Publications

Cao, L 2009, Data mining and multi-agent integration.
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Data Mining and Multi-agent Integration presents cutting-edge research, applications and solutions in data mining, and the practical use of innovative information technologies written by leading international researchers in the field. Topics examined include: Integration of multiagent applications and data mining Mining temporal patterns to improve agents behavior Information enrichment through recommendation sharing Automatic web data extraction based on genetic algorithms and regular expressions A multiagent learning paradigm for medical data mining diagnostic workbench A multiagent data mining framework Streaming data in complex uncertain environments Large data clustering A multiagent, multi-objective clustering algorithm Interactive web environment for psychometric diagnostics Anomalies detection on distributed firewalls using data mining techniques Automated reasoning for distributed and multiple source of data Video contents identification Data Mining and Multi-agent Integration is intended for students, researchers, engineers and practitioners in the field, interested in the synergy between agents and data mining. This book is also relevant for readers in related areas such as machine learning, artificial intelligence, intelligent systems, knowledge engineering, human-computer interaction, intelligent information processing, decision support systems, knowledge management, organizational computing, social computing, complex systems, and soft computing. © Springer Science+Business Media, LLC 2009. All rights reserved.

Cao, L, Yu, PS, Zhang, C & Zhang, H 2009, Data mining for business applications.
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Data Mining for Business Applications presents state-of-the-art data mining research and development related to methodologies, techniques, approaches and successful applications. The contributions of this book mark a paradigm shift from "data-centered pattern mining" to "domain-driven actionable knowledge discovery (AKD)" for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future data mining research and development in the dialogue between academia and business. Part I centers on developing workable AKD methodologies, including: domain-driven data mining post-processing rules for actions domain-driven customer analytics the role of human intelligence in AKD maximal pattern-based cluster ontology mining Part II focuses on novel KDD domains and the corresponding techniques, exloring the mining of emergent areas and domains such as: social security data community security data gene sequences mental health information traditional Chinese medicine data cancer related data blog data sentiment information web data procedures moving object trajectories land use mapping higher education data flight scheduling algorithmic asset management Researchers, practitioners and university students in the areas of data mining and knowledge discovery, knowledge engineering, human-computer interaction, artificial intelligence, intelligent information processing, decision support systems, knowledge management, and KDD project management are sure to find this a practical and effective means of enhancing their understanding of and using data mining in their own projects. © 2009 Springer Science+Business Media, LLC All rights reserved.

Tao, D, Xu, D & Li, X 2009, Semantic mining technologies for multimedia databases.
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Multimedia searching and management have become popular due to demanding applications and competition among companies. Despite the increase in interest, there is no existing book covering basic knowledge on state-of-the-art techniques within the field. Semantic Mining Technologies for Multimedia Databases provides an introduction to the most recent techniques in multimedia semantic mining necessary to researchers new to the field. This book serves as an important reference in multimedia for academicians, multimedia technologists and researchers, and academic libraries. © 2009 by IGI Global. All rights reserved.

Cao, L 2009, 'Actionable Knowledge Discovery' in Mehdi Khosrow-Pour (ed), Encyclopedia of Information Science and Technology, IGI Global, Hershey, PA, USA, pp. 8-13.
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Actionable knowledge discovery is selected as one of the greatest challenges (Ankerst, 2002; Fayyad, Shapiro, & Uthurusamy, 2003) of next-generation knowledge discovery in database (KDD) studies (Han & Kamber, 2006). In the existing data mining, often mined patterns are nonactionable to real user needs. To enhance knowledge actionability, domain-related social intelligence is substantially essential (Cao et al., 2006b). The involvement of domain-related social intelligence into data mining leads to domaindriven data mining (Cao & Zhang, 2006a, 2007a), which complements traditional data-centered mining methodology. Domain-related social intelligence consists of intelligence of human, domain, environment, society and cyberspace, which complements data intelligence. The extension of KDD toward domain-driven data mining involves many challenging but promising research and development issues in KDD. Studies in regard to these issues may promote the paradigm shift of KDD from data-centered interesting pattern mining to domain-driven actionable knowledge discovery, and the deployment shift from simulated data set-based to real-life data and business environment-oriented as widely predicted.

Cao, L 2009, 'Developing Actionable Trading Strategies' in Jain, LC & Nguyen, NT (eds), Knowledge Processing and Decision Making in Agent-Based Systems, Springer, Berlin, Germany, pp. 193-215.
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Actionable trading strategies for trading agents determine the potential of the simulated models in real-life markets. The development of actionable strategies is a non-trivial task, which needs to consider real-life constraints and organizational factors in the market. In this paper, we first analyze such constraints on developing actionable trading strategies. Further we propose an actionable trading strategy development framework. These points are deployed into developing a series of actionable trading strategies through optimizing, enhancing, discovering and integrating actionable trading strategies. We demonstrate working case studies in market data. These approaches and their performance are evaluated from both technical and business perspectives. Actionable trading strategies have potential to supporting smart trading decision for brokerage firms and financial companies.

Cao, L 2009, 'Introduction to Agent Mining Interaction and Integration' in Longbing Cao (ed), Data Mining and Multi-agent Integration, Springer, New York, USA, pp. 3-36.
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In recent years, more and morc researchers have been involved in research on both agent technology and data mining. A clear disciplinary effort has been activa ted toward removing the boundary between them, that is the interaction and integrati on be tween agent technology and data mining. We refer this to agent mining as a new area. The marriage of agents and data mining is driven by challenges faced by both communities, and the need of developing more advanced intelligence, in formation processi ng and systems. This chapter presents an overall picture of agent mining from the perspective of positioning it as an emerging area. We summarize the main driving forces, compleme ntary essence, di sci plinary framework , applications, case studies, and trends and directions, as well as brief observation on agent-driven data mining, data mining-driven agents, and mutual issues in agent mini ng. Arguably, we draw the following conclusions: (I) agent mining emerges as a new area in the scientific fam il y, (2) both agent technology and data mining can greatly benefit from agent mining, (3) it is very promising to resu lL in additional advancement in intelligent information processing and systems. However, as a new open area, there are many issues waiting for research and development from theoretical, technological and practical perspectives.

Cao, L 2009, 'Preface' in Cao, L (ed), Data Mining and Multi-agentIntegration, Springer, pp. v-vii.

Cao, L, Yu, P, Zhang, C & Zhang, H 2009, 'Introduction to Domain Driven Data Mining' in Cao, L, Yu, PS, Zhang, C & Zhang, H (eds), Data Mining for Business Applications, Springer, New York, USA, pp. 3-10.
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Data Mining for Business Applications presents state-of-the-art data mining research and development related to methodologies, techniques, approaches and successful applications. The contributions of this book mark a paradigm shift from "data-centered pattern mining" to "domain-driven actionable knowledge discovery (AKD)" for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future data mining research and development in the dialogue between academia and business.

Cao, L, Yu, PS, Zhang, C & Zhang, H 2009, 'Preface' in Cao, L, Yu, PS, Zhang, C & Zhang, H (eds), Data Mining for Business Applications, Springer, pp. v-vi.

Chiarella, C, Dieci, R & He, X 2009, 'Heterogeneity, market mechanisms and asset price dynamics' in Hens, T & Schenk-Hoppe, KR (eds), Handbook of Financial Markets: Dynamics and Evolution, Elsevier, USA, pp. 277-344.
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Dong, G, Li, J, Liu, G & Wong, L 2009, 'Mining conditional contrast patterns' in Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, pp. 294-310.
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This chapter considers the problem of "conditional contrast pattern mining." It is related to contrast mining, where one considers the mining of patterns/models that contrast two or more datasets, classes, conditions, time periods, and so forth. Roughly speaking, conditional contrasts capture situations where a small change in patterns is associated with a big change in the matching data of the patterns. More precisely, a conditional contrast is a triple (B, F1, F2) of three patterns; B is the condition/context pattern of the conditional contrast, and F 1 and F 2 are the contrasting factors of the conditional contrast. Such a conditional contrast is of interest if the difference between F 1 and F 2 as itemsets is relatively small, and the difference between the corresponding matching dataset of B∪F 1 and that of B∪F2 is relatively large. It offers insights on "discriminating" patterns for a given condition B. Conditional contrast mining is related to frequent pattern mining and analysis in general, and to the mining and analysis of closed pattern and minimal generators in particular. It can also be viewed as a new direction for the analysis (and mining) of frequent patterns. After formalizing the concepts of conditional contrast, the chapter will provide some theoretical results on conditional contrast mining. These results (i) relate conditional contrasts with closed patterns and their minimal generators, (ii) provide a concise representation for conditional contrasts, and (iii) establish a so-called dominance-beam property. An efficient algorithm will be proposed based on these results, and experiment results will be reported. Related works will also be discussed. © 2009, IGI Global.

Feng, M, Li, J, Dong, G & Wong, L 2009, 'Maintenance of frequent patterns: A survey' in Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, pp. 273-293.
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This chapter surveys the maintenance of frequent patterns in transaction datasets. It is written to be accessible to researchers familiar with the field of frequent pattern mining. The frequent pattern maintenance problem is summarized with a study on how the space of frequent patterns evolves in response to data updates. This chapter focuses on incremental and decremental maintenance. Four major types of maintenance algorithms are studied: Apriori-based, partition-based, prefix-tree-based, and concise-representation-based algorithms. The authors study the advantages and limitations of these algorithms from both the theoretical and experimental perspectives. Possible solutions to certain limitations are also proposed. In addition, some potential research opportunities and emerging trends in frequent pattern maintenance are also discussed1. © 2009, IGI Global.

Gao, X, Xiao, B, Tao, D & Li, X 2009, 'A Comparative Study of Three Graph Edit Distance Algorithms' in Abraham, A, Hassanien, AE & Snasel, V (eds), Studies in Computational Intelligence Vol 205: Foundations of Computational Intelligence vol 5, Springer, Berlin, pp. 223-242.
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Graph edit distance (GED) is widely applied to similarity measurement of graphs in inexact graph matching. Due to the difficulty of defining cost functions reasonably, we do research oil two GED algorithms without cost function definition: the first is c

Geweke, J 2009, 'The SETAR Model of Tong and Lim and Advances in Computation' in Chan, KS (ed), Exploration of a Nonlinear World: An Appreciation of Howell Tong's Contributions to Statistics., World Scientific, Singapore, pp. 85-94.
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This discussion revisits Tong and Lim's seminal 1980 paper on the SETAR model in the context of advances in computation since that time. Using the Canadian lynx data set from that paper, it compares exact maximum likelihood estimates with those in the original paper. It illustrates the application of Bayesian MCMC methods, developed in the intervening years, to this model and data set. It shows that SETAR is a limiting case of mixture of experts models and studies the application of one variant of those models to the lynx data set. The application is successful, despite the small size of the data set and the complexity of the model. Predictive likelihood ratios favor Tong and Lim's original model.

McNicholas, PD & Zhao, Y 2009, 'Association Rules: An Overview' in Zhao, Y, Zhang, C & Cao, L (eds), Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, IGI Global, USA, pp. 1-10.
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Association rules present one of the most versatile techniques for the analysis of binary data, with applications in areas as diverse as retail, bioinformatics, and sociology. In this chapter, the origin of association rules is discussed along with the functions by which association rules are traditionally characterised. Following the formal definition of an association rule, these functions â support, confidence and lift â are defined and various methods of rule generation are presented, spanning 15 years of development. There is some discussion about negations and negative association rules and an analogy between association rules and 2Ã2 tables is outlined. Pruning methods are discussed, followed by an overview of measures of interestingness. Finally, the post-mining stage of the association rule paradigm is put in the context of the preceding stages of the mining process.

Moemeng, C, Gorodetsky, V, Zuo, Z, Yang, Y & Zhang, C 2009, 'Agent-Based Distributed Data Mining: A Survey' in Longbing Cao (ed), Data Mining and Multi-agent Integration, Springer, New York, USA, pp. 1-12.
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Distributed data mining is originated from the need of mining over decentralised data sources. Data mining techniques involving in such complex environment must encounter great dynamics due to changes in the system can affect the overall performance of the system. Agent computing whose aim is to deal with complex systems has revealed opportunities to improve distributed data mining systems in a number of ways. This paper surveys the integration of multi-agent system and distributed data mining, also known as agent-based distributed data mining, in terms of significance, system overview, existing systems, and research trends.

Stoianoff, NP 2009, 'The Recognition of Traditional Knowledge under Australian Biodiscovery Regimes: Why Bother with Intellectual Property Rights?' in Antons, C (ed), Traditional Knowledge, Traditional Cultural Expressions and Intellectual Property Law in the Asia-Pacific Region, Kluwer Law International, The Hague, Netherlands, pp. 293-311.
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Ubaudi, FA, Kennedy, PJ, Catchpoole, DR, Guo, D & Simoff, SJ 2009, 'Microarray data mining: selecting trustworthy genes with gene feature ranking' in Data Mining for Business Applications, Springer, New York, USA, pp. 159-168.
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Gene expression datasets used in biomedical data mining frequently have two characteristics: they have many thousand attributes but only relatively few sample points and the measurements are noisy. In other words, individual expression measurements may be untrustworthy. Gene Feature Ranking (GFR) is a feature selection methodology that addresses these domain specific characteristics by selecting features (i.e. genes) based on two criteria: (i) how well the gene can discriminate between classes of patient and (ii) the trustworthiness of the microarray data associated with the gene. An example from the pediatric cancer domain demonstrates the use of GFR and compares its performance with a feature selection method that does not explicitly address the trustworthiness of the underlying data.

Wu, S, Zhao, Y, Zhang, H, Zhang, C, Cao, L & Bohlscheid, H 2009, 'Debt Detection in Social Security by Adaptive Sequence Classification' in Karagiannis, D & Jin, Z (eds), Knowledge Science, Engineering and Management, Springer, Germany, pp. 192-203.
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Debt detection is important for improving payment accuracy in social security. Since debt detection from customer transaction data can be generally modelled as a fraud detection problem, a straightforward solution is to extract features from transaction sequences and build a sequence classifier for debts. For long-running debt detections, the patterns in the transaction sequences may exhibit variation from time to time, which makes it imperative to adapt classification to the pattern variation. In this paper, we present a novel adaptive sequence classification framework for debt detection in a social security application. The central technique is to catch up with the pattern variation by boosting discriminative patterns and depressing less discriminative ones according to the latest sequence data.

Ye, Y, He, S, Li, J, Jia, W & Wu, Q 2009, 'Image Transformation on Hexagonal Structure Based on Conversion between 1D and 2D Coordinates' in Wen, P, Li, Y, Polkowski, L, Yao, Y, Tsumoto, S & Wang, G (eds), Rough Sets and Knowledge Technology, Springer, Berlin, Germany, pp. 571-578.
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Spiral Architecture, a hexagonal image structure is a novel and powerful approach to machine vision system. The pixels on Spiral architecture are geometrically arranged using a 1D (Spiral) addressing scheme in an ascending order along a spiral-like curve. Spiral addition and Spiral multiplication are defined based on the Spiral addresses on Spiral Architecture. These two fundamental operations result in fast and easy translation, rotation and separation on images, and hence play very important roles for image processing on Spiral Architecture. Moreover, 2D coordinates according to rows and columns defined on Spiral Structure provide a good mapping to the ordinary 2D coordinates defined on the common square image structure. Therefore, how to convert the 1D Spiral addresses from and to the 2D coordinates on Spiral Architecture has become very important to apply the theory developed on a hexagonal image structure for image processing (e.g., rotation). In this paper, we perform a fast way to correctly locate any hexagonal pixel when its Spiral address is known, and compute the Spiral address of any hexagonal pixel when its location is known. As an illustration of the use of conversions, we demonstrate the accurate image translation and rotation using experimental results.

Zhang, Y & Xu, G 2009, 'Singular Value Decomposition' in Encyclopedia of Database Systems, pp. 2657-2658.
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Zhao, Y, Cao, L, Zhang, H & Zhang, C 2009, 'Data Clustering' in Ferraggine, VE, Doorn, JH & Rivero, LC (eds), Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Tr, IGI Global, USA, pp. 562-572.
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Clustering is one of the most important techniques in data mining. This chapter presents a survey of popular approaches for data clustering, including well-known clustering techniques, such as partitioning clustering, hierarchical clustering, density-based clustering and grid-based clustering, and recent advances in clustering, such as subspace clustering, text clustering and data stream clustering. The major challenges and future trends of data clustering will also be introduced in this chapter. The remainder of this chapter is organized as follows. The background of data clustering will be introduced in Section 2, including the definition of clustering, categories of clustering techniques, features of good clustering algorithms, and the validation of clustering. Section 3 will present main approaches for clustering, which range from the classic partitioning and hierarchical clustering to recent approaches of bi-clustering and semisupervised clustering. Challenges and future trends will be discussed in Section 4, followed by the conclusions in the last section.

Zhao, Y, Zhang, H, Cao, L, Bohlscheid, H, Ou, Y & Zhang, C 2009, 'Data Mining Applications in Social Security' in Cao, L, Yu, PS, Zhang, C & Zhang, H (eds), Data Mining for Business Applications, Springer, New York, USA, pp. 81-96.
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This chapter presents four applications of data mining in social security. The first is an application of decision tree and association rules to find the demographic patterns of customers. Sequence mining is used in the second application to find activity sequence patterns related to debt occurrence. In the third application, combined association rules are mined from heterogeneous data sources to discover patterns of slow payers and quick payers. In the last application, clustering and analysis of variance are employed to check the effectiveness of a new policy.

Ackerberg, D, Geweke, J & Hahn, J 2009, 'Comments on 'Convergence Properties Likelihood of Computed Dynamic Models'', Econometrica, vol. 77, no. 6, pp. 2009-2017.
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We show by counterexample that Proposition 2 in Fernández-Villaverde, Rubio- Ramírez, and Santos (Econometrica (2006), 74, 93119) is false. We also show that even if their Proposition 2 were corrected, it would be irrelevant for parameter estimates. As a more constructive contribution, we consider the effects of approximation error on parameter estimation, and conclude that second order approximation errors in the policy function have at most second order effects on parameter estimates.

Cao, L & He, X 2009, 'Developing actionable trading agents', Knowledge And Information Systems, vol. 18, no. 2, pp. 183-192.
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Trading agents are useful for developing and back-testing quality trading strategies to support smart trading actions in the market. However, most of the existing trading agent research oversimplifies trading strategies, and focuses on simulated ones. As a result, there exists a big gap between the deliverables and business needs when the developed strategies are deployed into the real life. Therefore, the actionable capability of developed trading agents is often very limited. This paper for the first time introduces effective approaches for optimizing and integrating multiple classes of strategies through trading agent collaboration. An integration and optimization approach is proposed to identify optimal trading strategy in each category, and further integrate optimal strategies crossing classes. Positions associated with these optimal strategies are recommended for trading agents to take actions in the market. Extensive experiments on a large quantity of real-life market data show that trading agents following the recommended strategies have great potential to obtain high benefits while low costs. This verifies that it is promising to develop trading agents toward workable and satisfying business needs.

Cao, L & Yu, P 2009, 'Behavior Informatics: An Informatics Perspective for Behavior Studies', IEEE Computational Intelligence Bulletin, vol. 10, no. 1, pp. 6-11.
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Behavior is increasingly recognized as a key entity in business intelligence and problem-solving. Even though behavior analysis has been extensively investigated in social sciences and behavior sciences, in which qualitative and psychological methods have been the main means, nevertheless to conduct formal representation and deep quantitative analysis it is timely to investigate behavior from the informatics perspective. This article highlights the basic framework of behavior informatics, which aims to supply methodologies, approaches, means and tools for formal behavior modeling and representation, behavioral data construction, behavior impact modeling, behavior network analysis, behavior pattern analysis, behavior presentation, management and use. Behavior informatics can greatly complement existing studies in terms of providing more formal, quantitative and computable mechanisms and tools for deep understanding and use.

Cao, L, Dai, RW & Zhou, M 2009, 'Metasynthesis: M-Space, M-Interaction, and M-Computing for Open Complex Giant Systems', IEEE Transactions On Systems Man And Cyberneti..., vol. 39, no. 5, pp. 1007-1021.
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The studies of complex systems have been recognized as one of the greatest challenges for current and future science and technology. Open complex giant systems (OCGSs) are a family of specially complex systems with system complexities such as openness, human involvement, societal characteristic, and intelligence emergence. They greatly challenge multiple disciplines such as system sciences, system engineering, cognitive sciences, information systems, artificial intelligence, and computer sciences. As a result, traditional problem-solving methodologies can help deal with them but are far from a mature solution methodology. The theory of qualitative-to-quantitative metasynthesis has been proposed as a breakthrough and effective methodology for the understanding and problem solving of OCGSs. In this paper, we propose the concepts of M-Interaction, M-Space, and M-Computing which are three key components for studying OCGS and building problem-solving systems. M-Interaction forms the main problem-solving mechanism of qualitative-to-quantitative metasynthesis; M-Space is the OCGS problem-solving system embedded with M-Interactions, while M-Computing consists of engineering approaches to the analysis, design, and implementation of M-Space and M-Interaction. We discuss the theoretical framework, problem-solving process, social cognitive evolution, intelligence emergence, and pitfalls of certain types of cognitions in developing M-Space and M-Interaction from the perspectives of cognitive sciences and social cognitive interaction. These can help one understand complex systems and develop effective problem-solving methodologies.

Cao, L, Gorodetsky, V & Mitkas, P 2009, 'Agent Mining: The Synergy of Agents and Data Mining', IEEE Intelligent Systems, vol. 24, no. 3, pp. 64-72.
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Autonomous agents and multiagent systems (or agents) and data mining and knowledge discovery (or data mining) are two of the most active areas in information technology. Ongoing research has revealed a number of intrinsic challenges and problems facing each area, which can't be addressed solely within the confines of the respective discipline. A profound insight of bringing these two communities together has unveiled a tremendous potential for new opportunities and wider applications through the synergy of agents and data mining. With increasing interest in this synergy, agent mining is emerging as a new research field studying the interaction and integration of agents and data mining. In this paper, we give an overall perspective of the driving forces, theoretical underpinnings, main research issues, and application domains of this field, while addressing the state-of-the-art of agent mining research and development. Our review is divided into three key research topics: agent-driven data mining, data mining-driven agents, and joint issues in the synergy of agents and data mining. This new and promising field exhibits a great potential for groundbreaking work from foundational, technological and practical perspectives.

Cao, L, Gorodetsky, V & Mitkas, PA 2009, 'Agents and data mining', IEEE Intelligent Systems, vol. 24, no. 3, pp. 14-15.

Cao, L, Gorodetsky, V & Mitkas, PA 2009, 'Guest Editors' Introduction: Agents and Data Mining', IEEE Intelligent Systems, vol. 24, no. 3, pp. 14-15.
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On top of two active research streams, agents and data mining, a most recent and exciting trend is their interaction and integration. Agent mining has emerged as a very promising field due to its unique contributions to complementary and innovative methodologies, techniques, and applications for complex problem-solving. This editorial summarizes the structure of this special issue.

Cao, LQ, Wang, XL, Wang, Q, Xue, P, Jiao, XY, Peng, HP, Lu, HW, Zheng, Q, Chen, XL, Huang, XH, Fu, XH & Chen, JS 2009, 'Rosiglitazone sensitizes hepatocellular carcinoma cell lines to 5-fluorouracil antitumor activity through activation of the PPARγ signaling pathway', Acta Pharmacologica Sinica, vol. 30, no. 9, pp. 1316-1322.
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Aim: Resistance to 5-fluorouracil (5-FU) is a major cause of chemotherapy failure in advanced hepatocellular carcinoma (HCC).Rosiglitazone, a peroxisome proliferator-activated receptor γ (PPARγ) agonist, has a crucial role in growth inhibition and induction of apoptosis in several carcinoma cell lines. In this study, we examine rosiglitazone-induced sensitization of HCC cell lines (BEL-7402 and Huh-7 cells) to 5-FU.Methods:The 3-(4,5-dimethylthiazol-2- yl)-2,5-diphenyl tetrazolium bromide (MTT) assay was used to evaluate cell viability. Western blotting analysis was performed to detect the protein expression (PPARγ, PTEN, and COX-2) in BEL-7402 cells. Immunohistochemistry staining was used to examine the expression of PTEN in 100 advanced HCC tissues and paracancerous tissues. In addition, small interfering RNA was used to suppress PPARγ, PTEN, and COX-2 expression.Results: Rosiglitazone facilitates the anti-tumor effect of 5-FU in HCC cell lines, which is mediated by the PPARγ signaling pathway. Activation of PPARγ by rosiglitazone increases PTEN expression and decreases COX-2 expression. Since distribution of PTEN in HCC tissues is significantly decreased compared with the paracancerous tissue, over-expression of PTEN by rosiglitazone enhances 5-FU-inhibited cell growth of HCC. Moreover, down-regulation of COX-2 is implicated in the synergistic effect of 5-FU.Conclusion:Rosiglitazone sensitizes hepatocellular carcinoma cell lines to 5-FU antitumor activity through the activation of PPARγ. The results suggest potential novel therapies for the treatment of advanced liver cancer. © 2009 CPS and SIMM.

Chen, JS, Wang, Q, Fu, XH, Huang, XH, Chen, XL, Cao, LQ, Chen, LZ, Tan, HX, Li, W, Bi, J & Zhang, LJ 2009, 'Involvement of PI3K/PTEN/AKT/mTOR pathway in invasion and metastasis in hepatocellular carcinoma: Association with MMP-9', Hepatology Research, vol. 39, no. 2, pp. 177-186.
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Aim: To investigate the status of Phosphatidylinositol 3-kinase (PI3K)/PTEN/AKT/mammalian target of rapamycin (mTOR) pathway and its correlation with clinicopathological features and matrix metalloproteinase-2, -9 (MMP-2, 9) in human hepatocellular carcinoma (HCC). Methods: PTEN, Phosphorylated AKT (p-AKT), Phosphorylated mTOR (p-mTOR), MMP-2, MMP-9 and Ki-67 expression levels were evaluated by immunohistochemistry on tissue microarrays containing 200 HCCs with paired adjacent non-cancerous liver tissues. PTEN, MMP-2 and MMP-9 mRNA levels were determined by real-time RT-PCR in 36 HCCs. The relationships between PI3K/PTEN/AKT/mTOR pathway and clinicopathological factors and MMP-2, 9 were analyzed in HCC. Results: In HCC, PTEN loss and overexpression of p-AKT and p-mTOR were associated with tumor grade, intrahepatic metastasis, vascular invasion, TNM stage and high Ki-67 labeling index (P < 0.05). PTEN loss was correlated with p-AKT, p-mTOR and MMP-9 overexpression. Furthermore, PTEN and MMP-2, 9 mRNA levels were down-regulated and up-regulated in HCC compared with paired non-cancerous liver tissues, respectively (P < 0.01). PTEN, MMP-2 and MMP-9 mRNA levels were correlated with tumor stage and metastasis. There was an inverse correlation between PTEN and MMP-9 mRNA expression. However, PI3K/PTEN/AKT/mTOR pathway was not correlated with MMP-2. Conclusions: PI3K/PTEN/AKT/mTOR pathway, which is activated in HCC, is involved in invasion and metastasis through up-regulating MMP-9 in HCC. © 2009 The Japan Society of Hepatology.

Chen, X, Liu, W & Williams, M 2009, 'Introduction: Practical Cognitive Agnets and Robots', Autonomous Agents And Multi-Agent Systems, vol. 19, no. 3, pp. 245-247.

Christensen, BL & Williams, M 2009, 'Assessing postprandial glucose using 1,5-anhydroglucitol: An integrative literature review', Journal of the American Academy of Nurse Practitioners, vol. 21, no. 10, pp. 542-548.
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Purpose: Recent studies have determined postprandial blood glucose is an independent risk factor for macrovascular complications. This risk exists, despite having HbA1C results within acceptable ranges for diabetes. 1,5-Anhydroglucitol (1,5AG) has been proposed as an appropriate indicator to detect and screen for postprandial hyperglycemia (PPHG). This review discusses the efficacy of 1,5AG to predict PPHG in order to reveal those who may be at risk for macrovascular complications. Data Sources: An electronic search was conducted from 2003 to 2008 in the following databases: Medline, CINAHL, Health Source: Nursing/Academic Edition, and Pre-CINAHL. Any articles relating to 1,5AG as a marker for PPHG were used. The search was limited to any human research articles published in English. All articles were reviewed for additional relevant studies. Conclusions: 1,5AG was found to be a reliable indicator of PPHG, even when HbA1C levels were within target ranges. 1,5AG may be a simple and effective tool for primary care providers to identify those at risk for macrovascular complications, who would otherwise go unnoticed if assessed by HbA1C alone. © 2009 American Academy of Nurse Practitioners.

Clark, C, Kawachi, I, Ryan, LM, Ertel, K, Fay, M & Berkman, L 2009, 'Perceived Neighborhood Safety And Incident Mobility Disability Among Elders: The Hazards Of Poverty', BMC Public Health, vol. 9, no. 162, pp. 0-0.
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Collins, C, Fister, KR, Key, B & Williams, M 2009, 'Blasting neuroblastoma using optimal control of chemotherapy', Mathematical Biosciences and Engineering, vol. 6, no. 3, pp. 451-467.
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A mathematical model is used to investigate the effectiveness of the chemotherapy drug Topotecan against neuroblastoma. Optimal control theory is applied to minimize the tumor volume and the amount of drug utilized. The model incorporates a state constraint that requires the level of circulating neutrophils (white blood cells that form an integral part of the immune system) to rabove an acceptable value. The treatment schedule is designed to simultaneously satisfy this constraint and achieve the best results in fighting the tumor. Existence and uniqueness of the solution of the optimality system, which is the state system coupled with the adjoint system, is established. Numerical simulations are given to demonstrate the behavior of the tumor and the immune system components represented in the model.

Deng, C, Gao, X, Li, X & Tao, D 2009, 'A local Tchebichef moments-based robust image watermarking', Signal Processing, vol. 89, no. 8, pp. 1531-1539.
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Protection against geometric distortions and common image processing operations with blind detection becomes a much challenging task in image watermarking. To achieve this, in this paper we propose a content-based watermarking scheme that combines the invariant feature extraction with watermark embedding by using Tchebichef moments. Harris-Laplace detector is first adopted to extract feature points, and then non-overlapped disks centered at feature points are generated. These disks are invariant to scaling and translation distortions. For each disk, orientation alignment is then performed to achieve rotation invariant. Finally, the watermark is embedded in magnitudes of Tchebichef moments of each disk via dither modulation to realize the robustness to common image processing operations and the blind detection. Thorough simulation results obtained by using the standard benchmark, Stirmark, demonstrate that the proposed method is robust against various geometric distortions as well as common image processing operations and outperforms representative image watermarking schemes.

Dong, H, Hussain, FK & Chang, E 2009, 'A QoS-based service retrieval methodology for digital ecosystems', International Journal of Web and Grid Services, vol. 5, no. 3, pp. 261-283.
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The emergence of the World Wide Web and its influence on the fields of industry, commerce, healthcare and so on, has led to an innovative, dynamic, open, collaborative and interactive environment - the digital ecosystem. Whereas service plays an importan

Du, C, Yang, J, Wu, Q & Zhang, TT 2009, 'Face Recognition Using Message Passing Based Clustering Method', Journal of Visual Communication, vol. 20, no. 8, pp. 608-613.
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Traditional subspace analysis methods are inefficient and tend to be affected by noise as they compare the test image to all training images, especifically when there are large numbers of training images. To solve such problem, we propose a fast face recognition (FR) technique called APLDA by combining a novel clustering method affinity propagation (AP) with linear discriminant analysis (LDA). By using AP on the reduced features derived from LDA, a representative face image for each subject can be reached. Thus, our APLDA uses only the representative images rather than all training images for identification. Obviously, APLDA is much more computationally efficient than Fisherface. Also, unlike Fisherface who uses pattern classifier for identification, APLDA performs the identification using AP once again to cluster the test image into one of the representative images. Experimental results also indicate that APLDA outperforms Fisherface in terms of recognition rate.

Forno, E, Onderdonk, AB, McCracken, J, Litonjua, AA, Laskey, D, Delaney, ML, DuBois, AM, Gold, DR, Ryan, LM, Weiss, ST & Celedon, JC 2009, 'Diversity of the Gut Microbiota and Eczema in Infants', AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, vol. 179.

Gao, X, An, L, Li, X & Tao, D 2009, 'Reversibility improved lossless data hiding', Signal Processing, vol. 89, no. 10, pp. 2053-2065.
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Recently, lossless data hiding has attracted increasing interests. As a reversible watermark scheme, the host media and hidden data should be recovered without distortion. A latest lossless data hiding technique based on image blocking and block classification has achieved good performance for image authentication. However, this method cannot always fully restore all the blocks of host images and watermarks. For this purpose, we propose an improved algorithm, which is characterized by two aspects. First, a block skipping scheme (BSS) is developed for the host blocks selection to embed watermark; secondly, the embedding level is modified by a novel parameter model to guarantee that the host blocks can be recovered without distortion as well as the embedded data. Extensive experiments conducted on standard grayscale images, medical images, and color images have demonstrated the effectiveness of the improved lossless data hiding scheme.

Gao, X, Li, X, Fen, J & Tao, D 2009, 'Shot-based video retrieval with optical flow tensor and HMMs', Pattern Recognition Letters, vol. 30, no. 2, pp. 140-147.
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Video retrieval and indexing research aims to efficiently and effectively manage very large video databases, e.g., CCTV records, which is a key component in video-based object and event analysis. In this paper, for the purpose of video retrieval, we prop

Gao, X, Lu, W, Tao, D & Li, X 2009, 'Image Quality Assessment Based on Multiscale Geometric Analysis', IEEE Transactions On Image Processing, vol. 18, no. 7, pp. 1409-1423.
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Reduced-reference (RR) image quality assessment (IQA) has been recognized as an effective and efficient way to predict the visual quality of distorted images. The current standard is the wavelet-domain natural image statistics model (WNISM), which applie

Gao, X, Su, Y, Li, X & Tao, D 2009, 'Gabor texture in active appearance models', Neurocomputing, vol. 72, no. 13-15, pp. 3174-3181.
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In computer vision applications, Active Appearance Models (AAMs) is usually used to model the shape and the gray-level appearance of an object of interest using statistical methods, such as PCA. However, intensity values used in standard AAMs cannot provide enough information for image alignment. In this paper, we firstly propose to utilize Gabor filters to represent the image texture. The benefit of Gabor-based representation is that it can express local structures of an image. As a result, this representation can lead to more accurate matching when condition changes. Given the problem of the excessive storage and computational complexity of the Gabor. three different Gabor-based image representations are used in AAMs: (1) GaborD is the sum of Gabor filter responses over directions, (2) GaborS is the sum of Gabor filter responses over scales, and (3) GaborSD is the sum of Gabor filter responses over scales and directions. Through a large number of experiments, we show that the proposed Gabor representations lead to more accurate and robust matching between model and images.

Gao, X, Yang, Y, Tao, D & Li, X 2009, 'Discriminative optical flow tensor for video semantic analysis', Computer Vision And Image Understanding, vol. 113, no. 3, pp. 372-383.
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This paper presents a novel framework for effective video semantic analysis. This framework has two major components, namely, optical flow tensor (OFF) and hidden Markov models (HMMs). OFT and HMMs are employed because: (I) motion is one of the fundament

Guha, S, Ryan, LM & Morara, M 2009, 'Gauss-Seidel Estimation Of Generalized Linear Mixed Models With Application To Poisson Modeling Of Spatially Varying Disease Rates', Journal of Computational and Graphical Statistics, vol. 18, no. 4, pp. 818-837.
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Generalized linear mixed models (GLMMs) are often fit by computational procedures such as penalized quasi-likelihood (PQL). Special cases of GLMMs are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least sq

Gunes, H & Piccardi, M 2009, 'Automatic Temporal Segment Detection and Affect Recognition From Face and Body Display', IEEE Transactions on Systems, Man, and Cybernetic..., vol. 39, no. 1, pp. 64-84.
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Psychologists have long explored mechanisms with which humans recognize other humans' affective states from modalities, such as voice and face display. This exploration has led to the identification of the main mechanisms, including the important role played in the recognition process by the modalities' dynamics. Constrained by the human physiology, the temporal evolution of a modality appears to be well approximated by a sequence of temporal segments called onset, apex, and offset. Stemming from these findings, computer scientists, over the past 15 years, have proposed various methodologies to automate the recognition process. We note, however, two main limitations to date. The first is that much of the past research has focused on affect recognition from single modalities. The second is that even the few multimodal systems have not paid sufficient attention to the modalities' dynamics: The automatic determination of their temporal segments, their synchronization to the purpose of modality fusion, and their role in affect recognition are yet to be adequately explored. To address this issue, this paper focuses on affective face and body display, proposes a method to automatically detect their temporal segments or phases, explores whether the detection of the temporal phases can effectively support recognition of affective states, and recognizes affective states based on phase synchronization/alignment. The experimental results obtained show the following: 1) affective face and body displays are simultaneous but not strictly synchronous; 2) explicit detection of the temporal phases can improve the accuracy of affect recognition; 3) recognition from fused face and body modalities performs better than that from the face or the body modality alone; and 4) synchronized feature-level fusion achieves better performance than decision-level fusion.

Hart, J, Yanosky, J, Puett, R, Ryan, LM, Dockery, D, Smith, T, Garshick, E & Laden, F 2009, 'Spatial Modeling Of Pm10 And No2 In The Continental United States, 1985-2000', Environmental Health Perspectives, vol. 117, no. 11, pp. 1690-1696.
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Background: Epidemiologic studies of air pollution have demonstrated a link between long-term air pollution exposures and mortality. However, many have been limited to city-specific average pollution measures or spatial or land-use regression exposure models in small geographic areas. Objectives: Our objective was to develop nationwide models of annual exposure to particulate matter < 10 Êm in diameter (PM10) and nitrogen dioxide during 1985.2000. Methods: We used generalized additive models (GAMs) to predict annual levels of the pollutants using smooth spatial surfaces of available monitoring data and geographic information system. derived covariates. Model performance was determined using a cross-validation (CV) procedure with 10% of the data. We also compared the results of these models with a commonly used spatial interpolation, inverse distance weighting. Results: For PM10, distance to road, elevation, proportion of low-intensity residential, highintensity residential, and industrial, commercial, or transportation land use within 1 km were all statistically significant predictors of measured PM10 (model R2 = 0.49, CV R2 = 0.55). Distance to road, population density, elevation, land use, and distance to and emissions of the nearest nitrogen oxides.emitting power plant were all statistically significant predictors of measured NO2 (model R2 = 0.88, CV R2 = 0.90). The GAMs performed better overall than the inverse distance models, with higher CV R2 and higher precision. Conclusions: These models provide reasonably accurate and unbiased estimates of annual exposures for PM10 and NO2. This approach provides the spatial and temporal variability necessary to describe exposure in studies assessing the health effects of chronic air pollution.

He, X, Li, K, Wei, J & Zheng, M 2009, 'Market stability switches in a continuous-time financial market with heterogeneous beliefs', Economic Modelling, vol. 26, no. 6, pp. 1432-1442.
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By considering a financial market of fundamentalists and trend followers in which the price trend of trend followers is formed as a weighted average of historical prices, we establish a continuous-time financial market model with time delay and examine the impact of time delay on market price dynamics. Conditions for the stability of the fundamental price in terms of agents' behavior parameters and time delay are obtained. In particular, it is found that an increase in time delay can not only destabilize the market price but also stabilize an otherwise unstable market price, leading to stability switching as delay increases. These interesting phenomena shed new light in understanding of mechanism on the market stability. When the fundamental price becomes unstable through Hopf bifurcations, sufficient conditions on the stability and global existence of the periodic solution are obtained.

Huang, K, Tao, D, Yuan, Y, Li, X & Tan, T 2009, 'View-Independent Behavior Analysis', IEEE Transactions On Systems Man And Cybernetics Part B-Cybernetics, vol. 39, no. 4, pp. 1028-1035.
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The motion analysis of the human body is an important topic of research in computer vision devoted to detecting, tracking, and understanding people's physical behavior. This strong interest is driven by a wide spectrum of applications in various areas su

Huang, XH, Wang, Q, Chen, JS, Fu, XH, Chen, XL, Chen, LZ, Li, W, Bi, J, Zhang, LJ, Fu, Q, Zeng, WT, Cao, LQ, Tan, HX & Su, Q 2009, 'Bead-based microarray analysis of microRNA expression in hepatocellular carcinoma: MiR-338 is downregulated', Hepatology Research, vol. 39, no. 8, pp. 786-794.
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Aim: Recent studies have underlined causative links between microRNA (miRNA) deregulation and cancer development. However, the relevance of abnormally expressed miRNA to tumor biology has not been well understood in hepatocellular carcinoma (HCC). Methods: A bead-based miRNA expression profiling method was performed on 20 pairs of surgically removed HCC and adjacent non-tumorous tissue (NT). Special miR-338 downregulations and miR-338 associated with clinical characteristics was validated in an extended samples set of 36 paired HCC and adjacent non-tumorous liver tissues by real-time reverse transcription polymerase chain reaction (RT-PCR) analysis. Results: Out of our bead-based microarray data, 12 upregulated and 19 downregulated miRNA were found to be associated with HCC. Further characterization of miRNA-338, in which 20 pairs of the samples were clustered clearly into two groups according to expression of miR-338, revealed that the level of miR-338 expression can be associated with clinical aggressiveness, such as, tumor size, tumor-node-metastasis stage, vascular invasion and intrahepatic metastasis. These results were validated by real-time RT-PCR analysis. Conclusion: Our study suggests that miRNA expression could have relevance to the clinical behavior of HCC and that the bead-based miRNA expression profiling method might be a suitable system to assay miRNA expression in large-scale diagnostic trails. © 2009 The Japan Society of Hepatology.

Juszczyszyn, K, Musial, K, Kazienko, P & Gabrys, B 2009, 'TEMPORAL CHANGES IN LOCAL TOPOLOGY OF AN EMAIL-BASED SOCIAL NETWORK', COMPUTING AND INFORMATICS, vol. 28, no. 6, pp. 763-779.

Kelly, AH & Stoianoff, NP 2009, 'Biodiversity conservation, local government finance and differential rates: The good, the bad and the potentially attractive', Environmental and Planning Law Journal, vol. 26, no. 1, pp. 5-18.
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Kile, M, Hoffman, E, Hsueh, Y, Afroz, S, Quamruzzaman, Q, Rahman, M, Mahiuddin, G, Ryan, LM & Christiani, D 2009, 'Variability In Biomarkers Of Arsenic Exposure And Metabolism In Adults Over Time', Environmental Health Perspectives, vol. 117, no. 3, pp. 455-460.
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OBJECTIVES: To characterize inter- and intraindividual variability in IUAs in healthy individuals. METHODS: In a longitudinal study conducted in Bangladesh, we collected water and spot urine samples from 196 participants every 3 months for 2 years. Water arsenic (As) was measured by inductively coupled plasma-mass spectrometry and urinary As [arsenite, arsenate, monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA)] were detected using high-performance liquid chromatography-hydride-generated atomic absorption spectrometry. We used linear mixed-effects models to compute variance components and evaluate the association between UAs and selected factors. RESULTS: The concentrations of IUAs were fairly reproducible within individuals, with intraclass correlation coefficients (ICCs) of 0.41, 0.35, 0.47, and 0.49 for inorganic As (InAs), MMA, DMA, and total urinary As (TUA). However, when expressed as a ratio, the percent InAs (%InAs), %MMA, and %DMA were poorly reproducible within individuals, with ICCs of 0.16, 0.16, and 0.17, respectively. Arsenic metabolism was significantly associated with sex, exposure, age, smoking, chewing betel nut, urinary creatinine, and season. Specificity and sensitivity analyses showed that a single urine sample adequately classified a participant's urinary As profile as high or low, but TUA had only moderate specificity for correctly classifying drinking water exposures. CONCLUSIONS: Epidemiologic studies should use both urinary As concentrations and the relative proportion of UAs to minimize measurement error and to facilitate interpretation of factors that influence As metabolism.

Li, J & Liu, Q 2009, ''Double water exclusion': A hypothesis refining the O-ring theory for the hot spots at protein interfaces', Bioinformatics, vol. 25, no. 6, pp. 743-750.
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Motivation: The O-ring theory reveals that the binding hot spot at a protein interface is surrounded by a ring of residues that are energetically less important than the residues in the hot spot. As this ring of residues is served to occlude water molecu

Li, J, Zhang, L, Tao, D, Sun, H & Zhao, Q 2009, 'A Prior Neurophysiologic Knowledge Free Tensor-Based Scheme for Single Trial EEG Classification', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 17, no. 2, pp. 107-115.
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Single trial electroencephalogram (EEG) classification is essential in developing brain-computer interfaces (BCIs). However, popular classification algorithms, e.g., common spatial patterns (CSP), usually highly depend on the prior neurophysiologic knowl

Li, X, Tao, D, Gao, X & Lu, W 2009, 'A natural image quality evaluation metric', Signal Processing, vol. 89, no. 4, pp. 548-555.
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Reduced-reference (RR) image quality assessment (IQA) metrics evaluate the quality of a distorted (or degraded) image by using some, not all, information of the original (reference) image. In this paper, we propose a novel RR IQA metric based on hybrid wavelets and directional filter banks (HWD). With HWD as a pre-processing stage, the newly proposed metric mainly focuses on subbands coefficients of the distorted and original images. it performs well under low data rate, because only a threshold and several proportion values are recorded from the original images and transmitted. Experiments are carried out upon well recognized data sets and the results demonstrate advantages of the metric compared with existing ones. Moreover, a separate set of experiments shows that this proposed metric has good consistency with human subjective perception.

Liu, G, Sim, K, Li, J & Wong, L 2009, 'Efficient Mining of Distance-Based Subspace Clusters', Statistical Analysis and Data Mining, vol. 2, no. 5-6, pp. 427-444.
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Traditional similarity measurements often become meaningless when dimensions of datasets increase. Subspace clustering has been proposed to find clusters embedded in subspaces of high-dimensional datasets. Many existing algorithms use a grid-based approach to partition the data space into nonoverlapping rectangle cells, and then identify connected dense cells as clusters. The rigid boundaries of the grid-based approach may cause a real cluster to be divided into several small clusters. In this paper, we propose to use a sliding-window approach to partition the dimensions to preserve significant clusters. We call this model nCluster model. The sliding-window approach generates more bins than the grid-based approach, thus it incurs higher mining cost. We develop a deterministic algorithm, called MaxnCluster, to mine nClusters efficiently. MaxnCluster uses several techniques to speed up the mining, and it produces only maximal nClusters to reduce result size. Non-maximal nClusters are pruned without the need of storing the discovered nClusters in the memory, which is key to the efficiency of MaxnCluster. Our experiment results show that (i) the nCluster model can indeed preserve clusters that are shattered by the grid-based approach on synthetic datasets; (ii) the nCluster model produces more significant clusters than the grid-based approach on two real gene expression datasets and (iii) MaxnCluster is efficient in mining maximal nClusters.

Liu, Q, Li, X, Elgammal, A, Hua, XS, Xu, D & Tao, D 2009, 'Introduction to computer vision and image understanding the special issue on video analysis', Computer Vision and Image Understanding, vol. 113, no. 3, pp. 317-318.
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Lu, S, Zhang, J & Feng, DD 2009, 'Detecting ghost and left objects insurveillance video', International Journal of Pattern Recognition and Artificial Intelligence, vol. 23, no. 7, pp. 1503-1525.
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This paper proposes an efficient method for detecting ghost and left objects in surveillance video, which, if not identified, may lead to errors or wasted computational power in background modeling and object tracking in video surveillance systems. This method contains two main steps: the first one is to detect stationary objects, which narrows down the evaluation targets to a very small number of regions in the input image; the second step is to discriminate the candidates between ghost and left objects. For the first step, we introduce a novel stationary object detection method based on continuous object tracking and shape matching. For the second step, we propose a fast and robust inpainting method to differentiate between ghost and left objects by reconstructing the real background using the candidate's corresponding regions in the current input and background image. The effectiveness of our method has been validated by experiments over a variety of video sequences and comparisons with existing state-of-art methods. © 2009 World Scientific Publishing Company.

Mccracken, J, Schwartz, J, Bruce, N, Mittleman, M, Ryan, LM & Smith, K 2009, 'Combining Individual- And Group-level Exposure Information Child Carbon Monoxide In The Guatemala Woodstove Randomized Control Trial', Epidemiology, vol. 20, no. 1, pp. 127-136.
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Background: Epidemiology frequently relies on surrogates of long-term exposures, often either individual-level short-term measurements or group-level based on long-term characteristics of subjects and their environment. Whereas individual-level measures

Meeker, J, Missmer, S, Altshul, L, Vitonis, A, Ryan, LM, Cramer, D & Hauser, R 2009, 'Serum And Follicular Fluid Organochlorine Concentrations Among Women Undergoing Assisted Reproduction Technologies', Environmental health, vol. 8, no. 32, pp. 1-10.
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Merigo, JM & Casanovas, M 2009, 'Induced Aggregation Operators in Decision Making with the Dempster-Shafer Belief Structure', INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, vol. 24, no. 8, pp. 934-954.
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Merigó, JM & Casanovas, M 2009, 'Geometric operators in decision making with minimization of regret', World Academy of Science, Engineering and Technology, vol. 39, pp. 514-521.
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We study different types of aggregation operators and the decision making process with minimization of regret. We analyze the original work developed by Savage and the recent work developed by Yager that generalizes the MMR method creating a parameterized family of minimal regret methods by using the ordered weighted averaging (OWA) operator. We suggest a new method that uses different types of geometric operators such as the weighted geometric mean or the ordered weighted geometric operator (OWG) to generalize the MMR method obtaining a new parameterized family of minimal regret methods. The main result obtained in this method is that it allows to aggregate negative numbers in the OWG operator. Finally, we give an illustrative example.

Merigo, JM & Gil-Lafuente, AM 2009, 'The induced generalized OWA operator', INFORMATION SCIENCES, vol. 179, no. 6, pp. 729-741.
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Merigó, JM & Gil-Lafuente, AM 2009, 'OWA operators in generalized distances', World Academy of Science, Engineering and Technology, vol. 33, pp. 866-873.
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Different types of aggregation operators such as the ordered weighted quasi-arithmetic mean (Quasi-OWA) operator and the normalized Hamming distance are studied. We introduce the use of the OWA operator in generalized distances such as the quasiarithmetic distance. We will call these new distance aggregation the ordered weighted quasi-arithmetic distance (Quasi-OWAD) operator. We develop a general overview of this type of generalization and study some of their main properties such as the distinction between descending and ascending orders. We also consider different families of Quasi-OWAD operators such as the Minkowski ordered weighted averaging distance (MOWAD) operator, the ordered weighted averaging distance (OWAD) operator, the Euclidean ordered weighted averaging distance (EOWAD) operator, the normalized quasi-arithmetic distance, etc.

Mu, Y, Tao, D, Li, X & Murtagh, F 2009, 'Biologically Inspired Tensor Features', Cognitive Computation, vol. 1, no. 4, pp. 327-341.
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According to the research results reported in the past decades, it is well acknowledged that face recognition is not a trivial task. With the development of electronic devices, we are gradually revealing the secret of object recognition in the primate's

Ou, Y, Cao, L & Zhang, C 2009, 'Adaptive Anomaly Detection of Coupled Activity Sequences', The IEEE Intelligent Informatics Bulletin, vol. 10, no. 1, pp. 12-16.
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Many real-life applications often involve multiple sequences, which are coupled with each other. It is unreasonable to either study the multiple coupled sequences separately or simply merge them into one sequence, because the information about their interacting relationships would be lost. Furthermore, such coupled sequences also have frequently significant changes which are likely to degrade the performance of trained model. Taking the detection of abnormal trading activity patterns in stock markets as an example, this paper proposes a Hidden Markov Model-based approach to address the above two issues. Our approach is suitable for sequence analysis on multiple coupled sequences and can adapt to the significant sequence changes automatically. Substantial experiments conducted on a real dataset show that our approach is effective.

Owen, M, Ryan, LM & Holmes, L 2009, 'Effects Of Retinoic Acid On Dominant Bemimelia Expression In Mice', Birth Defects Research Part A: Clinical and Molecular Teratology, vol. 85, no. 1, pp. 36-41.
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BACKGROUND: Dominant hemimelia (Dh) is an autosomal dominant mutation that arose spontaneously in mice. Dh animals are asplenic and they exhibit asymmetric hindlimb defects in association with reduced numbers of lumbar vertebrae. These defects suggest that Dh acts early in embryonic development to affect patterning of the anterior-posterior (A-P) and left-right axes. This study was undertaken to determine whether retinoic acid (RA), which is involved in A-P patterning and coordination of bilaterally synchronized somitogenesis, affects phenotypic expression of the Dh gene. METHODS: Thirty-four pregnant females were given, by oral intubation, a single dose of 50 or 75 mg all-trans RA per kilogram body weight at GD 9, 10, or 11. The pregnant females were then euthanized at GD 18 and fetuses removed by cesarean section. A total of 326 fetuses were identified by phenotype and linked DNA and their skeletons were analyzed. RESULTS: There was a differential effect of RA on the axial skeleton and hindlimb of Dh/+ mice as compared to their wild-type littermates. Dose- and stage-specific effects on sternebrae and vertebrae were observed. CONCLUSIONS: The effects of RA dosing on numbers of sternebrae and vertebrae suggest that Dh embryos have a primary defect in retinoid-mediated A-P patterning. Dosing with RA may produce the observed effects on phenotypic expression of Dh/+ by indirectly or directly modifying an already existing altered Hox expression pattern. As the relationship between axial patterning and the asymmetric limb is unknown, Dh is an important model for studying this relationship. Birth Defects Research (Part A) 85:36-41, 2009,

Pang, Y, Li, X, Yuan, Y, Tao, D & Pan, J 2009, 'Fast Haar Transform Based Feature Extraction for Face Representation and Recognition', IEEE Transactions on Information Forensics and Security, vol. 4, no. 3, pp. 441-450.
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Subspace learning is the process of finding a proper feature subspace and then projecting high-dimensional data onto the learned low-dimensional subspace. The projection operation requires many floating-point multiplications and additions, which makes th

Pham, TD, Brandl, M & Beck, D 2009, 'Fuzzy declustering-based vector quantization', Pattern Recognition, vol. 42, no. 11, pp. 2570-2577.
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Vector quantization is a useful approach for multi-dimensional data compression and pattern classification. One of the most popular techniques for vector quantization design is the LBG (Linde, Buzo, Gray) algorithm. To address the problem of producing poor estimate of vector centroids which are subjected to biased data in vector quantization; we propose a fuzzy declustering strategy for the LBG algorithm. The proposed technique calculates appropriate declustering weights to adjust the global data distribution. Using the result of fuzzy declustering-based vector quantization design, we incorporate the notion of fuzzy partition entropy into the distortion measures that can be useful for classification of spectral features. Experimental results obtained from simulated and real data sets demonstrate the effective performance of the proposed approach. © 2009 Elsevier Ltd. All rights reserved.

Qin, Y, Zhang, S, Zhu, X, Zhang, J & Zhang, C 2009, 'Estimating confidence intervals for structural differences between contrast groups with missing data', Expert Systems with Applications, vol. 36, no. 3, pp. 6431-6438.
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Difference detection is actual and extremely useful for evaluating a new medicine B against a specified disease by comparing to an old medicine A, which has been used to treat the disease for many years. The datasets generated by applying A and B to the disease are called contrast groups and, main differences between the groups are the mean and distribution differences, referred to structural differences in this paper. However, contrast groups are only two samples obtained by limited applications or tests on A and B, and may be with missing values. Therefore, the differences derived from the groups are inevitably uncertain. In this paper, we propose a statistically sound approach for measuring this uncertainty by identifying the confidence intervals of structural differences between contrast groups. This method is designed significantly against most of those applications whose exact data distributions are unknown a priori, and the data may also be with missing values. We apply our approach to UCI datasets to illustrate its power as a new data mining technique for, such as, distinguishing spam from non-spam emails; and the benign breast cancer from the malign one.

Qin, Y, Zhang, S, Zhu, X, Zhang, J & Zhang, C 2009, 'POP algorithm: Kernel-based imputation to treat missing values in knowledge discovery from databases', Expert Systems with Applications, vol. 36, no. 2, pp. 2794-2804.
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To complete missing values a solution is to use correlations between the attributes of the data. The problem is that it is difficult to identify relations within data containing missing values. Accordingly, we develop a kernel-based missing data imputation in this paper. This approach aims at making an optimal inference on statistical parameters: mean, distribution function and quantile after missing data are imputed. And we refer this approach to parameter optimization method (POP algorithm). We experimentally evaluate our approach, and demonstrate that our POP algorithm (random regression imputation) is much better than deterministic regression imputation in efficiency and generating an inference on the above parameters.

Ryan, L 2009, 'Spatial Epidemiology Some Pitfalls and Opportunities', EPIDEMIOLOGY, vol. 20, no. 2, pp. 242-244.
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Sanchez, B, Budtz-jorgensen, E & Ryan, LM 2009, 'An Estimating Equations Approach To Fitting Latent Exposure Models With Longitudinal Health Outcomes', Annals of Applied Statistics, vol. 3, no. 2, pp. 830-856.
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The analysis of data arising from environmental health studies which collect a large number of measures of exposure can benefit from using latent variable models to summarize exposure information. However, difficulties with estimation of model parameters may arise since existing fitting procedures for linear latent variable models require correctly specified residual variance structures for unbiased estimation of regression parameters quantifying the association between (latent) exposure and health outcomes. We propose an estimating equations approach for latent exposure models with longitudinal health outcomes which is robust to misspecification of the outcome variance. We show that compared to maximum likelihood, the loss of efficiency of the proposed method is relatively small when the model is correctly specified. The proposed equations formalize the ad-hoc regression on factor scores procedure, and generalize regression calibration. We propose two weighting schemes for the equations, and compare their efficiency. We apply this method to a study of the effects of in-utero lead exposure on child development.

Sanchez, BN, Houseman, EA & Ryan, LM 2009, 'Residual-Based Diagnostics for Structural Equation Models', BIOMETRICS, vol. 65, no. 1, pp. 104-115.
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Shan, S, Liu, Q, Tao, D, Xu, D, Yan, S & Li, X 2009, 'Introduction to the special issue on Video-based Object and Event Analysis', PATTERN RECOGNITION LETTERS, vol. 30, no. 2, pp. 87-87.
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Shen, J, Tao, D & Li, X 2009, 'QUC-Tree: Integrating Query Context Information for Efficient Music Retrieval', IEEE Transactions On Multimedia, vol. 11, no. 2, pp. 313-323.
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In this paper, we introduce a novel indexing scheme-QUery Context tree (QUC-tree) to facilitate efficient query sensitive music search under different query contexts. Distinguished from the previous approaches, QUC-tree is a balanced multiway tree struct

Sim, K, Li, J, Gopalkrishnan, V & Liu, G 2009, 'Mining maximal quasi-bicliques: Novel algorithm and applications in the stock market and protein networks', Statistical Analysis and Data Mining, vol. 2, no. 4, pp. 255-273.
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Several real-world applications require mining of bicliques, as they represent correlated pairs of data clusters. However, the mining quality is adversely affected by missing and noisy data. Moreover, some applications only require strong interactions between data members of the pairs, but bicliques are pairs that display complete interactions. We address these two limitations by proposing maximal quasi-bicliques. Maximal quasi-bicliques tolerate erroneous and missing data, and also relax the interactions between the data members of their pairs. Besides, maximal quasi-bicliques do not suffer from skewed distribution of missing edges that prior quasi-bicliques have. We develop an algorithm MQBminer, which mines the complete set of maximal quasi-bicliques from either bipartite or non-bipartite graphs. We demonstrate the versatility and effectiveness of maximal quasi-bicliques to discover highly correlated pairs of data in two diverse real-world datasets. First, we propose to solve a novel financial stocks analysis problem using maximal quasi-bicliques to co-cluster stocks and financial ratios. Results show that the stocks in our co-clusters usually have significant correlations in their price performance. Second, we use maximal quasi-bicliques on a mining protein network problem and we show that pairs of protein groups mined by maximal quasi-bicliques are more significant than those mined by maximal bicliques.

Sturgeon, JL, Williams, M & van Servellen, G 2009, 'Efficacy of green tea in the prevention of cancers', Nursing and Health Sciences, vol. 11, no. 4, pp. 436-446.
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Understanding the literature about the efficacy of green tea consumption in preventing and slowing the progression of cancers is critical. A systematic review of the literature was conducted using an electronic search to identify studies from 2000 to 2008 in the following database: Alt HealthWatch, CINAHL, Medline, Health Source - Consumer Edition, Health Source: Nursing/Academic Edition, Web of Science (ISI), and the Cochrane Library. Although the evidence from this review suggested associations between green tea consumption and a decreased risk for some cancers, the findings were inconclusive. In selected cases, green tea was effective in slowing the progression of the earlier stages of cancer. However, contrary evidence is reported and the dose and duration of use is variable. Most evidence stems from self-reports. Research using more rigorous designs to investigate the efficacy of green tea in humans is needed. © 2009 The Authors. Journal Compilation © 2009 Blackwell Publishing Asia Pty Ltd.

Tao, D, Li, X & Tang, YY 2009, 'Learning semantics from multimedia content', Pattern Recognition, vol. 42, no. 2, p. 217.
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Tao, D, Li, X, Lu, W & Gao, X 2009, 'Reduced-Reference IQA in Contourlet Domain', IEEE Transactions On Systems Man And Cybernetics Part B-Cybernetics, vol. 39, no. 6, pp. 1623-1627.
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The human visual system (HVS) provides a suitable cue for image quality assessment (IQA). In this paper, we develop a novel reduced-reference (RR) IQA scheme by incorporating the merits from the contourlet transform, contrast sensitivity function (CSF),

Tao, D, Li, X, Wu, X & Maybank, S 2009, 'Geometric Mean for Subspace Selection', IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 31, no. 2, pp. 260-274.
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Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction step in the Fisher's linear discriminant analysis (FLDA), which has been successfully employed in many fields such as biometrics, bioinformatics, and multimedia information management. However, the linear dimensionality reduction step in FLDA has a critical drawback: for a classification task with c classes, if the dimension of the projected subspace is strictly lower than c - 1, the projection to a subspace tends to merge those classes, which are close together in the original feature space. If separate classes are sampled from Gaussian distributions, all with identical covariance matrices, then the linear dimensionality reduction step in FLDA maximizes the mean value of the Kullback-Leibler (KL) divergences between different classes. Based on this viewpoint, the geometric mean for subspace selection is studied in this paper. Three criteria are analyzed: 1) maximization of the geometric mean of the KL divergences, 2) maximization of the geometric mean of the normalized KL divergences, and 3) the combination of 1 and 2. Preliminary experimental results based on synthetic data, UCI Machine Learning Repository, and handwriting digits show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.

Tao, D, Yuan, Y, Shen, J, Huang, K & Li, X 2009, 'Visual information analysis for security', Signal Processing, vol. 89, no. 12, pp. 2311-2312.
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Thongkam, J, Xu, G, Zhang, Y & Huang, F 2009, 'Toward breast cancer survivability prediction models through improving training space', Expert Systems with Applications, vol. 36, no. 10, pp. 12200-12209.
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Due to the difficulties of outlier and skewed data, the prediction of breast cancer survivability has presented many challenges in the field of data mining and pattern precognition, especially in medical research. To solve these problems, we have proposed a hybrid approach to generating higher quality data sets in the creation of improved breast cancer survival prediction models. This approach comprises two main steps: (1) utilization of an outlier filtering approach based on C-Support Vector Classification (C-SVC) to identify and eliminate outlier instances; and (2) application of an over-sampling approach using over-sampling with replacement to increase the number of instances in the minority class. In order to assess the capability and effectiveness of the proposed approach, several measurement methods including basic performance (e.g., accuracy, sensitivity, and specificity), Area Under the receiver operating characteristic Curve (AUC) and F-measure were utilized. Moreover, a 10-fold cross-validation method was used to reduce the bias and variance of the results of breast cancer survivability prediction models. Results have indicated that the proposed approach leads to improving the performance of breast cancer survivability prediction models by up to 28.34% due to the improved training data space.

Tran, TP, Cao, L, Tran, D & Nguyen, CD 2009, 'Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting', International Journal of Computer Science and Information Security, vol. 6, no. 1, pp. 83-91.
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This article applies Machine Learning techniques to solve Intrusion Detection problems within computer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. To overcome such performance limitations, we propose a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), which integrates an adaptive boosting technique and a semi parametric neural network to obtain good tradeoff between accuracy and generality. As the result, learning bias and generalization variance can be significantly minimized. Substantial experiments on KDD 99 intrusion benchmark indicate that our model outperforms other state of the art learning algorithms, with significantly improved detection accuracy, minimal false alarms and relatively small computational complexity.

Tsai, PC, Cao, L, Hintz, TB & Jan, T 2009, 'A bi-modal face recognition framework integrating facial expression with facial appearance', Pattern Recognition Letters, vol. 30, no. 12, pp. 1096-1109.
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Among many biometric characteristics, the facial biometric is considered to be the least intrusive technology that can be deployed in the real-world visual surveillance environment. However, in facial biometric, little research attention has been paid to facial expression changes. In fact, facial expression changes have often been treated as noise that would degrade the recognition performance. This paper studies an innovative viewpoint: (1) whether facial expression changes, namely facial behavior, can be positively used for face recognition or not? (2) furthermore, can facial behavior be integrated with facial appearance for assisting the extra-personal separation to enhance face recognition performance? We propose a bi-modal face recognition framework which integrates facial expression with facial appearance. Substantial experiments on multiple facial appearance and facial expression data have been conducted. Our experimental results have validated that facial behavior can play a positive role in face recognition and can assist facial appearance in extra-personal separation in multiple modalities for personal identification improvement.

Wang, L, Wu, Q, Li, M, Gonzàlez, J & Geng, X 2009, 'Editorial: Video analysis and understanding for surveillance applications', International Journal of Pattern Recognition and Artificial Intelligence, vol. 23, no. 7, pp. 1221-1222.
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Wang, L, Wu, Q, Wang, H, Geng, X & Li, M 2009, 'Image/video-based pattern analysis and HCI applications', PATTERN RECOGNITION LETTERS, vol. 30, no. 12, pp. 1047-1047.
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Weuve, J, Korrick, S, Weisskopf, M, Ryan, LM, Schwartz, J, Nie, H, Grodstein, F & Hu, H 2009, 'Cumulative Exposure To Lead In Relation To Cognitive Function In Older Women', Environmental Health Perspectives, vol. 117, no. 4, pp. 574-580.
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BACKGROUND: Recent data indicate that chronic low-level exposure to lead is associated with accelerated declines in cognition in older age, but this has not been examined in women. OBJECTIVE: We examined biomarkers of lead exposure in relation to performance on a battery of cognitive tests among older women. METHODS: Patella and tibia bone lead-measures of cumulative exposure over many years-and blood lead, a measure of recent exposure, were assessed in 587 women 47-74 years of age. We assessed their cognitive function 5 years later using validated telephone interviews. RESULTS: Mean +/- SD lead levels in tibia, patella, and blood were 10.5 +/- 9.7 mu g/g bone, 12.6 +/- 11.6 mu g/g bone, and 2.9 +/- 1.9 mu g/dL, respectively, consistent with community-level exposures. In multivariable-adjusted analyses of all cognitive tests combined, levels of all three lead biomarkers were associated with worse cognitive performance. The association between bone lead and letter fluency score differed dramatically from the other bone lead-cognitive score associations, and exclusion of this particular score from the combined analyses strengthened the associations between bone lead and cognitive performance. Results were statistically significant only for tibia lead: one SD increase in tibia lead corresponded to a 0.051-unit lower standardized summary cognitive score (95% confidence interval: -0.099 to -0.003; P = 0.04), similar to the difference in cognitive scores we observed between women who were 3 years apart in age. CONCLUSIONS: These findings suggest that cumulative exposure to lead, even at low levels experienced in community settings, may have adverse consequences for women's cognition in older age.

Williams, M, McCarthy, J, Gardenfors, P, Stanton, CJ & Karol, A 2009, 'A Grounding Framework', Autonomous Agents And Multi-Agent Systems, vol. 19, no. 3, pp. 272-296.
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In order for an agent to achieve its objectives, make sound decisions, communicate and collaborate with others effectively it must have high quality representations. Representations can encapsulate objects, situations, experiences, decisions and behavior just to name a few. Our interest is in designing high quality representations, therefore it makes sense to ask of any representation; what does it represent; why is it represented; how is it represented; and importantly how well is it represented. This paper identifies the need to develop a better understanding of the grounding process as key to answering these important questions. The lack of a comprehensive understanding of grounding is a major obstacle in the quest to develop genuinely intelligent systems that can make their own representations as they seek to achieve their objectives. We develop an innovative framework which provides a powerful tool for describing, dissecting and inspecting grounding capabilities with the necessary flexibility to conduct meaningful and insightful analysis and evaluation. The framework is based on a set of clearly articulated principles and has three main applications. First, it can be used at both theoretical and practical levels to analyze grounding capabilities of a single system and to evaluate its performance. Second, it can be used to conduct comparative analysis and evaluation of grounding capabilities across a set of systems. Third, it offers a practical guide to assist the design and construction of high performance systems with effective grounding capabilities.

Xiao, B, Gao, X, Tao, D & Li, X 2009, 'A new approach for face recognition by sketches in photos', Signal Processing, vol. 89, no. 8, pp. 1576-1588.
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Face recognition by sketches in photos remains a challenging task. Unlike the existing sketch-photo recognition methods, which convert a photo into sketch and then perform the sketch-photo recognition through sketch-sketch recognition, this paper devotes to synthesizing a photo from the sketch and transforming the sketch-photo recognition to photo-photo recognition to achieve better performance in mixture pattern recognition. The contribution of this paper mainly focuses on two aspects: (1) in view of that there are no many research findings of sketch-photo recognition based on the pseudo-photo synthesis and the existing methods require a large set of training samples, which is nearly impossible to achieve for the high cost of sketch acquisition, we make use of embedded hidden Markov model (EHMM), which can learn the nonlinearity of sketch-photo pair with less training samples, to produce pseudo-photos in terms of sketches; and (2) photos and sketches are divided into patches and pseudo-photo is generated by combining pseudo-photo patches, which makes pseudo-photo more recognizable. Experimental results demonstrate that the newly proposed method is effective to identify face sketches in photo set.

Yan, X, Zhang, C & Zhang, S 2009, 'Confidence Metrics For Association Rule Mining', Applied Artificial Intelligence, vol. 23, no. 8, pp. 713-737.
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We propose a simple, novel, and yet effective confidence metric for measuring the interestingness of association rules. Distinguishing from existing confidence measures, our metrics really indicate the positively companionate correlations between frequent itemsets. Furthermore, some desired properties are derived for examining the goodness of confidence measures in terms of probabilistic significance. We systematically analyze our metrics and traditional ones, and demonstrate that our new algorithm significantly captures the mainstream properties. Our approach will be useful to many association analysis tasks where one must provide actionable association rules and assist users to make quality decisions.

Yan, X, Zhang, C & Zhang, S 2009, 'Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support', Expert Systems with Applications, vol. 36, no. 2, pp. 3066-3076.
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We design a genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. In this approach, an elaborate encoding method is developed, and the relative confidence is used as the fitness function. With genetic algorithm, a global search can be performed and system automation is implemented, because our model does not require the user-specified threshold of minimum support. Furthermore, we expand this strategy to cover quantitative association rule discovery. For efficiency, we design a generalized FP-tree to implement this algorithm. We experimentally evaluate our approach, and demonstrate that our algorithms significantly reduce the computation costs and generate interesting association rules only.

Yuan, Y, Li, X, Pang, Y, Lu, X & Tao, D 2009, 'Binary Sparse Nonnegative Matrix Factorization', IEEE Transactions On Circuits And Systems For Video Technology, vol. 19, no. 5, pp. 772-777.
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This paper presents a fast part-based subspace selection algorithm, termed the binary sparse nonnegative matrix factorization (B-SNMF). Both the training process and the testing process of B-SNMF are much faster than those of binary principal component a

Zeng, X, Pei, J, Wang, K & Li, J 2009, 'PADS: A simple yet effective pattern-aware dynamic search method for fast maximal frequent pattern mining', Knowledge And Information Systems, vol. 20, no. 3, pp. 375-391.
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While frequent pattern mining is fundamental for many data mining tasks, mining maximal frequent patterns efficiently is important in both theory and applications of frequent pattern mining. The fundamental challenge is how to search a large space of ite

Zhang, H, Zhao, Y, Cao, L, Zhang, C & Bohlscheid, H 2009, 'Customer Activity Sequence Classification for Debt Prevention in Social Security', Journal Of Computer Science And Technology, vol. 24, no. 6, pp. 1000-1009.
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From a data mining perspective, sequence classification is to build a classifier using frequent sequential patterns. However, mining for a complete set of sequential patterns on a large dataset can be extremely time-consuming and the large number of patterns discovered also makes the pattern selection and classifier building very time-consuming. The fact is that, in sequence classification, it is much more important to discover discriminative patterns than a complete pattern set. In this paper, we propose a novel hierarchical algorithm to build sequential classifiers using discriminative sequential patterns. Firstly, we mine for the sequential patterns which are the most strongly correlated to each target class. In this step, an aggressive strategy is employed to select a small set of sequential patterns. Secondly, pattern pruning and serial coverage test are done on the mined patterns. The patterns that pass the serial test are used to build the sub-classifier at the first level of the final classifier. And thirdly, the training samples that cannot be covered are fed back to the sequential pattern mining stage with updated parameters. This process continues until predefined interestingness measure thresholds are reached, or all samples are covered. The patterns generated in each loop form the sub-classifier at each level of the final classifier. Within this framework, the searching space can be reduced dramatically while a good classification performance is achieved. The proposed algorithm is tested in a real-world business application for debt prevention in social security area. The novel sequence classification algorithm shows the effectiveness and efficiency for predicting debt occurrences based on customer activity sequence data.

Zhang, T, Tao, D, Li, X & Yang, J 2009, 'Patch Alignment for Dimensionality Reduction', IEEE Transactions On Knowledge And Data Engineering, vol. 21, no. 9, pp. 1299-1313.
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Spectral analysis-based dimensionality reduction algorithms are important and have been popularly applied in data mining and computer vision applications. To date many algorithms have been developed, e. g., principal component analysis, locally linear em

Zhang, Y & Xu, G 2009, 'On Web Communities Mining And Recommendation', Concurrency And Computation-practice & Experience, vol. 21, no. 5, pp. 561-582.
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Because of the lack of a uniform schema for web documents and the sheer amount and dynamics of web data, both the effectiveness and the efficiency of information management and retrieval of web data are often unsatisfactory when using conventional data m

Zhang, Z, Yang, PH, Wu, X & Zhang, C 2009, 'An Agent-Based Hybrid System for Microarray Data Analysis', IEEE Intelligent Systems, vol. 24, no. 5, pp. 53-63.
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This article reports our experience in agent-based hybrid construction for microarray data analysis. The contributions are twofold: We demonstrate that agent-based approaches are suitable for building hybrid systems in general, and that a genetic ensemble system is appropriate for microarray data analysis in particular. Created using an agent-based framework, this genetic ensemble system for microarray data analysis excels in both sample classification accuracy and gene selection reproducibility.

Zheng, M, Wang, D & He, X 2009, 'Asymmetry of technical analysis and market price volatility', China Finance Review, vol. 61, no. 2, pp. 61-89.
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Zhu, M, Chiarella, C, He, X & Wang, D 2009, 'Does the market maker stabilize the market?', Physica A: Statistical Mechanics and its Applications, vol. 388, no. 15-16, pp. 3164-3180.
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The market maker plays an important role in price formation, but his/her behavior and stabilizing impact on the market are relatively unclear, in particular in speculative markets. This paper develops a financial market model that examines the impact on market stability of the market maker, who acts as both a liquidity provider and an active investor in a market consisting of two types of boundedly rational speculative investorsâthe fundamentalists and trend followers. We show that the market maker does not necessarily stabilize the market when he/she actively manages the inventory to maximize profits, and that rather the market makerâs impact depends on the behavior of the speculators. Numerical simulations show that the model is able to generate outcomes for asset returns and market inventories that are consistent with empirical findings.

Al-Sharawneh, JA & Williams, M 2009, 'A Social Network Approach in Semantic Web Services Selection using Follow the Leader Behavior', Enterprise Distributed Object Computing Conference Workshops, 2009., Enterprise Distributed Object Computing Conference, IEEE, Auckland, New Zealand, pp. 310-319.
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Automatic discovery of web services is a crucial task for e-Business communities. Locating and selecting âthe best❠web service from a vast number of similar services that matches the user's requirements and preferences is a cognitive challenge and requires the use of an intelligent decision making framework. This paper develops a flexible ontological architecture and framework for Semantic Web Service Selection that exploits Goldbaum's innovative "Follow the Leader" model originally designed as an analytic tool for studying social network behavior and evolution. The framework proposes two new ontologies integrated in a recommender system, which guides a user to select the best service that matches their requirements and preferences. We test and evaluate several behaviors of market leader scenarios using a simulation agent.

Al-Sharawneh, JA & Williams, M 2009, 'ABMS: Agent-based Modeling and Simulation in Web Service Selection', International Conference on Engineering Management and Service Sciences (EMS 2009), International Conference on Engineering Management and Service Sciences, IEEE, Beijing, China., pp. 1-6.
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Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous interacting agents. It promises to have an important role in research and education. Some researchers have contended that ABMS âis a third way of doing scienceâ. ABMS has been applied to a wide range of research in a varied number of complex domain problems. Social simulation is playing an increasingly important role in todayâs interconnected society. In this paper we apply agent based modeling and simulation to investigate the impact of Goldbaum's innovative "Follow the Leader" in social networks in web services selection using a recommender system that guides a user to select the best service that matches his requirements and preferences. We test and evaluate several customersâ behaviors scenarios using our simulation tool âSSSS: Service Selection Simulation Studioâ.

Beck, D & Wong, STC 2009, 'Conference scence: Wake-up call for the engineering and biomedical science communities in nanomedicine', Nanomedicine, pp. 515-517.
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The IEEE-NIH 4th life Science Systems and Applications Workshop 2009 (LISSA '09) was jointly organized by the IEEE LISSA Technical Committee and the NIH Nano Task Force. It was endorsed by the NIH Biomedical Information Science and Technology Initiative (BISTI) and the National Library of Medicine. The workshop was held in the Natcher Conference Center on the NIH campus in Bethesda, MD, USA. It took place on the 9-10 April, 2009, during the NIH NanoWeek and had approximately 200 delegates from around the globe (including North America, Europe, Asia and Australia) from both engineering and biomedical science disciplines. The conference featured around 40 talks, including nine plenary speakers emphasizing current state-of-the-art nanotechnology practices, developments and industry applications. All talks were scheduled in three oral and seven special sessions, as well as three breakout sessions. In addition, the interactive poster sessions hosted over 30 abstracts and attracted much attention from the audience; these sessions were readily used by many attendees to connect with colleagues of similar interest. In this article, we provide some of the highlights from the workshop. © 2009 Future Medicine Ltd.

Beck, D, Zhou, X, Pham, T, Sabatini, B & Wong, STC 2009, 'An image driven systems biology approach for neurodegenerative disease studies in the TSC-mTOR pathway', 2009 IEEE/NIH Life Science Systems and Applications Workshop, LiSSA 2009, pp. 36-39.
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In this brief paper we present an overview of the TSC-mTOR pathway and its importance in neurodegenerative disease (ND). We illustrate the influence of ND on dendritic spine morphology. Then we discuss some details of functional gene networks (FGN) and use this information to propose an image driven systems biology approach for the construction of a FGN for ND. We conclude on its importance and the prospective outcome of our study. © 2009 IEEE.

Benferhat, S, Dubios, D, Prade, H & Williams, M 2009, 'A General Framework for Revising Belief Bases using Qualitative Jeffrey's Rule', Foundations of Intelligent Systems - 18th International Symposium, ISMIS 2009: Lecture Notes in Artificial Intelligence vol 5722, International Symposium on Foundations of Intelligent Systems, Springer, Prague, Czech Republic, pp. 612-621.
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Intelligent agents require methods to revise their epistemic state as they acquire new information. Jeffreys rule, which extends conditioning to uncertain inputs, is currently used for revising probabilistic epistemic states when new information is uncertain. This paper analyses the expressive power of two possibilistic counterparts of Jeffreys rule for modeling belief revision in intelligent agents. We show that this rule can be used to recover most of the existing approaches proposed in knowledge base revision, such as adjustment, natural belief revision, drastic belief revision, revision of an epistemic by another epistemic state. In addition, we also show that that some recent forms of revision, namely improvement operators, can also be recovered in our framework.

Benferhat, S, Dubois, D, Prade, H & Williams, MA 2009, 'A general framework for revising belief bases using qualitative Jeffrey's rule', Commonsense 2009 - Proceedings of the 9th International Symposium on Logical Formalizations of Commonsense Reasoning, pp. 7-12.
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Intelligent agents require methods to revise their epis- temic state as they acquire new information. Jerey's rule, which extends conditioning to uncertain inputs, is used to revise probabilistic epistemic states when new information is uncertain. This paper analyses the expressive power of two possibilistic counterparts of Jerey's rule for modeling belief revision in intelligent agents. We show that this rule can be used to recover most of the existing approaches proposed in knowledge base revision, such as adjustment, natural belief revi- sion, drastic belief revision, revision of an epistemic by another epistemic state. In addition, we also show that that some recent forms of revision, namely reinforce- ment operators, can also be recovered in our frame- work.

Benter, A, Xu, R, Moore, W, Antolovich, M & Gao, J 2009, 'Fragment size detection within homogeneous material using ground penetrating radar', 2009 International Radar Conference "Surveillance for a Safer World", RADAR 2009.
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Ground Penetrating Radar (GPR) offers the ability to observe the internal structure of a pile of rocks. Large fragments within the pile may not be visible on the surface. Determining these large fragment sizes before collection can improve mine productivity. This research has examined the potential to identify objects where the background media and the object exhibit the same dielectric properties. Preliminary results are presented which show identification is possible using standard GPR equipment.

Bian, W & Tao, D 2009, 'Dirichlet Mixture Allocation For Multiclass Document Collections Modeling', 2009 9th IEEE International Conference On Data Mining, IEEE International Conference on Data Mining, IEEE, Miami Beach, FL, pp. 711-715.
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Topic model, Latent Dirichlet Allocation (LDA), is an effective tool for statistical analysis of large collections of documents. In LDA, each document is modeled as a mixture of topics and the topic proportions are generated from the unimodal Dirichlet d

Bian, W & Tao, D 2009, 'Manifold Regularization for SIR with Rate Root-n Convergence', Proceedings of the 2009 Conference ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 22, Annual Conference on Neural Information Processing Systems, Curran Associates, Inc, Vancouver, British Columbia, Canada, pp. 1-9.
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In this paper, we study the manifold regularization for the Sliced Inverse Regression (SIR). The manifold regularization improves the standard SIR in two aspects: 1) it encodes the local geometry for SIR and 2) it enables SIR to deal with transductive and semi-supervised learning problems. We prove that the proposed graph Laplacian based regularization is convergent at rate root-n. The projection directions of the regularized SIR are optimized by using a conjugate gradient method on the Grassmann manifold. Experimental results support our theory.

Bian, W & Tao, D 2009, 'Manifold regularization for SIR with rate root-n convergence', Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference, pp. 117-125.
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In this paper, we study the manifold regularization for the Sliced Inverse Regression (SIR). The manifold regularization improves the standard SIR in two aspects: 1) it encodes the local geometry for SIR and 2) it enables SIR to deal with trans-ductive and semi-supervised learning problems. We prove that the proposed graph Laplacian based regularization is convergent at rate root-n. The projection directions of the regularized SIR are optimized by using a conjugate gradient method on the Grassmann manifold. Experimental results support our theory.

Bian, W, Cheng, JL & Tao, D 2009, 'Biased Isomap Projections For Interactive Reranking', ICME: 2009 IEEE International Conference On Multimedia And Expo, Vols 1-3, IEEE International Conference on Multimedia and Expo, IEEE, New York, NY, pp. 1632-1635.
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Image search has recently gained more and more attention for various applications. To capture users' intensions and to bridge the gap between the low level visual features and the high level semantics, a dozen of interactive reranking (IR) or relevance f

Brodka, P, Musial, K & Kazienko, P 2009, 'A Performance of Centrality Calculation in Social Networks', 2009 INTERNATIONAL CONFERENCE ON COMPUTATIONAL ASPECTS OF SOCIAL NETWORKS, PROCEEDINGS, International Conference on Computational Aspects of Social Networks, IEEE COMPUTER SOC, ESIGETEL, Fontainebleau, FRANCE, pp. 24-31.
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Brodka, P, Musial, K & Kazienko, P 2009, 'Efficiency of Node Position Calculation in Social Networks', KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II, PROCEEDINGS, 13th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, SPRINGER-VERLAG BERLIN, Univ Chile, Fac Phys Sci & Math, Santiago, CHILE, pp. 455-463.

Cao, L 2009, 'Data Mining in Financial Markets', Advanced Data Mining and Applications, 5th International Conference, ADMA 2009, International Conference on Advanced Data Mining and Applications, Springer, Budapest, Hungary, pp. 4-4.

Cao, L, Luo, D & Zhang, C 2009, 'Ubiquitous Intelligence in Agent Mining', ADMI 2009, International Workshop on Agents and Data Mining Interaction, Springer, Budapest, Hungary, pp. 23-35.
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Agent mining, namely the interaction and integration of multi-agent and data mining, has emerged as a very promising research area. While many mutual issues exist in both multi-agent and data mining areas, most of them can be described in terms of or related to ubiquitous intelligence. It is certainly very important to define, specify, represent, analyze and utilize ubiquitous intelligence in agents, data mining, and agent mining. This paper presents a novel but preliminary investigation of ubiquitous intelligence in these areas. We specify five types of ubiquitous intelligence: data intelligence, human intelligence, domain intelligence, network and web intelligence, organizational intelligence, and social intelligence. We define and illustrate them, and discuss techniques for involving them into agents, data mining, and agent mining for complex problem-solving. Further investigation on involving and synthesizing ubiquitous intelligence into agents, data mining, and agent mining will lead to a disciplinary upgrade from methodological, technical and practical perspectives.

Chen, P & Li, J 2009, 'Prediction of protein long-range contacts using GaMC approach with sequence profile centers', Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009, pp. 128-135.
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In this paper, we apply an evolutionary optimization classifier, referred to as genetic algorithm-based multiple classifier (GaMC), to the long-range contacts prediction. As a result, about 44.1% contacts between long-range residues (with a sequence separation of at least 24 amino acids) are founded around the sequence profile (SP) centre when evaluating the top L/5 (L is the sequence length of protein) classified contacts if the SP centers are known. Meanwhile, with the knowledge of sequence profile center and the GaMC method, about 20.42% long-range contacts are correctly predicted. Results showed that SP center may be a sound pathway to predict contact map in protein structures. ©2009 IEEE.

Chen, S & Williams, M 2009, 'Privacy In Social Networks: A Comparative Study', Pacific Asia Conference on Information Systems (PACIS) 2009 Proceedings, Pacific Asia Conference on Information Systems, Association for Information Systems, India School of Business, Hyderabad, India, pp. 1-12.
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Social networks provide unprecedented opportunity for individuals and organizations to share information. At the same time they present significant challenges to privacy that left unaddressed will stifle information sharing and innovation. In this paper we analyse four different prototypical existing social networks, and identify key problems that arise for a privacy-by-design approach to the development of a new breed of social networks.

Chen, W, Huang, K, Tan, T & Tao, D 2009, 'A convergent solution to two dimensional linear discriminant analysis', Proceedings - International Conference on Image Processing, ICIP, pp. 4133-4136.
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The matrix based data representation has been recognized to be effective for face recognition because it can deal with the undersampled problem. One of the most popular algorithms, the two dimensional linear discriminant analysis (2DLDA), has been identified to be effective to encode the discriminative information for training matrix represented samples. However, 2DLDA does not converge in the training stage. This paper presents an evolutionary computation based solution, referred to as E-2DLDA, to provide a convergent training stage for 2DLDA. In E-2DLDA, every randomly generated candidate projection matrices are first normalized. The evolutionary computation method optimizes the projection matrices to best separate different classes. Experimental results show E-2DLDA is convergent and outperforms 2DLDA. ©2009 IEEE.

Chiarella, C, Dieci, R & He, X 2009, 'Time-varying beta: A bounded rational equilibrium approach', Seminar Presentation, Bocconi University, Milan, Italy.

Chiarella, C, Dieci, R & He, X 2009, 'Time-varying beta: A bounded rational equlibrium approach', Evolution and Market Behavior in Economics and Finance Workshop, Pisa, Italy.

Chiarella, C, He, X & Zheng, M 2009, 'Consensus investor and intertemporal asset pricing with heterogeneous beliefs', AsianFA International Conference, Brisbane, Australia.

Chiarella, C, He, X & Zheng, M 2009, 'Consensus investor and intertemporal asset pricing with heterogeneous beliefs', 15th International Conference on Computing in Economics and Finance, Sydney, Australia.

Chiarella, C, He, X & Zheng, M 2009, 'Consensus investor and intertemporal asset pricing with heterogeneous beliefs', International Conference on Economic Science with Heterogeneous Interacting Agents, Beijing, China.

Dong, H, Hussain, FK & Chang, E 2009, 'A Hybrid Concept Similarity Measure Model For Ontology Environment', On The Move To Meaningful Internet Systems: Otm 2009 Workshops, On the Move Confederated International Conference and Workshops, Springer-verlag Berlin, Vilamoura, PORTUGAL, pp. 848-857.
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In this paper, we present a hybrid concept similarity measure model for the ontology environment. Whilst to date many similar technologies have been developed for semantic networks, few of them can be directly applied to the semantic-rich ontology enviro

Dong, H, Hussain, FK & Chang, E 2009, 'A Hybrid Service Metadata Clustering Methodology In The Digital Ecosystem Environment', 2009 International Conference On Advanced Information Networking And Applications Workshops: Waina, Vols 1 And 2, International Conference on Advanced Information Networking and Applications, IEEE, Bradford, ENGLAND, pp. 238-243.
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Digital Ecosystem is defined as 'an open, loosely coupled, domain clustered, demand-driven, self-organizing and agent-based environment, in which each species is proactive and responsive for its own benefit and profit' [1]. Species in the Digital Ecosyst

Dong, H, Hussain, FK & Chang, E 2009, 'An Ontology-based Webpage Classification Approach For The Knowledge Grid Environment', 2009 Fifth International Conference On Semantics, Knowledge And Grid (skg 2009), International Conference on Semantics, Knowledge and Grid, IEEE, Zhuhai, PEOPLES R CHINA, pp. 120-127.
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With the rapid growth of the amount of information available in the Web, webpage classification technologies are widely employed by many search engines in order to formulate user queries and make users' search tasks easier. Knowledge Grid is a new form o

Dong, H, Hussain, FK & Chang, E 2009, 'Paradox in applications of semantic similarity models in information retrieval', Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, pp. 60-68.
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Semantic similarity models are a series of mathematical models for computing semantic similarity values among nodes in a semantic net. In this paper we reveal the paradox in the applications of these semantic similarity models in the field of information retrieval, which is that these models rely on a common prerequisite - the words of a user query must correspond to the nodes of a semantic net. In certain situations, this sort of correspondence can not be carried out, which invalidates the further working of these semantic similarity models. By means of two case studies, we analyze these issues. In addition, we discuss some possible solutions in order to address these issues. Conclusion and future works are drawn in the final section. © 2009 ICST Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering.

Dong, H, Hussain, FK & Chang, E 2009, 'Semantic service search, service evaluation and ranking in Service Oriented Environment', Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, pp. 111-117.
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The theory of Service Oriented Environment (SOE) emerges with advanced connectivity of the Internet technologies, openness of business environment and prosperousness of business activities. Service, as a critical object impenetrating every corner of SOE, is a hot research topic in many research domains. Software Engineering (SE), as a subject in engineering field, its researchers pay more attention to supporting advanced technologies for promoting service activities in SOE. In this paper, we draw the position in the research field of semantic service search, service evaluation and ranking in SOE. By means of the case study and literature review research approach, we discover the research motivations and research issues in this field. © 2009 ICST Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering.

Dong, H, Hussain, FK & Chang, E 2009, 'State Of The Art In Semantic Focused Crawlers', Computational Science And Its Applications - Iccsa 2009, Pt Ii, International Conference on Computational Science and Its Applications, Springer-verlag Berlin, Seoul, SOUTH KOREA, pp. 910-924.
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Nowadays, the research of focused crawler approaches the field of semantic web, along with the appearance of increasing semantic web documents and the rapid development of ontology mark-up languages. Semantic focused crawlers are a series of focused craw

Du, R, Wu, Q, He, S, Jia, W & Wei, D 2009, 'Facial Expression Recognition Using Histogram Variances Faces', Proceedings of the Ninth IEEE Computer Society Workshop on Application of Computer Vision (WACV 2009), IEEE Workshop on Applications of Computer Vision, IEEE, Snowbird, USA, pp. 341-347.
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In human's expression recognition, the representation of expression features is essential for the recognition accuracy. In this work we propose a novel approach for extracting expression dynamic features from facial expression videos. Rather than utilising statistical models e.g. Hidden Markov Model (HMM), our approach integrates expression dynamic features into a static image, the Histogram Variances Face (HVF), by fusing histogram variances among the frames in a video. The HVFs can be automatically obtained from videos with different frame rates and immune to illumination interference. In our experiments, for the videos picturing the same facial expression, e.g., surprise, happy and sadness etc., their corresponding HVFs are similar, even though the performers and frame rates are different. Therefore the static facial recognition approaches can be utilised for the dynamic expression recognition. We have applied this approach on the well-known Cohn-Kanade AUCoded Facial Expression database then classified HVFs using PCA and Support Vector Machine (SVMs), and found the accuracy of HVFs classification is very encouraging.

Fachrunnisa, O, Hussain, FK & Chang, E 2009, 'Current research trends and directions for future research in trust maintenance for virtual environments', iiWAS2009 - The 11th International Conference on Information Integration and Web-based Applications and Services, pp. 718-724.
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Trust is widely acknowledged as being important for the efficient and effective operation of business in virtual environments. This is because trust functions like a glue that holds and links virtual agents together as they relate and collaborate remotely. In virtual environments, trust needs to be established swiftly as there is little time to build it gradually in the absence of face-to-face meetings. However, the manner in which trust develops and is maintained is a critical factor in relationships, both physically and virtually. In virtual environments, trust needs to be managed including network connection as well as social aspects of interaction. Maintaining trust in virtual environments is defined as an effort to maximize the benefits of such virtual relationship and to prevent the level of trust from decreasing. In this paper, we undertake a general survey of the current situation of trust maintenance in virtual environments. We review several researches from the perspective of terminology, strategies presented, and types of strategies. We describe the benefits and shortcomings of each strategy, conduct an integrative review of these strategic approaches, and make suggestions for future research. © 2010 ACM.

Geng, B, Tao, D, Xu, C, Yang, L & Hua, X 2009, 'Ensemble Manifold Regularization', IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, Miami USA, pp. 2396-2402.
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We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, pure cross-validation is considered but it does not necessarily scale up. A second problem derives from the suboptimality incurred by discrete grid search and overfitting problems. As a consequence, we developed an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR very carefully so that it (a) learns both the composite manifold and the semi-supervised classifier jointly; (b) is fully automatic for learning the intrinsic manifold hyperparameters implicitly; (c) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption; and (d) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Extensive experiments over both synthetic and real datasets show the effectiveness of the proposed framework.

Geng, B, Tao, D, Xu, C, Yang, L & Hua, XS 2009, 'Ensemble manifold regularization', 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, pp. 2396-2402.
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We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, pure cross-validation is considered but it does not necessarily scale up. A second problem derives from the suboptimality incurred by discrete grid search and overfitting problems. As a consequence, we developed an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR very carefully so that it (a) learns both the composite manifold and the semi-supervised classifier jointly; (b) is fully automatic for learning the intrinsic manifold hyperparameters implicitly; (c) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption; and (d) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Extensive experiments over both synthetic and real datasets show the effectiveness of the proposed framework. © 2009 IEEE.

Goh, T-T, Bose, S, Ng, WK, Cao, L & Lee, VCS 2009, 'Editorial to the Proceedings of Mobile Technologies in Enterprise Computing Systems Workshop (MTECS 2009)', 2009 13TH ENTERPRISE DISTRIBUTED OBJECT COMPUTING CONFERENCE WORKSHOPS (EDOCW 2009), 13th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2009), IEEE, Auckland, NEW ZEALAND, pp. 138-+.
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He, S, Li, J, Wei, D, Jia, W & Wu, Q 2009, 'Canny edge detection on a virtual hexagonal image structure', 2009 Joint Conferences on Pervasive Computing (JCPC2009), IEEE, Taipei, Taiwan, pp. 167-172.
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Canny edge detector is the most popular tool for edge detection and has many applications in the areas of image processing, multimedia and computer vision. The Canny algorithm optimizes the edge detection through noise filtering using an optimal function approximated by the first derivative of a Gaussian. It identifies the edge points by computing the gradients of light intensity function based on the fact that the edge points likely appear where the gradient magnitudes are large. Hexagonal structure is an image structure alternative to traditional square image structure. Because all the existing hardware for capturing image and for displaying image are produced based on square structure, an approach that uses linear interpolation described for conversion between square and hexagonal structures. Gaussian filtering together with gradient computation is performed on the hexagonal structure. The pixel edge strengths on the square structure are then estimated before the thresholds of Canny algorithm are applied to determine the final edge map. The experimental results show the edge detection on hexagonal structure using static and video images, and the comparison with the results using Canny algorithm on square structure.

He, X 2009, 'Does the market maker stabilize the market?', Paul Woolley Centre for Capital Market Dysfunctionality 2009 Annual Conference, Sydney, Australia.

He, X & Shi, L 2009, 'Bounded rational equilibrium and risk premium', International Conference of Financial Crises, Perth, Australia.

He, X & Zheng, M 2009, 'Dynamics of moving average rules in a continuous-time financial market model', Seminar Presentation, University of Adelaide, Adelaide, Australia.

Homayounfard, H & Kennedy, PJ 2009, 'HDAX: Historical Symbolic Modelling of Delay Time Series in a Communications Network', Data Mining and Analytics 2009 (AusDM'09): Conference in Research and Practice in Information Technology Volume 101, Australian Data Mining Conference, Australian Computer Society, Melbourne, Australia, pp. 129-137.
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There are certain performance parameters like packet delay, delay variation (jitter) and loss, which are decision factors for online quality of service (QoS) traffic routing. Although considerable efforts have been placed on the Internet to assure QoS, the dominant TCP/IP - like the best-effort communications policy - does not provide sufficient guarantee without abrupt change in the protocols. Estimation and forecasting end-to-end delay and its variations are essential tasks in network routing management for detecting anomalies. A large amount of research has been done to provide foreknowledge of network anomalies by characterizing and forecasting delay with numerical forecasting methods.

Huang, Q, Jin, L & Tao, D 2009, 'An Unsupervised Feature Ranking Scheme by Discovering Biclusters', 2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9, IEEE International Conference on Systems, Man and Cybernetics, IEEE, San Antonio, TX, pp. 4970-+.
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Hussain, O, Dillon, TS, Chang, E & Hussain, FK 2009, 'A Fuzzy Inference Model For Risk Based Informed Decision-making In E-business', 2009 International Conference On Network-based Information Systems, International Conference on Network Based Information Systems, IEEE, Indianapolis, ID, pp. 519-524.
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Since its invention, the web has considerably advanced and it now provides its users with sophisticated technologies and processes for facilitating interactions. This has enabled users to complete their tasks by efficient ways and in turn increase their

Hussain, O, Dillon, TS, Chang, E & Hussain, FK 2009, 'A Methodology For Transactional Risk Assessment And Decision Making In E-business Interactions', IEEE International Conference on e-Business Engineering, 2009. ICEBE '09, IEEE International Conference on e-Business Engineering, IEEE Computer Soc, Macau, PEOPLES R CHINA, pp. 157-164.
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The development and advancement of technologies have enabled users to complete their tasks efficiently. They have also provided them with various options and alternatives to complete and achieve their tasks. In such an environment, it is imperative for a

Hussain, O, Dillon, TS, Chang, E & Hussain, FK 2009, 'Determining The Net Financial Risk For Decision Making In Business Interactions', 2009 International Conference On Advanced Information Networking And Applications, International Conference on Advanced Information Networking and Applications, IEEE, Bradford, ENGLAND, pp. 822-829.
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In a business interaction, transactional risk highlights the uncertainty associated in not achieving the desired outcomes. The assessment of transactional risk gives the interacting user the different levels of failure in achieving its desired outcomes a

Jamdagni, A, Tan, T, Liu, R, Nanda, P & He, S 2009, 'A Frame Work for Geometrical Structure Anomaly Detection Model', The sixth annual CSIRO ICT Centre Science and Engineering Conference, Centre Science and Engineering Conference, CSIRO, Australian Technology Park, NSW, Australia, pp. 109-110.
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The growth of Internet offers quality and convenience to human life, but at the same time provides a platform for hackers and criminals. The Internet security hence becomes an important issue. Intrusion Detection System (IDS) is designed to detect intrusion and also to prevent a system from being compromised. In this paper, we present a novel Geometrical Structure Anomaly Detection (GSAD) model. GSAD employs pattern recognition techniques previously used in human detection [2}.

Jamdagni, A, Tan, T, Nanda, P, He, S & Liu, R 2009, 'Intrusion Detection Using Geometrical Structure', Proceeding of 2009 International Conference on Frontier of Computer Science and Technology, International Conference on Frontier of Computer Science and Technology, IEEE Computer Society, Shanghai, China, pp. 327-333.
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Geometrical Structure Anomaly Detection (GSAD) model to detect intrusion using the packet payload in the network. GSAD takes into account the correlations among the packet payload features arranged in a geometrical structure. The representation is based on statistical analysis of Mahalanobis distances among payload features, which calculate the similarity of new data against precomputed profile.

Johnston, B & Williams, MA 2009, 'A formal framework for the symbol grounding problem', Proceedings of the 2nd Conference on Artificial General Intelligence, AGI 2009, pp. 79-84.
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A great deal of contention can be found within the published literature on grounding and the symbol grounding problem, much of it motivated by appeals to intuition and unfalsifiable claims. We seek to define a formal framework of representation grounding that is independent of any particular opinion, but that promotes classification and comparison. To this end, we identify a set of fundamental concepts and then formalize a hierarchy of six representational system classes that correspond to different perspectives on the representational requirements for intelligence, describing a spectrum of systems built on representations that range from symbolic through iconic to distributed and unconstrained. This framework offers utility not only in enriching our understanding of symbol grounding and the literature, but also in exposing crucial assumptions to be explored by the research community.

Johnston, BG & Williams, M 2009, 'A Formal Framework for the Symbol Grounding Problem', Proceedings of the Second Conference on Artificial General Intelligence, Conference on Artificial General Intelligence, Atlantis Press, Washington, USA, pp. 61-66.
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A great deal of contention can be found within the published literature on grounding and the symbol grounding problem, much of it motivated by appeals to intuition and unfalsifiable claims. We seek to define a formal framework of representa- tion grounding that is independent of any particular opinion, but that promotes classification and comparison. To this end, we identify a set of fundamental concepts and then formalize a hierarchy of six representational system classes that corre- spond to different perspectives on the representational require- ments for intelligence, describing a spectrum of systems built on representations that range from symbolic through iconic to distributed and unconstrained. This framework offers utility not only in enriching our understanding of symbol grounding and the literature, but also in exposing crucial assumptions to be explored by the research community.

Johnston, BG & Williams, M 2009, 'Autonomous Learning of Commonsense Simulations', International Symposium on Logical Formalizations of Commonsense Reasoning, Symposium on Logical Formalizations of Commonsense Reasoning, UTSePress, Toronto, Canada, pp. 73-78.
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Parameter-driven simulations are an effective and efficient method for reasoning about a wide range of commonsense scenarios that can complement the use of logical formalizations. The advantage of simulation is its simplified knowledge elicitation process: rather than building complex logical formulae, simulations are constructed by simply selecting numerical values and graphical structures. In this paper, we propose the application of machine learning techniques to allow an embodied autonomous agent to automatically construct appropriate simulations from its real-world experience. The automation of learning can dramatically reduce the cost of knowledge elicitation, and therefore result in models of commonsense with breadth and depth not possible with traditional engineering of logical formalizations.

Johnston, BG & Williams, M 2009, 'Conservative and Reward-driven Behavior Selection in a Commonsense Reasoning Framework', 2009 AAAI Symposium: Multirepresentational Architectures for Human-Level Intelligence, National Conference of the American Association for Artificial Intelligence, AAAI Press, Washington, USA, pp. 14-19.
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Comirit is a framework for commonsense reasoning that combines simulation, logical deduction and passive machine learning. While a passive, observation-driven approach to learning is safe and highly conservative, it is limited to interaction only with those objects that it has previously observed. In this paper we describe a preliminary exploration of methods for extending Comirit to allow safe action selection in uncertain situations, and to allow reward-maximizing selection of behaviors.

Juszczyszyn, K & Musial, K 2009, 'Structural Changes in an Email-Based Social Network', AGENT AND MULTI-AGENT SYSTEMS: TECHNOLOGIES AND APPLICATIONS, PROCEEDINGS, 3rd KES International Symposium on Agent and Multi-Agent Systems, SPRINGER-VERLAG BERLIN, Uppsala Univ, Uppsala, SWEDEN, pp. 40-49.

Juszczyszyn, K, Musial, A, Musial, K & Brodka, P 2009, 'Molecular Dynamics Modelling of the Temporal Changes in Complex Networks', 2009 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-5, IEEE Congress on Evolutionary Computation, IEEE, Trondheim, NORWAY, pp. 553-+.
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Kasisopha, N, Wongthongtham, P & Hussain, FK 2009, 'Semantic Wild As A Basis For Software Engineering Ontology Evolution', On The Move To Meaningful Internet Systems: Otm 2009 Workshops, On the Move Confederated International Conference and Workshops, Springer-verlag Berlin, Vilamoura, PORTUGAL, pp. 858-865.
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Ontology plays a vital role in sharing a common understanding of the domain among groups of people and provides terminology interpretable by machines. Recently, ontology has grown and continued to evolve constantly, but there are not many tools to provid

Kazienko, P, Musial, K & Zgrzywa, A 2007, 'Evaluation of node position based on email communication', CONTROL AND CYBERNETICS, Conference on Data Processing Technologies, POLISH ACAD SCIENCES SYSTEMS RESEARCH INST, Poznan, POLAND, pp. 67-86.

Kennedy, PJ, Ong, K & Christen, P 2009, 'Data Mining and Analytics', Data Mining and Analytics 2009 (AusDM'09), Australian Data Mining Conference, Australian Computer Society, Melbourne, Australia, pp. 1-218.

Kennedy, PJ, Ong, KL & Christen, P 2009, 'Preface', Conferences in Research and Practice in Information Technology Series.

Kusakunniran, W, Li, H & Zhang, J 2009, 'A direct method to self-calibrate a surveillance camera by observing a walking pedestrian', 2009 Digital Image Computing: Techniques and Applications, Digital Image Computing Techniques and Applications, IEEE, Melbourne, VIC, pp. 250-255.
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Recent efforts show that it is possible to calibrate a surveillance camera simply from observing a walking human. This procedure can be seen as a special application of the camera self-calibration technique. Several methods have been proposed along this

Kusakunniran, W, Wu, Q, Li, H & Zhang, J 2009, 'Automatic gait recognition using weighted binary pattern on video', Proceedings of Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, Advanced Video and Signal Based Surveillance, IEEE Computer Society, Genoa, Italy, pp. 49-54.
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Human identification by recognizing the spontaneous gait recorded in real-world setting is a tough and not yet fully resolved problem in biometrics research. Several issues have contributed to the difficulties of this task. They include various poses, different clothes, moderate to large changes of normal walking manner due to carrying diverse goods when walking, and the uncertainty of the environments where the people are walking. In order to achieve a better gait recognition, this paper proposes a new method based on Weighted Binary Pattern (WBP). WBP first constructs binary pattern from a sequence of aligned silhouettes. Then, adaptive weighting technique is applied to discriminate significances of the bits in gait signatures. Being compared with most of existing methods in the literatures, this method can better deal with gait frequency, local spatial-temporal human pose features, and global body shape statistics. The proposed method is validated on several well known benchmark databases. The extensive and encouraging experimental results show that the proposed algorithm achieves high accuracy, but with low complexity and computational time.

Kusakunniran, W, Wu, Q, Li, H & Zhang, J 2009, 'Multiple Views Gait Recognition using View Transformation Model Based on Optimized Gait Energy Image', Proceedings of 2009 IEEE 12th International Conference on Computer Vision Workshops, IEEE International Conference on Computer Vision Workshops, IEEE, Kyoto, Japan, pp. 1058-1064.
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Gait is one of well recognized biometrics that has been widely used for human identification. However, the current gait recognition might have difficulties due to viewing angle being changed. This is because the viewing angle under which the gait signature database was generated may not be the same as the viewing angle when the probe data are obtained. This paper proposes a new multi-view gait recognition approach which tackles the problems mentioned above. Being different from other approaches of same category, this new method creates a so called View Transformation Model (VTM) based on spatial-domain Gait Energy Image (GEI) by adopting Singular Value Decomposition (SVD) technique. To further improve the performance of the proposed VTM, Linear Discriminant Analysis (LDA) is used to optimize the obtained GEI feature vectors. When implementing SVD there are a few practical problems such as large matrix size and over-fitting. In this paper, reduced SVD is introduced to alleviate the effects caused by these problems. Using the generated VTM, the viewing angles of gallery gait data and probe gait data can be transformed into the same direction. Thus, gait signatures can be measured without difficulties. The extensive experiments show that the proposed algorithm can significantly improve the multiple view gait recognition performance when being compared to the similar methods in literature.

Lakemeyer, G, Morgenstern, L & Williams, MA 2009, 'Preface', Commonsense 2009 - Proceedings of the 9th International Symposium on Logical Formalizations of Commonsense Reasoning.

Li, L, Xu, G, Zhang, Y & Kitsuregawa, M 2009, 'Enhancing Web Search by Aggregating Results of Related Web Queries', Web Information Systems Engineering - WISE 2009 Lecture Notes in Computer Science, Web Information Systems Engineering - WISE 2009, Springer Berlin / Heidelberg, Poznan, Poland, pp. 203-217.
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Currently, commercial search engines have implemented methods to suggest alternative Web queries to users, which helps them specify alternative related queries in pursuit of finding needed Web pages. In this paper, we address the Web search problem on related queries to improve retrieval quality by devising a novel search rank aggregation mechanism. Given an initial query and the suggested related queries, our search system concurrently processes their search result lists from an existing search engine and then forms a single list aggregated by all the retrieved lists. In particular we propose a generic rank aggregation framework which considers not only the number of wins that an item won in a competition, but also the quality of its competitor items in calculat- ing the ranking of Web items. The framework combines the traditional and random walk based rank aggregation methods to produce a more reasonable list to users. Experimental results show that the proposed approach can clearly improve the retrieval quality in a parallel man- ner over the traditional search strategy that serially returns result lists. Moreover, we also empirically investigate how different rank aggregation methods affect the retrieval performance.

Li, Q & Tao, D 2009, 'Detecting image points of general imbalance', Proceedings - International Conference on Image Processing, ICIP, pp. 337-340.
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Imbalance oriented selection scheme was recently proposed to detect stable image points in weakly or sparsely textured images. The scheme chooses image points whose one-pixel-wide directional intensity variations can be clustered into two imbalanced classes as candidates. In this paper, we propose general imbalance decided by multi-pixel-wide directional intensity variations. We present a case study of general imbalanced points in road sign images, which demonstrates the good potential of general imbalanced points. ©2009 IEEE.

Li, Q, Xia, Z & Tao, D 2009, 'A GLOBAL-TO-LOCAL SCHEME FOR IMBALANCED POINT MATCHING', 2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6, 16th IEEE International Conference on Image Processing, IEEE, Cairo, EGYPT, pp. 2117-+.
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Li, Z, Wright, S, Fu, Y, Zhai, F & Tao, D 2009, 'Message from the MCC'09 Chairs', Proceedings - International Conference on Computer Communications and Networks, ICCCN.
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Liu, Q, Chen, Y-PP & Li, J 2009, 'High Functional Coherence in k-Partite Protein Cliques of Protein Interaction Networks', 2009 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, IEEE International Conference on Bioinformatics and Biomedicine (BIBMW 2009), IEEE COMPUTER SOC, Washington, DC, pp. 111-+.
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Luo, S, Hu, Q, He, S, Li, J, Jin, J & Park, M 2009, 'Automatic Liver Parenchyma Segmentation from Abdominal CT Images Using Support Vector Machines', 2009 IEEE/ICME International Conference on Complex Medical Engineering, IEEE, Tempe, AZ, USA, pp. 1-5.
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This paper presents an automatic liver parenchyma segmentation algorithm that can segment liver in abdominal CT images. There are three major steps in the proposed approach. Firstly, a texture analysis is applied to input abdominal CT images to extract pixel level features. In this step, wavelet coefficients are used as texture descriptors. Secondly, support vector machines (SVMs) are implemented to classify the data into pixel-wised liver area or non-liver area. Finally, integrated morphological operations are designed to remove noise and finally delineate the liver. Our unique contributions to liver segmentation are twofold: one is that it has been proved through experiments that wavelet features present good classification result when SVMs are used; the other is that the combination of morphological operations with the pixel-wised SVM classifier can delineate volumetric liver accurately. The algorithm can be used in an advanced computer-aided liver disease diagnosis and liver surgical planning system. Examples of applying the proposed algorithm on real CT data are presented with performance validation based on the comparison between the automatically segmented results and manually segmented ones.

Ma, X, Pan, R, Wang, L & He, S 2009, 'A Method Based on Orientation Field for Skew Correction of License Plate', 2009 Second Asia-Pacific Conference on Computational Intelligence and Industrial Applications, Asia-Pacific Conference on Computational Intelligence and Industrial Applications, IEEE Industrial Electronics Society, Wuhan, China, pp. 308-311.
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Character recognitions are generally very sensitive to skew in the automatic license plate recognition (ALPR) system. The skew correction of license plate thus is an important step in ALPR. In this paper, we propose an orientation-based method for skew detection. The license plate image is firstly divided into a set of 5x5 non-overlapping blocks. The local orientation of each black is estimated by gradients of pixels in the block. Next, the direction histogram which can reveal the overall orientation information in the license plate image is counted. The skew angle of license plate is detected by the local maximum of the direction histogram. The experimental results demonstrate the great robustness and efficiency of our method.

Merigo, JM 2009, 'On the Use of the OWA Operator in the Weighted Average and its Application in Decision Making', WORLD CONGRESS ON ENGINEERING 2009, VOLS I AND II, World Congress on Engineering 2009, INT ASSOC ENGINEERS-IAENG, Imperial Coll London, London, ENGLAND, pp. 82-87.

Merigo, JM 2009, 'Probabilistic Decision Making with the OWA Operator and its Application in Investment Management', PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE, Joint World Congress of International-Fuzzy-Systems-Association (IFSA)/European Conference of European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT), EUROPEAN SOC FUZZY LOGIC & TECHNOLOGY, Lisbon, PORTUGAL, pp. 1364-1369.

Merigo, JM 2009, 'The Fuzzy Probabilistic Weighted Averaging Operator and its Application in Decision Making', 2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, 9th International Conference on Intelligent Systems Design and Applications, IEEE, Univ Pisa, Pisa, ITALY, pp. 485-490.
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Merigo, JM & Gil-Lafuente, AM 2009, 'An Overview of Fuzzy Research in the ISI Web of Knowledge', WORLD CONGRESS ON ENGINEERING 2009, VOLS I AND II, World Congress on Engineering 2009, INT ASSOC ENGINEERS-IAENG, Imperial Coll London, London, ENGLAND, pp. 43-48.

Merigo, JM & Gil-Lafuente, AM 2009, 'Some Basic Results of Fuzzy Research in the ISI Web of Knowledge', 2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, 9th International Conference on Intelligent Systems Design and Applications, IEEE, Univ Pisa, Pisa, ITALY, pp. 1215-1220.
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Merigo, JM & Gil-Lafuente, AM 2009, 'The Fuzzy Induced Generalized OWA Operator and its Application in Business Decision Making', PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE, Joint World Congress of International-Fuzzy-Systems-Association (IFSA)/European Conference of European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT), EUROPEAN SOC FUZZY LOGIC & TECHNOLOGY, Lisbon, PORTUGAL, pp. 1661-1666.

Merigo, JM, Gil-Lafuente, AM & Gil-Aluja, J 2009, 'Induced Aggregation Operators in the Generalized Adequacy Coefficient', WORLD CONGRESS ON ENGINEERING 2009, VOLS I AND II, World Congress on Engineering 2009, INT ASSOC ENGINEERS-IAENG, Imperial Coll London, London, ENGLAND, pp. 7-11.

Merigo, JM, Gil-Lafuente, AM & Martorell, O 2009, 'On the Use of the Uncertain Induced OWA Operator and the Uncertain Weighted Average and its Application in Tourism Management', 2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, 9th International Conference on Intelligent Systems Design and Applications, IEEE, Univ Pisa, Pisa, ITALY, pp. 856-+.
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Moghaddam, Z & Piccardi, M 2009, 'Deterministic Initialization of Hidden Markov Models for Human Action Recognition', DICTA (Digital Image Computing: Techniques and Applications) 2009, Digital Image Computing Techniques and Applications, IEEE Computer Society, Conference Publishing Services (CPS), Melbourne, Australia, pp. 188-195.
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Human action recognition is often approached in terms of probabilistic models such as the hidden Markov model or other graphical models. When learning such models by way of Expectation- Maximisation algorithms, arbitrary choices must be made for their initial parameters. Often, solutions for the selection of the initial parameters are based on random functions. However, in this paper, we argue that deterministic alternatives are preferable, and propose various methods. Experiments on a video dataset prove that the deterministic initialization is capable of achieving an accuracy that is comparable to or above the average from random initializations and suffers from no deviation thanks to its deterministic nature. The methods proposed naturally extend to be used with other graphical models such as dynamic Bayesian networks and conditional random fields.

Mudugamuwa, D, He, S, Ahn, C & Yang, J 2009, 'Higher order prediction for sub-pixel motion estimation', IEEE 16th International Conference on Image Processing (ICIP2009), IEEE International Conference on Image Processing, IEEE, Cairo, Egypt, pp. 1585-1588.
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Estimating motion between two frames of a video sequence, up to sub-pixel accuracy, is a critical task for many image processing applications. Efficient block matching algorithms were proposed for motion estimation up to pixel accuracy. Applying these fast block search algorithms to up-sampled and interpolated frames can produce good results but with significant increase in computations. To reduce the number of search points, and therefore the computational cost, quadratic prediction was proposed earlier to predict the location of minimum block matching error, and then to limit the search window to the vicinity of the predicted location. In this paper we investigate the typical behavior of block matching error surface and propose an improved higher order prediction that models the error surface more accurately, utilizing additional local image behavior. Initial experiments have proved promising results of about 50% more improvement in PSNR compared to quadratic prediction with only a marginal increase in the computational cost

Mudugamuwa, D, He, S, Wei, D & Ahn, C 2009, 'Super-Resolution by Prediction Based Sub-pel Motion Estimation', Proceedings of Image and Vision Computing New Zealand 2009 (IVCNZ2009), Image and Vision Computing Conference, IEEE, Wellington, New Zealand, pp. 282-287.
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Super-resolution reconstruction produces highresolution images from a set of low-resolution images of the same scene. In the last two and a half decades, many super-resolution algorithms have been proposed. These algorithms are usually very sensitive to their assumed models of data and noise, and also computationally expensive for many practical applications. In this paper we adopt computationally efficient prediction based sub-pel motion estimation to produce a fast super-resolution reconstruction that can also accommodate generic motion patterns. The proposed algorithm adaptively exploits the available high frequency content in adjacent video frames to generate high resolution video frames. Initial experiments showed promising results of around 2dB, PSNR improvement, over single frame bi-linear interpolation.

Musial, K & Juszczyszyn, K 2009, 'Motif-based Analysis of Social Position Influence on Interconnection Patterns in Complex Social Network', 2009 FIRST ASIAN CONFERENCE ON INTELLIGENT INFORMATION AND DATABASE SYSTEMS, 1st Asian Conference on Intelligent Information and Database Systems, IEEE, Dong Hoi, VIETNAM, pp. 34-39.
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Musial, K & Juszczyszyn, K 2009, 'Properties of Bridge Nodes in Social Networks', COMPUTATIONAL COLLECTIVE INTELLIGENCE: SEMANTIC WEB, SOCIAL NETWORKS AND MULTIAGENT SYSTEMS, 1st International Conference on Computational Collective Intelligence, SPRINGER-VERLAG BERLIN, Wroclaw, POLAND, pp. 357-364.

Musial, K, Juszczyszyn, K, Gabrys, B & Kazienko, P 2008, 'Patterns of Interactions in Complex Social Networks Based on Coloured Motifs Analysis', ADVANCES IN NEURO-INFORMATION PROCESSING, PT II, 15th International Conference on Neuro-Information Processing, SPRINGER-VERLAG BERLIN, Auckland, NEW ZEALAND, pp. 607-+.

Musiał, K, Kazienko, P & Bródka, P 2009, 'User position measures in social networks', Proceedings of the 3rd Workshop on Social Network Mining and Analysis, SNA-KDD '09.
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Network analysis offers many centrality measures that are successfully utilized in the process of investigating the social network profile. The most important and representative measures are presented in the paper. It includes indegree centrality, proximity prestige, rank prestige, node position, outdegree centrality, eccentrality, closeness centrality, and betweenes centrality. Both feature analysis and experimental comparative studies revealed the general profile of selected measures.

Novianto, R & Williams, M 2009, 'The Role of Attention in Robot Self-Awareness', Robot and Human Interactive Communication, 2009. RO-MAN 2009. The 18th IEEE International Symposium on, IEEE International Symposium on Robot and Human Interactive Communication, IEEE, Toyama, pp. 1047-1053.
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A robot may not be truly self-aware even though it can have some characteristics of self-awareness, such as having emotional states or the ability to recognize itself in the mirror. We define self-awareness in robots to be characterized by the capacity to direct attention toward their own mental state. This paper explores robot self-awareness and the role that attention plays in the achievement self-awareness. We propose a new attention based approach to self-awareness called ASMO and conduct a comparative analysis of approaches that highlights the innovation and benefits of ASMO. We then describe how our attention based self-awareness can be designed and used to develop self-awareness in state-of-the-art humanoidal robots.

Otoom, AF, Concha, OP, Gunes, H & Piccardi, M 2009, 'Mixtures of Normalized Linear Projections', Proceedings of 11th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2009, Advanced Concepts for Intelligent Vision Systems, Springer, Bordeaux, France, pp. 66-76.
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High dimensional spaces pose a challenge to any classification task. In fact, these spaces contain much redundancy and it becomes crucial to reduce the dimensionality of the data to improve analysis, density modeling, and classification. In this paper, we present a method for dimensionality reduction in mixture models and its use in classification. For each component of the mixture, the data are projected by a linear transformation onto a lower-dimensional space. Subsequently, the projection matrices and the densities in such compressed spaces are learned by means of an Expectation Maximization (EM) algorithm. However, two main issues arise as a result of implementing this approach, namely: 1) the scale of the densities can be different across the mixture components and 2) a singularity problem may occur. We suggest solutions to these problems and validate the proposed method on three image data sets from the UCI Machine Learning Repository. The classification performance is compared with that of a mixture of probabilistic principal component analysers (MPPCA). Across the three data sets, our accuracy always compares favourably, with improvements ranging from 2.5% to 35.4%.

Paisitkriangkrai, S, Shen, C & Zhang, J 2009, 'Efficiently training a better visual detector with sparse eigenvectors', 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Miami, FL, pp. 1129-1136.
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Face detection plays an important role in many vision applications. Since Viola and Jones [1] proposed the first real-time AdaBoost based object detection system, much ef- fort has been spent on improving the boosting method. In this work, we first show

Pan, J, Pang, Y, Li, X, Yuan, Y & Tao, D 2009, 'A fast feature extraction method', ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 1797-1800.
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A fast subspace analysis and feature extraction algorithm is proposed which is based on fast Haar transform and integral vector. In rapid object detection and conventional binary subspace learning, Haar-like functions have been frequently used but true Haar functions are seldom employed. In this paper we have shown that true Haar functions can be successfully used to accelerate subspace analysis and feature extraction. Both the training and testing speed of the proposed method is higher than conventional algorithms. Experimental results on face database demonstrated its effectiveness. ©2009 IEEE.

Parvin, S, Ali, S, Singh, J, Hussain, FK & Han, S 2009, 'Towards DOS Attack Prevention Based On Clustering Architecture In Mobile IP Communication', IECON: 2009 35th Annual Conference Of IEEE Industrial Electronics, Vols 1-6, IEEE Industrial Electronics Society Annual Conference, IEEE, Porto, Portugal, pp. 3007-3012.
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Mobile IP communication, like wired communication and mobile ad hoc networking, is vulnerable to Denial-of-Service (DOS) attacks. In this paper, we propose using a lightweight packet filtering technique in different domains and base stations to reduce/el

Raza, M, Hussain, FK & Chang, E 2009, 'Quality measures for digital business ecosystems formation', Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, pp. 118-121.
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To execute a complex business task, business entities may need to collaborate with each other as individually they may not have the capability or willingness to perform the task on its own. Such collaboration can be seen implemented in digital business ecosystems in the form of simple coalitions using multi-agent systems or by employing Electronic Institutions. A major challenge is choosing optimal partners who will deliver the agreed commitments, and act in the coalition's interest. Business entities are scaled according to their quality level. Determining the quality of previously unknown business entities and predicting the quality of such an entity in a dynamic environment are crucial issues in Business Ecosystems. A comprehensive quality management system grounded in the concepts of Trust and Reputation can help address these issues. © 2009 ICST Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering.

Shen, J, Pang, H, Tao, D & Li, X 2009, 'Dual phase learning for large scale video gait recognition', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 500-510.
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Accurate gait recognition from video is a complex process involving heterogenous features, and is still being developed actively. This article introduces a novel framework, called GC2F, for effective and efficient gait recognition and classification. Adopting a "refinement-and- classification" principle, the framework comprises two components: 1) a classifier to generate advanced probabilistic features from low level gait parameters; and 2) a hidden classifier layer (based on multilayer perceptron neural network) to model the statistical properties of different subject classes. To validate our framework, we have conducted comprehensive experiments with a large test collection, and observed significant improvements in identification accuracy relative to other state-of-the-art approaches. © 2010 Springer-Verlag Berlin Heidelberg.

Shen, J, Tao, D & Li, X 2009, 'Robust semantic concept detection in large video collections', Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, pp. 635-638.
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With explosive amounts of video data emerging from the Internet, automatic video concept detection is becoming very important and has been received great attention. However, reported approaches mainly suffer from low identification accuracy and poor robustness over different concepts. One of the main reason is that the existing approaches typically isolate the video signature generation from the process of classifier training. Also, very few approaches consider effects of multiple video features. The paper describes a novel approach fusing different information from diverse knowledge sources to facilitate effective video concept detection. The system is designed based on CM*F scheme [7], [5] and its basic architecture contains two core components including 1) CM*F based video signature generation scheme and 2) CM*F based video concept detector. To evaluate the approach proposed, an extensive experimental study on two large video databases has been carried out. The results demonstrate the superiority of the method in terms of effectiveness and robustness. ©2009 IEEE.

Si, S, Tao, D & Chan, KP 2009, 'Cross-domain web image annotation', ICDM Workshops 2009 - IEEE International Conference on Data Mining, pp. 184-189.
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In recent years, cross-domain learning algorithms have attracted much attention to solve labeled data insufficient problem. However, these cross-domain learning algorithms cannot be applied for subspace learning, which plays a key role in multimedia, e.g., web image annotation. This paper envisions the cross-domain discriminative subspace learning and provides an effective solution to cross-domain subspace learning. In particular, we propose the cross-domain discriminative Hessian Eigenmaps or CDHE for short. CDHE connects the training and the testing samples by minimizing the quadratic distance between the distribution of the training samples and that of the testing samples. Therefore, a common subspace for data representation can be preserved. We basically expect the discriminative information to separate the concepts in the training set can be shared to separate the concepts in the testing set as well and thus we have a chance to address above cross-domain problem duly. The margin maximization is duly adopted in CDHE so the discriminative information for separating different classes can be well preserved. Finally, CDHE encodes the local geometry of each training class in the local tangent space which is locally isometric to the data manifold and thus can locally preserve the intra-class local geometry. Experimental evidence on real world image datasets demonstrates the effectiveness of CDHE for cross-domain web image annotation. © 2009 IEEE.

Si, S, Tao, D & Chan, KP 2009, 'Transfer discriminative logmaps', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 131-143.
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In recent years, transfer learning has attracted much attention in multimedia. In this paper, we propose an efficient transfer dimensionality reduction algorithm called transfer discriminative Logmaps (TDL). TDL finds a common feature so that 1) the quadratic distance between the distribution of the training set and that of the testing set is minimized and 2) specific knowledge of the training samples can be conveniently delivered to or shared with the testing samples. Drawing on this common feature in the representation space, our objective is to develop a linear subspace in which discriminative and geometric information can be exploited. TDL adopts the margin maximization to identify discriminative information between different classes, while Logmaps is used to preserve the local-global geodesic distance as well as the direction information. Experiments carried out on both synthetic and real-word image datasets show the effectiveness of TDL for cross-domain face recognition and web image annotation. © 2009 Springer-Verlag Berlin Heidelberg.

Song, D & Tao, D 2009, 'Discrminative Geometry Preserving Projections', 2009 16th IEEE International Conference On Image Processing, Vols 1-6, IEEE International Conference on Image Processing, IEEE, Cairo, Egypt, pp. 2429-2432.
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Dimension reduction algorithms have attracted a lot of attentions in face recognition and human gait recognition because they can select a subset of effective and efficient discriminative features. In this paper, we apply the Discriminative Geometry Pres

Stiglic, G, Kocbek, S & Kokol, P 2009, 'Unsupervised Variance Based Preprocessing of Microarray Data', 2009 22ND IEEE INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, 22nd IEEE International Symposium on Computer-Based Medical Systems, IEEE, Albuquerque, NM, pp. 69-72.

Su, Y, Tao, D, Li, X & Gao, X 2009, 'Texture Representation In AAM Using Gabor Wavelet And Local Binary Patterns', 2009 IEEE International Conference On Systems, Man And Cybernetics (SMC 2009), Vols 1-9, IEEE International Conference on Systems, Man and Cybernetics, IEEE, San Antonio, TX, pp. 3274-3279.
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Active appearance model (AAM) has been widely used for modeling the shape and the texture of deformable objects and matching new ones effectively. The traditional AAM consists of two parts, shape model and texture model. In the texture model, for the sak

Tan, T, He, S & Nanda, P 2009, 'Web Service Locating Unit in RFID-centric Anti-counterfeit System', Proceeding of 2009 IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE Computer Society, Chengdu, Sichuan, China, pp. 389-393.
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Web Service Locating Unit (WSLU) is able to simplify the deployment of RFID-centric anti-counterfeit system over the Internet.

Tang, M, Zhou, Y, Cui, P, Wang, W, Li, J, Zhang, H, Hou, Y & Yan, B 2009, 'Discovery of Migration Habitats and Routes of Wild Bird Species by Clustering and Association Analysis', ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS, 5th International Conference on Advanced Data Mining and Applications, SPRINGER-VERLAG BERLIN, Beijing, PEOPLES R CHINA, pp. 288-+.

Thi, T, Lu, S, Zhang, J, Cheng, L & Wang, L 2009, 'Human body articulation for action recognition in video sequences', 6th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009, IEEE International Conference on Video and Signal Based Surveillance (AVSS), IEEE, Genova, pp. 92-97.
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This paper presents a new technique for action recognition in video using human body part-based approach, combining both local feature description of each body part, and global graphical model structure of the human action. The human body is divided into

Tian, B, Chang, E, Dillon, TS, Han, S & Hussain, FK 2009, 'An Authenticated Self-healing Key Distribution Scheme Based On Bilinear Pairings', 2009 6th IEEE Consumer Communications And Networking Conference, Vols 1 And 2, IEEE Consumer Communications and Networking Conference, IEEE, Las Vegas, NV, pp. 1061-1065.
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Self-healing key distribution mechanism can be utilized for distributing session keys over an unreliable network. A self-healing key distribution scheme using bilinear pairings is proposed in this paper. As far as we know, it is the first pairing-based a

Tsai, PC, Tran, TP & Cao, L 2009, 'Expression-invariant Facial Identification', Proceedings 2009 IEEE International Conference on Systems, Man and Cybernetics, IEEE Conference on Systems, Man and Cybernetics, IEEE, San Antonio, Texas, USA, pp. 5151-5155.
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Facial identification has been recognized as most simple and non-intrusive technology that can be applied in many places. However, there are still many unsolved facial identification problems due to different intra-personal variations. In particular, when images of the databases appear at different facial expressions, most currently available facial recognition approaches encounter the expression-invariant problem in which neutral faces are difficult to be recognized. In this paper, a new approach is proposed to transform facial expressions to neutral-face like images; hence enabling image retrieval systems to robustly identify a persons face for which its learning and testing face images differ in facial expression.

Vezzani, R, Piccardi, M & Cucchiara, R 2009, 'An Efficient Bayesian Framework For On-Line Action Recognition', Proceedings of the 16th IEEE International Conference on Image Processing (ICIP), 2009, IEEE International Conference on Image Processing, IEEE, Cairo, Egypt, pp. 3553-3556.
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On-line action recognition from a continuous stream of actions is still an open problem with fewer solutions proposed compared to time-segmented action recognition. The most challenging task is to classify the current action while finding its time boundaries at the same time. In this paper we propose an approach capable of performing on-line action segmentation and recognition by means of batteries of HMM taking into account all the possible time boundaries and action classes. A suitable Bayesian normalization is applied to make observation sequences of different length comparable and computational optimizations are introduce to achieve real-time performances. Results on a well known action dataset prove the efficacy of the proposed method.

Wang, B, Gao, X, Tao, D, Li, X & Li, J 2009, 'The Gabor-based Tensor Level Set Method For Multiregional Image Segmentation', Computer Analysis Of Images And Patterns, Proceedings, International Conference on Computer Analysis of Images and Patterns, Springer-Verlag Berlin, Munster, Germany, pp. 987-994.
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This paper represents a new level set method for multiregional image segmentation. It employs the Gabor filter bank to extract local geometrical features and builds the pixel tensor representation whose dimensionality is reduced by using the offline tens

Wang, W, Shen, C, Zhang, J & Paisitkriangkrai, S 2009, 'A two-layer night-time vehicle detector', 2009 Digital Image Computing: Techniques and Applications, Digital Image Computing Techniques and Applications, IEEE, Melbourne, VIC, pp. 162-167.
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We present a two-layer night time vehicle detector in this work. At the first layer, vehicle headlight detection [1, 2, 3] is applied to find areas (bounding boxes) where the possible pairs of headlights locate in the image, the Haar feature based AdaBoo

Wang, W, Xiao, C, Lin, X & Zhang, C 2009, 'Efficient approximate entity extraction with edit distance constraints', Proceedings of the 35th SIGMOD international conference on Management of data, ACM Special Interest Group on Management of Data Conference, ACM, Rhode Island, USA, pp. 759-770.
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Named entity recognition aims at extracting named entities from unstructured text. A recent trend of named entity recognition is finding approximate matches in the text with respect to a large dictionary of known entities, as the domain knowledge encoded in the dictionary helps to improve the extraction performance. In this paper, we study the problem of approximate dictionary matching with edit distance constraints. Compared to existing studies using token-based similarity constraints, our problem definition enables us to capture typographical or orthographical errors, both of which are common in entity extraction tasks yet may be missed by token-based similarity constraints. Our problem is technically challenging as existing approaches based on q-gram filtering have poor performance due to the existence of many short entities in the dictionary. Our proposed solution is based on an improved neighborhood generation method employing novel partitioning and prefix pruning techniques. We also propose an efficient document processing algorithm that minimizes unnecessary comparisons and enumerations and hence achieves good scalability. We have conducted extensive experiments on several publicly available named entity recognition datasets. The proposed algorithm outperforms alternative approaches by up to an order of magnitude.

Wang, Y, Gao, X, Li, X, Tao, D & Wang, B 2009, 'Embedded Geometric Active Contour With Shape Constraint For Mass Segmentation', Computer Analysis Of Images And Patterns, Proceedings, International Conference on Computer Analysis of Images and Patterns, Springer-Verlag Berlin, Munster, Germany, pp. 995-1002.
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Mass boundary segmentation plays an important role in computer aided diagnosis (CAD) system. Since the shape and boundary are crucial discriminant features in CAD, the active contour methods are more competitive in mass segmentation. However, the general

Wen, J, Gao, X, Li, X & Tao, D 2009, 'Incremental Learning Of Weighted Tensor Subspace For Visual Tracking', 2009 IEEE International Conference On Systems, Man And Cybernetics (SMC 2009), IEEE International Conference on Systems, Man and Cybernetics, IEEE, San Antonio, TX, pp. 3688-3693.
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Tensor analysis has been widely utilized in image-related machine learning applications, which has preferable performance over the vector-based approaches for its capability of holding the spatial structure information in some research field. The traditi

Williams, M 2009, 'Evidence Transmutations: Gathering Admissible Evidence using Belief Revision', Proceedings of the 12th International Conference on Artificial Intelligence and Law, 12th International Conference on Artificial Intelligence and Law, ACM, Barcelona, Spain, pp. 216-217.
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In this paper we explore the potential of using extended belief change operators for modeling the evolution of legal evidence. We introduce a new representation, an evidence structure that can be used to support rich change operators called transmutations. Evidence structures and transmutations support the iterated nature of evidence gathering and acquisition. Importantly reliability is maintained during transmutations and modified where necessary using the Principle of Minimal Change. We establish that this process possesses desirable properties; in particular we construct transmutations that do not assume logical omniscience and that satisfy the widely accepted AGM rationality postulates for revision and contraction but at the same time preserve a key value of the evidence, its reliability.

Williams, M 2009, 'Privacy Management, the Law & Business Strategies: A Case for Privacy Driven Design', International Conference on Computational Science and Engineering, International Conference on Computational Science and Engineering, IEEE Press, Vancouver, Canada, pp. 60-68.
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This paper explores the adage that good privacy is good business. Businesses, like social networks, often seek to create value from personal information and monetize it. Unlocking and harvesting value embedded in personal information can lead to disclosure of private and sensitive information, and subsequent harm. Personal information management practices can be a means to competitive and strategic advantage, however they are also subject to privacy law. We explore the underlying tension between transparency and disclosure in the privacy verses business strategy in the pursuit of innovation arena, and argue that in order achieve sustained innovation next generation applications and services will require a fresh imaginative and strategic privacy by design approach. Personal information management is a complex task and cannot be adequately achieved without significant attention and commitment to privacy requirements in systems analysis and design. Due to the potential power, magnitude, complexity and scope of web technologies there is a pressing need to understand privacy requirements better, and to invest in developing tools and techniques for modeling, analyzing, designing and building more effective personal information management systems that seek consent where appropriate and that offer users natural choices and sophisticated mechanisms for controlling their personal information.

Williams, M & Elliot, S 2009, 'Strategic Treasury Risk Management in Uncertain and Changing Environments', Strategic Management Society Conference, Strategic Management Society, Washington, USA.

Williams, MA 2009, 'Privacy management, the law and global business strategies: A case for privacy driven design', AAAI Spring Symposium - Technical Report, pp. 71-79.
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This paper is based on the adage that 'goodprivacy is good business'. Personal information holds significant value and web based businesses often seek to monetize that value. Unlocking personal information value in web based businesses, like social networks, can lead to disclosure of private and sensitive information, and subsequent harm. Personal information management business practices are subject to privacy law, but perhaps more importantly practices that protect personal information can be a means to competitive advantage, and as a result they can form the basis of effective business strategy. We explore the underlying tension between transparency and disclosure in the privacy verses business strategy arena, and argue that the next generation of web businesses based on powerful Web 3.0 applications and services will demand a privacy by design approach, rather than addressing privacy concerns as an afterthought. Due to the potential power, magnitude, complexity and scope of Web 3.0 there is a need to use sophisticated technology-enabled approaches to assist users to monitor and manage personal information and its usage in a more transparent proactive fashion. Copyright © 2009, Association for the Advancement of Artificial Intelligence.

Xiao, Y, Liu, B, Cao, L, Wu, X, Zhang, C, Hao, Z, Yang, F & Cao, J 2009, 'Multi-sphere Support Vector Data for Outliers Detection on Multi-distribution Data', Data Mining Workshops, 2009. ICDMW '09. IEEE International Conference on, IEEE International Conference on Data Mining, IEEE Computer Society Press, Miami, Florida, pp. 82-87.
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SVDD has been proved a powerful tool for outlier detection. However, in detecting outliers on multi-distribution data, namely there are distinctive distributions in the data, it is very challenging for SVDD to generate a hyper-sphere for distinguishing outliers from normal data. Even if such a hyper-sphere can be identified, its performance is usually not good enough. This paper proposes an multi-sphere SVDD approach, named MS-SVDD, for outlier detection on multi-distribution data. First, an adaptive sphere detection method is proposed to detect data distributions in the dataset. The data is partitioned in terms of the identified data distributions, and the corresponding SVDD classifiers are constructed separately. Substantial experiments on both artificial and real-world datasets have demonstrated that the proposed approach outperforms original SVDD.

Xu, RYD & Kemp, M 2009, 'Multiple curvature based approach to human upper body parts detection with connected ellipse model fine-tuning', Proceedings - International Conference on Image Processing, ICIP, pp. 2577-2580.
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In this paper, we discuss an effective method for detecting human upper body parts from a 2D image silhouette using curvature analysis and ellipse fitting. First we smooth the silhouette so that we can determine just the global features: the head, hands and armpits. Next we reduce the smoothing to detect the local features of the neck and elbows. We model the human upper body by multiple connected ellipses. Thus we segment the body by the extracted features. Ellipses are fitted to each segment. Lastly, we apply a non-linear least square method to minimize the differences between the connected ellipse model and the edge of the silhouette. ©2009 IEEE.

Yang, Y, Zhuang, Y, Xu, D, Pan, Y, Tao, D & Maybank, S 2009, 'Retrieval Based Interactive Cartoon Synthesis via Unsupervised Bi-Distance Metric Learning', 2009 ACM International Conference on Multimedia Compilation E-Proceedings (with co-located workshops & symposiums), ACM international conference on Multimedia, Association for Computing Machinery, Inc. (ACM), Beijing, China, pp. 311-320.
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Cartoons play important roles in many areas, but it requires a lot of labor to produce new cartoon clips. In this paper, we propose a gesture recognition method for cartoon character images with two applications, namely content-based cartoon image retrieval and cartoon clip synthesis. We first define Edge Features (EF) and Motion Direction Features (MDF) for cartoon character images. The features are classified into two different groups, namely intra-features and inter-features. An Unsupervised Bi-Distance Metric Learning (UBDML) algorithm is proposed to recognize the gestures of cartoon character images. Different from the previous research efforts on distance metric learning, UBDML learns the optimal distance metric from the heterogeneous distance metrics derived from intra-features and inter-features. Content-based cartoon character image retrieval and cartoon clip synthesis can be carried out based on the distance metric learned by UBDML. Experiments show that the cartoon character image retrieval has a high precision and that the cartoon clip synthesis can be carried out efficiently.

Ye, Y, He, X, Li, J, Jia, W & Wu, Q 2009, 'Image transformation on hexagonal structure based on conversion between 1d and 2d coordinates', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 571-578.
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Spiral Architecture, a hexagonal image structure is a novel and powerful approach to machine vision system. The pixels on Spiral architecture are geometrically arranged using a 1D (Spiral) addressing scheme in an ascending order along a spiral-like curve. Spiral addition and Spiral multiplication are defined based on the Spiral addresses on Spiral Architecture. These two fundamental operations result in fast and easy translation, rotation and separation on images, and hence play very important roles for image processing on Spiral Architecture. Moreover, 2D coordinates according to rows and columns defined on Spiral Structure provide a good mapping to the ordinary 2D coordinates defined on the common square image structure. Therefore, how to convert the 1D Spiral addresses from and to the 2D coordinates on Spiral Architecture has become very important to apply the theory developed on a hexagonal image structure for image processing (e.g., rotation). In this paper, we perform a fast way to correctly locate any hexagonal pixel when its Spiral address is known, and compute the Spiral address of any hexagonal pixel when its location is known. As an illustration of the use of conversions, we demonstrate the accurate image translation and rotation using experimental results. © 2009 Springer Berlin Heidelberg.

Zhang, C 2009, 'Developing Actionable Trading Strategies for Trading Agents', Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM international Conference on Web Intelligence and Intelligent Agent Technology, IEEE Computer Society, Milano, Italy, pp. 9-9.
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Trading agents are useful for developing and back-testing quality trading strategies for taking actions in the real world. The existing trading agent research mainly focuses on simulation using artificial data. As a result, the actionable capability of developed trading strategies is often limited, and the trading agents therefore lack power. Actionable trading strategies can empower trading agents with workable decisionmaking in real-life markets. The development of actionable strategies is a non-trivial task, which needs to consider real-life constraints and organisational factors in the market. In this talk, we first analyse such constraints on developing actionable trading strategies for trading agents and propose a trading strategy development framework for trading agents. We then develop a series of trading strategies for trading agents through optimising, enhancing and discovering actionable trading strategies. We demonstrate working case studies using agent mining technology in real market data. These approaches, and their performance, are evaluated from both technical and business perspectives. These evalualtions clearly show that the development of trading strategies for trading agents, using our approach, can lead to smart decisions for brokerage firms and financial companies.

Zhang, J, Paisitkriangkrai, S & Shen, C 2009, 'An overview of fast pedestrian detection: Feature selection and cascade framework of boosted features', Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009, pp. 1566-1567.
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Efficiently and accurately detecting pedestrians plays a crucial role in many vision applications such as video surveillance, multimedia retrieval and smart car etc. In order to find the right feature for this task, we first present a comprehensive experimental study on pedestrian detection using state-of-the-art locally-extracted features. Building upon our findings, we propose a new, simpler pedestrian detecting framework based on the covariance features. We conduct feature selection and weak classifier training in the Euclidean space for faster computation. To this end, two machine learning algorithms have been designed: AdaBoost with weighted Fisher linear discriminant analysis (WLDA) based weak classifiers and Greedy Sparse Linear Discriminant Analysis (GSLDA). To further accelerate the detection, we employ a faster strategy, multiple cascade layers with heterogeneous features, to exploit the efficiency of the Haar-like features and the discriminative power of the covariance features. Experimental results shown on different datasets prove that the new pedestrian detection is not only comparable to the performance of the state-of-the-art pedestrian detectors but it also performs at a faster speed. ©2009 IEEE.

Zhang, Z, Gunes, H & Piccardi, M 2009, 'Head Detection For Video Surveillance Based On Categorical Hair And Skin Colour Models', Proceeings of the 16th IEEE International Conference on Image Processing (ICIP), 2009, IEEE International Conference on Image Processing, IEEE, Cairo, Egypt, pp. 1137-1140.
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We propose a new robust head detection algorithm that is capable of handling significantly different conditions in terms of viewpoint, tilt angle, scale and resolution. To this aim, we built a new model for the head based on appearance distributions and shape constraints. We construct a categorical model for hair and skin, separately, and train the models for four categories of hair (brown, red, blond and black) and three categories of skin representing the different illumination conditions (bright, standard and dark). The shape constraint fits an elliptical model to the candidate region and compares its parameters with priors based on human anatomy. The experimental results validate the usability of the proposed algorithm in various video surveillance and multimedia applications.

Zhao, G, Xiong, Y, Cao, L, Luo, D, Su, X & Zhu, Y 2009, 'A Cost-Effective LSH Filter for Fast Pairwise Mining', ICDM 2009, The Ninth IEEE International Conference on Data Mining, IEEE International Conference on Data Mining, IEEE Computer Society, Miami, Florida, USA, pp. 1088-1093.
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The pairwise mining problem is to discover pairwise objects having measures greater than the user-specified minimum threshold from a collection of objects. It is essential in a large variety of database and data-mining applications. Of late, there has been increasing interest in applying a Locality-Sensitive Hashing (LSH) scheme for pairwise mining. LSH-type methods have shown themselves to be simply implementable and capable of achieving significant performance gain in running time over most exact methods. However, the present LSH-type methods still suffer from some bottlenecks, such as the curse of threshold. In this paper, we proposed a novel LSHbased method, namely Cost-effective LSH filter (Ce-LSH for short), for pairwise mining. Compared with previous LSH-type methods, it uses a lower fixed number of LSH functions and is thus more cost-effective. Substantial experiments evidence that our method gives significant improvement in running time over existing LSH-type methods and some recently reported method based on upper-bound. Experimental results also indicate that it scales well even for a relatively low minimum threshold and for a fairly small miss ratio.

Zhao, L & Li, J 2009, 'Sequence-based B-cell epitope prediction by using associations in antibody-antigen structural complexes', Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009, pp. 165-172.
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B-cell secreted antibodies play a critical role in fighting against the invaders and abnormal self tissues. Identifying the epitope on antigens recognized by the paratope on antibodies can enlighten the understanding of this important immune mechanism. Predicting B-cell epitope can also pave the way for vaccine design and disease therapy. However, due to the high complexity of this problem, previous prediction methods that focus on linear and conformational epitope are both unsatisfactory. In this work, we propose a novel method to predict B-cell epitopes, when a pair of sequences is given, by using associations and cooperativity patterns from a relatively small antigen-antibody structural data set. More exactly, our classifier is trained on only PDB protein complexes, but it can be applied to any sequence data. Our evaluation results show that the accuracy of our method is very competitive to, sometimes even much better than, previous structure-based prediction methods which have a smaller applicability scope than ours. ©2009 IEEE.

Zhao, Y, Zhang, H, Cao, L, Zhang, C & Bohlscheid, H 2009, 'Mining Both Positive and Negative Impact-Oriented Sequential Rules from Transactional Data', Advances in Knowledge Discovery and Data Mining, 13th Pacific-Asia Conference, PAKDD 2009, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Bangkok, Thailand, pp. 656-663.
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Traditional sequential pattern mining deals with positive correlation between sequential patterns only, without considering negative relationship between them. In this paper, we present a notion of impact-oriented negative sequential rules, in which the left side is a positive sequential pattern or its negation, and the right side is a predefined outcome or its negation. Impact-oriented negative sequential rules are formally defined to show the impact of sequential patterns on the outcome, and an efficient algorithm is designed to discover both positive and negative impact-oriented sequential rules. Experimental results on both synthetic data and real-life data show the efficiency and effectiveness of the proposed technique.

Zhao, Y, Zhang, H, Wu, S, Pei, J, Cao, L, Zhang, C & Bohlscheid, H 2009, 'Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns', Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2009, European Conference on Machine Learning, Springer, Bled, Slovenia, pp. 648-663.
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Debt detection is important for improving payment accuracy in social security. Since debt detection from customer transactional data can be generally modelled as a fraud detection problem, a straightforward solution is to extract features from transaction sequences and build a sequence classifier for debts. The existing sequence classification methods based on sequential patterns consider only positive patterns. However, according to our experience in a large social security application, negative patterns are very useful in accurate debt detection. In this paper, we present a successful case study of debt detection in a large social security application. The central technique is building sequence classification using both positive and negative sequential patterns.

Zheng, Z, Zhao, Y, Zuo, Z & Cao, L 2009, 'Negative-GSP: An Efficient Method for Mining Negative Sequential Patterns', Proceedings of the 8th Australasian Data Mining Conference (AusDM'09): Data Mining and Analytics - Conferences in Research and Practice in Information Technology Volume 101, Australian Data Mining Conference, Australian Computer Society, Melbourne, Australia, pp. 63-67.
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Different from traditional positive sequential pattern mining, negative sequential pattern mining considers both positive and negative relationships between items. Negative sequential pattern mining doesn't necessarily follow the Apriori principle, and the searching space is much larger than positive pattern mining. Giving definitions and some constraints of negative sequential patterns, this paper proposes a new method for mining negative sequential patterns, called Negative-GSP. Negative-GSP can find negative sequential patterns effectively and efficiently by joining and pruning, and extensive experimental results show the efficiency of the method.

Zhou, H, Tao, D, Yuan, Y & Li, X 2009, 'OBJECT TRAJECTORY CLUSTERING VIA TENSOR ANALYSIS', 2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6, 16th IEEE International Conference on Image Processing, IEEE, Cairo, EGYPT, pp. 1945-+.

Zhou, Q, Xu, G & Zong, Y 2009, 'Web co-clustering of usage network using tensor decomposition', Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2009, pp. 311-314.
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Web clustering is an approach to aggregate web objects into various categories according to underlying relationships among them. Finding co-clusters of web objects is an emerging topic in the context of web usage mining. In this paper we will present an algorithm using tensor decomposition to co-cluster web objects based on analysis of user navigational tasks. The usage data of users visiting web sites is collected as experimental data to construct the usage network and validate the presented method. Experimental results have demonstrated the proposed method can clearly reveal the aggregations of web objects as a result of different navigational tasks. © 2009 IEEE.

Zhou, T & Tao, D 2009, 'Manifold Elastic Net For Sparse Learning', 2009 IEEE International Conference On Systems, Man And Cybernetics (SMC 2009), Vols 1-9, IEEE International Conference on Systems, Man and Cybernetics, IEEE, San Antonio, TX, pp. 3699-3704.
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In this paper, we present the manifold elastic net (MEN) for sparse variable selection. MEN combines merits of the manifold regularization and the elastic net regularization, so it considers both the nonlinear manifold structure of a dataset and the spar

Zhu, X, Wu, X & Zhang, C 2009, 'Vague One-Class Learning for Data Streams', Proceedings of the 9th IEEE International Conference on Data Mining (ICDM-09), IEEE International Conference on Data Mining, IEEE Computer Society, Miami, Florida, pp. 657-666.
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In this paper, we formulate a new research problem of learning from vaguely labeled one-class data streams, where the main objective is to allow users to label instance groups, instead of single instances, as positive samples for learning. The batch-labeling, however, raises serious issues because labeled groups may contain non-positive samples, and users may change their labeling interests at any time. To solve this problem, we propose a Vague One-Class Learning (VOCL) framework which employs a double weighting approach, at both instance and classifier levels, to build an ensembling framework for learning. At instance level, both local and global filterings are considered for instance weight adjustment. Two solutions are proposed to take instance weight values into the classifier training process. At classifier level, a weight value is assigned to each classifier of the ensemble to ensure that learning can quickly adapt to usersâ interests. Experimental results on synthetic and real-world data streams demonstrate that the proposed VOCL framework significantly outperforms other methods for vaguely labeled one-class data streams.

Williams, M 2009, 'Robots: Future Challenges', Powerhouse Museum.
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Public Robot Performance

Chiarella, C, Dieci, R & He, X 2009, 'A framework for CAPM with heterogenous beliefs', Research Paper Series, Quantitative Finance Research Centre, University of Technology, Sydney.
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Research Paper Number: 254 Abstract: We introduce heterogeneous beliefs in to the mean-variance framework of the standard CAPM, in contrast to the standard approach which assumes homogeneous beliefs. By assuming that agents form optimal portfolios based upon their heterogeneous beliefs about conditional means and covariances of the risky asset returns, we set up a framework for the CAPM that incorporates the heterogeneous beliefs when the market is in equilibrium. In this framework we first construct a consensus belief (with respect to the means and covariances of the risky asset returns) to represent the aggregate market belief when the market is in equilibrium. We then extend the analysis to a repeated one-period set-up and establish a framework for a dynamic CAPM using a market fraction model in which agents are grouped according to their beliefs. The exact relation between heterogeneous beliefs, the market equilibrium returns and the ex-ante beta-coeffcients is obtained. CAPM and Heterogeneous beliefs.

Chiarella, C, He, X & Pellizzari, P 2009, 'A dynamic analysis of the microstructure of moving average rules in a double auction market', Research Paper Series, Quantitative Finance Research Centre, University of Technology, Sydney.
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Research Paper Number: 251 Abstract: Inspired by the theoretically oriented dynamic analysis of moving average rules in Chiarella, He and Hommes (CHH) (2006a) model, this paper conducts a dynamic analysis of a microstructure model of continuous double auctions in which the probability of heterogeneous agents to trade is determined by the rules of either fundamentalists mean-reverting to the fundamental or chartists choosing moving average rules based their relative performance. With such a realistic market microstructure, the model is able not only to obtain the results of the CHH model but also to characterise most of the stylized facts including the power-law behaviour of volatility. The results seem to suggest that a comprehensive explanation of several statistical properties of returns is possible in a framework where both behavioral traits and realistic microstructure have a role.

Chiarella, C, He, X & Zheng, M 2009, 'Heterogeneous Expectations and Exchange Rate Dynamics (243)', Quantitative Finance Research Paper Series.
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This paper presents a continuous-time model of exchange rates relying not only on macroeconomic factors but also having a market microstructure component. The driving macroeconomic factor is the interest rate differential, while the market microstructure element is described by the expectations of boundedly rational portfolio managers who use a weighted average of the expectations of fundamentalists and chartists. Within this framework, the different roles of the macroeconomic factors and market microstructure elements on the determination of the exchange rate are examined explicitly. We show that this simple model generates very complicated market behaviour, including the existence of multiple steady state equilibria, the deviations of the market exchange rate from the fundamental, and market fluctuations. Numerical simulation of the corresponding stochastic version of the model shows that the model is able to generate typical time series and volatility clustering patterns observed in exchange rate markets.

He, X & Shi, L 2009, 'Portfolio analysis and zero-beta CAPM with heterogeneous beliefs', Research Paper Series, Quantitative Finance Research Centre, University of Technology, Sydney.
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Research Paper Number: 244 Abstract: With the standard mean variance framework, by assuming heterogeneity and bounded rationality of investors, this paper examines their impact on the market equilibrium and implications to the portfolio analysis. By constructing a market consensus belief, we establish market equilibrium prices of risky assets and show that the standard Blackâs zero-beta CAPM under homogeneous beliefs holds under the heterogeneous belief. We demonstrate that the biased belief (from the market consensus belief) of investors makes their optimal portfolio not necessarily locate on the market mean-variance frontier. We show that the traditional geometric relation of the mean variance frontiers with and without the riskless asset under the homogeneous beliefs does not hold under the heterogeneous beliefs. The results shed light on the risk premium puzzle, Millerâs hypothesis, the lower market performance when the access to the risk free asset is impossible, and the empirical finding that managed funds under-perform comparing to the market indices on average.

He, X, Li, K, Wei, J & Zheng, M 2009, 'Market stability switches in a continuous-time financial market with heterogeneous beliefs', Research Paper Series, Quantitative Finance Research Centre, University of Technology, Sydney.
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Research Paper Number: 252 Abstract: By considering a financial market of fundamentalists and trend followers in which the price trend of the trend followers is formed as a weighted average of historical prices, we establish a continuous-time financial market model with time delay and examines the impact of time delay on market price dynamics. Conditions for the stability of the fundamental price in terms of agents' behavior parameters and time delay are obtained. In particular, it is found that an increase in time delay can not only destabilize the market price but also stabilize an otherwise unstable market price, leading to stability switching as delay increases. This interesting phenomena shed new light in understanding of mechanism on the market stability. When the fundamental price becomes unstable through Hopf bifurcations, suffcient conditions on the stability and global existence of the periodic solution are obtained.