323 resultados para improving convergence


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The 'Queensland Model' grew out of three convergent agendas: educational renewal, urban redevelopment, and the Queensland state government's 'Smart State' strategy.

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Seven endemic governance problems are shown to be currently present in governments around the globe and at any level of government as well (for example municipal, federal). These problems have their roots traced back through more than two thousand years of political, specifically ‘democratic’, history. The evidence shows that accountability, transparency, corruption, representation, campaigning methods, constitutionalism and long-term goals were problematic for the ancient Athenians as well as modern international democratisation efforts encompassing every major global region. Why then, given the extended time period humans have had to deal with these problems, are they still present? At least part of the answer to this question is that philosophers, academics and NGOs as well as MNOs have only approached these endemic problems in a piecemeal manner with a skewed perspective on democracy. Their works have also been subject to the ebbs and flows of human history which essentially started and stopped periods of thinking. In order to approach the investigation of endemic problems in relation to democracy (as the overall quest of this thesis was to generate prescriptive results for the improvement of democratic government), it was necessary to delineate what exactly is being written about when using the term ‘democracy’. It is common knowledge that democracy has no one specific definition or practice, even though scholars and philosophers have been attempting to create a definition for generations. What is currently evident, is that scholars are not approaching democracy in an overly simplified manner (that is, it is government for the people, by the people) but, rather, are seeking the commonalities that democracies share, in other words, those items which are common to all things democratic. Following that specific line of investigation, the major practiced and theoretical versions of democracy were thematically analysed. After that, their themes were collapsed into larger categories, at which point the larger categories were comparatively analysed with the practiced and theoretical versions of democracy. Four democratic ‘particles’ (selecting officials, law, equality and communication) were seen to be present in all practiced and theoretical democratic styles. The democratic particles fused with a unique investigative perspective and in-depth political study created a solid conceptualisation of democracy. As such, it is argued that democracy is an ever-present element of any state government, ‘democratic’ or not, and the particles are the bodies which comprise the democratic element. Frequency- and proximity-based analyses showed that democratic particles are related to endemic problems in international democratisation discourse. The linkages between democratic particles and endemic problems were also evident during the thematic analysis as well historical review. This ultimately led to the viewpoint that if endemic problems are mitigated the act may improve democratic particles which might strengthen the element of democracy in the governing apparatus of any state. Such may actively minimise or wholly displace inefficient forms of government, leading to a government specifically tailored to the population it orders. Once the theoretical and empirical goals were attained, this thesis provided some prescriptive measures which government, civil society, academics, professionals and/or active citizens can use to mitigate endemic problems (in any country and at any level of government) so as to improve the human condition via better democratic government.

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The traditional searching method for model-order selection in linear regression is a nested full-parameters-set searching procedure over the desired orders, which we call full-model order selection. On the other hand, a method for model-selection searches for the best sub-model within each order. In this paper, we propose using the model-selection searching method for model-order selection, which we call partial-model order selection. We show by simulations that the proposed searching method gives better accuracies than the traditional one, especially for low signal-to-noise ratios over a wide range of model-order selection criteria (both information theoretic based and bootstrap-based). Also, we show that for some models the performance of the bootstrap-based criterion improves significantly by using the proposed partial-model selection searching method. Index Terms— Model order estimation, model selection, information theoretic criteria, bootstrap 1. INTRODUCTION Several model-order selection criteria can be applied to find the optimal order. Some of the more commonly used information theoretic-based procedures include Akaike’s information criterion (AIC) [1], corrected Akaike (AICc) [2], minimum description length (MDL) [3], normalized maximum likelihood (NML) [4], Hannan-Quinn criterion (HQC) [5], conditional model-order estimation (CME) [6], and the efficient detection criterion (EDC) [7]. From a practical point of view, it is difficult to decide which model order selection criterion to use. Many of them perform reasonably well when the signal-to-noise ratio (SNR) is high. The discrepancies in their performance, however, become more evident when the SNR is low. In those situations, the performance of the given technique is not only determined by the model structure (say a polynomial trend versus a Fourier series) but, more importantly, by the relative values of the parameters within the model. This makes the comparison between the model-order selection algorithms difficult as within the same model with a given order one could find an example for which one of the methods performs favourably well or fails [6, 8]. Our aim is to improve the performance of the model order selection criteria in cases where the SNR is low by considering a model-selection searching procedure that takes into account not only the full-model order search but also a partial model order search within the given model order. Understandably, the improvement in the performance of the model order estimation is at the expense of additional computational complexity.

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Online dating networks, a type of social network, are gaining popularity. With many people joining and being available in the network, users are overwhelmed with choices when choosing their ideal partners. This problem can be overcome by utilizing recommendation methods. However, traditional recommendation methods are ineffective and inefficient for online dating networks where the dataset is sparse and/or large and two-way matching is required. We propose a methodology by using clustering, SimRank to recommend matching candidates to users in an online dating network. Data from a live online dating network is used in evaluation. The success rate of recommendation obtained using the proposed method is compared with baseline success rate of the network and the performance is improved by double.

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Personalised social matching systems can be seen as recommender systems that recommend people to others in the social networks. However, with the rapid growth of users in social networks and the information that a social matching system requires about the users, recommender system techniques have become insufficiently adept at matching users in social networks. This paper presents a hybrid social matching system that takes advantage of both collaborative and content-based concepts of recommendation. The clustering technique is used to reduce the number of users that the matching system needs to consider and to overcome other problems from which social matching systems suffer, such as cold start problem due to the absence of implicit information about a new user. The proposed system has been evaluated on a dataset obtained from an online dating website. Empirical analysis shows that accuracy of the matching process is increased, using both user information (explicit data) and user behavior (implicit data).

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Intelligible and accurate risk-based decision-making requires a complex balance of information from different sources, appropriate statistical analysis of this information and consequent intelligent inference and decisions made on the basis of these analyses. Importantly, this requires an explicit acknowledgement of uncertainty in the inputs and outputs of the statistical model. The aim of this paper is to progress a discussion of these issues in the context of several motivating problems related to the wider scope of agricultural production. These problems include biosecurity surveillance design, pest incursion, environmental monitoring and import risk assessment. The information to be integrated includes observational and experimental data, remotely sensed data and expert information. We describe our efforts in addressing these problems using Bayesian models and Bayesian networks. These approaches provide a coherent and transparent framework for modelling complex systems, combining the different information sources, and allowing for uncertainty in inputs and outputs. While the theory underlying Bayesian modelling has a long and well established history, its application is only now becoming more possible for complex problems, due to increased availability of methodological and computational tools. Of course, there are still hurdles and constraints, which we also address through sharing our endeavours and experiences.

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Interaction Design is a fast developing branch of Industrial Design. The availability of cheap microprocessors and sensor electronics allow interactions between people and products that were until recently impossible. This has added additional layers of complexity to the design process. Novice designers find it difficult to effectively juggle these complexities and typically tend to focus on one aspect at a time. They also tend to take a linear, step-by-step approach to the design process in contrast to expert designers who pursue “parallel lines of thought” whilst simultaneously co-evolving both problem and solution. (Lawson, 1993) This paper explores an approach that encourages designers (in this case novice designers) to take a parallel rather than linear approach to the design process. It also addresses the problem of social loafing that tends to occur in team activities.

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In this paper, we consider the variable-order Galilei advection diffusion equation with a nonlinear source term. A numerical scheme with first order temporal accuracy and second order spatial accuracy is developed to simulate the equation. The stability and convergence of the numerical scheme are analyzed. Besides, another numerical scheme for improving temporal accuracy is also developed. Finally, some numerical examples are given and the results demonstrate the effectiveness of theoretical analysis. Keywords: The variable-order Galilei invariant advection diffusion equation with a nonlinear source term; The variable-order Riemann–Liouville fractional partial derivative; Stability; Convergence; Numerical scheme improving temporal accuracy

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An initialisation process is a key component in modern stream cipher design. A well-designed initialisation process should ensure that each key-IV pair generates a different key stream. In this paper, we analyse two ciphers, A5/1 and Mixer, for which this does not happen due to state convergence. We show how the state convergence problem occurs and estimate the effective key-space in each case.

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Human facial expression is a complex process characterized of dynamic, subtle and regional emotional features. State-of-the-art approaches on facial expression recognition (FER) have not fully utilized this kind of features to improve the recognition performance. This paper proposes an approach to overcome this limitation using patch-based ‘salient’ Gabor features. A set of 3D patches are extracted to represent the subtle and regional features, and then inputted into patch matching operations for capturing the dynamic features. Experimental results show a significant performance improvement of the proposed approach due to the use of the dynamic features. Performance comparison with pervious work also confirms that the proposed approach achieves the highest CRR reported to date on the JAFFE database and a top-level performance on the Cohn-Kanade (CK) database.

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While supportive-expressive group therapy (SEGT) has been found to be effective in significantly reducing distress associated with life-threatening illness, the challenge in Australia is to develop a means of providing supportive interventions to rural women who may be isolated both by the experience of illness and by geographical location. In this study an adaptation of SEGT was provided to women with metastatic breast cancer (n =21), who attended face-to-face or by telephone conference call. Participants showed significant gains on standardised measures of well-being, including a reduction in negative affect and an increase in positive affect, over a 12-month period. A reduction in intrusive and avoidant stress symptoms was also observed over 12 months; however, this difference was not significant. These outcomes suggest that SEGT, delivered in an innovative way within a community setting, may be an effective means of moderating the adverse effects of a diagnosis of metastatic breast cancer while improving access to supportive care for rural women. These results are considered exploratory, as the study did not include a matched control group.

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This paper is based on the premise that universities have an obligation to provide adequate student support services, such as learning assistance (that is, assistance with academic writing and other study skills) and that in order to be effective such services must be responsive to the wider policy and social implications of student attrition and retention. The paper outlines briefly some of the factors that have influenced the development of learning assistance practices in Australia and America. This is followed by an account of experiences at one Australian metropolitan university where learning assistance service provision shifted from a decentralised, faculty-based model to a centralised model of service delivery. This shift was in response to concerns about lack of quality and consistency in a support model dependent upon faculty resources yet a follow up study identified other problems in the centralised delivery of learning assistance services. These problems, clustered under the heading contextualised versus decontextualised learning assistance, include the relevance of generic learning assistance services to students struggling with specific course related demands; the apparent tensions between challenging students and assisting students at risk of failure; and variations in the level of collaboration between learning advisers and academic staff in supporting students in the learning environment. These problems are analysed using the theoretical modelling derived from the tools made available through cultural historical activity theory and expansive visibilisation (Engeström & Miettinen, 1999).