Publications

Durao, F, Bayyapu, K, Xu, G, Dolog, P & Lage, R 2013, Medical Information Retrieval Enhanced with User’s Query Expanded with Tag-Neighbors, Springer New York.
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Under-specified queries often lead to undesirable search results that do not contain the information needed. This problem gets worse when it comes to medical information, a natural human demand everywhere. Existing search engines on the Web often are unable to handle medical search well because they do not consider its special requirements. Often a medical information searcher is uncertain about his exact questions and unfamiliar with medical terminology. To overcome the limitations of under-specified queries, we utilize tags to enhance information retrieval capabilities by expanding users’ original queries with context-relevant information. We compute a set of significant tag neighbor candidates based on the neighbor frequency and weight, and utilize the qualified tag neighbors to expand an entry query. The proposed approach is evaluated by using MedWorm medical article collection and results show considerable precision improvements over state-of-the-art approaches.

Howlett, RJ & Gabrys, B 2013, InnovationKT-2012 Preface.

Luo, T, Chen, S, Xu, G & Zhou, J 2013, Trust-based Collective View Prediction, 1, Springer New York, Germany.
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Collective view prediction is to judge the opinions of an active web user based on unknown elements by referring to the collective mind of the whole community. Content-based recommendation and collaborative filtering are two mainstream collective view prediction techniques. They generate predictions by analyzing the text features of the target object or the similarity of users past behaviors. Still, these techniques are vulnerable to the artificially-injected noise data, because they are not able to judge the reliability and credibility of the information sources. Trust-based Collective View Prediction describes new approaches for tackling this problem by utilizing users trust relationships from the perspectives of fundamental theory, trust-based collective view prediction algorithms and real case studies. The book consists of two main parts a theoretical foundation and an algorithmic study. The first part will review several basic concepts and methods related to collective view prediction, such as state-of-the-art recommender systems, sentimental analysis, collective view, trust management, the Relationship of Collective View and Trustworthy, and trust in collective view prediction. In the second part, the authors present their models and algorithms based on a quantitative analysis of more than 300 thousand users data from popular product-reviewing websites. They also introduce two new trust-based prediction algorithms, one collaborative algorithm based on the second-order Markov random walk model, and one Bayesian fitting model for combining multiple predictors.

Xu, G, Zong, Y & Yang, Z 2013, Applied data mining, 1, CRC Press, USA.
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Data mining has witnessed substantial advances in recent decades. New research questions and practical challenges have arisen from emerging areas and applications within the various fields closely related to human daily life, e.g. social media and social networking. This book aims to bridge the gap between traditional data mining and the latest adv.

Cao, L, Motoda, H, Srivastava, J, Lim, EP, King, I, Yu, PS, Nejdl, W, Xu, G, Li, G & Zhang, Y 2013, 'Behavior and Social Computing' in Cao, L, Motoda, H, Srivastava, J, Lim, E-P, King, I, Yu, PS, Nejdl, W, Xu, G, Li, G & Zhang, Y (eds), Behavior and Social Computing, Springer International Publishing, Germany, pp. v-vi.
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Le, M, Nauck, D, Gabrys, B & Martin, T 2013, 'KNNs and Sequence Alignment for Churn Prediction' in Research and Development in Intelligent Systems XXX, Springer International Publishing, pp. 279-285.
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Li, L, Xiao, H & Xu, G 2013, 'Recommending Related Microblogs' in Xu, G & Li, L (eds), Social Media Mining and Social Network Analysis, IGI Global, Hershey, USA, pp. 202-210.
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Computing similarity between short microblogs is an important step in microblog recommendation. In this chapter, the authors utilize three kinds of approaches—traditional term-based approach, WordNet-based semantic approach, and topic-based approach—to compute similarities between micro-blogs and recommend top related ones to users. They conduct experimental study on the effectiveness of the three approaches in terms of precision. The results show that WordNet-based semantic similarity approach has a relatively higher precision than that of the traditional term-based approach, and the topic-based approach works poorest with 548 tweets as the dataset. In addition, the authors calculated the Kendall tau distance between two lists generated by any two approaches from WordNet, term, and topic approaches. Its average of all the 548 pair lists tells us the WordNet-based and term-based approach have generally high agreement in the ranking of related tweets, while the topic-based approach has a relatively high disaccord in the ranking of related tweets with the WordNet-based approach.

Xu, G, Gu, Y & Yi, X 2013, 'On Group Extraction and Fusion for Tag-Based Social Recommendation' in Xu, G & Li, L (eds), Social Media Mining and Social Network Analysis, IGI Global, Hershey, USA, pp. 211-223.
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With the recent information explosion, social websites have become popular in many Web 2.0 applications where social annotation services allow users to annotate various resources with freely chosen words, i.e., tags, which can facilitate users’ finding preferred resources. However, obtaining the proper relationship among user, resource, and tag is still a challenge in social annotation-based recommendation researches. In this chapter, the authors aim to utilize the affinity relationship between tags and resources and between tags and users to extract group information. The key idea is to obtain the implicit relationship groups among users, resources, and tags and then fuse them to generate recommendation. The authors experimentally demonstrate that their strategy outperforms the state-of-the-art algorithms that fail to consider the latent relationships among tagging data.

Zong, Y & Xu, G 2013, 'Clustering Algorithms for Tags' in Xu, G & Li, L (eds), Social Media Mining and Social Network Analysis, IGI Global, Hershey, USA, pp. 39-53.
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With the development and application of social media, more and more user-generated contents are created. Tag data, a kind of typical user generated content, has attracted lots of interests of researchers. In general, tags are the freely chosen textual descriptions by users to label digital data sources in social tagging systems. Poor retrieval performance remains a major problem of most social tagging systems resulting from the severe difficulty of ambiguity, redundancy, and less semantic nature of tags. Clustering method is a useful tool to increase the ability of information retrieval in the aforementioned systems. In this chapter, the authors (1) review the background of state-of-the-art tagging clustering and the tag data description, (2) present five kinds of tag similarity measurements proposed by researchers, and (3) finally propose a new clustering algorithm for tags based on local information that is derived from Kernel function. This chapter aims to benefit both academic and industry communities who are interested in the techniques and applications of tagging clustering.

AlAamri, H, Abolhasan, M, Franklin, D & Lipman, J 2013, 'Optimised relay selection for route discovery in reactive routing', AD HOC NETWORKS, vol. 11, no. 1, pp. 70-88.
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On-demand routing protocols have the potential to provide scalable information delivery in large ad hoc networks. The novelty of these protocols is in their approach to route discovery, where a route is determined only when it is required by initiating a route discovery procedure. Much of the research in this area has focused on reducing the route discovery overhead when prior knowledge of the destination is available at the source or by routing through stable links. Hence, many of the protocols proposed to date still resort to flooding the network when prior knowledge about the destination is un-available. This paper proposes a novel routing protocol for ad hoc networks, called On-demand Tree-based Routing Protocol (OTRP). This protocol combines the idea of hop-by-hop routing (as used by AODV) with an efficient route discovery algorithm called Tree-based Optimised Flooding (TOF) to improve scalability of ad hoc networks when there is no prior knowledge about the destination. To achieve this in OTRP, route discovery overheads are minimised by selectively flooding the network through a limited set of nodes, referred to as branching nodes. The key factors governing the performance of OTRP are theoretically analysed and evaluated, including the number of branch nodes, location of branching nodes and number of Route REQuest (RREQ) retries. It was found that the performance of OTRP (evaluated using a variety of well-known metrics) improves as the number of branching nodes increases and the number of consumed RREQ retries is reduced. Additionally, theoretical analysis and simulation results shows that OTRP outperforms AODV, DYMO, and OLSR with reduced overheads as the number of nodes and traffic load increases. © 2012 Elsevier B.V. All rights reserved.

Anaissi, A, Kennedy, PJ, Goyal, M & Catchpoole, DR 2013, 'A balanced iterative random forest for gene selection from microarray data', BMC BIOINFORMATICS, vol. 14, no. 1, pp. 1-10.
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Background: The wealth of gene expression values being generated by high throughput microarray technologies leads to complex high dimensional datasets. Moreover, many cohorts have the problem of imbalanced classes where the number of patients belonging to each class is not the same. With this kind of dataset, biologists need to identify a small number of informative genes that can be used as biomarkers for a disease.Results: This paper introduces a Balanced Iterative Random Forest (BIRF) algorithm to select the most relevant genes for a disease from imbalanced high-throughput gene expression microarray data. Balanced iterative random forest is applied on four cancer microarray datasets: a childhood leukaemia dataset, which represents the main target of this paper, collected from The Children's Hospital at Westmead, NCI 60, a Colon dataset and a Lung cancer dataset. The results obtained by BIRF are compared to those of Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Multi-class SVM-RFE (MSVM-RFE), Random Forest (RF) and Naive Bayes (NB) classifiers. The results of the BIRF approach outperform these state-of-the-art methods, especially in the case of imbalanced datasets. Experiments on the childhood leukaemia dataset show that a 7% ∼ 12% better accuracy is achieved by BIRF over MSVM-RFE with the ability to predict patients in the minor class. The informative biomarkers selected by the BIRF algorithm were validated by repeating training experiments three times to see whether they are globally informative, or just selected by chance. The results show that 64% of the top genes consistently appear in the three lists, and the top 20 genes remain near the top in the other three lists.Conclusion: The designed BIRF algorithm is an appropriate choice to select genes from imbalanced high-throughput gene expression microarray data. BIRF outperforms the state-of-the-art methods, especially the ability to handle the class-imbalanced data. Moreover, the...

Apeh, E & Gabrys, B 2013, 'Detecting and Visualizing the Change in Classification of Customer Profiles based on Transactional Data', Evolving Systems, vol. 4, no. 1, pp. 27-42.
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Customer transactions tend to change over time with changing customer behaviour patterns. Classifier models, however, are often designed to perform prediction on data which is assumed to be static. These classifier models thus deteriorate in performance over time when predicting in the context of evolving data. Robust adaptive classification models are therefore needed to detect and adjust to the kind of changes that are common in transactional data. This paper presents an investigation into using change mining to monitor the adaptive classification of customers based on their transactions through moving time windows. The classification performance of two-class decision tree ensembles built using the data binning process based on the number of items purchased was monitored over varying 3, 6, 9 and 12 months time windows. The changing class values of the customer profiles were analysed and described. Results from our experiments show that the proposed approach can be used for learning and adapting to changing customer profiles. © 2012 Springer-Verlag.

Arsene, CTC & Gabrys, B 2013, 'Probabilistic finite element predictions of the human lower limb model in total knee replacement', Medical Engineering & Physics, vol. 35, no. 8, pp. 1116-1132.
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Ashraf, J, Hussain, OK & Hussain, FK 2013, 'A Framework for Measuring Ontology Usage on the Web', COMPUTER JOURNAL, vol. 56, no. 9, pp. 1083-1101.
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A decade-long conscious effort by the Semantic Web community has resulted in the formation of a decentralized knowledge platform which enables data interoperability at a syntactic and semantic level. For information interoperability, at a syntactic level, RDF provides the standard format for publishing data and RDFS gives structure to the information. For semantic-level interoperability, ontologies are used which allow information dissemination and assimilation among diverse applications and systems; where information is equally accessible and useful to humans and machines. The success of the linked open data project, recognition of explicit semantics (annotated through web ontologies) by search engines and the realized potential advantages of semantic data for publishers have resulted in tremendous growth in the use of web ontologies on the web. In order to promote the adoption of ontologies (to new users), reusability of adopted ontologies, effective and efficient utilization on ontological knowledge and evolving the ontological model, erudite insight on the usage of ontologies is imperative. While ontology evaluation attempts to evaluate a developed ontology to assess its fitness and quality, it does not provide any insight into how ontologies are being used and what is the state of prevalent knowledge patterns. Realizing the importance of measuring and analysing ontology usage to advance the adoption, reusability and exploitation of ontologies, we present a semantic framework for measuring and analysing ontology usage on the Web on empirical grounding. Our methodological approach is discussed to highlight the detail and role of each step. A framework is presented along with the set of metrics developed to measure ontology usage from different aspects such as ontology richness, usage and incentives to provide a holistic view on the state of ontology usage. The framework is then evaluated using an important use-case scenario to identify the prevalent knowledge ...

Beck, D, Thoms, JAI, Perera, D, Schütte, J, Unnikrishnan, A, Knezevic, K, Kinston, SJ, Wilson, NK, O’Brien, TA, Göttgens, B, Wong, JWH & Pimanda, JE 2013, 'Genome-wide analysis of transcriptional regulators in human HSPCs reveals a densely interconnected network of coding and noncoding genes', Blood, vol. 122, no. 14, pp. e12-e22.
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Key Points Genome-wide binding profiles of FLI1, ERG, GATA2, RUNX1, SCL, LMO2, and LYL1 in human HSPCs reveals patterns of combinatorial TF binding. Integrative analysis of transcription factor binding reveals a densely interconnected network of coding and noncoding genes in human HSPCs.

Belles-Sampera, J, Merigó, JM & Santolino, M 2013, 'Some New Definitions of Indicators for the Choquet Integral', AGGREGATION FUNCTIONS IN THEORY AND IN PRACTISE, vol. 228, pp. 467-476.
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Belles-Sampera, J, Merigó, JM, Guillén, M & Santolino, M 2013, 'The connection between distortion risk measures and ordered weighted averaging operators', Insurance: Mathematics and Economics, vol. 52, no. 2, pp. 411-420.
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Borzeshi, EZ, Perez Concha, O, Xu, RYD & Piccardi, M 2013, 'Joint Action Segmentation and Classification by an Extended Hidden Markov Model', IEEE Signal Processing Letters, vol. 20, no. 12, pp. 1207-1210.
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Hidden Markov models (HMMs) provide joint segmentation and classification of sequential data by efficient inference algorithms and have therefore been employed in fields as diverse as speech recognition, document processing, and genomics. However, conventional HMMs do not suit action segmentation in video due to the nature of the measurements which are often irregular in space and time, high dimensional and affected by outliers. For this reason, in this paper we present a joint action segmentation and classification approach based on an extended model: the hidden Markov model for multiple, irregular observations (HMM-MIO). Experiments performed over a concatenated version of the popular KTH action dataset and the challenging CMU multi-modal activity dataset (CMU-MMAC) report accuracies comparable to or higher than those of a bag-of-features approach, showing the usefulness of improved sequential models for joint action segmentation and classification tasks. © 1994-2012 IEEE.

Budka, M & Gabrys, B 2013, 'Density-Preserving Sampling: Robust and Efficient Alternative to Cross-Validation for Error Estimation', IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 1, pp. 22-34.
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Budka, M, Juszczyszyn, K, Musial, K & Musial, A 2013, 'Molecular model of dynamic social network based on e-mail communication', Social Network Analysis and Mining, vol. 3, no. 3, pp. 543-563.
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In this work we consider an application of physically inspired sociodynamical model to the modelling of the evolution of email-based social network. Contrary to the standard approach of sociodynamics, which assumes expressing of system dynamics with heuristically defined simple rules, we postulate the inference of these rules from the real data and their application within a dynamic molecular model. We present how to embed the n-dimensional social space in Euclidean one. Then, inspired by the Lennard-Jones potential, we define a data-driven social potential function and apply the resultant force to a real e-mail communication network in a course of a molecular simulation, with network nodes taking on the role of interacting particles. We discuss all steps of the modelling process, from data preparation, through embedding and the molecular simulation itself, to transformation from the embedding space back to a graph structure. The conclusions, drawn from examining the resultant networks in stable, minimum-energy states, emphasize the role of the embedding process projecting the non–metric social graph into the Euclidean space, the significance of the unavoidable loss of information connected with this procedure and the resultant preservation of global rather than local properties of the initial network. We also argue applicability of our method to some classes of problems, while also signalling the areas which require further research in order to expand this applicability domain.

Cao, L 2013, 'Combined mining: Analyzing object and pattern relations for discovering and constructing complex yet actionable patterns', WIREs Data Mining and Knowledge Discovery, vol. 3, no. 2, pp. 140-155.
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AbstractCombined mining is a technique for analyzing object relations and pattern relations, and for extracting and constructing actionable knowledge (patterns or exceptions). Although combined patterns can be built within a single method, such as combined sequential patterns by aggregating relevant frequent sequences, this knowledge is composed of multiple constituent components (the left hand side) from multiple data sources, which are represented by different feature spaces, or identified by diverse modeling methods. In some cases, this knowledge is also associated with certain impacts (influence, action, or conclusion, on the right hand side). This paper presents an abstract high‐level picture of combined mining and the combined patterns from the perspective of object and pattern relation analysis. Several fundamental aspects of combined pattern mining are discussed, including feature interaction, pattern interaction, pattern dynamics, pattern impact, pattern relation, pattern structure, pattern paradigm, pattern formation criteria, and pattern presentation (in terms of pattern ontology and pattern dynamic charts). We also briefly illustrate the concepts and discuss how they can be applied to mining complex data for complex knowledge in either a multifeature, multisource, or multimethod scenario. © 2013 Wiley Periodicals, Inc.This article is categorized under:Algorithmic Development > Ensemble Methods

Cao, L, Yu, PS, Motoda, H & Williams, G 2013, 'Special issue on behavior computing', Knowledge and Information Systems, vol. 37, no. 2, pp. 245-249.
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Chan, THT & Zhu, X 2013, 'Preface', Journal of Civil Structural Health Monitoring, vol. 3, no. 2, pp. 63-64.
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Chen, P, Li, J, Wong, L, Kuwahara, H, Huang, JZ & Gao, X 2013, 'Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences', PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, vol. 81, no. 8, pp. 1351-1362.
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Hot spot residues of proteins are fundamental interface residues that help proteins perform their functions. Detecting hot spots by experimental methods is costly and time-consuming. Sequential and structural information has been widely used in the compu

Diffner, E, Beck, D, Gudgin, E, Thoms, JAI, Knezevic, K, Pridans, C, Foster, S, Goode, D, Lim, WK, Boelen, L, Metzeler, KH, Micklem, G, Bohlander, SK, Buske, C, Burnett, A, Ottersbach, K, Vassiliou, GS, Olivier, J, Wong, JWH, Göttgens, B, Huntly, BJ & Pimanda, JE 2013, 'Activity of a heptad of transcription factors is associated with stem cell programs and clinical outcome in acute myeloid leukemia', Blood, vol. 121, no. 12, pp. 2289-2300.
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Key Points The ERG stem cell enhancer is active in acute myeloid leukemia and is regulated by a heptad of transcription factors. Expression signatures derived from ERG promoter–enhancer activity and heptad expression are associated with clinical outcome.

Do, QNT & Hussain, FK 2013, 'A hybrid approach for the personalisation of cloud-based e-governance services', International Journal of High Performance Computing and Networking, vol. 7, no. 3, pp. 205-205.
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Cloud computing is a new and promising paradigm for service delivery including computing resources over the internet. Cloud computing standards and architecture play an important role in benefiting governments by reducing operating costs and increasing governance effectiveness. Cloud-based e-governance contributes to managing security, reducing cost based on a pay-as-you-go method, IT labour cost reduction, and increasing scalability. Given the importance of cloud computing in the today's emerging technologies, personalisation in cloud computing is also significant in supporting users to obtain what they need without being required to request it explicitly. This research will focus mainly on a personalisation algorithm to for cloud computing. A case study in which a user can suggest the language they want to use without making an explicit request will be provided to assist further understanding of the new algorithm, which is a combination of the TOPSIS and Pearson correlation coefficient methods.

Dong, H & Hussain, FK 2013, 'SOF: a semi-supervised ontology-learning-based focused crawler', CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, vol. 25, no. 12, pp. 1755-1770.
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The rapid increase in the volume of data available on the Internet makes it increasingly impractical for a crawler to index the whole Web. Instead, many intelligent crawlers, known as ontology-based semantic focused crawlers, have been designed by making use of Semantic Web technologies for topic-centered Web information crawling. Ontologies, however, have constraints of validity and time, which may influence the performance of the crawlers. Ontology-learning-based focused crawlers are therefore designed to automatically evolve ontologies by integrating ontology learning technologies. Nevertheless, surveys indicate that the existing ontology-learning-based focused crawlers do not have the capability to automatically enrich the content of ontologies, which makes these crawlers unreliable in the open and heterogeneous Web environment. Hence, in this paper, we propose a framework for a novel semi-supervised ontology-learning-based focused (SOF) crawler, the SOF crawler, which embodies a series of schemas for ontology generation and Web information formatting, a semi-supervised ontology learning framework, and a hybrid Web page classification approach aggregated by a group of support vector machine models. A series of tests are implemented to evaluate the technical feasibility of this proposed framework. The conclusion and the future work are summarized in the final section

Dong, H, Hussain, FK & Chang, E 2013, 'Semantic Web Service matchmakers: state of the art andchallenges', CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, vol. 25, no. 7, pp. 961-988.
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Web services provide a standard means for the interoperable operations between electronic devices in a network. The mission of Web service discovery is to seek an appropriate Web service for a service requester on the basis of the service descriptions in Web service advertisements and the service requesterâs requirements. Nevertheless, the standard language used for encoding service descriptions does not have the capacity to specify the capabilities of a Web service, leading to the problem of ambiguity in the service discovery process. This brings up the vision of SemanticWeb Services and SemanticWeb Service discovery, which make use of the SemanticWeb technologies to enrich the semantics of service descriptions for service discovery. Semantic Web Service matchmakers are the programs or frameworks designed to implement the task of Semantic Web Service discovery and have drawn a significant amount of attention from both academia and industry from the start of this century. In this paper, we conduct a survey of the contemporary Semantic Web Service matchmakers in order to obtain an overview of the state of the art in this research area. We summarize six technical dimensions from the past literature and analyze the typical Semantic Web Service matchmakers mostly developed during the past 4 or 5 years in terms of the six dimensions. By means of this analysis, we gain an understanding of the current research and summarize a series of potential issues to that would provide the foundation for future research in this area. Copyright © 2012 John Wiley & Sons, Ltd

Dong, X, Liu, E, Yang, J & Wu, Q 2013, 'MEGH: A New Affine Invariant Descriptor', KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, vol. 7, no. 7, pp. 1690-1704.
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An affine invariant descriptor is proposed, which is able to well represent the affine covariant regions. Estimating main orientation is still problematic in many existing method, such as SIFT (scale invariant feature transform) and SURF (speeded up robust features). Instead of aligning the estimated main orientation, in this paper ellipse orientation is directly used. According to ellipse orientation, affine covariant regions are firstly divided into 4 sub-regions with equal angles. Since affine covariant regions are divided from the ellipse orientation, the divided sub-regions are rotation invariant regardless the rotation, if any, of ellipse. Meanwhile, the affine covariant regions are normalized into a circular region. In the end, the gradients of pixels in the circular region are calculated and the partition-based descriptor is created by using the gradients. Compared with the existing descriptors including MROGH, SIFT, GLOH, PCA-SIFT and spin images, the proposed descriptor demonstrates superior performance according to extensive experiments. © 2013 KSII.

Du, R, Wu, Q, He, X & Yang, J 2013, 'MIL-SKDE: Multiple-instance learning with supervised kernel density estimation', Signal Processing, vol. 93, no. 6, pp. 1471-1484.
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Multiple-instance learning (MIL) is a variation on supervised learning. Instead of receiving a set of labeled instances, the learner receives a set of bags that are labeled. Each bag contains many instances. The aim of MIL is to classify new bags or instances. In this work, we propose a novel algorithm, MIL-SKDE (multiple-instance learning with supervised kernel density estimation), which addresses MIL problem through an extended framework of KDE (kernel density estimation)+mean shift. Since the KDE+mean shift framework is an unsupervised learning method, we extend KDE to its supervised version, called supervised KDE (SKDE), by considering class labels of samples. To seek the modes (local maxima) of SKDE, we also extend mean shift to a supervised version by taking into account sample labels. SKDE is an alternative of the well-known diverse density estimation (DDE) whose modes are called concepts. Comparing to DDE, SKDE is more convenient to learn multi-modal concepts and robust to labeling noise (mistakenly labeled bags). Finally, each bag is mapped into a concept space where the multi-class SVM classifiers are learned. Experimental results demonstrate that our approach outperforms the state-of-the-art MIL approaches. © 2012 Elsevier B.V.

Esfijani, A, Hussain, FK & Chang, E 2013, 'University social responsibility ontology', Engineering Intelligent Systems, vol. 21, no. 4, pp. 271-281.
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This paper draws on the existing body of knowledge to develop an ontology for university social responsibility (USR). There are numerous terms and definitions for USR in the existing literature. However, there is no consensus among them. In order to address this issue, we used a semi-automated text mining approach for ontology engineering. The developed ontology covered USR and its associated terms by which social responsibilities of a university to its communities have been described in the existing literature. The developed ontology, which is an explicit specification of USR concept, its components and their relationships, can contribute to develop a unified understanding of the concept for measurement purposes. © 2013 CRL Publishing Ltd.

Gil-Lafuente, AM & Merigo, JM 2013, 'Modelling and Simulation in Enterprises – MS’10 Barcelona', Kybernetes, vol. 42, no. 5, pp. 251-269.
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Gil-Lafuente, AM & Merigo, JM 2013, 'Modelling and Simulation in Enterprises - MS'10 Barcelona', KYBERNETES, vol. 42, no. 5, pp. 671-673.
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Goldberg, L, Tijssen, MR, Birger, Y, Hannah, RL, Kinston, SJ, Schütte, J, Beck, D, Knezevic, K, Schiby, G, Jacob-Hirsch, J, Biran, A, Kloog, Y, Marcucci, G, Bloomfield, CD, Aplan, PD, Pimanda, JE, Göttgens, B & Izraeli, S 2013, 'Genome-scale expression and transcription factor binding profiles reveal therapeutic targets in transgenic ERG myeloid leukemia', Blood, vol. 122, no. 15, pp. 2694-2703.
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Key PointsERG overexpression in transgenic mice induces a transcriptional leukemia stem cell program characteristic of human AML. PIM1 and RAS are relevant ERG therapeutic targets.

Goodswen, SJ, Kennedy, PJ & Ellis, JT 2013, 'A guide to in silico vaccine discovery for eukaryotic pathogens', BRIEFINGS IN BIOINFORMATICS, vol. 14, no. 6, pp. 753-774.
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In this article, a framework for an in silico pipeline is presented as a guide to high-throughput vaccine candidate discovery for eukaryotic pathogens, such as helminths and protozoa. Eukaryotic pathogens are mostly parasitic and cause some of the most damaging and difficult to treat diseases in humans and livestock. Consequently, these parasitic pathogens have a significant impact on economy and human health. The pipeline is based on the principle of reverse vaccinology and is constructed from freely available bioinformatics programs. There are several successful applications of reverse vaccinology to the discovery of subunit vaccines against prokaryotic pathogens but not yet against eukaryotic pathogens. The overriding aim of the pipeline, which focuses on eukaryotic pathogens, is to generate through computational processes of elimination and evidence gathering a ranked list of proteins based on a scoring system. These proteins are either surface components of the target pathogen or are secreted by the pathogen and are of a type known to be antigenic. No perfect predictive method is yet available; therefore, the highest-scoring proteins from the list require laboratory validation.

Goodswen, SJ, Kennedy, PJ & Ellis, JT 2013, 'A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms', BMC BIOINFORMATICS, vol. 14, no. 1, pp. 315-327.
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An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets.

Goodswen, SJ, Kennedy, PJ & Ellis, JT 2013, 'A review of the infection, genetics, and evolution of Neospora caninum: From the past to the present', INFECTION GENETICS AND EVOLUTION, vol. 13, no. 1, pp. 133-150.
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This paper is a review of current knowledge on Neospora caninum in the context of other apicomplexan parasites and with an emphasis on: life cycle, disease, epidemiology, immunity, control and treatment, evolution, genomes, and biological databases and web resources. N. caninum is an obligate, intracellular, coccidian, protozoan parasite of the phylum Apicomplexa. Infection can cause the clinical disease neosporosis, which most notably is associated with abortion in cattle. These abortions are a major root cause of economic loss to both the dairy and beef industries worldwide. N. caninum has been detected in every country in which a study has been specifically conducted to detect this parasite in cattle. The major mode of transmission in cattle is transplacental (or vertical) transmission and several elements of the N. caninum life cycle are yet to be studied in detail. The outcome of an infection is inextricably linked to the precise timing of the infection coupled with the status of the immune system of the dam and foetus. There is no community consensus as to whether it is the dams pro-inflammatory cytotoxic response to tachyzoites that kills the foetus or the tachyzoites themselves. From economic analysis the most cost-effective approach to control neosporosis is a vaccine. The perfect vaccine would protect against both infection and the clinical disease, and this implies a vaccine is needed that can induce a non-foetopathic cell mediated immunity response. Researchers are beginning to capitalise on the vast potential of -omics data (e.g. genomes, transcriptomes, and proteomes) to further our understanding of pathogens but especially to identify vaccine and drug targets. The recent publication of a genome for N. caninum offers vast opportunities in these areas.

Hammadi, A, Hussain, OK, Dillon, T & Hussain, FK 2013, 'A framework for SLA management in cloud computing for informed decision making', CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, vol. 16, no. 4, pp. 961-977.
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In cloud computing, service providers offer cost-effective and on-demand IT services to service users on the basis of Service Level Agreements (SLAs). However the effective management of SLAs in cloud computing is essential for the service users to ensure that they achieve the desired outcomes from the formed service. In this paper, we introduce a SLA management framework that will enable service users to select the best available service provider on the basis of its reputation and then monitor the run time performance of the service provider to determine whether or not it will fulfill its promise defined in the SLA. Such analysis will assist the service user to make an informed decision about the continuation of service with the service provider.

Janjua, NK, Hussain, FK & Hussain, OK 2013, 'Semantic information and knowledge integration through argumentative reasoning to support intelligent decision making', INFORMATION SYSTEMS FRONTIERS, vol. 15, no. 2, pp. 167-192.
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The availability of integrated, high quality information is a pre-requisite for a decision support system (DSS) to aid in the decision-making process. The introduction of semantic web ensures the seamless integration of information derived from diverse sources and transforms the DSS to an adoptable and flexible Semantic Web-DSS (Web-DSS). However, due to the monotonic nature of the layered development of semantic web, it lacks the capability to represent, reason and integrate incomplete and conflicting information. This, in turn, renders an enterprise incapable of knowledge integration; that is, integration of information about a subject that could potentially be incomplete, inconsistent and distributed among different Web-DSS within or across enterprises. In this article, we address the issues of incomplete and inconsistent semantic information and knowledge integration by using argumentation and argumentation schemes. We discuss the Argumentation-enabled Information Integration Web-DSS (Web@IDSS) along with its syntax and semantics for semantic information integration, and devise a methodology for sharing the results of Web@IDSS in Argument Interchange Format (AIF) format. We also discuss Argumentation-enabled Knowledge Integration Web-DSS (Web@KIDSS) for semantic knowledge integration. We provide formal syntax and semantics for the Web@KIDSS, propose a conceptual framework, and describe it in detail. We present the algorithms for knowledge integration and the prototype application for validation of results

Kazem, A, Sharifi, E, Hussain, FK, Saberi, M & Hussain, OK 2013, 'Support vector regression with chaos-based firefly algorithm for stock market price forecasting', APPLIED SOFT COMPUTING, vol. 13, no. 2, pp. 947-958.
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Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box-Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price. The forecasting model has three stages. In the first stage, a delay coordinate embedding method is used to reconstruct unseen phase space dynamics. In the second stage, a chaotic firefly algorithm is employed to optimize SVR hyperparameters. Finally in the third stage, the optimized SVR is used to forecast stock market price. The significance of the proposed algorithm is 3-fold. First, it integrates both chaos theory and the firefly algorithm to optimize SVR hyperparameters, whereas previous studies employ a genetic algorithm (GA) to optimize these parameters. Second, it uses a delay coordinate embedding method to reconstruct phase space dynamics. Third, it has high prediction accuracy due to its implementation of structural risk minimization (SRM). To show the applicability and superiority of the proposed algorithm, we selected the three most challenging stock market time series data from NASDAQ historical quotes, namely Intel, National Bank shares and Microsoft daily closed (last) stock price, and applied the proposed algorithm to these data. Compared with genetic algorithm-based SVR (SVR-GA), chaotic genetic algorithm-based SVR (SVR-CGA), firefly-based SVR (SVR-FA), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), the proposed model performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE). Copyright © 2012 Published by Elsevier B.V. All rights reserved.

Kazienko, P, Musial, K & Kajdanowicz, T 2013, 'Multidimensional Social Network in the Social Recommender System', Kazienko, P.; Musial, K.; Kajdanowicz, T.;, 'Multidimensional Social Network in the Social Recommender System,' Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol.41, no.4, pp.746-759, July 2011, vol. 41, no. 4, pp. 746-759.
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All online sharing systems gather data that reflects users' collectivebehaviour and their shared activities. This data can be used to extractdifferent kinds of relationships, which can be grouped into layers, and whichare basic components of the multidimensional social network proposed in thepaper. The layers are created on the basis of two types of relations betweenhumans, i.e. direct and object-based ones which respectively correspond toeither social or semantic links between individuals. For better understandingof the complexity of the social network structure, layers and their profileswere identified and studied on two, spanned in time, snapshots of the Flickrpopulation. Additionally, for each layer, a separate strength measure wasproposed. The experiments on the Flickr photo sharing system revealed that therelationships between users result either from semantic links between objectsthey operate on or from social connections of these users. Moreover, thedensity of the social network increases in time. The second part of the studyis devoted to building a social recommender system that supports the creationof new relations between users in a multimedia sharing system. Its main goal isto generate personalized suggestions that are continuously adapted to users'needs depending on the personal weights assigned to each layer in themultidimensional social network. The conducted experiments confirmed theusefulness of the proposed model.

Kusakunniran, W, Wu, Q, Zhang, J, Ma, Y & Li, H 2013, 'A New View-Invariant Feature for Cross-View Gait Recognition', IEEE Transactions on Information Forensics and Security, vol. 8, no. 10, pp. 1642-1653.
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Human gait is an important biometric feature which is able to identify a person remotely. However, change of view causes significant difficulties for recognizing gaits. This paper proposes a new framework to construct a new view-invariant feature for cross-view gait recognition. Our view-normalization process is performed in the input layer (i.e., on gait silhouettes) to normalize gaits from arbitrary views. That is, each sequence of gait silhouettes recorded from a certain view is transformed onto the common canonical view by using corresponding domain transformation obtained through invariant low-rank textures (TILTs). Then, an improved scheme of procrustes shape analysis (PSA) is proposed and applied on a sequence of the normalized gait silhouettes to extract a novel view-invariant gait feature based on procrustes mean shape (PMS) and consecutively measure a gait similarity based on procrustes distance (PD). Comprehensive experiments were carried out on widely adopted gait databases. It has been shown that the performance of the proposed method is promising when compared with other existing methods in the literature.

Lemke, C, Riedel, S & Gabrys, B 2013, 'Evolving forecast combination structures for airline revenue management', Journal of Revenue and Pricing Management, vol. 12, no. 3, pp. 221-234.
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Forecasting is at the heart of every revenue management system, providing necessary input to capacity control, pricing and overbooking functionalities. For airlines, the key to efficient capacity control is determining the time of when to restrict bookings in a lower-fare class to leave space for later booking high-fare customers. This work presents findings of a collaboration project between Bournemouth University and Lufthansa Systems AG, a company providing revenue management software for airline carriers. The main aim is to increase net booking forecast accuracy by modifying one of its components, the cancellation forecast. Complementing an available set of three traditional individual algorithms, an additional method is presented and added to the method pool. Furthermore, diversification of model parameters and level of learning is discussed to increase the number of individual forecasts even further. Finally, the evolution of forecast combination structures is investigated and shown to be beneficial on an airline data set. © 2013 Macmillan Publishers Ltd.

Li, B, Chen, L, Zhu, X & Zhang, C 2013, 'Noisy but non-malicious user detection in social recommender systems', World Wide Web, vol. 16, no. 5-6, pp. 677-699.
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Social recommender systems largely rely on user-contributed data to infer users' preference. While this feature has enabled many interesting applications in social networking services, it also introduces unreliability to recommenders as users are allowed to insert data freely. Although detecting malicious attacks from social spammers has been studied for years, little work was done for detecting Noisy but Non-Malicious Users (NNMUs), which refers to those genuine users who may provide some untruthful data due to their imperfect behaviors. Unlike colluded malicious attacks that can be detected by finding similarly-behaved user profiles, NNMUs are more difficult to identify since their profiles are neither similar nor correlated from one another. In this article, we study how to detect NNMUs in social recommender systems. Based on the assumption that the ratings provided by a same user on closely correlated items should have similar scores, we propose an effective method for NNMU detection by capturing and accumulating user's 'self-contradictions', i.e., the cases that a user provides very different rating scores on closely correlated items. We show that self-contradiction capturing can be formulated as a constrained quadratic optimization problem w.r.t. a set of slack variables, which can be further used to quantify the underlying noise in each test user profile. We adopt three real-world data sets to empirically test the proposed method. The experimental results show that our method (i) is effective in real-world NNMU detection scenarios, (ii) can significantly outperform other noisy-user detection methods, and (iii) can improve recommendation performance for other users after removing detected NNMUs from the recommender system. © 2012 Springer Science+Business Media, LLC.

Li, J, Bian, W, Tao, D & Zhang, C 2013, 'Learning colours from textures by sparse manifold embedding.', Signal Process., vol. 93, no. 6, pp. 1485-1495.
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The capability of inferring colours from the texture (grayscale contents) of an image is useful in many application areas, when the imaging device/environment is limited. Traditional manual or limited automatic colour assignment involves intensive human effort. In this paper, we have developed a user-friendly colourisation technique, where the algorithm learns the relation between textures and colours in a user-provided example image and applies the relation to predict the colours in the target image. The key contribution of the proposed technique is trifold. First, we have explicitly built a linear model for the texture-colour relation. Second, we have considered the global non-linear structure of the data distribution by applying the linear model locally; and the local area is determined automatically by sparsity constraints. Third, we have introduced semantic information to further improve the colourisation. Examples demonstrate the effectiveness of the proposed techniques. Moreover, we have conducted a subjective study, where user experience supports the superiority of our method over existing techniques. © 2012 Elsevier B.V.

Li, L, Xu, G, Yang, Z, Dolog, P, Zhang, Y & Kitsuregawa, M 2013, 'An efficient approach to suggesting topically related web queries using hidden topic model', World Wide Web, vol. 16, no. 3, pp. 273-297.
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Keyword-based Web search is a widely used approach for locating information on the Web. However, Web users usually suffer from the difficulties of organizing and formulating appropriate input queries due to the lack of sufficient domain knowledge, which greatly affects the search performance. An effective tool to meet the information needs of a search engine user is to suggest Web queries that are topically related to their initial inquiry. Accurately computing query-to-query similarity scores is a key to improve the quality of these suggestions. Because of the short lengths of queries, traditional pseudo-relevance or implicit-relevance based approaches expand the expression of the queries for the similarity computation. They explicitly use a search engine as a complementary source and directly extract additional features (such as terms or URLs) from the top-listed or clicked search results. In this paper, we propose a novel approach by utilizing the hidden topic as an expandable feature. This has two steps. In the offline model-learning step, a hidden topic model is trained, and for each candidate query, its posterior distribution over the hidden topic space is determined to re-express the query instead of the lexical expression. In the online query suggestion step, after inferring the topic distribution for an input query in a similar way, we then calculate the similarity between candidate queries and the input query in terms of their corresponding topic distributions; and produce a suggestion list of candidate queries based on the similarity scores. Our experimental results on two real data sets show that the hidden topic based suggestion is much more efficient than the traditional term or URL based approach, and is effective in finding topically related queries for suggestion. © 2011 Springer Science+Business Media, LLC.

Li, Z, He, Y, Liu, Q, Zhao, L, Wong, L, Kwoh, CK, Nguyen, H & Li, J 2013, 'Structural analysis on mutation residues and interfacial water molecules for human TIM disease understanding', BMC BIOINFORMATICS, vol. 14, no. SUPPL16, pp. 1-15.
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Background: Human triosephosphate isomerase (HsTIM) deficiency is a genetic disease caused often by the pathogenic mutation E104D. This mutation, located at the side of an abnormally large cluster of water in the inter-subunit interface, reduces the thermostability of the enzyme. Why and how these water molecules are directly related to the excessive thermolability of the mutant have not been investigated in structural biology.Results: This work compares the structure of the E104D mutant with its wild type counterparts. It is found that the water topology in the dimer interface of HsTIM is atypical, having a 'wet-core-dry-rim' distribution with 16 water molecules tightly packed in a small deep region surrounded by 22 residues including GLU104. These water molecules are co-conserved with their surrounding residues in non-archaeal TIMs (dimers) but not conserved across archaeal TIMs (tetramers), indicating their importance in preserving the overall quaternary structure. As the structural permutation induced by the mutation is not significant, we hypothesize that the excessive thermolability of the E104D mutant is attributed to the easy propagation of atoms' flexibility from the surface into the core via the large cluster of water. It is indeed found that the B factor increment in the wet region is higher than other regions, and, more importantly, the B factor increment in the wet region is maintained in the deeply buried core. Molecular dynamics simulations revealed that for the mutant structure at normal temperature, a clear increase of the root-mean-square deviation is observed for the wet region contacting with the large cluster of interfacial water. Such increase is not observed for other interfacial regions or the whole protein. This clearly suggests that, in the E104D mutant, the large water cluster is responsible for the subunit interface flexibility and overall thermolability, and it ultimately leads to the deficiency of this enzyme.Conclusions: O...

Liu, B, Xiao, Y, Cao, L, Hao, Z & Deng, F 2013, 'SVDD-based outlier detection on uncertain data', Knowledge and Information Systems, vol. 34, no. 3, pp. 597-618.
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Outlier detection is an important problem that has been studied within diverse research areas and application domains. Most existing methods are based on the assumption that an example can be exactly categorized as either a normal class or an outlier. However, in many real-life applications, data are uncertain in nature due to various errors or partial completeness. These data uncertainty make the detection of outliers far more difficult than it is from clearly separable data. The key challenge of handling uncertain data in outlier detection is how to reduce the impact of uncertain data on the learned distinctive classifier. This paper proposes a new SVDD-based approach to detect outliers on uncertain data. The proposed approach operates in two steps. In the first step, a pseudo-training set is generated by assigning a confidence score to each input example, which indicates the likelihood of an example tending normal class. In the second step, the generated confidence score is incorporated into the support vector data description training phase to construct a global distinctive classifier for outlier detection. In this phase, the contribution of the examples with the least confidence score on the construction of the decision boundary has been reduced. The experiments show that the proposed approach outperforms state-of-art outlier detection techniques.

Liu, L, Chen, X, Luo, D, Lu, Y, Xu, G & Liu, M 2013, 'HSC: A SPECTRAL CLUSTERING ALGORITHM COMBINED WITH HIERARCHICAL METHOD', Neural Network World, vol. 23, no. 6, pp. 499-521.
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Most of the traditional clustering algorithms are poor for clustering more complex structures other than the convex spherical sample space. In the past few years, several spectral clustering algorithms were proposed to cluster arbitrarily shaped data in various real applications. However, spectral clustering relies on the dataset where each cluster is approximately well separated to a certain extent. In the case that the cluster has an obvious inflection point within a non-convex space, the spectral clustering algorithm would mistakenly recognize one cluster to be different clusters. In this paper, we propose a novel spectral clustering algorithm called HSC combined with hierarchical method, which obviates the disadvantage of the spectral clustering by not using the misleading information of the noisy neighboring data points. The simple clustering procedure is applied to eliminate the misleading information, and thus the HSC algorithm could cluster both convex shaped data and arbitrarily shaped data more efficiently and accurately. The experiments on both synthetic data sets and real data sets show that HSC outperforms other popular clustering algorithms. Furthermore, we observed that HSC can also be used for the estimation of the number of clusters

Liu, Q, Kwoh, CK & Li, J 2013, 'Binding Affinity Prediction for Protein–Ligand Complexes Based on β Contacts and B Factor', Journal of Chemical Information and Modeling, vol. 53, no. 11, pp. 3076-3085.
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Accurate determination of proteinligand binding affinity is a fundamental problem in biochemistry useful for many applications including drug design and proteinligand docking. A number of scoring functions have been proposed for the prediction of proteinligand binding affinity. However, accurate prediction is still a challenging problem because poor performance is often seen in the evaluation under the leave-one-cluster-out cross-validation (LCOCV). We introduce a new scoring function named B2BScore to improve the prediction performance. B2BScore integrates two physicochemical properties for proteinligand binding affinity prediction. One is the property of ß contacts. A ß contact between two atoms requires no other atoms to interrupt the atomic contact and assumes that the two atoms should have enough direct contact area. The other is the property of B factor to capture the atomic mobility in the dynamic proteinligand binding process.

Liu, Z, Yang, L, Dai, N, Chu, Y, Chen, Q & Li, J 2013, 'Intense ultra-broadband down-conversion in co-doped oxide glass by multipolar interaction process', Optics Express, vol. 21, no. 10, pp. 12635-12635.
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Lu, S, Zhang, J, Wang, Z & Feng, DD 2013, 'Fast human action classification and VOI localization with enhanced sparse coding', Journal of Visual Communication and Image Representation, vol. 24, no. 2, pp. 127-136.
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Sparse coding which encodes the natural visual signal into a sparse space for visual codebook generation and feature quantization, has been successfully utilized for many image classification applications. However, it has been seldom explored for many video analysis tasks. In particular, the increased complexity in characterizing the visual patterns of diverse human actions with both the spatial and temporal variations imposes more challenges to the conventional sparse coding scheme. In this paper, we propose an enhanced sparse coding scheme through learning discriminative dictionary and optimizing the local pooling strategy. Localizing when and where a specific action happens in realistic videos is another challenging task. By utilizing the sparse coding based representations of human actions, this paper further presents a novel coarse-to-fine framework to localize the Volumes of Interest (VOIs) for the actions. Firstly, local visual features are transformed into the sparse signal domain through our enhanced sparse coding scheme. Secondly, in order to avoid exhaustive scan of entire videos for the VOI localization, we extend the Spatial Pyramid Matching into temporal domain, namely Spatial Temporal Pyramid Matching, to obtain the VOI candidates. Finally, a multi-level branch-and-bound approach is developed to refine the VOI candidates. The proposed framework is also able to avoid prohibitive computations in local similarity matching (e.g., nearest neighbors voting). Experimental results on both two popular benchmark datasets (KTH and YouTube UCF) and the widely used localization dataset (MSR) demonstrate that our approach reduces computational cost significantly while maintaining comparable classification accuracy to that of the state-of-the-art methods. © 2012 Elsevier Inc. All rights reserved.

Meng, HD, Wu, PF, Song, YC & Xu, GD 2013, 'Research of Clustering Algorithm Based on Different Data Field Model', Advanced Materials Research, vol. 760-762, pp. 1925-1929.
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Data field clustering algorithm possesses dynamic characteristics compared with other clustering algorithms. By changing the parameters of the data field model, the results can be dynamically adjusted to meet the target of feature extraction and knowledge discovery in different scales, but the selection and construction of data field model can give rise to different clustering results. This paper presents the different effectiveness of clustering based on various of data field models and its parameters, provides with the scheme to chose the best data field model fitting to the characteristics of the data radiation, and verifies that the best clustering effectiveness can be achieved with the value of radial energy in the golden section.

Merigo, JM 2013, 'The probabilistic weighted averaging distance and its application in group decision making', Kybernetes, vol. 42, no. 5, pp. 686-697.
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Merigó, JM & Gil-Lafuente, AM 2013, 'A Method for Decision Making Based on Generalized Aggregation Operators', International Journal of Intelligent Systems, vol. 28, no. 5, pp. 453-473.
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Merigó, JM & Gil-Lafuente, AM 2013, 'Induced 2-tuple linguistic generalized aggregation operators and their application in decision-making', Information Sciences, vol. 236, pp. 1-16.
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MERIGÓ, JM & YAGER, RR 2013, 'GENERALIZED MOVING AVERAGES, DISTANCE MEASURES AND OWA OPERATORS', International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 21, no. 04, pp. 533-559.
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The concept of moving average is studied. We analyze several extensions by using generalized aggregation operators, obtaining the generalized moving average. The main advantage is that it provides a general framework that includes a wide range of specific cases including the geometric and the quadratic moving average. This analysis is extended by using the generalized ordered weighted averaging (GOWA) and the induced GOWA (IGOWA) operator. Thus, we get the generalized ordered weighted moving average (GOWMA) and the induced GOWMA (IGOWMA) operator. Some of their main properties are studied. We further extend this approach by using distance measures suggesting the concept of distance moving average and generalized distance moving average. We also consider the case with the OWA and the IOWA operator, obtaining the generalized ordered weighted moving averaging distance (GOWMAD) and the induced GOWMAD (IGOWMAD) operator. The paper ends with an application in multi-period decision making.

Merigó, JM & Yager, RR 2013, 'Norm Aggregations and OWA Operators', AGGREGATION FUNCTIONS IN THEORY AND IN PRACTISE, vol. 228, pp. 141-151.
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Merigó, JM, Gil-Lafuente, AM & Xu, Y 2013, 'Decision making with induced aggregation operators and the adequacy coefficient', Economic Computation and Economic Cybernetics Studies and Research, vol. 9.
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We present a method for decision making by using induced aggregation operators. This method is very useful for business decision making problems such as product management, investment selection and strategic management. We introduce a new aggregation operator that uses the induced ordered weighted averaging (IOWA) operator and the weighted average in the adequacy coefficient. We call it the induced ordered weighted averaging weighted averaging adequacy coefficient (IOWAWAAC) operator. The main advantage is that it is able to deal with complex attitudinal characters in the aggregation process. Thus, we are able to give a better representation of the problem considering the complex environment that affects the decisions. Moreover, it is able to provide a unified framework between the OWA and the weighted average. We generalize it by using generalized aggregation operators, obtaining the induced generalized OWAWAAC (IGOWAWAAC) operator. We study some of the main properties of this approach. We end the paper with a numerical example of the new approach in a group decision making problem in strategic management.

Merigó, JM, Gil-Lafuente, AM & Xu, Y 2013, 'Decision making with induced aggregation operators and the adequacy coefficient', Economic Computation and Economic Cybernetics Studies and Research, vol. 47, no. 1, pp. 185-202.
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We present a method for decision making by using induced aggregation operators. This method is very useful for business decision making problems such as product management, investment selection and strategic management. We introduce a new aggregation operator that uses the induced ordered weighted averaging (IOWA) operator and the weighted average in the adequacy coefficient. We call it the induced ordered weighted averaging weighted averaging adequacy coefficient (IOWAWAAC) operator. The main advantage is that it is able to deal with complex attitudinal characters in the aggregation process. Thus, we are able to give a better representation of the problem considering the complex environment that affects the decisions. Moreover, it is able to provide a unified framework between the OWA and the weighted average. We generalize it by using generalized aggregation operators, obtaining the induced generalized OWAWAAC (IGOWAWAAC) operator. We study some of the main properties of this approach. We end the paper with a numerical example of the new approach in a group decision making problem in strategic management.

Merigó, JM, Rocha, C & Garcia-Agreda, S 2013, 'Entrepreneurial intervention in electronic markets: the influence of customer participation', International Entrepreneurship and Management Journal, vol. 9, no. 4, pp. 521-529.
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Merigó, JM, Xu, Y & Zeng, S 2013, 'Group decision making with distance measures and probabilistic information', Knowledge-Based Systems, vol. 40, pp. 81-87.
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Movassaghi, S, Abolhasan, M & Lipman, J 2013, 'A Review of Routing Protocols in Wireless Body Area Networks', Journal of Networks, vol. 8, no. 3, pp. 559-575.
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Recent technological advancements in wireless communication, integrated circuits and Micro-Electro-Mechanical Systems (MEMs) has enabled miniaturized, low-power, intelligent, invasive/ non-invasive micro and nano-technology sensor nodes placed in or on the human body for use in monitoring body function and its immediate environment referred to as Body Area Networks (BANs). BANs face many stringent requirements in terms of delay, power, temperature and network lifetime which need to be taken into serious consideration in the design of different protocols. Since routing protocols play an important role in the overall system performance in terms of delay, power consumption, temperature and so on, a thorough study on existing routing protocols in BANs is necessary. Also, the specific challenges of BANs necessitates the design of new routing protocols specifically designed for BANs. This paper provides a survey of existing routing protocols mainly proposed for BANs. These protocols are further classified into five main categories namely, temperature based, cross-layer, cluster based, cost-effective and QoS-based routing, where each protocol is described under its specified category. Also, comparison among routing protocols in each category is given. © 2013 ACADEMY PUBLISHER.

Musiał, K & Kazienko, P 2013, 'Social networks on the Internet', World Wide Web, vol. 16, no. 1, pp. 31-72.
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Musial, K, Budka, M & Blysz, W 2013, 'Understanding the Other Side – The Inside Story of the INFER Project', Smart Innovation, Systems and Technologies, vol. 18, pp. 1-9.
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In the last few years, the collaboration between research institutions and industry has become a well established process. Transfer of Knowledge (ToK) is required to accelerate the development of both sides and to enable them to unlock their full potential. European Commission within the Marie Curie Industry and Academia Partnerships & Pathways (IAPP) programme supports the cooperation between these two sectors at the international scale by funding research projects that as one of the objectives aim at enhancing human mobility. IAPP projects offer people from different institutions the possibility to move sector and country in order to provide, absorb and implement new knowledge in a professional industrial-academic environment. In this paper, one of such projects is presented and both academia and industry perspectives in regard to opportunities and challenges in Transfer of Knowledge are described. Computational Intelligence Platform for Evolving and Robust Predictive Systems (INFER) is the IAPP project that serves as a case study for this paper. © Springer-Verlag Berlin Heidelberg 2013.

Musial, K, Budka, M & Juszczyszyn, K 2013, 'Creation and growth of online social network', World Wide Web, vol. 16, no. 4, pp. 421-447.
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Palacios‐Marqués, D, Peris‐Ortiz, M & Merigó, JM 2013, 'The effect of knowledge transfer on firm performance', Management Decision, vol. 51, no. 5, pp. 973-985.
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PurposeThis work aims to analyse the effect of a holistic business view, competency‐based management, continuous learning and information technology infrastructure on knowledge transfer and the subsequent effect on firm performance.Design/methodology/approachStructural equation models and a survey of 222 firms from the Spanish biotechnology and telecommunications industries verify the mediator role of knowledge transfer.FindingsThe implications of confirming these hypotheses for managers are that by emphasising the creation of a holistic business view, competency‐based management, promoting continuous learning and improving information technology infrastructure, managers will improve knowledge transfer and positively influence the creation of superior firm performance.Originality/valueIt is shown that in knowledge‐intensive industries, knowledge transfer acts as a mediating variable between a holistic view of the firm, competency‐based management, continuous learning and information and communication technologies infrastructure and firm performance.

Parvin, S, Hussain, FK & Hussain, OK 2013, 'Conjoint trust assessment for secure communication in cognitive radio networks', MATHEMATICAL AND COMPUTER MODELLING, vol. 58, no. 5-6, pp. 1340-1350.
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With the rapid development of wireless communication, the growth of Cognitive Radio (CR) is increasing day by day. Because CR is flexible and operates on the wireless network, there are more security threats to CR technology than to the traditional radio environment. In addition, there is no comprehensive framework for achieving security in Cognitive Radio Networks (CRNs), and the role of trust for achieving security in CRNs has not been explored previously. Security vulnerability in cognitive radio technology is unavoidable due to the intrinsic nature of the technology, so it is critical to ensure system security in CRNs. The issue of secure communication in CRNs thus becomes more important than it is in conventional wireless networks. In this paper, we propose a conjoint trust assessment approach (combining trust assessment from the Primary User Network and the Secondary User Network) in a CRN to solve the security threats brought about by untrustworthy entities, such as selfish, malicious, and faultless nodes, and to ensure secure spectrum sharing in CRNs. A numerical analysis shows the feasibility of our proposed approach.

Parvin, S, Hussain, FK, Hussain, OK, Thein, T & Park, JS 2013, 'Multi-cyber framework for availability enhancement of cyber physical systems', COMPUTING, vol. 95, no. 10-11, pp. 927-948.
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With the rapid growth of wireless communication, the deployment of cyber-physical system (CPS) is increasing day by day. As a cyber physical system involves a tight coupling between the physical and computational components, it is critical to ensure that the system, apart from being secure, is available for both the cyber and physical processes. Traditional methods have generally been employed to defend an infrastructure system against physical threats. However, this does not guarantee that the availability of the system will always be high. In this paper, we propose a multi-cyber (computational unit) framework to improve the availability of CPS based on Markov model. We evaluate the effectiveness of our proposed framework in terms of availability, downtime, downtime cost and reliability of the CPS framework. © 2012 Her Majesty the Queen in Right of Australia.

Rehman, ZU, Hussain, FK & Hussain, OK 2013, 'Frequency-based similarity measure for multimedia recommender systems', MULTIMEDIA SYSTEMS, vol. 19, no. 2, pp. 95-102.
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Personalized recommendation has become a pivotal aspect of online marketing and e-commerce as a means of overcoming the information overload problem. There are several recommendation techniques but collaborative recommendation is the most effective and widely used technique. It relies on either item-based or user-based nearest neighborhood algorithms which utilize some kind of similarity measure to assess the similarity between different users or items for generating the recommendations. In this paper, we present a new similarity measure which is based on rating frequency and compare its performance with the current most commonly used similarity measures. The applicability and use of this similarity measure from the perspective of multimedia content recommendation is presented and discussed

Saberi, M, Mirtalaie, MS, Hussain, FK, Azadeh, A, Hussain, OK & Ashjari, B 2013, 'A granular computing-based approach to credit scoring modeling', NEUROCOMPUTING, vol. 122, no. 1, pp. 100-115.
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The credit card industry has been growing rapidly and thus huge numbers of consumers' credit data are collected by the credit department of the banks. The credit scoring managers often evaluate the consumer's credit with intuitive experience. However, with the support of the credit classification models, the managers can accurately evaluate the applicants' credit score. In this study, a neurocomputing-based granular approach is proposed to model credit scoring. Granular computing is used to compute the size of training and testing groups. Artificial neural networks (ANN) and data envelopment analysis (DEA) are used to model credit lending decisions in the online and offline manner, respectively. Proposed method is composed of three distinct stages based on trust and credibility concept. Trust is introduced and modeled via ANN in online module. Also credibility is modeled via DEA in offline module in present study. This paper is a pioneer in examining the concept of granularity for selecting the optimum size of testing and training group in machine learning area. In addition, proposing flexible trust ranges comparing to the current constant ones will support the importance of customers with higher credit scores to financial markets. To show the applicability and superiority of the proposed algorithm, it is applied to a credit-card data set obtained from the UCI repository. © 2013 Elsevier B.V.

Stahl, F, Gabrys, B, Gaber, MM & Berendsen, M 2013, 'An overview of interactive visual data mining techniques for knowledge discovery', WIREs Data Mining and Knowledge Discovery, vol. 3, no. 4, pp. 239-256.
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In the past decade, the analysis of data has faced the challenge of dealing with very large and complex datasets and the real‐time generation of data. Technologies to store and access these complex and large datasets are in place. However, robust and scalable analysis technologies are needed to extract meaningful information from these datasets. The research field of Information Visualization and Visual Data Analytics addresses this need. Information visualization and data mining are often used complementary to each other. Their common goal is the extraction of meaningful information from complex and possibly large data. However, though data mining focuses on the usage of silicon hardware, visualization techniques also aim to access the powerful image‐processing capabilities of the human brain. This article highlights the research on data visualization and visual analytics techniques. Furthermore, we highlight existing visual analytics techniques, systems, and applications including a perspective on the field from the chemical process industry.This article is categorized under:Application Areas > Data Mining Software ToolsFundamental Concepts of Data and Knowledge > Knowledge RepresentationTechnologies > Visualization

Tafavogh, S, Navarro, KF, Catchpoole, DR & Kennedy, PJ 2013, 'Non-parametric and integrated framework for segmenting and counting neuroblastic cells within neuroblastoma tumor images', MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, vol. 51, no. 6, pp. 645-655.
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Neuroblastoma is a malignant tumor and a cancer in childhood that derives from the neural crest. The number of neuroblastic cells within the tumor provides significant prognostic information for pathologists. An enormous number of neuroblastic cells makes the process of counting tedious and error-prone. We propose a user interaction-independent framework that segments cellular regions, splits the overlapping cells and counts the total number of single neuroblastic cells. Our novel segmentation algorithm regards an image as a feature space constructed by joint spatial-intensity features of color pixels. It clusters the pixels within the feature space using mean-shift and then partitions the image into multiple tiles. We propose a novel color analysis approach to select the tiles with similar intensity to the cellular regions. The selected tiles contain a mixture of single and overlapping cells. We therefore also propose a cell counting method to analyse morphology of the cells and discriminate between overlapping and single cells. Ultimately, we apply watershed to split overlapping cells. The results have been evaluated by a pathologist. Our segmentation algorithm was compared against adaptive thresholding. Our cell counting algorithm was compared with two state of the art algorithms. The overall cell counting accuracy of the system is 87.65 %. © 2013 International Federation for Medical and Biological Engineering.

Tang, J, Chen, L, King, I & Wang, J 2013, 'Introduction to Special section on Large-scale Data Mining', Data & Knowledge Engineering, vol. 87, pp. 355-356.
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Tsakonas, A & Gabrys, B 2013, 'A fuzzy evolutionary framework for combining ensembles', Applied Soft Computing, vol. 13, no. 4, pp. 1800-1812.
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Wang, Z, Chen, S, Mo, H, Huang, Y, Li, J, Sun, J, Liu, L & Zhao, S 2013, 'A simple and economical method in purifying dairy goat luteal cells', Tissue and Cell, vol. 45, no. 4, pp. 269-274.
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Wei, HF, Chen, HW, Chen, SP, Yan, PG, Liu, T, Guo, L, Lei, Y, Chen, ZL, Li, J, Zhang, XB, Zhang, GL, Hou, J, Tong, WJ, Luo, J, Li, JY & Chen, KK 2013, 'A compact seven-core photonic crystal fiber supercontinuum source with 42.3 W output power', Laser Physics Letters, vol. 10, no. 4, pp. 045101-045101.
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Wei, W, Li, J, Cao, L, Ou, Y & Chen, J 2013, 'Effective detection of sophisticated online banking fraud on extremely imbalanced data', World Wide Web, vol. 16, no. 4, pp. 449-475.
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Sophisticated online banking fraud reflects the integrative abuse of resources in social, cyber and physical worlds. Its detection is a typical use case of the broad-based Wisdom Web of Things (W2T) methodology. However, there is very limited information available to distinguish dynamic fraud from genuine customer behavior in such an extremely sparse and imbalanced data environment, which makes the instant and effective detection become more and more important and challenging. In this paper, we propose an effective online banking fraud detection framework that synthesizes relevant resources and incorporates several advanced data mining techniques. By building a contrast vector for each transaction based on its customer's historical behavior sequence, we profile the differentiating rate of each current transaction against the customer's behavior preference. A novel algorithm, ContrastMiner, is introduced to efficiently mine contrast patterns and distinguish fraudulent from genuine behavior, followed by an effective pattern selection and risk scoring that combines predictions from different models. Results from experiments on large-scale real online banking data demonstrate that our system can achieve substantially higher accuracy and lower alert volume than the latest benchmarking fraud detection system incorporating domain knowledge and traditional fraud detection methods. © 2012 Springer Science+Business Media, LLC.

Whitney, M & Ryan, L 2013, 'Uncertainty due to low-dose extrapolation: modified BMD methodology for epidemiological data', ENVIRONMETRICS, vol. 24, no. 5, pp. 289-297.
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Traditional environmental risk assessment methodologies, including benchmark dose (BMD) estimation, were originally developed to be used with animal toxicology data. We discuss some problems that can occur when toxicology-based methods are applied to hum

Whitney, M, Ryan, L & Walkowiak, J 2013, 'On the Use of Bayesian Model Averaging for Covariate Selection in Epidemiological Modeling', Journal of Statistical Theory and Practice, vol. 7, no. 2, pp. 233-247.
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Bayesian model averaging (BMA) is a powerful technique to address model selection uncertainty and recent computational advances have led to a proliferation of usage. BMA methods are of particular interest in environmental health risk assessment because of the high degree of uncertainty that typically arises in that context. In this article, we review a variety of approaches to conducting BMA and compare four implementations in a setting where there are a number of potential predictors. We then use these four methods to calculate risk assessment measures that account for the uncertainty involved in modeling environmental exposures. These methods are used to reexamine data from a study conducted by Walkowiak et al. (2001) to investigate the effects of maternal polychlorinated biphenyl exposure on cognitive development in early childhood. This case study reveals that different strategies for implementing BMA can yield varying risk assessment results. We conclude with some practical recommendations. © 2013 Copyright Grace Scientific Publishing, LLC.

Wu, Z, Xu, G, Lu, C, Chen, E, Zhang, Y & Zhang, H 2013, 'Position-wise contextual advertising: Placing relevant ads at appropriate positions of a web page', Neurocomputing, vol. 120, no. 1, pp. 524-535.
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Web advertising, a form of online advertising, which uses the Internet as a medium to post product or service information and attract customers, has become one of the most important marketing channels. As one prevalent type of web advertising, contextual

Xin, J, Chen, K, Bai, L, Liu, D & Zhang, J 2013, 'Depth Adaptive Zooming Visual Servoing for a Robot with a Zooming Camera', International Journal of Advanced Robotic Systems, vol. 10, no. 2, pp. 120-120.
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To solve the view visibility problem and keep the observed object in the field of view (FOV) during the visual servoing, a depth adaptive zooming visual servoing strategy for a manipulator robot with a zooming camera is proposed. Firstly, a zoom control mechanism is introduced into the robot visual servoing system. It can dynamically adjust the camera's field of view to keep all the feature points on the object in the field of view of the camera and get high object local resolution at the end of visual servoing. Secondly, an invariant visual servoing method is employed to control the robot to the desired position under the changing intrinsic parameters of the camera. Finally, a nonlinear depth adaptive estimation scheme in the invariant space using Lyapunov stability theory is proposed to estimate adaptively the depth of the image features on the object. Three kinds of robot 4DOF visual positioning simulation experiments are conducted. The simulation experiment results show that the proposed approach has higher positioning precision. © 2013 Xin et al.

Xinwang Liu, Jianping Yin, Lei Wang, Lingqiao Liu, Jun Liu, Chenping Hou & Jian Zhang 2013, 'An Adaptive Approach to Learning Optimal Neighborhood Kernels', IEEE Transactions on Cybernetics, vol. 43, no. 1, pp. 371-384.
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Learning an optimal kernel plays a pivotal role in kernel-based methods. Recently, an approach called optimal neighborhood kernel learning (ONKL) has been proposed, showing promising classification performance. It assumes that the optimal kernel will reside in the neighborhood of a 'pre-specified' kernel. Nevertheless, how to specify such a kernel in a principled way remains unclear. To solve this issue, this paper treats the pre-specified kernel as an extra variable and jointly learns it with the optimal neighborhood kernel and the structure parameters of support vector machines. To avoid trivial solutions, we constrain the pre-specified kernel with a parameterized model. We first discuss the characteristics of our approach and in particular highlight its adaptivity. After that, two instantiations are demonstrated by modeling the pre-specified kernel as a common Gaussian radial basis function kernel and a linear combination of a set of base kernels in the way of multiple kernel learning (MKL), respectively. We show that the optimization in our approach is a min-max problem and can be efficiently solved by employing the extended level method and Nesterov's method. Also, we give the probabilistic interpretation for our approach and apply it to explain the existing kernel learning methods, providing another perspective for their commonness and differences. Comprehensive experimental results on 13 UCI data sets and another two real-world data sets show that via the joint learning process, our approach not only adaptively identifies the pre-specified kernel, but also achieves superior classification performance to the original ONKL and the related MKL algorithms. © 2012 IEEE.

Xinwang Liu, Lei Wang, Jianping Yin, En Zhu & Jian Zhang 2013, 'An Efficient Approach to Integrating Radius Information into Multiple Kernel Learning', IEEE Transactions on Cybernetics, vol. 43, no. 2, pp. 557-569.
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Integrating radius information has been demonstrated by recent work on multiple kernel learning (MKL) as a promising way to improve kernel learning performance. Directly integrating the radius of the minimum enclosing ball (MEB) into MKL as it is, however, not only incurs significant computational overhead but also possibly adversely affects the kernel learning performance due to the notorious sensitivity of this radius to outliers. Inspired by the relationship between the radius of the MEB and the trace of total data scattering matrix, this paper proposes to incorporate the latter into MKL to improve the situation. In particular, in order to well justify the incorporation of radius information, we strictly comply with the radius-margin bound of support vector machines (SVMs) and thus focus on the ℓ2-norm soft-margin SVM classifier. Detailed theoretical analysis is conducted to show how the proposed approach effectively preserves the merits of incorporating the radius of the MEB and how the resulting optimization is efficiently solved. Moreover, the proposed approach achieves the following advantages over its counterparts: 1) more robust in the presence of outliers or noisy training samples; 2) more computationally efficient by avoiding the quadratic optimization for computing the radius at each iteration; and 3) readily solvable by the existing off-the-shelf MKL packages. Comprehensive experiments are conducted on University of California, Irvine, protein subcellular localization, and Caltech-101 data sets, and the results well demonstrate the effectiveness and efficiency of our approach. © 2012 IEEE.

Xu, G, Yu, J & Lee, W 2013, 'Guest editorial: Social networks and social Web mining', World Wide Web, vol. 16, no. 5-6, pp. 541-544.
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Xu, Y, Shi, P, Merigó, JM & Wang, H 2013, 'Some proportional 2-tuple geometric aggregation operators for linguistic decision making', Journal of Intelligent & Fuzzy Systems, vol. 25, no. 3, pp. 833-843.
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Yang, W, Gao, Y & Cao, L 2013, 'TRASMIL: A local anomaly detection framework based on trajectory segmentation and multi-instance learning', Computer Vision and Image Understanding, vol. 117, no. 10, pp. 1273-1286.
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Local anomaly detection refers to detecting small anomalies or outliers that exist in some subsegments of events or behaviors. Such local anomalies are easily overlooked by most of the existing approaches since they are designed for detecting global or large anomalies. In this paper, an accurate and flexible threephase framework TRASMIL is proposed for local anomaly detection based on TRAjectory Segmentation and Multi-Instance Learning. Firstly, every motion trajectory is segmented into independent subtrajectories, and a metric with Diversity and Granularity is proposed to measure the quality of segmentation. Secondly, the segmented sub-trajectories are modeled by a sequence learning model. Finally, multi-instance learning is applied to detect abnormal trajectories and sub-trajectories which are viewed as bags and instances, respectively. We validate the TRASMIL framework in terms of 16 different algorithms built on the three-phase framework. Substantial experiments show that algorithms based on the TRASMIL framework outperform existing methods in effectively detecting the trajectories with local anomalies in terms of the whole trajectory. In particular, the MDL-C algorithm (the combination of HDP-HMM with MDL segmentation and Citation kNN) achieves the highest accuracy and recall rates. We further show that TRASMIL is generic enough to adopt other algorithms for identifying local anomalies. Crown Copyright © 2012 Published by Elsevier Inc. All rights reserved.

Yu, D, Merigó, JM & Zhou, L 2013, 'Interval-valued multiplicative intuitionistic fuzzy preference relations', International Journal of Fuzzy Systems, vol. 15, no. 4, pp. 412-422.
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Inspired by the idea of multiplicative intuitionistic preference relation (Xia MM et al. Preference relations based on intuitionistic multiplicative information, IEEE Transactions on Fuzzy Systems, 2013, 21(1): 113-133), in this paper, a new preference relation called the interval-valued multiplicative intuitionistic preference relation is developed. It is analyzed the basic operations for interval-valued multiplicative intuitionistic preference information and its aggregation techniques. An interval-valued multiplicative intuitionistic group decision making model is presented in which experts provide their preference relation by interval-valued multiplicative intuitionistic fuzzy expressions, and give a real case about talent introduction in Zhejiang University of Finance and Economics to illustrate our methods. © 2013 TFSA.

Yu, D, Nanda, P, Cao, L & He, X 2013, 'TCTM: an evaluation framework for architecture design on wireless sensor networks', International Journal of Sensor Networks, vol. 14, no. 3, pp. 168-168.
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This paper presents an evaluation framework for architecture designs on wireless sensor networks (WSNs). We introduce a simple evaluation model: triangular constraint tradeoffs model (TCTM) to grasp the essence of the architecture design consideration under transient wireless media characteristic and stringent limitation on energy and computing resource of WSNs. Based on this evaluation framework, we investigate the existing architectures proposed in literature from three main competing constraint aspects, namely generality, cost, and performance. Two important concepts: performance efficiency and deployment efficiency are identified and distinguished in overall architecture efficiency. With this powerful abstract and simple model, we describe the motivations of major body of WSNs architectures proposed in current literature. We also analyse the fundamental advantage and limitations of each class of architectures from TCTM perspective. We foresee the influence of evolving technology to futuristic architecture design. We believe our efforts will serve as a reference to orient researchers and system designers in this area

Zare Borzeshi, E, Piccardi, M, Riesen, K & Bunke, H 2013, 'Discriminative prototype selection methods for graph embedding', Pattern Recognition, vol. 46, no. 6, pp. 1648-1657.
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Graphs possess a strong representational power for many types of patterns. However, a main limitation in their use for pattern analysis derives from their difficult mathematical treatment. One way of circumventing this problem is that of transforming the graphs into a vector space by means of graph embedding. Such an embedding can be conveniently obtained by using a set of prototype graphs and a dissimilarity measure. However, when we apply this approach to a set of class-labelled graphs, it is challenging to select prototypes capturing both the salient structure within each class and inter-class separation. In this paper, we introduce a novel framework for selecting a set of prototypes from a labelled graph set taking their discriminative power into account. Experimental results showed that such a discriminative prototype selection framework can achieve superior results in classification compared to other well-established prototype selection approaches. © 2012 Elsevier Ltd.

ZENG, S, LI, WEI & MERIGÓ, JM 2013, 'EXTENDED INDUCED ORDERED WEIGHTED AVERAGING DISTANCE OPERATORS AND THEIR APPLICATION TO GROUP DECISION-MAKING', International Journal of Information Technology & Decision Making, vol. 12, no. 04, pp. 789-811.
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The induced ordered weighted averaging distance (IOWAD) approach is very suitable in situations in which the available information is represented with exact numerical values. In this paper, we develop some extended IOWAD operators: the linguistic induced ordered weighted averaging distance (LIOWAD) operator, the uncertain induced ordered weighted averaging distance (UIOWAD) operator and the fuzzy induced ordered weighted averaging distance (FIOWAD) operator. Their main objective is to assess uncertain situations in which the available information is given in the form of linguistic variables, interval numbers and fuzzy numbers. Some special cases of these three new extensions are studied. Finally, we develop an application of the new operators in a group decision-making problem under an uncertain environment and illustrate it with a numerical example.

Zeng, S, Merigó, JM & Su, W 2013, 'The uncertain probabilistic OWA distance operator and its application in group decision making', Applied Mathematical Modelling, vol. 37, no. 9, pp. 6266-6275.
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Zhang, X, Zhu, X, Xing, R, Yang, X, Jiang, F, Li, H, Peng, J, Dai, N & Li, J 2013, 'Microstructure core photonic crystal fiber for blue extension of supercontinuum generation', Optics Communications, vol. 298-299, pp. 191-195.
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Zhou, J, Cao, L & Yang, N 2013, 'On the convergence of some possibilistic clustering algorithms', Fuzzy Optimization and Decision Making, vol. 12, no. 4, pp. 415-432.
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In this paper, an analysis of the convergence performance is conducted for a class of possibilistic clustering algorithms (PCAs) utilizing the Zangwill convergence theorem. It is shown that under certain conditions the iterative sequence generated by a PCA converges, at least along a subsequence, to either a local minimizer or a saddle point of the objective function of the algorithm. The convergence performance of more general PCAs is also discussed. © 2013 Springer Science+Business Media New York.

Zong, Y, Jin, P, Xu, D & Pan, R 2013, 'A Clustering Algorithm based on Local Accumulative Knowledge', Journal of Computers, vol. 8, no. 2, pp. 365-371.
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Clustering as an important unsupervised learning technique is widely used to discover the inherent structure of a given data set. For clustering is depended on applications, researchers use different models to defined clustering problems. Heuristic clustering algorithm is an efficient way to deal with clustering problem defined by combining optimization model, but initialization sensitivity is an inevitable problem. In the past decades, a lot of methods have been proposed to deal with such problem. In this paper, on the contrary, we take the advantage of the initialization sensitivity to design a new clustering algorithm. We, firstly, run K-means, a widely used heuristic clustering algorithm, on data set for multiple times to generate several clustering results; secondly, propose a structure named Local Accumulative Knowledge (LAKE) to capture the common information of clustering results; thirdly, execute the Single-linkage algorithm on LAKE to generate a rough clustering result; eventually, assign the rest data objects to the corresponding clusters. Experimental results on synthetic and real world data sets demonstrate the superiority of the proposed approach in terms of clustering quality measures. © 2013 ACADEMY PUBLISHER.

Bakirov, R & Gabrys, B 1970, 'Investigation of Expert Addition Criteria for Dynamically Changing Online Ensemble Classifiers with Multiple Adaptive Mechanisms', ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2013, 9th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Springer Berlin Heidelberg, Paphos, CYPRUS, pp. 646-656.
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Balvey, A, Gil Lafuente, AM, Merigo, JM & Garriga, X 1970, 'APPLICATION OF THE FORGOTTEN EFFECTS MODEL TO THE EARLY DIAGNOSIS OF HEREDITARY HEMOCHROMATOSIS', DECISION MAKING SYSTEMS IN BUSINESS ADMINISTRATION, International Conference on Modeling and Simulation in Engineering, Economics and Management for Sustainable Development, WORLD SCIENTIFIC PUBL CO PTE LTD, Rio de Janeiro, BRAZIL, pp. 407-420.

Beck, D, Thoms, J, Perera, D, Unnikrishnan, A, Knezevic, K, O'Brien, T, Gottgens, B, Wong, J & Pimanda, J 1970, 'Genome-wide analysis of transcriptional regulators in human hscs reveals a densely interconnected network of coding and non-coding genes', Experimental Hematology, 42nd Annual Scientific Meeting of the International-Society-for-Experimental-Hematology-and-Stem-Cells (ISEH), Elsevier BV, Vienna, AUSTRIA, pp. S17-S17.
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Benaben, F, Hussain, F & Pereira, E 1970, 'Track I: Collaborative platforms for sustainable logistics and transportation', 2013 7th IEEE International Conference on Digital Ecosystems and Technologies (DEST), 2013 7th IEEE International Conference on Digital Ecosystems and Technologies (DEST) - Complex Environment Engineering, IEEE.
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Cao, W, Cao, L & Song, Y 1970, 'Coupled market behavior based financial crisis detection', The 2013 International Joint Conference on Neural Networks (IJCNN), 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas), IEEE, Dallas, TX, USA, pp. 1-8.
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Financial crisis detection is a long-standing challenging issue with significant practical values and impact on economy, society and globalization. The challenge lies in many aspects, in particular, the nonlinear and dynamic characteristics associated with financial crisis. Most of existing methods rely on selecting individual indicators associated with one market indicator, and the linear assumption is often behind the models for prediction. In practice, a linear assumption may be too strong to be applicable to the real market dynamics. More importantly, instruments in different markets such as gold price and petrol price are often coupled. A financial crisis may significantly change the couplings between different market indicators. In addition, such couplings in cross-market interaction are likely nonlinear. In this paper, we present a new approach for financial crisis detection by catering for the often nonlinear couplings between major indicators selected from different markets, called coupled market behavior analysis, to detect different coupled market behaviors at crisis and non-crisis periods. A Coupled Hidden Markov Model (CHMM) is built to characterize the coupled market behaviors of equity, commodity and interest markets as case studies. The empirical results show the need of catering for nonlinear couplings between various markets and the proposed approach is much more effective in capturing the coupling and nonlinear relations associated with financial crisis compared with other traditionally used approaches, such as Signal, Logistic and ANN models.

Cao, W, Wang, C & Cao, L 1970, 'Trading Strategy Based Portfolio Selection for Actionable Trading Agents', Agents and Data Mining Interaction - 8th International Workshop, ADMI 2012, International Workshop on Agents and Data Mining Interaction, Springer Berlin Heidelberg, Valencia, Spain, pp. 191-202.
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Trading agents are very useful for supporting investors in making decisions in financial markets, but the existing trading agent research focuses on simulation on artificial data. This leads to limitations in its usefulness. As for investors, how trading agents help them manipulate their assets according to their risk appetite and thus obtain a higher return is a big issue. Portfolio optimization is an approach used by many researchers to resolve this issue, but the focus is mainly on developing more accurate mathematical estimation methods, and overlooks an important factor: trading strategy. Since the global financial crisis added uncertainty to financial markets, there is an increasing demand for trading agents to be more active in providing trading strategies that will better capture trading opportunities. In this paper, we propose a new approach, namely trading strategy based portfolio selection, by which trading agents combine assets and their corresponding trading strategies to construct new portfolios, following which, trading agents can help investors to obtain the optimal weights for their portfolios according to their risk appetite. We use historical data to test our approach, the results show that it can help investors make more profit according to their risk tolerance by selecting the best portfolio in real financial markets.

Cheng, X, Miao, D, Wang, C & Cao, L 1970, 'Coupled term-term relation analysis for document clustering', The 2013 International Joint Conference on Neural Networks (IJCNN), 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas), IEEE, Dallas, TX, USA, pp. 1-8.
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Traditional document clustering approaches are usually based on the Bag of Words model, which is limited by its assumption of the independence among terms. Recent strategies have been proposed to capture the relation between terms based on statistical analysis, and they estimate the relation between terms purely by their co-occurrence across the documents. However, the implicit interactions with other link terms are overlooked, which leads to the discovery of incomplete information. This paper proposes a coupled term-term relation model for document representation, which considers both the intra-relation (i.e. co-occurrence of terms) and inter-relation (i.e. dependency of terms via link terms) between a pair of terms. The coupled relation for each pair of terms is further used to map a document onto a new feature space, which includes more semantic information. Substantial experiments verify that the document clustering incorporated with our proposed relation achieves a significant performance improvement compared to the state-of-the-art techniques. © 2013 IEEE.

Cuzzocrea, A, Moussa, R & Xu, G 1970, 'OLAP*: Effectively and Efficiently Supporting Parallel OLAP over Big Data', Lecture Notes in Computer Science, International Conference on Model and Data Engineering, Springer Berlin Heidelberg, Amantea, Italy, pp. 38-49.
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In this paper, we investigate solutions relying on data partitioning schemes for parallel building of OLAP data cubes, suitable to novel Big Data environments, and we propose the framework OLAP*, along with the associated benchmark TPC-H*d, a suitable transformation of the well-known data warehouse benchmark TPC-H. We demonstrate through performance measurements the efficiency of the proposed framework, developed on top of the ROLAP server Mondrian

Deng, C, Ji, R, Liu, W, Tao, D & Gao, X 1970, 'Visual Reranking through Weakly Supervised Multi-graph Learning.', ICCV, IEEE International Conference on Computer Vision, IEEE Computer Society, Sydney, Australia, pp. 2600-2607.
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Visual reranking has been widely deployed to refine the quality of conventional content-based image retrieval en- gines. The current trend lies in employing a crowd of re- trieved results stemming from multiple feature modalities to boost the overall performance of visual reranking. Howev- er, a major challenge pertaining to current reranking meth- ods is how to take full advantage of the complementary property of distinct feature modalities. Given a query im- age and one feature modality, a regular visual reranking framework treats the top-ranked images as pseudo positive instances which are inevitably noisy, difficult to reveal this complementary property, and thus lead to inferior ranking performance. This paper proposes a novel image rerank- ing approach by introducing a Co-Regularized Multi-Graph Learning (Co-RMGL) framework, in which the intra-graph and inter-graph constraints are simultaneously imposed to encode affinities in a single graph and consistency across d- ifferent graphs. Moreover, weakly supervised learning driv- en by image attributes is performed to denoise the pseudo- labeled instances, thereby highlighting the unique strength of individual feature modality. Meanwhile, such learning can yield a few anchors in graphs that vitally enable the alignment and fusion of multiple graphs. As a result, an edge weight matrix learned from the fused graph automat- ically gives the ordering to the initially retrieved results. We evaluate our approach on four benchmark image re- trieval datasets, demonstrating a significant performance gain over the state-of-the-arts

Fang, M, Yin, J, Zhu, X & Zhang, C 1970, 'Active class discovery and learning for networked data', Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013, pp. 315-323.
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With the recent explosion of social network applications, active learning has increasingly become an important paradigm for classifying networked data. While existing research has shown promising results by exploiting network properties to improve the active learning performance, they are all based on a static setting where the number and the type of classes underlying the networked data remain stable and unchanged. For most social network applications, the dynamic change of users and their evolving relationships, along with the emergence of new social events, often result in new classes that need to be immediately discovered and labeled for classification. This paper proposes a novel approach called ADLNET for active class discovery and learning with networked data. Our proposed method uses the Dirichlet process defined over class distributions to enable active discovery of new classes, and explicitly models label correlations in the utility function of active learning. Experimental results on two real-world networked data sets demonstrate that our proposed approach outperforms other state-of-the-art methods.

Fariha, A, Ahmed, CF, Leung, CK-S, Abdullah, SM & Cao, L 1970, 'Mining Frequent Patterns from Human Interactions in Meetings Using Directed Acyclic Graphs', Lecture Notes in Computer Science, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer Berlin Heidelberg, Gold Coast, Australia, pp. 38-49.
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In modern life, interactions between human beings frequently occur in meetings, where topics are discussed. Semantic knowledge of meetings can be revealed by discovering interaction patterns from these meetings. An existing method mines interaction patterns from meetings using tree structures. However, such a tree-based method may not capture all kinds of triggering relations between interactions, and it may not distinguish a participant of a certain rank from another participant of a different rank in a meeting. Hence, the tree-based method may not be able to find all interaction patterns such as those about correlated interaction. In this paper, we propose to mine interaction patterns from meetings using an alternative data structurenamely, a directed acyclic graph (DAG). Specifically, a DAG captures both temporal and triggering relations between interactions in meetings. Moreover, to distinguish one participant of a certain rank from another, we assign weights to nodes in the DAG. As such, a meeting can be modeled as a weighted DAG, from which weighted frequent interaction patterns can be discovered. Experimental results showed the effectiveness of our proposed DAG-based method for mining interaction patterns from meetings.

Fong, S, Zhuang, Y, Li, J & Khoury, R 1970, 'Sentiment Analysis of Online News Using MALLET', 2013 International Symposium on Computational and Business Intelligence, 2013 International Symposium on Computational and Business Intelligence (ISCBI), IEEE, New Delhi, INDIA, pp. 301-304.
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Fu, B, Xu, G, Wang, Z & Cao, L 1970, 'Leveraging Supervised Label Dependency Propagation for Multi-label Learning', 2013 IEEE 13TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), IEEE International Conference on Data Mining, IEEE, Dallas, TX, USA, pp. 1061-1066.
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Exploiting label dependency is a key challenge in multi-label learning, and current methods solve this problem mainly by training models on the combination of related labels and original features. However, label dependency cannot be exploited dynamically and mutually in this way. Therefore, we propose a novel paradigm of leveraging label dependency in an iterative way. Specifically, each label's prediction will be updated and also propagated to other labels via an random walk with restart process. Meanwhile, the label propagation is implemented as a supervised learning procedure via optimizing a loss function, thus more appropriate label dependency can be learned. Extensive experiments are conducted, and the results demonstrate that our method can achieve considerable improvements in terms of several evaluation metrics. © 2013 IEEE.

Gay, V, Leijdekkers, P, Agcanas, J, Wong, F & Wu, Q 1970, 'CaptureMyEmotion: Helping autistic children understand their emotions using facial expression recognition and mobile technologies', 26th Bled eConference - eInnovations: Challenges and Impacts for Individuals, Organizations and Society, Proceedings, Bled eConference - eInnovations: Challenges and Impacts for Individuals, Organizations and Society, Proceedings, AIS Electronic Library (AISeL), Bled, Slovenia, pp. 409-420.
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One of the main challenges for autistic children is to identify and express emotions. Many emotion-learning apps are available for smartphones and tablets to assist autistic children and their carers. However, they do not use the full potential offered by mobile technology, such as using facial expression recognition and wireless biosensors to recognise and sense emotions. To fill this gap we developed CaptureMyEmotion, an Android App that uses wireless sensors to capture physiological data together with facial expression recognition to provide a very personalised way to help autistic children learn about their emotions. The App enables children to capture photos, videos or sounds, and simultaneously attach emotion data and a self-portrait photo. The material can then be reviewed and discussed together with a carer at a later stage. CaptureMyEmotion has the potential to help autistic children integrate better in the society by providing a new way for them to understand their emotions.

Gay, VC, Leijdekkers, P & Wu, Q 1970, 'Helping Autistic Children Understand Their Emotions Using Facial Expression Recognition and Mobile Technologies', Proceedings of the 26th Bled eConference eInnovations, Bled eConference, AISeL, Bled, Slovenia, pp. 409-420.
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One of the main challenges for autistic children is to identify and express emotions. Many emotion-learning apps are available for smartphones and tablets to assist autistic children and their carers. However, they do not use the full potential offered by mobile technology, such as using facial expression recognition and wireless biosensors to recognise and sense emotions. To fill this gap we developed CaptureMyEmotion, an Android App that uses wireless sensors to capture physiological data together with facial expression recognition to provide a very personalised way to help autistic children learn about their emotions. The App enables children to capture photos, videos or sounds, and simultaneously attach emotion data and a self-portrait photo. The material can then be reviewed and discussed together with a carer at a later stage. CaptureMyEmotion has the potential to help autistic children integrate better in the society by providing a new way for them to understand their emotions.

Homayounfard, H, Kennedy, PJ & Braun, R 1970, 'NARGES: Prediction Model for Informed Routing in a Communications Network', Lecture Notes in Computer Science, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer Berlin Heidelberg, Gold Coast, Australia, pp. 327-338.
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There is a dependency between packet-loss and the delay and jitter time-series derived from a telecommunication link. Multimedia applications such as Voice over IP (VoIP) are sensitive to loss and packet recovery is not a merely efficient solution with the increasing number of Internet users. Predicting packet-loss from network dynamics of past transmissions is crucial to inform the next generation of routers in making smart decisions. This paper proposes a hybrid data mining model for routing management in a communications network, called NARGES. The proposed model is designed and implemented for predicting packet-loss based on the forecasted delays and jitters. The model consists of two parts: a historical symbolic time-series approximation module, called HDAX, and a Multilayer Perceptron (MLP). It is validated with heterogeneous quality of service (QoS) datasets, namely delay, jitter and packet-loss time-series. The results show improved precision and quality of prediction compared to autoregressive moving average, ARMA.

Hu, L, Cao, J, Xu, G, Cao, L, Gu, Z & Zhu, C 1970, 'Personalized recommendation via cross-domain triadic factorization', Proceedings of the 22nd international conference on World Wide Web, WWW '13: 22nd International World Wide Web Conference, ACM, Rio de Janeiro, Brazil, pp. 595-605.
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Collaborative filtering (CF) is a major technique in recommender systems to help users find their potentially desired items. Since the data sparsity problem is quite commonly encountered in real-world scenarios, Cross-Domain Collaborative Filtering (CDCF) hence is becoming an emerging research topic in recent years. However, due to the lack of sufficient dense explicit feedbacks and even no feedback available in users' uninvolved domains, current CDCF approaches may not perform satisfactorily in user preference prediction. In this paper, we propose a generalized Cross Domain Triadic Factorization (CDTF) model over the triadic relation user-item-domain, which can better capture the interactions between domain-specific user factors and item factors. In particular, we devise two CDTF algorithms to leverage user explicit and implicit feedbacks respectively, along with a genetic algorithm based weight parameters tuning algorithm to trade off influence among domains optimally. Finally, we conduct experiments to evaluate our models and compare with other state-of-the-art models by using two real world datasets. The results show the superiority of our models against other comparative models. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Hu, L, Cao, J, Xu, G, Wang, J, Gu, Z & Cao, L 1970, 'Cross-domain collaborative filtering via bilinear multilevel analysis', IJCAI International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence, IJCAI/AAAI, Beijing, China, pp. 2626-2632.
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Cross-domain collaborative filtering (CDCF), which aims to leverage data from multiple domains to relieve the data sparsity issue, is becoming an emerging research topic in recent years. However, current CDCF methods that mainly consider user and item factors but largely neglect the heterogeneity of domains may lead to improper knowledge transfer issues. To address this problem, we propose a novel CDCF model, the Bilinear Multilevel Analysis (BLMA), which seamlessly introduces multilevel analysis theory to the most successful collaborative filtering method, matrix factorization (MF). Specifically, we employ BLMA to more efficiently address the determinants of ratings from a hierarchical view by jointly considering domain, community, and user effects so as to overcome the issues caused by traditional MF approaches. Moreover, a parallel Gibbs sampler is provided to learn these effects. Finally, experiments conducted on a realworld dataset demonstrate the superiority of the BLMA over other state-of-the-art methods.

Kajdanowicz, T, Michalski, R, Musial, K & Kazienko, P 1970, 'Active learning and inference method for within network classification', Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM '13: Advances in Social Networks Analysis and Mining 2013, ACM, Niagara Falls, CANADA, pp. 1299-1306.
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Kusakunniran, W, Satoh, S, Jian Zhang & Qiang Wu 1970, 'Attribute-based learning for large scale object classification', 2013 IEEE International Conference on Multimedia and Expo (ICME), 2013 IEEE International Conference on Multimedia and Expo (ICME), IEEE, San Jose, California, USA, pp. 1-6.
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Scalability to large numbers of classes is an important challenge for multi-class classification. It can often be computationally infeasible at test phase when class prediction is performed by using every possible classifier trained for each individual class. This paper proposes an attribute-based learning method to overcome this limitation. First is to define attributes and their associations with object classes automatically and simultaneously. Such associations are learned based on greedy strategy under certain conditions. Second is to learn a classifier for each attribute instead of each class. Then, these trained classifiers are used to predict classes based on their attribute representations. The proposed method also allows trade-off between test-time complexity (which grows linearly with the number of attributes) and accuracy. Experiments based on Animals-with-Attributes and ILSVRC2010 datasets have shown that the performance of our method is promising when compared with the state-of-the-art. © 2013 IEEE.

Lei Shi, Kodagoda, S & Piccardi, M 1970, 'Towards simultaneous place classification and object detection based on conditional random field with multiple cues', 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), IEEE, Tokyo, Japan, pp. 2806-2811.
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Li, F, Xu, G, Cao, L, Fan, X & Niu, Z 1970, 'CGMF: Coupled Group-Based Matrix Factorization for Recommender System', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Web Information Systems Engineering, Springer Berlin Heidelberg, Nanjing, China, pp. 189-198.
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With the advent of social influence, social recommender systems have become an active research topic for making recommendations based on the ratings of the users that have close social relations with the given user. The underlying assumption is that a user's taste is similar to his/her friends' in social networking. In fact, users enjoy different groups of items with different preferences. A user may be treated as trustful by his/her friends more on some specific rather than all groups. Unfortunately, most of the extant social recommender systems are not able to differentiate user's social influence in different groups, resulting in the unsatisfactory recommendation results. Moreover, most extant systems mainly rely on social relations, but overlook the influence of relations between items. In this paper, we propose an innovative coupled group-based matrix factorization model for recommender system by leveraging the user and item groups learned by topic modeling and incorporating couplings between users and items and within users and items. Experiments conducted on publicly available data sets demonstrate the effectiveness of our approach. © 2013 Springer-Verlag.

Li, L, Chen, X & Xu, G 1970, 'Suggestions for Fresh Search Queries by Mining Mircoblog Topics', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Workshop on Behavior and Social Informatics, Springer International Publishing, Gold Coast, QLD, Australia, pp. 214-223.
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Query suggestion of Web search has been an effective approach to help users quickly express their information need and more accurately get the information they need. All major web-search engines and most proposed methods that suggest queries rely on query logs of search engine to determine possible query suggestions. However, for search systems, it is much more difficult to effectively suggest relevant queries to a fresh search query which has no or few historical evidences in query logs. In this paper, we propose a suggestion approach for fresh queries by mining the new social network media, i.e, mircoblog topics. We leverage the comment information in the microblog topics to mine potential suggestions. We utilize word frequency statistics to extract a set of ordered candidate words. As soon as a user starts typing a query word, words that match with the partial user query word are selected as completions of the partial query word and are offered as query suggestions. We collect a dataset from Sina microblog topics and compare the final results by selecting different suggestion context source. The experimental results clearly demonstrate the effectiveness of our approach in suggesting queries with high quality. Our conclusion is that the suggestion context source of a topic consists of the tweets from authenticated Sina users is more effective than the tweets from all Sina users. © Springer International Publishing Switzerland 2013.

Li, W, Cao, L, Zhao, D, Cui, X & Yang, J 1970, 'CRNN: Integrating classification rules into neural network', The 2013 International Joint Conference on Neural Networks (IJCNN), 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas), IEEE, Dallas, TX, USA, pp. 1-8.
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Association classification has been an important type of the rule-based classification. A variety of approaches have been proposed to build a classifier based on classification rules. In the prediction stage of the extant approaches, most of the existing association classifiers use the ensemble quality measurement of each rule in a subset of rules to predict the class label of the new data. This method still suffers the following two problems. The classification rules are used individually thus the coupling relations between rules [1] are ignored in the prediction. However, in real-world rule set, rules are often inter-related and a new data object may partially satisfy many rules. Furthermore, the classification rule based prediction model lacks a general expression of the decision methodology. This paper proposes a classification method that integrating classification rules into neural network (CRNN, for short), which presents a general form of the rule based decision methodology by rule-based network. In comparison with the extant rule-based classifiers, such as C4.5, CBA, CMAR and CPAR, our approach has two advantages. First, CRNN takes the coupling relations between rules from the training data into account in the prediction step. Second, CRNN automatically obtains higher performance on the structure and parameter learning than traditional neural network. CRNN uses the linear computing algorithm in neural network instead of the costly iterative learning algorithm. Two ways of the classification rule set generation are conducted in this paper for the CRNN evaluation, and CRNN achieves the satisfactory performance. © 2013 IEEE.

Li, W, Zhao, D, Yang, J & Cao, L 1970, 'An approach of hierarchical concept clustering on Medical Short Text corpus', 2013 6th International Conference on Biomedical Engineering and Informatics, 2013 6th International Conference on Biomedical Engineering and Informatics (BMEI), IEEE, Hangzhou, China, pp. 509-518.
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Hierarchical clustering and conceptual clustering are two important types of clustering analysis methods. A variety of approaches have been proposed in previous works. However, seldom methods are designed to run on the medical short text database and construct a hierarchical concept taxonomy. This paper proposes a new clustering method of Hierarchical Concept Clustering on Medical Short Text corpus (HCCST), which presents a new solution on actionable disease taxonomy construction from the actual medical data. Our approach has three advantages. Firstly, HCCST takes a new similarity method which covers all the problems in medical short text distance computing. Secondly, an adaptive clustering method is proposed for synonymous disease names without predefining the size of clusters. Thirdly, this paper uses a mutual information based potential hierarchy concept pair recognition method which improves the subsumption method to create hierarchical disease taxonomy. The evaluation is conducted on Chinese medical disease name text data set and the result shows that HCCST achieves satisfactory performance. © 2013 IEEE.

Li, X, Zhang, L, Chen, E, Zong, Y & Xu, G 1970, 'Mining Frequent Patterns in Print Logs with Semantically Alternative Labels', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Advanced Data Mining and Applications, Springer Berlin Heidelberg, Hangzhou, pp. 107-119.
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It is common today for users to print the informative information from webpages due to the popularity of printers and internet. Thus, many web printing tools such as Smart Print and PrintUI are developed for online printing. In order to improve the users' printing experience, the interaction data between users and these tools are collected to form a so-called print log data, where each record is the set of urls selected for printing by a user within a certain period of time. Apparently, mining frequent patterns from these print log data can capture user intentions for other applications, such as printing recommendation and behavior targeting. However, mining frequent patterns by directly using url as item representation in print log data faces two challenges: data sparsity and pattern interpretability. To tackle these challenges, we attempt to leverage delicious api (a social bookmarking web service) as an external thesaurus to expand the semantics of each url by selecting tags associated with the domain of each url. In this setting, the frequent pattern mining is employed on the tag representation of each url rather than the url or domain representation. With the enhancement of semantically alternative tag representation, the semantics of url is substantially improved, thus yielding the useful frequent patterns. To this end, in this paper we propose a novel pattern mining problem, namely mining frequent patterns with semantically alternative labels, and propose an efficient algorithm named PaSAL (Frequent Patterns with Semantically Alternative Labels Mining Algorithm) for this problem. Specifically, we propose a new constraint named conflict matrix to purify the redundant patterns to achieve a high efficiency. Finally, we evaluate the proposed algorithm on a real print log data. © 2013 Springer-Verlag.

Linares Mustaros, S, Gil Lafuente, AM, Ferrer Comalat, JC & Merigo, JM 1970, 'A MODEL FOR THE GENERALIZATION OF THE FORGOTTEN EFFECTS', DECISION MAKING SYSTEMS IN BUSINESS ADMINISTRATION, International Conference on Modeling and Simulation in Engineering, Economics and Management for Sustainable Development, WORLD SCIENTIFIC PUBL CO PTE LTD, Rio de Janeiro, BRAZIL, pp. 495-508.

Linares-Mustarós, S, Merigó, JM & Ferrer-Comalat, JC 1970, 'PEV: A Computer Program for Fuzzy Sales Forecasting', MODELING AND SIMULATION IN ENGINEERING, ECONOMICS, AND MANAGEMENT, International Conference on Modeling and Simulation in Engineering, Economics, and Management, Springer Berlin Heidelberg, Castellon de la Plana, SPAIN, pp. 200-209.
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Liu, B, Xiao, Y, Yu, PS, Cao, L & Hao, Z 1970, 'Robust Textual Data Streams Mining Based on Continuous Transfer Learning', Proceedings of the 2013 SIAM International Conference on Data Mining, Proceedings of the 2013 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Austin, Texas, USA, pp. 731-739.
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Copyright © SIAM. In textual data stream environment, concept drift can occur at any time, existing approaches partitioning streams into chunks can have problem if the chunk boundary does not coincide with the change point which is impossible to predict. Since concept drift can occur at any point of the streams, it will certainly occur within chunks, which is called random concept drift. The paper proposed an approach, which is called chunk level-based concept drift method (CLCD), that can overcome this chunking problem by continuously monitoring chunk characteristics to revise the classifier based on transfer learning in positive and unlabeled (PU) textual data stream environment. Our proposed approach works in three steps. In the first step, we propose core vocabulary-based criteria to justify and identify random concept drift. In the second step, we put forward the extension of LELC (PU learning by extracting likely positive and negative microclusters)[ 1], called soft-LELC, to extract representative examples from unlabeled data, and assign a confidence score to each extracted example. The assigned confidence score represents the degree of belongingness of an example towards its corresponding class. In the third step, we set up a transfer learning-based SVM to build an accurate classifier for the chunks where concept drift is identified in the first step. Extensive experiments have shown that CLCD can capture random concept drift, and outperforms state-of-the-art methods in positive and unlabeled textual data stream environments.

Liu, C, Chen, L & Zhang, C 1970, 'Mining Probabilistic Representative Frequent Patterns From Uncertain Data', Proceedings of the 2013 SIAM International Conference on Data Mining, Proceedings of the 2013 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Austin, Texas, USA, pp. 73-81.
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Copyright © SIAM. Probabilistic frequent pattern mining over uncertain data has received a great deal of attention recently due to the wide applications of uncertain data. Similar to its counterpart in deterministic databases, however, probabilistic frequent pattern mining suffers from the same problem of generating an exponential number of result patterns. The large number of discovered patterns hinders further evaluation and analysis, and calls for the need to find a small number of representative patterns to approximate all other patterns. This paper formally defines the problem of probabilistic representative frequent pattern (P-RFP) mining, which aims to find the minimal set of patterns with sufficiently high probability to represent all other patterns. The problem's bottleneck turns out to be checking whether a pattern can probabilistically represent another, which involves the computation of a joint probability of supports of two patterns. To address the problem, we propose a novel and efficient dynamic programming-based approach. Moreover, we have devised a set of effective optimization strategies to further improve the computation efficiency. Our experimental results demonstrate that the proposed P-RFP mining effectively reduces the size of probabilistic frequent patterns. Our proposed approach not only discovers the set of P-RFPs efficiently, but also restores the frequency probability information of patterns with an error guarantee.

Liu, C, Chen, L & Zhang, C 1970, 'Summarizing probabilistic frequent patterns', Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Chicago, Illinois USA, pp. 527-535.
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Copyright © 2013 ACM. Mining probabilistic frequent patterns from uncertain data has received a great deal of attention in recent years due to the wide applications. However, probabilistic frequent pattern mining suffers from the problem that an exponential number of result patterns are generated, which seriously hinders further evaluation and analysis. In this paper, we focus on the problem of mining probabilistic representative frequent patterns (P-RFP), which is the minimal set of patterns with adequately high probability to represent all frequent patterns. Observing the bottleneck in checking whether a pattern can probabilistically represent another, which involves the computation of a joint probability of the supports of two patterns, we introduce a novel approximation of the joint probability with both theoretical and empirical proofs. Based on the approximation, we propose an Approximate P-RFP Mining (APM) algorithm, which effectively and efficiently compresses the set of probabilistic frequent patterns. To our knowledge, this is the first attempt to analyze the relationship between two probabilistic frequent patterns through an approximate approach. Our experiments on both synthetic and real-world datasets demonstrate that the APM algorithm accelerates P-RFP mining dramatically, orders of magnitudes faster than an exact solution. Moreover, the error rate of APM is guaranteed to be very small when the database contains hundreds transactions, which further affirms APM is a practical solution for summarizing probabilistic frequent patterns.

Mao, R, Wu, Q, Qiao, Y, Bai, L & Yang, J 1970, 'Multi-view urban scene reconstruction in non-uniform volume', SPIE Proceedings, Sixth International Conference on Machine Vision (ICMV 13), SPIE, London, United Kingdom.
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This paper presents a new fully automatic approach for multi-view urban scene reconstruction. Our algorithm is based on the Manhattan-World assumption, which can provide compact models while preserving fidelity of synthetic architectures. Starting from a dense point cloud, we extract its main axes by global optimization, and construct a nonuniform volume based on them. A graph model is created from volume facets rather than voxels. Appropriate edge weights are defined to ensure the validity and quality of the surface reconstruction. Compared with the common pointcloud- to-model methods, the proposed methodology exploits image information to unveil the real structures of holes in the point cloud. Experiments demonstrate the encouraging performance of the algorithm. © 2013 SPIE.

Meng, Q & Kennedy, PJ 1970, 'Discovering influential authors in heterogeneous academic networks by a co-ranking method', Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13, the 22nd ACM international conference, ACM Press, San Francisco, California, USA, pp. 1029-1036.
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Research in ranking networked entities is widely applicable to many problems such as optimizing search engines, building recommendation systems and discovering influential nodes in social networks. However, many famous ranking approaches like PageRank are limited to solving this problem in homogeneous networks and are not applicable to heterogeneous networks. Faced with this problem, we propose a co--ranking method to evaluate scientific publications and authors. This novel approach is a flexible framework based on a set of customized rules taking into account both topological features of networks and the included citations. The approach ranks authors and publications iteratively and uses the results of each round to reinforce the ranks of authors and publications. Unlike traditional approaches to assessing publication, which require a great number of citations, our method lowers this requirement. This co--ranking approach has been validated using data collected from DBLP and CiteSeer, and the results suggest that it is effective and efficient in ranking authors and publications based on limited numbers of citations in heterogeneous networks and that it has fast convergence.

Merige, JM, Jian-Bo Yang & Dong-Ling Xu 1970, 'Supply Analysis and Aggregation Systems', 2013 IEEE International Conference on Systems, Man, and Cybernetics, 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013), IEEE, Manchester, ENGLAND, pp. 97-102.
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Merigó, JM, Guillén, M & Sarabia, JM 1970, 'A Generalization of the Variance by Using the Ordered Weighted Average', MODELING AND SIMULATION IN ENGINEERING, ECONOMICS, AND MANAGEMENT, International Conference on Modeling and Simulation in Engineering, Economics, and Management, Springer Berlin Heidelberg, Castellon de la Plana, SPAIN, pp. 222-231.
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Merigó, JM, Yang, J-B & Xu, D-L 1970, 'Decision Making with Fuzzy Moving Averages and OWA Operators', MODELING AND SIMULATION IN ENGINEERING, ECONOMICS, AND MANAGEMENT, International Conference on Modeling and Simulation in Engineering, Economics, and Management, Springer Berlin Heidelberg, Castellon de la Plana, SPAIN, pp. 210-221.
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Musial, K, Gabrys, B & Buczko, M 1970, 'What kind of network are you?', Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM '13: Advances in Social Networks Analysis and Mining 2013, ACM, Niagara Falls, CANADA, pp. 1366-1373.
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Musial, K, Kazienkol, P & Kajdanowicz, T 1970, 'Social Recommendations within the Multimedia Sharing Systems', Musial K., Kazienko P., Kajdanowicz T.: Social Recommendations within the Multimedia Sharing Systems. The First World Summit on the Knowledge Society, WSKS'08, Lecture Notes in Computer Science LNCS 5288, 2008, pp. 364-372, 1st World Summit on the Knowledge Society (WSKS 2008), SPRINGER-VERLAG BERLIN, Athens, GREECE, pp. 364-+.
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The social recommender system that supports the creation of new relationsbetween users in the multimedia sharing system is presented in the paper. Togenerate suggestions the new concept of the multirelational social network wasintroduced. It covers both direct as well as object-based relationships thatreflect social and semantic links between users. The main goal of the newmethod is to create the personalized suggestions that are continuously adaptedto users' needs depending on the personal weights assigned to each layer fromthe social network. The conducted experiments confirmed the usefulness of theproposed model.

Pan, R, Dolog, P & Xu, G 1970, 'KNN-Based Clustering for Improving Social Recommender Systems', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Workshop on Agents and Data Mining Interaction, Springer Berlin Heidelberg, Valencia, Spain, pp. 115-125.
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Clustering is useful in tag based recommenders to reduce sparsity of data and by doing so to improve also accuracy of recommendation. Strategy for the selection of tags for clusters has an impact on the accuracy. In this paper we propose a KNN based approach for ranking tag neighbors for tag selection. We study the approach in comparison to several baselines by using two datasets in different domains. We show, that in both cases the approach outperforms the compared approaches. © 2013 Springer-Verlag.

Qin, Z, Wang, AT, Zhang, C & Zhang, S 1970, 'Cost-Sensitive Classification with k-Nearest Neighbors', Lecture Notes in Computer Science, International Conference on Knowledge Science, Engineering and Management, Springer Berlin Heidelberg, China, pp. 112-131.
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Cost-sensitive learning algorithms are typically motivated by imbalance data in clinical diagnosis that contains skewed class distribution. While other popular classification methods have been improved against imbalance data, it is only unsolved to extend k-Nearest Neighbors (kNN) classification, one of top-10 datamining algorithms, to make it cost-sensitive to imbalance data. To fill in this gap, in this paper we study two simple yet effective cost-sensitive kNN classification approaches, called Direct-CS-kNN and Distance-CS-kNN. In addition, we utilize several strategies (i.e., smoothing, minimum-cost k value selection, feature selection and ensemble selection) to improve the performance of Direct-CS-kNN and Distance-CS-kNN. We conduct several groups of experiments to evaluate the efficiency with UCI datasets, and demonstrate that the proposed cost-sensitive kNN classification algorithms can significantly reduce misclassification cost, often by a large margin, as well as consistently outperform CS-4.5 with/without additional enhancements.

Ramezani, F, Lu, J & Hussain, F 1970, 'An Online Fuzzy Decision Support System for Resource Management in Cloud Environments', PROCEEDINGS OF THE 2013 JOINT IFSA WORLD CONGRESS AND NAFIPS ANNUAL MEETING (IFSA/NAFIPS), Joint IFSA World Congress and NAFIPS Annual Meeting, IEEE, Edmonton, Canada, pp. 754-759.
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Cloud computing is a large-scale distributed computing paradigm driven by economies of scale, in which a pool of abstracted, virtualized, dynamically-scalable, managed computing power, storage, platforms, and services are delivered on demand to external customers over the Internet. Although a significant amount of studies have been developed to optimize resource management and task scheduling in cloud computing, none of them considered the impact of task scheduling patterns on resource management and vice versa. To overcome this drawback, and considering the lack of resources in cloud environments and growing customer demands for cloud services, this paper proposes an Online Resource Management Decision Support System (ORMDSS) that addresses both tasks scheduling and resource management optimization in a unique system. In addition, ORMDSS contains a fuzzy prediction method for predicting VM workload patterns and VM migration time by applying neural networks and fuzzy expert systems. This ORMDSS helps cloud providers to automatically allocate scare resources to the applications and services in an optimal way. It is expected that the ORMDSS not only increases cloud utilization and QoS, but also decreases cost and response time.

Ramezani, F, Lu, J & Hussain, F 1970, 'Task Scheduling Optimization in Cloud Computing Applying Multi-Objective Particle Swarm Optimization', SERVICE-ORIENTED COMPUTING, ICSOC 2013, IEEE International Conference on Service-Oriented Computing and Applications, Springer, Berlin, Germany, pp. 237-251.
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Optimizing the scheduling of tasks in a distributed heterogeneous computing environment is a nonlinear multi-objective NP-hard problem which is playing an important role in optimizing cloud utilization and Quality of Service (QoS). In this paper, we develop a comprehensive multi-objective model for optimizing task scheduling to minimize task execution time, task transferring time, and task execution cost. However, the objective functions in this model are in conflict with one another. Considering this fact and the supremacy of Particle Swarm Optimization (PSO) algorithm in speed and accuracy, we design a multi-objective algorithm based on multi-objective PSO (MOPSO) method to provide an optimal solution for the proposed model. To implement and evaluate the proposed model, we extend Jswarm package to multi-objective Jswarm (MO-Jswarm) package. We also extend Cloudsim toolkit applying MO-Jswarm as its task scheduling algorithm. MO-Jswarm in Cloudsim determines the optimal task arrangement among VMs according to MOPSO algorithm. The simulation results show that the proposed method has the ability to find optimal trade-off solutions for multi-objective task scheduling problems that represent the best possible compromises among the conflicting objectives, and significantly increases the QoS. © 2013 Springer-Verlag.

Rehman, ZU, Hussain, OK & Hussain, FK 1970, 'Multi-Criteria IaaS Service Selection based on QoS History', 2013 IEEE 27TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS (AINA), International Conference on Advanced Information Networking and Applications (was ICOIN), IEEE, Barcelona, Spain, pp. 1129-1135.
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The growing number of cloud services have made service selection a challenging decision-making problem by providing wide ranging choices for cloud service consumers. This necessitates the use of formal decision making methodologies to assist a decision maker in selecting the service that best fulfils the user's requirements. In this paper, we present a cloud service selection methodology that utilizes QoS history over different time periods, performs Multi-Criteria Decision Analysis to rank all cloud services in each time period in accordance with user preferences before aggregating the results to determine the overall service rank of all the available services for cloud service selection. © 2013 IEEE.

Shirui Pan, Xingquan Zhu, Chengqi Zhang & Yu, PS 1970, 'Graph stream classification using labeled and unlabeled graphs', 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 29th IEEE International Conference on Data Engineering (ICDE 2013), IEEE, Brisbane, QLD, Australia, pp. 398-409.
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Graph Stream Classification using Labeled and Unlabeled Graphs

Shu Wang, Zhenjiang Miao & Jian Zhang 1970, 'Simultaneously detect and segment pedestrian', 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), IEEE, San Jose, USA, pp. 1-4.
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We present a framework to simultaneously detect and segment pedestrian in images. Our work is based on part-based method. We first segment the image into superpixels, then assemble superpixels into body part candidates by comparing the assembled shape with pre-built template library. A structure-based shape matching algorithm is developed to measure the shape similarity. All the body part candidates are input into our modified AND/OR graph to generate the most reasonable combination. The graph describes the possible variation of body configuration and model the constrain relationship between body parts. We perform comparison experiments on the public database and the results show the effectiveness of our framework.

Song, Y, Cao, L, Yin, J & Wang, C 1970, 'Extracting discriminative features for identifying abnormal sequences in one-class mode', The 2013 International Joint Conference on Neural Networks (IJCNN), 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas), IEEE, Dallas, TX, USA, pp. 1-8.
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This paper presents a novel framework for detecting abnormal sequences in an one-class setting (i.e., only normal data are available), which is applicable to various domains. Examples include intrusion detection, fault detection and speaker verification. Detecting abnormal sequences with only normal data presents several challenges for anomaly detection: the weak discrimination of normal and abnormal sequences; the unavailability of the abnormal data and other issues. Traditional model-based anomaly detection techniques can solve some of the above issues but with limited discrimination power (because of directly modeling the normal data). In order to enhance the discriminative power for anomaly detection, we turn to extracting discriminative features from the generative model based on the principle deducted from the corresponding theoretical analysis. Then a new anomaly detection framework is developed on top of that. The proposed approach firstly projects all the sequential data into a model-based equal length feature space (this is theoretically proven to have better discriminative power than the model itself), and then adopts a classifier learned from the transformed data to detect anomalies. Experimental evaluation on both the synthetic and real-world data shows that our proposed approach outperforms several anomaly detection baseline algorithms for sequential data. © 2013 IEEE.

Song, Y, Zhang, J, Cao, L & Sangeux, M 1970, 'On Discovering the Correlated Relationship between Static and Dynamic Data in Clinical Gait Analysis', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer Berlin Heidelberg, Prague, Czech Republic, pp. 563-578.
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'Gait' is a person's manner of walking. Patients may have an abnormal gait due to a range of physical impairment or brain damage. Clinical gait analysis (CGA) is a technique for identifying the underlying impairments that affect a patient's gait pattern. The CGA is critical for treatment planning. Essentially, CGA tries to use patients' physical examination results, known as static data, to interpret the dynamic characteristics in an abnormal gait, known as dynamic data. This process is carried out by gait analysis experts, mainly based on their experience which may lead to subjective diagnoses. To facilitate the automation of this process and form a relatively objective diagnosis, this paper proposes a new probabilistic correlated static-dynamic model (CSDM) to discover correlated relationships between the dynamic characteristics of gait and their root cause in the static data space. We propose an EM-based algorithm to learn the parameters of the CSDM. One of the main advantages of the CSDM is its ability to provide intuitive knowledge. For example, the CSDM can describe what kinds of static data will lead to what kinds of hidden gait patterns in the form of a decision tree, which helps us to infer dynamic characteristics based on static data. Our initial experiments indicate that the CSDM is promising for discovering the correlated relationship between physical examination (static) and gait (dynamic) data. © 2013 Springer-Verlag.

Tafavogh, S, Navarro, KF, Catchpoole, DR & Kennedy, PJ 1970, 'Segmenting Neuroblastoma Tumor Images and Splitting Overlapping Cells Using Shortest Paths between Cell Contour Convex Regions.', AIME, Artificial Intelligence in Medicine in Europe, Springer, Murcia, Spain, pp. 171-175.
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Neuroblastoma is one of the most fatal paediatric cancers. One of the major prognostic factors for neuroblastoma tumour is the total number of neuroblastic cells. In this paper, we develop a fully automated system for counting the total number of neuroblastic cells within the images derived from Hematoxylin and Eosin stained histological slides by considering the overlapping cells. We finally propose a novel multi-stage cell counting algorithm, in which cellular regions are extracted using an adaptive thresholding technique. Overlapping and single cells are discriminated using morphological differences. We propose a novel cell splitting algorithm to split overlapping cells into single cells using the shortest path between contours of convex regions. © 2013 Springer-Verlag.

Wan, L, Chen, L & Zhang, C 1970, 'Mining Dependent Frequent Serial Episodes from Uncertain Sequence Data', 2013 IEEE 13th International Conference on Data Mining, 2013 IEEE International Conference on Data Mining (ICDM), IEEE, Dallas, TX, USA, pp. 1211-1216.
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In this paper, we focus on the problem of mining Probabilistic Dependent Frequent Serial Episodes (P-DFSEs) from uncertain sequence data. By observing that the frequentness probability of an episode in an uncertain sequence is a Markov Chain imbeddable variable, we first propose an Embeded Markov Chain-based algorithm that efficiently computes the frequentness probability of an episode by projecting the probability space into a set of limited partitions. To further improve the computation efficiency, we devise an optimized approach that prunes candidate episodes early by estimating the upper bound of their frequentness probabilities. © 2013 IEEE.

Wan, L, Chen, L & Zhang, C 1970, 'Mining frequent serial episodes over uncertain sequence data', Proceedings of the 16th International Conference on Extending Database Technology, EDBT/ICDT '13: Joint 2013 EDBT/ICDT Conferences, ACM, Genoa, Italy, pp. 215-226.
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Data uncertainty has posed many unique challenges to nearly all types of data mining tasks, creating a need for uncertain data mining. In this paper, we focus on the particular task of mining probabilistic frequent serial episodes (P-FSEs) from uncertain sequence data, which applies to many real applications including sensor readings as well as customer purchase sequences. We first define the notion of P-FSEs, based on the frequentness probabilities of serial episodes under possible world semantics. To discover P-FSEs over an uncertain sequence, we propose: 1) an exact approach that computes the accurate frequentness probabilities of episodes; 2) an approximate approach that approximates the frequency of episodes using probability models; 3) an optimized approach that efficiently prunes a candidate episode by estimating an upper bound of its frequentness probability using approximation techniques. We conduct extensive experiments to evaluate the performance of the developed data mining algorithms. Our experimental results show that: 1) while existing research demonstrates that approximate approaches are orders of magnitudes faster than exact approaches, for P-FSE mining, the efficiency improvement of the approximate approach over the exact approach is marginal; 2) although it has been recognized that the normal distribution based approximation approach is fairly accurate when the data set is large enough, for P-FSE mining, the binomial distribution based approximation achieves higher accuracy when the the number of episode occurrences is limited; 3) the optimized approach clearly outperforms the other two approaches in terms of the runtime, and achieves very high accuracy. © 2013 ACM.

Wang, S, He, X, Wu, Q & Yang, J 1970, 'Generalized local N-ary patterns for texture classification', 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, Krakow, Poland, pp. 324-329.
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Local Binary Pattern (LBP) has been well recognised and widely used in various texture analysis applications of computer vision and image processing. It integrates properties of texture structural and statistical texture analysis. LBP is invariant to monotonic gray-scale variations and has also extensions to rotation invariant texture analysis. In recent years, various improvements have been achieved based on LBP. One of extensive developments was replacing binary representation with ternary representation and proposed Local Ternary Pattern (LTP). This paper further generalises the local pattern representation by formulating it as a generalised weight problem of Bachet de Meziriac and proposes Local N-ary Pattern (LNP). The encouraging performance is achieved based on three benchmark datasets when compared with its predecessors. © 2013 IEEE.

Wang, S, Zhang, J & Miao, Z 1970, 'A new edge feature for head-shoulder detection', 2013 IEEE International Conference on Image Processing, 2013 20th IEEE International Conference on Image Processing (ICIP), IEEE, Melbourne, Australia, pp. 2822-2826.
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In this work, we introduce a new edge feature to improve the head-shoulder detection performance. Since Head-shoulder detection is much vulnerable to vague contour, our new edge feature is designed to extract and enhance the head-shoulder contour and suppress the other contours. The basic idea is that head-shoulder contour can be predicted by filtering edge image with edge patterns, which are generated from edge fragments through a learning process. This edge feature can significantly enhance the object contour such as human head and shoulder known as En-Contour. To evaluate the performance of the new En-Contour, we combine it with HOG+LBP [1] as HOG+LBP+En-Contour. The HOG+LBP is the state-of-the-art feature in pedestrian detection. Because the human head-shoulder detection is a special case of pedestrian detection, we also use it as our baseline. Our experiments have indicated that this new feature significantly improve the HOG+LBP.

Wang, Z, Luo, T, Xu, G & Wang, X 1970, 'A New Indexing Technique for Supporting By-attribute Membership Query of Multidimensional Data', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Web-Age Information Management, Springer Berlin Heidelberg, China, pp. 266-277.
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Multidimensional Data indexing and lookup has been widely used in online data-intensive applications involving in data with multiple attributes. However, there remains a long way to go for the high performance multi-attribute data representation and lookup: the performance of index drops down with the increase of dimensions. In this paper, we present a novel data structure called Bloom Filter Matrix (BFM) to support multidimensional data indexing and by-attribute search. The proposed matrix is based on the Cartesian product of different bloom filters, each representing one attribute of the original data. The structure and parameter of each bloom filter is designed to fit the actual data characteristic and system demand, enabling fast object indexing and lookup, especially by-attribute search of multidimensional data. Experiments show that Bloom Filter Matrix is a fast and accurate data structure for multi-attribute data indexing and by-attribute search with high-correlated queries. © 2013 Springer-Verlag.

Wu, L, Chin, A, Xu, G, Du, L, Wang, X, Meng, K, Guo, Y & Zhou, Y 1970, 'Who Will Follow Your Shop? Exploiting Multiple Information Sources in Finding Followers', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Database Systems for Advanced Applications, Springer Berlin Heidelberg, Wuhan, pp. 401-415.
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WuXianGouXiang is an O2O(offline to online and vice versa)-based mobile application that recommends the nearby coupons and deals for users, by which users can also follow the shops they are interested in. If the potential followers of a shop can be discovered, the merchant's targeted advertising can be more effective and the recommendations for users will also be improved. In this paper, we propose to predict the link relations between users and shops based on the following behavior. In order to better model the characteristics of the shops, we first adopt Topic Modeling to analyze the semantics of their descriptions and then propose a novel approach, named INtent Induced Topic Search (INITS) to update the hidden topics of the shops with and without a description. In addition, we leverage the user logs and search engine results to get the similarity between users and shops. Then we adopt the latent factor model to calculate the similarity between users and shops, in which we use the multiple information sources to regularize the factorization. The experimental results demonstrate that the proposed approach is effective for detecting followers of the shops and the INITS model is useful for shop topic inference. © Springer-Verlag 2013.

Wu, Z, Yin, W, Cao, J, Xu, G & Cuzzocrea, A 1970, 'Community Detection in Multi-relational Social Networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Web Information Systems Engineering, Springer Berlin Heidelberg, Nanjing, pp. 43-56.
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Multi-relational networks are ubiquitous in many fields such as bibliography, twitter, and healthcare. There have been many studies in the literature targeting at discovering communities from social networks. However, most of them have focused on single-relational networks. A hint of methods detected communities from multi-relational networks by converting them to single-relational networks first. Nevertheless, they commonly assumed different relations were independent from each other, which is obviously unreal to real-life cases. In this paper, we attempt to address this challenge by introducing a novel co-ranking framework, named MutuRank. It makes full use of the mutual influence between relations and actors to transform the multi-relational network to the single-relational network. We then present GMM-NK (Gaussian Mixture Model with Neighbor Knowledge) based on local consistency principle to enhance the performance of spectral clustering process in discovering overlapping communities. Experimental results on both synthetic and real-world data demonstrate the effectiveness of the proposed method. © 2013 Springer-Verlag.

Xu, J, Wu, Q, Zhang, J, Shen, F & Tang, Z 1970, 'Training boosting-like algorithms with semi-supervised subspace learning', 2013 IEEE International Conference on Image Processing, 2013 20th IEEE International Conference on Image Processing (ICIP), IEEE, Melbourne, Australia, pp. 4302-4306.
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Boosting algorithms have attracted great attention since the first real-time face detector by Viola & Jones through feature selection and strong classifier learning simultaneously. On the other hand, researchers have proposed to decouple such two procedures to improve the performance of Boosting algorithms. Motivated by this, we propose a boosting-like algorithm framework by embedding semi-supervised subspace learning methods. It selects weak classifiers based on class-separability. Combination weights of selected weak classifiers can be obtained by subspace learning. Three typical algorithms are proposed under this framework and evaluated on public data sets. As shown by our experimental results, the proposed methods obtain superior performances over their supervised counterparts and AdaBoost. © 2013 IEEE.

Xu, W, Miao, Z, Zhang, J, Zhang, Q & Wu, H 1970, 'Spatial-Temporal Context for Action Recognition Combined with Confidence and Contribution Weight', 2013 2nd IAPR Asian Conference on Pattern Recognition, 2013 2nd IAPR Asian Conference on Pattern Recognition (ACPR), IEEE, Naha, JAPAN, pp. 576-580.
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Yi, X, Paulet, R, Bertino, E & Xu, G 1970, 'Private data warehouse queries', Proceedings of the 18th ACM symposium on Access control models and technologies, SACMAT '13: 18th ACM Symposium on Access Control Models and Technologies, ACM, Amsterdam, pp. 25-35.
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Publicly accessible data warehouses are an indispensable resource for data analysis. But they also pose a significant risk to the privacy of the clients, since a data warehouse operator may follow the client's queries and infer what the client is interested in. Private Information Retrieval (PIR) techniques allow the client to retrieve a cell from a data warehouse without revealing to the operator which cell is retrieved. However, PIR cannot be used to hide OLAP operations performed by the client, which may disclose the client's interest. This paper presents a solution for private data warehouse queries on the basis of the Boneh-Goh-Nissim cryptosystem which allows one to evaluate any multi-variate polynomial of total degree 2 on ciphertexts. By our solution, the client can perform OLAP operations on the data warehouse and retrieve one (or more) cell without revealing any information about which cell is selected. Furthermore, our solution supports some types of statistical analysis on data warehouse, such as regression and variance analysis, without revealing the client's interest. Our solution ensures both the server's security and the client's security. Copyright 2013 ACM.

Yin, H, Sun, Y, Cui, B, Hu, Z & Chen, L 1970, 'LCARS', Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Chicago, Illinois USA, pp. 221-229.
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Newly emerging location-based and event-based social network services provide us with a new platform to understand users preferences based on their activity history. A user can only visit a limited number of venues/events and most of them are within a limited distance range, so the user-item matrix is very sparse, which creates a big challenge for traditional collaborative filtering-based recommender systems. The problem becomes more challenging when people travel to a new city where they have no activity history. In this paper, we propose LCARS, a location-content-aware recommender system that offers a particular user a set of venues (e.g., restaurants) or events (e.g., concerts and exhibitions) by giving consideration to both personal interest and local preference. This recommender system can facilitate peoples travel not only near the area in which they live, but also in a city that is new to them. Specifically, LCARS consists of two components: offline modeling and online recommendation. The offline modeling part, called LCA- LDA, is designed to learn the interest of each individual user and the local preference of each individual city by capturing item co- occurrence patterns and exploiting item contents. The online recommendation part automatically combines the learnt interest of the querying user and the local preference of the querying city to produce the top-k recommendations. To speed up this online process, a scalable query processing technique is developed by extending the classic Threshold Algorithm (TA). We evaluate the performance of our recommender system on two large-scale real data sets, Douban- Event and Foursquare. The results show the superiority of LCARS in recommending spatial items for users, especially when traveling to new cities, in terms of both effectiveness and efficiency.

You, Y, Xu, G, Cao, J, Zhang, Y & Huang, G 1970, 'Leveraging Visual Features and Hierarchical Dependencies for Conference Information Extraction', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Joint International Conference on Asia-Pacific Web Conference (APWeb)/Web-Age Information Management (WAIM), Springer Berlin Heidelberg, Sydney, pp. 404-416.
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Traditional information extraction methods mainly rely on visual feature assisted techniques; but without considering the hierarchical dependencies within the paragraph structure, some important information is missing. This paper proposes an integrated approach for extracting academic information from conference Web pages. Firstly, Web pages are segmented into text blocks by applying a new hybrid page segmentation algorithm which combines visual feature and DOM structure together. Then, these text blocks are labeled by a Tree-structured Random Fields model, and the block functions are differentiated using various features such as visual features, semantic features and hierarchical dependencies. Finally, an additional post-processing is introduced to tune the initial annotation results. Our experimental results on real-world data sets demonstrated that the proposed method is able to effectively and accurately extract the needed academic information from conference Web pages. © 2013 Springer-Verlag.

Yu, Y, Wang, C, Gao, Y, Cao, L & Chen, X 1970, 'A Coupled Clustering Approach for Items Recommendation', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer Berlin Heidelberg, Gold Coast, Australia, pp. 365-376.
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Recommender systems are very useful due to the huge volume of information available on the Web. It helps users alleviate the information overload problem by recommending users with the personalized information, products or services (called items). Collaborative filtering and content-based recommendation algorithms have been widely deployed in e-commerce web sites. However, they both suffer from the scalability problem. In addition, there are few suitable similarity measures for the content-based recommendation methods to compute the similarity between items. In this paper, we propose a hybrid recommendation algorithm by combing the content-based and collaborative filtering techniques as well as incorporating the coupled similarity. Our method firstly partitions items into several item groups by using a coupled version of k-modes clustering algorithm, where the similarity between items is measured by the Coupled Object Similarity considering coupling between items. The collaborative filtering technique is then used to produce the recommendations for active users. Experimental results show that our proposed hybrid recommendation algorithm effectively solves the scalability issue of recommender systems and provides a comparable recommendation quality when lacking most of the item features. © Springer-Verlag 2013.

Zhu, X, Yu, Y, Ou, Y, Luo, D, Zhang, C & Chen, J 1970, 'System Modeling of a Smart-Home Healthy Lifestyle Assistant', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Workshop on Agents and Data Mining Interaction, Springer Berlin Heidelberg, Valencia, Spain, pp. 65-78.
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A system modeling is presented for a Smart-home Healthy Lifestyle Assistant System (SHLAS), covering healthy lifestyle promotion by intelligently collecting and analyzing context information, executing control instruction and suggesting health plans for users. SHLAS is Multi-agent based. Each agent has three levels: the Goal Layer has business rules for representing agent goals; the Strategy Layer provides technical rules and processes for guiding how the agent reacts to events; the Component Layer is made up of components, some components are called by technical rules and processes in the Strategy Layer, some others are used for communicating with third party systems. This agent framework enables the customizability of agents in SHLAS. We also introduce an Ontology-based domain knowledge and context model to capture and represent the agents, and agent behavior which provides agents with reasoning ability. SHLAS helps users with healthy lifestyle promotion by tracking and analyzing their behaviors, and recommending health plans. The paper closes with an empirical evaluation of the approach from the point of view of customizability. © 2013 Springer-Verlag.

Bargi, A, Xu, RYD & Piccardi, M 2013, 'A non-parametric conditional factor regression model for high-dimensional input and response'.

Cao, L 2013, 'Agents and Data Mining Interaction - 8th International Workshop, ADMI 2012', Springer, Heidelberg.
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Revised Selected Papers. Lecture Notes in Computer Science 7607, Springer 2013

Howlett, RJ, Gabrys, B, Musial-Gabrys, K & Roach, J 2013, 'Innovation through Knowledge Transfer 2012', Springer Berlin Heidelberg.
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Xu, G 2013, 'Advances in Knowledge Discovery and Data Mining', Springer Berlin Heidelberg.
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Xu, G & Li, L 2013, 'Social Media Mining and Social Network Analysis', IGI Global, pp. 1-254.
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Social Media Mining and Social Network Analysis: Emerging Research highlights the advancements made in social network analysis and social web mining and its influence in the fields of computer science, information systems, sociology, organization science discipline and much more. This collection of perspectives on developmental practice is useful for industrial practitioners as well as researchers and scholars. © 2013 by IGI Global. All rights reserved.