis allows us to quantify the e ciency of the code in each benchmark and nd real optimisation opportunities, providing improvements of up to 36% in our case studies.

New modalities of state power: neoliberal responsibilisation and the work of academy chains

This paper draws on interview data gathered as part of a broader study around issues of equity and schooling. It features the voices of the Executive Director and four Head Teachers from one of England's top performing academy chains, ‘CONNECT’. The notion of neoliberal responsibilisation is drawn on to examine, first, the ways in which Head Teachers describe their work and, second, the chain's expectations of them as CONNECT leaders. Responsibilisation of the self was apparent in Head Teachers' construction of themselves as ideal neoliberal workers – performing and enterprising subjects who readily accept the business principles and results-orientation of their ‘data-driven’ environment. Responsibilising of Head Teachers by the organisation was evident in the rigorous ‘non-negotiable’ standards and accountabilities at CONNECT that they were expected to comply with. These non-negotiables cultivated and rewarded Head Teachers’ entrepreneurial identity of achievement motivation. The paper illustrates how such neoliberal responsibilisation is both a crucial and highly troubling element in the work of academy chains as new modalities of state power.