Technologies for supporting reasoning communities and collaborative decision making: Cooperative approaches
- Authors: Yearwood, John , Stranieri, Andrew
- Date: 2011
- Type: Text , Book
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- Description: The information age has enabled unprecedented levels of data to be collected and stored. At the same time, society and organizations have become increasingly complex. Consequently, decisions in many facets have become increasingly complex but have the potential to be better informed. Technologies for Supporting Reasoning Communities and Collaborative Decision Making: Cooperative Approaches includes chapters from diverse fields of enquiry including decision science, political science, argumentation, knowledge management, cognitive psychology and business intelligence. Each chapter illustrates a perspective on group reasoning that ultimately aims to lead to a greater understanding of reasoning communities and inform technological developments.
Automated opinion detection : Implications of the level of agreement between human raters
- Authors: Osman, Deanna , Yearwood, John , Vamplew, Peter
- Date: 2010
- Type: Text , Journal article
- Relation: Information Processing and Management Vol. 46, no. 3 (2010), p. 331-342
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- Description: The ability to agree with the TREC Blog06 opinion assessments was measured for seven human assessors and compared with the submitted results of the Blog06 participants. The assessors achieved a fair level of agreement between their assessments, although the range between the assessors was large. It is recommended that multiple assessors are used to assess opinion data, or a pre-test of assessors is completed to remove the most dissenting assessors from a pool of assessors prior to the assessment process. The possibility of inconsistent assessments in a corpus also raises concerns about training data for an automated opinion detection system (AODS), so a further recommendation is that AODS training data be assembled from a variety of sources. This paper establishes an aspirational value for an AODS by determining the level of agreement achievable by human assessors when assessing the existence of an opinion on a given topic. Knowing the level of agreement amongst humans is important because it sets an upper bound on the expected performance of AODS. While the AODSs surveyed achieved satisfactory results, none achieved a result close to the upper bound. © 2009 Elsevier Ltd. All rights reserved.
Editorial
- Authors: Yearwood, John
- Date: 2010
- Type: Text , Journal article
- Relation: Journal of Research and Practice in Information Technology Vol. 42, no. 1 (2010), p. 1
- Full Text: false
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Internet security applications of Grobner-Shirvov bases
- Authors: Kelarev, Andrei , Yearwood, John , Watters, Paul
- Date: 2010
- Type: Text , Journal article
- Relation: Asian-European Journal of Mathematics Vol. 3, no. 3 (2010), p. 435-442
- Relation: http://purl.org/au-research/grants/arc/DP0211866
- Full Text: false
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Learning parse-free event-based features for textual entailment recognition
- Authors: Ofoghi, Bahadorreza , Yearwood, John
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 23rd Australasian Joint Conference on Artificial Intelligence, AI 2010 Vol. 6464 LNAI, p. 184-193
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- Description: We propose new parse-free event-based features to be used in conjunction with lexical, syntactic, and semantic features of texts and hypotheses for Machine Learning-based Recognizing Textual Entailment. Our new similarity features are extracted without using shallow semantic parsers, but still lexical and compositional semantics are not left out. Our experimental results demonstrate that these features can improve the effectiveness of the identification of entailment and no-entailment relationships. © 2010 Springer-Verlag.
A stochastic version of Expectation Maximization algorithm for better estimation of Hidden Markov Model
- Authors: Huda, Shamsul , Yearwood, John , Togneri, Roberto
- Date: 2009
- Type: Text , Journal article
- Relation: Pattern Recognition Letters Vol. 30, no. 14 (2009), p. 1301-1309
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- Description: This paper attempts to overcome the local convergence problem of the Expectation Maximization (EM) based training of the Hidden Markov Model (HMM) in speech recognition. We propose a hybrid algorithm, Simulated Annealing Stochastic version of EM (SASEM), combining Simulated Annealing with EM that reformulates the HMM estimation process using a stochastic step between the EM steps and the SA. The stochastic processes of SASEM inside EM can prevent EM from converging to a local maximum and find improved estimation for HMM using the global convergence properties of SA. Experiments on the TIMIT speech corpus show that SASEM obtains higher recognition accuracies than the EM. © 2009 Elsevier B.V. All rights reserved.
An algorithm for the optimization of multiple classifers in data mining based on graphs
- Authors: Kelarev, Andrei , Ryan, Joe , Yearwood, John
- Date: 2009
- Type: Text , Journal article
- Relation: The Journal of Combinatorial Mathematics and Combinatorial Computing Vol. 71, no. (2009), p. 65-85
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- Description: This article develops an efficient combinatorial algorithm based on labeled directed graphs and motivated by applications in data mining for designing multiple classifiers. Our method originates from the standard approach described in [37]. It defines a representation of a multiclass classifier in terms of several binary classifiers. We are using labeled graphs to introduce additional structure on the classifier. Representations of this sort are known to have serious advantages. An important property of these representations is their ability to correct errors of individual binary classifiers and produce correct combined output. For every representation like this we develop a combinatorial algorithm with quadratic running time to compute the largest number of errors of individual binary classifiers which can be corrected by the combined multiple classifier. In addition, we consider the question of optimizing the classifiers of this type and find all optimal representations for these multiple classifiers.
- Description: 2003007563
Can shallow semantic class information help answer passage retrieval?
- Authors: Ofoghi, Bahadorreza , Yearwood, John
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 22nd Australasian Joint Conference, AI 2009: Advances in Artificial Intelligence, Melbourne, Victoria : 1st-4th December 2009 p. 587–596
- Full Text: false
- Description: In this paper, the effect of using semantic class overlap evidence in enhancing the passage retrieval effectiveness of question answering (QA) systems is tested. The semantic class overlap between questions and passages is measured by evoking FrameNet semantic frames using a shallow term-lookup procedure. We use the semantic class overlap evidence in two ways: i) fusing passage scores obtained from a baseline retrieval system with those obtained from the analysis of semantic class overlap (fusion-based approach), and ii) revising the passage scoring function of the baseline system by incorporating semantic class overlap evidence (revision-based approach). Our experiments with the TREC 2004 and 2006 datasets show that the revision-based approach significantly improves the passage retrieval effectiveness of the baseline system.
- Description: 2003007254
Deliberative discourse and reasoning from generic argument structures
- Authors: Yearwood, John , Stranieri, Andrew
- Date: 2009
- Type: Text , Journal article
- Relation: AI and Society Vol. 23, no. 3 (2009), p. 353-377
- Full Text: false
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- Description: In this article a dialectical model for practical reasoning within a community, based on the Generic/Actual Argument Model (GAAM) is advanced and its application to deliberative dialogue discussed. The GAAM, offers a dynamic template for structuring knowledge within a domain of discourse that is connected to and regulated by a community. The paper demonstrates how the community accepted generic argument structure acts to normatively influence both admissible reasoning and the progression of dialectical reasoning between participants. It is further demonstrated that these types of deliberation dialogues supported by the GAAM comply with criteria for normative principles for deliberation, specifically, Alexy's rules for discourse ethics and Hitchcock's Principles of Rational Mutual Inquiry. The connection of reasoning to the community in a documented and transparent structure assists in providing best justified reasons, principles of deliberation and ethical discourse which are important advantages for reasoning communities. © Springer-Verlag London Limited 2006.
MRF model based unsupervised color textured image segmentation using multidimensional spatially variant finite mixture model
- Authors: Islam, Mofakharul , Vamplew, Peter , Yearwood, John
- Date: 2009
- Type: Text , Book chapter
- Relation: Technological developments in Education and Automation p. 375-380
- Full Text: false
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- Description: We investigate and propose a novel approach to implement an unsupervised color image segmentation model that segments a color image meaningfully and partitions into its constituent parts automatically. The aim is to devise a robust unsupervised segmentation approach that can segment a color textured image more accurately. Here, color and texture information of each individual pixel along with the spatial relationship within its neighborhood have been considered for producing more accuracy in segmentation. In this particular work, the problem we want to investigate is to implement a robust unsupervised Multidimensional Spatially Variant Finite Mixture Model (MSVFMM) based color image segmentation approach using Cluster Ensembles and MRF model along with Daubechies wavelet transforms for increasing the content sensitivity of the segmentation model in order to get a better accuracy in segmentation. Here, Cluster Ensemble has been utilized as a robust automatic tool for finding the number of components in an image. The main idea behind this work is introducing a Bayesian inference based approach to estimate the Maximum a Posteriori (MAP) to identify the different objects/components in a color image. Markov Random Field (MRF) plays a crucial role in capturing the relationships among the neighboring pixels. An Expectation Maximization (EM) model fitting MAP algorithm segments the image utilizing the pixel’s color and texture features and the captured neighborhood relationships among them. The algorithm simultaneously calculates the model parameters and segments the pixels iteratively in an interleaved manner. Finally, it converges to a solution where the model parameters and pixel labels are stabilized within a specified criterion. Finally, we have compared our results with another recent segmentation approach [10], which is similar in nature. The experimental results reveal that the proposed approach is capable of producing more accurate and faithful segmentation and can be employed in different practical image content understanding applications.
Online group deliberation for the elicitation of shared values to underpin decision making
- Authors: Feldman, Yishai , Kraft, Donald , Kuflik, Tsvi , Afshar, Faezeh , Stranieri, Andrew , Yearwood, John
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 7th International Conference, NGITS 2009, Next generation information technologies and systems, Haifa, Israel : 16th-18th June 2009 Vol. 5831, p. 158-168
- Full Text: false
- Description: Values have been shown to underpin our attitudes, behaviour and motivate our decisions. Values do not exist in isolation but have meaning in relation to other values. However, values are not solely the purview of individuals as communities and organisations have core values implicit in their culture, policies and practices. Values for a group can be determined by a minority in power, derived by algorithmically merging values each group member holds, or set by deliberative consensus. The elicitation of values for the group by deliberation is likely to lead to widespread acceptance of values arrived at, however enticing individuals to engage in face to face discussion about values has been found to be very difficult. We present an online deliberative communication approach for the anonymous deliberation of values and claim that the framework has the elements required for the elicitation of shared values.
- Description: 2003007509
Optimization methods and the k-committees algorithm for clustering of sequence data
- Authors: Yearwood, John , Bagirov, Adil , Kelarev, Andrei
- Date: 2009
- Type: Text , Journal article
- Relation: Applied and Computational Mathematics Vol. 8, no. 1 (2009), p. 92-101
- Relation: http://purl.org/au-research/grants/arc/DP0211866
- Relation: http://purl.org/au-research/grants/arc/DP0666061
- Full Text: false
- Description: The present paper is devoted to new algorithms for unsupervised clustering based on the optimization approaches due to [2], [3] and [4]. We consider a novel situation, where the datasets consist of nucleotide or protein sequences and rather sophisticated biologically significant alignment scores have to be used as a measure of distance. Sequences of this kind cannot be regarded as points in a finite dimensional space. Besides, the alignment scores do not satisfy properties of Minkowski metrics. Nevertheless the optimization approaches have made it possible to introduce a new k-committees algorithm and compare its performance with previous algorithms for two datasets. Our experimental results show that the k-committees algorithms achieves intermediate accuracy for a dataset of ITS sequences, and it can perform better than the discrete k-means and Nearest Neighbour algorithms for certain datasets. All three algorithms achieve good agreement with clusters published in the biological literature before and can be used to obtain biologically significant clusterings.
The impact of frame semantic annotation levels, frame-alignment techniques, and fusion methods on factoid answer processing
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Liping, Ma
- Date: 2009
- Type: Text , Journal article
- Relation: Journal of the American Society for Information Science and Technology Vol. 60, no. 2 (2009), p. 247-263
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- Description: The impact of frame semantic enrichment of texts on the task of factoid question answering (QA) is studied in this paper. In particular, we consider different techniques for answer processing with frame semantics: the level of semantic class identification and role assignment to texts, and the fusion of frame semantic-based answerprocessing approaches with other methods used in the Text REtrieval Conference (TREC). The impact of each of these aspects on the overall performance of a QA system is analyzed in this paper. The TREC 2004 and TREC 2006 factoid question sets were used for the experiments. These demonstrate that the exploitation of encapsulated frame semantics in FrameNet in a shallow semantic parsing process can enhance answer-processing performance in factoid QA systems. This improvement is dependent on the level of semantic annotation, the frame semantic alignment method, and the method of fusing frame semantic-based answer-processing models with other existing models. A more comprehensively annotated environment with all different part-of-speech target predicates provides a higher chance of correct factoid answer retrieval where semantic alignment is based on both semantic classes and a relaxed set of semantic roles for answer span identification. Our experiments on fusion techniques of frame semantic-based and entity-based answer-processing models show that merging answer lists with respect to their scores and redundancy by exploiting a fusion function leads to a more effective overall factoid QA system compared to the use of individual models.
Unsupervised segmentation of Industrial Images using Markov Random Field Model
- Authors: Islam, Mofakharul , Yearwood, John , Vamplew, Peter
- Date: 2009
- Type: Text , Book chapter
- Relation: Technogical Developments in Education and Automation p. 369-374
- Full Text: false
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- Description: We propose a novel approach to investigate and implement unsupervised image content understanding and segmentation of color industrial images like medical imaging, forensic imaging, security and surveillance imaging, biotechnical imaging, biometrics, mineral and mining imaging, material science imaging, and many more. In this particular work, our focus will be on medical images only. The aim is to develop a computer aided diagnosis (CAD) system based on a newly developed Multidimensional Spatially Variant Finite Mixture Model (MSVFMM) using Markov Random Fields (MRF) Model. Unsupervised means automatic discovery of classes or clusters in images rather than generating the class or cluster descriptions from training image sets. The aim of this work is to produce precise segmentation of color medical images on the basis of subtle color and texture variation. Finer segmentation of images has tremendous potential in medical imaging where subtle information related to color and texture is required to analyze the image accurately. In this particular work, we have used CIE-Luv and Daubechies wavelet transforms as color and texture descriptors respectively. Using the combined effect of a CIE-Luv color model and Daubechies transforms, we can segment color medical images precisely in a meaningful manner. The evaluation of the results is done through comparison of the segmentation quality with another similar alternative approach and it is found that the proposed approach is capable of producing more faithful segmentation.
Weblogs for market research : Finding more relevant opinion documents using system fusion
- Authors: Osman, Deanna , Yearwood, John , Vamplew, Peter
- Date: 2009
- Type: Text , Journal article
- Relation: Online Information Review Vol. 33, no. 5 (2009), p. 873-888
- Full Text: false
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- Description: Purpose - The purpose of this paper is to examine the usefulness of fusion as a means of improving the precision of automated opinion detection. Design/methodology/approach - Five system fusion methods are proposed and tested using runs submitted by the Text REtrieval Conference (TREC) Blog06 participants as input. The methods include a voting method, an inverse rank method (IRM), a linear-normalised score method and two weighted methods that use a weighted IRM score to rank the document. Findings - Mean average precision (MAP) is used as an indicator of the performance of the runs in this study. The best system fusion method achieves a 55.5 percent higher MAP result compared with the highest MAP result of any individual run submitted by the Blog06 participants. This equates to an increase in detection of 2,398 relevant opinion documents (21 percent). Practical implications - System fusion can be used to improve upon the results achieved by existing individual opinion detection systems. On the other hand, multiple opinion detection approaches can be combined into one system and fusion used to combine the results to build in diversity. Diversity within fusion inputs can increase the improvements achieved by fusion methods. The improved output from a diverse opinion detection system will then contain a higher number of relevant documents and reduce the incidence of high-ranking non-relevant documents and low-ranking relevant documents. Originality/value - The fusion methods proposed in this study demonstrate that simple fusion of opinion detection systems can improve performance.
Workload coverage through nonsmooth optimization
- Authors: Sukhorukova, Nadezda , Ugon, Julien , Yearwood, John
- Date: 2009
- Type: Text , Journal article
- Relation: Optimization Methods and Software Vol. 24, no. 2 (2009), p. 285-298
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- Description: In this paper, workload coverage is the problem of identifying a pattern of days worked and days off, along with the number of hours worked on each work day. This pattern must satisfy certain work-related constraints and fit best to a predefined workload. In our study, we formulate the problem of workload coverage as an optimization problem. We propose a number of models which take into consideration various staffing constraints. For each of these models, our study aims to find a compromise between an accurate workload coverage and the ability to solve the corresponding optimization problems in a reasonable time. Numerical experiments on each model are carried out and the results are presented. Interestingly, the nonlinear programming approaches are found to be competitive with linear programming ones. © 2009 Taylor & Francis.
A study of the use of structured reasoning frameworks for improving students' reasoning quality
- Authors: Yearwood, John , Stranieri, Andrew
- Date: 2008
- Type: Text , Journal article
- Relation: Learning and Teaching: an international journal in classroom pedagogy Vol. 1, no. 1 (2008), p. 71-90
- Full Text: false
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- Description: C1
- Description: 2003006498
AWSum - Data mining for insight
- Authors: Quinn, Anthony , Stranieri, Andrew , Yearwood, John , Hafen, Gaudenz
- Date: 2008
- Type: Text , Journal article
- Relation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 5139 LNAI, no. (8 October 2008 through 10 October 2008 2008), p. 524-531
- Full Text: false
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- Description: Many classifiers achieve high levels of accuracy but have limited use in real world problems because they provide little insight into data sets, are difficult to interpret and require expertise to use. In areas such as health informatics not only do analysts require accurate classifications but they also want some insight into the influences on the classification. This can then be used to direct research and formulate interventions. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classifier that gives accuracy comparable to other techniques whist providing insight into the data. AWSum achieves this by calculating a weight for each feature value that represents its influence on the class value. The merits of AWSum in classification and insight are tested on a Cystic Fibrosis dataset with positive results. © 2008 Springer-Verlag Berlin Heidelberg.
- Description: 2003006692
AWSum -Combining classification with knowledge acquisition
- Authors: Quinn, Anthony , Stranieri, Andrew , Yearwood, John , Hafen, Gaudenz , Jelinek, Herbert
- Date: 2008
- Type: Text , Journal article
- Relation: International Journal of Software and Informatics Vol. 2, no. 2 (2008), p. 199-214
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- Description: Many classifiers achieve high levels of accuracy but have limited applicability in real world situations because they do not lead to a greater understanding or insight into the way features influence the classification. In areas such as health informatics a classifier that clearly identifies the influences on classification can be used to direct research and formulate interventions. This research investigates the practical aplications of Automated Weighted Sum, (AWSum), a classifier that provides accuracy comparable to other techniques whist providing insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. The merits of this approach in classification and insight are evaluated on a Cystic Fibrosis and diabetes datasets with positive results.
Enhancing learning outcomes with an interactive knowledge-based learning environment providing narrative feedback
- Authors: Stranieri, Andrew , Yearwood, John
- Date: 2008
- Type: Text , Journal article
- Relation: Interactive Learning Environments Vol. 16, no. 3 (2008), p. 265-281
- Full Text: false
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- Description: This paper describes a narrative-based interactive learning environment which aims to elucidate reasoning using interactive scenarios that may be used in training novices in decision-making. Its design is based on an approach to generating narrative from knowledge that has been modelled in specific decision/reasoning domains. The approach uses a narrative model that is guided partially by inference and contextual information contained in the particular knowledge representation used, the generic/actual argument model of structured reasoning. The approach is described with examples in the area of critical care nursing training. A study of the effectiveness of this approach on learning outcomes was conducted with final year nursing students and provides evidence of improved learning outcomes.
- Description: C1