DOWL : A dynamic ontology language
- Authors: Avery, John , Yearwood, John
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at IADIS International Conference WWW/Internet 2003, Algarve, Portugal : 5th August, 2003
- Full Text:
- Reviewed:
- Description: Abstract: Ontologies in a web setting, particularly those used in a group context (such as a virtual community), need to be flexible and open to changes that reflect the evolution of knowledge. OWL the ontology language of the semantic web provides very little for facilitating the description of evolutionary changes in an ontology. We propose a dynamic web ontology language (dOWL), an extension to OWL, which consists of a set of elements that can be used to model these evolutionary changes in an ontology.
- Description: E1
- Description: 2003000552
- Authors: Avery, John , Yearwood, John
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at IADIS International Conference WWW/Internet 2003, Algarve, Portugal : 5th August, 2003
- Full Text:
- Reviewed:
- Description: Abstract: Ontologies in a web setting, particularly those used in a group context (such as a virtual community), need to be flexible and open to changes that reflect the evolution of knowledge. OWL the ontology language of the semantic web provides very little for facilitating the description of evolutionary changes in an ontology. We propose a dynamic web ontology language (dOWL), an extension to OWL, which consists of a set of elements that can be used to model these evolutionary changes in an ontology.
- Description: E1
- Description: 2003000552
A formal description of ontology change in OWL
- Authors: Avery, John , Yearwood, John
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at the Third International Conference on Information Technology and Applications, ICITA 2005, Sydney : 4th - 7th July, 2005
- Full Text:
- Reviewed:
- Description: There are three main activities involved in managing ontology change. Firstly we need to identify changes, secondly describe these identified changes, and finally describe and handle the ramifications of the changes. In previous work we have presented a language (DOWL) for describing ontology change and in this paper we demonstrate how changes described in this language can be represented in the RDF abstract syntax which enables us to describe the ramifications of a change in a formal manner. This formalism can provide the basis for an automated ontology change management system.
- Description: E1
- Description: 2003001448
- Authors: Avery, John , Yearwood, John
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at the Third International Conference on Information Technology and Applications, ICITA 2005, Sydney : 4th - 7th July, 2005
- Full Text:
- Reviewed:
- Description: There are three main activities involved in managing ontology change. Firstly we need to identify changes, secondly describe these identified changes, and finally describe and handle the ramifications of the changes. In previous work we have presented a language (DOWL) for describing ontology change and in this paper we demonstrate how changes described in this language can be represented in the RDF abstract syntax which enables us to describe the ramifications of a change in a formal manner. This formalism can provide the basis for an automated ontology change management system.
- Description: E1
- Description: 2003001448
A global optimisation approach to classification in medical diagnosis and prognosis
- Bagirov, Adil, Rubinov, Alex, Yearwood, John, Stranieri, Andrew
- Authors: Bagirov, Adil , Rubinov, Alex , Yearwood, John , Stranieri, Andrew
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
- Full Text:
- Description: In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
- Description: 2003003950
- Authors: Bagirov, Adil , Rubinov, Alex , Yearwood, John , Stranieri, Andrew
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
- Full Text:
- Description: In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
- Description: 2003003950
Consensus clustering and supervised classification for profiling phishing emails in internet commerce security
- Dazeley, Richard, Yearwood, John, Kang, Byeongho, Kelarev, Andrei
- Authors: Dazeley, Richard , Yearwood, John , Kang, Byeongho , Kelarev, Andrei
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services, PKAW 2010 Vol. 6232 LNAI, p. 235-246
- Full Text:
- Reviewed:
- Description: This article investigates internet commerce security applications of a novel combined method, which uses unsupervised consensus clustering algorithms in combination with supervised classification methods. First, a variety of independent clustering algorithms are applied to a randomized sample of data. Second, several consensus functions and sophisticated algorithms are used to combine these independent clusterings into one final consensus clustering. Third, the consensus clustering of the randomized sample is used as a training set to train several fast supervised classification algorithms. Finally, these fast classification algorithms are used to classify the whole large data set. One of the advantages of this approach is in its ability to facilitate the inclusion of contributions from domain experts in order to adjust the training set created by consensus clustering. We apply this approach to profiling phishing emails selected from a very large data set supplied by the industry partners of the Centre for Informatics and Applied Optimization. Our experiments compare the performance of several classification algorithms incorporated in this scheme. © 2010 Springer-Verlag Berlin Heidelberg.
- Authors: Dazeley, Richard , Yearwood, John , Kang, Byeongho , Kelarev, Andrei
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services, PKAW 2010 Vol. 6232 LNAI, p. 235-246
- Full Text:
- Reviewed:
- Description: This article investigates internet commerce security applications of a novel combined method, which uses unsupervised consensus clustering algorithms in combination with supervised classification methods. First, a variety of independent clustering algorithms are applied to a randomized sample of data. Second, several consensus functions and sophisticated algorithms are used to combine these independent clusterings into one final consensus clustering. Third, the consensus clustering of the randomized sample is used as a training set to train several fast supervised classification algorithms. Finally, these fast classification algorithms are used to classify the whole large data set. One of the advantages of this approach is in its ability to facilitate the inclusion of contributions from domain experts in order to adjust the training set created by consensus clustering. We apply this approach to profiling phishing emails selected from a very large data set supplied by the industry partners of the Centre for Informatics and Applied Optimization. Our experiments compare the performance of several classification algorithms incorporated in this scheme. © 2010 Springer-Verlag Berlin Heidelberg.
An experiment in task decomposition and ensembling for a modular artificial neural network
- Ferguson, Brent, Ghosh, Ranadhir, Yearwood, John
- Authors: Ferguson, Brent , Ghosh, Ranadhir , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at Innovations in Applied Artificial Intelligence: 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Ottawa, Canada : 17th May, 2004
- Full Text:
- Reviewed:
- Description: Modular neural networks have the possibility of overcoming common scalability and interference problems experienced by fully connected neural networks when applied to large databases. In this paper we trial an approach to constructing modular ANN's for a very large problem from CEDAR for the classification of handwritten characters. In our approach, we apply progressive task decomposition methods based upon clustering and regression techniques to find modules. We then test methods for combining the modules into ensembles and compare their structural characteristics and classification performance with that of an ANN having a fully connected topology. The results reveal improvements to classification rates as well as network topologies for this problem.
- Description: E1
- Description: 2003000852
- Authors: Ferguson, Brent , Ghosh, Ranadhir , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at Innovations in Applied Artificial Intelligence: 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Ottawa, Canada : 17th May, 2004
- Full Text:
- Reviewed:
- Description: Modular neural networks have the possibility of overcoming common scalability and interference problems experienced by fully connected neural networks when applied to large databases. In this paper we trial an approach to constructing modular ANN's for a very large problem from CEDAR for the classification of handwritten characters. In our approach, we apply progressive task decomposition methods based upon clustering and regression techniques to find modules. We then test methods for combining the modules into ensembles and compare their structural characteristics and classification performance with that of an ANN having a fully connected topology. The results reveal improvements to classification rates as well as network topologies for this problem.
- Description: E1
- Description: 2003000852
A fully automated CAD system using multi-category feature selection with restricted recombination
- Ghosh, Ranadhir, Ghosh, Moumita, Yearwood, John, Mukherjee, Subhasis
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Mukherjee, Subhasis
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 106-111
- Full Text:
- Description: In pattern recognition problems features plays an important role for classification results. It is very important which features are used and how many features are used for the classification process. Most of the real life classification problem uses different category of features. It is desirable to find the optimal combination of features that improves the performance of the classifier. There exists different selection framework that selects the features. Mostly do not incorporate the impact of one category of features on another. Even if they incorporate, they produce conflict between the categories. In this paper we proposed a restricted crossover selection framework which incorporate the impact of different categories on each other, as well as it restricts the search within the category which searching in the global region of the search space. The results obtained by the proposed framework are promising.
- Description: 2003005429
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Mukherjee, Subhasis
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 106-111
- Full Text:
- Description: In pattern recognition problems features plays an important role for classification results. It is very important which features are used and how many features are used for the classification process. Most of the real life classification problem uses different category of features. It is desirable to find the optimal combination of features that improves the performance of the classifier. There exists different selection framework that selects the features. Mostly do not incorporate the impact of one category of features on another. Even if they incorporate, they produce conflict between the categories. In this paper we proposed a restricted crossover selection framework which incorporate the impact of different categories on each other, as well as it restricts the search within the category which searching in the global region of the search space. The results obtained by the proposed framework are promising.
- Description: 2003005429
A modular framework for multi category feature selection in digital mammography
- Ghosh, Ranadhir, Ghosh, Moumita, Yearwood, John
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at ESANN 2004 Proceedings: European Symposium on Artificial Neural Networks, Bruges, Belguim : 28/04/2004 Vol. Elsevier, p. 175-180
- Full Text:
- Reviewed:
- Description: Many existing researches utilized many different approaches for recognition in digital mammography using various ANN classifier-modeling techniques. Different types of feature extraction techniques are also used. It has been observed that, beyond a certain point, the inclusion of additional features leads to a worse rather than better performance. Moreover, the choice of features to represent the patterns affects several aspects of pattern recognition problem such as accuracy, required learning time and necessary number of samples. A common problem with the multi category feature classification is the conflict between the categories. None of the feasible solutions allow simultaneous optimal solution for all categories. In order to find an optimal solutions the searching space can be divided based on individual category in each sub region and finally merging them through decision spport system. In this paper we propose a canonical GA based modular feature selection approach combined with standard MLP.
- Description: E1
- Description: 2003000872
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at ESANN 2004 Proceedings: European Symposium on Artificial Neural Networks, Bruges, Belguim : 28/04/2004 Vol. Elsevier, p. 175-180
- Full Text:
- Reviewed:
- Description: Many existing researches utilized many different approaches for recognition in digital mammography using various ANN classifier-modeling techniques. Different types of feature extraction techniques are also used. It has been observed that, beyond a certain point, the inclusion of additional features leads to a worse rather than better performance. Moreover, the choice of features to represent the patterns affects several aspects of pattern recognition problem such as accuracy, required learning time and necessary number of samples. A common problem with the multi category feature classification is the conflict between the categories. None of the feasible solutions allow simultaneous optimal solution for all categories. In order to find an optimal solutions the searching space can be divided based on individual category in each sub region and finally merging them through decision spport system. In this paper we propose a canonical GA based modular feature selection approach combined with standard MLP.
- Description: E1
- Description: 2003000872
Visual tools for analysing evolution, emergence, and error in data streams
- Hart, Sol, Yearwood, John, Bagirov, Adil
- Authors: Hart, Sol , Yearwood, John , Bagirov, Adil
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 987-992
- Full Text:
- Description: The relatively new field of stream mining has necessitated the development of robust drift-aware algorithms that provide accurate, real time, data handling capabilities. Tools are needed to assess and diagnose important trends and investigate drift evolution parameters. In this paper, we present two new and novel visualisation techniques, Pixie and Luna graphs, which incorporate salient group statistics coupled with intuitive visual representations of multidimensional groupings over time. Through the novel representations presented here, spatial interactions between temporal divisions can be diagnosed and overall distribution patterns identified. It provides a means of evaluating in non-constrained capacity, commonly constrained evolutionary problems.
- Description: 2003005432
- Authors: Hart, Sol , Yearwood, John , Bagirov, Adil
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 987-992
- Full Text:
- Description: The relatively new field of stream mining has necessitated the development of robust drift-aware algorithms that provide accurate, real time, data handling capabilities. Tools are needed to assess and diagnose important trends and investigate drift evolution parameters. In this paper, we present two new and novel visualisation techniques, Pixie and Luna graphs, which incorporate salient group statistics coupled with intuitive visual representations of multidimensional groupings over time. Through the novel representations presented here, spatial interactions between temporal divisions can be diagnosed and overall distribution patterns identified. It provides a means of evaluating in non-constrained capacity, commonly constrained evolutionary problems.
- Description: 2003005432
Exploring novel features and decision rules to identify cardiovascular autonomic neuropathy using a hybrid of wrapper-filter based feature selection
- Huda, Shamsul, Jelinek, Herbert, Ray, Biplob, Stranieri, Andrew, Yearwood, John
- Authors: Huda, Shamsul , Jelinek, Herbert , Ray, Biplob , Stranieri, Andrew , Yearwood, John
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at the 2010 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2010 p. 297-302
- Full Text:
- Reviewed:
- Description: Cardiovascular autonomic neuropathy (CAN) is one of the important causes of mortality among diabetes patients. Statistics shows that more than 22% of people with type 2 diabetes mellitus suffer from CAN and which in turn leads to cardiovascular disease (heart attack, stroke). Therefore early detection of CAN could reduce the mortality. Traditional method for detection of CAN uses Ewing's algorithm where five noninvasive cardiovascular tests are used. Often for clinician, it is difficult to collect data from for the Ewing Battery patients due to onerous test conditions. In this paper, we propose a hybrid of wrapper-filter approach to find novel features from patients' ECG records and then generate decision rules for the new features for easier detection of CAN. In the proposed feature selection, a hybrid of filter (Maximum Relevance, MR) and wrapper (Artificial Neural Net Input Gain Measurement Approximation ANNIGMA) approaches (MR-ANNIGMA) would be used. The combined heuristics in the hybrid MRANNIGMA takes the advantages of the complementary properties of the both filter and wrapper heuristics and can find significant features. The selected features set are used to generate a new set of rules for detection of CAN. Experiments on real patient records shows that proposed method finds a smaller set of features for detection of CAN than traditional method which are clinically significant and could lead to an easier way to diagnose CAN. © 2010 IEEE.
- Authors: Huda, Shamsul , Jelinek, Herbert , Ray, Biplob , Stranieri, Andrew , Yearwood, John
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at the 2010 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2010 p. 297-302
- Full Text:
- Reviewed:
- Description: Cardiovascular autonomic neuropathy (CAN) is one of the important causes of mortality among diabetes patients. Statistics shows that more than 22% of people with type 2 diabetes mellitus suffer from CAN and which in turn leads to cardiovascular disease (heart attack, stroke). Therefore early detection of CAN could reduce the mortality. Traditional method for detection of CAN uses Ewing's algorithm where five noninvasive cardiovascular tests are used. Often for clinician, it is difficult to collect data from for the Ewing Battery patients due to onerous test conditions. In this paper, we propose a hybrid of wrapper-filter approach to find novel features from patients' ECG records and then generate decision rules for the new features for easier detection of CAN. In the proposed feature selection, a hybrid of filter (Maximum Relevance, MR) and wrapper (Artificial Neural Net Input Gain Measurement Approximation ANNIGMA) approaches (MR-ANNIGMA) would be used. The combined heuristics in the hybrid MRANNIGMA takes the advantages of the complementary properties of the both filter and wrapper heuristics and can find significant features. The selected features set are used to generate a new set of rules for detection of CAN. Experiments on real patient records shows that proposed method finds a smaller set of features for detection of CAN than traditional method which are clinically significant and could lead to an easier way to diagnose CAN. © 2010 IEEE.
Smokers' characteristics and cluster based quitting rule discovery model for enhancement of government's tobacco control systems
- Huda, Shamsul, Yearwood, John, Borland, Ron
- Authors: Huda, Shamsul , Yearwood, John , Borland, Ron
- Date: 2010
- Type: Text , Conference paper
- Relation: Proceedings of the 14th Pacific Asia Conference on Information Systems (PACIS 2010)
- Full Text:
- Reviewed:
- Description: Discovery of cluster characteristics and interesting rules describing smokers' clusters and the behavioural patterns of smoker's quitting intentions is an important task in the development of an effective tobacco control systems. In this paper, we attempt to determine the characteristics smokers' cluster and simplified rule for predicting smokers' quitting behaviour that can provide feedback to build a scientific evidence-based adaptive tobacco control systems. Standard clustering algorithm groups the data based on there inherent pattern. "From abstract"
- Description: Discovery of cluster characteristics and interesting rules describing smokers' clusters and the behavioural patterns of smoker's quiiting intentios is an important task in the development of an effective tobacco control systems. In this paper, we attempt to determine the characteristics smokers' cluster and simplified rule for predicting smokers' quitting behaviour that can provide feedback to build a scientific evidence-based adaptive tobacco control systems. Standard clustering algorithm groups the data based on there inherent pattern. "From abstract"
- Authors: Huda, Shamsul , Yearwood, John , Borland, Ron
- Date: 2010
- Type: Text , Conference paper
- Relation: Proceedings of the 14th Pacific Asia Conference on Information Systems (PACIS 2010)
- Full Text:
- Reviewed:
- Description: Discovery of cluster characteristics and interesting rules describing smokers' clusters and the behavioural patterns of smoker's quitting intentions is an important task in the development of an effective tobacco control systems. In this paper, we attempt to determine the characteristics smokers' cluster and simplified rule for predicting smokers' quitting behaviour that can provide feedback to build a scientific evidence-based adaptive tobacco control systems. Standard clustering algorithm groups the data based on there inherent pattern. "From abstract"
- Description: Discovery of cluster characteristics and interesting rules describing smokers' clusters and the behavioural patterns of smoker's quiiting intentios is an important task in the development of an effective tobacco control systems. In this paper, we attempt to determine the characteristics smokers' cluster and simplified rule for predicting smokers' quitting behaviour that can provide feedback to build a scientific evidence-based adaptive tobacco control systems. Standard clustering algorithm groups the data based on there inherent pattern. "From abstract"
Using association and overlapping time window approach to detect drug reaction signals
- Ivkovic, Sasha, Saunders, Gary, Ghosh, Ranadhir, Yearwood, John
- Authors: Ivkovic, Sasha , Saunders, Gary , Ghosh, Ranadhir , Yearwood, John
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at CIMCA 2005 International Conference on Computational Intelligence for Modelling Control & Automation jointly with IAWTIC 2005 International Conference on Intelligent Agents, Web Technologies & Internet Commerce, Vienna, Austria : 28th November, 2005 p. 1045-1053
- Full Text:
- Reviewed:
- Description: The problem with detecting adverse drug reactions (ADRs) from drugs is that they may not be obvious until long after they are widely prescribed. Part of the problem is these events are rare. This work describes an approach to signal detection of ADRs based on association rules (AR) in Australian drug safety data. This work was carried out using the Australian Adverse Drug Reactions Advisory Committee (ADRAC) database, which contains a hundred and thirty seven thousand records collected in 1972-2001 period. Many signal detection methods have been developed for drug safety data, most of which use a classical statistical approach. Some of these stratify the data using an ontology for reactions, but the application of drug ontologies to ADR signal detection methods has not been reported. We propose a novel approach for detecting various signal levels by using an overlapped windowing approach. The overlapping windows help to detect smooth transition of signal. We use association rules for measuring significant change over time for different hierarchical levels of drugs (using the Anatomical-Therapeutic-Chemical (ATC) system of drug classification ontology) and their reactions based on the System Organ Classes (SOC) ontology. Using association rules and their strength for different levels in the drug and reaction hierarchy, helps in the detection of signals at particular levels in higher order using a bottom up approach. The results of a preliminary investigation of ADRAC data using our method demonstrate that this approach could produce a powerful and robust ADR signal detection method.
- Description: E1
- Description: 2003001838
- Authors: Ivkovic, Sasha , Saunders, Gary , Ghosh, Ranadhir , Yearwood, John
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at CIMCA 2005 International Conference on Computational Intelligence for Modelling Control & Automation jointly with IAWTIC 2005 International Conference on Intelligent Agents, Web Technologies & Internet Commerce, Vienna, Austria : 28th November, 2005 p. 1045-1053
- Full Text:
- Reviewed:
- Description: The problem with detecting adverse drug reactions (ADRs) from drugs is that they may not be obvious until long after they are widely prescribed. Part of the problem is these events are rare. This work describes an approach to signal detection of ADRs based on association rules (AR) in Australian drug safety data. This work was carried out using the Australian Adverse Drug Reactions Advisory Committee (ADRAC) database, which contains a hundred and thirty seven thousand records collected in 1972-2001 period. Many signal detection methods have been developed for drug safety data, most of which use a classical statistical approach. Some of these stratify the data using an ontology for reactions, but the application of drug ontologies to ADR signal detection methods has not been reported. We propose a novel approach for detecting various signal levels by using an overlapped windowing approach. The overlapping windows help to detect smooth transition of signal. We use association rules for measuring significant change over time for different hierarchical levels of drugs (using the Anatomical-Therapeutic-Chemical (ATC) system of drug classification ontology) and their reactions based on the System Organ Classes (SOC) ontology. Using association rules and their strength for different levels in the drug and reaction hierarchy, helps in the detection of signals at particular levels in higher order using a bottom up approach. The results of a preliminary investigation of ADRAC data using our method demonstrate that this approach could produce a powerful and robust ADR signal detection method.
- Description: E1
- Description: 2003001838
Establishing phishing provenance using orthographic features
- Liping, Ma, Yearwood, John, Watters, Paul
- Authors: Liping, Ma , Yearwood, John , Watters, Paul
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 2009 eCrime Researchers Summit, eCRIME '09, Tacoma, Washington : 20th-21st October 2009
- Full Text:
- Description: After phishing message detection, determining the provenance of phishing messages and Websites is the second step to tracing cybercriminals. In this paper, we present a novel method to cluster phishing emails automatically using orthographic features. In particular, we develop an algorithm to cluster documents and remove redundant features at the same time. After collecting all the possible features based on observation, we adapt the modified global k-mean method repeatedly, and generate the objective function values over a range of tolerance values across different subsets of features. Finally, we identify the appropriate clusters based on studying the distribution of the objective function values. Experimental evaluation of a large number of computations demonstrates that our clustering and feature selection techniques are highly effective and achieve reliable results.
- Description: 2003007842
- Authors: Liping, Ma , Yearwood, John , Watters, Paul
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 2009 eCrime Researchers Summit, eCRIME '09, Tacoma, Washington : 20th-21st October 2009
- Full Text:
- Description: After phishing message detection, determining the provenance of phishing messages and Websites is the second step to tracing cybercriminals. In this paper, we present a novel method to cluster phishing emails automatically using orthographic features. In particular, we develop an algorithm to cluster documents and remove redundant features at the same time. After collecting all the possible features based on observation, we adapt the modified global k-mean method repeatedly, and generate the objective function values over a range of tolerance values across different subsets of features. Finally, we identify the appropriate clusters based on studying the distribution of the objective function values. Experimental evaluation of a large number of computations demonstrates that our clustering and feature selection techniques are highly effective and achieve reliable results.
- Description: 2003007842
Dramatic level analysis for interactive narrative
- Macfadyen, Alyx, Stranieri, Andrew, Yearwood, John
- Authors: Macfadyen, Alyx , Stranieri, Andrew , Yearwood, John
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at NILE 2008: 5th International Conference on Narrative and Interactive Learning Environments, Edinburgh, Scotland : 6th-8th August 2008 p. 17-22
- Full Text:
- Description: In interactive 3D narratives, a user’s narrative emerges through interactions with the system and embodied agencies (characters) mediated through the 3D environment. We present a methodology that identifies and measures four factors in interactive narrative where agency is present. We describe a technique for measuring drama, agency and engagement and compare the centrality of a designed interactive narrative with the emergent participatory narrative. This methodology has application as an analytic device for any interactive narrative where agency is fundamental. The adoption of the FrameNet semantic resource and the interpretation of interaction in narrative, situate this work in the domain of 3D interactive narratives, mixed and augmented realities and polymorphic narratives that cross forms of media.
- Description: 2003006540
- Authors: Macfadyen, Alyx , Stranieri, Andrew , Yearwood, John
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at NILE 2008: 5th International Conference on Narrative and Interactive Learning Environments, Edinburgh, Scotland : 6th-8th August 2008 p. 17-22
- Full Text:
- Description: In interactive 3D narratives, a user’s narrative emerges through interactions with the system and embodied agencies (characters) mediated through the 3D environment. We present a methodology that identifies and measures four factors in interactive narrative where agency is present. We describe a technique for measuring drama, agency and engagement and compare the centrality of a designed interactive narrative with the emergent participatory narrative. This methodology has application as an analytic device for any interactive narrative where agency is fundamental. The adoption of the FrameNet semantic resource and the interpretation of interaction in narrative, situate this work in the domain of 3D interactive narratives, mixed and augmented realities and polymorphic narratives that cross forms of media.
- Description: 2003006540
An interaction framework for scenario-based three dimensional environments
- Macfadyen, Alyx, Stranieri, Andrew, Yearwood, John
- Authors: Macfadyen, Alyx , Stranieri, Andrew , Yearwood, John
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at IE 2006, the 3rd Australasian Conference on Interactive Entertainment, Perth : 4th December, 2006
- Full Text:
- Reviewed:
- Description: Although popular and engaging, three dimensional environments are rarely deployed to depict strong narratives involving complex characters engaged in reasoning. The design of three dimensional environments rich in narrative and character depth can be facilitated with a detailed representation of interactions between characters. However, the representation of interaction in current 3D development environments such as game engines is quite basic. This work advances a scheme for representing interactions that integrates a representation of semantics from linguistics called FrameNet with conceptualizations of drama and narrative by Georges Polti and Joseph Campbell. The resulting interaction frame facilitates the design of 3D environments by providing designers rich, yet standard elements that include spatial and temporal data, with which to represent complex interactions in 3D environments. This has application for the authoring of dynamically generated interactive narrative environments.
- Description: E1
- Description: 2003001839
- Authors: Macfadyen, Alyx , Stranieri, Andrew , Yearwood, John
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at IE 2006, the 3rd Australasian Conference on Interactive Entertainment, Perth : 4th December, 2006
- Full Text:
- Reviewed:
- Description: Although popular and engaging, three dimensional environments are rarely deployed to depict strong narratives involving complex characters engaged in reasoning. The design of three dimensional environments rich in narrative and character depth can be facilitated with a detailed representation of interactions between characters. However, the representation of interaction in current 3D development environments such as game engines is quite basic. This work advances a scheme for representing interactions that integrates a representation of semantics from linguistics called FrameNet with conceptualizations of drama and narrative by Georges Polti and Joseph Campbell. The resulting interaction frame facilitates the design of 3D environments by providing designers rich, yet standard elements that include spatial and temporal data, with which to represent complex interactions in 3D environments. This has application for the authoring of dynamically generated interactive narrative environments.
- Description: E1
- Description: 2003001839
Dramatic flow in interactive 3D narrative
- Macfadyen, Alyx, Stranieri, Andrew, Yearwood, John
- Authors: Macfadyen, Alyx , Stranieri, Andrew , Yearwood, John
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Fourth Australiasian Conference on Interactive Entertainment, IE2007, RMIT University, Melbourne, Victoria : 3rd-5th December 2007
- Full Text:
- Description: The concept of dramatic level is crucial for a model of dramatic flow. We present a framework to maintain optimal dramatic flow in an interactive 3D environment where both linear and emergent narratives co-exist. Unlike all other interactive narrative prototypes the framework advanced focuses on the optimal dramatic flow of the emerging user narrative so that although fragmented, it can be engaging and make sense. Using a sample narrative from Ovid’s Metamorphoses [18] we demonstrate a method to evaluate dramatic levels as plot points so that movement across narratives retains a strong dramatic flow. Although users may never choose to explore any given linear narrative in its entirety, the result is an engaging and rich narrative experience.
- Description: 2003004706
- Authors: Macfadyen, Alyx , Stranieri, Andrew , Yearwood, John
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Fourth Australiasian Conference on Interactive Entertainment, IE2007, RMIT University, Melbourne, Victoria : 3rd-5th December 2007
- Full Text:
- Description: The concept of dramatic level is crucial for a model of dramatic flow. We present a framework to maintain optimal dramatic flow in an interactive 3D environment where both linear and emergent narratives co-exist. Unlike all other interactive narrative prototypes the framework advanced focuses on the optimal dramatic flow of the emerging user narrative so that although fragmented, it can be engaging and make sense. Using a sample narrative from Ovid’s Metamorphoses [18] we demonstrate a method to evaluate dramatic levels as plot points so that movement across narratives retains a strong dramatic flow. Although users may never choose to explore any given linear narrative in its entirety, the result is an engaging and rich narrative experience.
- Description: 2003004706
A new supervised term ranking method for text categorization
- Mammadov, Musa, Yearwood, John, Zhao, Lei
- Authors: Mammadov, Musa , Yearwood, John , Zhao, Lei
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 23rd Australasian Joint Conference on Artificial Intelligence, AI 2010 Vol. 6464 LNAI, p. 102-111
- Full Text:
- Reviewed:
- Description: In text categorization, different supervised term weighting methods have been applied to improve classification performance by weighting terms with respect to different categories, for example, Information Gain, χ2 statistic, and Odds Ratio. From the literature there are three term ranking methods to summarize term weights of different categories for multi-class text categorization. They are Summation, Average, and Maximum methods. In this paper we present a new term ranking method to summarize term weights, i.e. Maximum Gap. Using two different methods of information gain and χ2 statistic, we setup controlled experiments for different term ranking methods. Reuter-21578 text corpus is used as the dataset. Two popular classification algorithms SVM and Boostexter are adopted to evaluate the performance of different term ranking methods. Experimental results show that the new term ranking method performs better. © 2010 Springer-Verlag.
- Authors: Mammadov, Musa , Yearwood, John , Zhao, Lei
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 23rd Australasian Joint Conference on Artificial Intelligence, AI 2010 Vol. 6464 LNAI, p. 102-111
- Full Text:
- Reviewed:
- Description: In text categorization, different supervised term weighting methods have been applied to improve classification performance by weighting terms with respect to different categories, for example, Information Gain, χ2 statistic, and Odds Ratio. From the literature there are three term ranking methods to summarize term weights of different categories for multi-class text categorization. They are Summation, Average, and Maximum methods. In this paper we present a new term ranking method to summarize term weights, i.e. Maximum Gap. Using two different methods of information gain and χ2 statistic, we setup controlled experiments for different term ranking methods. Reuter-21578 text corpus is used as the dataset. Two popular classification algorithms SVM and Boostexter are adopted to evaluate the performance of different term ranking methods. Experimental results show that the new term ranking method performs better. © 2010 Springer-Verlag.
Are schools of education failing the tertiary mathematics sector?
- Mays, Heather, Yearwood, John
- Authors: Mays, Heather , Yearwood, John
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at 2003 International Symposium on Information Technology, Las Vegas, USA : 28th April, 2003
- Full Text:
- Reviewed:
- Description: In the 1990s, two major issues emerged globally for tertiary mathematics educators: declining standards in the mathematical proficiency of students at tertiary-entry level and the advocates for the incorporation of IT into the processes of teaching and learning. Addressing these issues required significant reform of both curriculum content and classroom practice to ensure that the technology was used appropriately and effectively. These reforms were largely implemented by staff from tertiary Schools of Mathematics. Rather than leading the reform, the Education community lagged behind and in some instances has taken research into the use of IT in Mathematics Education in a questionable direction. In this paper, we outline what we contend has been a failure on the behalf of the Education community to tackle and address problems experienced by mathematics educators (particularly at the tertiary level) and make some suggestions for the directions of future research in Mathematics Education.
- Description: E1
- Description: 2003000464
- Authors: Mays, Heather , Yearwood, John
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at 2003 International Symposium on Information Technology, Las Vegas, USA : 28th April, 2003
- Full Text:
- Reviewed:
- Description: In the 1990s, two major issues emerged globally for tertiary mathematics educators: declining standards in the mathematical proficiency of students at tertiary-entry level and the advocates for the incorporation of IT into the processes of teaching and learning. Addressing these issues required significant reform of both curriculum content and classroom practice to ensure that the technology was used appropriately and effectively. These reforms were largely implemented by staff from tertiary Schools of Mathematics. Rather than leading the reform, the Education community lagged behind and in some instances has taken research into the use of IT in Mathematics Education in a questionable direction. In this paper, we outline what we contend has been a failure on the behalf of the Education community to tackle and address problems experienced by mathematics educators (particularly at the tertiary level) and make some suggestions for the directions of future research in Mathematics Education.
- Description: E1
- Description: 2003000464
Two-step comprehensive open domain text annotation with frame semantics
- Ofoghi, Bahadorreza, Yearwood, John, Ma, Liping
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ma, Liping
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Australasian Language Technology Workshop 2007, Melbourne Zoo, Melbourne, Victoria : 10th-11th December 2007 p. 83-91
- Full Text:
- Description: With shallow semantic parsing tasks receiving more attention in many natural language applications, there is a need for labelled corpora for learning the specific tags under consideration. In this paper, we discuss a two-step semantic class and semantic role assignment based on the FrameNet elements over a subset of the AQUAINT collection with a reasonable coverage over the semantic frames in FrameNet. The quality of the annotation task is examined through inter-annotator agreement. The methodology described in this work for measuring inter-annotator agreement can be adapted for similar tasks. Some central aspects of the task are also detailed in this paper.
- Description: 2003005522
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ma, Liping
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Australasian Language Technology Workshop 2007, Melbourne Zoo, Melbourne, Victoria : 10th-11th December 2007 p. 83-91
- Full Text:
- Description: With shallow semantic parsing tasks receiving more attention in many natural language applications, there is a need for labelled corpora for learning the specific tags under consideration. In this paper, we discuss a two-step semantic class and semantic role assignment based on the FrameNet elements over a subset of the AQUAINT collection with a reasonable coverage over the semantic frames in FrameNet. The quality of the annotation task is examined through inter-annotator agreement. The methodology described in this work for measuring inter-annotator agreement can be adapted for similar tasks. Some central aspects of the task are also detailed in this paper.
- Description: 2003005522
A semantic approach to boost passage retrieval effectiveness for question answering
- Ofoghi, Bahadorreza, Yearwood, John, Ghosh, Ranadhir
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ghosh, Ranadhir
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at Computer Science 2006 Twenty-Ninth Australian Computer Science Conference, Hobart : 16th January, 2006 p. 95-101
- Full Text:
- Reviewed:
- Description: In the current state of the rapid growth of information resources and the huge number of requests submitted by users to existing information retrieval systems; recently, Question Answering systems have attracted more attention to meet information needs providing users with more precise and focused retrieval units. As one of the most challenging and important processes of such systems is to retrieve the best related text excerpts with regard to the questions, we propose a novel approach to exploit not only the syntax of the natural language of the questions and texts, but also the semantics relayed beneath them via a semantic question rewriting and passage retrieval task. The semantic structure used to address the surface mismatch of the semantically related passages and queries is FrameNet which is a lexical resource for English constituted based on frame semantics. We have run our proposed approach on a subset of the TREC 2004 factoid questions to retrieve passages containing correct answers from the AQUAINT collection and we have obtained promising results.
- Description: E1
- Description: 2003001803
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ghosh, Ranadhir
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at Computer Science 2006 Twenty-Ninth Australian Computer Science Conference, Hobart : 16th January, 2006 p. 95-101
- Full Text:
- Reviewed:
- Description: In the current state of the rapid growth of information resources and the huge number of requests submitted by users to existing information retrieval systems; recently, Question Answering systems have attracted more attention to meet information needs providing users with more precise and focused retrieval units. As one of the most challenging and important processes of such systems is to retrieve the best related text excerpts with regard to the questions, we propose a novel approach to exploit not only the syntax of the natural language of the questions and texts, but also the semantics relayed beneath them via a semantic question rewriting and passage retrieval task. The semantic structure used to address the surface mismatch of the semantically related passages and queries is FrameNet which is a lexical resource for English constituted based on frame semantics. We have run our proposed approach on a subset of the TREC 2004 factoid questions to retrieve passages containing correct answers from the AQUAINT collection and we have obtained promising results.
- Description: E1
- Description: 2003001803
From lexical entailment to recognizing textual entailment using linguistic resources
- Ofoghi, Bahadorreza, Yearwood, John
- Authors: Ofoghi, Bahadorreza , Yearwood, John
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at Australasian Language Technology Association Workshop 2009, Sydney, New South Wales : 3rd-4th December 2009 p. 119–123
- Full Text:
- Description: In this paper, we introduce our Recognizing Textual Entailment (RTE) system developed on the basis of Lexical Entailment between two text excerpts, namely the hypothesis and the text. To extract atomic parts of hypotheses and texts, we carry out syntactic parsing on the sentences. We then utilize WordNet and FrameNet lexical resources for estimating lexical coverage of the text on the hypothesis. We report the results of our RTE runs on the Text Analysis Conference RTE datasets. Using a failure analysis process, we also show that the main difficulty of our RTE system relates to the underlying difficulty of syntactic analysis of sentences.
- Description: 2003007910
- Authors: Ofoghi, Bahadorreza , Yearwood, John
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at Australasian Language Technology Association Workshop 2009, Sydney, New South Wales : 3rd-4th December 2009 p. 119–123
- Full Text:
- Description: In this paper, we introduce our Recognizing Textual Entailment (RTE) system developed on the basis of Lexical Entailment between two text excerpts, namely the hypothesis and the text. To extract atomic parts of hypotheses and texts, we carry out syntactic parsing on the sentences. We then utilize WordNet and FrameNet lexical resources for estimating lexical coverage of the text on the hypothesis. We report the results of our RTE runs on the Text Analysis Conference RTE datasets. Using a failure analysis process, we also show that the main difficulty of our RTE system relates to the underlying difficulty of syntactic analysis of sentences.
- Description: 2003007910