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
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  • 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

An intelligent offline handwriting recognition system using evolutionary neural learning algorithm and rule based over segmented data points

- Ghosh, Ranadhir, Ghosh, Moumita

  • Authors: Ghosh, Ranadhir , Ghosh, Moumita
  • Date: 2005
  • Type: Text , Journal article
  • Relation: Journal of Research and Practice in Information Technology Vol. 37, no. 1 (2005), p. 73-86
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  • Description: In this paper we propose a novel technique of using a hybrid evolutionary method, which uses a combination of genetic algorithm and matrix based solution methods such as QR factorization. The training of the model is based on a layer based hierarchical structure for the architecture and the weights for the Artificial Neural Network classifier. The architecture for the classifier is found using a binary search type procedure. The hierarchical structured algorithm (EALS-BT) is also a hybrid, because it combines the Genetic Algorithm based method with the Matrix based solution method for finding weights. A heuristic segmentation algorithm is initially used to over segment each word. Then the segmentation points are passed through the rule-based module to discard the incorrect segmentation points and include any missing segmentation points. Following the segmentation the contour is extracted between two correct segmentation points. The contour is passed through the feature extraction module that extracts the angular features, after which the EALS-BT algorithm finds the architecture and the weights for the classifier network. These recognized characters are grouped into words and passed to a variable length lexicon that retrieves words that have the highest confidence value.
  • Description: C1
  • Description: 2003001367

A smart proxy for a next generation web services transaction

- Pradhan, Sunam, Zaslavsky, Arkady

  • Authors: Pradhan, Sunam , Zaslavsky, Arkady
  • 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. 646-651
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  • Description: In this paper, we propose and describe sProxy - smart proxy, a software tool in Web Services transaction. sProxy acts as a gateway between transaction management systems and Web services which implements a key abstraction of proxy management systems. This enables to perform transactions in the loosely coupled environment i.e. loose coupling among services. Proxies are useful to invoke Web services to allow an easy programming model that facilitates the serialization and transmission of service invocations. Our proposed model supports relaxation of traditional ACID properties with existing commit and recovery protocols. The model works on non-ACID type of transactions which encapsulates Web services. It also uses multithreading proxies to check and update transaction simultaneously. The proposed model solves the current problems with distributed computational activities which involves both transactions and Web Services. The proposed model is more abstract and generic as demonstrated in the paper.
  • Description: 2003005442

An argumentation shell for supporting the development and drafting of legal arguments

- Yearwood, John, Stranieri, Andrew

  • Authors: Yearwood, John , Stranieri, Andrew
  • Date: 2002
  • Type: Text , Journal article
  • Relation: Information and Communication Technology Law Vol. 11, no. 1 (2002), p. 75-86
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  • Description: This article describes an argumentation shell to support the formulation, representation and drafting of legal arguments. The shell can be used to capture generic arguments in many legal domains as well as to assist decision-makers in constructing their own actual arguments . The shell demonstrates that knowledge represented using the generic/actual argument model (GAAM) (a variant of Toulmin's argument structure) can be used to: (a) support the development of complex arguments, (b) add context and increase specificity for the retrieval of relevant documents, (c) incorporate background knowledge, (d) assist in the drafting of documents that represent arguments made, and (e) provide a structure for complex inferences requiring a range of mechanisms. The shell can be used to support decision making in a range of legal domains, including discretionary domains.
  • Description: C1
  • Description: 2003000141

Managing privacy, trust, security, and context relationships using weighted graph representations

- Skinner, Geoff, Miller, Mirka

  • Authors: Skinner, Geoff , Miller, Mirka
  • Date: 2006
  • Type: Text , Journal article
  • Relation: Transactions on Information Science and Applications Vol. 3, no. 2 (2006), p. 283-290
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  • Description: Determining who has access to personal data is an ongoing problem facing information system entities. The establishment of trust and its representation for known and unknown entities within the system further complicates access control rights allocation. One unique solution is through the application of graph representation to aid in the identification and management of privacy, trust and security requirements. Graphs provide a much better mental map than would textual information. In this paper we use graphs to represent informational relations concerning trust levels between entities for privacy and security requirements.
  • Description: C1
  • Description: 2003001598

Antimagic valuations for the special class of plane graphs

- Baca, Martin, Baskoro, Edy, Miller, Mirka

  • Authors: Baca, Martin , Baskoro, Edy , Miller, Mirka
  • Date: 2005
  • Type: Text , Journal article
  • Relation: Lecture Notes in Computer Science Vol. 3350, no. (2005), p. 58-64
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  • Description: We deal with the problem of labeling the vertices, edges and faces of a special class of plane graphs with 3-sided internal faces in such a way that the label of a face and the labels of the vertices and edges surrounding that face all together add up to the weight of that face. These face weights then form an arithmetic progression with common difference d.
  • Description: C1
  • Description: 2003001410

Massively multiplayer online role-playing games : The past, present, and future

- Achterbosch, Leigh, Pierce, Robyn, Simmons, Gregory

  • Authors: Achterbosch, Leigh , Pierce, Robyn , Simmons, Gregory
  • Date: 2008
  • Type: Text , Journal article
  • Relation: Computers in Entertainment Vol. 5, no. 4 (2008), p. 1-33
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  • Description: Massively multiplayer online role-playing games (MMORPGs) are emerging in the computer game industry as a very popular genre. These games have existed since the late 1990s, but in the last few years the market has become increasingly strong. This relatively new genre is attracting a widespread audience, bringing together those who previously enjoyed both pen and paper and computer role-playing games, as well as those who enjoy socializing with other players in a virtual environment. Game developers see MMORPGs as a potentially profitable business due to its widespread appeal, but the reality is that only a small percentage of MMORPGs that are released become a success [Kosak 2006]. This article attempts to determine the many aspects that make a successful MMORPG; it also attempts to ascertain what new and innovative features are expected by the users from the next generation of MMORPGs. This is achieved by looking at and discussing past literature and surveying the MMORPG community's perception of previous and current MMORPGs, as well as their expectations of the next generation. An online survey attracted 122 participants to provide their perceptions of current and past MMORPGs. This article determines and outlines the respondents' preferences in the MMORPG genre, discussing what implications these could have on its future. The survey also gave insight into the respondents' expectations of the future of MMORPGs. We conclude this article with a discussion of aspects of current MMORPGs that the participants would like improved, as well as new features they would like incorporated into the next generation of games. © 2008 ACM.
  • Description: C1

Predicting Australian stock market index using neural networks exploiting dynamical swings and intermarket influences

- Pan, Heping, Tilakaratne, Chandima, Yearwood, John

  • Authors: Pan, Heping , Tilakaratne, Chandima , Yearwood, John
  • Date: 2005
  • Type: Text , Journal article
  • Relation: Journal of Research and Practice in Information Technology Vol. 37, no. 1 (2005), p. 43-55
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  • Description: This paper presents a computational approach for predicting the Australian stock market index AORD using multi-layer feed-forward neural networks front the time series data of AORD and various interrelated markets. This effort aims to discover an effective neural network, or a set of adaptive neural networks for this prediction purpose, which can exploit or model various dynamical swings and inter-market influences discovered from professional technical analysis and quantitative analysis. Within a limited range defined by our empirical knowledge, three aspects of effectiveness on data selection are considered: effective inputs from the target market (AORD) itself, a sufficient set of interrelated markets,. and effective inputs from the interrelated markets. Two traditional dimensions of the neural network architecture are also considered: the optimal number of hidden layers, and the optimal number of hidden neurons for each hidden layer. Three important results were obtained: A 6-day cycle was discovered in the Australian stock market during the studied period; the time signature used as additional inputs provides useful information; and a basic neural network using six daily returns of AORD and one daily, returns of SP500 plus the day of the week as inputs exhibits up to 80% directional prediction correctness.
  • Description: C1
  • Description: 2003001440

Trustworthiness of self-driving vehicles for intelligent transportation systems in industry applications

- Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder, Islam, Syed

  • Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Islam, Syed
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 2 (2021), p. 961-970
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  • Description: To enhance industrial production and automation, rapid and faster transportation of raw materials and finished products to and from distributed factories, warehouses and outlets are essential. To reduce cost with increased efficiency, this will increasingly see the use of connected and self-driving commercial vehicles fitted with industrial grade sensors on roads, shared with normal and self-driving passenger vehicles. For its wide adoption, the trustworthiness of self-driving vehicles in the intelligent transportation system (ITS) is pivotal. In this article, we introduce a novel model to measure the overall trustworthiness of a self-driving vehicle considering on-Board unit (OBU) components, GPS data and safety messages. In calculating the trustworthiness of individual OBU components, CertainLogic and beta distribution function (BDF) are used. Those trust values are fused using both the dempster-Shafer Theory (DST) and a logical operator of CertainLogic. Results of our simulation show that our proposed method can effectively determine the trust of self-driving vehicles. © 2005-2012 IEEE.

Electric vehicle participated electricity market model considering flexible ramping product provisions

- Zhang, Xian, Hu, Jiefeng, Wang, Huaizhi, Wang, Guibin, Chan, Ka, Qiu, Jing

  • Authors: Zhang, Xian , Hu, Jiefeng , Wang, Huaizhi , Wang, Guibin , Chan, Ka , Qiu, Jing
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industry Applications Vol. 56, no. 5 (2020), p. 5868-5879
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  • Description: This article studies electric vehicle (EV) potential to participate in the energy market and provide flexible ramping products (FRPs). EV traffic flows are predicted by the deep belief network, and the availability of flexible EVs is estimated based on the predicted EV traffic flows. Then, a novel market mechanism in distribution system is proposed to encourage the dispatchable EV demand to react to economic signals and provide ramping services. The designed market model is based on locational marginal pricing of energy and marginal pricing of FRPs. System ramping capacity constraints and EV operation constraints are incorporated in the proposed model to achieve the balance between the system social cost minimization and the EV traveling convenience. Moreover, typical uncertainties are considered by the scenario-based approach. Finally, simulations are conducted to verify the effectiveness of the established model and demonstrate the contributions of EVs to the system reliability and flexibility. © 1972-2012 IEEE.
  • Description: ITIAC: Funding details: JCYJ20170817100412438, 2019-AAAE-1307, JCYJ20190808141019317

Using links to aid web classification

- Xie, Wei, Mammadov, Musa, Yearwood, John

Editors' cut : Managing scholarly journals in mathematics and IT

- Hofmann, Karl, Morris, Sidney

  • Authors: Hofmann, Karl , Morris, Sidney
  • Date: 2005
  • Type: Text , Journal article
  • Relation: Journal of Research and Practice in Information Technology Vol. 37, no. 4 (2005), p. 299-309
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  • Description: The first version of this essay was jointly delivered by the authors as a colloquium lecture at the University of Ballarat on 24 November, 2004. A second, expanded and illustrated version was published in German in the Mitteilungen der Deutschen Mathematikervereinigung early in 2005. Because of the very positive feedback, the authors decided it would be useful to publish a version in English in a computing journal. The purpose of the essay is to provide advice and information to authors of articles about publishing in scholarly journals from an editor's perspective. Of particular importance are remarks about etiquette.
  • Description: C1

Hybridization of neural learning algorithms using evolutionary and discrete gradient approaches

- Ghosh, Ranadhir, Yearwood, John, Ghosh, Moumita, Bagirov, Adil

  • Authors: Ghosh, Ranadhir , Yearwood, John , Ghosh, Moumita , Bagirov, Adil
  • Date: 2005
  • Type: Text , Journal article
  • Relation: Journal of Computer Science Vol. 1, no. 3 (2005), p. 387-394
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  • Description: In this study we investigated a hybrid model based on the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. Also we discuss different variants for hybrid models using the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. The Discrete Gradient method has the advantage of being able to jump over many local minima and find very deep local minima. However, earlier research has shown that a good starting point for the discrete gradient method can improve the quality of the solution point. Evolutionary algorithms are best suited for global optimisation problems. Nevertheless they are cursed with longer training times and often unsuitable for real world application. For optimisation problems such as weight optimisation for ANNs in real world applications the dimensions are large and time complexity is critical. Hence the idea of a hybrid model can be a suitable option. In this study we propose different fusion strategies for hybrid models combining the evolutionary strategy with the discrete gradient method to obtain an optimal solution much quicker. Three different fusion strategies are discussed: a linear hybrid model, an iterative hybrid model and a restricted local search hybrid model. Comparative results on a range of standard datasets are provided for different fusion hybrid models.
  • Description: C1
  • Description: 2003001357

Knowledge based regulation of statistical databases

- Mishra, Vivek, Stranieri, Andrew, Miller, Mirka, Ryan, Joe

  • Authors: Mishra, Vivek , Stranieri, Andrew , Miller, Mirka , Ryan, Joe
  • Date: 2006
  • Type: Text , Journal article
  • Relation: WSEAS Transactions on Information Science and Applications Vol. 3, no. 2 (2006), p. 239-244
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  • Description: A statistical database system is a system that contains information about individuals, companies or organisations that enables authorized users to retrieve aggregate statistics such as mean and count. The regulation of a statistical database involves limiting the use of the database so that no sequence of queries is sufficient to infer protected information about an individual. The database is said to be compromised when individual confidential information is obtained as a result of a statistical query. Devices to protect against compromise include adding noise to the data or restricting a query. While effective, these techniques are sometimes too strong in that legitimate compromises for reasons of public safety are always blocked. Further, a statistical database can be often be compromised with some knowledge about the database attributes (working knowledge), the real world (supplementary knowledge) or the legal system (legal knowledge). In this paper we illustrate that a knowledge based system that represents working, supplementary and legal knowledge can contribute to the regulation of a statistical database.
  • Description: C1
  • Description: 2003001608

Editorial

- Yearwood, John

Narrative-based interactive learning environments from modelling reasoning

- Yearwood, John, Stranieri, Andrew

  • Authors: Yearwood, John , Stranieri, Andrew
  • Date: 2007
  • Type: Text , Journal article
  • Relation: Educational Technology and Society Vol. 10, no. 3 (2007), p. 192-208
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  • Description: Narrative and story telling has a long history of use in structuring, organising and communicating human experience. This paper describes a narrative based interactive intelligent learning environment which aims to elucidate practical reasoning using interactive emergent narratives that can 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 and positive learning outcomes are reported. © International Forum of Educational Technology & Society (IFETS).
  • Description: C1
  • Description: 2003002522

A fast scalable implementation of the two-dimensional triangular discrete element Method on a GPU platform

- Zhang, Ling, Quigley, Steven, Chan, Andrew

  • Authors: Zhang, Ling , Quigley, Steven , Chan, Andrew
  • Date: 2014
  • Type: Text , Journal article
  • Relation: Advances in Engineering Software Vol. 60-61, no. June-July (2014), p. 70-80
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  • Description: Real-time solution of the Discrete Element Method is a computational challenge that is hardly achievable on standard PCs, especially when a large number of triangular shaped particles are involved. This paper presents a scalable architecture, including a domain decomposition technique, of a GPU accelerator for the two-dimensional Discrete Element Method for triangular shaped particles. This approach achieved a speed up of about 140 times as a single core and about 80 after domain decomposition on a consumer level GPU compared to a similar algorithm run on a fast desktop PC.

A direct optimization method for low group delay FIR filter design

- Wu, Changzhi, Gao, David, Lay Teo, Kok

  • Authors: Wu, Changzhi , Gao, David , Lay Teo, Kok
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Signal Processing Vol. 93, no. 7 (2013), p. 1764-1772
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  • Description: This paper studies the design of FIR filter with low group delay, where the desired phase response is not being approximated. It is formulated as a constrained optimization problem, which is then solved globally. Numerical experiments show that our design method can produce a filter with smaller group delay than that obtained by the existing convex optimization method used in conjunction with a minimum phase spectral factorization method under the same design criteria. Furthermore, our formulation offers us the flexibility for the trade-off between the group delay and the magnitude response directly. It also allows the feasibility of imposing constraints on the group delay. © 2013 Elsevier B.V.
  • Description: 2003011019

Using an instructional design model to evaluate a blended learning subject in a pre-service teacher education degree

- Johnson, Nicola

  • Authors: Johnson, Nicola
  • Date: 2010
  • Type: Text , Journal article
  • Relation: The International Journal of Learning Vol. 17, no. 2 (2010 2010), p. 65-80
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  • Description: Over 2007-2008, a pedagogy subject in a pre-service teacher education degree was (re)designed to help students develop their understandings and skills and a wider, more critical appreciation of the work of teachers and approaches to curriculum. The rationale for designing and including the online modules in the subject was to develop information and communication technology (ICT) skills, and to deliver a blended learning approach, argued by some to be more effective, that is, have more advantages than traditional approaches. In this paper, the face-to-face teaching alongside the eLearning that occurred in the blended learning approach is analysed using Tom Reeves and John Hedberg's model (2003) for evaluating interactive learning systems. Arguably, this evaluation model can be usefully applied to higher education teaching that is not fully online, and can help to comprise an integral part of an action research approach. This paper is a 'proof of concept' piece, demonstrating the applicability of the model to a blended learning course. Demonstrating the application of Reeves and Hedberg's model fills a knowledge void on the literature surrounding blended learning. [ABSTRACT FROM AUTHOR]

Combining segmental semi-Markov models with neural networks for protein secondary structure prediction

- Bidargaddi, Niranjan, Chetty, Madhu, Kamruzzaman, Joarder

  • Authors: Bidargaddi, Niranjan , Chetty, Madhu , Kamruzzaman, Joarder
  • Date: 2009
  • Type: Text , Journal article
  • Relation: Neurocomputing Vol. 72, no. 3943-3950 (2009), p.3943-3950
  • Full Text: false
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  • Description: Predicting the secondary structure of proteins from a primary sequence alone has been variously approached from either a classification or a generative model perspective. The most prominent classification methods have used neural networks, which involves mappings from a local window of residues in the sequence to the structural state of the central residue in the window, thus capturing the local interactions effectively. However, they fail to capture distant interactions among residues. The generative models based on Bayesian segmentation capture sequence structure relationships using generalized hidden Markov models with explicit state duration. They capture non-local interactions through a joint sequence-structure probability distribution based on structural segments. In this paper, we investigate a combined architecture of Bayesian segmentation at the first stage and neural network at the second stage which captures both local and non-local correlation, to increase the single sequence prediction accuracy. The combined architecture is further enhanced by using neural network optimization and ensemble techniques.