A theoretical foundation of demand driven web services
- Authors: Sun, Zhaohao , Yearwood, John
- Date: 2014
- Type: Text , Book chapter
- Relation: Demand-driven web services p. 1-32
- Full Text: false
- Reviewed:
- Description: Web services are playing a pivotal role in business, management, governance, and society with the dramatic development of the Internet and the Web. However, many fundamental issues are still ignored to some extent. For example, what is the unified perspective to the state-of-the-art of Web services? What is the foundation of Demand-Driven Web Services (DDWS)? This chapter addresses these fundamental issues by examining the state-of-the-art of Web services and proposing a theoretical and technological foundation for demand-driven Web services with applications. This chapter also presents an extended Service-Oriented Architecture (SOA), eSMACS SOA, and examines main players in this architecture. This chapter then classifies DDWS as government DDWS, organizational DDWS, enterprise DDWS, customer DDWS, and citizen DDWS, and looks at the corresponding Web services. Finally, this chapter examines the theoretical, technical foundations for DDWS with applications. The proposed approaches will facilitate research and development of Web services, mobile services, cloud services, and social services.
Illicit image detection : An MRF model based stochastic approach
- Authors: Islam, Mofakharul , Watters, Paul , Yearwood, John , Hussain, Mazher , Swarna, Lubaba
- Date: 2013
- Type: Text , Book chapter
- Relation: Innovations and Advances in Computer, Information, Systems Sciences, and Engineering p. 467-479
- Full Text:
- Reviewed:
- Description: The steady growth of the Internet, sophisticated digital image processing technology, the cheap availability of storage devices and surfer's ever-increasing interest on images have been contributing to make the Internet an unprecedented large image library. As a result, The Internet quickly became the principal medium for the distribution of pornographic content favouring pornography to become a drug of the millennium. With the arrival of GPRS mobile telephone technology, and with the large scale arrival of the 3G networks, along with the cheap availability of latest mobile sets and a variety of forms of wireless connections, the internet has already gone to mobile, driving us toward a new degree of complexity. In this paper, we propose a stochastic model based novel approach to investigate and implement a pornography detection technique towards a framework for automated detection of pornography based on contextual constraints that are representatives of actual pornographic activity. Compared to the results published in recent works, our proposed approach yields the highest accuracy in detection. © 2013 Springer Science+Business Media.
Illicit image detection using erotic pose estimation based on kinematic constraints
- Authors: Islam, Mofakharul , Watters, Paul , Yearwood, John , Hussain, Mazher , Swarna, Lubaba
- Date: 2013
- Type: Text , Book chapter
- Relation: Innovations and Advances in Computer, Information, Systems Sciences, and Engineering p. 481-495
- Full Text:
- Reviewed:
- Description: With the advent of the Internet along with sophisticated digital image processing technology, the Internet quickly became the principal medium for the distribution of pornographic content favouring pornography to become a drug of the millennium. With the advent of GPRS mobile telephone networks, and with the large scale arrival of the 3G networks, along with the cheap availability of latest mobile sets and a variety of forms of wireless connections, the internet has already gone to mobile, drives us toward a new degree of complexity. The detection of pornography remains an important and significant research problem, since there is great potential to minimize harm to the community. In this paper, we propose a novel approach to investigate and implement a pornography detection technique towards a framework for automated detection of pornography based on most commonly found erotic poses. Compared to the results published in recent works, our proposed approach yields the highest accuracy in recognition. © 2013 Springer Science+Business Media.
Applications of machine learning for linguistic analysis of texts
- Authors: Torney, Rosemary , Yearwood, John , Vamplew, Peter , Kelarev, Andrei
- Date: 2012
- Type: Text , Book chapter
- Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. 133-148
- Full Text: false
- Reviewed:
- Description: This chapter describes a novel multistage method for linguistic clustering of large collections of texts available on the Internet as a precursor to linguistic analysis of these texts. This method addresses the practicalities of applying clustering operations to a very large set of text documents by using a combination of unsupervised clustering and supervised classification. The method relies on creating a multitude of independent clusterings of a randomized sample selected from the International Corpus of Learner English. Several consensus functions and sophisticated algorithms are applied in two substages to combine these independent clusterings into one final consensus clustering, which is then used to train fast classifiers in order to enable them to perform the profiling of very large collections of text and web data. This approach makes it possible to apply advanced highly accurate and sophisticated clustering techniques by combining them with fast supervised classification algorithms. For the effectiveness of this multistage method it is crucial to determine how well the supervised classification algorithms are going to perform at the final stage, when they are used to process large data sets available on the Internet. This performance may also serve as an indication of the quality of the combined consensus clustering obtained in the preceding stages. The authors' experimental results compare the performance of several classification algorithms incorporated in this multistage scheme and demonstrate that several of these classification algorithms achieve very high precision and recall and can be used in practical implementations of their method.
Detection of CAN by ensemble classifiers based on Ripple Down rules
- Authors: Kelarev, Andrei , Dazeley, Richard , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
- Date: 2012
- Type: Text , Book chapter
- Relation: Knowledge Management and Acquisition for Intelligent Systems p. 147-159
- Full Text: false
- Reviewed:
- Description: It is well known that classification models produced by the Ripple Down Rules are easier to maintain and update. They are compact and can provide an explanation of their reasoning making them easy to understand for medical practitioners. This article is devoted to an empirical investigation and comparison of several ensemble methods based on Ripple Down Rules in a novel application for the detection of cardiovascular autonomic neuropathy (CAN) from an extensive data set collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments included essential ensemble methods, several more recent state-of-the-art techniques, and a novel consensus function based on graph partitioning. The results show that our novel application of Ripple Down Rules in ensemble classifiers for the detection of CAN achieved better performance parameters compared with the outcomes obtained previously in the literature.
Machine learning algorithms for analysis of DNA data sets
- Authors: Yearwood, John , Bagirov, Adil , Kelarev, Andrei
- Date: 2012
- Type: Text , Book chapter
- Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. 47-58
- Relation: http://purl.org/au-research/grants/arc/LP0990908
- Full Text: false
- Reviewed:
- Description: The applications of machine learning algorithms to the analysis of data sets of DNA sequences are very important. The present chapter is devoted to the experimental investigation of applications of several machine learning algorithms for the analysis of a JLA data set consisting of DNA sequences derived from non-coding segments in the junction of the large single copy region and inverted repeat A of the chloroplast genome in Eucalyptus collected by Australian biologists. Data sets of this sort represent a new situation, where sophisticated alignment scores have to be used as a measure of similarity. The alignment scores do not satisfy properties of the Minkowski metric, and new machine learning approaches have to be investigated. The authors' experiments show that machine learning algorithms based on local alignment scores achieve very good agreement with known biological classes for this data set. A new machine learning algorithm based on graph partitioning performed best for clustering of the JLA data set. Our novel k-committees algorithm produced most accurate results for classification. Two new examples of synthetic data sets demonstrate that the authors' k-committees algorithm can outperform both the Nearest Neighbour and k-medoids algorithms simultaneously.
A case for the re-use of community reasoning
- Authors: Stranieri, Andrew , Yearwood, John
- Date: 2011
- Type: Text , Book chapter
- Relation: Technologies for supporting reasoning communities and collaborative decision making: Cooperative approaches p.
- Full Text: false
- Reviewed:
- Description: In software engineering, the re-use concept is a design principle that improves efficency, quality and maintainability by ensuring that software artifacts are developed once and re-used may times. In an analogous way, a group's reasoning can be imagined to be re-used by that or another group to enhance efficiency, transparency and consistency in decison-making. However, the re-use of reasoning is difficult to achieve because group reasoning cannot easily be captured and the way in which a group reasoning artifact is subsequently used is not obvious. This chapter explores the case for the re-use of community reasoning and concludes that individuals can benefit from a representation of a previous groups's coalesced reasoning to be modeled and the scheme to represent the reasoning have been selected to suit the task. The authors contend that specifying the future community like to re-use the reasoning, called the intended audience, informs a decision regarding whether an exercise aimed at coalescing a group's reasoning is best performed verbally, in writing or with the use of more structured schemes such as Argument visualization.
A reasoning community perspective on deliberate democracy
- Authors: Yearwood, John , Stranieri, Andrew
- Date: 2011
- Type: Text , Book chapter
- Relation: Technologies for supporting reasoning communities and collaborative decision making: Cooperative approaches p.237-246
- Full Text: false
- Reviewed:
- Description: This chapter describes some of the current approaches to delibertative democracy and the considers them from the perspective of a reasoning community framework. This approach highlights important tasks, process and structures that can be used to enhance the process of groups engaging in deliberative democracy approaches. In particular it focuses attention on the potential for technologies to support groups in achieving broad agreed structured reasoning bases that capture the scope of an issue from multiple perspectives.
A reinforcement learning approach with spline-fit object tracking for AIBO Robot's high level decision making
- Authors: Mukherjee, Subhasis , Huda, Shamsul , Yearwood, John
- Date: 2011
- Type: Text , Book chapter
- Relation: Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing p. 169-183
- Full Text: false
- Reviewed:
- Description: Robocup is a popular test bed for AI programs around the world. Robosoccer is one of the two major parts of Robocup, in which AIBO entertainment robots take part in the middle sized soccer event. The three key challenges that robots need to face in this event are manoeuvrability, image recognition and decision making skills. This paper focuses on the decision making problem in Robosoccer-The goal keeper problem. We investigate whether reinforcement learning (RL) as a form of semi-supervised learning can effectively contribute to the goal keeper's decision making process when penalty shot and two attacker problem are considered. Currently, the decision making process in Robosoccer is carried out using rule-base system. RL also is used for quadruped locomotion and navigation purpose in Robosoccer using AIBO. Moreover the ball distance is being calculated using IR sensors available at the nose of the robot. In this paper, we propose a reinforcement learning based approach that uses a dynamic state-action mapping using back propagation of reward and Q-learning along with spline fit (QLSF) for the final choice of high level functions in order to save the goal. The novelty of our approach is that the agent learns while playing and can take independent decision which overcomes the limitations of rule-base system due to fixed and limited predefined decision rules. The spline fit method used with the nose camera was also able to find out the location and the ball distance more accurately compare to the IR sensors. The noise source and near and far sensor dilemma problem with IR sensor was neutralized using the proposed spline fit method. Performance of the proposed method has been verified against the bench mark data set made with Upenn'03 code logic and a base line experiment with IR sensors. It was found that the efficiency of our QLSF approach in goalkeeping was better than the rule based approach in conjunction with the IR sensors. The QLSF develops a semi-supervised learning process over the rule-base system's input-output mapping process, given in the Upenn'03 code. © 2011 Springer-Verlag Berlin Heidelberg.
Case based web services
- Authors: Sun, Zhaohao , Finnie, Gavin , Yearwood, John
- Date: 2011
- Type: Text , Book chapter
- Relation: Encyclopedia of E-Business Development and Management in the Global Economy p. 871-882
- Full Text: false
- Reviewed:
- Description: Web services are Internet-based application components published using standard interface description languages and universally available via uniform communication protocols (Singh & Huhns, 2005). Web services can be also considered the provision of services over electronic networks such as the Internet and wireless networks (Rust & Kannan, 2003). Web services is a new computing paradigm that has drawn increasing attention in information technology (Deitel, et al, 2004, p.13), information systems, and is playing a pivotal role in service computing and service intelligence (Singh & Huhns, 2005). Web services is a new business paradigm that is playing an important role in e-business, ecommerce and business intelligence (Wang, et al, 2006). The key motive for the rapid development of web services is the ability to discover services that fulfil users’ demands, negotiate service contracts and have the services delivered where and when the users request them (Tang, et al, 2007). The current research trend is to add intelligent techniques to web services to facilitate discovery, invocation, composition, and recommendation of web services (Wang, et al, 2006)
Demand driven web services
- Authors: Sun, Zhaohao , Dong, Dong , Yearwood, John
- Date: 2011
- Type: Text , Book chapter
- Relation: Service intelligence and service science: Evolutionary technologies and challenges p. 35-55
- Full Text: false
- Reviewed:
- Description: Web services are playing a pivotal role in e-business, service intelligence, and service science. Demand-driven web services are becoming important for web services and service computing. However, many fundamental issues are still ignored to some extent. For example, what is the demand theory for web services, what is a demand-driven architecture for web services and what is a demand-driven web service lifecycle remain open. This chapter addresses these issues by examining fundamentals for demand analysis in web services, and proposing a demand-driven architecture for web services. It also proposes a demand-driven web service lifecycle for the main players in web services: Service providers, service requestors and service brokers, respectively. It then provides a unified perspective on demand-driven web service lifecycles. The proposed approaches will facilitate research and development of web services, e-services, service intelligence, service science and service computing.
- Description: 2003009207
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
- Reviewed:
- 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.
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
- Reviewed:
- 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.
Unsupervised color textured image segmentation using cluster ensembles and MRF mdel
- Authors: Islam, Mofakharul , Yearwood, John , Vamplew, Peter
- Date: 2008
- Type: Text , Book chapter
- Relation: Advances in computer and information sciences and engineering p. 323-328
- Full Text: false
- Reviewed:
- Description: We propose a novel approach to implement robust unsupervised color image content understanding approach that segments a color image into its constituent parts automatically. The aim of this work is to produce precise segmentation of color images using color and texture information along with neighborhood relationships among image pixels which will provide more accuracy in segmentation. Here, unsupervised means automatic discovery of classes or clusters in images rather than generating the class or cluster descriptions from training image sets. As a whole, in this particular work, the problem we want to investigate is to implement a robust unsupervised SVFM model based color medical image segmentation tool using Cluster Ensembles and MRF model along with wavelet transforms for increasing the content sensitivity of the segmentation model. In addition, Cluster Ensemble has been utilized for introducing a robust technique for finding the number of components in an image automatically. The experimental results reveal that the proposed tool is able to find the accurate number of objects or components in a color image and eventually capable of producing more accurate and faithful segmentation and can. A statistical model based approach has been developed to estimate the Maximum a posteriori (MAP) to identify the different objects/components in a color image. The approach utilizes a Markov Random Field model to capture the relationships among the neighboring pixels and integrate that information into the Expectation Maximization (EM) model fitting MAP algorithm. 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 well-known segmentation approach.
A Tool for Assisting Group Decision-Making for Consensus Outcomes in Organizations
- Authors: Afshar, Faye , Yearwood, John , Stranieri, Andrew
- Date: 2006
- Type: Text , Book chapter
- Relation: E-Supply Chain Technologies and Management p. 316-343
- Full Text: false
- Reviewed:
Dynamical systems described by relational elasticities with applications to global optimization
- Authors: Mammadov, Musa , Rubinov, Alex , Yearwood, John
- Date: 2005
- Type: Text , Book chapter
- Relation: Continuous Optimization: Current Trends and Modern Applications Chapter p. 365-385
- Full Text: false
- Reviewed:
- Description: B1
Visual grouping of association rules by clustering conditional probabilities for categorical data
- Authors: Ivkovic, Sasha , Ghosh, Ranadhir , Yearwood, John
- Date: 2005
- Type: Text , Book chapter
- Relation: Business Applications and Computational Intelligence p. 248-266
- Full Text: false
- Reviewed:
- Description: We demonstrate the use of a visual data-mining tool for non-technical domain experts within organizations to facilitate the extraction of meaningful information and knowledge from in-house databases. The tool is mainly based on the basic notion of grouping association rules. Association rules are useful in discovering items that are frequently found together. However in many applications, rules with lower frequencies are often interesting for the user. Grouping of association rules is one way to overcome the rare item problem. However some groups of association rules are too large for ease of understanding. In this chapter we propose a method for clustering categorical data based on the conditional probabilities of association rules for data sets with large numbers of attributes. We argue that the proposed method provides non-technical users with a better understanding of discovered patterns in the data set.