973 resultados para MATCH
Resumo:
The middle years of schooling are increasingly recognised as a crucial stage in students' lives, one that has significant consequences for ongoing educational success. International research indicates that young adolescents benefit from programs designed especially for their needs, and the middle years have become an important reform issue for education systems. Teaching Middle Years offers a systematic overview of the philosophy, principles and issues in middle schooling. It includes contributions from academics and school-based practitioners on intellectual and emotional development in early adolescence, pedagogy, curriculum and assessment of middle years students. Written for teachers, student teachers, education leaders and policy makers, Teaching Middle Years is an essential resource for anyone involved in educating young adolescents. Teaching Middle Years is the first comprehensive Australian book to match and surpass the quality of many overseas publications.'
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Aims: To describe a local data linkage project to match hospital data with the Australian Institute of Health and Welfare (AIHW) National Death Index (NDI) to assess longterm outcomes of intensive care unit patients. Methods: Data were obtained from hospital intensive care and cardiac surgery databases on all patients aged 18 years and over admitted to either of two intensive care units at a tertiary-referral hospital between 1 January 1994 and 31 December 2005. Date of death was obtained from the AIHW NDI by probabilistic software matching, in addition to manual checking through hospital databases and other sources. Survival was calculated from time of ICU admission, with a censoring date of 14 February 2007. Data for patients with multiple hospital admissions requiring intensive care were analysed only from the first admission. Summary and descriptive statistics were used for preliminary data analysis. Kaplan-Meier survival analysis was used to analyse factors determining long-term survival. Results: During the study period, 21 415 unique patients had 22 552 hospital admissions that included an ICU admission; 19 058 surgical procedures were performed with a total of 20 092 ICU admissions. There were 4936 deaths. Median follow-up was 6.2 years, totalling 134 203 patient years. The casemix was predominantly cardiac surgery (80%), followed by cardiac medical (6%), and other medical (4%). The unadjusted survival at 1, 5 and 10 years was 97%, 84% and 70%, respectively. The 1-year survival ranged from 97% for cardiac surgery to 36% for cardiac arrest. An APACHE II score was available for 16 877 patients. In those discharged alive from hospital, the 1, 5 and 10-year survival varied with discharge location. Conclusions: ICU-based linkage projects are feasible to determine long-term outcomes of ICU patients
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This paper explains, somewhat along a Simmelian line, that political theory may produce practical and universal theories like those developed in theoretical physics. The reasoning behind this paper is to show that the Element of Democracy Theory may be true by way of comparing it to Einstein’s Special Relativity – specifically concerning the parameters of symmetry, unification, simplicity, and utility. These parameters are what make a theory in physics as meeting them not only fits with current knowledge, but also produces paths towards testing (application). As the Element of Democracy Theory meets these same parameters, it could settle the debate concerning the definition of democracy. This will be shown firstly by discussing why no one has yet achieved a universal definition of democracy; secondly by explaining the parameters chosen (as in why these and not others confirm or scuttle theories); and thirdly by comparing how Special Relativity and the Element of Democracy match the parameters.
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Even for a casual observer of the journalistic industry it is becoming difficult to escape the conclusion that journalism is entering a time of crisis. At the same time that revenues and readerships for traditional publications from newspapers to broadcast news are declining, journalistic content is being overtaken by a flotilla of alternative options ranging from the news satire of The Daily Show in the United States to the citizen journalism of South Korea’s OhmyNews and a myriad of other news blogs and citizen journalism Websites. Worse still, such new competitors with the products of the journalism industry frequently take professional journalists themselves to task where their standards have appeared to have slipped, and are beginning to match the news industry’s incumbents in terms of insight and informational value: recent studies have shown, for example, that avid Daily Show viewers are as if not better informed about the U.S. political process as those who continue to follow mainstream print or television news (see e.g. Fox et al., 2007). The show’s host Jon Stewart – who has consistently maintained his self-description as a comedian, not a journalist – even took the fight directly to the mainstream with his appearance on CNN’s belligerent talk show Crossfire, repeatedly making the point that the show’s polarised and polarising ‘left vs. right’ format was “hurting” politics in America (the show disappeared from CNN’s line-up a few months after Stewart’s appearance; Stewart, 2004). Similarly, news bloggers and citizen journalists have shown persistence and determination both in uncovering political and other scandals, and in highlighting the shortcomings of professional journalism as it investigates and reports on such scandals.
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This paper illustrates the prediction of opponent behaviour in a competitive, highly dynamic, multi-agent and partially observable environment, namely RoboCup small size league robot soccer. The performance is illustrated in the context of the highly successful robot soccer team, the RoboRoos. The project is broken into three tasks; classification of behaviours, modelling and prediction of behaviours and integration of the predictions into the existing planning system. A probabilistic approach is taken to dealing with the uncertainty in the observations and with representing the uncertainty in the prediction of the behaviours. Results are shown for a classification system using a Naïve Bayesian Network that determines the opponent’s current behaviour. These results are compared to an expert designed fuzzy behaviour classification system. The paper illustrates how the modelling system will use the information from behaviour classification to produce probability distributions that model the manner with which the opponents perform their behaviours. These probability distributions are show to match well with the existing multi-agent planning system (MAPS) that forms the core of the RoboRoos system.
Resumo:
This paper explains, somewhat along a Simmelian line, that political theory may produce practical and universal theories like those developed in theoretical physics. The reasoning behind this paper is to show that the Element of Democracy Theory may be true by way of comparing it to Einstein’s Special Relativity – specifically concerning the parameters of symmetry, unification, simplicity, and utility. These parameters are what make a theory in physics as meeting them not only fits with current knowledge, but also produces paths towards testing (application). As the Element of Democracy Theory meets these same parameters, it could settle the debate concerning the definition of democracy. This will be shown firstly by discussing why no one has yet achieved a universal definition of democracy; secondly by explaining the parameters chosen (as in why these and not others confirm or scuttle theories); and thirdly by comparing how Special Relativity and the Element of Democracy match the parameters.
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Existing literature has failed to find robust relationships between individual differences and the ability to fake psychological tests, possibly due to limitations in how successful faking is operationalised. In order to fake, individuals must alter their original profile to create a particular impression. Currently, successful faking is operationalised through statistical definitions, informant ratings, known groups comparisons, the use of archival and baseline data, and breaches of validity indexes. However, there are many methodological limitations to these approaches. This research proposed a three component model of successful faking to address this, where an original response is manipulated into a strategic response, which must match a criteria target. Further, by operationalising successful faking in this manner, this research takes into account the fact that individuals may have been successful in reaching their implicitly created profile, but that this may not have matched the criteria they were instructed to fake.Participants (N=48, 22 students and 26 non-students) completed the BDI-II honestly. Participants then faked the BDI-II as if they had no, mild, moderate and severe depression, as well as completing a checklist revealing which symptoms they thought indicated each level of depression. Findings were consistent with a three component model of successful faking, where individuals effectively changed their profile to what they believed was required, however this profile differed from the criteria defined by the psychometric norms of the test.One of the foremost issues for research in this area is the inconsistent manner in which successful faking is operationalised. This research allowed successful faking to be operationalised in an objective, quantifiable manner. Using this model as a template may allow researchers better understanding of the processes involved in faking, including the role of strategies and abilities in determining the outcome of test dissimulation.
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Water environments are greatly valued in urban areas as ecological and aesthetic assets. However, it is the water environment that is most adversely affected by urbanisation. Urban land use coupled with anthropogenic activities alters the stream flow regime and degrade water quality with urban stormwater being a significant source of pollutants. Unfortunately, urban water pollution is difficult to evaluate in terms of conventional monetary measures. True costs extend beyond immediate human or the physical boundaries of the urban area and affect the function of surrounding ecosystems. Current approaches for handling stormwater pollution and water quality issues in urban landscapes are limited as these are primarily focused on ‘end-of-pipe’ solutions. The approaches are commonly based either on, insufficient design knowledge, faulty value judgements or inadequate consideration of full life cycle costs. It is in this context that the adoption of a triple bottom line approach is advocated to safeguard urban water quality. The problem of degradation of urban water environments can only be remedied through innovative planning, water sensitive engineering design and the foresight to implement sustainable practices. Sustainable urban landscapes must be designed to match the triple bottom line needs of the community, starting with ecosystem services first such as the water cycle, then addressing the social and immediate ecosystem health needs, and finally the economic performance of the catchment. This calls for a cultural change towards urban water resources rather than the current piecemeal and single issue focus approach. This paper discusses the challenges in safeguarding urban water environments and the limitations of current approaches. It then explores the opportunities offered by integrating innovative planning practices with water engineering concepts into a single cohesive framework to protect valuable urban ecosystem assets. Finally, a series of recommendations are proposed for protecting urban water resources within the context of a triple bottom line approach.
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A mathematical model is developed to simulate the discharge of a LiFePO4 cathode. This model contains 3 size scales, which match with experimental observations present in the literature on the multi-scale nature of LiFePO4 material. A shrinking-core is used on the smallest scale to represent the phase-transition of LiFePO4 during discharge. The model is then validated against existing experimental data and this validated model is then used to investigate parameters that influence active material utilisation. Specifically the size and composition of agglomerates of LiFePO4 crystals is discussed, and we investigate and quantify the relative effects that the ionic and electronic conductivities within the oxide have on oxide utilisation. We find that agglomerates of crystals can be tolerated under low discharge rates. The role of the electrolyte in limiting (cathodic) discharge is also discussed, and we show that electrolyte transport does limit performance at high discharge rates, confirming the conclusions of recent literature.
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Abstract Providing water infrastructure in times of accelerating climate change presents interesting new problems. Expanding demands must be met or managed in contexts of increasingly constrained sources of supply, raising ethical questions of equity and participation. Loss of agricultural land and natural habitats, the coastal impacts of desalination plants and concerns over re-use of waste water must be weighed with demand management issues of water rationing, pricing mechanisms and inducing behaviour change. This case study examines how these factors impact on infrastructure planning in South East Queensland, Australia: a region with one of the developed world’s most rapidly growing populations, which has recently experienced the most severe drought in its recorded history. Proposals to match forecast demands and potential supplies for water over a 20 year period are reviewed by applying ethical principles to evaluate practical plans to meet the water needs of the region’s activities and settlements.
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Robust image hashing seeks to transform a given input image into a shorter hashed version using a key-dependent non-invertible transform. These image hashes can be used for watermarking, image integrity authentication or image indexing for fast retrieval. This paper introduces a new method of generating image hashes based on extracting Higher Order Spectral features from the Radon projection of an input image. The feature extraction process is non-invertible, non-linear and different hashes can be produced from the same image through the use of random permutations of the input. We show that the transform is robust to typical image transformations such as JPEG compression, noise, scaling, rotation, smoothing and cropping. We evaluate our system using a verification-style framework based on calculating false match, false non-match likelihoods using the publicly available Uncompressed Colour Image database (UCID) of 1320 images. We also compare our results to Swaminathan’s Fourier-Mellin based hashing method with at least 1% EER improvement under noise, scaling and sharpening.
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Hollywood has dominated the global film business since the First World War. Economic formulas used by governments to assess levels of industry dominance typically measure market share to establish the degree of industry concentration. The business literature reveals that a marketing orientation strongly correlates with superior market performance and that market leaders that possess a set of six superior marketing capabilities are able to continually outperform rival firms. This paper argues that the historical evidence shows that the Hollywood Majors have consistently outperformed rival firms and rival film industries in each of those six marketing capabilities and that unless rivals develop a similarly integrated and cohesive strategic marketing management approach to the movie business and match the Major studios’ superior capabilities, then Hollywood’s dominance will continue. This paper also proposes that in cyberspace, whilst the Internet does provide a channel that democratises film distribution, the flat landscape of the world wide web means that in order to stand out from the clutter of millions of cyber-voices seeking attention, independent film companies need to possess superior strategic marketing management capabilities and develop effective e-marketing strategies to find a niche, attract a loyal online audience and prosper. However, mirroring a recent CIA report forecasting a multi-polar world economy, this paper also argues that potentially serious longer-term rivals are emerging and will increasingly take a larger slice of an expanding global box office as India, China and other major developing economies and their respective cultural channels grow and achieve economic parity with or surpass the advanced western economies. Thus, in terms of global market share over time, Hollywood’s slice of the pie will comparatively diminish in an emerging multi-polar movie business.
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Stereo vision is a method of depth perception, in which depth information is inferred from two (or more) images of a scene, taken from different perspectives. Practical applications for stereo vision include aerial photogrammetry, autonomous vehicle guidance, robotics and industrial automation. The initial motivation behind this work was to produce a stereo vision sensor for mining automation applications. For such applications, the input stereo images would consist of close range scenes of rocks. A fundamental problem faced by matching algorithms is the matching or correspondence problem. This problem involves locating corresponding points or features in two images. For this application, speed, reliability, and the ability to produce a dense depth map are of foremost importance. This work implemented a number of areabased matching algorithms to assess their suitability for this application. Area-based techniques were investigated because of their potential to yield dense depth maps, their amenability to fast hardware implementation, and their suitability to textured scenes such as rocks. In addition, two non-parametric transforms, the rank and census, were also compared. Both the rank and the census transforms were found to result in improved reliability of matching in the presence of radiometric distortion - significant since radiometric distortion is a problem which commonly arises in practice. In addition, they have low computational complexity, making them amenable to fast hardware implementation. Therefore, it was decided that matching algorithms using these transforms would be the subject of the remainder of the thesis. An analytic expression for the process of matching using the rank transform was derived from first principles. This work resulted in a number of important contributions. Firstly, the derivation process resulted in one constraint which must be satisfied for a correct match. This was termed the rank constraint. The theoretical derivation of this constraint is in contrast to the existing matching constraints which have little theoretical basis. Experimental work with actual and contrived stereo pairs has shown that the new constraint is capable of resolving ambiguous matches, thereby improving match reliability. Secondly, a novel matching algorithm incorporating the rank constraint has been proposed. This algorithm was tested using a number of stereo pairs. In all cases, the modified algorithm consistently resulted in an increased proportion of correct matches. Finally, the rank constraint was used to devise a new method for identifying regions of an image where the rank transform, and hence matching, are more susceptible to noise. The rank constraint was also incorporated into a new hybrid matching algorithm, where it was combined a number of other ideas. These included the use of an image pyramid for match prediction, and a method of edge localisation to improve match accuracy in the vicinity of edges. Experimental results obtained from the new algorithm showed that the algorithm is able to remove a large proportion of invalid matches, and improve match accuracy.
Resumo:
Keyword Spotting is the task of detecting keywords of interest within continu- ous speech. The applications of this technology range from call centre dialogue systems to covert speech surveillance devices. Keyword spotting is particularly well suited to data mining tasks such as real-time keyword monitoring and unre- stricted vocabulary audio document indexing. However, to date, many keyword spotting approaches have su®ered from poor detection rates, high false alarm rates, or slow execution times, thus reducing their commercial viability. This work investigates the application of keyword spotting to data mining tasks. The thesis makes a number of major contributions to the ¯eld of keyword spotting. The ¯rst major contribution is the development of a novel keyword veri¯cation method named Cohort Word Veri¯cation. This method combines high level lin- guistic information with cohort-based veri¯cation techniques to obtain dramatic improvements in veri¯cation performance, in particular for the problematic short duration target word class. The second major contribution is the development of a novel audio document indexing technique named Dynamic Match Lattice Spotting. This technique aug- ments lattice-based audio indexing principles with dynamic sequence matching techniques to provide robustness to erroneous lattice realisations. The resulting algorithm obtains signi¯cant improvement in detection rate over lattice-based audio document indexing while still maintaining extremely fast search speeds. The third major contribution is the study of multiple veri¯er fusion for the task of keyword veri¯cation. The reported experiments demonstrate that substantial improvements in veri¯cation performance can be obtained through the fusion of multiple keyword veri¯ers. The research focuses on combinations of speech background model based veri¯ers and cohort word veri¯ers. The ¯nal major contribution is a comprehensive study of the e®ects of limited training data for keyword spotting. This study is performed with consideration as to how these e®ects impact the immediate development and deployment of speech technologies for non-English languages.
Resumo:
Camera calibration information is required in order for multiple camera networks to deliver more than the sum of many single camera systems. Methods exist for manually calibrating cameras with high accuracy. Manually calibrating networks with many cameras is, however, time consuming, expensive and impractical for networks that undergo frequent change. For this reason, automatic calibration techniques have been vigorously researched in recent years. Fully automatic calibration methods depend on the ability to automatically find point correspondences between overlapping views. In typical camera networks, cameras are placed far apart to maximise coverage. This is referred to as a wide base-line scenario. Finding sufficient correspondences for camera calibration in wide base-line scenarios presents a significant challenge. This thesis focuses on developing more effective and efficient techniques for finding correspondences in uncalibrated, wide baseline, multiple-camera scenarios. The project consists of two major areas of work. The first is the development of more effective and efficient view covariant local feature extractors. The second area involves finding methods to extract scene information using the information contained in a limited set of matched affine features. Several novel affine adaptation techniques for salient features have been developed. A method is presented for efficiently computing the discrete scale space primal sketch of local image features. A scale selection method was implemented that makes use of the primal sketch. The primal sketch-based scale selection method has several advantages over the existing methods. It allows greater freedom in how the scale space is sampled, enables more accurate scale selection, is more effective at combining different functions for spatial position and scale selection, and leads to greater computational efficiency. Existing affine adaptation methods make use of the second moment matrix to estimate the local affine shape of local image features. In this thesis, it is shown that the Hessian matrix can be used in a similar way to estimate local feature shape. The Hessian matrix is effective for estimating the shape of blob-like structures, but is less effective for corner structures. It is simpler to compute than the second moment matrix, leading to a significant reduction in computational cost. A wide baseline dense correspondence extraction system, called WiDense, is presented in this thesis. It allows the extraction of large numbers of additional accurate correspondences, given only a few initial putative correspondences. It consists of the following algorithms: An affine region alignment algorithm that ensures accurate alignment between matched features; A method for extracting more matches in the vicinity of a matched pair of affine features, using the alignment information contained in the match; An algorithm for extracting large numbers of highly accurate point correspondences from an aligned pair of feature regions. Experiments show that the correspondences generated by the WiDense system improves the success rate of computing the epipolar geometry of very widely separated views. This new method is successful in many cases where the features produced by the best wide baseline matching algorithms are insufficient for computing the scene geometry.