Publications

Cao, L & Ruwei, D 2008, Open Complex Intelligent Systems: Fundamentals, Concepts, Analysis, Design and Implementation, 1, Posts & Telecom Press, Beijing, China.
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one of nine books in computer science selected into China Key-Book Publishing Plan in 11th Five-Years (2006-2010)

Chen, Q, Zhang, C & Zhang, S 2008, Secure Transaction Protocol Analysis, 1, Springer Berlin Heidelberg, Berlin, Germany.
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Security protocols (cryptographic protocol) have been widely used to not only achieve traditional goals of data confidentiality, integrity and authentication, but also secure a variety of other desired characteristics of computer-mediated transactions recently. To guarantee reliable protocols, a great deal of formal methods has been undertaken not only to develop diverse tools with specialized purpose or general purpose, but also to apply them to the analysis of realistic protocols. Many of them have been proved to be useful in detecting some intuitive attacks in security protocols. In many cases, a useful feedback is supplied to designers in order to improve the protocolâs security. For both beginners and experienced researchers, this book will present useful information on relevant technologies that can be extended or adapted. A comprehensive introduction to the basic concepts and core techniques will be presented. In this chapter, we explain what is security protocols and how they can be used to ensure secure transactions, what challenging issues in e-commerce (electronic commerce) are, why security protocol analysis important, how they are performed, and what are the ongoing efforts and relevant work. We will also explain the limitations in previous work and why it is important to develop new approaches. These questions will be briefly answered. In particular, we will focus on the discussion regarding secure transaction protocols. Finally, some emerging issues and the ways they are being met are also described.

Reynolds, R & Stoianoff, NP 2008, Intellectual Property : Text and Essential cases, Third, Federation Press, Sydney.

Cao, L & Zhang, C 2008, 'Domain Driven Data Mining' in Data Mining and Knowledge Discovery Technologies, IGI Global, pp. 196-223.
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Quantitative intelligence based traditional data mining is facing grand challenges from real-world enterprise and cross-organization applications. For instance, the usual demonstration of specific algorithms cannot support business users to take actions to their advantage and needs. We think this is due to Quantitative Intelligence focused data-driven philosophy. It either views data mining as an autonomous data-driven, trial-and-error process, or only analyzes business issues in an isolated, case-by-case manner. Based on experience and lessons learnt from real-world data mining and complex systems, this article proposes a practical data mining methodology referred to as Domain-Driven Data Mining. On top of quantitative intelligence and hidden knowledge in data, domain-driven data mining aims to meta-synthesize quantitative intelligence and qualitative intelligence in mining complex applications in which human is in the loop. It targets actionable knowledge discovery in constrained environment for satisfying user preference. Domain-driven methodology consists of key components including understanding constrained environment, business-technical questionnaire, representing and involving domain knowledge, human-mining cooperation and interaction, constructing next-generation mining infrastructure, in-depth pattern mining and postprocessing, business interestingness and actionability enhancement, and loop-closed human-cooperated iterative refinement. Domain-driven data mining complements the data-driven methodology, the metasynthesis of qualitative intelligence and quantitative intelligence has potential to discover knowledge from complex systems, and enhance knowledge actionability for practical use by industry and business.

Cao, L & Zhang, C 2008, 'Domain-Driven Data Mining' in Data Warehousing and Mining, IGI Global, pp. 831-848.
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Extant data mining is based on data-driven methodologies. It either views data mining as an autonomous data-driven, trial-and-error process or only analyzes business issues in an isolated, case-by-case manner. As a result, very often the knowledge discovered generally is not interesting to real business needs. Therefore, this article proposes a practical data mining methodology referred to as domain-driven data mining, which targets actionable knowledge discovery in a constrained environment for satisfying user preference. The domain-driven data mining consists of a DDID-PD framework that considers key components such as constraint-based context, integrating domain knowledge, human-machine cooperation, in-depth mining, actionability enhancement, and iterative refinement process. We also illustrate some examples in mining actionable correlations in Australian Stock Exchange, which show that domain-driven data mining has potential to improve further the actionability of patterns for practical use by industry and business.

Gillies, P, Stoianoff, NP & Dahdal, A 2008, 'Chinese Company Law' in Blazey, P & Chan, K (eds), The Chinese Commercial Legal System, Lawbook Co, Pyrmont, NSW, pp. 241-257.

Gunes, H, Piccardi, M & Pantic, M 2008, 'From the Lab to the Real World: Affect Recognition Using Multiple Cues and Modalities' in Or, J (ed), Affective Computing, I-Tech Education and Publishing, Vienna, Austria, pp. 185-218.
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Kennedy, P, Simoff, SJ, Catchpoole, DR, Skillicorn, DB, Ubaudi, F & Al-Oqaily, A 2008, 'Integrative Visual Data Mining of Biomedical Data: Investigating Cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia' in Simoff, SJ, Bohlen, MH & Mazeika, A (eds), Lecture Notes in Computer Science, Springer Berlin Heidelberg, Berlin Heidelberg, pp. 367-388.
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This chapter presents an integrative visual data mining approach towards biomedical data. This approach and supporting methodology are presented at a high level. They combine in a consistent manner a set of visualisation and data mining techniques that operate over an integrated data set of several diverse components, including medical (clinical) data, patient outcome and interview data, corresponding gene expression and SNP data, domain ontologies and health management data. The practical application of the methodology and the specific data mining techniques engaged are demonstrated on two case studies focused on the biological mechanisms of two different types of diseases: Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia, respectively. The common between the cases is the structure of the data sets. © 2008 Springer-Verlag Berlin Heidelberg.

Sidhu, AS, Kennedy, PJ, Simoff, S, Dillon, TS & Chang, E 2008, 'Knowledge Discovery in Biomedical Data Facilitated by Domain Ontologies' in Medical Informatics, IGI Global, pp. 2096-2108.
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In some real-world areas, it is important to enrich the data with external background knowledge so as to provide context and to facilitate pattern recognition. These areas may be described as data rich but knowledge poor. There are two challenges to incorporate this biological knowledge into the data mining cycle: (1) generating the ontologies; and (2) adapting the data mining algorithms to make use of the ontologies. This chapter presents the state-of-the-art in bringing the background ontology knowledge into the pattern recognition task for biomedical data.

Stoianoff, NP 2008, 'Chinese Taxation Law' in Blazey, P & Chan, KW (eds), The Chinese Commercial Legal System, Lawbook Co, Pyrmont, NSW, pp. 307-319.

Yang, C, Meng, J, Zhu, S & Dai, M 2008, 'Model Free Data Mining' in Taniar, D (ed), Data Mining and Knowledge Discovery Technologies, IGI Global, USA, pp. 224-252.
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Input selection is a crucial step for nonlinear regression modeling problem, which contributes to build an interpretable model with less computation. Most of the available methods are model-based, and few of them are model-free. Model-based methods often make use of prediction error or sensitivity analysis for input selection and Model-free methods exploit consistency. In this paper, we show the underlying relationship between sensitivity analysis and consistency analysis for input selection, and then derive an efficient model-free method from our common sense, and then formulate this common sense by fuzzy logic, thus it can be called Fuzzy Consistency Analysis (FCA). In contrast to available methods, FCA has the following desirable properties: 1) it is a model-free method so that it will not be biased on a specific model, exploiting “what the data say” rather than “what the model say”, which is the essential point of data mining – input selection should not be biased on a specific model. 2) it is implemented as efficiently as classical model-free methods, but more flexible than them. 3) it can be directly applied to a data set with mix continuous and discrete inputs without doing rotation. Four benchmark problems study indicates that the proposed method works effectively for nonlinear problems. With the input selection procedure, the underlying reasons which effect the prediction are work out, which helps to gain an insight into a specific problem and servers the purpose of data mining very well.

Beard, JR, Earnest, A, Morgan, G, Chan, H, Summerhayes, R, Dunn, TM, Tomaska, NA & Ryan, L 2008, 'Socioeconomic disadvantage and acute coronary events - A spatiotemporal analysis', EPIDEMIOLOGY, vol. 19, no. 3, pp. 485-492.
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BACKGROUND: The associations between socioeconomic disadvantage and ischemic heart disease are not well understood. We explore the relationship between socioeconomic factors and acute coronary events using spatiotemporal analysis. METHODS: We studied all deaths from acute myocardial infarction and hospital admissions for acute coronary syndrome and related revascularization procedures for the state of New South Wales, Australia, from 1996 through 2002. We used conditional autoregressive models to describe how characteristics of subjects' place of residence (socioeconomic disadvantage, proportion of the population of indigenous background, and metropolitan versus nonmetropolitan area) influenced admissions and mortality. RESULTS: There were 32,534 deaths due to acute myocardial infarction and 129,045 admissions for acute coronary syndrome. We found a relationship between increasing socioeconomic disadvantage and mortality (unadjusted relative risk for highest quartile of disadvantage relative to lowest = 1.40; 95% confidence interval = 1.27-1.54) as well as admissions (1.41; 1.28-1.55). After accounting for admission rates, socioeconomic disadvantage was associated with lower rates of angiography (0.75; 0.63-0.88) and interventional angiography (0.70; 0.56-0.85). After adjusting for socioeconomic disadvantage, areas with higher proportions of the population identified as indigenous had higher rates of admission and mortality, while residency in the state capital was associated with higher admission rates and more interventional angiography. After accounting for admission rates, the association of socioeconomic disadvantage with mortality was reduced. CONCLUSIONS: Socioeconomic disadvantage increases both the risk of acute coronary syndrome and related mortality. A contributing factor appears to be a reduced chance of receiving appropriate care. Regions with a higher proportion of indigenous residents show risk beyond the effects of general socioeconomic ...

Boynton-Jarrett, R, Ryan, LM, Berkman, LF & Wright, RJ 2008, 'Cumulative Violence Exposure and Self-Rated Health: Longitudinal Study of Adolescents in the United States', PEDIATRICS, vol. 122, no. 5, pp. 961-970.
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Objective. The goal was to determine whether cumulative exposure to violence in childhood and adolescence contributes to disparities in self-rated health among a nationally representative sample of US adolescents. Methods. The National Longitudinal Survey of Youth 1997 is an ongoing, 8-year (1997-2004), longitudinal, cohort study of youths who were 12 to 18 years of age at baseline (N = 8224). Generalized estimating equations were constructed to investigate the relationship between cumulative exposure to violence and risk for poor health. Results. At baseline, 75% of subjects reported excellent or very good health, 21.5% reported good health, and 4.5% reported fair or poor health. Cumulative violence exposures (witnessed gun violence, threat of violence, repeated bullying, perceived safety, and criminal victimization) were associated with a graded increase in risk for poor health and reduced the strength of the relationship between household income and poor health. In comparison with subjects with no violence exposure, risk for poor self-rated health was 4.6 times greater among subjects who reported ≥5 forms of cumulative exposure to violence, controlling for demographic features and household income. Trend analysis revealed that, for each additional violence exposure, the risk of poor health increased by 38%. Adjustment for alcohol use, drug use, smoking, depressive symptoms, and family and neighborhood environment reduced the strength of the relationships between household income and cumulative exposure to violence scores and poor self-rated health, which suggests partial mediation of the effects of socioeconomic status and cumulative exposure to violence by these factors. Conclusions. In this nationally representative sample, social inequality in risk for poor self-rated health during the transition from adolescence to adulthood was partially attributable to disparities in cumulative exposure to violence. A strong graded association was noted between...

CAO, L 2008, 'INTEGRATING AGENT, SERVICE AND ORGANIZATIONAL COMPUTING', International Journal of Software Engineering and Knowledge Engineering, vol. 18, no. 05, pp. 573-596.
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Engineering open complex systems is challenging because of system complexities such as openness, the involvement of organizational factors and service delivery. It cannot be handled well by the single use of existing computing techniques such as agent-based computing and service-oriented computing. Due to the intrinsic organizational characteristics and the request of service delivery, an integrative computing paradigm combining agent, service, organizational and social computing can open complex systems more effectively engineering. In this paper, we briefly introduce an integrative computing approach named OASOC for system analysis and design. It combines and complements the strengths of agent, service and organizational computing to handle the complexities of open complex systems. OASOC provides facilities for organization-oriented analysis and agent service-oriented design. It also supports transition between analysis and design. Compared with the existing approaches, our approach can (1) support service and organization that are either rarely or weakly covered by single computing methods, (2) provide effective mechanisms to integrate agent, service and organizational computing, and (3) complement the strengths of various methods. Experiences in engineering an online trading support system have further shown the workable capability of integrating agent, service and organizational computing for engineering open complex systems.

Cao, L & Nguyen, NT 2008, 'Intelligence metasynthesis and knowledge processing in intelligent systems', Journal of Universal Computer Science, vol. 14, no. 14, pp. 2256-2262.
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Intelligence and Knowledge play more and more important roles in building complex intelligent systems, for instance, intrusion detection systems, and operational analysis systems. Knowledge processing in complex intelligent systems faces new challenges from the increased number of applications and environment, such as the requirements of representing domain and human knowledge in intelligent systems, and discovering actionable knowledge on a large scale in distributed web applications. In this paper, we discuss the main challenges of, and promising approaches to, intelligence metasynthesis and knowledge processing in open complex intelligent systems. We believe (1) ubiquitous intelligence, including data intelligence, domain intelligence, human intelligence, network intelligence and social intelligence, is necessary for OCIS, which needs to be meta-synthesized; and (2) knowledge processing should pay more attention to developing innovative and workable methodologies, techniques, tools and systems for representing, modelling, transforming, discovering and servicing the uncertain, large-scale, deep, distributed, domain-oriented, human-involved, and actionable knowledge highly expected in constructing open complex intelligent systems. To this end, the meta-synthesis of ubiquitous intelligence is an appropriate way in designing complex intelligent systems. To support intelligence meta-synthesis, m-interaction can play as the working mechanism to form rn-spaces as problem-solving systems. In building such m-spaces, advancement in knowledge processing is necessary. © J.UCS.

Cao, L & Ou, Y 2008, 'Market microstructure patterns powering trading and surveillance agents', Journal of Universal Computer Science, vol. 14, no. 14, pp. 2288-2308.
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Market Surveillance plays important mechanism roles in constructing market models. From data analysis perspective, we view it valuable for smart trading in designing legal and profitable trading strategies and smart regulation in maintaining market integrity, transparency and fairness. The existing trading pattern analysis only focuses on interday data which discloses explicit and high-level market dynamics. In the mean time, the existing market surveillance systems available from large exchanges are facing crucial challenges of diversified, dynamic, distributed and cyber-based misuse, mis-disclosure and misdealing of information, announcement and orders in one market or crossing multiple markets. Therefore, there is a crucial need to develop innovative and workable methods for smart trading and surveillance. To deal with such issues, we propose the innovative concept microstructure pattern analysis and corresponding approaches in this paper. Microstructure pattern analysis studies trading behaviour patterns of traders in market microstructure data by utilizing market microstructure knowledge. The identified market microstructure patterns are then used for powering market trading and surveillance agents for automatically detecting/designing profitable and legal trading strategies or monitoring abnormal market dynamics and trader's behaviour. Such trading/surveillance agent-driven market trading/surveillance systems can greatly enhance the analytical, discovery and decision-support capability of market trading/surveillance than the current predefined rule/alert-based systems. © J.UCS.

Cao, L, Zhao, Y & Zhang, C 2008, 'Mining Impact-Targeted Activity Patterns in Imbalanced Data', IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 8, pp. 1053-1066.
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Impact-targeted activities are rare but they may have a significant impact on the society. For example, isolated terrorism activities may lead to a disastrous event, threatening the national security. Similar issues can also be seen in many other areas. Therefore, it is important to identify such particular activities before they lead to having a significant impact to the world. However, it is challenging to mine impact-targeted activity patterns due to their imbalanced structure. This paper develops techniques for discovering such activity patterns. First, the complexities of mining imbalanced impact-targeted activities are analyzed. We then discuss strategies for constructing impact-targeted activity sequences. Algorithms are developed to mine frequent positive-impact-oriented (P → T) and negative-impact-oriented (P → T̄) activity patterns, sequential impact-contrasted activity patterns (P is frequently associated with both patterns P → T and P → T̄ in separated data sets), and sequential impact-reversed activity patterns (both P → T and PQ → T̄ are frequent). Activity impact modeling is also studied to quantify the pattern impact on business outcomes. Social security debt-related activity data is used to test the proposed approaches. The outcomes show that they are promising for information and security informatics (ISI) applications to identify impact-targeted activity patterns in imbalanced data. © 2008 IEEE.

CAO, L, ZHAO, Y, ZHANG, C & ZHANG, H 2008, 'ACTIVITY MINING: FROM ACTIVITIES TO ACTIONS', International Journal of Information Technology & Decision Making, vol. 07, no. 02, pp. 259-273.
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Activity data accumulated in real life, such as terrorist activities and governmental customer contacts, present special structural and semantic complexities. Activity data may lead to or be associated with significant business impacts, and result in important actions and decision making leading to business advantage. For instance, a series of terrorist activities may trigger a disaster to society, and large amounts of fraudulent activities in social security programs may result in huge government customer debt. Uncovering these activities or activity sequences can greatly evidence and/or enhance corresponding actions in business decisions. However, mining such data challenges the existing KDD research in aspects such as unbalanced data distribution and impact-targeted pattern mining. This paper investigates the characteristics and challenges of activity data, and the methodologies and tasks of activity mining based on case-study experience in the area of social security. Activity mining aims to discover high impact activity patterns in huge volumes of unbalanced activity transactions. Activity patterns identified can be used to prevent disastrous events or improve business decision making and processes. We illustrate the above issues and prospects in mining governmental customer contacts data to recover customer debt.

Chen, W, Cao, L & Qin, Z 2008, 'An integrated investment decision-support framework analysing and synthesising multidimensional market dynamics', International Journal of Intelligent Systems Technologies and Applications, vol. 4, no. 3-4, pp. 239-253.
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In stock markets, the performance of traditional technology-based investment methods is limited because such methods only take into account single-dimensional market dynamics. The paper shows how the integration of multi-dimensional dynamics can improve performance. We propose a novel three-layer integrated framework composed of Analysis, Synthesis, and Investment Decision Support. At the first layer, multi-dimensional market dynamics are identified, in which we emphasize two key aspects that previous studies have neglected: unique trends of stocks, and a two-way reflexivity relationship of investors’ decisions and market reactions. At the second layer, multi-dimensional dynamics are synthesized to reflect real and potential market situations. At the third layer, a prototype integrates the functions of first two layers for investment decision support. The framework covers multi-dimensional dynamics, and incorporates the concepts and advantages of traditional investment methods. The framework is promising, and our experimental results indicated that it outperformed market baselines and single-dimensional conventional methods. © 2008 Inderscience Enterprises Ltd.

Chen, W, Cao, L & Qin, Z 2008, 'An integrated investment decision-support framework analysing and synthesising multidimensional market dynamics', International Journal of Intelligent Systems Technologies and Applications, vol. 4, no. 3/4, pp. 239-239.
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In stock markets, the performance of traditional technology-based investment methods is limited because such methods only take into account single-dimensional market dynamics. The paper shows how the integration of multi-dimensional dynamics can improve performance. We propose a novel three-layer integrated framework composed of Analysis, Synthesis, and Investment Decision Support. At the first layer, multi-dimensional market dynamics are identified, in which we emphasise two key aspects that previous studies have neglected: unique trends of stocks, and a two-way reflexivity relationship of investors' decisions and market reactions. At the second layer, multi-dimensional dynamics are synthesized to reflect real and potential market situations. At the third layer, a prototype integrates the functions of first two layers for investment decision support. The framework covers multi-dimensional dynamics, and incorporates the concepts and advantages of traditional investment methods. The framework is promising, and our experimental results indicated that it outperformed market baselines and single-dimensional conventional methods.

Chen, W, Li, J, Li, S, Jiang, Z, Li, H & Peng, J 2008, 'High nonlinear photonic crystal fiber and its supercontinuum spectrum', Frontiers of Optoelectronics in China, vol. 1, no. 1-2, pp. 75-78.
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Clark, C, Ryan, L, Kawachi, I, Canner, MJ, Berkman, L & Wright, RJ 2008, 'Witnessing community violence in residential neighborhoods: A mental health hazard for urban women', JOURNAL OF URBAN HEALTH-BULLETIN OF THE NEW YORK ACADEMY OF MEDICINE, vol. 85, no. 1, pp. 22-38.
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We examined the prevalence and psychological correlates of witnessing community violence among women of low socioeconomic status living in urban neighborhoods in the northeastern United States. Three hundred eighty-six women receiving their health care at an urban community health center were sampled to assess their violence exposures. Women were asked to report the location and timing of their exposure to witnessing violent neighborhood events in which they were not participants. The Brief Symptoms Inventory was used to assess anxiety and depressive symptoms. Controlling for marital status, educational attainment, age, and intimate partner violence victimization, women who witnessed violent acts in their neighborhoods were twice as likely to experience depressive and anxiety symptoms compared to women who did not witness community violence. Central American-born women had particularly high exposures. We conclude that witnessing neighborhood violence is a pervasive experience in this urban cohort, and is associated with anxiety and depressive symptoms, even among women who are not direct participants in violence to which they are exposed. Community violence interventions must incorporate efforts to protect the mental health of adult women who witness events in their neighborhoods. © 2007 The New York Academy of Medicine.

Forno, E, Onderdonk, AB, McCracken, J, Litonjua, AA, Laskey, D, Delaney, ML, DuBois, AM, Gold, DR, Ryan, LM, Weiss, ST & Celedón, JC 2008, 'Diversity of the gut microbiota and eczema in early life', Clinical and Molecular Allergy, vol. 6, no. 1.
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AbstractBackgroundA modest number of prospective studies of the composition of the intestinal microbiota and eczema in early life have yielded conflicting results.ObjectiveTo examine the relationship between the bacterial diversity of the gut and the development of eczema in early life by methods other than stool culture.MethodsFecal samples were collected from 21 infants at 1 and 4 months of life. Nine infants were diagnosed with eczema by the age of 6 months (cases) and 12 infants were not (controls). After conducting denaturating gradient gel electrophoresis (DGGE) of stool samples, we compared the microbial diversity of cases and controls using the number of electrophoretic bands and the Shannon index of diversity (H') as indicators.ResultsControl subjects had significantly greater fecal microbial diversity than children with eczema at ages 1 (meanH'for controls = 0.75 vs. 0.53 for cases, P = 0.01) and 4 months (meanH'for controls = 0.92 vs. 0.59 for cases, P = 0.02). The increase in diversity from 1 to 4 months of age was significant in controls (P = 0.04) but not in children who developed eczema by 6 months of age (P = 0.32).ConclusionOur findings suggest that reduced microbial diversity is associated with the development of eczema in early life.

Frazier, SK, Lennie, TA & Moser, DK 2008, 'Preface', Nursing Clinics of North America, vol. 43, no. 1, pp. xi-xii.
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He, X, Wu, Q, Jia, W & Hintz, T 2008, 'Edge Detection on Hexagonal Structure', Journal of Algorithms & Computational Technology, vol. 2, no. 1, pp. 61-78.
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Gradient-based edge detection is a straightforward method to identify the edge points in the original grey-level image. It is intuitive that in the human vision system the edge points always appear where the grey-level value is greatly changed. Spiral Architecture is a relatively new image data structure that is inspired from anatomical considerations of the primate's vision. In Spiral Architecture, each image is represented as a collection of hexagonal pixels. Edge detection on Spiral Architecture has features of fast computation and accurate localization. In this paper, we present and compare gradient-based edge detection algorithms on Spiral Architecture. The experimental results show that the edge detection on Spiral Architecture outperforms that on traditional square image structure.

Horton, NJ, Roberts, K, Ryan, L, Suglia, SF & Wright, RJ 2008, 'A maximum likelihood latent variable regression model for multiple informants', STATISTICS IN MEDICINE, vol. 27, no. 24, pp. 4992-5004.
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Studies pertaining to childhood psychopathology often incorporate information from multiple sources (or informants). For example, measurement of some factor of particular interest might be collected from parents, teachers as well as the children being studied. We propose a latent variable modeling framework to incorporate multiple informant predictor data. Several related models are presented, and likelihood ratio tests are introduced to formally compare fit. The incorporation of partially observed subjects is addressed under a variety of missing data mechanisms. The methods are motivated by and applied to a study of the association of chronic exposure to violence on asthma in children. Copyright © 2008 John Wiley & Sons, Ltd.

Jinyan Li & Junghwan Kim 2008, 'Performance Analysis of MF-TDMA Multi-Carrier Demultiplexer/Demodulators (MCDDs) in the Presence of Critical Degrading Factors', IEEE Transactions on Broadcasting, vol. 54, no. 3, pp. 371-382.
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Kennedy, P, Francis, N, Rovnyak, D & Kastner, ME 2008, 'Redetermination ofcis-diaquadiglycolatozinc(II)', Acta Crystallographica Section E Structure Reports Online, vol. 64, no. 12, pp. m1635-m1635.
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Kim, S, Kang, J, Chung, YJ, Li, J & Ryu, KH 2008, 'Clustering orthologous proteins across phylogenetically distant species', Proteins: Structure, Function, and Bioinformatics, vol. 71, no. 3, pp. 1113-1122.
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AbstractThe quality of orthologous protein clusters (OPCs) is largely dependent on the results of the reciprocal BLAST (basic local alignment search tool) hits among genomes. The BLAST algorithm is very efficient and fast, but it is very difficult to get optimal solution among phylogenetically distant species because the genomes with large evolutionary distance typically have low similarity in their protein sequences. To reduce the false positives in the OPCs, thresholding is often employed on the BLAST scores. However, the thresholding also eliminates large numbers of true positives as the orthologs from distant species likely have low BLAST scores. To rectify this problem, we introduce a new hybrid method combining the Recursive and the Markov CLuster (MCL) algorithms without using the BLAST thresholding. In the first step, we use InParanoid to produce n(n−1)/2 ortholog tables from n genomes. After combining all the tables into one, our clustering algorithm clusters ortholog pairs recursively in the table. Then, our method employs MCL algorithm to compute the clusters and refines the clusters by adjusting the inflation factor. We tested our method using six different genomes and evaluated the results by comparing against Kegg Orthology (KO) OPCs, which are generated from manually curated pathways. To quantify the accuracy of the results, we introduced a new intuitive similarity measure based on our Least‐move algorithm that computes the consistency between two OPCs. We compared the resulting OPCs with the KO OPCs using this measure. We also evaluated the performance of our method using InParanoid as the baseline approach. The experimental results show that, at the inflation factor 1.3, we produced 54% more orthologs than InParanoid sacrificing a little less accuracy (1.7% less) than InParanoid, and at the factor 1.4, produced not only 15% more orthologs than InParanoid but also a higher accuracy (1.4% more) than InPara...

Li, L, Lou, Q, Zhou, J, Dong, J, Wei, Y & Li, J 2008, 'Influence of bending diameter on output capability of multimode fiber laser', Frontiers of Optoelectronics in China, vol. 1, no. 1-2, pp. 91-94.
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Lin, L & Cao, L 2008, 'Mining in-depth patterns in stock market', International Journal of Intelligent Systems Technologies and Applications, vol. 4, no. 3/4, pp. 225-225.
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Stock trading plays an important role for supporting profitable stock investment. In particular, more and more data mining-based technical trading rules have been developed and used in stock trading systems to assist investors with their smart trading decisions. However, many mined trading rules are of no interest to traders and brokers because they are discovered based on statistical significance without checking traders' interestingness concerns. To this end, this paper proposes in-depth data mining technologies to overcome the disadvantages of current data mining methods. We implement a decision support in-depth trading pattern discovery system with Robust Genetic Algorithms (RGA). The system integrates expert knowledge and considers domain constraints into the trading rule development. We further utilise this technique to mine actionable stock-rule pairs targeting behaviour with high return at low risk. The proposed approaches are tested in real stock orderbook data with varying investment strategies.

Liu, G, Li, J & Wong, L 2008, 'A new concise representation of frequent itemsets using generators and a positive border', KNOWLEDGE AND INFORMATION SYSTEMS, vol. 17, no. 1, pp. 35-56.
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A complete set of frequent itemsets can get undesirably large due to redundancy when the minimum support threshold is low or when the database is dense. Several concise representations have been previously proposed to eliminate the redundancy. Generator based representations rely on a negative border to make the representation lossless. However, the number of itemsets on a negative border sometimes even exceeds the total number of frequent itemsets. In this paper, we propose to use a positive border together with frequent generators to form a lossless representation. A positive border is usually orders of magnitude smaller than its corresponding negative border. A set of frequent generators plus its positive border is always no larger than the corresponding complete set of frequent itemsets, thus it is a true concise representation. The generalized form of this representation is also proposed. We develop an efficient algorithm, called GrGrowth, to mine generators and positive borders as well as their generalizations. The GrGrowth algorithm uses the depth-first-search strategy to explore the search space, which is much more efficient than the breadth-first-search strategy adopted by most of the existing generator mining algorithms. Our experiment results show that the GrGrowth algorithm is significantly faster than level-wise algorithms for mining generator based representations, and is comparable to the state-of-the-art algorithms for mining frequent closed itemsets. © Springer-Verlag London Limited 2007.

Liu, G, Li, J & Wong, L 2008, 'Assessing and predicting protein interactions using both local and global network topological metrics.', Genome Inform, vol. 21, pp. 138-149.
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High-throughput protein interaction data, with ever-increasing volume, are becoming the foundation of many biological discoveries. However, high-throughput protein interaction data are often associated with high false positive and false negative rates. It is desirable to develop scalable methods to identify these errors. In this paper, we develop a computational method to identify spurious interactions and missing interactions from high-throughput protein interaction data. Our method uses both local and global topological information of protein pairs, and it assigns a local interacting score and a global interacting score to every protein pair. The local interacting score is calculated based on the common neighbors of the protein pairs. The global interacting score is computed using globally interacting protein group pairs. The two scores are then combined to obtain a final score called LGTweight to indicate the interacting possibility of two proteins. We tested our method on the DIP yeast interaction dataset. The experimental results show that the interactions ranked top by our method have higher functional homogeneity and localization coherence than existing methods, and our method also achieves higher sensitivity and precision under 5-fold cross validation than existing methods.

Longbing Cao, Chengqi Zhang & MengChu Zhou 2008, 'Engineering Open Complex Agent Systems: A Case Study', IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 38, no. 4, pp. 483-496.
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Open complex agent systems (OCAS) are becoming increasingly important in constructing problem-solving systems for enterprise applications. are challenging because they present very high system complexities involving human users and interactions with a changing environment. The existing agent-oriented software engineering (AOSE) approaches have trouble in engineering OCAS because of a number of deficiencies, e.g., lacking the capability of handling system dynamics analysis. This paper introduces an effective AOSE approach, i.e., organization- and service-oriented system analysis and design (OSOAD). It is used to extract and model system members and design a real-life OCAS system called financial trading rule automated development and evaluation (F-Trade). Through the case studies of visual and formal modeling and design of major organizational members, relations, and subsystems in F-Trade, this paper demonstrates the effective mechanisms and capabilities of the OSOAD approach. System implementation and evaluation results further show that OSOAD provides comprehensive AOSE support for engineering real-world open complex agent organizations. © 2008 IEEE.

Luo, D, Cao, L, Luo, C, Zhang, C & Wang, W 2008, 'Towards business interestingness in actionable knowledge discovery', Frontiers in Artificial Intelligence and Applications, vol. 177, no. 1, pp. 99-109.
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From the evolution of developing a pattern interestingness perspective, data mining has experienced two phases, which are Phase 1: technical objective interestingness focused research, and Phase 2: technical objective and subjective interestingness focused studies. As a result of these efforts, patterns mined are of significant interest to technical concern. However, technically interesting patterns are not necessarily of interest to business. In fact, real-world experience shows that many mined patterns, which are interesting from the perspective of the data mining method used, are out of business expectations when they are delivered to the final user. This scenario actually involves a grand challenge in next-generation KDD (Knowledge Discovery in Databases) studies, defined as actionable knowledge discovery. To discover knowledge that can be used for taking actions to business advantages, this paper addresses a framework that extends the evolution process of knowledge evaluation to Phase 3 and Phase 4. In Phase 3, concerns with objective interestingness from a business perspective are added on top of Phase 2, while in Phase 4 both technical and business interestingness should be satisfied in terms of objective and subjective perspectives. The introduction of Phase 4 provides a comprehensive knowledge actionability framework for actionable knowledge discovery. We illustrate applications in governmental data mining showing that the considerations and adoption of the framework described in Phase 4 has potential to enhance both sides of interestingness and expectation. As a result, knowledge discovered has better chances to support action-taking in the business world. © 2008 The authors and IOS Press. All rights reserved.

Merigó, JM & Gil Lafuente, AM 2008, 'THE GENERALIZED ADEQUACY COEFFICIENT AND ITS APPLICATION IN STRATEGIC DECISION MAKING', FUZZY ECONOMIC REVIEW, vol. 13, no. 02, pp. 17-36.
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The adequacy coefficient is a very useful technique that provides a more complete formulation than the Hamming distance in decision making problems. In this paper, we suggest a generalization by using generalized and quasi-arithmetic means. As a result, we will get the generalized ordered weighted averaging adequacy coe fficient (GOWAAC) and the Quasi- OWAAC operator. These new aggregation operators generalize a wide range of particular cases such as the generalized adequacy coefficient (GAC), the weighted generalized adequacy coefficient (WGAC), the ordered weighted averaging adequacy coefficient (OWAAC), the ordered weighted quadratic averaging adequacy coefficient (OWQAAC), and others. We study different families and properties of these aggregation operators. We also analyze the unification point with distance measures and we find that in these situations, the GOWAAC and the Quasi-OWAAC become the Minkowski ordered weighted averaging distance (MOWAD) operator and the Quasi- OWAD operator, respectively. Finally, we also develop an application of the new approach in a strategic decision making problem about selection of strategies.

Musiał, K, Juszczyszyn, K & Kazienko, P 2008, 'Ontology-based recommendation in multimedia sharing systems', Systems Science, vol. 34, no. 1, pp. 97-106.
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In this paper, a new framework for recommendation of multimedia objects in the environment of the multimedia sharing system has been proposed. It uses two kinds of individual ontologies, one is created for multimedia objects and the second one for system users. The final recommendation process takes into account similarities calculated both between objects' and users' ontologies. These individual ontologies respect all the social and semantic features existing in the system. The entire recommender framework was developed for the use in Flickr, a typical photo sharing system.

Ni, J, Cao, L & Zhang, C 2008, 'Evolutionary optimization of trading strategies', Frontiers in Artificial Intelligence and Applications, vol. 177, no. 1, pp. 11-24.
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It is a non-trivial task to effectively and efficiently optimize trading strategies, not to mention the optimization in real-world situations. This paper presents a general definition of this optimization problem, and discusses the application of evolutionary technologies (genetic algorithm in particular) to the optimization of trading strategies. Experimental results show that this approach is promising. © 2008 The authors and IOS Press. All rights reserved.

Ni, J, Luo, D, Ou, Y & Luo, C 2008, 'Agent-based evolutionary optimisation of trading strategies', International Journal of Intelligent Information and Database Systems, vol. 2, no. 1, pp. 25-25.
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The backtesting and optimisation of trading strategies has emerged as an interesting research and experimental problem in both finance and Information Technology (IT) fields. However, it is a non-trivial task to effectively and efficiently optimise trading strategies, not to mention the optimisation in the real-world situations. This paper discusses the application of evolutionary technologies (genetic algorithm in particular) to the optimisation of trading strategies. Experimental results show that this approach is promising. Due to the complexity involved in the optimisation process, we further present an agent-based system that can help users easily specify and execute optimisation jobs to their advantages. Copyright © 2008, Inderscience Publishers.

Paisitkriangkrai, S, Chunhua Shen & Jian Zhang 2008, 'Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features', IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 8, pp. 1140-1151.
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Efficiently and accurately detecting pedestrians plays a very important role in many computer vision applications such as video surveillance and smart cars. In order to find the right feature for this task, we first present a comprehensive experimental s

Paisitkriangkrai, S, Shen, C & Zhang, J 2008, 'Performance evaluation of local features in human classification and detection', IET Computer Vision, vol. 2, no. 4, pp. 236-236.
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Detecting pedestrians accurately is the first fundamental step for many computer vision applications such as video surveillance, smart vehicles, intersection traffic analysis and so on. The authors present an experimental study on pedestrian detection us

Pham, TD, Honghui Wang, Xiaobo Zhou, Dominik Beck, Brandl, M, Hoehn, G, Azok, J, Brennan, M-L, Hazen, SL, Li, K & Wong, STC 2008, 'Computational Prediction Models for Early Detection of Risk of Cardiovascular Events Using Mass Spectrometry Data', IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 5, pp. 636-643.
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Early prediction of the risk of cardiovascular events in patients with chest pain is critical in order to provide appropriate medical care for those with positive diagnosis. This paper introduces a computational methodology for predicting such events in the context of robust computerized classification using mass spectrometry data of blood samples collected from patients in emergency departments. We applied the computational theories of statistical and geostatistical linear prediction models to extract effective features of the mass spectra and a simple decision logic to classify disease and control samples for the purpose of early detection. While the statistical and geostatistical techniques provide better results than those obtained from some other methods, the geostatistical approach yields superior results in terms of sensitivity and specificity in various designs of the data set for validation, training, and testing. The proposed computational strategies are very promising for predicting major adverse cardiac events within six months. © 2008 IEEE.

Phipatanakul, W, Celedon, JC, Hoffman, EB, Abdulkerim, H, Ryan, LM & Gold, DR 2008, 'Mouse allergen exposure, wheeze and atopy in the first seven years of life', ALLERGY, vol. 63, no. 11, pp. 1512-1518.
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Background: Little is known about mouse allergen exposure in home environments and the development of wheezing, asthma and atopy in childhood. Objective: To examine the relation between mouse allergen exposure and wheezing, atopy, and asthma in the first 7 years of life. Methods: Prospective study of 498 children with parental history of allergy or asthma followed from birth to age 7 years, with longitudinal questionnaire ascertainment of reported mouse exposure and dust sample mouse urinary protein allergen levels measured at age 2-3 months. Results: Parental report of mouse exposure in the first year of life was associated with increased risk of transient wheeze and wheezing in early life. Current report of mouse exposure was also significantly associated with current wheeze throughout the first 7 years of life in the longitudinal analysis (P = 0.03 for overall relation of current mouse to current wheeze). However, early life mouse exposure did not predict asthma, eczema or allergic rhinitis at age 7 years. Exposure to detectable levels of mouse urinary protein in house dust samples collected at age 2-3 months was associated with a twofold increase in the odds of atopy (sensitization to >=1 allergen) at school age (95% confidence interval for odds ratio = 1.1-3.7; P = 0.03 in a multivariate analysis. Conclusions: Among children with parental history of asthma or allergies, current mouse exposure is associated with increased risk of wheeze during the first 7 years of life. Early mouse exposure was associated with early wheeze and atopy later in life. © 2008 The Authors.

Ryan, L 2008, 'Combining data from multiple sources, with applications to environmental risk assessment', STATISTICS IN MEDICINE, vol. 27, no. 5, pp. 698-710.
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The classical statistical paradigm emphasizes the development and application of methods to account for sampling variability. Many modern day applications, however, require consideration of other sources of uncertainty that are not so easy to quantify. This paper presents a case study involving an assessment of the impact of in-utero methylmercury exposure on the Intelligence Quotient (IQ) of young children. We illustrate how familiar techniques such as hierarchical modeling, Bayesian methods and sensitivity analysis can be used to aid decision making in settings that involve substantial uncertainty. Copyright © 2007 John Wiley & Sons, Ltd.

Schwartz, J, Coull, B, Laden, F & Ryan, L 2008, 'The effect of dose and timing of dose on the association between airborne particles and survival', ENVIRONMENTAL HEALTH PERSPECTIVES, vol. 116, no. 1, pp. 64-69.
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Background: Understanding the shape of the concentration-response curve for particles is important for public health, and lack of such understanding was recently cited by U.S. Environmental Protection Agency (EPA) as a reason for not tightening the standards. Similarly, the delay between changes in exposure and changes in health is also important in public health decision making. We addressed these issues using an extended follow-up of the Harvard Six Cities Study. Methods: Cox proportional hazards models were fit controlling for smoking, body mass index, and other covariates. Two approaches were used. First, we used penalized splines, which fit a flexible functional form to the concentration response to examine its shape, and chose the degrees of freedom for the curve based on Akaike's information criterion. Because the uncertainties around the resultant curve do not reflect the uncertainty in model choice, we also used model averaging as an alternative approach, where multiple models are fit explicitly and averaged, weighted by their probability of being correct given the data. We examined the lag relationship by model averaging across a range of unconstrained distributed lag models. Results: We found that the concentration-response curve is linear, clearly continuing below the current U.S. standard of 15 μg/m3, and that the effects of changes in exposure on mortality are seen within two years. Conclusions: Reduction in particle concentrations below U.S. EPA standards would increase life expectancy.

Suglia, SF, Ryan, L & Wright, RJ 2008, 'Creation of a Community Violence Exposure Scale: Accounting for What, Who, Where, and How Often', JOURNAL OF TRAUMATIC STRESS, vol. 21, no. 5, pp. 479-486.
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Previous research has used the Rasch model, a method for obtaining a continuous scale from dichotomous survey items measuring a single latent construct, to create a scale of community violence exposure. The authors build upon previous work and describe the application of a Rasch model using the continuation ratio model to create an exposure to community violence (ETV) scale including event circumstance information previously shown to modify the impact of experienced events. They compare the Rasch ETV scale to a simpler sum ETV score, and estimate the effect of ETV on child posttraumatic stress symptoms. Incorporating detailed event circumstance information that is grounded in traumatic stress theory may reduce measurement error in the assessment of children's community violence exposure. © 2008 International Society for Traumatic Stress Studies.

Suglia, SF, Ryan, L, Laden, F, Dockery, DW & Wright, RJ 2008, 'Violence exposure, a-chronic psychosocial stressor, and childhood lung function', PSYCHOSOMATIC MEDICINE, vol. 70, no. 2, pp. 160-169.
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OBJECTIVE: Chronic psychosocial stressors, including violence, and neuropsychological and behavioral development in children as well as physiologic alterations that may lead to broader health effects. METHODS: We studied the relationship between violence and childhood lung function in a prospective birth cohort of 313 urban children (age range = 6-7 years). Mothers reported on their child's lifetime exposure to community violence (ETV) and interparental conflict in the home (Conflict Tactics Scale (CTS)) within 1 year of the lung function assessment. RESULTS: In linear regression analyses, adjusting for maternal education, child's age, race, birthweight, tobacco smoke exposure, and medical history, girls in the highest CTS verbal aggression tertile had a 5.5% (95% confidence interval (CI) = -9.6, -1.5) decrease in percent predicted forced expiratory volume (FEV1) and a 5.4% (95% CI = -9.7, -1.1) decrease in forced vital capacity (FVC) compared with girls in the lowest tertile. The CTS verbal aggression subscale was associated with lung function among boys in the same direction, albeit this was not statistically significant. Boys in the highest ETV tertile had a 3.4% (95% CI = -8.0, 1.1) lower FEV1 and 5.3% lower FVC (95% CI = -10.2, -0.4) compared with boys in the lowest tertile. The ETV score was not a significant predictor of girls' lung function. CONCLUSIONS: Interparental conflict, specifically verbal aggression, and ETV were associated with decreased childhood lung function independent of socioeconomic status, tobacco smoke exposure, birthweight, and respiratory illness history. Gender differences were noted based on the type of violence exposure, which may warrant further exploration. Copyright © 2008 by American Psychosomatic Society.

Surkan, PJ, Kawachi, I, Ryan, LM, Berkman, LF, Vieira, C & Peterson, KE 2008, 'Maternal depressive symptoms, parenting self-efficacy, and child growth', AMERICAN JOURNAL OF PUBLIC HEALTH, vol. 98, no. 1, pp. 125-132.
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Objectives. We assessed whether maternal depressive symptoms and parenting self-efficacy were associated with child growth delay. Methods. We collected data from a random sample of 595 low-income mothers and their children aged 6 to 24 months in Teresina, Piauí, Brazil, including information on sociodemographic characteristics, mothers' depressive symptoms and parenting self-efficacy, and children's anthropometric characteristics. We used adjusted logistic regression models in our analyses. Results. Depressive symptoms among mothers were associated with 1.8 times higher odds (95% confidence interval [CI]=1.1, 2.9) of short stature among children. Parenting self-efficacy was not associated with short stature, nor did it mediate or modify the relationship between depressive symptoms and short stature. Maternal depressive symptoms and self-efficacy were not related to child underweight. Conclusions. Our results showed that among low-income Brazilian families maternal depressive symptoms, but not self-efficacy, were associated with short stature in children aged 6 to 24 months after adjustment for known predictors of growth.

Wang, H, He, X, Hintz, T & Wu, Q 2008, 'Fractal Image Compression on Hexagonal Structure', Journal of Algorithms & Computational Technology, vol. 2, no. 1, pp. 79-98.
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Fractal image compression (FIC) is a relatively recent image compression method. Its basic idea is to represent images as a fixed point of a contractive Iterated Function System (IFS). Spiral Architecture (SA) is a novel hexagonal image structure on which images are displayed as a collection of hexagonal pixels. The efficiency and accuracy of image processing on SA have been demonstrated in many recently published papers. In this paper, two presentations of SA on the traditional display device will be discussed. Then we will review the current research work on fractal image compression based on SA using both presentations. The FIC performance on SA will be compared with it on the traditional square structure in terms of compression ratio and PSNR. In the experimental results, higher PSNR values can be achieved at various compression ratios for all test images. The preliminary research on this direction has shown a promising future of applying FIC on SA to further improve the compression performance.

Xiangjian He, Wenjing Jia & Qiang Wu 2008, 'An approach of canny edge detection with virtual hexagonal image structure', 2008 10th International Conference on Control, Automation, Robotics and Vision, vol. 3, no. 1, pp. 133-143.
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Gradient-based edge detection is a straightforward method to identify the edge points in the original grey-level image. It is intuitive that in the human vision system the edge points always appear where the grey-level value is greatly changed. Spiral Architecture is a relatively new image data structure that is inspired from anatomical considerations of the primate's vision. In Spiral Architecture, each image is represented as a collection of hexagonal pixels. Edge detection on Spiral Architecture has features of fast computation and accurate localization. In this paper, we present and compare gradient-based edge detection algorithms on Spiral Architecture. The experimental results show that the edge detection on Spiral Architecture outperforms that on traditional square image structure.

Xiao, Y, Liu, B & Cao, L 2008, 'A Chinese question classification using one-vs-one method as a learning tool', International Journal of Intelligent Information and Database Systems, vol. 2, no. 4, pp. 446-446.
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Question classification plays an important role in the question answering system and the errors of question classification will probably result in the failure of question answering. Thus, how to enhance the accuracy is an open question. In order to enhance the accuracies of the Chinese question classification, this paper extends one-against-one method based on SVMs to resolve the problems. The results show the good performance of the algorithm for Chinese question classification problems. © 2008, Inderscience Publishers.

Zhang, S, Wu, X, Zhang, C & Lu, J 2008, 'Computing the minimum-support for mining frequent patterns', Knowledge and Information Systems, vol. 15, no. 2, pp. 233-257.
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Frequent pattern mining is based on the assumption that users can specify the minimum-support for mining their databases. It has been recognized that setting the minimum-support is a difficult task to users. This can hinder the widespread applications of these algorithms. In this paper we propose a computational strategy for identifying frequent itemsets, consisting of polynomial approximation and fuzzy estimation. More specifically, our algorithms (polynomial approximation and fuzzy estimation) automatically generate actual minimum-supports (appropriate to a database to be mined) according to users' mining requirements. We experimentally examine the algorithms using different datasets, and demonstrate that our fuzzy estimation algorithm fittingly approximates actual minimum-supports from the commonly-used requirements. © Springer-Verlag London Limited 2007.

Zhang, S, Zhang, J, Zhu, X, Qin, Y & Zhang, C 2008, 'Missing Value Imputation Based on Data Clustering', Lecture Notes in Computer Science, vol. 4750, no. 2008, pp. 128-138.
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We propose an efficient nonparametric missing value imputation method based on clustering, called CMI (Clustering-based Missing value Imputation), for dealing with missing values in target attributes. In our approach, we impute the missing values of an instance A with plausible values that are generated from the data in the instances which do not contain missing values and are most similar to the instance A using a kernel-based method. Specifically, we first divide the dataset (including the instances with missing values) into clusters. Next, missing values of an instance A are patched up with the plausible values generated from Aâs cluster. Extensive experiments show the effectiveness of the proposed method in missing value imputation task.

Al-Oqaily, A, Kennedy, PJ, Catchpoole, D & Simoff, S 1970, 'Comparison of visualization methods of genome-wide SNP profiles in childhood acute lymphoblastic leukaemia', Conferences in Research and Practice in Information Technology Series, Australian Data Mining Conference, Australian Computer Society, Adelaide, Australia, pp. 111-121.
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Data mining and knowledge discovery have been applied to datasets in various industries including biomedical data. Modelling, data mining and visualization in biomedical data address the problem of extracting knowledge from large and complex biomedical data. The current challenge of dealing with such data is to develop statistical-based and data mining methods that search and browse the underlying patterns within the data. In this paper, we employ several data reduction methods for visualizing genome- wide Single Nucleotide Polymorphism (SNP) datasets based on state-of-art data reduction techniques. Visualization approach has been selected based on the trustworthiness of the resultant visualizations. To deal with large amounts of genetic variation data, we have chosen to apply different data reduction methods to deal with the problem induced by high dimensionality. Based on the trustworthiness metric we found that neighbour Retrieval Visualizer (NeRV) outperformed other methods. This method optimizes the retrieval quality of Stochastic neighbour Embedding. The quality measure of the visualization (i.e. NeRV) showed excellent results, even though the dataset was reduced from 13917 to 2 dimensions. The visualization results will assist clinicians and biomedical researchers in understanding the systems biology of patients and how to compare different groups of clusters in visualizations. © 2008, Australian Computer Society, Inc.

Cao, L 1970, 'Behavior Informatics and Analytics: Let Behavior Talk', 2008 IEEE International Conference on Data Mining Workshops, 2008 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, Pisa, Italy, pp. 87-96.
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Behavior is increasingly recognized as a key component in business intelligence and problem-solving. Different from traditional behavior analysis, which mainly focus on implicit behavior and explicit business appearance as a result of business usage and customer demographics, this paper proposes the field of Behavior Informatics and Analytics (BIA), to support explicit behavior involvement through a conversion from transactional data to behavioral data, and further genuine analysis of native behavior patterns and impacts. BIA consists of key components including behavior modeling and representation, behavioral data construction, behavior impact modeling, behavior pattern analysis, and behavior presentation. BIA can greatly complement the existing means for combined, more informative and social patterns and solutions for critical problem-solving in areas such as dealing with customer-officer interaction, counterterrorism and monitoring online communities.

Cao, L 1970, 'Metasynthetic Computing for Solving Open Complex Problems', 2008 32nd Annual IEEE International Computer Software and Applications Conference, 2008 32nd Annual IEEE International Computer Software and Applications Conference, IEEE, Turku, Finland, pp. 896-901.
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Complex systems, in particular, open complex giant systems have become one of major challenges to many current disciplines such as system sciences, cognitive sciences, intelligence sciences, computer sciences, and information sciences. An appropriate methodology for dealing with them is the theory of qualitative-to-quantitative metasynthesis. From the perspective of engineering, we propose the concept of metasynthetic computing. This paper discusses the theoretical framework, problem-solving process and intelligence emergence of metasynthetic computing from both engineering and cognition perspectives. These efforts can help one understand complex systems and design effective problem-solving systems.

Cao, L, Luo, D, Xiao, Y & Zheng, Z 1970, 'Agent Collaboration for Multiple Trading Strategy Integration.', KES-AMSTA, International KES Symposium on Agents and Multiagent systems - Technologies and Applications, Springer, Incheon, Korea,, pp. 361-370.
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The collaboration of agents can undertake complicated tasks that cannot be handled well by a single agent. This is even true for excecuting multiple goals at the same time. In this paper, we demonstrate the use of trading agent collaboration in integrating multiple trading strategies. Trading agents are used for developing quality trading strategies to support smart actions in the market. Evolutionary trading agents are armed with evolutionary computing capability to optimize strategy parameters. To develop even smarter trading strategies (we call golden strategies), multiple Evolutionary and Collaborative trading agents negotiate with each other for m loops to search multiple local strategies with best parameter combinations. They also integrate multiple classes of strategies for trading agents to achieve the best global objectives acceptable for trader needs. Tests of five classes of trading strategies in ten years of five markets of data have shown that agent collaboration for strategy integration can achieve much better performance of trading compared with that of either individually optimized or randomly chosen strategies. © 2008 Springer-Verlag Berlin Heidelberg.

Chen, Q, Zhang, C & Zhang, S 1970, 'Introduction', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Berlin Heidelberg, pp. 1-15.
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Chen, Y, Hong, Q, Chen, X & Zhang, C 1970, 'Real-Time Speaker Verification Based on GMM-UBM for PDA', 2008 Fifth IEEE International Symposium on Embedded Computing, 2008 Fifth IEEE International Symposium on Embedded Computing (SEC), IEEE.
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Chen, Y, Wu, Q & He, X 1970, 'Human Action Recognition by Radon Transform', 2008 IEEE International Conference on Data Mining Workshops, 2008 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, Pisa, Italy, pp. 862-868.
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A new feature description is used for human behaviour representation and recognition. The feature is based on Radon transforms of extracted silhouettes. Key postures are selected based on the Radon transform. Key postures are combined to construct an action template for each sequence. Linear Discriminant Analysis (LDA) is applied to the set of key postures to obtain low dimensional feature vectors. Different classification methods are used to classify each sequence. Experiments are carried out based on a publically available human behaviour database and the results are exciting. © 2008 IEEE.

Chen, Y, Wu, Q & He, X 1970, 'Motion Based Pedestrian Recognition', 2008 Congress on Image and Signal Processing, 2008 Congress on Image and Signal Processing, IEEE, Sanya, China, pp. 376-380.
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This paper proposed a method for discriminating pedestrians from rigid objects in a video. The method is a motion-based recognition of moving objects. This method is motivated by the assumptions that human beings are non-rigid and their movements are periodic. Moving objects and their skeletons are extracted. The motion cue is determined by the angle formed by the centroid point and the two bottom end points at object's skeleton. The histogram of the cue over a time period is used to determine if the object is pedestrian or not. This cue does not require any pre-built models. Neither does it need Fourier Transform to obtain the cycle of the objects. The proposed method is computation inexpensive, and it can be used for real-time video surveillance. ©2008 IEEE.

Chen, Y, Wu, Q & He, X 1970, 'Using dynamic programming to match human behavior sequences', 2008 10th International Conference on Control, Automation, Robotics and Vision, 2008 10th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE, Hanoi, Vietnam, pp. 1498-1503.
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This paper proposed a new approach for recognition and matching the human behavior sequence. Each human behavior sequence is represented by its key postures to greatly reduce the computation time. Normalization is applied to all the behavior sequences key postures for matching. A dynamic time warping (DTW) algorithm is used to perform the alignment of two time series. Experiments are carried out on an open human behavior database and exciting results have been obtained. © 2008 IEEE.

Chen, Y, Wu, Q, He, X, Du, C & Yang, J 1970, 'Extracting key postures in a human action video sequence', 2008 IEEE 10th Workshop on Multimedia Signal Processing, 2008 IEEE 10th Workshop on Multimedia Signal Processing (MMSP), IEEE, Cairns, Queensland, Australia, pp. 569-573.
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Human key posture extraction from videos will benefit video storage, video retrieval, human action recognition, human behaviour understanding and so on. This paper presents an approach to select key postures from human action sequences using 2D information. There are two steps in the proposed method. Information measurement which is a kind of global feature of a frame is used to roughly find key posture candidates. Then, a body skeleton feature which is a kind of local feature is applied to select final key postures from the candidates obtained in the first step. The experiments show that the proposed method is efficient. © 2008 IEEE.

Chen, Y, Wu, Q, He, X, Du, C & Yang, T 1970, 'Extracting Key Postures in a Human Action Video Sequence', 2008 IEEE 10TH WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING, VOLS 1 AND 2, 10th IEEE Workshop on Multimedia Signal Processing, IEEE, Cairns, AUSTRALIA, pp. 573-+.

Chunhua Du, Qiang Wu, Jie Yang & Zheng Wu 1970, 'SVM based ASM for facial landmarks location', 2008 8th IEEE International Conference on Computer and Information Technology, 2008 8th IEEE International Conference on Computer and Information Technology (CIT), IEEE, Sydney, Australia, pp. 321-326.
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Finding a new position for each landmark is a crucial step in active shape model (ASM). Mahalanobis distance minimization is used for this finding, provided there are enough training data such that the grey-level profiles for each landmark follow a multivariate Gaussian distribution. However, this condition could not be satisfied in most cases. In this paper, a new method support vector machine (SVM) based ASM (SVMBASM) is proposed. It approaches the finding task as a small sample size classification problem, and uses SVM classifier to deal with this problem. Moreover, considering imbalanced dataset which contains more negative instances(incorrect candidates for new position) than positive instances(correct candidates for new position), a multi-class classification framework is adopted. Performance evaluation on SJTU face database show that the proposed SVMBASM outperforms the original ASM in terms of the average error as well as the average frequency of convergence. © 2008 IEEE.

da Xu, RY 1970, 'A Computer Vision based Whiteboard Capture System', 2008 IEEE Workshop on Applications of Computer Vision, 2008 IEEE Workshop on Applications of Computer Vision (WACV), IEEE.
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Conventional whiteboard video capture using a static camera usually results in a poor quality. In this paper, we present an autonomous whiteboard scan and capture prototype system, which consist a pair of static and Pan-Tilt-Zoom (PTZ) cameras. The PTZ camera is used to scan the newly-updated whiteboard regions without interrupting the instructor. We will illustrate several computer vision techniques used in our system: Firstly, we present our unique camera calibration method using rough hand-drawn gridlines. Secondly, we present the image processing methods used to determine where the newly updated whiteboard region to be scanned is. Our method also accounts for the whiteboard region occlusion from the instructor.

Dong, H, Hussain, FK & Chang, E 1970, 'A Semantic Crawler Based on an Extended CBR Algorithm', ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2008 WORKSHOPS, On the Move Confederated International Conference and Workshops, Springer-verlag Berlin, Monterrey, MEXICO, pp. 1076-1085.
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A semantic (web) crawler refers to a series of web crawlers designed for harvesting semantic web content. This paper presents the frame-work of a semantic crawler that call abstract metadata from online webpages and Cluster the metadata by associating th

Dong, H, Hussain, FK & Chang, E 1970, 'A survey in semantic web technologies-inspired focused crawlers', 2008 Third International Conference on Digital Information Management, 2008 Third International Conference on Digital Information Management (ICDIM), IEEE, pp. 934-936.
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Crawlers are software which can traverse the internet and retrieve webpages by hyperlinks. In the face of the inundant spam websites, traditional web crawlers cannot function well to solve this problem. Semantic focused crawlers utilize semantic web technologies to analyze the semantics of hyperlinks and web documents. This paper briefly reviews the recent studies on one category of semantic focused crawlers - ontology-based focused crawlers, which are a series of crawlers that utilize ontologies to link the fetched web documents with the ontological concepts (topics). The purpose of this is to organize and categorize web documents, or filtering irrelevant webpages with regards to the topics. A brief comparison are made among these crawlers, from six perspectives - domain, working environment, special functions, technologies utilized, evaluation metrics and evaluation results. The conclusion with respect to this comparison is made in the final section. © 2008 IEEE.

Dong, H, Hussain, FK & Chang, E 1970, 'A Transport Service Ontology-based Focused Crawler', 2008 Fourth International Conference on Semantics, Knowledge and Grid, 2008 Fourth International Conference on Semantics, Knowledge and Grid (SKG), IEEE, pp. 49-56.
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Ontology is a technology for conceptualizing specific domain knowledge, which can provide machine-readable definitions to the severed domain. Therefore, ontology can be utilized to enhance the performance of focused crawlers, by precisely defining the crawling boundary. In this paper, we will exhibit a conceptual framework of an ontology-based focused crawler serving in the domain of transport services. Here, a transport service ontology is designed for filtering non-relevant metadata, by means of logically linking the metadata with ontological concepts. In addition, we will provide the evaluation process in order to assess the power of ontology in the focused crawler. Conclusion and further works based on our current evaluation results will be made in the final section. © 2008 IEEE.

Du, C, Wu, Q, Yang, J, He, X & Chen, Y 1970, 'Subspace Analysis Methods plus Motion History Image for Human Action Recognition', 2008 Digital Image Computing: Techniques and Applications, 2008 Digital Image Computing: Techniques and Applications, IEEE, Canberra, Australia, pp. 606-611.
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This paper proposes a new human action recognition method which deals with recognition task in a quite different way when compared with traditional methods which use sequence matching scheme. Our method compresses a sequence of an action into a Motion History Image (MHI) on which low-dimensional features are extracted using subspace analysis methods. Unlike other methods which use a sequence consisting of several frames for recognition, our method uses only a MHI per action sequence for recognition. Obviously, our method avoids the complexity as well as the large computation in sequence matching based methods. Encouraging experimental results on a widely used database demonstrate the effectiveness of the proposed method. © 2008 IEEE.

Feng, D, Sikora, T, Siu, WC, Zhang, J, Guan, L & Dugelay, JL 1970, 'Preface', 2008 IEEE 10th Workshop on Multimedia Signal Processing, 2008 IEEE 10th Workshop on Multimedia Signal Processing, IEEE.
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Geng, X, Wang, L, Li, M, Wu, Q & Smith-Miles, K 1970, 'Adaptive fusion of gait and face for human identification in video', 2008 IEEE WORKSHOP ON APPLICATIONS OF COMPUTER VISION, IEEE Workshop on Applications of Computer Vision, IEEE, CO, Copper Mt, pp. 94-+.

Geng, X, Wang, L, Li, M, Wu, Q & Smith-Miles, K 1970, 'Adaptive Fusion of Gait and Face for Human Identification in Video', 2008 IEEE Workshop on Applications of Computer Vision, 2008 IEEE Workshop on Applications of Computer Vision (WACV), IEEE, Copper Mountain, CO, USA, pp. 1-6.
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Most work on multi-biometric fusion is based on static fusion rules which cannot respond to the changes of the environment and the individual users. This paper proposes adaptive multi-biometric fusion, which dynamically adjusts the fusion rules to suit the real-time external conditions. As a typical example, the adaptive fusion of gait and face in video is studied. Two factors that may affect the relationship between gait and face in the fusion are considered, i.e., the view angle and the subject-to-camera distance. Together they determine the way gait and face are fused at an arbitrary time. Experimental results show that the adaptive fusion performs significantly better than not only single biometric traits, but also those widely adopted static fusion rules including SUM, PRODUCT, MIN, and MAX.

Ghous, H, Kennedy, PJ, Catchpoole, DR & Simoff, SJ 1970, 'Kernel-based visualisation of genes with the gene ontology', Conferences in Research and Practice in Information Technology Series, Australian Data Mining Conference, Australian Computer Society, Adelaide, pp. 133-140.
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With the development of microarray-based high- throughput technologies for examining genetic and biological information en masse, biologists are now faced with making sense of large lists of genes identi-ffed from their biological experiments. There is a vital need for \system biology' approaches which can allow biologists to see new or unanticipated potential relationships which will lead to new hypotheses and eventual new knowledge. Finding and understanding relationships in this data is a problem well suited to visualisation. We augment genes with their associated terms from the Gene Ontology and visualise them using kernel Principal Component Analysis with both specialised linear and Gaussian kernels. Our results show that this method can correctly visualise genes by their functional relationships and we describe the difference between using the linear and Gaussian kernels on the problem. © 2008, Australian Computer Society, Inc.

Hai Dong, Hussain, FK & Chang, E 1970, 'A survey in semantic search technologies', 2008 2nd IEEE International Conference on Digital Ecosystems and Technologies, 2008 2nd IEEE International Conference on Digital Ecosystems and Technologies (DEST), IEEE, Phitsanulok, Thailand, pp. 403-408.
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In this paper, we make a survey over the primary literature regarding semantic search technologies. By classifying the literature into six main categories, we review their characteristics respectively. In addition, the issues within the reviewed semantic search methods and engines are analysed and concluded based on four perspectives.

He, X, Jia, W & Wu, Q 1970, 'An approach of canny edge detection with virtual hexagonal image structure', 2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008, International Conference on Control, Automation, Robotics and Vision, IEEE, Hanoi, Vietnam, pp. 879-882.
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Edge detection plays an important role in the areas of image processing, multimedia and computer vision. Gradient-based edge detection is a straightforward method to identify the edge points in the original grey-level image. It is intuitive that, in the human vision system, the edge points always appear where the gradient magnitude assumes a maximum. Hexagonal structure is an image structure alternative to traditional square image structure. The geometrical arrangement of pixels on a hexagonal structure can be described as a collection of hexagonal pixels. Because all the existing hardware for capturing image and for displaying image are produced based on square structure, an approach that uses bilinear interpolation and tri-linear interpolation is applied for conversion between square and hexagonal structures. Based on this approach, an edge detection method is proposed. This method performs Gaussian filtering to suppress image noise and computes gradients on the hexagonal structure. The pixel edge strengths on the square structure are then estimated before Canny' edge detector is applied to determine the final edge map. The experimental results show that the proposed method improves the edge detection accuracy and efficiency. © 2008 IEEE.

He, X, Zheng, L, Qiang Wu, Wenjing Jia, Bijan Samali & Palaniswami, M 1970, 'Segmentation of characters on car license plates', 2008 IEEE 10th Workshop on Multimedia Signal Processing, 2008 IEEE 10th Workshop on Multimedia Signal Processing (MMSP), IEEE, Cairns, Australia, pp. 399-402.
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License plate recognition usually contains three steps, namely license plate detection/localization, character segmentation and character recognition. When reading characters on a license plate one by one after license plate detection step, it is crucial to accurately segment the characters. The segmentation step may be affected by many factors such as license plate boundaries (frames). The recognition accuracy will be significantly reduced if the characters are not properly segmented. This paper presents an efficient algorithm for character segmentation on a license plate. The algorithm follows the step that detects the license plates using an AdaBoost algorithm. It is based on an efficient and accurate skew and slant correction of license plates, and works together with boundary (frame) removal of license plates. The algorithm is efficient and can be applied in real-time applications. The experiments are performed to show the accuracy of segmentation. © 2008 IEEE.

Hussain, FK 1970, 'Papers in track 15 - Social networks', 2008 2nd IEEE International Conference on Digital Ecosystems and Technologies, 2008 2nd IEEE International Conference on Digital Ecosystems and Technologies (DEST), IEEE.
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Hussain, FK, Chang, E & Hussain, O 1970, 'A robust methodology for prediction of trust and reputation values', Proceedings of the 2008 ACM workshop on Secure web services, CCS08: 15th ACM Conference on Computer and Communications Security 2008, ACM, pp. 97-108.
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In this paper, we present the FC direct trust value-based decision making methodology, for making direct trust value based decisions regarding interactions in (a) a given context and during the current time slot, and (b) a given context and at a future time slot. The direct trust value-based decision making methodology models the context specific nature of trust and the dynamic nature of trust to make direct trust value-based decisions regarding interactions. Additionally in this paper, we present the FC reputation-based trust decision making methodology, for making reputation-based trust decisions regarding interactions, if direct trust value-based decisions cannot be made. The FC reputationbased trust decision making methodology can make reputationbased trust decisions regarding interactions in (a) a given context and during the current time slot, and (b) a given context and at a future time slot. Copyright 2008 ACM.

Juszczyszyn, K, Kazienko, P & Musiał, K 1970, 'Local Topology of Social Network Based on Motif Analysis', KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGS, 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, Springer Berlin Heidelberg, Zagreb, CROATIA, pp. 97-105.
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Juszczyszyn, K, Kazienko, P, Musial, K & Gabrys, B 1970, 'Temporal Changes in Connection Patterns of an Email-Based Social Network', 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, IEEE, pp. 9-12.
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Network motifs are small subgraphs that reflect local network topology and were shown to be useful for creating profiles that reveal several properties of the network. Analysis of three-node motifs (triads) was used in this paper to track the temporal changes in the structure of large social network derived from email communication between the employees of Wroclaw University of Technology. © 2008 IEEE.

Kadlec, P & Gabrys, B 1970, 'Adaptive Local Learning Soft Sensor for Inferential Control Support', 2008 International Conference on Computational Intelligence for Modelling Control & Automation, 2008 International Conference on Computational Intelligence for Modelling Control & Automation, IEEE, Vienna, AUSTRIA, pp. 243-248.
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Kadlec, P & Gabrys, B 1970, 'Gating Artificial Neural Network Based Soft Sensor', NEW CHALLENGES IN APPLIED INTELLIGENCE TECHNOLOGIES, 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Springer Berlin Heidelberg, Wroclaw, POLAND, pp. 193-202.
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Kadlec, P & Gabrys, B 1970, 'Learnt Topology Gating Artificial Neural Networks', 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008 - Hong Kong), IEEE, Hong Kong, PEOPLES R CHINA, pp. 2604-2611.
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Kazienko, P & Musiał, K 1970, 'Mining Personal Social Features in the Community of Email Users', SOFSEM 2008: THEORY AND PRACTICE OF COMPUTER SCIENCE, 34th Conference on Current Trends in Theory and Practice of Computer Science, Springer Berlin Heidelberg, Novy Smokovec, SLOVAKIA, pp. 708-719.
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Kazienko, P, Musiał, K & Juszczyszyn, K 1970, 'Recommendation of Multimedia Objects Based on Similarity of Ontologies', KNOWLEDGE - BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS, 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, Springer Berlin Heidelberg, Zagreb, CROATIA, pp. 194-201.
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Kazienko, P, Musiał, K & Kajdanowicz, T 1970, 'Profile of the social network in photo sharing systems', 14th Americas Conference on Information Systems, AMCIS 2008, pp. 2815-2826.
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People, who interact, cooperate or share common activities within the photo sharing system can be seen as a multirelational social network. The results of their activities, i.e. tags, comments, references to favourites and others that semantically connect users through multimedia objects, i.e. pictures are the crucial component of the semantic web concept. Every online sharing system provides data that can be used for extraction of different kinds of relations grouped in layers in the multirelational social network. Layers and their profiles were identified and studied on two, spanned in time, snapshots of Flickr population for better understanding of social network structure complexity. Additionally, for each of the identified layers, a separate strength measure was proposed in the paper. The experiments on the Flickr photo sharing system revealed that users are inspired by both the semantic relationships between objects they operate on and social links they have to other users. Moreover, the density and affluence of the social network grows over course of time.

Li, J, Sim, K, Liu, G & Wong, L 1970, 'Maximal Quasi-Bicliques with Balanced Noise Tolerance: Concepts and Co-clustering Applications', Proceedings of the 2008 SIAM International Conference on Data Mining, Proceedings of the 2008 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Atlanta, pp. 72-83.
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Lihong Zheng, Xiangjian He, Qiang Wu, Wenjing Jia, Samali, B & Palaniswami, M 1970, 'A hierarchically combined classifier for license plate recognition', 2008 8th IEEE International Conference on Computer and Information Technology, 2008 8th IEEE International Conference on Computer and Information Technology (CIT), IEEE, Sydney, pp. 372-377.
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High accuracy and fast recognition speed are two requirements for real-time and automatic license plate recognition system. In this paper, we propose a hierarchically combined classifier based on an Inductive Learning Based Method and an SVM-based classification. This approach employs the inductive learning based method to roughly divide all classes into smaller groups. Then the SVM method is used for character classification in individual groups. Both start from a collection of samples of characters from license plates. After a training process using some known samples in advance, the inductive learning rules are extracted for rough classification and the parameters used for SVM-based classification are obtained. Then, a classification tree is constructed for further fast training and testing processes for SVMbased classification. Experimental results for the proposed approach are given. From the experimental results, we can make the conclusion that the hierarchically combined classifier is better than either the inductive learning based classification or the SVMbased classification in terms of error rates and processing speeds. © 2008 IEEE.

Liu, G, Li, J & Wong, L 1970, 'Assessing and Predicting Protein Interactions Using Both Local and Global Network Topological Metrics', Genome Informatics 2008, Proceedings of the 19th International Conference, IMPERIAL COLLEGE PRESS.
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Liu, X, Li, J & Wang, L 1970, 'Quasi-bicliques: Complexity and Binding Pairs', Proceedings of the 14th Annual International Conference, COCOON 2008, Annual International Computing and Combinatorics Conference, Springer Berlin Heidelberg, Dalian, pp. 255-264.
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Protein-protein interactions (PPIs) are one of the most important mechanisms in cellular processes. To model protein interaction sites, recent studies have suggested to find interacting protein group pairs from large PPI networks at the first step, and then to search conserved motifs within the protein groups to form interacting motif pairs. To consider noise effect and incompleteness of biological data, we propose to use quasi-bicliques for finding interacting protein group pairs. We investigate two new problems which arise from finding interacting protein group pairs: the maximum vertex quasi-biclique problem and the maximum balanced quasi-biclique problem. We prove that both problems are NP-hard. This is a surprising result as the widely known maximum vertex biclique problem is polynomial time solvable [16]. We then propose a heuristic algorithm which uses the greedy method to find the quasi-bicliques from PPI networks. Our experiment results on real data show that this algorithm has a better performance than a benchmark algorithm for identifying highly matched BLOCKS and PRINTS motifs.

Lo, D, Khoo, S-C & Li, J 1970, 'Mining and Ranking Generators of Sequential Patterns', Proceedings of the 2008 SIAM International Conference on Data Mining, Proceedings of the 2008 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Atlanta, pp. 553-564.
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Luo, C, Cai, X & Zhang, J 1970, 'GATE: A Novel Robust Object Tracking Method Using the Particle Filtering and Level Set Method', 2008 Digital Image Computing: Techniques and Applications, 2008 Digital Image Computing: Techniques and Applications, IEEE, Canberra, ACT, pp. 378-385.
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This paper presents a novel algorithm for robust object tracking based on the particle filtering method employed in recursive Bayesian estimation and image segmentation and optimisation techniques employed in active contour models and level set methods.

Luo, C, Cai, X & Zhang, J 1970, 'Robust object tracking using the particle filtering and level set methods: A comparative experiment', 2008 IEEE 10th Workshop on Multimedia Signal Processing, 2008 IEEE 10th Workshop on Multimedia Signal Processing (MMSP), IEEE, Cairns, QLD, pp. 359-364.
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Robust visual tracking has become an important topic of research in computer vision. A novel method for robust object tracking, GATE [11], improves object tracking in complex environments using the particle filtering and the level set-based active contou

Luo, C, Zhao, Y, Cao, L, Ou, Y & Liu, L 1970, 'Outlier Mining on Multiple Time Series Data in Stock Market', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific Rim International Conference on Artificial Intelligence, Springer Berlin Heidelberg, Hanoi, Vietnam, pp. 1010-1015.
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With the dramatic increase of stock market data, traditional outlier mining technologies have shown their limitations in efficiency and precision. In this paper, an outlier mining model on stock market data is proposed, which aims to detect the anomalies from multiple complex stock market data. This model is able to improve the precision of outlier mining on individual time series. The experiments on real-world stock market data show that the proposed outlier mining model is effective and outperforms traditional technologies. © 2008 Springer Berlin Heidelberg.

Luo, C, Zhao, Y, Cao, L, Ou, Y & Zhang, C 1970, 'Exception Mining on Multiple Time Series in Stock Market', 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, IEEE, Sydney, Australia, pp. 690-693.
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This paper presents our research on exception mining on multiple time series data which aims to assist stock market surveillance by identifying market anomalies. Traditional technologies on stock market surveillance have shown their limitations to handle large amount of complicated stock market data. In our research, the Outlier Mining on Multiple time series (OMM) is proposed to improve the effectiveness of exception detection for stock market surveillance. The idea of our research is presented, challenges on the research are analyzed, and potential research directions are summarized. © 2008 IEEE.

MERIGÓ, JM & CASANOVAS, M 1970, 'DECISION MAKING WITH DEMPSTER-SHAFER BELIEF STRUCTURE USING THE 2-TUPLE LINGUISTIC REPRESENTATION MODEL', Computational Intelligence in Decision and Control, Proceedings of the 8th International FLINS Conference, WORLD SCIENTIFIC, Madrid, SPAIN, pp. 325-330.
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MERIGÓ, JM & CASANOVAS, M 1970, 'DECISION MAKING WITH DISTANCE MEASURES AND INDUCED AGGREGATION OPERATORS', Computational Intelligence in Decision and Control, Proceedings of the 8th International FLINS Conference, WORLD SCIENTIFIC, Madrid, SPAIN, pp. 483-488.
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Merigó, JM & Casanovas, M 1970, 'Fuzzy induced aggregation operators in decision making with Dempster-Shafer belief structure', ICEIS 2008 - Proceedings of the 10th International Conference on Enterprise Information Systems, 10th International Conference on Enterprise Information Systems, INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION, Barcelona, SPAIN, pp. 548-552.
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We develop a new approach for decision making with Dempster-Shafer theory of evidence when the available information is uncertain and it can be assessed with fuzzy numbers. With this approach, we are able to represent the problem without losing relevant information, so the decision maker knows exactly which are the different alternatives and their consequences. For doing so, we suggest the use of different types of fuzzy induced aggregation operators in the problem. As a result, we get new types of fuzzy induced aggregation operators such as the belief structure - fuzzy induced ordered weighted averaging (BS-FIOWA) operator. We also develop an application of the new approach in a financial decision making problem.

Merigó, JM & Casanovas, M 1970, 'The generalized hybrid averaging operator and its application in financial decision making', ICEIS 2008 - Proceedings of the 10th International Conference on Enterprise Information Systems, 10th International Conference on Enterprise Information Systems, INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION, Barcelona, SPAIN, pp. 467-471.
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We present the generalized hybrid averaging (GHA) operator. It is a new aggregation operator that generalizes the hybrid averaging (HA) operator by using the generalized mean. Then, we are able to generalize a wide range of mean operators such as the HA, the hybrid quadratic averaging (HQA), etc. The HA is an aggregation operator that includes the ordered weighted averaging (OWA) operator and the weighted average (WA). Then, with the GHA, we are able to get all the particular cases obtained by using generalized means in the OWA and in the WA such as the weighted geometric mean, the ordered weighted geometric (OWG) operator, the weighted quadratic mean (WQM), etc. We further generalize the GHA by using quasi-arithmetic means. Then, we obtain the quasi-arithmetic hybrid averaging (Quasi-HA) operator. Finally, we apply the new approach in a financial decision making problem.

MERIGÓ, JM & GIL-LAFUENTE, AM 1970, 'THE INDUCED LINGUISTIC GENERALIZED OWA OPERATOR', Computational Intelligence in Decision and Control, Proceedings of the 8th International FLINS Conference, WORLD SCIENTIFIC, Madrid, SPAIN, pp. 513-518.
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Merigó, JM & Gil-Lafuente, AM 1970, 'The linguistic generalized OWA operator and its application in strategic decision making', ICEIS 2008 - Proceedings of the 10th International Conference on Enterprise Information Systems, 10th International Conference on Enterprise Information Systems, INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION, Barcelona, SPAIN, pp. 219-224.
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We introduce the linguistic generalized ordered weighted averaging (LGOWA) operator. It is a new aggregation operator that uses linguistic information and generalized means in the OWA operator. It is very useful for uncertain situations where the available information can not be assessed with numerical values but it is possible to use linguistic assessments. This aggregation operator generalizes a wide range of aggregation operators that use linguistic information such as the linguistic generalized mean (LGM), the linguistic weighted generalized mean (LWGM), the linguistic OWA (LOWA) operator, the linguistic ordered weighted geometric (LOWG) operator and the linguistic ordered weighted quadratic averaging (LOWQA) operator. We also introduce a new type of Quasi-LOWA operator by using quasi-arithmetic means in the LOWA operator. Finally, we develop an application of the new approach. We analyze a decision making problem about selection of strategies.

Merigó, JM, Casanovas, M & Martínez, L 1970, 'A Decision Making Model Based on Dempster-Shafer Theory and Linguistic Hybrid Aggregation Operators', 2008 Eighth International Conference on Hybrid Intelligent Systems, 2008 8th International Conference on Hybrid Intelligent Systems (HIS), IEEE, pp. 180-185.
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The solving processes for decision making problems based on the use of the Dempster-Shafer (D-S) theory can be accomplished in different ways according to the necessities of each single problem. In this contribution we present a decision making scheme based on the D-S defined in a linguistic framework and then, we propose the use of an hybrid averaging operator (2-THA) that use the 2-tuple linguistic representation model. By using the 2-THA in D-S theory, we obtain a new aggregation operator: the belief structure - 2-THA (BS-2-THA) operator. We study some of its main properties and then show an illustrative example of the new approach in a decision making problem. © 2008 IEEE.

Moemeng, P, Cao, L & Zhang, C 1970, 'F-TRADE 3.0: An Agent-Based Integrated Framework for Data Mining Experiments', 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, IEEE, University of Technology, Sydney, Australia, pp. 612-615.
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Data mining researches focus on algorithms that mine valuable patterns from particular domain. Apart from the theoretical research, experiments take a vast amount of effort to build. In this paper, we propose an integrated framework that utilises a multi-agent system to support the researchers to rapidly develop experiments. Moreover, the proposed framework allows extension and integration for future researches in mutual aspects of agent and data mining. The paper describes the details of the framework and also presents a sample implementation.

Ong, C, Lu, S & Zhang, J 1970, 'An Approach for Enhancing the Results of Detecting Foreground Objects and Their Moving Shadows in Surveillance Video', 2008 Digital Image Computing: Techniques and Applications, 2008 Digital Image Computing: Techniques and Applications, IEEE, Canberra, ACT, pp. 242-249.
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Automated surveillance system is becoming increasingly important especially in the fields of computer vision and video processing. This paper describes a novel approach for improving the results of detecting foreground objects and their shadows in indoor

Otoom, AF, Gunes, H & Piccardi, M 1970, 'Automatic classification of abandoned objects for surveillance of public premises', CISP 2008: FIRST INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, VOL 4, PROCEEDINGS, International Congress on Image and Signal Processing, IEEE, Sanya, Hainan, China, pp. 542-549.
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One of the core components of any visual surveillance system is object classification, where detected objects are classified into different categories of interest. Although in airports or train stations, abandoned objects are mainly luggage or trolleys, none of the existing works in the literature have attempted to classify or recognize trolleys. In this paper, we analyzed and classified images of trolley(s), bag(s), single person(s), and group(s) of people by using various shape features with a number of uncluttered and cluttered images and applied multi-frame integration to overcome partial occlusions and obtain better recognition results. We also tested the proposed techniques on data extracted from a well-recognized and recent data set, PETS 2007 benchmark data set [16]. Our experimental results show that the features extracted are invariant to data set and classification scheme chosen. For our four-class object recognition problem, we achieved an average recognition accuracy of 70%.

Otoom, AF, Gunes, H & Piccardi, M 1970, 'Comparative performance analysis of feature sets for abandoned object classification', 2008 IEEE International Conference on Systems, Man and Cybernetics, 2008 IEEE International Conference on Systems, Man and Cybernetics (SMC), IEEE, Singapore City, Singapore, pp. 1-6.
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Accurate classification of abandoned objects is crucial in video surveillance systems. In this paper, we experiment with different validation techniques (hold-out and 10-fold cross validation), with the aim of determining which feature set proves more useful for accurate object classification in a video surveillance context (scale invariant image transform (SIFT) keypoints vs. geometric primitive features). Moreover, we show how the resulting features affect classification performance across different classifiers. We also further analyze the best performing classifier in order to have better understanding of its classification results. Objects are classified into four different categories: bag (s), person (s), trolley (s), and group (s) of people. Our experimental results show that the highest recognition accuracy and the lowest false alarm rate are achieved by building a classifier based on our proposed set of statistics of geometric primitives' features. This set of features maximizes inter-class separation and simplifies the classification process. Classification based on this set of features thus outperforms the second best approach based on SIFT keypoint histograms by providing on average 22% higher recognition accuracy and 7% lower false alarm rate.

Otoom, AF, Gunes, H, Piccardi, M & IEEE 1970, 'FEATURE EXTRACTION TECHNIQUES FOR ABANDONED OBJECT CLASSIFICATION IN VIDEO SURVEILLANCE', 2008 15TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-5, IEEE International Conference on Image Processing, IEEE, San Diego, CA, USA, pp. 1368-1371.
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We address the problem of abandoned object classification in video surveillance. Our aim is to determine (i) which feature extraction technique proves more useful for accurate object classification in a video surveillance context (scale invariant image transform (SIFT) keypoints vs. geometric primitive features), and (ii) how the resulting features affect classification accuracy and false positive rates for different classification schemes used. Objects are classified into four different categories: bag (s), person (s), trolley (s), and group (s) of people. Our experimental results show that the highest recognition accuracy and the lowest false alarm rate are achieved by building a classifier based on our proposed set of statistics of geometric primitives' features. Moreover, classification performance based on this set of features proves to be more invariant across different learning algorithms. © 2008 IEEE.

Ou, Y, Cao, L, Luo, C & Liu, L 1970, 'Mining Exceptional Activity Patterns in Microstructure Data', 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, IEEE, University of Technology, Sydney, Australia, pp. 884-887.
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Market Surveillance plays an important role in maintaining market integrity, transparency and fairnesss. The existing trading pattern analysis only focuses on interday data which discloses explicit and high-level market dynamics. In the mean time, the existing market surveillance systems are facing challenges of misuse, mis-disclosure and misdealing of information, announcement and order in one market or crossing multiple markets. Therefore, there is a crucial need to develop workable methods for smart surveillance. To deal with such issues, we propose an innovative methodology - microstructure activity pattern analysis. Based on this methodology, a case study in identifying exceptional microstructure activity patterns is carried out. The experiments on real-life stock data show that microstructure activity pattern analysis opens a new and effective means for crucially understanding and analysing market dynamics. The resulting findings such as exceptional microstructure activity patterns can greatly enhance the learning, detection, adaption and decision-making capability of market surveillance. © 2008 IEEE.

Ou, Y, Cao, L, Luo, C & Zhang, C 1970, 'Domain-Driven Local Exceptional Pattern Mining for Detecting Stock Price Manipulation', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific Rim International Conference on Artificial Intelligence, Springer Berlin Heidelberg, Hanoi,Vietnam, pp. 849-858.
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Recently, a new data mining methodology, Domain Driven Data Mining (D 3M), has been developed. On top of data-centered pattern mining, D3M generally targets the actionable knowledge discovery under domain-specific circumstances. It strongly appreciates the involvement of domain intelligence in the whole process of data mining, and consequently leads to the deliverables that can satisfy business user needs and decision-making. Following the methodology of D3M, this paper investigates local exceptional patterns in real-life microstructure stock data for detecting stock price manipulations. Different from existing pattern analysis mainly on interday data, we deal with tick-by-tick data. Our approach proposes new mechanisms for constructing microstructure order sequences by involving domain factors and business logics, and for measuring the interestingness of patterns from business concern perspective. Real-life data experiments on an exchange data demonstrate that the outcomes generated by following D3M can satisfy business expectations and support business users to take actions for market surveillance. © 2008 Springer Berlin Heidelberg.

Paisitkriangkra, S, Shen, C & Zhang, J 1970, 'Real-time Pedestrian Detection Using a Boosted Multi-layer Classifier', The Eighth International Workshop on Visual Surveillance, in conjunction with European Conference on Computer Vision (ECCV'08), 2008, IEEE International Workshop on Visual Surveillance, Institute of Electrical and Electronics Engineers, Marseille France.
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Techniques for detecting pedestrian in still images haveattached considerable research interests due to its wide applicationssuch as video surveillance and intelligent transportationsystems. In this paper, we propose a novel simplerpedestrian detector using state-of-the-art locally extractedfeatures, namely, covariance features. Covariancefeatures were originally proposed in [1, 2]. Unlike the workin [2], where the feature selection and weak classifier trainingare performed on the Riemannian manifold, we selectfeatures and train weak classifiers in the Euclidean spacefor faster computation. To this end, AdaBoost with weightedFisher linear discriminant analysis based weak classifiersare adopted. Multiple layer boosting with heterogeneousfeatures is constructed to exploit the efficiency of the Haarlikefeature and the discriminative power of the covariancefeature simultaneously. Extensive experiments show that bycombining the Haar-like and covariance features, we speedup the original covariance feature detector [2] by up to anorder of magnitude in processing time without compromisingthe detection performance. For the first time, the proposedwork enables covariance feature based pedestriandetection to work real-time.

Paisitkriangkrai, S, Chunhua Shen & Jian Zhang 1970, 'An experimental study on pedestrian classification using local features', 2008 IEEE International Symposium on Circuits and Systems (ISCAS), 2008 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, Seattle, WA, pp. 2741-2744.
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This paper presents an experimental study on pedestrian detection using state-of-the-art local feature extraction and support vector machine (SVM) classifiers. The performance of pedestrian detection using region covariance, histogram of oriented gradien

Pham, TD, Beck, D, Brandl, M & Zhou, X 1970, 'Classification of Proteomic Signals by Block Kriging Error Matching', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Berlin Heidelberg, pp. 281-288.
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One of recent advances in biotechnology offers high-throughput mass-spectrometry data for disease detection, prevention, and biomarker discovery. In fact proteomics has recently become an attractive topic of research in biomedicine. Signal processing and pattern classification techniques are inherently essential for analyzing proteomic data. In this paper the estimation method of block kriging is utilized to derive an error matching strategy for classifying proteomic signals with a particular application to the prediction of cardiovascular events using clinical mass spectrometry data. The proposed block kriging based classification technique has been found to be superior to other recently developed methods. © 2008 Springer-Verlag.

Pham, TD, Brandl, M & Beck, D 1970, 'A new approach for cancer classification using microarray gene expression data', IASTED International Symposium on Computational Biology and Bioinformatics, CBB 2008, pp. 247-253.
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We propose in this paper a new approach for classification of cancers using microarray gene expression data. The proposed method adopts the concept of fuzzy declustering strategy for vector quantization algorithm. The notion of fuzzy partition entropy is coupled with the distortion measures for classifying spectral features of microarray data. Experimental results obtained from real datasets demonstrate the effective performance of the proposed approach.

Piccardi, M, Gunes, H & Otoom, AF 1970, 'Maximum-likelihood dimensionality reduction in gaussian mixture models with an application to object classification', 2008 19th International Conference on Pattern Recognition, 2008 19th International Conference on Pattern Recognition (ICPR), IEEE, Tampa, FL, USA, pp. 1-4.
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Accurate classification of objects of interest for video surveillance is difficult due to occlusions, deformations and variable views/illumination. The adopted feature sets tend to overcome these issues by including many and complementary features; however, their large dimensionality poses an intrinsic challenge to the classification task. In this paper, we present a novel technique providing maximum-likelihood dimensionality reduction in Gaussian mixture models for classification. The technique, called hereafter mixture of maximum-likelihood normalized projections (mixture of ML-NP), was used in this work to classify a 44-dimensional data set into 4 classes (bag, trolley, single person, group of people). The accuracy achieved on an independent test set is 98% vs. 80% of the runner-up (MultiBoost/AdaBoost).

Piccardi, M, Gunes, H, Otoom, AF & IEEE 1970, 'Maximum-Likelihood Dimensionality Reduction in Gaussian Mixture Models with an Application to Object Classification', 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6, pp. 2986-2989.

Qiang Wu, Chunhua Du, Jie Yang, Xiangjian He & Yan Chen 1970, 'Pedestrian detection using hybrid statistical feature', 2008 IEEE 10th Workshop on Multimedia Signal Processing, 2008 IEEE 10th Workshop on Multimedia Signal Processing (MMSP), IEEE, Cairns, Queensland, Australia, pp. 101-106.
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A novel approach for walking people detection is proposed in this paper, which is inspired by the idea of Gait Energy Image (GEI). Unlike most of common human detection methods where usually a trained detector scans a single image and then generates a detection result, the proposed method detects people on a sequence of silhouettes which contain both appearance characteristics and motion characteristics. Thus, our method is more robust. Encouraging experimental results are obtained based on CASIA Gait Database and the additional nonhuman objects data. © 2008 IEEE.

Rahman, A, Kennedy, P, Simmonds, A & Edwards, J 1970, 'Fuzzy logic based modelling and analysis of network traffic', 2008 8th IEEE International Conference on Computer and Information Technology, 2008 8th IEEE International Conference on Computer and Information Technology (CIT), IEEE, Sydney, Australia, pp. 652-657.
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Accurate computer network traffic models are required for many network tasks such as network traffic analysis and performance optimization. Existing statistical traffic modelling techniques rely on precise mathematical analysis of extensive measured data such as packet arrival time, packet size and server-side or client-side round trip time. With the advent of high speed broadband networks, gathering an acceptable quantity of data needed for the precise representation of traffic is a difficult, time consuming, expensive and in some cases almost an impossible task. In this work we developed a fuzzy logic based traffic models using imprecise data sets that can be obtained realistically. The model include a parameter, the R parameter, which is also useful for analysis of network traffic.

Roddick, J, Li, J, Christen, P & Kennedy, PJ 1970, 'Data Mining & Analytics 20068: Proceedings of the 7th Australasian Data Mining Conference (AusDM 2008)', Data Mining & Analytics 20068: Proceedings of the 7th Australasian Data Mining Conference (AusDM 2008), Australian Data Mining Conference, Australian Computer Society, Adelaide.

Roddick, JF, Li, J, Christen, P & Kennedy, P 1970, 'Preface', Conferences in Research and Practice in Information Technology Series.

Saesue, W, Jian Zhang & Chun Tung Chou 1970, 'Hybrid frame-recursive block-based distortion estimation model for wireless video transmission', 2008 IEEE 10th Workshop on Multimedia Signal Processing, 2008 IEEE 10th Workshop on Multimedia Signal Processing (MMSP), IEEE, Cairns, QLD, pp. 774-779.
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In wireless environments, video quality can be severely degraded due to channel errors. Improving error robustness towards the impact of packet loss in error-prone network is considered as a critical concern in wireless video networking research. Data pa

Shen, C, Paisitkriangkrai, S & Zhang, J 1970, 'Face detection from few training examples', 2008 15th IEEE International Conference on Image Processing, 2008 15th IEEE International Conference on Image Processing - ICIP 2008, IEEE, San Diego, CA, pp. 2764-2767.
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Sidhu, A, Hussain, FK & Madzic, M 1970, 'Papers in track 14 - Health ecosystems', 2008 2nd IEEE International Conference on Digital Ecosystems and Technologies, 2008 2nd IEEE International Conference on Digital Ecosystems and Technologies (DEST), IEEE.
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Stoianoff, NP 1970, 'An Institutional Analysis of Intellectual Property Rights Enforcement in China', Australian Intellectual Property Academics Conference, University of Victoria, Wellington, New Zealand.

Thi, TH, Lu, S & Zhang, J 1970, 'Self-Calibration of Traffic Surveillance Camera using Motion Tracking', 2008 11th International IEEE Conference on Intelligent Transportation Systems, 2008 11th International IEEE Conference on Intelligent Transportation Systems (ITSC), IEEE, Beijing, China, pp. 304-309.
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A statistical and computer vision approach using tracked moving vehicle shapes for auto-calibrating traffic surveillance cameras is presented. Vanishing point of the traffic direction is picked up from Linear Regression of all tracked vehicle points. Pre

Thi, TH, Robert, K, Lu, S & Zhang, J 1970, 'Vehicle Classification at Nighttime Using Eigenspaces and Support Vector Machine', 2008 Congress on Image and Signal Processing, 2008 Congress on Image and Signal Processing, IEEE, Sanya, Hainan, pp. 422-426.
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A robust framework to classify vehicles in nighttime traffic using vehicle eigenspaces and support vector machine is presented. In this paper, a systematic approach has been proposed and implemented to classify vehicles from roadside camera video sequenc

Thongkam, J, Xu, G & Zhang, Y 1970, 'AdaBoost algorithm with random forests for predicting breast cancer survivability', 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008 - Hong Kong), IEEE, Hong Kong, China, pp. 3062-3069.
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Thongkam, J, Xu, G, Zhang, Y & Huang, F 1970, 'Support Vector Machine for Outlier Detection in Breast Cancer Survivability Prediction', Advanced Web and NetworkTechnologies, and Applications Lecture Notes in Computer Science, Asia Pacific Web Conference, Springer Berlin Heidelberg, Shenyang, China, pp. 99-109.
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Finding and removing misclassified instances are important steps in data mining and machine learning that affect the performance of the data mining algorithm in general. In this paper, we propose a C-Support Vector Classification Filter (C-SVCF) to identify and remove the misclassified instances (outliers) in breast cancer survivability samples collected from Srinagarind hospital in Thai- land, to improve the accuracy of the prediction models. Only instances that are correctly classified by the filter are passed to the learning algorithm. Perform- ance of the proposed technique is measured with accuracy and area under the re- ceiver operating characteristic curve (AUC), as well as compared with several popular ensemble filter approaches including AdaBoost, Bagging and ensemble of SVM with AdaBoost and Bagging filters. Our empirical results indicate that C-SVCF is an effective method for identifying misclassified outliers. This ap- proach significantly benefits ongoing research of developing accurate and robust prediction models for breast cancer survivability.

Xu, G, Zhang, Y & Yi, X 1970, 'Modelling User Behaviour for Web Recommendation Using LDA Model', 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, IEEE, Sydney, NSW, Australia, pp. 529-532.
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Xuekan Qiu, Shuqiang Jiang, Huiying Liu, Qingming Huang & Longbing Cao 1970, 'Spatial-temporal attention analysis for home video', 2008 IEEE International Conference on Multimedia and Expo, 2008 IEEE International Conference on Multimedia and Expo (ICME), IEEE, Hannover Congress Centrum, Hannover, Germany, pp. 1517-1520.
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In this paper, by considering the multiple spatial-temporal characteristic of visual perception system, we propose a novel home video attention analysis method. Firstly, each frame of the video is segmented into regions which are more informative than pixels and image blocks. Then the saliency of each region is analyzed by combining static, motion and location attentions. Finally a region based saliency map is generated for each frame, and an attention score curve is obtained for the video clip by combining attention scores of all regions in each frame. Both of them can be utilized in wide applications. This method takes advantage of the properties of human visual perception and can well present the attention information of home videos. Experimental results show the effectiveness of this approach.

Yan Chen, Qiang Wu, Xiangjian He, Chunhua Du & Jie Yang 1970, 'Extracting key postures using radon transform', 2008 23rd International Conference Image and Vision Computing New Zealand, 2008 23rd International Conference Image and Vision Computing New Zealand (IVCNZ), IEEE, Christchurch, New Zealand, pp. 1-5.
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Human key posture extraction will benefit for human action recognition, human action retrieval, human behaviour understanding and so on. This paper proposes an approach to select key postures from a human action video based on Radon transform. Cluster is used on the Radon transform to select the final key postures of human action video. The approach does not require motion extraction from the human action video. The experiments results show that the proposed approach is efficient. © 2008 IEEE.

Yan Chen, Qiang Wu, Xiangjian He, Wenjing Jia & Hintz, T 1970, 'A modified Mahalanobis distance for human detection in out-door environments', 2008 First IEEE International Conference on Ubi-Media Computing, 2008 First IEEE International Conference on Ubi-media Computing (U-Media 2008), IEEE, Lanzhou, China, pp. 243-248.
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This paper proposes a novel method for human detection from static images based on pixel structure of input images. Each image is divided into four parts, and a weight is assigned to each part of the image. In training stage, all sample images including human images and non-human images are used to construct a Mahalanobis distance map through statistically analyzing the difference between the different blocks on each original image. A projection matrix will be created with Linear Discriminant Method (LDM) based on the Mahalanobis distance map. This projection matrix will be used to transform multidimensional feature vectors into one dimensional feature domain according to a pre-calculated threshold to distinguish human figures from non-human figures. In comparison with the method without introducing weights, the proposed method performs much better. Encouraging experimental results have been obtained based on MIT dataset and our own dataset. © 2008 IEEE.

Yi Da Xu, R, Junbin Gao & Antolovich, M 1970, 'Novel methods for high-resolution facial image capture using calibrated PTZ and static cameras', 2008 IEEE International Conference on Multimedia and Expo, 2008 IEEE International Conference on Multimedia and Expo (ICME), IEEE, Hannover, GERMANY, pp. 45-48.
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Zhang, C, Cercone, N & Jain, LC 1970, 'IAT 2008 Welcome Message from Conference Chairs and Program Chair', 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, IEEE.
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Zhang, C, Cercone, N & Jain, LC 1970, 'WI 2008 Welcome Message from Conference Chairs and Program Chair', 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, IEEE.
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Zhang, H, Zhao, Y, Cao, L & Zhang, C 1970, 'Combined Association Rule Mining', Lecture Notes in Artificial Intelligence Vol 5012: Advances in Knowledge Discovery and Data Mining, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer Berlin Heidelberg, Osaka, Japan, pp. 1069-1074.
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This paper proposes an algorithm to discover novel association rules, combined association rules. Compared with conventional association rule, this combined association rule allows users to perform actions directly. Combined association rules are always organized as rule sets, each of which is composed of a number of single combined association rules. These single rules consist of non-actionable attributes, actionable attributes, and class attribute, with the rules in one set sharing the same non-actionable attributes. Thus, for a group of objects having the same non-actionable attributes, the actions corresponding to a preferred class can be performed directly. However, standard association rule mining algorithms encounter many difficulties when applied to combined association rule mining, and hence new algorithms have to be developed for combined association rule mining. In this paper, we will focus on rule generation and interestingness measures in combined association rule mining. In rule generation, the frequent itemsets are discovered among itemset groups to improve efficiency. New interestingness measures are defined to discover more actionable knowledge. In the case study, the proposed algorithm is applied into the field of social security. The combined association rule provides much greater actionable knowledge to business owners and users.

Zhang, Y & Xu, G 1970, 'Using Web Clustering for Web Communities Mining and Analysis', 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, IEEE, Sydney, NSW, Australia.
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Zhang, Z, Gunes, H, Piccardi, M & IEEE 1970, 'AN ACCURATE ALGORITHM FOR HEAD DETECTION BASED ON XYZ AND HSV HAIR AND SKIN COLOR MODELS', 2008 15TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-5, IEEE International Conference on Image Processing, IEEE, San Diego, CA, USA, pp. 1644-1647.
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Head detection in images and videos plays an important role in a wide range of computer vision and multimedia applications. In this paper, we propose a new head detection algorithm that is capable of handling significantly variable conditions in terms of viewpoint (i.e. frontal, profile, back view, from -180 degrees to +180 degrees), tilt angle (i.e. from horizontal to aerial), scale and resolution. To this aim, we built a new model for the head based on appearance distributions and shape constraints. The appearance distribution models the colors of hair and skin by sets of Gaussian mixtures in the XYZ and HSV color spaces. The shape constraint fits an elliptical model to the candidate region and compares its parameters with priors based on the human anatomy. This presents a pixel-level measurement of accuracy for the proposed algorithm both prior and after applying the spatial constraints referenced by the elliptical model. The excellent accuracy at both levels confirms the accuracy of the appearance model and the appropriateness of the spatial and topological process.

Zhao, Y, Zhang, H, Cao, L, Zhang, C & Bohlscheid, H 1970, 'Combined Pattern Mining: From Learned Rules to Actionable Knowledge', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Australasian Joint Conference on Artificial Intelligence, Springer Berlin Heidelberg, Auckland, Newzealand, pp. 393-403.
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Association mining often produces large collections of association rules that are difficult to understand and put into action. In this paper, we have designed a novel notion of combined patterns to extract useful and actionable knowledge from a large amount of learned rules. We also present definitions of combined patterns, design novel metrics to measure their interestingness and analyze the redundancy in combined patterns. Experimental results on real-life social security data demonstrate the effectiveness and potential of the proposed approach in extracting actionable knowledge from complex data. © 2008 Springer Berlin Heidelberg.

Zhao, Y, Zhang, H, Cao, L, Zhang, C & Bohlscheid, H 1970, 'Efficient Mining of Event-Oriented Negative Sequential Rules', 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, IEEE, University of Technology, Sydney, Australia, pp. 336-342.
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Traditional sequential pattern mining deals with positive sequential patterns only, that is, only frequent sequential patterns with the appearance of items are discovered. However, it is often interesting in many applications to find frequent sequential patterns with the non-occurrence of some items, which are referred to as negative sequential patterns. This paper analyzes three types of negative sequential rules and presents a new technique to find event-oriented negative sequential rules. Its effectiveness and efficiency are shown in our experiments. © 2008 IEEE.

Bruti-Liberati, N, Martini, F, Piccardi, M & Platen, E 2008, 'A hardware generator of multi-point distributed random numbers for Monte Carlo simulation'.
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ISSN 1441-8010 www.business.uts.edu.au/qfrc/research/research_papers/rp156.pdf

Xu, G 2008, 'Advanced Web and Network Technologies, and Applications', Springer Berlin Heidelberg.
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