122 resultados para cs.LG

em Queensland University of Technology - ePrints Archive


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The University of Queensland has recently established a new design-focused, studio-based computer science degree. The Bachelor of Information Environments degree augments the core courses from the University's standard CS degree with a stream of design courses and integrative studio-based projects undertaken every semester. The studio projects integrate and reinforce learning by requiring students to apply the knowledge and skills gained in other courses to open-ended real-world design projects. The studio model is based on the architectural studio and involves teamwork, collaborative learning, interactive problem solving, presentations and peer review. This paper describes the degree program, its curriculum and rationale, and reports on experiences in the first year of delivery.

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We have studied the borate mineral rhodizite (K, Cs)Al4Be4(B, Be)12O28 using a combination of DEM with EDX and vibrational spectroscopic techniques. The mineral occurs as colorless, gray, yellow to white crystals in the triclinic crystal system. The studied sample is from the Antandrokomby Mine, Sahatany valley, Madagascar. The mineral is prized as a semi-precious jewel. Semi-quantitative chemical composition shows a Al, Ca, borate with minor amounts of K, Mg and Cs. The mineral has a characteristic borate Raman spectrum and bands are assigned to the stretching and bending modes of B, Be and Al. No Raman bands in the OH stretching region were observed.

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Hybrid face recognition, using image (2D) and structural (3D) information, has explored the fusion of Nearest Neighbour classifiers. This paper examines the effectiveness of feature modelling for each individual modality, 2D and 3D. Furthermore, it is demonstrated that the fusion of feature modelling techniques for the 2D and 3D modalities yields performance improvements over the individual classifiers. By fusing the feature modelling classifiers for each modality with equal weights the average Equal Error Rate improves from 12.60% for the 2D classifier and 12.10% for the 3D classifier to 7.38% for the Hybrid 2D+3D clasiffier.

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Person tracking systems are dependent on being able to locate a person accurately across a series of frames. Optical flow can be used to segment a moving object from a scene, provided the expected velocity of the moving object is known; but successful detection also relies on being able segment the background. A problem with existing optical flow techniques is that they don’t discriminate the foreground from the background, and so often detect motion (and thus the object) in the background. To overcome this problem, we propose a new optical flow technique, that is based upon an adaptive background segmentation technique, which only determines optical flow in regions of motion. This technique has been developed with a view to being used in surveillance systems, and our testing shows that for this application it is more effective than other standard optical flow techniques.

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Discrete event-driven simulations of digital communication networks have been used widely. However, it is difficult to use a network simulator to simulate a hybrid system in which some objects are not discrete event-driven but are continuous time-driven. A networked control system (NCS) is such an application, in which physical process dynamics are continuous by nature. We have designed and implemented a hybrid simulation environment which effectively integrates models of continuous-time plant processes and discrete-event communication networks by extending the open source network simulator NS-2. To do this a synchronisation mechanism was developed to connect a continuous plant simulation with a discrete network simulation. Furthermore, for evaluating co-design approaches in an NCS environment, a piggybacking method was adopted to allow the control period to be adjusted during simulations. The effectiveness of the technique is demonstrated through case studies which simulate a networked control scenario in which the communication and control system properties are defined explicitly.

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This article investigates work related learning and development amongst mature aged workers from a lifespan developmental psychology perspective. The current study follows on from research regarding the construction and revision of the Learning and Development Survey (LDS; Tones & Pillay, 2008). Designed to measure adaptive development for work related learning, the revised LDS (R-LDS) encompasses goal selection, engagement and disengagement from individual and organisational perspectives. Previous survey findings from a mixed age sample of local government workers suggest that mature aged workers aged over 45 years are less likely to report engagement in learning and development goals than younger workers, which is partly due to insufficient opportunities at work. In the current paper, exploratory factor analysis was used to investigate responses to the R-LDS amongst two groups of mature aged workers from a local government (LG) and private healthcare (PH) organisation to determine the stability of the R-LDS. Organisational constraints to development accounted for almost a quarter of the variance in R-LDS scores for both samples, while remaining factors emerged in different orders for each data set. Organisational opportunities for development explained about 17% of the variance in R-LDS scores in the LG sample, while the individual goal disengagement factor contributed a comparable proportion of variance to R-LDS scores for the PH sample. Findings from the current study indicate that opportunities for learning and development at work may be age structured and biased towards younger workers. Implications for professional practice are discussed and focus on improving the engagement of mature aged workers.

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ABSTRACT: Neuropathy is a cause of significant disability in patients with Fabry disease, yet its diagnosis is difficult. In this study we compared the novel noninvasive techniques of corneal confocal microscopy (CCM) to quantify small-fiber pathology, and non-contact corneal esthesiometry (NCCA) to quantify loss of corneal sensation, with established tests of neuropathy in patients with Fabry disease. Ten heterozygous females with Fabry disease not on enzyme replacement therapy (ERT), 6 heterozygous females, 6 hemizygous males on ERT, and 14 age-matched, healthy volunteers underwent detailed quantification of neuropathic symptoms, neurological deficits, neurophysiology, quantitative sensory testing (QST), NCCA, and CCM. All patients with Fabry disease had significant neuropathic symptoms and an elevated Mainz score. Peroneal nerve amplitude was reduced in all patients and vibration perception threshold was elevated in both male and female patients on ERT. Cold sensation (CS) threshold was significantly reduced in both male and female patients on ERT (P < 0.02), but warm sensation (WS)and heat-induced pain (HIP) were only significantly increased in males onERT (P<0.01). However, corneal sensation assessed withNCCAwas significantly reduced in female (P < 0.02) and male (P < 0.04) patients on ERT compared with control subjects. According to CCM, corneal nerve fiber and branch density was significantly reduced in female (P < 0.03) and male (P < 0.02) patients on ERT compared with control subjects. Furthermore, the severity of neuropathic symptoms and the neurological component of the Mainz Severity Score Index correlated significantly with QSTand CCM. This study shows that CCM and NCCA provide a novel means to detect early nerve fiber damage and dysfunction, respectively, in patients with Fabry disease.

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Dealing with the ever-growing information overload in the Internet, Recommender Systems are widely used online to suggest potential customers item they may like or find useful. Collaborative Filtering is the most popular techniques for Recommender Systems which collects opinions from customers in the form of ratings on items, services or service providers. In addition to the customer rating about a service provider, there is also a good number of online customer feedback information available over the Internet as customer reviews, comments, newsgroups post, discussion forums or blogs which is collectively called user generated contents. This information can be used to generate the public reputation of the service providers’. To do this, data mining techniques, specially recently emerged opinion mining could be a useful tool. In this paper we present a state of the art review of Opinion Mining from online customer feedback. We critically evaluate the existing work and expose cutting edge area of interest in opinion mining. We also classify the approaches taken by different researchers into several categories and sub-categories. Each of those steps is analyzed with their strength and limitations in this paper.

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In paper has been to investigate the morphological patterns and kinetics of PDMS spreading on silicon wafer using combination of techniques like ellipsometry, atomic force microscope (AFM), scanning electron microscope (SEM) and optical microscopy. A macroscopic silicone oil drops as well as PDMS water based emulsions were studied after deposition on a flat surface of silicon wafer in air, water and vacuum. our own measurements using an imaging ellipsometer, which also clearly shows the presence of a precursor film. The diffusion constant of this film, measured with a 60 000 cS PDMS sample spreading on a hydrophilic silicon wafer, is Df = 1.4  10-11 m2/s. Regardless of their size, density and method of deposition, droplets on both types of wafer (hydrophilic and hydrophobic) flatten out over a period of many hours, up to 3 days. During this process neighbouring droplets may coalesce, but there is strong evidence that some of the PDMS from the droplets migrates into a thin, continuous film that covers the surface in between droplets. The thin film appears to be ubiquitous if there has been any deposition of PDMS. However, this statement needs further verification. One question is whether the film forms immediately after forced drying, or whether in some or all cases it only forms by spreading from isolated droplets as they slowly flatten out.

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This paper explores the potential therapeutic role of the naturally occurring sugar heparan sulfate (HS) for the augmentation of bone repair. Scaffolds comprising fibrin glue loaded with 5 lg of embryonically derived HS were assessed, firstly as a release-reservoir, and secondly as a scaffold to stimulate bone regeneration in a critical size rat cranial defect. We show HS-loaded scaffolds have a uniform distribution of HS, which was readily released with a typical burst phase, quickly followed by a prolonged delivery lasting several days. Importantly, the released HS contributed to improved wound healing over a 3-month period as determined by microcomputed tomography (lCT) scanning, histology, histomorphometry, and PCR for osteogenic markers. In all cases, only minimal healing was observed after 1 and 3 months in the absence of HS. In contrast, marked healing was observed by 3 months following HS treatment, with nearly full closure of the defect site. PCR analysis showed significant increases in the gene expression of the osteogenic markers Runx2, alkaline phosphatase, and osteopontin in the heparin sulfate group compared with controls. These results further emphasize the important role HS plays in augmenting wound healing, and its successful delivery in a hydrogel provides a novel alternative to autologous bone graft and growth factorbased therapies.

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This dissertation is primarily an applied statistical modelling investigation, motivated by a case study comprising real data and real questions. Theoretical questions on modelling and computation of normalization constants arose from pursuit of these data analytic questions. The essence of the thesis can be described as follows. Consider binary data observed on a two-dimensional lattice. A common problem with such data is the ambiguity of zeroes recorded. These may represent zero response given some threshold (presence) or that the threshold has not been triggered (absence). Suppose that the researcher wishes to estimate the effects of covariates on the binary responses, whilst taking into account underlying spatial variation, which is itself of some interest. This situation arises in many contexts and the dingo, cypress and toad case studies described in the motivation chapter are examples of this. Two main approaches to modelling and inference are investigated in this thesis. The first is frequentist and based on generalized linear models, with spatial variation modelled by using a block structure or by smoothing the residuals spatially. The EM algorithm can be used to obtain point estimates, coupled with bootstrapping or asymptotic MLE estimates for standard errors. The second approach is Bayesian and based on a three- or four-tier hierarchical model, comprising a logistic regression with covariates for the data layer, a binary Markov Random field (MRF) for the underlying spatial process, and suitable priors for parameters in these main models. The three-parameter autologistic model is a particular MRF of interest. Markov chain Monte Carlo (MCMC) methods comprising hybrid Metropolis/Gibbs samplers is suitable for computation in this situation. Model performance can be gauged by MCMC diagnostics. Model choice can be assessed by incorporating another tier in the modelling hierarchy. This requires evaluation of a normalization constant, a notoriously difficult problem. Difficulty with estimating the normalization constant for the MRF can be overcome by using a path integral approach, although this is a highly computationally intensive method. Different methods of estimating ratios of normalization constants (N Cs) are investigated, including importance sampling Monte Carlo (ISMC), dependent Monte Carlo based on MCMC simulations (MCMC), and reverse logistic regression (RLR). I develop an idea present though not fully developed in the literature, and propose the Integrated mean canonical statistic (IMCS) method for estimating log NC ratios for binary MRFs. The IMCS method falls within the framework of the newly identified path sampling methods of Gelman & Meng (1998) and outperforms ISMC, MCMC and RLR. It also does not rely on simplifying assumptions, such as ignoring spatio-temporal dependence in the process. A thorough investigation is made of the application of IMCS to the three-parameter Autologistic model. This work introduces background computations required for the full implementation of the four-tier model in Chapter 7. Two different extensions of the three-tier model to a four-tier version are investigated. The first extension incorporates temporal dependence in the underlying spatio-temporal process. The second extensions allows the successes and failures in the data layer to depend on time. The MCMC computational method is extended to incorporate the extra layer. A major contribution of the thesis is the development of a fully Bayesian approach to inference for these hierarchical models for the first time. Note: The author of this thesis has agreed to make it open access but invites people downloading the thesis to send her an email via the 'Contact Author' function.