Grouping people in social networks using a weighted multi-constraints clustering method

Grouping users in social networks is an important process that improves matching and recommendation activities in social networks. The data mining methods of clustering can be used in grouping the users in social networks. However, the existing general purpose clustering algorithms perform poorly on the social network data due to the special nature of users' data in social networks. One main reason is the constraints that need to be considered in grouping users in social networks. Another reason is the need of capturing large amount of information about users which imposes computational complexity to an algorithm. In this paper, we propose a scalable and effective constraint-based clustering algorithm based on a global similarity measure that takes into consideration the users' constraints and their importance in social networks. Each constraint's importance is calculated based on the occurrence of this constraint in the dataset. Performance of the algorithm is demonstrated on a dataset obtained from an online dating website using internal and external evaluation measures. Results show that the proposed algorithm is able to increases the accuracy of matching users in social networks by 10% in comparison to other algorithms.

Shift

Shift was an exhibition held in October 2008, and was the culmination of a 10 month artist in residence held at Metro Arts, Brisbane in 2008. A number of works were produced and exhibited, and were a response to the ambient urban landscape of inner city Brisbane. The research component contributes to the discussion of the form and display of digital animation, and builds upon strategies of presentation developed from series of works completed in 2005-2007 as part of an MA (research) at QUT, Brisbane. For the two week exhibition, one interactive kiosk and several large scale digital prints were produced, and also a site specific digital animation sequence was projected onto urban landscape features next to the gallery.

Uncertainty analysis of pollutant build-up modelling based on a Bayesian Weighted Least Squares approach

Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality data sets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares Regression and Bayesian Weighted Least Squares Regression for the estimation of uncertainty associated with pollutant build-up prediction using limited data sets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in the prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling.

Locally weighted learning model predictivek control for elastic joint robots

This paper proposes an efficient and online learning control system that uses the successful Model Predictive Control (MPC) method in a model based locally weighted learning framework. The new approach named Locally Weighted Learning Model Predictive Control (LWL-MPC) has been proposed as a solution to learn to control complex and nonlinear Elastic Joint Robots (EJR). Elastic Joint Robots are generally difficult to learn to control due to their elastic properties preventing standard model learning techniques from being used, such as learning computed torque control. This paper demonstrates the capability of LWL-MPC to perform online and incremental learning while controlling the joint positions of a real three Degree of Freedom (DoF) EJR. An experiment on a real EJR is presented and LWL-MPC is shown to successfully learn to control the system to follow two different figure of eight trajectories.

V1-inspired features induce a weighted margin in SVMs

Image representations derived from simplified models of the primary visual cortex (V1), such as HOG and SIFT, elicit good performance in a myriad of visual classification tasks including object recognition/detection, pedestrian detection and facial expression classification. A central question in the vision, learning and neuroscience communities regards why these architectures perform so well. In this paper, we offer a unique perspective to this question by subsuming the role of V1-inspired features directly within a linear support vector machine (SVM). We demonstrate that a specific class of such features in conjunction with a linear SVM can be reinterpreted as inducing a weighted margin on the Kronecker basis expansion of an image. This new viewpoint on the role of V1-inspired features allows us to answer fundamental questions on the uniqueness and redundancies of these features, and offer substantial improvements in terms of computational and storage efficiency.

Determining process model precision and generalization with weighted artificial negative events

Process mining encompasses the research area which is concerned with knowledge discovery from event logs. One common process mining task focuses on conformance checking, comparing discovered or designed process models with actual real-life behavior as captured in event logs in order to assess the “goodness” of the process model. This paper introduces a novel conformance checking method to measure how well a process model performs in terms of precision and generalization with respect to the actual executions of a process as recorded in an event log. Our approach differs from related work in the sense that we apply the concept of so-called weighted artificial negative events towards conformance checking, leading to more robust results, especially when dealing with less complete event logs that only contain a subset of all possible process execution behavior. In addition, our technique offers a novel way to estimate a process model’s ability to generalize. Existing literature has focused mainly on the fitness (recall) and precision (appropriateness) of process models, whereas generalization has been much more difficult to estimate. The described algorithms are implemented in a number of ProM plugins, and a Petri net conformance checking tool was developed to inspect process model conformance in a visual manner.

Histogram of weighted local directions for gait recognition

In this paper, we explore the effectiveness of patch-based gradient feature extraction methods when applied to appearance-based gait recognition. Extending existing popular feature extraction methods such as HOG and LDP, we propose a novel technique which we term the Histogram of Weighted Local Directions (HWLD). These 3 methods are applied to gait recognition using the GEI feature, with classification performed using SRC. Evaluations on the CASIA and OULP datasets show significant improvements using these patch-based methods over existing implementations, with the proposed method achieving the highest recognition rate for the respective datasets. In addition, the HWLD can easily be extended to 3D, which we demonstrate using the GEV feature on the DGD dataset, observing improvements in performance.

Control, shift, insert : living and enacting policy in teachers' day/night work

This thesis investigates the experiences of teachers who trialled an electronic curriculum and assessment tool in the wider context of text-mediated ruling relations organising their work. Problematised as policy and text, this tool is interrogated as a 'solution' to problems perceived in teachers' work in an era of increased accountability. It provides evidence that teachers' work is shaped by forces operating outside their control and mediated by the policy discourses and subjectivities available to them.

Locally weighted learning model predictive control for nonlinear and time varying dynamics

This paper proposes an online learning control system that uses the strategy of Model Predictive Control (MPC) in a model based locally weighted learning framework. The new approach, named Locally Weighted Learning Model Predictive Control (LWL-MPC), is proposed as a solution to learn to control robotic systems with nonlinear and time varying dynamics. This paper demonstrates the capability of LWL-MPC to perform online learning while controlling the joint trajectories of a low cost, three degree of freedom elastic joint robot. The learning performance is investigated in both an initial learning phase, and when the system dynamics change due to a heavy object added to the tool point. The experiment on the real elastic joint robot is presented and LWL-MPC is shown to successfully learn to control the system with and without the object. The results highlight the capability of the learning control system to accommodate the lack of mechanical consistency and linearity in a low cost robot arm.

Parenting from the outside-in : reflections on parent training during a potential paradigm shift

This article proposes that a paradigm shift that has implications for practitioners of parenting interventions is emerging. This shift represents a challenge to the dominant model of parent training. The Triple P Parenting Program is discussed as an example of parent training programme to highlight the relevant issues for practitioners, including common practitioner objections encountered in dissemination as identified, in part, by Mazzucchelli and Sanders. It is argued that apart fromthese objections, there are more essential concerns in relation to the adoption of parent training programmes by practitioners. Rather, the article argues that parent training is “mind-blind” and that approaches emerging from the field of interpersonal neurobiology represent developmentally sophisticated alternatives for intervention. The Circle of Security programme is discussed as one example of this emerging paradigm shift that integrates attachment, social neuroscience, and psychodynamic theory. Contrasts are highlighted between the models, and considerations for future issues in parent intervention conclude the article.

Supplementary information : weighted tree kernels for sequence analysis

Genomic sequences are fundamentally text documents, admitting various representations according to need and tokenization. Gene expression depends crucially on binding of enzymes to the DNA sequence at small, poorly conserved binding sites, limiting the utility of standard pattern search. However, one may exploit the regular syntactic structure of the enzyme's component proteins and the corresponding binding sites, framing the problem as one of detecting grammatically correct genomic phrases. In this paper we propose new kernels based on weighted tree structures, traversing the paths within them to capture the features which underpin the task. Experimentally, we and that these kernels provide performance comparable with state of the art approaches for this problem, while offering significant computational advantages over earlier methods. The methods proposed may be applied to a broad range of sequence or tree-structured data in molecular biology and other domains.

The Limited Benefit of Prison in Controlling Crime : On the Threshold of a Political Shift

In 2012 the New Zealand government spent $3.4 billion, or nearly $800 per person, on responses to crime via the justice system. Research shows that much of this spending does little to reduce the changes of re-offending. Relatively little money is spent on victims, the rehabilitation of offenders or to support the families of offenders. This book is based on papers presented at the Costs of Crime forum held by the Institute of Policy Studies in February 2011. It presents lessons from what is happening in Australia, Britain and the United States and focuses on how best to manage crime, respond to victims, and reduce offending in a cost-effective manner in a New Zealand context. It is clear that strategies are needed that are based on better research and a more informed approach to policy development. Such strategies must assist victims constructively while also reducing offending. Using public resources to lock as many people in our prisons as possible cannot be justified by the evidence and is fiscally unsustainable; nor does such an approach make society safer. To reduce the costs of crime we need to reinvest resources in effective strategies to build positive futures for those at risk and the communities needed to sustain them.

Benthic shift in a Solomon Islands' lagoon : corals to cyanobacteria

In June 2011 a large phytoplankton bloom resulted in a catastrophic mortality event that affected a large coastal embayment in the Solomon Islands. This consisted of an area in excess of 20 km2 of reef and soft sandy habitats in Marovo Lagoon, the largest double barrier lagoon in the world. This embayment is home to over 1200 people leading largely subsistence lifestyles depending on the impacted reefs for majority of their protein needs. A toxic diatom (Psuedo-nitzchia spp.) and toxic dinoflagellate (Pyrodinium bahamense var. compressum) reached concentrations of millions of cells per litre. The senescent phytoplankton bloom led to complete de-oxygenation of the water column that reportedly caused substantial mortality of marine animal life in the immediate area within a rapid timeframe (24 h). Groups affected included holothurians, crabs and reef and pelagic fish species. Dolphins, reptiles and birds were also found dead within the area, indicating algal toxin accumulation in the food chain. Deep reefs and sediments, whilst initially unaffected, have now been blanketed in large cyanobacterial mats which have negatively impacted live coral cover especially within the deep reef zone (> 6 m depth). Reef recovery within the deep zone has been extremely slow and may indicate an alternative state for the system.

Noise levels in a general intensive care unit : a descriptive study.

The aim of this small-scale study was to measure, analyse and compare levels of acoustic noise, in a nine-bedded general intensive care unit (ICU). Measurements were undertaken using the Norsonic 116 sound level meter recording noise levels in the internationally agreed ‘A’ weighted scale. Noise level data were obtained and recorded at 5 min over 3 consecutive days. Results of noise level analysis indicated that mean noise levels within this clinical area was 56·42 dB(A), with acute spikes reaching 80 dB(A). The quietest noise level attained was that of 50 dB(A) during sporadic intervals throughout the 24-h period. Parametric testing using analysis of variance found a positive relationship (p ≤ 0·001) between the nursing shifts and the day of the week. However, Scheffe multiple range testing showed significant differences between the morning shift, and the afternoon and night shifts combined (p ≤ 0·05). There was no statistical difference between the afternoon and night shifts (p ≥ 0·05). While the results of this study may seem self-evident in many respects, what it has highlighted is that the problem of excessive noise exposure within the ICU continues to go unabated. More concerning is that the prolonged effects of excessive noise exposure on patients and staff alike can have deleterious effect on the health and well-being of these individuals.

Half-wave cycloconverter-based photovoltaic microinverter topology with phase-shift power modulation

A grid-connected microinverter with a reduced number of power conversion stages and fewer passive components is proposed. A high-frequency transformer and a series-resonant tank are used to interface the full-bridge inverter to the half-wave cycloconverter. All power switches are switched with zero-voltage switching. Phase-shift power modulation is used to control the output power of the inverter. A steady-state analysis of the proposed topology is presented to determine the average output power of the inverter. Analysis of soft switching of the full-bridge and the half-wave cycloconverter is presented with respect to voltage gain, quality factor, and phase shift of the inverter. Simulation and experimental results are presented to validate the operation of the proposed topology.