Publications

Li, G, Demazeau, Y, Xu, G, Wang, P, Wang, LSL & Liu, G 2016, Proceedings of 2016 International Conference on Behavioral, Economic and Socio-cultural Computing.
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Tomamichel, M 2016, Quantum Information Processing with Finite Resources - Mathematical Foundations, Springer, Cham, Switzerland.
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One of the predominant challenges when engineering future quantum information processors is that large quantum systems are notoriously hard to maintain and control accurately. It is therefore of immediate practical relevance to investigate quantum information processing with limited physical resources, for example to ask: How well can we perform information processing tasks if we only have access to a small quantum device? Can we beat fundamental limits imposed on information processing with classical resources? This book will introduce the reader to the mathematical framework required to answer such questions. A strong emphasis is given to information measures that are essential for the study of devices of finite size, including R\'enyi entropies and smooth entropies. The presentation is self-contained and includes rigorous and concise proofs of the most important properties of these measures. The first chapters will introduce the formalism of quantum mechanics, with particular emphasis on norms and metrics for quantum states. This is necessary to explore quantum generalizations of R\'enyi divergence and conditional entropy, information measures that lie at the core of information theory. The smooth entropy framework is discussed next and provides a natural means to lift many arguments from information theory to the quantum setting. Finally selected applications of the theory to statistics and cryptography are discussed.

Gao, L, Luan, TH, Liu, B, Zhou, W & Yu, S 2016, 'Fog computing and its applications in 5G' in 5G Mobile Communications, pp. 571-593.
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© Springer International Publishing Switzerland 2017. With smartphones becoming our everyday companions, high-quality mobile applications have become an important integral of people’s lives. The intensive and ubiquitous use of mobile applications have led to explosive growth of mobile data traffics. To accommodate the surge mobile traffic yet providing the guaranteed service quality to mobile users represent a key issue of 5G mobile networks. This motivates the emergence of Fog computing as a promising, practical and efficient solution tailored to serving mobile traffics. Fog computing deploys highly virtualized computing and communication facilities at the proximity of mobile users. Dedicated to serving the mobile users, Fog computing explores the predictable service demand patterns of mobile users and typically provides desirable localized services accordingly. Stitching above features, Fog computing can provide mobile users with the demanded services through low-latency and short-distance local connections. In this chapter, we introduce the main features of Fog computing and describe its concept, architecture and design goals. Lastly, we discuss on the potential research issues from the perspective of 5G networking.

Kenning, G & Law, J 2016, 'The Phenomena of Knitting and Crochet: Craft-based textiles and Computational Media' in Luckman, S (ed), The Craft Economy: Making, Materiality and Meaning, Bloomsbury (UK).

Mathieson, L, Gallardo, JE, Cotta, C & Moscato, P 2016, 'Memetic Algorithms: A Contemporary Introduction' in Webster, JG (ed), Wiley Encyclopedia of Electrical and Electronics Engineering, John Wiley & Sons, USA.
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Memetic algorithms (MAs) constitute a search and optimization paradigm based on the orchestrated interplay between global and local search components, and have the exploitation of specific problem knowledge as one of their central tenets. MAs can take different forms although a classical incarnation involves the integration of independent search processes within a population-based optimization approach. We discuss this basic structure as well as several of the issues arising in the design process. This paves the way for providing a glimpse of some algorithmic extensions of this basic scheme. After providing an overview of the numerous practical applications of MAs, we close this article with some current perspectives of these techniques.

van den Hoven, E & Connell, JA 2016, 'Joint Doctoral Supervision across Countries: Changes, Challenges and Considerations' in Blessinger, P & Stockley, D (eds), Emerging Directions in Doctoral Education (Innovations in Higher EducationTeaching and Learning, Volume 6), Emerald, UK, pp. 259-280.
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Many universities international activities have increased enormously in volume, scope, and complexity in recent years (Altbach & Knight, 2007; Altbach, 2015) with education providers seeking more innovative ways to provide education across boundaries. Joint doctoral degrees are one example of such an initiative, focusing on international collaboration between institutions. Joint doctorates can provide richer and more rewarding learning experiences for PhD students, supervisors and colla- borating institutions. However, all the parties involved also need to be aware of the potential challenges and considerations that underpin effec- tive outcomes, as well as the key differences between joint degree docto- rates and doctorates with more traditional approaches. It has been pointed out that the literature on joint degree programmes is ‘thin’ pro- viding limited information for institutional leaders (and other parties involved in their setting up and conduct) who may be contemplating joint degree initiatives (Michael & Balraj, 2003). This chapter draws on a unique case study of a joint doctoral programme that operates across continents and academic cultures to illustrate the challenges and consid- erations that should be borne in mind prior to entering into joint doctoral arrangements. Various ways in which the associated challenges may be overcome are also suggested in order to support effective outcomes for all the parties involved.

Ahadi, A, Brennan, S, Kennedy, P, Hutvagner, G & Tran, N 2016, 'Long non-coding RNAs harboring miRNA seed regions are enriched in prostate cancer exosomes', Scientific Reports, vol. 6, pp. 1-14.
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Long non-coding RNAs (lncRNAs) form the largest transcript class in the human transcriptome. These lncRNA are expressed not only in the cells, but they are also present in the cell-derived extracellular vesicles such as exosomes. The function of these lncRNAs in cancer biology is not entirely clear, but they appear to be modulators of gene expression. In this study, we characterize the expression of lncRNAs in several prostate cancer exosomes and their parental cell lines. We show that certain lncRNAs are enriched in cancer exosomes with the overall expression signatures varying across cell lines. These exosomal lncRNAs are themselves enriched for miRNA seeds with a preference for let-7 family members as well as miR-17, miR-18a, miR-20a, miR-93 and miR-106b. The enrichment of miRNA seed regions in exosomal lncRNAs is matched with a concomitant high expression of the same miRNA. In addition, the exosomal lncRNAs also showed an over representation of RNA binding protein binding motifs. The two most common motifs belonged to ELAVL1 and RBMX. Given the enrichment of miRNA and RBP sites on exosomal lncRNAs, their interplay may suggest a possible function in prostate cancer carcinogenesis

Ahadi, A, Hutvagner, G & Connerty, P 2016, 'RNA Binding Proteins in the miRNA Pathway', International Journal of Molecular Sciences, vol. 17, no. 31, pp. 1-16.
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microRNAs (miRNAs) are short ~22 nucleotides (nt) ribonucleic acids which post-transcriptionally regulate gene expression. miRNAs are key regulators of all cellular processes, and the correct expression of miRNAs in an organism is crucial for proper development and cellular function. As a result, the miRNA biogenesis pathway is highly regulated. In this review, we outline the basic steps of miRNA biogenesis and miRNA mediated gene regulation focusing on the role of RNA binding proteins (RBPs). We also describe multiple mechanisms that regulate the canonical miRNA pathway, which depends on a wide range of RBPs. Moreover, we hypothesise that the interaction between miRNA regulation and RBPs is potentially more widespread based on the analysis of available high-throughput datasets.

Ahadi, A, Khoury, S, losseva, M & Tran, N 2016, 'A comparative analysis of lncRNAs in prostate cancer exosomes and theirparental cell lines', Genomics Data, vol. 9, pp. 7-9.
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Prostate cancer is the second leading cancer in men world-wide. Due to its heterogeneous nature, a considerable amount of research effort has been dedicated in identifying effective clinical biomarkers with a focus on proteins, messenger RNA and microRNAs. However, there is limited data on the role and expression of long noncoding RNAs (lncRNAs) in prostate cancer exosomes. This array dataset which is linked to our publication describes the profiling of human lncRNAs in prostate cancer and their exosomes from five different cell lines. From this dataset, we identified a list of statistically significant prostate cancer lncRNAs which are differentially expressed in the exosomes compared to their parent cell lines. This dataset has been deposited into Gene Expression Omnibus (GSE81034).

Ahadi, A, Khoury, S, Tran, N & Zhang, X 2016, 'Expression of microRNAs in HPV negative tonsil cancers and their regulation of PDCD4', Genomics Data, vol. 8, pp. 93-96.
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Global rates of tonsil cancer have been increasing since the turn of the millennia, however we still have a limited understanding of the genes and pathways which control this disease. This array dataset which is linked to our publication (Zhang et al., 2015) describes the profiling of human miRNAs in tonsil and normal adjacent tissues. With this dataset, we identified a list of microRNA (miRNA) which were highly over represented in tonsil cancers and showed that several miRNAs were able to regulate the tumour suppressor PDCD4 in a temporal manner. The dataset has been deposited into Gene Expression Omnibus (GSE75630).

Alzoubi, Y, Gill, AQ & Al-ani, A 2016, 'Empirical studies of geographically distributed agile development communication challenges: A systematic review', Information and Management, vol. 53, no. 1, pp. 22-37.
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There is increasing interest in studying and applying geographically distributed agile development (GDAD). Much has been published on GDAD communication. There is a need to systematically review and synthesize the literature on GDAD communication challenges. Using the SLR approach and applying customized search criteria derived from the research questions, 21 relevant empirical studies were identified and reviewed in this paper. The data from these papers were extracted to identify communication challenges and the techniques used to overcome these challenges. The findings of this research serve as a resource for GDAD practitioners and researchers when setting future research priorities and directions.

Anaissi, A, Goyal, M, Catchpoole, DR, Braytee, A & Kennedy, PJ 2016, 'Ensemble Feature Learning of Genomic Data Using Support Vector Machine.', PLoS ONE, vol. 11, no. 6, pp. 1-17.
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The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random decision trees with bagging to improve overall feature selection and classification accuracy. Surprisingly, the adoption of these methods in support vector machines has only recently received attention but mostly on classification not gene selection. This paper introduces an ensemble SVM-Recursive Feature Elimination (ESVM-RFE) for gene selection that follows the concepts of ensemble and bagging used in random forest but adopts the backward elimination strategy which is the rationale of RFE algorithm. The rationale behind this is, building ensemble SVM models using randomly drawn bootstrap samples from the training set, will produce different feature rankings which will be subsequently aggregated as one feature ranking. As a result, the decision for elimination of features is based upon the ranking of multiple SVM models instead of choosing one particular model. Moreover, this approach will address the problem of imbalanced datasets by constructing a nearly balanced bootstrap sample. Our experiments show that ESVM-RFE for gene selection substantially increased the classification performance on five microarray datasets compared to state-of-the-art methods. Experiments on the childhood leukaemia dataset show that an average 9% better accuracy is achieved by ESVM-RFE over SVM-RFE, and 5% over random forest based approach. The selected genes by the ESVM-RFE algorithm were further explored with Singular Value Decomposition (SVD) which reveals significant clusters with the selected data.

Anshar, M & Williams, MA 2016, 'Evolving synthetic pain into an adaptive self-awareness framework for robots', Biologically Inspired Cognitive Architectures, vol. 16, pp. 8-18.
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In human-robot interaction, physical contact is the most common medium to be used, and the more physical interaction occurs, at certain times, the higher possibilities of causing humans to experience pain. Humans, at times, send this message out through social cues, such as verbal and facial expressions in which requires robots to have the skill to capture and translate these cues into useful information. The task of understanding human pain concept and its implementation on robots plays a dominant factor in allowing robots to acquire this social skill. However, it is reported that the concept of human pain is strongly related to the concept of human self-awareness concept and cognitive aspects with complex nerve mechanisms, hence, it is crucial to evolving appropriate self-awareness and pain concepts for robots. This paper focuses on imitating the concept of pain into a synthetic pain model, utilised in justifying the integration and implementation an adaptive self-awareness into a real robot design framework, named ASAF. The framework develops an appropriate robot cognitive system-"self-consciousness" that includes two primary levels of self-concept, namely subjective and objective. Novel experiments designated to measure whether a robot is capable of generating appropriate synthetic pain; whether the framework's reasoning skills support an accurate synthetic pain acknowledgement, and at the same time, develop appropriate counter responses. We find that the proposed framework enhances the awareness of robot's body parts and prevent further catastrophic impact on robot hardware and possible harm to human peers.

Berta, M & Tomamichel, M 2016, 'The Fidelity of Recovery Is Multiplicative', IEEE TRANSACTIONS ON INFORMATION THEORY, vol. 62, no. 4, pp. 1758-1763.
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Bremner, MJ, Montanaro, A & Shepherd, DJ 2016, 'Average-Case Complexity Versus Approximate Simulation of Commuting Quantum Computations', PHYSICAL REVIEW LETTERS, vol. 117, no. 8.
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Cao, Z, Lin, C-T, Chuang, C-H, Lai, K-L, Yang, AC, Fuh, J-L & Wang, S-J 2016, 'Resting-state EEG power and coherence vary between migraine phases', JOURNAL OF HEADACHE AND PAIN, vol. 17.
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Chen, C, Zhang, J, Xiang, Y, Zhou, W & Oliver, J 2016, 'Spammers Are Becoming "smarter" on Twitter', IT Professional, vol. 18, no. 2, pp. 66-70.
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© 2016 IEEE. While researchers develop various approaches to detect Twitter spam, spammers thwart their efforts with more complex spamming strategies. The authors' in-depth analysis of more than 570 million tweets revealed three new spamming strategies: coordinated posting behavior, finite-state machine-based spam template, and passive spam.

Chen, C, Zhang, J, Xie, Y, Xiang, Y, Zhou, W, Hassan, MM & Alelaiwi, A 2016, 'Erratum: A Performance Evaluation of Machine Learning Based Streaming Spam Tweets Detection (IEEE Transactions on Computational Social Systems (2015) 2:3 (65-76))', IEEE Transactions on Computational Social Systems, vol. 3, no. 1, p. 42.
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Chen, S, Yuan, X, Wang, Z, Guo, C, Liang, J, Wang, Z, Zhang, XL & Zhang, J 2016, 'Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data', IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 270-279.
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Social media data with geotags can be used to track people's movements in their daily lives. By providing both rich text and movement information, visual analysis on social media data can be both interesting and challenging. In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. To facilitate the understanding of people's movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory data from social media. We propose a heuristic model to reduce the uncertainty caused by the nature of social media data. In the proposed system, users can filter and select reliable data from each derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. We provide two cases to demonstrate how our system can help users to explore the movement patterns.

Chen, Y, Zhen, Y, Hu, H, Liang, J & Ma, K 2016, 'Visualization Technique for Multi-Attribute in Hierarchical Structure', Journal of Software, vol. 27, no. 5, pp. 1091-1102.
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Nowadays, there is increasing need to analyze the complex data with both hierarchical and multi-attributes in many fields such as food safety, stock market, and network security. The visual analytics appeared in recent years provides a good solution to analyze this kind of data. So far, many visualization methods for multi-dimensional data and hierarchical data, the typical data objects in the field of information visualization, have been presented to solve data analyzing problems effectively. However, the existing solutions can't meet requirements of visual analysis for the complex data with both multi-dimensional and hierarchical attributes. This paper presents a technology named Multi-Coordinate in Treemap (MCT), which combines rectangle treemap and multi-dimensional coordinates techniques. MCT uses treemap created with Squarified and Strip layout algorithm to represent hierarchical structure, uses four edges of treemap's rectangular node as the attribute axis, and through mapping property values to attribute axis, connecting attribute points and fitting curve, to achieve visualization of multi-attribute in hierarchical structure. This work applies MCT technology to visualize pesticide residue detection data and implements the visualization for detecting excessive pesticide residue in fruits and vegetables distributed in each provinces of China. This technology provides an efficient analysis tool for field experts. MCT can also be applied in other fields which require visual analysis of complex data with both hierarchical and multi-attribute.

Chuang, S-W, Chuang, C-H, Yu, Y-H, King, J-T & Lin, C-T 2016, 'EEG Alpha and Gamma Modulators Mediate Motion Sickness-Related Spectral Responses', INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, vol. 26, no. 2.
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Datta, N, Tomamichel, M & Wilde, MM 2016, 'On the second-order asymptotics for entanglement-assisted communication', QUANTUM INFORMATION PROCESSING, vol. 15, no. 6, pp. 2569-2591.
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Ding, W-P, Lin, C-T, Prasad, M, Chen, S-B & Guan, Z-J 2016, 'Attribute Equilibrium Dominance Reduction Accelerator (DCCAEDR) Based on Distributed Coevolutionary Cloud and Its Application in Medical Records', IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, vol. 46, no. 3, pp. 384-400.
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Dzeng, R-J, Lin, C-T & Fang, Y-C 2016, 'Using eye-tracker to compare search patterns between experienced and novice workers for site hazard identification', SAFETY SCIENCE, vol. 82, pp. 56-67.
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Gan, C, Yang, Y, Zhu, L, Zhao, D & Zhuang, Y 2016, 'Recognizing an Action Using Its Name: A Knowledge-Based Approach', INTERNATIONAL JOURNAL OF COMPUTER VISION, vol. 120, no. 1, pp. 61-77.
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Garcia, JA, Schoene, D, Lord, SR, Delbaere, K, Valenzuela, T & Navarro, KF 2016, 'A Bespoke Kinect Stepping Exergame for Improving Physical and Cognitive Function in Older People: A Pilot Study', Games for Health Journal, vol. 5, no. 6, pp. 382-388.
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© 2016 Mary Ann Liebert, Inc. Background: Systematic review evidence has shown that step training reduces the number of falls in older people by half. This study investigated the feasibility and effectiveness of a bespoke Kinect stepping exergame in an unsupervised home-based setting. Materials and Methods: An uncontrolled pilot trial was conducted in 12 community-dwelling older adults (mean age 79.3 ± 8.7 years, 10 females). The stepping game comprised rapid stepping, attention, and response inhibition. Participants were recommended to exercise unsupervised at home for a minimum of three 20-minute sessions per week over the 12-week study period. The outcome measures were choice stepping reaction time (CSRT) (main outcome measure), standing balance, gait speed, five-time sit-to-stand (STS), timed up and go (TUG) performance, and neuropsychological function (attention: letter-digit and executive function:Stroop tests) assessed at baseline, 4 weeks, 8 weeks, and trial end (12 weeks). Results: Ten participants (83%) completed the trial and reassessments. A median 8.2 20-minute sessions were completed and no adverse events were reported. Across the trial period, participants showed significant improvements in CSRT (11%), TUG (13%), gait speed (29%), standing balance (7%), and STS (24%) performance (all P < 0.05). There were also nonsignificant, but meaningful, improvements for the letter-digit (13%) and Stroop tests (15%). Conclusions: This study found that a bespoke Kinect step training program was safe and feasible for older people to undertake unsupervised at home and led to improvements in stepping, standing balance, gait speed, and mobility. The home-based step training program could therefore be included in exercise programs designed to prevent falls.

Gill, AQ, Phennel, N, Lane, D & Phung, VL 2016, 'IoT-enabled Emergency Information Supply Chain Architecture for Elderly People: The Australian Context', Information Systems, vol. 58, pp. 75-86.
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The effective delivery of emergency information to elderly people is a challenging task. Failure to deliver appropriate information can have an adverse impact on the well-being of the elderly people. This paper addresses this challenge and proposes an IoT-enabled information architecture driven approach, which is called “Resalert”. Resalert offers IoT-enabled emergency information supply chain architecture pattern, IoT device architecture and system architecture. The applicability of the Resalert is evaluated by the means of an example scenario, a portable Raspberry Pi based system prototype and user evaluation. The results of this research indicate that the proposed approach seems useful to the effective delivery of emergency information to elderly people.

Gupta, P, Lin, C-T, Mehlawat, MK & Grover, N 2016, 'A New Method for Intuitionistic Fuzzy Multiattribute Decision Making', IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, vol. 46, no. 9, pp. 1167-1179.
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Han, S, Zhao, S, Li, Q, Ju, CH & Zhou, W 2016, 'PPM-HDA: Privacy-Preserving and Multifunctional Health Data Aggregation with Fault Tolerance', IEEE Transactions on Information Forensics and Security, vol. 11, no. 9, pp. 1940-1955.
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© 2005-2012 IEEE. Wireless body area networks (WBANs), as a promising health-care system, can provide tremendous benefits for timely and continuous patient care and remote health monitoring. Owing to the restriction of communication, computation and power in WBANs, cloud-assisted WBANs, which offer more reliable, intelligent, and timely health-care services for mobile users and patients, are receiving increasing attention. However, how to aggregate the health data multifunctionally and efficiently is still an open issue to the cloud server (CS). In this paper, we propose a privacy-preserving and multifunctional health data aggregation (PPM-HDA) mechanism with fault tolerance for cloud-assisted WBANs. With PPM-HDA, the CS can compute multiple statistical functions of users' health data in a privacy-preserving way to offer various services. In particular, we first propose a multifunctional health data additive aggregation scheme (MHDA+) to support additive aggregate functions, such as average and variance. Then, we put forward MHDA as an extension of MHDA+ to support nonadditive aggregations, such as min/max, median, percentile, and histogram. The PPM-HDA can resist differential attacks, which most existing data aggregation schemes suffer from. The security analysis shows that the PPM-HDA can protect users' privacy against many threats. Performance evaluations illustrate that the computational overhead of MHDA+ is significantly reduced with the assistance of CSs. Our MHDA scheme is more efficient than previously reported min/max aggregation schemes in terms of communication overhead when the applications require large plaintext space and highly accurate data.

Hausmann, V & Williams, SP 2016, 'Issues for the long-term management of Social Business Documents', International Journal of Information Systems and Project Management, vol. 4, no. 3, pp. 45-61.
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Social business documents are currently one of the fastest growing content types within organizations. As carriers of important business information they require systematic management to ensure their content is available, accurate and protected over the long-term. To achieve this requires a deep understanding of their structure, nature and use. In this paper we present the findings of a preliminary study of social business documents. The aim of the study is to understand how social business documents are structured and to identify the issues and challenges that surround their management. Through an analysis of social business documents in four different systems we identify and compare their structural components from a user perspective. From this cross document/cross system analysis we develop a conceptual model for social business documents and identify issues for their long-term management. Our findings also identify the need for more in-depth modeling for which we propose methods to assist in understanding the syntactic and semantic structure of social business documents and how these change over the life of a social business document.

Hayashi, M & Tomamichel, M 2016, 'Correlation detection and an operational interpretation of the Renyi mutual information', JOURNAL OF MATHEMATICAL PHYSICS, vol. 57, no. 10.
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Hazber, MAG, Li, R, Gu, X & Xu, G 2016, 'Integration mapping rules: Transforming relational database to semantic web ontology', Applied Mathematics and Information Sciences, vol. 10, no. 3, pp. 881-901.
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© 2016 NSP. Semantic integration became an attractive area of research in several disciplines, such as information integration, databases and ontologies. Huge amount of data is still stored in relational databases (RDBs) that can be used to build ontology, and the database cannot be used directly by the semantic web. Therefore, one of the main challenges of the semantic web is mapping relational databases to ontologies (RDF(S)-OWL). Moreover, the use of manual work in the mapping of web contents to ontologies is impractical because it contains billions of pages and the most of these contents are generated from relational databases. Hence, we propose a new approach, which enables semantic web applications to access relational databases and their contents by semantic methods. Domain ontologies can be used to formulate relational database schema and data in order to simplify the mapping (transformation) of the underlying data sources. Our method consists of two main phases: building ontology from an RDB schema and the generation of ontology instances from an RDB data automatically. In the first phase, we studied different cases of RDB schema to be mapped into ontology represented in RDF(S)-OWL, while in the second phase, the mapping rules are used to transform RDB data to ontological instances represented in RDF triples. Our approach is demonstrated with examples, validated by ontology validator and implemented using Apache Jena in Java Language and MYSQL. This approach is effective for building ontology and important for mining semantic information from huge web resources.

He, W & Xu, G 2016, 'Social media analytics: unveiling the value, impact and implications of social media analytics for the management and use of online information', Online Information Review, vol. 40, no. 1.
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Heijboer, M, van den Hoven, E, Bongers, B & Bakker, S 2016, 'Facilitating peripheral interaction: design and evaluation of peripheral interaction for a gesture-based lighting control with multimodal feedback', PERSONAL AND UBIQUITOUS COMPUTING, vol. 20, no. 1, pp. 1-22.
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Hou, S, Zhou, S, Chen, L, Feng, Y & Awudu, K 2016, 'Multi-label learning with label relevance in advertising video', Neurocomputing, vol. 171, pp. 932-948.
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The recent proliferation of videos has brought out the need for applications such as automatic annotation and organization. These applications could greatly benefit from the respective thematic content depending on the type of video. Unlike the other kinds of video, an advertising video usually conveys a specific theme in a certain time period (e.g. drawing the audience׳s attention to a product or emphasizing the brand). Traditional multi-label algorithms may not work effectively with advertising videos due mainly to their heterogeneous nature. In this paper, we propose a new learning paradigm to resolve the problems arising out of traditional multi-label learning in advertising videos through label relevance. Aiming to address the issue of label relevance, we firstly assign each label with label degree (LD) to classify all the labels into three groups such as first label (FL), important label (IL) and common label (CL), and then propose a Directed Probability Label Graph (DPLG) model to mine the most related labels from the multi-label data with label relevance, in which the interdependency between labels is considered. In the implementation of DPLG, the labels that appear occasionally and possess inconspicuous co-occurrences are consequently eliminated effectively, employing λ-filtering and τ-pruning processes, respectively. And then the graph theory is utilized in DPLG to acquire Correlative Label-Sets (CLSs). Lastly, the searched Correlative Label-Sets (CLSs) are utilized to enhance multi-label annotation. Experimental results on advertising videos and several publicly available datasets demonstrate the effectiveness of the proposed method for multi-label annotation with label relevance

Hu, L, Cao, L, Cao, J, Gu, Z, Xu, G & Yang, D 2016, 'Learning Informative Priors from Heterogeneous Domains to Improve Recommendation in Cold-Start User Domains', ACM TRANSACTIONS ON INFORMATION SYSTEMS, vol. 35, no. 2.
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Huang, G, He, J, Zhou, W, Huang, GL, Guo, L, Zhou, X & Tang, F 2016, 'Discovery of stop regions for understanding repeat travel behaviors of moving objects', Journal of Computer and System Sciences, vol. 82, no. 4, pp. 582-593.
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© 2015 Elsevier Inc. All rights reserved. GPS trajectory dataset with high sampling-rates is usually in large volume that challenges the processing efficiency. Most of the data points on trajectories are useless. This paper summarizes trajectories using stop points. We define a new concept of stay stability (i.e., time dividing distance or reciprocal of speed) between any two GPS points to detect stop points on individual trajectories. We propose a novel Mining Repeat Travel Behaviors Using Stop Regions (MRTBUSR) method. In MRTBUSR, a stop region is a popular region containing a certain number of close stop points that can be grouped into a cluster. We then retrieve common sequences of stop regions to denote repeat route patterns and further analyze the stop durations on a stop region to find repeat travel behaviors. The experiments on 20 labeled trajectories selected from GeoLife demonstrated the semantic effect, accuracy and near linear efficiency of our proposed method.

Huang, K-C, Huang, T-Y, Chuang, C-H, King, J-T, Wang, Y-K, Lin, C-T & Jung, T-P 2016, 'An EEG-Based Fatigue Detection and Mitigation System.', International journal of neural systems, vol. 26, no. 4, pp. 1650018-1650018.
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Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual's internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant's EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha- and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject's cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.

Huang, M, Lu, L & Zhang, J 2016, 'Two Axes Re-ordering Methods in Parallel Coordinates Plots', Journal of Visual Languages & Computing, vol. 33, pp. 3-12.
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Visualization and interaction of multidimensional data are challenges in visual data analytics, which requires optimized solutions to integrate the display, exploration and analytical reasoning of data into one visual pipeline for human-centered data analysis and interpretation. Though it is considered to be one of the most popular techniques for visualization and analysis of multidimensional data, parallel coordinate visualization is also suffered from the visual clutter problem as well as the computational complexity problem, same as other visualization methods in which visual clutter occurs where the volume of data needs to be visualized to be increasing. One straightforward way to address these problems is to change the ordering of axis to reach the minimal number of visual clutters. However, the optimization of the ordering of axes is actually a NP-complete problem. In this paper, two axes re-ordering methods are proposed in parallel coordinates visualization: (1) a contribution-based method and (2) a similarity-based method.

Huang, ML, Huang, TH & Zhang, X 2016, 'A novel virtual node approach for interactive visual analytics of big datasets in parallel coordinates', Future Generation Computer Systems, vol. 55, pp. 510-523.
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© 2015 Elsevier B.V. Big data is a collection of large and complex datasets that commonly appear in multidimensional and multivariate data formats. It has been recognized as a big challenge in modern computing/information sciences to gain (or find out) due to its massive volume and complexity (e.g. its multivariate format). Accordingly, there is an urgent need to find new and effective techniques to deal with such huge datasets. Parallel coordinates is a well-established geometrical system for visualizing multidimensional data that has been extensively studied for decades. There is also a variety of associated interaction techniques currently used with this geometrical system. However, none of these existing techniques can achieve the functions that are covered by the Select layer of Yi's Seven-Layer Interaction Model. This is because it is theoretically impossible to find a select of data items via a mouse-click (or mouse-rollover) operation over a particular visual poly-line (a visual object) with no geometric region. In this paper, we present a novel technique that uses a set of virtual nodes to practically achieve the Select interaction which has hitherto proven to be such a challenging sphere in parallel coordinates visualization.

Huang, W, Huang, ML & Lin, CC 2016, 'Evaluating overall quality of graph visualizations based on aesthetics aggregation', Information Sciences, vol. 330, pp. 444-454.
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© 2015 Elsevier Inc. Aesthetics are often used to measure the layout quality of graph drawings and it is commonly accepted that drawings with good layout are effective in conveying the embedded data information to end users. However, existing aesthetic criteria are useful only in judging the extents to which a drawing conforms to specific drawing rules. They have limitations in evaluating overall quality. Currently graph visualizations are mainly evaluated based on personal judgments and user studies for their overall quality. Personal judgments are not reliable while user studies can be costly to run. Therefore, there is a need for a direct measure of overall quality. In an attempt to meet this need, we propose a measurement that measures overall quality based on individual aesthetics and gives a single numerical score. We present a user study that validates this measure by demonstrating its sensibility in detecting quality changes and its capacity in predicting the performance of human graph comprehension. The implications of our proposed measure for future research are discussed.

Hussain, W, Hussain, FK, Hussain Omar K & Chang E 2016, 'Provider-Based Optimized Personalized Viable SLA (OPV-SLA) Framework to Prevent SLA Violation', The Computer Journal, vol. 59, no. 12, pp. 1760-1783.
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Service level agreement (SLA) is an essential agreement formed between a consumer and a provider in business activities. The SLA defines the business terms, objectives, obligations and commitment of both parties to a business activity, and in cloud computing it also defines a consumer's request for both fixed and variable resources, due to the elastic and dynamic nature of the cloud-computing environment. Providers need to thoroughly analyze such variability when forming SLAs to ensure they commit to the agreements with consumers and at the same time make the best use of available resources and obtain maximum returns. They can achieve this by entering into viable SLAs with consumers. A consumer's profile becomes a key element in determining the consumer's reliability, as a consumer who has previous service violation history is more likely to violate future service agreements; hence, a provider can avoid forming SLAs with such consumers. In this paper, we propose a novel optimal SLA formation architecture from the provider's perspective, enabling the provider to consider a consumer's reliability in committing to the SLA. We classify existing consumers into three categories based on their reliability or trustworthiness value and use that knowledge to ascertain whether to accept a consumer request for resource allocation, and then to determine the extent of the allocation. Our proposed architecture helps the service provider to monitor the behavior of service consumers in the post-interaction time phase and to use that information to form viable SLAs in the pre-interaction time phase to minimize service violations and penalties.

Jay, B 2016, 'Programs as Data Structures in $\l SF$-Calculus', Proceedings of MFPS XXXII.
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Jay, B 2016, 'Programs as Data Structures in λSF-Calculus', Electronic Notes in Theoretical Computer Science, vol. 325, pp. 221-236.
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Abstract Lambda-SF-calculus can represent programs as closed normal forms. In turn, all closed normal forms are data structures, in the sense that their internal structure is accessible through queries defined in the calculus, even to the point of constructing the Goedel number of a program. Thus, program analysis and optimisation can be performed entirely within the calculus, without requiring any meta-level process of quotation to produce a data structure. Lambda-SF-calculus is a confluent, applicative rewriting system derived from lambda-calculus, and the combinatory SF-calculus. Its superior expressive power relative to lambda-calculus is demonstrated by the ability to decide if two programs are syntactically equal, or to determine if a program uses its input. Indeed, there is no homomorphism of applicative rewriting systems from lambda-SF-calculus to lambda-calculus. Program analysis and optimisation can be illustrated by considering the conversion of a programs to combinators. Traditionally, a program p is interpreted using fixpoint constructions that do not have normal forms, but combinatory techniques can be used to block reduction until the program arguments are given. That is, p is interpreted by a closed normal form M. Then factorisation (by F) adapts the traditional account of lambda-abstraction in combinatory logic to convert M to a combinator N that is equivalent to M in the following two senses. First, N is extensionally equivalent to M where extensional equivalence is defined in terms of eta-reduction. Second, the conversion is an intensional equivalence in that it does not lose any information, and so can be reversed by another definable conversion. Further, the standard optimisations of the conversion process are all definable within lambda-SF-calculus, even those involving free variable analysis. Proofs of all theorems in the paper have been verified using the Coq theorem prover.

Jialin, H, Guangquan, Z, Yaoguang, H & Jie, L 2016, 'A solution to bi/tri-level programming problems using particle swarm optimization', INFORMATION SCIENCES, vol. 370, pp. 519-537.
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Johnston, A 2016, 'Opportunities for practice-based research in musical instrument design', Leonardo, vol. 49, no. 1, pp. 82-83.
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© 2016 ISAST. This paper considers the relationship between design, practice and research in the area of New Interfaces for Musical Expression (NIME). The author argues that NIME practitioner-researchers should embrace the instability and dynamism inherent in digital musical interactions in order to explore and document the evolving processes of musical expression.

Johnston, A & Ferguson, S 2016, 'Practice-based research and new interfaces for musical expression', Leonardo, vol. 49, no. 1.
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Jungnickel, E & Gramann, K 2016, 'Mobile Brain/Body Imaging (MoBI) of Physical Interaction with Dynamically Moving Objects', FRONTIERS IN HUMAN NEUROSCIENCE, vol. 10.
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Kaiwartya, O, Abdullah, AH, Cao, Y, Altameem, A, Prasad, M, Lin, CT & Liu, X 2016, 'Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects', IEEE Access, vol. 4, pp. 5356-5373.
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© 2013 IEEE. Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of 'smart transport,' Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs). This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle-to-vehicle, vehicle-to-roadside, vehicle-to-infrastructure of cellular networks, vehicle-to-personal devices, and vehicle-to-sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned.

Kamaleswaran, R & McGregor, C 2016, 'A Review of Visual Representations of Physiologic Data.', JMIR medical informatics, vol. 4, no. 4, p. e31.
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Physiological data is derived from electrodes attached directly to patients. Modern patient monitors are capable of sampling data at frequencies in the range of several million bits every hour. Hence the potential for cognitive threat arising from information overload and diminished situational awareness becomes increasingly relevant. A systematic review was conducted to identify novel visual representations of physiologic data that address cognitive, analytic, and monitoring requirements in critical care environments.The aims of this review were to identify knowledge pertaining to (1) support for conveying event information via tri-event parameters; (2) identification of the use of visual variables across all physiologic representations; (3) aspects of effective design principles and methodology; (4) frequency of expert consultations; (5) support for user engagement and identifying heuristics for future developments.A review was completed of papers published as of August 2016. Titles were first collected and analyzed using an inclusion criteria. Abstracts resulting from the first pass were then analyzed to produce a final set of full papers. Each full paper was passed through a data extraction form eliciting data for comparative analysis.In total, 39 full papers met all criteria and were selected for full review. Results revealed great diversity in visual representations of physiological data. Visual representations spanned 4 groups including tabular, graph-based, object-based, and metaphoric displays. The metaphoric display was the most popular (n=19), followed by waveform displays typical to the single-sensor-single-indicator paradigm (n=18), and finally object displays (n=9) that utilized spatiotemporal elements to highlight changes in physiologic status. Results obtained from experiments and evaluations suggest specifics related to the optimal use of visual variables, such as color, shape, size, and texture have not been fully understood. Relationships betwe...

Kamaleswaran, R, Collins, C, James, A & McGregor, C 2016, 'PhysioEx: Visual Analysis of Physiological Event Streams', COMPUTER GRAPHICS FORUM, vol. 35, no. 3, pp. 331-340.
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Kenning, G 2016, 'Viewing art brings ‘in the moment’ pleasure', Australian Journal of Dementia Care, vol. 5, no. October/November 2016.
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Khanna, S, Finlay, JK, Jatana, V, Gouffe, AM & Redshaw, S 2016, 'The Impact of Observed Trauma on Parents in a PICU*', PEDIATRIC CRITICAL CARE MEDICINE, vol. 17, no. 4, pp. E154-E158.
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Konyagin, SV, Luca, F, Mans, B, Mathieson, L, Sha, M & Shparlinski, IE 2016, 'Functional graphs of polynomials over finite fields', JOURNAL OF COMBINATORIAL THEORY SERIES B, vol. 116, pp. 87-122.
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Li, DL, Prasad, M, Lin, CT & Chang, JY 2016, 'Self-adjusting feature maps network and its applications', Neurocomputing, vol. 2017, pp. 78-94.
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This paper, proposes a novel artificial neural network, called self-adjusting feature map (SAM), and develop its unsupervised learning ability with self-adjusting mechanism. The trained network structure of representative connected neurons not only displays the spatial relation of the input data distribution but also quantizes the data well. The SAM can automatically isolate a set of connected neurons, in which, the used number of the sets may indicate the number of clusters. The idea of self-adjusting mechanism is based on combining of mathematical statistics and neurological advantages and retreat of waste. In the training process, for each representative neuron has are three phases, growth, adaptation and decline. The network of representative neurons, first create the necessary neurons according to the local density of the input data in the growth phase. In the adaption phase, it adjusts neighborhood neuron pair's connected/disconnected topology constantly according to the statistics of input feature data. Finally, the unnecessary neurons of the network are merged or remove in the decline phase. In this paper, we exploit the SAM to handle some peculiar cases that cannot be handled easily by classical unsupervised learning networks such as self-organizing map (SOM) network. The remarkable characteristics of the SAM can be seen on various real world cases in the experimental results.

Li, F, Xu, G & Cao, L 2016, 'Two-level matrix factorization for recommender systems', Neural Computing and Applications, vol. 27, no. 8, pp. 2267-2278.
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Many existing recommendation methods such as matrix factorization (MF) mainly rely on user–item rating matrix, which sometimes is not informative enough, often suffering from the cold-start problem. To solve this challenge, complementary textual relations between items are incorporated into recommender systems (RS) in this paper. Specifically, we first apply a novel weighted textual matrix factorization (WTMF) approach to compute the semantic similarities between items, then integrate the inferred item semantic relations into MF and propose a two-level matrix factorization (TLMF) model for RS. Experimental results on two open data sets not only demonstrate the superiority of TLMF model over bench-mark methods, but also show the effectiveness of TLMF for solving the cold-start problem.

Li, Q, Xie, B, You, J, Bian, W & Tao, D 2016, 'Correlated Logistic Model With Elastic Net Regularization for Multilabel Image Classification', IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 25, no. 8, pp. 3801-3813.
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Li, Y, Li, Y & Xu, G 2016, 'Protecting private geosocial networks against practical hybrid attacks with heterogeneous information', Neurocomputing, vol. 210, pp. 81-90.
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© 2016 Elsevier B.V.GeoSocial Networks (GSNs) are becoming increasingly popular due to its power in providing high-performance and flexible service capabilities. More and more Internet users have accepted this innovative service model. However, even GSNs have great business value for data analysis by integrated with location information, it may seriously compromise users' privacy in publishing the GSN data. In this paper, we study the identity disclosure problem in publishing GSN data. We first discuss the attack problem by considering both the location-based and structure-based properties, as background knowledge, and then formalize two general models, named (k,m)-anonymity and (k,m,l)-anonymity Then we propose a complete solution to achieve (k,m)-anonymization and (k,m,l)-anonymization to prevent the released data from the above attacks above. We also take data utility into consideration by defining specific information loss metrics. It is validated by real-world data that the proposed methods can prevent GSN dataset from the attacks while retaining good utility.

Liang, J, Huang, M & Nguyen, Q 2016, 'Navigation in large hierarchical graph through chain-context views', Journal of Visualization, vol. 19, no. 3, pp. 543-559.
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The most commonly used interaction techniques in space-filling visualization are drilling-down + semantic-zooming and focus + context methods. However, under these schemes, users often have insufficient knowledge about contextual information to guide them exploring through very large and deep hierarchical structures. This paper proposes an efficient interaction method called “chain-context view” (CCV) for the navigation in space-filling visualizations. Instead of displaying a no or one context views, we provide users with a progressive sequence of context views, which maximize the display area of contextual information. The rich contextual information provided in the exploration path could greatly increase the accuracy of user’s decisions and reduce the “unsuccessful trips” and “unnecessary views” while locating the target object by browsing in deep levels of hierarchical structures with CCVs. The new method allows the users to trace each step of their interactions and make it easy to jump or return to any level of the hierarchy that they have previously visited. A usability study was conducted to evaluate the effectiveness of the CCV, by measuring the user performance and satisfaction on the navigation of deep levelled relational structures.

Light, A, Pedell, S, Robertson, T, Waycott, J, Bell, J, Durick, J & Leong, TW 2016, 'What's Special about Aging', Interactions, vol. 23, no. 2, pp. 66-69.
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Community + Culture features practitioner perspectives on designing technologies for and with communities. We highlight compelling projects and provocative points of view that speak to both community technology practice and the interaction design field as a whole. - Christopher A. Le Dantec, Editor.

Lin, C, Hajinoroozi, M, Mao, Z, Jung, TP & Huang, YF 2016, 'EEG- based prediction of drivers cognitive performance by deep convolutional neural network', Signal Processing: Image Communication, vol. 47, pp. 549-555.
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We considered the prediction of driver's cognitive states related to driving performance using EEG signals. We proposed a novel channel-wise convolutional neural network (CCNN) whose architecture considers the unique characteristics of EEG data. We also discussed CCNN-R, a CCNN variation that uses Restricted Boltzmann Machine to replace the convolutional filter, and derived the detailed algorithm. To test the performance of CCNN and CCNN-R, we assembled a large EEG dataset from 3 studies of driver fatigue that includes samples from 37 subjects. Using this dataset, we investigated the new CCNN and CCNN-R on raw EEG data and also Independent Component Analysis (ICA) decomposition. We tested both within-subject and cross-subject predictions and the results showed CCNN and CCNN-R achieved robust and improved performance over conventional DNN and CNN as well as other non-DL algorithms.

Lin, C-T & Garibaldi, JJ 2016, 'Untitled', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 24, no. 6, pp. 1257-1258.
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Lin, C-T, Chuang, C-H, Kerick, S, Mullen, T, Jung, T-P, Ko, L-W, Chen, S-A, King, J-T & McDowell, K 2016, 'Mind-Wandering Tends to Occur under Low Perceptual Demands during Driving', SCIENTIFIC REPORTS, vol. 6.
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Liu, B, Zhou, W, Zhu, T, Gao, L, Luan, TH & Zhou, H 2016, 'Silence is Golden: Enhancing Privacy of Location-Based Services by Content Broadcasting and Active Caching in Wireless Vehicular Networks', IEEE Transactions on Vehicular Technology, vol. 65, no. 12, pp. 9942-9953.
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© 2016 IEEE. The privacy issue of location-based service (LBS) applications represents a fundamental and critical research topic that shows distinguished challenges. Specifically, in the scenario of vehicular networks, due to connectivity transitory and throughput degradation caused by high vehicle mobility, traditional methods such as k-anonymity are not sufficient. In an effort to address this issue, we propose a framework that enhances the privacy of LBS by actively caching in the wireless vehicular network scenario. The scheme is designed under the dedicated short-range communication (DSRC) standard, with three original contributions. First, a point-of-interest (POI) query probability model for in-vehicle users is defined, considering the spatial relationship between the current location and the queried location. Second, three broadcasting content selection algorithms of the roadside unit (RSU) are proposed, including two adaptive updating methods and one knowledge-based precaching (KBPC) method. Third, we identify critical parameters that affect privacy, including RSU distance, vehicle speed, query set size of POI, size of each POI entry, and the proportion of channel capacity used for RSU broadcast (defined as channel occupancy). Finally, the proposed scheme is evaluated by extensive simulations, and the results are discussed in detail.

Liu, Y, Li, L, Li, X, Wang, Y, Ren, X & Liang, J 2016, 'Antibacterial modification of microcrystalline cellulose by grafting copolymerization', BioResources, vol. 11, no. 1, pp. 519-529.
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Microcrystalline cellulose (MCC) has the advantage of a high specific surface area as compared to that of conventional cellulose fibers. In this study the monomer methacrylamide (MAM) was used to treat MCC by grafting copolymerization. SEM, FTIR, and solid 13 C NMR were used to characterize the morphology and composition of MAM-g-MCC. After the chlorination of MAM-g-MCC with 10% sodium hypochlorite solution, the grafted MCC exhibited antibacterial activity as a result of the formation of N-Cl bonds. The thermal stability, antibacterial ability, and storage stability of chlorinated MAM-g-MCC were also studied. The results showed that the chlorinated MAM-g-MCC had excellent storage stability and could inactivate all S. aureus and E. coli O157:H7 within 10 min.

Liu, Y, Xiao, C, Li, X, Li, L, Ren, X, Liang, J & Huang, TS 2016, 'Antibacterial efficacy of functionalized silk fabrics by radical copolymerization with quaternary ammonium salts', Journal of Applied Polymer Science, vol. 133, no. 21.
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© 2016 Wiley Periodicals, Inc. Quaternary ammonium salts Quats-C 8 , Quats-C 12 , and Quats-C 18 with different alkyl chain lengths have been successfully synthesized, and used for modifying silk fabrics. The optimum reaction conditions of initiator concentration, curing temperature, curing time, and monomer concentration have been studied. The modified fabrics of silk-g-C 8 , silk-g-C 12 , and silk-g-C 18 were characterized by FTIR spectra. Antibacterial test showed that the modified silk fabrics possessed potent antibacterial activity against both Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli. The carbon number in the alkyl chain of monomers Quats-C 8 , Quats-C 12 , and Quats-C 18 can affect the antibacterial efficacy. With longer alkyl chain, the antibacterial efficacy increased. The quaternary ammonium salts-modified silk fabrics have small change on the tensile strength and wrinkle recovery angle, and have shown potential practical application.

Liu, YT, Chuang, CH, Wang JM & Lin, CT 2016, 'Changes in Alertness and the EEG Effective Connectivity in a Sustained-Attention Driving Task', Journal of Neuroscience and Neuroengineering.

Liu, YT, Lin, C, Chuang, CH, Wang, YK, Huang, SH, King, JT, Chen, SA & Lu, SW 2016, 'Novel Neurotechnology and Computational Intelligence Method Applied to EEG-based Brain-Computer Interfaces', IEEE Systems, Man, and Cybernetics Magazine.

Liu, Y-T, Lin, Y-Y, Wu, S-L, Chuang, C-H & Lin, C-T 2016, 'Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network', IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, vol. 27, no. 2, pp. 347-360.
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Lu, J, Han, J, Hu, Y & Zhang, G 2016, 'Multilevel decision-making: A survey', INFORMATION SCIENCES, vol. 346, pp. 463-487.
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Lu, M, Liang, J, Wang, Z & Yuan, X 2016, 'Exploring OD patterns of interested region based on taxi trajectories', Journal of Visualization, vol. 19, no. 4, pp. 811-821.
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Traffics of different regions in a city have different Origin-Destination (OD) patterns, which potentially reveal the surrounding traffic context and social functions. In this work, we present a visual analysis system to explore OD patterns of interested regions based on taxi trajectories. The system integrates interactive trajectory filtering with visual OD patterns exploration. Trajectories related to interested region are selected by a suite of graphical filtering tools, from which OD clusters are detected automatically. OD traffic patterns can be explored at two levels: overview of OD and detailed exploration on dynamic OD patterns, including information of dynamic traffic volume and travel time. By testing on real taxi trajectory data sets, we demonstrate the effectiveness of our system.

Lu, N, Lu, J, Zhang, G & Lopez de Mantaras, R 2016, 'A concept drift-tolerant case-base editing technique', ARTIFICIAL INTELLIGENCE, vol. 230, pp. 108-133.
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Luccio, F, Mans, B, Mathieson, L & Pagli, L 2016, 'Complete Balancing via Rotation', COMPUTER JOURNAL, vol. 59, no. 8, pp. 1252-1263.
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Luo, X, Xuan, J, Lu, J & Zhang, G 2016, 'Measuring the Semantic Uncertainty of News Events for Evolution Potential Estimation', ACM TRANSACTIONS ON INFORMATION SYSTEMS, vol. 34, no. 4.
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Malomo, L, Pietroni, N, Bickel, B & Cignoni, P 2016, 'FlexMolds: automatic design of flexible shells for molding', ACM Transactions on Graphics (TOG), vol. 35, pp. 223-223.

Mathieson, L 2016, 'Synergies in critical reflective practice and science: Science as reflection and reflection as science', Journal of University Teaching and Learning Practice, vol. 13, no. 2, pp. 1-13.
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© 2016, University of Wollongong. All rights reserved. The conceptions of reflective practice in education have their roots at least partly in the work of Dewey, who describes reflection as “the active, persistent, and careful consideration of any belief or supposed form of knowledge in the light of the grounds that support it and the further conclusions to which it tends” (Dewey 1933, p.9). This conception of reflection has carried on into more-focused efforts to describe critical reflection as a tool for improving professional practice (where academic and educational practice is the particular interest of this study); “… some puzzling or troubling or interesting phenomenon” allows the practitioner to access “the understandings which have been implicit in his action, understandings which he surfaces, criticizes, restructures, and embodies in further action” (Schön 1983, p. 50). Both of these descriptions embody a central idea of critical reflective practice: that the examination of practice involves the divination (in a rational, critical sense) of order and perhaps meaning from the facts at hand (which, in turn, are brought to light by the events that occur as the results of implementation of theory). As part of a lecture series, Gottlieb defined science as “an intellectual activity carried out by humans to understand the structure and functions of the world in which they live” (Gottlieb 1997). While science and critical reflective practice attempt to build models about different parts of our world – the natural world and the world of professional (educational) practice respectively – both embody certain underlying aims and methodologies. Indeed, it is striking that in these definitions the simple replacement of the terminology of reflective practice with the terminology of science (or vice versa) leads to a perfectly comprehensible definition of either.It is this confluence that this paper studies, building from two separate foundations, critical reflective pr...

Meert, KL & Eggly, S 2016, 'Observed Trauma in the PICU and Parental Meaning Making', Pediatric Critical Care Medicine, vol. 17, no. 4, pp. 375-376.
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Mols, I, van den Hoven, E & Eggen, B 2016, 'Ritual Camera: Exploring Domestic Technology to Remember Everyday Life', IEEE Pervasive Computing, vol. 15, no. 2, pp. 48-58.
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Motes, KR, Mann, RL, Olson, JP, Studer, NM, Bergeron, EA, Gilchrist, A, Dowling, JP, Berry, DW & Rohde, PP 2016, 'Efficient recycling strategies for preparing large Fock states from single-photon sources: Applications to quantum metrology', PHYSICAL REVIEW A, vol. 94, no. 1.
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Musial, K, Brodka, P & Magnani, M 2016, 'Social Network Analysis in Applications', AI COMMUNICATIONS, vol. 29, no. 1, pp. 55-56.
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Naderpour, M, Lu, J & Zhang, G 2016, 'A safety-critical decision support system evaluation using situation awareness and workload measures', RELIABILITY ENGINEERING & SYSTEM SAFETY, vol. 150, pp. 147-159.
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Nguyen, Q, Khalifa, N, Alzamora, P, Gleeson, A, Catchpoole, D, Kennedy, P & Simoff, S 2016, 'Visual Analytics of Complex Genomics Data to Guide Effective Treatment Decisions', Journal of Imaging, vol. 2, no. 4, pp. 1-17.
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In cancer biology, genomics represents a big data problem that needs accurate visual data processing and analytics. The human genome is very complex with thousands of genes that contain the information about the individual patients and the biological mechanisms of their disease. Therefore, when building a framework for personalised treatment, the complexity of the genome must be captured in meaningful and actionable ways. This paper presents a novel visual analytics framework that enables effective analysis of large and complex genomics data. By providing interactive visualisations from the overview of the entire patient cohort to the detail view of individual genes, our work potentially guides effective treatment decisions for childhood cancer patients. The framework consists of multiple components enabling the complete analytics supporting personalised medicines, including similarity space construction, automated analysis, visualisation, gene-to-gene comparison and user-centric interaction and exploration based on feature selection. In addition to the traditional way to visualise data, we utilise the Unity3D platform for developing a smooth and interactive visual presentation of the information. This aims to provide better rendering, image quality, ergonomics and user experience to non-specialists or young users who are familiar with 3D gaming environments and interfaces. We illustrate the effectiveness of our approach through case studies with datasets from childhood cancers, B-cell Acute Lymphoblastic Leukaemia (ALL) and Rhabdomyosarcoma (RMS) patients, on how to guide the effective treatment decision in the cohort.

Oberst, S, Lai, JCS & Evans, TA 2016, 'Termites utilise clay to build structural supports and so increase foraging resources', SCIENTIFIC REPORTS, vol. 6.
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Oberst, S, Zhang, Z & Lai, JCS 2016, 'The Role of Nonlinearity and Uncertainty in Assessing Disc Brake Squeal Propensity', SAE INTERNATIONAL JOURNAL OF PASSENGER CARS-MECHANICAL SYSTEMS, vol. 9, no. 3, pp. 980-986.
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Park, J & Zhou, W 2016, 'Special issue on entropy-based applied cryptography and enhanced security for ubiquitous computing', Entropy, vol. 18, no. 9.
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© 2016 by the authors. Entropy is a basic and important concept in information theory. It is also often used as a measure of the unpredictability of a cryptographic key in cryptography research areas. Ubiquitous computing (Ubi-comp) has emerged rapidly as an exciting new paradigm. In this special issue, we mainly selected and discussed papers related with ore theories based on the graph theory to solve computational problems on cryptography and security, practical technologies; applications and services for Ubi-comp including secure encryption techniques, identity and authentication; credential cloning attacks and countermeasures; switching generator with resistance against the algebraic and side channel attacks; entropy-based network anomaly detection; applied cryptography using chaos function, information hiding and watermark, secret sharing, message authentication, detection and modeling of cyber attacks with Petri Nets, and quantum flows for secret key distribution, etc.

Percival, J & McGregor, C 2016, 'An Evaluation of Understandability of Patient Journey Models in Mental Health.', JMIR human factors, vol. 3, no. 2, p. e20.
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There is a significant trend toward implementing health information technology to reduce administrative costs and improve patient care. Unfortunately, little awareness exists of the challenges of integrating information systems with existing clinical practice. The systematic integration of clinical processes with information system and health information technology can benefit the patients, staff, and the delivery of care.This paper presents a comparison of the degree of understandability of patient journey models. In particular, the authors demonstrate the value of a relatively new patient journey modeling technique called the Patient Journey Modeling Architecture (PaJMa) when compared with traditional manufacturing based process modeling tools. The paper also presents results from a small pilot case study that compared the usability of 5 modeling approaches in a mental health care environment.Five business process modeling techniques were used to represent a selected patient journey. A mix of both qualitative and quantitative methods was used to evaluate these models. Techniques included a focus group and survey to measure usability of the various models.The preliminary evaluation of the usability of the 5 modeling techniques has shown increased staff understanding of the representation of their processes and activities when presented with the models. Improved individual role identification throughout the models was also observed. The extended version of the PaJMa methodology provided the most clarity of information flows for clinicians.The extended version of PaJMa provided a significant improvement in the ease of interpretation for clinicians and increased the engagement with the modeling process. The use of color and its effectiveness in distinguishing the representation of roles was a key feature of the framework not present in other modeling approaches. Future research should focus on extending the pilot case study to a more diversified group of clinicians...

Pfister, C, Kaniewski, J, Tomamichel, M, Mantri, A, Schmucker, R, McMahon, N, Milburn, G & Wehner, S 2016, 'A universal test for gravitational decoherence.', Nature Communications, vol. 7, pp. 13022-13022.
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Quantum mechanics and the theory of gravity are presently not compatible. A particular question is whether gravity causes decoherence. Several models for gravitational decoherence have been proposed, not all of which can be described quantum mechanically. Since quantum mechanics may need to be modified, one may question the use of quantum mechanics as a calculational tool to draw conclusions from the data of experiments concerning gravity. Here we propose a general method to estimate gravitational decoherence in an experiment that allows us to draw conclusions in any physical theory where the no-signalling principle holds, even if quantum mechanics needs to be modified. As an example, we propose a concrete experiment using optomechanics. Our work raises the interesting question whether other properties of nature could similarly be established from experimental observations alone-that is, without already having a rather well-formed theory of nature to make sense of experimental data.

Pham, PG & Huang, M 2016, 'Qstack: Multi-tag Visual Rankings', Journal of Software, vol. 11, no. 7, pp. 695-703.
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Multi-tag-based search is quite popular on collaborative websites and sharing-online-content systems. For this kind of search results, the challenge is how to compare grouped-tag values of tag collections on heterogeneous alternatives. This paper introduces a new visualization approach named Qstack for dealing with the challenge. Qstack purpose is to help users to visually rank multi-tags based on grouped-score combination within and across the categorized alternatives. The methodology applying interactive stacked bars, dynamic queries and adaptive focus+context techniques enables users to easily create and adjust grouped-tag rankings of a large number of heterogeneous alternatives. A case study on Flickr photo award allocation will be presented for Qstack demonstration. We conducted a qualitative study for evaluating Qstack effectiveness, and the result indicates that our approach is useful for multi-tag rankings.

Pratama, M, Lu, J & Zhang, G 2016, 'Evolving Type-2 Fuzzy Classifier', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 24, no. 3, pp. 574-589.
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Pratama, M, Lu, J, Lughofer, E, Zhang, G & Anavatti, S 2016, 'Scaffolding type-2 classifier for incremental learning under concept drifts', Neurocomputing, vol. 191, pp. 304-329.
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© 2016 Elsevier B.V. The proposal of a meta-cognitive learning machine that embodies the three pillars of human learning: what-to-learn, how-to-learn, and when-to-learn, has enriched the landscape of evolving systems. The majority of meta-cognitive learning machines in the literature have not, however, characterized a plug-and-play working principle, and thus require supplementary learning modules to be pre-or post-processed. In addition, they still rely on the type-1 neuron, which has problems of uncertainty. This paper proposes the Scaffolding Type-2 Classifier (ST2Class). ST2Class is a novel meta-cognitive scaffolding classifier that operates completely in local and incremental learning modes. It is built upon a multivariable interval type-2 Fuzzy Neural Network (FNN) which is driven by multivariate Gaussian function in the hidden layer and the non-linear wavelet polynomial in the output layer. The what-to-learn module is created by virtue of a novel active learning scenario termed the uncertainty measure; the how-to-learn module is based on the renowned Schema and Scaffolding theories; and the when-to-learn module uses a standard sample reserved strategy. The viability of ST2Class is numerically benchmarked against state-of-the-art classifiers in 12 data streams, and is statistically validated by thorough statistical tests, in which it achieves high accuracy while retaining low complexity.

Qiao, M, Xu, RYD, Bian, W & Tao, D 2016, 'Fast Sampling for Time-Varying Determinantal Point Processes', ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, vol. 11, no. 1.
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Reis, MDL, Ferme, E & Peppas, P 2016, 'Construction of system of spheres-based transitively relational partial meet multiple contractions: An impossibility result', ARTIFICIAL INTELLIGENCE, vol. 233, pp. 122-141.
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Reis, MDL, Peppas, P & Ferme, E 2016, 'Two axiomatic characterizations for the system of spheres-based (and the Epistemic Entrenchment-based) multiple contractions', ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE, vol. 78, no. 3-4, pp. 181-203.
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Riaz-ud-Din, F, Zhou, W & Doss, R 2016, 'Query verification schemes for cloud-hosted databases: a brief survey', International Journal of Parallel, Emergent and Distributed Systems, vol. 31, no. 6, pp. 543-561.
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© 2016 Taylor & Francis. Database query verification schemes provide correctness guarantees for database queries. Typically such guarantees are required and advisable where queries are executed on untrusted servers. This need to verify query results, even though they may have been executed on one’s own database, is something new that has arisen with the advent of cloud services. The traditional model of hosting one’s own databases on one’s own servers did not require such verification because the hardware and software were both entirely within one’s control, and therefore fully trusted. However, with the economical and technological benefits of cloud services beckoning, many are now considering outsourcing both data and execution of database queries to the cloud, despite obvious risks. This survey paper provides an overview into the field of database query verification and explores the current state of the art in terms of query execution and correctness guarantees provided for query results. We also provide indications towards future work in the area.

Sayad Haghighi, M, Wen, S, Xiang, Y, Quinn, B & Zhou, W 2016, 'On the Race of Worms and Patches: Modeling the Spread of Information in Wireless Sensor Networks', IEEE Transactions on Information Forensics and Security, vol. 11, no. 12, pp. 2854-2865.
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© 2005-2012 IEEE. Sensor networks are a branch of distributed ad hoc networks with a broad range of applications in surveillance and environment monitoring. In these networks, message exchanges are carried out in a multi-hop manner. Due to resource constraints, security professionals often use lightweight protocols, which do not provide adequate security. Even in the absence of constraints, designing a foolproof set of protocols and codes is almost impossible. This leaves the door open to the worms that take advantage of the vulnerabilities to propagate via exploiting the multi-hop message exchange mechanism. This issue has drawn the attention of security researchers recently. In this paper, we investigate the propagation pattern of information in wireless sensor networks based on an extended theory of epidemiology. We develop a geographical susceptible-infective model for this purpose and analytically derive the dynamics of information propagation. Compared with the previous models, ours is more realistic and is distinguished by two key factors that had been neglected before: 1) the proposed model does not purely rely on epidemic theory but rather binds it with geometrical and spatial constraints of real-world sensor networks and 2) it extends to also model the spread dynamics of conflicting information (e.g., a worm and its patch). We do extensive simulations to show the accuracy of our model and compare it with the previous ones. The findings show the common intuition that the infection source is the best location to start patching from, which is not necessarily right. We show that this depends on many factors, including the time it takes for the patch to be developed, worm/patch characteristics as well as the shape of the network.

Singh, J, Prasad, M, Prasad, OK, Joo, EM, Saxena, AK & Lin, C-T 2016, 'A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query Expansion', INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, vol. 18, no. 6, pp. 980-989.
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Sui, Y, Ye, D, Su, Y & Xue, J 2016, 'Eliminating Redundant Bounds Checks in Dynamic Buffer Overflow Detection Using Weakest Preconditions', IEEE TRANSACTIONS ON RELIABILITY, vol. 65, no. 4, pp. 1682-1699.
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Sun, L, Ma, J, Zhang, Y, Dong, H & Hussain, FK 2016, 'Cloud-FuSeR: Fuzzy ontology and MCDM based cloud service selection', FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, vol. 57, pp. 42-55.
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Surden, H & Williams, M-A 2016, 'How Self-Driving Cars Work'.

Surden, H & Williams, M-A 2016, 'Technological Opacity, Predictability, and Self-Driving Cars', Cardozo Law Review, vol. 38, pp. 1-52.
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Autonomous or “self-driving” cars are vehicles that drive themselves without human supervision or input. Because of safety benefits that they are expected to bring, autonomous vehicles are likely to become more common. Notably, for the first time, people will share a physical environment with computer-controlled machines that can both direct their own activities and that have considerable range of movement. This represents a distinct change from our current context. Today people share physical spaces either with machines that have free range of movement, but are controlled by people (e.g. automobiles) or with machines that are controlled by computers, but highly constrained in their range of movement (e.g. elevators). The movements of today's machines are thus broadly predictable. The unrestricted, computer-directed movement of autonomous vehicles is an entirely novel phenomenon that may challenge certain unarticulated assumptions in our existing legal structure. Problematically, the movements of autonomous vehicles may be less predictable to the ordinary people who will share their physical environment--such as pedestrians--than the comparable movements of human-driven vehicles. Today, a great deal of physical harm that might otherwise occur is likely avoided through humanity's collective ability to predict the movements of other people. In anticipating the behavior of others, we employ what psychologists call a “theory of mind.” Theory of mind cognitive mechanisms allow us to extrapolate from our own internal mental states in order to estimate what others are thinking or likely to do. These cognitive systems allow us to make instantaneous, unconscious judgments about the likely actions of people around us, and therefore, to keep ourselves safe in the driving context. However, the theory of mind mechanisms that allow us to accurately model the minds of other people and interpret their communicative signals of attention and *122 intention will be challenged in th...

Sutter, D, Tomamichel, M & Harrow, AW 2016, 'Strengthened Monotonicity of Relative Entropy via Pinched Petz Recovery Map', IEEE Transactions on Information Theory, vol. 62, no. 5, pp. 2907-2913.
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The quantum relative entropy between two states satisfies a monotonicity property meaning that applying the same quantum channel to both states can never increase their relative entropy. It is known that this inequality is only tight when there is a recovery map that exactly reverses the effects of the quantum channel on both states. In this paper, we strengthen this inequality by showing that the difference of relative entropies is bounded below by the measured relative entropy between the first state and a recovered state from its processed version. The recovery map is a convex combination of rotated Petz recovery maps and perfectly reverses the quantum channel on the second state. As a special case, we reproduce recent lower bounds on the conditional mutual information, such as the one proved by Fawzi and Renner. Our proof only relies on the elementary properties of pinching maps and the operator logarithm

Tomamichel, M, Berta, M & Renes, JM 2016, 'Quantum coding with finite resources', NATURE COMMUNICATIONS, vol. 7.
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Tonelli, D, Pietroni, N, Cignoni, P & Scopigno, R 2016, 'Design and fabrication of grid-shells mockups', STAG: Smart Tools and Apps for Graphics, Eurographics Association.

Tonelli, D, Pietroni, N, Puppo, E, Froli, M, Cignoni, P, Amendola, G & Scopigno, R 2016, 'Stability of statics aware voronoi grid-shells', Engineering Structures, vol. 116, pp. 70-82.

Treadaway, C & Kenning, G 2016, 'Sensor e-Textiles: person centered co-design for people with late stage dementia', Working with Older People.

Vichitvanichphong, S, Talaei-Khoei, A, Kerr, D, Ghapanchi, AH & Scott-Parker, B 2016, 'Good old gamers, good drivers: Results from a correlational experiment among older drivers', Australasian Journal of Information Systems, vol. 20.
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In many situations, driving is essential for senior citizens to maintain their independent lifestyle. A systematic literature review was conducted that summarized the age-related physical, visual and cognitive functional declines and their associated risk to driving. Based on these findings, we explored whether the skills required in playing Xbox Kinect video games were correlated with measures of driving performance among older drivers. Fifty-two participants, 65 years of age or older (Mean = 72; SD = 3.84; range 65 - 85 years; 29 males) who have access to a car and drive frequently were invited to play Just dance, Table Tennis (ping pong), Bowling, and Dr Kawashima's Brain Training Exercises on an Xbox Kinect 360. Participants also completed a 25-minute on-road driving task along a predetermined route to assess and identify critical driving errors using a similar instrument as that used by a driving license examiner. Bivariate correlation examined the relationship between game scores and these objective driving skills. There was a significant correlation between the Xbox Kinect video games and on-road driving scores (r = 0.861, p < 0.001), indicating that 'good gamers are good drivers'. This correlation was significant for the males (r = 0.864, p < 0.001) as well as for the females (r = 0.878, p < 0.001). We suggest that performance on Xbox games may be a suitable, cost-effective and less-risky indicator of on-road driving skills for older drivers, particularly in jurisdictions in which mandatory testing of older citizens has been introduced or is being considered as a requirement in the driver licensing process.

Wang, W, Huang, M, Nguyen, Q, Huang, T, Zhang, K & Huang, T 2016, 'Enabling Decision Trend Analysis with Interactive Scatter Plot Matrices Visualization', Journal of Visual Languages & Computing, vol. 33, pp. 13-23.
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This paper presents a new interactive scatter plot visualization for multi-dimensional data analysis. We apply Rough Set Theory (RST) to reduce the visual complexity through dimensionality reduction. We use an innovative point-to-region mouse click concept to enable direct interactions with scatter points that are theoretically impossible. To show the decision trend we use a virtual Z dimension to display a set of linear flows showing approximation of the decision trend. We conducted case studies to demonstrate the effectiveness and usefulness of our new technique for analyzing the property of three popular data sets including wine quality, wages and cars. The paper also includes a pilot usability study to evaluate parallel coordinate visualization with scatter plot matrices visualization with RST results.

Wang, W, Zhang, G & Lu, J 2016, 'Member contribution-based group recommender system', Decision Support Systems, vol. 87, pp. 80-93.
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© 2016.Developing group recommender systems (GRSs) is a vital requirement in many online service systems to provide recommendations in contexts in which a group of users are involved. Unfortunately, GRSs cannot be effectively supported using traditional individual recommendation techniques because it needs new models to reach an agreement to satisfy all the members of this group, given their conflicting preferences. Our goal is to generate recommendations by taking each group member's contribution into account through weighting members according to their degrees of importance. To achieve this goal, we first propose a member contribution score (MCS) model, which employs the separable non-negative matrix factorization technique on a group rating matrix, to analyze the degree of importance of each member. A Manhattan distance-based local average rating (MLA) model is then developed to refine predictions by addressing the fat tail problem. By integrating the MCS and MLA models, a member contribution-based group recommendation (MC-GR) approach is developed. Experiments show that our MC-GR approach achieves a significant improvement in the performance of group recommendations. Lastly, using the MC-GR approach, we develop a group recommender system called GroTo that can effectively recommend activities to web-based tourist groups.

Wang, X, Zhang, Y, Zhang, W & Lin, X 2016, 'Efficient identification of local keyword patterns in microblogging platforms', IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 10, pp. 2621-2634.
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© 1989-2012 IEEE. Microblogging platforms, such as Twitter, serve as an important and efficient channel for sharing information. With the prevalence of geo-position enabled devices, a rapidly growing amount of microblogs are associated with geo-tags. Consequently, real-time analysis of the geo-tagged microblog stream has attracted great attentions. In this paper, we advocate the significance of keyword co-occurrence for geo-tagged microblogs analysis, which has been overlooked by existing studies. The co-occurrence of keywords is necessary to resolve the ambiguity in event analysis, especially when different events have overlapping descriptions. Given a geo-tagged microblog stream, we formally define the problem of identifying local (top-K ) maximal frequent keyword co-occurrence patterns over geo-tagged microblog stream, namely LFP (LKFP) query. Given a query region, LFP query aims to retrieve the local maximal keyword patterns with frequency exceeding a given threshold; while LKFP query aims to identify K maximal keyword patterns with highest local frequency, in case users do not have a threshold in mind. To handle the high volume microblog stream and meet the requirement when a large number of queries are issued, we develop novel data structures to maintain the data stream, and propose efficient algorithms to process LFP and LKFP queries with theoretical underpinnings. The extensive empirical study on real dataset confirms the effectiveness and efficiency of our approaches.

Wu, J, Wang, J, Qin, S & Lu, H 2016, 'Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm', International Journal of Green Energy, vol. 13, no. 11, pp. 1145-1162.
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© 2016 Taylor & Francis Group, LLC. In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.

Xiao, L, Shao, W, Wang, C, Zhang, K & Lu, H 2016, 'Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting', APPLIED ENERGY, vol. 180, pp. 213-233.
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Xu, G, Fu, B & Gu, Y 2016, 'Point-of-Interest Recommendations via a Supervised Random Walk Algorithm', IEEE Intelligent Systems, vol. 31, no. 1, pp. 15-23.
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Recently, location-based social networks (LBSNs) such as Foursquare and Whrrl have emerged as a new application for users to establish personal social networks and review various points of interest (POIs), triggering a new recommendation service aimed at helping users locate more preferred POIs. Although users' check-in activities could be explicitly considered as user ratings, in turn being utilized directly for collaborative filtering-based recommendations, such solutions don't differentiate the sentiment of reviews accompanying check-ins, resulting in unsatisfactory recommendations. This article proposes a new POI recommendation framework by simultaneously incorporating user check-ins and reviews along with side information into a tripartite graph and predicting personalized POI recommendations via a sentiment-supervised random walk algorithm. The experiments conducted on real data demonstrate the superiority of this approach in comparison with state-of-the-art techniques.

Xuan, J, Luo, X, Zhang, G, Lu, J & Xu, Z 2016, 'Uncertainty Analysis for the Keyword System of Web Events', IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, vol. 46, no. 6, pp. 829-842.
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Yaish, H, Goyal, M & Feuerlicht, G 2016, 'Elastic Extension Tables for Multi-tenant Cloud Apllications', International Journal of Computer Science and Information Security, vol. Vol. 14 No. 6, June 2016.
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Yi, X, Paulet, R, Bertino, E & Xu, G 2016, 'Private Cell Retrieval from Data Warehouses', IEEE Transactions on Information Forensics and Security, vol. 11, no. 6, pp. 1346-1361.
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© 2015 IEEE. Publicly accessible data warehouses are an indispensable resource for data analysis. However, 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 and, therefore, protects the privacy of the client's queries. However, PIR cannot be used to hide online analytical processing (OLAP) operations performed by the client, which may disclose the client's interest. This paper presents a solution for private cell retrieval from a data warehouse on the basis of the Paillier cryptosystem. By our solution, the client can privately perform OLAP operations on the data warehouse and retrieve one (or more) cell without revealing any information about which cell is selected. In addition, we propose a solution for private block download on the basis of the Paillier cryptosystem. Our private block download allows the client to download an encrypted block from a data warehouse without revealing which block in a cloaking region is downloaded and improves the feasibility of our private cell retrieval. Our solutions ensure both the server's privacy and the client's privacy. Our experiments have shown that our solutions are practical.

Yu, S, Zhou, W, Guo, S & Guo, M 2016, 'A Feasible IP Traceback Framework through Dynamic Deterministic Packet Marking', IEEE Transactions on Computers, vol. 65, no. 5, pp. 1418-1427.
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© 2015 IEEE. DDoS attack source traceback is an open and challenging problem. Deterministic packet marking (DPM) is a simple and effective traceback mechanism, but the current DPM based traceback schemes are not practical due to their scalability constraint. We noticed a factor that only a limited number of computers and routers are involved in an attack session. Therefore, we only need to mark these involved nodes for traceback purpose, rather than marking every node of the Internet as the existing schemes doing. Based on this finding, we propose a novel marking on demand (MOD) traceback scheme based on the DPM mechanism. In order to traceback to involved attack source, what we need to do is to mark these involved ingress routers using the traditional DPM strategy. Similar to existing schemes, we require participated routers to install a traffic monitor. When a monitor notices a surge of suspicious network flows, it will request a unique mark from a globally shared MOD server, and mark the suspicious flows with the unique marks. At the same time, the MOD server records the information of the marks and their related requesting IP addresses. Once a DDoS attack is confirmed, the victim can obtain the attack sources by requesting the MOD server with the marks extracted from attack packets. Moreover, we use the marking space in a round-robin style, which essentially addresses the scalability problem of the existing DPM based traceback schemes. We establish a mathematical model for the proposed traceback scheme, and thoroughly analyze the system. Theoretical analysis and extensive real-world data experiments demonstrate that the proposed traceback method is feasible and effective.

Yu, Y-H, Chen, S-H, Chang, C-L, Lin, C-T, Hairston, WD & Mrozek, RA 2016, 'New Flexible Silicone-Based EEG Dry Sensor Material Compositions Exhibiting Improvements in Lifespan, Conductivity, and Reliability', SENSORS, vol. 16, no. 11.
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Yu, Y-H, Lu, S-W, Chuang, C-H, King, J-T, Chang, C-L, Chen, S-A, Chen, S-F & Lin, C-T 2016, 'An Inflatable and Wearable Wireless System for Making 32-Channel Electroencephalogram Measurements', IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, vol. 24, no. 7, pp. 806-813.
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Zander, TO, Krol, LR, Birbaumer, NP & Gramann, K 2016, 'Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity', PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, vol. 113, no. 52, pp. 14898-14903.
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Zhang, C, Zhang, Y, Zhang, W & Lin, X 2016, 'Inverted Linear Quadtree: Efficient Top K Spatial Keyword Search', IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol. 28, no. 7, pp. 1706-1721.
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Zhang, G, Han, J & Lu, J 2016, 'Fuzzy Bi-level Decision-Making Techniques: A Survey', INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 9, pp. 25-34.
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Zhang, J & Huang, M 2016, 'Data Behaviours Model for Big Data Visual Analytics', International Journal of Big Data Intelligence, vol. 3, no. 1, pp. 1-17.
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Big Data is composed of text, image, video, audio, mobile or other forms of data collected from multiple datasets, and is rapidly growing in size and complexity. It has created a huge volume of multidimensional data within a very short time period. This raises several new challenges, including; how to classify Big Data for multiple datasets, how to analyze Big Data for different forms of data, and how to visualize Big Data without the loss of information. In this paper, we extended our 5Ws density methods to Big Data behaviours analysis and visualization. Our approach classifies Big Data into the 5Ws dimensions based on the data behaviours, and then further creates the 5Ws densities to measure Big Data patterns across multiple datasets for any form of data. We also establish non-dimensional data axes as additional parallel axes for Big Data visualization. The experimental results have shown that the proposed new model has significantly improved the accuracy of Big Data visualization, and has large potential benefits and applications.

Zhang, J & Huang, ML 2016, 'Density approach: A new model for BigData analysis and visualization', Concurrency and Computation: Practice and Experience, vol. 28, no. 3, pp. 661-673.
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Copyright © 2014 John Wiley & Sons, Ltd. In this paper, we extended our density model to BigData analysis and visualization. BigData, which contains images, videos, texts, audio files and other forms of data collected from multiple datasets, is difficult to process and visualize using traditional database management and visualization tools. The challenges are in representing multiple datasets and illustrating and visualizing data patterns to meet business, government and organization needs. We have established the 5Ws density model which uses the 5Ws dimensions for BigData analysis and visualization. The 5Ws dimensions are what the data contain, why the data were transferred, where the data came from, when the data occurred, who received the data and how the data were transferred. According to the network log dataset, an example of BigData, each data incident can be classified into these 5Ws dimensions. The network log dataset ISCX2012 is tested throughout our model. This new model not only classifies network attributes and patterns but also establishes density patterns that provide more analytical features for BigData analysis and visualization. The experimental result shows that this new model with clustered visualization can be efficiently used for BigData analysis and network intrusion detection. Concurrency and Computation: Practice and Experience, 2014.

Zhang, J, Yang, S, Li, Z, Yang, W, Wang, J, He, R & Huang, M 2016, 'Visualization Guided Document Reading by Citation and Text Summarization', Ruan Jian Xue Bao/Journal of Software, vol. 27, no. 5, pp. 1163-1173.
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With growing volume of publications in recent years, researchers have to read much more literatures. Therefore, how to read a scientific article in an efficient way becomes an importance issue. When reading an article, it's necessary to read its references in order to get a better understanding. However, how to differentiate between the relevant and non-relevant references, and how to stay in topic in a large document collection are still challenging tasks. This paper presents GUDOR (GUidedDOcument Reader), a visualization guided reader based on citation and summarization. It (1) extracts the important sentences from a scientific article with an objective-based summarization technique, and visualizes the extraction results by a multi-resolution method; (2) identifies the main topics of the references with a LDA (Latent Dirichlet Allocation) model; (3) tracks user's reading behavior to keep him or her focusing on the reading objective. In addition, the paper describes the functions and operations of the system in a usage scenario and validates its applicability by a user study.

Zhang, W, Lin, X, Zhang, Y, Zhu, K & Zhu, G 2016, 'Efficient Probabilistic Supergraph Search', IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 4, pp. 965-978.
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© 2015 IEEE. Given a query graph q, retrieving the data graphs g from a set D of data graphs such that q contains g, namely supergraph containment search, is fundamental in graph data analysis with a wide range of real applications. It is very challenging due to the NP-Completeness of subgraph isomorphism testing. Driven by many real applications, in this paper, we study the problem of probabilistic supergraph search; that is, given a set D of uncertain data graphs, a certain query graph q and a probability threshold θ, we retrieve the data graphs g u from D such that the probability of q containing g u is not smaller than θ. We show that besides the NP-Completeness of subgraph isomorphism testing, the problem of calculating probabilities is #P-Complete; thus, it is even more challenging than the supergraph containment search. To tackle the computational hardness, we first propose two novel pruning rules, based on probabilistic connectivity and features, respectively, to efficiently prune non-promising data graphs. Then, efficient verification algorithms are developed with the aim of sharing computation and terminating non-promising computation as early as possible. Extensive performance studies on both real and synthetic data demonstrate the efficiency and effectiveness of our techniques in practice.

Zhang, Y, Robinson, DKR, Porter, AL, Zhu, D, Zhang, G & Lu, J 2016, 'Technology roadmapping for competitive technical intelligence', Technological Forecasting and Social Change, vol. 110, no. 2016, pp. 175-186.
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© 2015 Elsevier Inc. Understanding the evolution and emergence of technology domains remains a challenge, particularly so for potentially breakthrough technologies. Though it is well recognized that emergence of new fields is complex and uncertain, to make decisions amidst such uncertainty, one needs to mobilize various sources of intelligence to identify known-knowns and known-unknowns to be able to choose appropriate strategies and policies. This competitive technical intelligence cannot rely on simple trend analyses because breakthrough technologies have little past to inform such trends, and positing the directions of evolution is challenging. Neither do qualitative tools, embracing the complexities, provide all the solutions, since transparent and repeatable techniques need to be employed to create best practices and evaluate the intelligence that comes from such exercises. In this paper, we present a hybrid roadmapping technique that draws on a number of approaches and integrates them into a multi-level approach (individual activities, industry evolutions and broader global changes) that can be applied to breakthrough technologies. We describe this approach in deeper detail through a case study on dye-sensitized solar cells. Our contribution to this special issue is to showcase the technique as part of a family of approaches that are emerging around the world to inform strategy and policy.

Zhang, Y, Shang, L, Huang, L, Porter, A, Zhu, D, Zhang, G & Lu, J 2016, 'A hybrid similarity measure method for patent portfolio analysis', Journal of Informetrics, vol. 10, no. 4, pp. 1108-1130.
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Similarity measures are fundamental tools for identifying relationships within or across patent portfolios. Many bibliometric indicators are used to determine similarity measures; for example, bibliographic coupling, citation and co-citation, and co-word distribution. This paper aims to construct a hybrid similarity measure method based on multiple indicators to analyze patent portfolios. Two models are proposed: categorical similarity and semantic similarity. The categorical similarity model emphasizes international patent classifications (IPCs), while the semantic similarity model emphasizes textual elements. We introduce fuzzy set routines to translate the rough technical (sub-) categories of IPCs into defined numeric values, and we calculate the categorical similarities between patent portfolios using membership grade vectors. In parallel, we identify and highlight core terms in a 3-level tree structure and compute the semantic similarities by comparing the tree-based structures. A weighting model is designed to consider: 1) the bias that exists between the categorical and semantic similarities, and 2) the weighting or integrating strategy for a hybrid method. A case study to measure the technological similarities between selected firms in China’s medical device industry is used to demonstrate the reliability our method, and the results indicate the practical meaning of our method in a broad range of informetric applications.

Zhang, Y, Wu, J, Cai, Z, Zhang, P & Chen, L 2016, 'Memetic Extreme Learning Machine', Pattern Recognition, vol. 58, pp. 135-148.
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© 2016. Extreme Learning Machine (ELM) is a promising model for training single-hidden layer feedforward networks (SLFNs) and has been widely used for classification. However, ELM faces the challenge of arbitrarily selected parameters, e.g., the network weights and hidden biases. Therefore, many efforts have been made to enhance the performance of ELM, such as using evolutionary algorithms to explore promising areas of the solution space. Although evolutionary algorithms can explore promising areas of the solution space, they are not able to locate global optimum efficiently. In this paper, we present a new Memetic Algorithm (MA)-based Extreme Learning Machine (M-ELM for short). M-ELM embeds the local search strategy into the global optimization framework to obtain optimal network parameters. Experiments and comparisons on 46 UCI data sets validate the performance of M-ELM. The corresponding results demonstrate that M-ELM significantly outperforms state-of-the-art ELM algorithms.

Zhang, Y, Zhang, G, Chen, H, Porter, AL, Zhu, D & Lu, J 2016, 'Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research', Technological Forecasting and Social Change, vol. 105, pp. 179-191.
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The number and extent of current Science, Technology & Innovation topics are changing all the time, and their induced accumulative innovation, or even disruptive revolution, will heavily influence the whole of society in the near future. By addressing and predicting these changes, this paper proposes an analytic method to (1) cluster associated terms and phrases to constitute meaningful technological topics and their interactions, and (2) identify changing topical emphases. Our results are carried forward to present mechanisms that forecast prospective developments using Technology Roadmapping, combining qualitative and quantitative methodologies. An empirical case study of Awards data from the United States National Science Foundation, Division of Computer and Communication Foundation, is performed to demonstrate the proposed method. The resulting knowledge may hold interest for R&D management and science policy in practice.

Zhang, Z, Liu, Y, Xu, G & Chen, H 2016, 'A weighted adaptation method on learning user preference profile', Knowledge-Based Systems, vol. 112, pp. 114-126.
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© 2016 Elsevier B.V. Recommender systems typically store personal preference profiles. Many items in the profiles can be represented by numerical attributes. However, the initial profile of each user is incomplete and imprecise. One important problem in the development of these systems is how to learn user preferences, and how to automatically adapted update the profiles. To address this issue, this paper presents an unsupervised approach for learning user preferences over numeric attributes by analyzing the interactions between users and recommender systems. When a list of recommendations shown to a target user, the favorite item will be selected by him/her, then the selected item and the over-ranked items will be employed as valuable feedback to learn the user profile. Specifically, two contributions are offered: 1), a learning approach to measure the influence of over-ranked items through analysis of user feedbacks and 2), a weighting algorithm to calculate weights of different attributes by analyzing user selections. These two approaches are integrated into a traditional adaption model for updating user preference profile. Extensive simulations and results show that both approaches are more effective than existing approaches.

Zhang, Z, Liu, Y, Xu, G & Luo, G 2016, 'Recommendation using DMF-based fine tuning method', Journal of Intelligent Information Systems, vol. 47, pp. 233-246.
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© 2016 Springer Science+Business Media New York Recommender Systems (RS) have been comprehensively analyzed in the past decade, Matrix Factorization (MF)-based Collaborative Filtering (CF) method has been proved to be an useful model to improve the performance of recommendation. Factors that inferred from item rating patterns shows the vectors which are useful for MF to characterize both items and users. A recommendation can concluded from good correspondence between item and user factors. A basic MF model starts with an object function, which is consisted of the squared error between original training matrix and predicted matrix as well as the regularization term (regularization parameters). To learn the predicted matrix, recommender systems minimize the squared error which has been regularized. However, two important details have been ignored: (1) the predicted matrix will be more and more accuracy as the iterations carried out, then a fix value of regularization parameters may not be the most suitable choice. (2) the final distribution trend of ratings of predicted matrix is not similar with the original training matrix. Therefore, we propose a Dynamic-MF algorithm and fine tuning method which is quite general to overcome the mentioned detail problems. Some other information, such as social relations, etc, can be easily incorporated into this method (model). The experimental analysis on two large datasets demonstrates that our approaches outperform the basic MF-based method.

Zhang, Z, Oberst, S & Lai, JCS 2016, 'Instability analysis of friction oscillators with uncertainty in the friction law distribution', PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, vol. 230, no. 6, pp. 948-958.
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Zhang, Z, Oberst, S & Lai, JCS 2016, 'On the potential of uncertainty analysis for prediction of brake squeal propensity', JOURNAL OF SOUND AND VIBRATION, vol. 377, pp. 123-132.
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Zheng, Y, Zhang, G, Han, J & Lu, J 2016, 'Pessimistic bilevel optimization model for risk-averse production-distribution planning', Information Sciences, vol. 372, pp. 677-689.
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© 2016Production-distribution (PD) planning problems are often addressed in an organizational hierarchy in which a distribution company that utilizes several depots is the leader and the manufacturing companies are the followers. The classical objective function of the leader is to minimize the total operating cost of the distribution company, and the followers optimize their respective production cost. However, the distribution company (the leader) frequently cannot obtain complete production information from the manufacturing companies, and may thus become risk-averse. In this case, a better description of the leader's objective function is the minimization of the maximum possible operating cost (Min-Max). In this paper, this type of PD problem is called a risk-averse PD planning problem and is formulated as a pessimistic mixed-integer bilevel optimization (PMIBO) model from the worst-case point of view. To solve the risk-averse PD planning problem, not yet well solved in literature, a penalty function-based method is presented which transforms the PMIBO model into a series of single-level optimization problems so that the latter ones can be solved by available optimization software. Finally, the feasibility of the proposed model is demonstrated using a set of case-based examples of PD planning.

Zhu, T, Li, G, Zhou, W, Xiong, P & Yuan, C 2016, 'Privacy-preserving topic model for tagging recommender systems', Knowledge and Information Systems, vol. 46, no. 1, pp. 33-58.
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© 2015, Springer-Verlag London. Tagging recommender systems provide users the freedom to explore tags and obtain recommendations. The releasing and sharing of these tagging datasets will accelerate both commercial and research work on recommender systems. However, releasing the original tagging datasets is usually confronted with serious privacy concerns, because adversaries may re-identify a user and her/his sensitive information from tagging datasets with only a little background information. Recently, several privacy techniques have been proposed to address the problem, but most of these lack a strict privacy notion, and rarely prevent individuals being re-identified from the dataset. This paper proposes a privacy- preserving tag release algorithm, PriTop. This algorithm is designed to satisfy differential privacy, a strict privacy notion with the goal of protecting users in a tagging dataset. The proposed PriTop algorithm includes three privacy-preserving operations: Private topic model generation structures the uncontrolled tags; private weight perturbation adds Laplace noise into the weights to hide the numbers of tags; while private tag selection finally finds the most suitable replacement tags for the original tags, so the exact tags can be hidden. We present extensive experimental results on four real-world datasets, Delicious, MovieLens, Last.fm and BibSonomy. While the recommendation algorithm is successful in all the cases, our results further suggest the proposed PriTop algorithm can successfully retain the utility of the datasets while preserving privacy.

Abdullaev, S, McBurney, P & Musial, K 2016, 'Pricing Options with Portfolio-Holding Trading Agents in Direct Double Auction', ECAI 2016: 22ND EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, 22nd European Conference on Artificial Intelligence (ECAI), IOS PRESS, Hague, NETHERLANDS, pp. 1754-1755.
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Abidi, S, Piccardi, M & Williams, M 2016, 'Static Action Recognition by Efficient Greedy Inference', Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision, IEEE Winter Conference on Applications of Computer Vision, IEEE, Lake Placid, NY, USA, pp. 1-8.
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Action recognition from a single image is an important task for applications such as image annotation, robotic navigation, video surveillance and several others. Existing methods for recognizing actions from still images mainly rely on either bag-of-feature representations or pose estimation from articulated body-part models. However, the relationship between the action and the containing image is still substantially unexplored. Actually, the presence of given objects or specific backgrounds is likely to provide informative clues for the recognition of the action. For this reason, in this paper we propose approaching action recognition by first partitioning the entire image into superpixels, and then using their latent classes as attributes of the action. The action class is predicted based on a graphical model composed of measurements from each superpixel and a fully-connected graph of superpixel classes. The model is learned using a latent structural SVM approach, and an efficient, greedy algorithm is proposed to provide inference over the graph. Differently from most existing methods, the proposed approach does not require annotation of the actor (usually provided as a bounding box). Experimental results over the challenging Stanford 40 Action dataset have reported an impressive mean average precision of 72.3%, the highest achieved to date.

Adak, C, Chaudhuri, BB & Blumenstein, M 2016, 'Named Entity Recognition from Unstructured Handwritten Document Images', Proceedings - 12th IAPR International Workshop on Document Analysis Systems, DAS 2016, International Workshop on Document Analysis Systems, IEEE, Santorini, Greece, pp. 375-380.
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© 2016 IEEE.Named entity recognition is an important topic in the field of natural language processing, whereas in document image processing, such recognition is quite challenging without employing any linguistic knowledge. In this paper we propose an approach to detect named entities (NEs) directly from offline handwritten unstructured document images without explicit character/word recognition, and with very little aid from natural language and script rules. At the preprocessing stage, the document image is binarized, and then the text is segmented into words. The slant/skew/baseline corrections of the words are also performed. After preprocessing, the words are sent for NE recognition. We analyze the structural and positional characteristics of NEs and extract some relevant features from the word image. Then the BLSTM neural network is used for NE recognition. Our system also contains a post-processing stage to reduce the true NE rejection rate. The proposed approach produces encouraging results on both historical and modern document images, including those from an Australian archive, which are reported here for the very first time.

Adak, C, Chaudhuri, BB & Blumenstein, M 2016, 'Offline cursive Bengali word recognition using CNNs with a recurrent model', Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR, International Conference on Frontiers in Handwriting Recognition, IEEE, Shenzhen, China, pp. 429-434.
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© 2016 IEEE. This paper deals with offline handwritten word recognition of a major Indic script: Bengali. Due to the structure of this script, the characters (mostly ortho-syllables) are frequently overlapping and hard to segment, especially when the writing is cursive. Individual character recognition and the combination of outputs can increase the likelihood of errors. Instead, a better approach can be sending the whole word to a suitable recognizer. Here we use the Convolutional Neural Network (CNN) integrated with a recurrent model for this purpose. Long short-term memory blocks are used as hidden units. Also, the CNN-derived features are employed in a recurrent model with a CTC (Connectionist Temporal Classification) layer to get the output. We have tested our method on three datasets: (a) a publicly available dataset, (b) a new dataset generated by our research group and (c) an unconstrained dataset. The dataset (a) contains 17,091 words, while our dataset (b) contains 107,550 number of words in total. In addition to these, the dataset (c) is comprised of 5,223 words. We have compared our results with those of some earlier work in the area and have found improved performance, which is due to the novel integration of CNNs with the recurrent model.

Adak, C, Chaudhuri, BB & Blumenstein, M 2016, 'Writer identification by training on one script but testing on another', Proceedings - International Conference on Pattern Recognition, International Conference on Pattern Recognition, Mexico, pp. 1153-1158.
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© 2016 IEEE. This paper deals with identifying a writer from his/her offline handwriting. In a multilingual country where a writer can scribe in multiple scripts, writer identification becomes challenging when we have individual handwriting data in one script while we need to verify/identify a writer from handwriting in another script. In this paper such an issue is addressed with two scripts: English and Bengali. Here we model the task as a classification problem, where training data contains only Bengali handwritten samples and testing is performed on English handwritten texts. This work is based on the understanding that a writer has some inherent stroke characteristics that are independent of the script in which (s)he writes. In this work, some implicit structural and statistical features are extracted, and multiple classifiers are employed for writer identification. Many training sessions are run on a database of 100 writers and the performances are analyzed. We have obtained encouraging results on this database, which show the effectiveness of our method.

Ahadi, A 2016, 'Early Identification of Novice Programmers' Challenges in Coding Using Machine Learning Techniques', Proceedings of the 2016 ACM Conference on International Computing Education Research, International Computing Education Research, ACM, Melbourne, Australia., pp. 263-264.
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It is well known that many first year undergraduate university students struggle with learning to program. Educational Data Mining (EDM) applies machine learning and statistics to information generated from educational settings. In this PhD project, EDM is used to study first semester novice programmers, using data collected from students as they work on computers to complete their normal weekly laboratory exercises. Analysis of the generated snapshots has shown the potential for early identification of students who later struggle in the course. The aim of this study is to propose a method for early identification of "at risk" students while providing suggestions on how they can improve their coding style. This PhD project is within its final year.

Ahadi, A, Behbood, V, Lister, R, Prior, J & Vihavainen, A 2016, 'Students’ Syntactic Mistakes in Writing Seven Different Types of SQL Queries and its Application to Predicting Students’ Success', Proceedings of the 47th ACM Technical Symposium on Computing Science Education, Special Interest Group in COmputer Science Education, ACM, Memphis, Tennessee, pp. 401-406.
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The computing education community has studied extensively the errors of novice programmers. In contrast, little attention has been given to student's mistake in writing SQL statements. This paper represents the first large scale quantitative analysis of the student's syntactic mistakes in writing different types of SQL queries. Over 160 thousand snapshots of SQL queries were collected from over 2000 students across eight years. We describe the most common types of syntactic errors that students make. We also describe our development of an automatic classifier with an overall accuracy of 0.78 for predicting student performance in writing SQL queries.

Ahadi, A, behbood, V, prior, J & Lister, R 2016, 'Students’ Semantic Mistakes in Writing Seven Different Types of SQL Queries', ITiCSE’16: Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education, Annual Conference on Innovation and Technology in Computer Science Education, ACM, Peru.
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Ahadi, A, Hellas, A, Ihantola, P, Korhonen, A & Petersen, A 2016, 'Replication in Computing Education Research: Researcher Attitudes and Experiences', Koli International Conference on Computing Education Research, Koli International Conference on Computing Education Research, University of Eastern Finland, Koli, FInland.
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Reproducibility is a core principle of the scientific method. However, several scientific disciplines have suffered crises in confidence caused, in large part, by attitudes toward replication. This work reports on the value the computing education research community associates with studies that aim to replicate, reproduce or repeat earlier research. The results were obtained from a large-scale (n=73) survey of computing education researchers. An analysis of the responses confirms that researchers in our field hold many of the same biases as those in other fields experiencing a crisis in replication. In particular, researchers agree that original works -- novel works that report new phenomena -- have more impact and are more prestigious. They also agree that originality is an important criteria for accepting a paper, making such work more likely to be published. Furthermore, while the respondents agree that verifiability is a desirable property of published work, they doubt this standard is widely met in the computing education field and, in addition, are not eager to do the work of verifying others' work themselves.

Ahadi, A, Lister, RF & Vihavainen, A 2016, 'On the Number of Attempts Students Made on Some Online Programming Exercises During Semester and their Subsequent Performance on Final Exam Questions', Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE, Annual Conference on Innovation and Technology in Computer Science Education, Association for Computing Machinery, Arequipa, Peru, pp. 218-223.
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This paper explores the relationship between student performance on online programming exercises completed during semester with subsequent student performance on a final exam. We introduce an approach that combines whether or not a student produced a correct solution to an online exercise with information on the number of attempts at the exercise submitted by the student. We use data collected from students in an introductory Java course to assess the value of this approach. We compare the approach that utilizes the number of attempts to an approach that simply considers whether or not a student produced a correct solution to each exercise. We found that the results for the method that utilizes the number of attempts correlates better with performance on a final exam.

Alkalbani, AM & Hussain, FK 2016, 'A comparative study and future research directions in cloud service discovery', Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016, IEEE Conference on Industrial Electronics and Applications, IEEE, Dearborn, MI, United States, pp. 1049-1056.
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© 2016 IEEE.Cloud computing technology is a new paradigm which provides Information Technology (IT) resources via the Internet. This new shift in the way that IT re-sources are offered to the user brings new challenges, such as cloud service discovery. Nowadays, cloud users are faced with a dilemma as they have an abundant choice of cloud services. Moreover, many cloud providers offer a range of services which deliver similar functionality. Locating the best and most appropriate cloud service with a suitable and capable provider is a primary concern for any consumer. In order to clearly comprehend the scope of this problem, a thorough analysis of the limitations of cloud service discovery approaches is required which, in turn, will empower researchers to deliver better solutions for consumers to make an informed decision and choose the right service. This paper presents an overview of the current cloud service discovery trends and challenges in recent studies. Additionally, the reviewed approaches are classified according to service discovery architecture and techniques. Furthermore, these approaches are compared and analysed from several perspectives including approach model/architecture, service type, ontology representation (domain, language, and reasoning), dynamic discovery model, evaluation model, user's preferences techniques, data updates, and public repositories.

Alkalbani, AM, Ghamry, AM, Hussain, FK & Hussain, OK 2016, 'Harvesting Multiple Resources for Software as a Service Offers: A Big Data Study', Neural Information Processing (LNCS), International Conference on Neural Information Processing, Springer, Kyoto, Japan, pp. 61-71.
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Currently, the World Wide Web (WWW) is the primary resource for cloud services information, including offers and providers. Cloud applications (Software as a Service), such as Google App, are one of the most popular and commonly used types of cloud services. Having access to a large amount of information on SaaS offers is critical for the potential cloud client to select and purchase an appropriate service. Web harvesting has become a primary tool for discovering knowledge from the Web source. This paper describes the design and development of Web scraper to collect information on SaaS offers from target Digital cloud services advertisement portals, namely www.getApp.com, and www.cloudreviews.com. The collected data were used to establish two datasets: a SaaS provider’s dataset and a SaaS reviews/feedback dataset. Further, we applied sentiment analysis on the reviews dataset to establish a third dataset called the SaaS sentiment polarity dataset. The significance of this study is that the first work focuses on Web harvesting for cloud computing domain, and it also establishes the first SaaS services datasets. Furthermore, we present statistical data that can be helpful to determine the current status of SaaS services and the number of services offered on the Web. In our conclusion, we provide further insight into improving Web scraping for SaaS service information. Our datasets are available online through www.bluepagesdataset.com

Alkalbani, AM, Ghamry, AM, Hussain, FK & Hussain, OK 2016, 'Predicting the sentiment of SaaS online reviews using supervised machine learning techniques', Proceedings of the International Joint Conference on Neural Networks 2016, IEEE International Joint Conference on Neural Networks, IEEE, Vancouver, CANADA, pp. 1547-1553.
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© 2016 IEEE.There has been a dramatic increase in the sharing of opinions and information across different web platforms and social media, especially online product reviews. Cloud web portals, such as getApp.com, were designed to amalgamate cloud service information and to also examine how consumers evaluate their experience of using cloud computing products. The current literature shows the growing importance of online users' reviews, hence this study focuses on investigating consumers' feedback on Software-as-a-Service (SaaS) products by developing models to predict reviewers' attitudes. The goal of this paper is to develop prediction models to predict the sentiment of SaaS consumers' reviews (positive or negative). This research proposes five models that are based on five algorithms, the Support Vector Machine algorithm, Naive Bayes algorithm, Naive Bayes (Kernel) algorithm, k-nearest neighbors algorithm, and the decision tree algorithm to predict the attitude of SaaS reviews. The prediction accuracy of the space vector algorithm (5-fold cross-validation) is 92.37% which suggests that this algorithm is able to better determine the sentiment of online reviews compared with the other models. The results of this study provide valuable insight into online SaaS reviews and will assist in the design of SaaS review websites.

Alkalbani, AM, Ghamry, AM, Hussain, FK & Hussain, OK 2016, 'Sentiment analysis and classification for software as a service reviews', Proceedings - International Conference on Advanced Information Networking and Applications, AINA, International Conference on Advanced Information Networking and Applications (was ICOIN), IEEE, Crans-Montana, Switzerland, pp. 53-58.
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© 2016 IEEE.With the rapid growth of cloud services, there has been a significant increase in the number of online consumer reviews and opinions on these services on different social media platforms. These reviews are a source of valuable information in regard to cloud market position and cloud consumer satisfaction. This study explores cloud consumers' reviews that reflect the user's experience with Software as a Service (SaaS) applications. The reviews were collected from different web portals, and around 4000 online reviews were analysed using sentiment analysis to identify the polarity of each review, that is, whether the sentiment being expressed is positive, negative, or neutral. Also, this research develops a model for predicting the sentiment of Software as a Service consumers' reviews using a supervised learning machine called a support vector machine (SVM). The sentiment results show that 62% of the reviews are positive which indicates that consumers are most likely satisfied with SaaS services. The results show that the prediction accuracy of the SVM-based Binary Occurrence approach (3-fold crossvalidation testing) is 92.30%, indicating it performs better in determining sentiment compared with other approaches (Term Occurrences, TFIDF). This work also provides valuable insight into online SaaS reviews and offers the research community the first SaaS polarity dataset.

Alzoubi, Y & Gill, A 2015, 'An Agile Enterprise Architecture-Driven Model for Geographically Distributed Agile Development', Transforming Healthcare Through Information Systems: Proceedings of the 24th International Conference on Information Systems Development, International Conference on Information Systems Development, Springer International Publishing, Harbin, China, pp. 67-77.
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Agile development is a highly collaborative environment, which requires active communication (i.e. effective and efficient communication) among stakeholders. The active communication in geographically distributed agile development (GDAD) environment is difficult to achieve due to many challenges. Literature has reported that active communication play critical role in enhancing GDAD performance through reducing the cost and time of a project. However, little empirical evidence is known about how to study and establish active communication construct in GDAD in terms of its dimensions, determinants and effects on GDAD performance. To address this knowledge gap, this paper describes an enterprise architecture (EA) driven research model to identify and empirically examine the GDAD active communication construct. This model can be used by researchers and practitioners to examine the relationships among two dimensions of GDAD active communication (effectiveness and efficiency), one antecedent that can be controlled (agile EA), and four dimensions of GDAD performance (on-time completion, on-budget completion, software functionality and software quality).

Bajwa, MA & Gill, AQ 2016, 'Enterprise IT Governance: Back to Basics', 25th International Conference on Information Systems Development, International Conference on Information Systems Development, AIS eLibrary, Katowice, Poland..

Bashir, MR & Gill, AQ 2016, 'Towards an IoT Big Data Analytics Framework: Smart Buildings Systems', http://ieeexplore.ieee.org/document/7828529/, IEEE International Conference on High Performance Computing and Communications, IEEE, Sydney, Australia, pp. 1325-1332.
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Berta, M, Fawzi, O & Tomamichel, M 2016, 'Exploiting variational formulas for quantum relative entropy', Proceedings IEEE International Symposium on Information Theory (ISIT 2016), IEEE International Symposium on Information Theory, IEEE, Barcelona, Spain, pp. 2844-2848.
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The relative entropy is the basic concept underlying various information measures like entropy, conditional entropy and mutual information. Here, we discuss how to make use of variational formulas for measured relative entropy and quantum relative entropy for understanding the additivity properties of various entropic quantities that appear in quantum information theory. In particular, we show that certain lower bounds on quantum conditional mutual information are superadditive.

Blezinger, D & Van Den Hoven, E 2016, 'Storytelling with objects to explore digital archives', Proceedings of the ACM International Conference Proceeding Series, European Conference on Cognitive Ergonomics, ACM Press, Nottingham, United Kingdom.
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© 2016 ACM.Finding media in archives is difficult while storytelling with photos can be fun and supports memory retrieval. Could the search for media become a natural part of the storytelling experience? This study investigates spatial interactions with objects as a means to encode information for retrieval while being embedded in the story flow. An experiment is carried out in which participants watch a short video and re-tell the story using cards each of which shows a character or object occurring in the video. Participants arrange the cards when telling the story. It is analyzed what information interactions with cards carry and how this information relates to the language of storytelling. Most participants align interactions with objects with the sentences of the story while some arrange the cards corresponding to the video scene. Spatial interactions with objects can carry information on their own or complemented by language.

Bown, O & Ferguson, S 2016, 'A musical game of bowls featuring the DIADs', http://www.nime.org/proceedings/2016, New Interfaces for Musical Expression, Griffith University, Brisbane, Australia, pp. 371-372.
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We describe a project in which a game of lawn bowls was recreated using Distributed Interactive Audio Devices (DIADs), to create an interactive musical experience in the form of a game. This paper details the design of the underlying digital music system, some of the compositional and design considerations, and the technical challenges involved. We discuss future directions for our system and compositional method.

Braytee, A, Catchpoole, DR, Kennedy, PJ & Liu, W 2016, 'Balanced Supervised Non-Negative Matrix Factorization for Childhood Leukaemia Patients', CIKM '16 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, ACM International Conference on Information and Knowledge Management, ACM, Indianapolis, Indiana, USA.
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Supervised feature extraction methods have received considerable attention in the data mining community due to their capability to improve the classification performance of the unsupervised dimensionality reduction methods. With increasing dimensionality, several methods based on supervised feature extraction are proposed to achieve a feature ranking especially on microarray gene expression data. This paper proposes a method with twofold objectives: it implements a balanced supervised non-negative matrix factorization (BSNMF) to handle the class imbalance problem in supervised non-negative matrix factorization techniques. Furthermore, it proposes an accurate gene ranking method based on our proposed BSNMF for microarray gene expression datasets. To the best of our knowledge, this is the first work to handle the class imbalance problem in supervised feature extraction methods. This work is part of a Human Genome project at The Children's Hospital at Westmead (TB-CHW), Australia. Our experiments indicate that the factorized components using supervised feature extraction approach have more classification capability than the unsupervised one, but it drastically fails at the presence of class imbalance problem. Our proposed method outperforms the state-of-the-art methods and shows promise in overcoming this concern.

Braytee, A, Liu, W & Kennedy, P 2016, 'A Cost-Sensitive Learning Strategy for Feature Extraction from Imbalanced Data', Springer International Publishing, International Conference on Neural Information Processing, Springer International Publishing, Kyoto, Japan, pp. 78-86.
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In this paper, novel cost-sensitive principal component analysis (CSPCA) and cost-sensitive non-negative matrix factorization (CSNMF) methods are proposed for handling the problem of feature extraction from imbalanced data. The presence of highly imbalanced data misleads existing feature extraction techniques to produce biased features, which results in poor classification performance especially for the minor class problem. To solve this problem, we propose a cost-sensitive learning strategy for feature extraction techniques that uses the imbalance ratio of classes to discount the majority samples. This strategy is adapted to the popular feature extraction methods such as PCA and NMF. The main advantage of the proposed methods is that they are able to lessen the inherent bias of the extracted features to the majority class in existing PCA and NMF algorithms. Experiments on twelve public datasets with different levels of imbalance ratios show that the proposed methods outperformed the state-of-the-art methods on multiple classifiers.

Bremner, MJ, Montanaro, A & Shepherd, D 2016, 'Average-case complexity versus approximate simulation of commuting quantum computations', 19th Conference on Quantum Information Processing, Banff, Canada.

Broekhuijsen, M, Mols, I & van den Hoven, E 2016, 'A Holistic Design Perspective on Media Capturing and Reliving', Proceedings of the 28th Australian Conference on Human-Computer Interaction (OzCHI’16), Australian Computer Human Interaction Conference, ACM New York, Launceston, Tasmania.
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Chang, CL, Huang, CS, Lu, SW & Lin, C 2016, 'Apply Artifact Rejection on Multi-Channel Dry EEG System under Motion'.

Chang, CL, Huang, CS, Lu, SW & Lin, C 2016, 'Real-Time Unsupervised Artifact Removal Algorithm Using Wearable Dry EEG System'.

Cheema, P, Khoa, NLD, Alamdari, MM, Liu, W, Wang, Y, Chen, F & Runcie, P 2016, 'On Structural Health Monitoring Using Tensor Analysis and Support Vector Machine with Artificial Negative Data', CIKM '16 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, ACM International Conference on Information and Knowledge Management, ACM, Indianapolis, Indiana, USA, pp. 1813-1822.
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Structural health monitoring is a condition-based technology to monitor infrastructure using sensing systems. Since we usually only have data associated with the healthy state of a structure, one-class approaches are more practical. However, tuning the parameters for one-class techniques (like one-class Support Vector Machines) still remains a relatively open and difficult problem. Moreover, in structural health monitoring, data are usually multi-way, highly redundant and correlated, which a matrix-based two-way approach cannot capture all these relationships and correlations together. Tensor analysis allows us to analyse the multi-way vibration data at the same time. In our approach, we propose the use of tensor learning and support vector machines with artificial negative data generated by density estimation techniques for damage detection, localization and estimation in a one-class manner. The artificial negative data can help tuning SVM parameters and calibrating probabilistic outputs, which is not possible to do with one-class SVM. The proposed method shows promising results using data from laboratory-based structures and also with data collected from the Sydney Harbour Bridge, one of the most iconic structures in Australia. The method works better than the one-class approach and the approach without using tensor analysis.

Chen, HT, Wei, LY, Hartmann, B & Agrawala, M 2016, 'Data-driven adaptive history for image editing', Proceedings - 20th ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, I3D 2016, ACM-SIGRAPH Interactive 3D Graphics, Association Computing Machinery, Redmond, Washington, United States, pp. 103-112.
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Digital image editing is usually an iterative process; users repetitively perform short sequences of operations, as well as undo and redo using history navigation tools. In our collected data, undo, redo and navigation constitute about 9 percent of the total commands and consume a significant amount of user time. Unfortunately, such activities also tend to be tedious and frustrating, especially for complex projects. We address this crucial issue by adaptive history, a UI mechanism that groups relevant operations together to reduce user workloads. Such grouping can occur at various history granularities. We present two that have been found to be most useful. On a fine level, we group repeating commands patterns together to facilitate smart undo. On a coarse level, we segment commands history into chunks for semantic navigation. The main advantages of our approach are that it is intuitive to use and easy to integrate into any existing tools with text-based history lists. Unlike prior methods that are predominately rule based, our approach is data driven, and thus adapts better to common editing tasks which exhibit sufficient diversity and complexity that may defy predetermined rules or procedures. A user study showed that our system performs quantitatively better than two other baselines, and the participants also gave positive qualitative feedbacks on the system features.

Chen, Q, Hu, L, Xu, J, Liu, W & cao, L 2015, 'Document Similarity Analysis via Involving Both Explicit and Implicit Semantic Couplings', Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015, International Conference on Data Science and Advanced Analytics, IEEE, Paris.
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Chen, S, Chen, S, Wang, Z, Liang, J, Yuan, X, Cao, N & Wu, Y 2016, 'D-Map: Visual Analysis of Ego-centric information Diffusion patterns in social media', Visual Analytics Science and Technology (VAST), 2016 IEEE Conference on, IEEE Symposium on Visual Analytics Science and Technology, IEEE, Baltimore, MD, USA.
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Popular social media platforms could rapidly propagate vital information over social networks among a significant number of people. In this work we present D-Map (Diffusion Map), a novel visualization method to support exploration and analysis of social behaviors during such information diffusion and propagation on typical social media through a map metaphor. In D-Map, users who participated in reposting (i.e., resending a message initially posted by others) one central user's posts (i.e., a series of original tweets) are collected and mapped to a hexagonal grid based on their behavior similarities and in chronological order of the repostings. With additional interaction and linking, D-Map is capable of providing visual portraits of the influential users and describing their social behaviors. A comprehensive visual analysis system is developed to support interactive exploration with D-Map. We evaluate our work with real world social media data and find interesting patterns among users. Key players, important information diffusion paths, and interactions among social communities can be identified.

Chen, S, Wang, Z, Liang, J & Yuan, X 2016, 'Uncertainty-aware Visual Analytics for Exploring Human Behaviors from Heterogeneous Spatial Temporal Data', Proceedings of the Third Conference of China Visualization and Visual Analytics (ChinaVis'16), the Third Conference of China Visualization and Visual Analytics (ChinaVis'16), Changsha, China.
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When analyzing human behaviors, we need to construct the human behaviors from multiple sources of data, e.g. trajectory data, transaction data, identity data, etc. The problem we’re facing is the data conflicts, different resolution, missing and conflicting data, which together lead to the uncertainty in the spatial temporal data. Such uncertainty in data leads to difficulties even failure in the visual analytics task for analyzing people behavior, pattern and outliers. However, traditional automatic methods can not solve the problems in such complex scenario, where the uncertain and conflicting patterns are not well-defined. To solve the problems, we proposed a semi-automatic approach, for users to solve the conflicts and identify the uncertainties. To be general, We summarized five types of uncertainties and solutions to conduct the tasks of behavior analysis. Combined with the uncertainty-aware methods, we proposed a visual analytics system to analyze human behaviors, detect patterns and find outliers. Case studies from the IEEE VAST Challenge 2014 dataset confirms the effectiveness of our approach.

Chen, Y, Li, X, Li, L, Liu, G & Xu, G 2016, 'Modeling user mobility via user psychological and geographical behaviors towards point of-interest recommendation', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Database Systems for Advanced Applications, Springer, Dallas, Texas, USA, pp. 364-380.
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© Springer International Publishing Switzerland 2016.The pervasive employments of Location-based Social Network call for precise and personalized Point-of-Interest (POI) recommendation to predict which places the users prefer. Modeling user mobility, as an important component of understanding user preference, plays an essential role in POI recommendation. However, existing methods mainly model user mobility through analyzing the check-in data and formulating a distribution without considering why a user checks in at a specific place from psychological perspective. In this paper, we propose a POI recommendation algorithm modeling user mobility by considering check-in data and geographical information. Specifically, with check-in data, we propose a novel probabilistic latent factor model to formulate user psychological behavior from the perspective of utility theory, which could help reveal the inner information underlying the comparative choice behaviors of users. Geographical behavior of all the historical check-ins captured by a power law distribution is then combined with probabilistic latent factor model to form the POI recommendation algorithm. Extensive evaluation experiments conducted on two real-world datasets confirm the superiority of our approach over state-of-the-art methods.

Chinchore, A, Xu, G & Jiang, F 2016, 'Classifying sybil in MSNs using C4.5', Proceedings of 2016 International Conference on Behavioral, Economic, Socio - Cultural Computing, International Conference on Behavioral, Economic, Socio - Cultural Computing (BESC), IEEE, Durham, USA.
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© 2016 IEEE.Sybil detection is an important task in cyber security research. Over past years, many data mining algorithms have been adopted to fulfill such task. Using classification and regression for sybil detection is a very challenging task. Despite of existing research made toward modeling classification for sybil detection and prediction, this research has proposed new solution on how sybil activity could be tracked to address this challenging issue. Prediction of sybil behaviour has been demonstrated by analysing the graph-based classification and regression techniques, using decision trees and described dependencies across different methods. Calculated gain and maxGain helped to trace some sybil users in the datasets.

Chu, C, Xu, G, Brownlow, J & Fu, B 2016, 'Deployment of churn prediction model in financial services industry', Proceedings of 2016 International Conference on Behavioral, Economic, Socio - Cultural Computing, International Conference on Behavioral, Economic, Socio - Cultural Computing (BESC), IEEE, Durham, USA, pp. 1-2.
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© 2016 IEEE.Nowadays, data analytics techniques are playing an increasingly crucial role in financial services due to the huge benefits they bring. To ensure a successful implementation of an analytics project, various factors and procedures need to be considered besides technical issues. This paper introduces some practical lessons from our deployment of a data analytics project in a leading wealth management company in Australia. Specifically, the process of building a customer churn prediction model is described. Besides common steps of data analysis, how to deal with other practical issues like data privacy and change management that are encountered by many financial companies are also introduced.

Cook, H, Nguyen, Q, Simoff, S & Huang, M 2016, 'Enabling Gesture Interaction with 3D Point Cloud', Proceedings of the 24th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision, Computer Science Research Notes CSRN 2602, Plzen, Czech Republic, pp. 59-68.
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This paper presents a novel 3D point cloud gesture recognition system, based on an existing low-cost, accurate and easy to implement 2D point cloud gesture recognition system called $P. Our work improves recognition rates and lowers algorithmic complexity. We develop new 3D gestures, such as the GUN gesture and the SHAKE gesture, while also developing 3D poses like the L pose, OK pose, ROCK pose and PEACE pose for the LeapMotion Device. We demonstrate proposed gesture and pose methods on various 3D environments including a Monsoon mini-game, a cave painting interaction and a target practice scene. The average recognition rates for 3D gestures and poses were compared against the 2D, 3D and 3D+ recognition systems. The results indicate that most gestures in the proposed system were improved in comparison to the existing ones.

Daniel, J & Talaei-Khoei, A 2016, 'Can fuzzy multi-criteria decision making improve strategic planning by balanced scorecard?', Pacific Asia Conference on Information Systems, PACIS 2016 - Proceedings, Pacific Asia Conference on Information Systems, AIS Electronic Library (AISeL), Chiayi, Taiwan.
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Daniel, J & Talaei-Khoei, A 2016, 'Developing a Conceptual Model to Evaluate Green Suppliers: Decision Making Method using DEMATEL', AMCIS 2016: Surfing the IT Innovation Wave - 22nd Americas Conference on Information Systems, Americas Conference on Information Systems, AMCIS, San Diego, US.
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Davis, JJ, Kozma, R, Lin, C & Freeman, WJ 2016, 'Spatio-temporal EEG pattern extraction using high-density scalp arrays', Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), IEEE International Joint Conference on Neural Networks, IEEE, Vancouver, BC, Canada, pp. 889-896.
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Previous experimental studies on rabbits using electrocorticograms (ECoGs) over the cortical surface indicate spatio-temporal dynamics in the form of amplitude modulation (AM) patterns, which intermittently collapse at theta rates and give rise to rapidly propagating phase modulated (PM) patterns. The observed dynamics have been shown to be of cognitive relevance carrying useful information on the meaning of sensory information perceived by the subject. We have extended these studies to human scalp EEG measurements, which show evidence that cognitively relevant AM and PM patterns are observable by non-intrusive experimental techniques as well. The present work develops experimental techniques for studying cognitively relevant spatio-temporal neural dynamics using a high-density EEG array. Theoretical considerations indicate that the required spatial resolution to detect and categorize amplitude and phase patterns should be in the range of 3-5 mm. A prototype 1-dimensional array (MINDO-48S) has been developed, which has 48 electrodes in a flexible linear array of 5 mm spacing. The present work focuses on the extraction of broadly distributed spatio-temporal patterns, which carry cognitively relevant information. Preliminary analysis of the signal-to-noise ratio indicates that the sensitivity of the experiment allows the predicted AM patterns to be measured.

de Vries, NJ, Arefin, AS, Mathieson, L, Lucas, B & Moscato, P 2016, 'Relative neighborhood graphs uncover the dynamics of social media engagement', 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, Gold Coast, Queensland, Australia, pp. 283-297.
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In this paper, we examine if the Relative Neighborhood Graph (RNG) can reveal related dynamics of page-level social media metrics. A statistical analysis is also provided to illustrate the application of the method in two other datasets (the Indo-European Language dataset and the Shakespearean Era Text dataset). Using social media metrics on the world’s ‘top check-in locations’ Facebook pages dataset, the statistical analysis reveals coherent dynamical patterns. In the largest cluster, the categories ‘Gym’, ‘Fitness Center’, and ‘Sports and Recreation’ appear closely linked together in the RNG. Taken together, our study validates our expectation that RNGs can provide a “parameter-free" mathematical formalization of proximity. Our approach gives useful insights on user behaviour in social media page-level metrics as well as other applications.

Demediuk, S, Raffe, WL & Li, X 2016, 'An adaptive training framework for increasing player proficiency in games and simulations', Proceedings of the Annual Symposium on Computer-Human Interaction in Play Companion, Annual Symposium on Computer-Human Interaction in Play, ACM, Austin, USA, pp. 125-131.
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To improve a player's proficiency at a particular video game, the player must be presented with an appropriate level of challenge. This level of challenge must remain relative to the player as their proficiency changes. The current fixed difficulty settings (e.g. easy, medium or hard) provide a limited range of difficulty for the player. This work aims to address this problem through developing an adaptive training framework that utilities existing work in Dynamic Difficulty Adjustment to construct an adaptive AI opponent. The framework also provides a way to measure the player's proficiency, by analysing the level of challenge the adaptive AI opponent provides for the player. This work tests part of the proposed adaptive training framework through a pilot study that uses a real-time fighting game. Copyright is held by the owner/author(s).

Di, P & Sui, Y 2016, 'Accelerating dynamic data race detection using static thread interference analysis', Proceedings of the 7th International Workshop on Programming Models and Applications for Multicores and Manycores, PMAM 2016, pp. 30-39.
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Copyright © 2016 ACM. Precise dynamic race detectors report an error if and only if more than one thread concurrently exhibits conict on a memory access. They insert instrumentations at compiletime to perform runtime checks on all memory accesses to ensure that all races are captured and no spurious warnings are generated. However, a dynamic race check for a particular memory access statement is guaranteed to be redundant if the statement can be statically identified as thread interference-free. Despite significant recent advances in dynamic detection techniques, the redundant check remains a critical factor that leads to prohibitive overhead of dynamic race detection for multithreaded programs. In this paper, we present a new framework that eliminates redundant race check and boosts the dynamic race detection by performing static optimizations on top of a series of thread interference analysis phases. Our framework is implemented on top of LLVM 3.5.0 and evaluated with an industry dynamic race detector TSAN which is available as a part of LLVM tool chain. 11 benchmarks from SPLASH2 are used to evaluate the effectiveness of our approach in accelerating TSAN by eliminating redundant interference-free checks. The experimental result demonstrates our new approach achieves from 1.4x to 4.0x (2.4x on average) speedup over original TSAN under 4 threads setting, and achieves from 1.3x to 4.6x (2.6x on average) speedup under 16 threads setting.

Do, D & Liu, W 2016, 'ASTEN: an Accurate and Scalable Approach to Coupled Tensor Factorization', the International Joint Conference in Neural Networks, The International Joint Conference in Neural Networks, IEEE, Vancouver, Canada, pp. 99-106.
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Coupled Tensor Factorization (CTF) has become one of the most popular methods for joint analysis of high dimensional data generated from multiple sources. The goal of CTF is to factorize correlated datasets into latent factors efficiently. This research was taken with a particular goal of improving the accuracy of CTF. It is important to optimize the factorization of each single tensor of the coupled tensors. To achieve this, we introduce ASTEN, an Accurate and Scalable Tensor factorization method, where the objective function is optimized with respect to every single tensor and matrix. Differing from algorithms with a traditional objective function which forces shared modes among tensors to have identical factors, ASTEN enables each tensor to have its own discriminative factor on the shared mode and thus is capable of finding the accurate approximation of every tensor. Furthermore, to make it highly scalable in handling big data, we design it to be fully distributed and scalable with respect to the number of tensors, their dimensions, their sizes and the number of data partitions. In addition, we provide our theoretical proof and experimental evidence that our algorithm converges to an optimum. Experiments on both real and synthetic datasets demonstrate that our proposed ASTEN outperforms alternative existing algorithms.

Do, Q, Pham, T, Liu, W & Ramamohanarao, K 2016, 'WTEN: An Advanced Coupled Tensor Factorization Strategy for Learning from Imbalanced Data', Web Information Systems Engineering – WISE 2016, International Conference on Web Information Systems Engineering, Springer International Publishing, Shanghai, China, pp. 537-552.
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Learning from imbalanced and sparse data in multi-mode and high-dimensional tensor formats efficiently is a significant problem in data mining research. On one hand, Coupled Tensor Factorization (CTF) has become one of the most popular methods for joint analysis of heterogeneous sparse data generated from different sources. On the other hand, techniques such as sampling, cost-sensitive learning, etc. have been applied to many supervised learning models to handle imbalanced data. This research focuses on studying the effectiveness of combining advantages of both CTF and imbalanced data learning techniques for missing entry prediction, especially for entries with rare class labels. Importantly, we have also investigated the implication of joint analysis of the main tensor and extra information. One of our major goals is to design a robust weighting strategy for CTF to be able to not only effectively recover missing entries but also perform well when the entries are associated with imbalanced labels. Experiments on both real and synthetic datasets show that our approach outperforms existing CTF algorithms on imbalanced data.

Do, QNT, Zhilin, A, Junior, CZP, Wang, G & Hussain, FK 2016, 'A network-based approach to detect spammer groups', Proceedings of the International Joint Conference on Neural Networks, IEEE International Joint Conference on Neural Networks, IEEE, Vancouver, BC, Canada, pp. 3642-3648.
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© 2016 IEEE.Online reviews nowadays are an important source of information for consumers to evaluate online services and products before deciding which product and which provider to choose. Therefore, online reviews have significant power to influence consumers' purchase decisions. Being aware of this, an increasing number of companies have organized spammer review campaigns, in order to promote their products and gain an advantage over their competitors by manipulating and misleading consumers. To make sure the Internet remains a reliable source of information, we propose a method to identify both individual and group spamming reviews by assigning a suspicion score to each user. The proposed method is a network-based approach combining clustering techniques. We demonstrate the efficiency and effectiveness of our approach on a real-world and manipulated dataset that contains over 8000 restaurants and 600,000 restaurant reviews from TripAdvisor website. We tested our method in three testing scenarios. The method was able to detect all spammers in two testing scenarios, however it did not detect all in the last scenario.

Fang, XS, Sheng, QZ & Wang, X 2016, 'An ensemble approach for better truth discovery', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 298-311.
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© Springer International Publishing AG 2016. Truth discovery is a hot research topic in the Big Data era, with the goal of identifying true values from the conflicting data provided by multiple sources on the same data items. Previously, many methods have been proposed to tackle this issue. However, none of the existing methods is a clear winner that consistently outperforms the others due to the varied characteristics of different methods. In addition, in some cases, an improved method may not even beat its original version as a result of the bias introduced by limited ground truths or different features of the applied datasets. To realize an approach that achieves better and robust overall performance, we propose to fully leverage the advantages of existing methods by extracting truth from the prediction results of these existing truth discovery methods. In particular, we first distinguish between the single-truth and multi-truth discovery problems and formally define the ensemble truth discovery problem. Then, we analyze the feasibility of the ensemble approach, and derive two models, i.e., serial model and parallel model, to implement the approach, and to further tackle the above two types of truth discovery problems. Extensive experiments over three large real-world datasets and various synthetic datasets demonstrate the effectiveness of our approach.

Gao, F & Musial-Gabrys, K 2016, 'Hybrid Structure-based Link Prediction Model', PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016, 8th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEEE, San Francisco, CA, pp. 1221-1228.

Gill, AQ & Hevary, S 2016, 'Cloud Monitoring Data Challenges: A Systematic Review', https://link.springer.com/chapter/10.1007/978-3-319-46687-3_8, International Conference on Neural Information Processing, Springer, Kyoto, Japan, pp. 72-79.
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Gill, AQ, Chew, EK, Kricker, D & Bird, G 2016, 'Adaptive Enterprise Resilience Management: Adaptive Action Design Research in Financial Services Case Study', http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7776665, IEEE Conference on Business Informatics (CBI), IEEE Computer Society, Paris, pp. 113-122.
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Greeven, CS & Williams, SP 2016, 'Enterprise collaboration systems: an analysis and classification of adoption challenges.', INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS/INTERNATIONAL CONFERENCE ON PROJECT MANAGEMENT/INTERNATIONAL CONFERENCE ON HEALTH AND SOCIAL CARE INFORMATION SYSTEMS AND TECHNOLOGIES, CENTERIS/PROJMAN / HCIST 2016, International Conference on ENTERprise Information Systems / International Conference on Project MANagement / International Conference on Health and Social Care Information Systems and Technologies (CENTERIS/ProjMAN/HCist), ELSEVIER SCIENCE BV, Porto, PORTUGAL, pp. 179-187.
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Grochow, JA, Mulmuley, KD & Qiao, Y 2016, 'Boundaries of VP and VNP', Leibniz International Proceedings in Informatics, LIPIcs, International Colloquium on Automata Languages and Programming, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, Rome, Italy.
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One fundamental question in the context of the geometric complexity theory approach to the VP vs. VNP conjecture is whether VP = !VP, where VP is the class of families of polynomials that can be computed by arithmetic circuits of polynomial degree and size, and VP is the class of families of polynomials that can be approximated infinitesimally closely by arithmetic circuits of polynomial degree and size. The goal of this article is to study the conjecture in (Mulmuley, FOCS 2012) that !VP is not contained in VP. Towards that end, we introduce three degenerations of VP (i.e., sets of points in VP), namely the stable degeneration Stable-VP, the Newton degeneration Newton-VP, and the p-definable one-parameter degeneration VP*. We also introduce analogous degenerations of VNP. We show that Stable-VP subseteq Newton-VP subseteq VP* subseteq VNP, and Stable-VNP = Newton-VNP = VNP* = VNP. The three notions of degenerations and the proof of this result shed light on the problem of separating VP from VP. Although we do not yet construct explicit candidates for the polynomial families in !VP\VP, we prove results which tell us where not to look for such families. Specifically, we demonstrate that the families in Newton-VP \VP based on semi-invariants of quivers would have to be nongeneric by showing that, for many finite quivers (including some wild ones), Newton degeneration of any generic semi-invariant can be computed by a circuit of polynomial size. We also show that the Newton degenerations of perfect matching Pfaffians, monotone arithmetic circuits over the reals, and Schur polynomials have polynomial-size circuits.

Gu, F, Zhang, G, Lu, J & Lin, CT 2016, 'Concept drift detection based on equal density estimation', Proceedings of the International Joint Conference on Neural Networks, IEEE International Joint Conference on Neural Networks, IEEE, Vancouver, Canada, pp. 24-30.
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© 2016 IEEE.An important problem that remains in online data mining systems is how to accurately and efficiently detect changes in the underlying distribution of large data streams. The challenge for change detection methods is to maximise the accumulative effect of changing regions with unknown distribution, while at the same time providing sufficient information to describe the nature of the changes. In this paper, we propose a novel change detection method based on the estimation of equal density regions, with the aim of overcoming the issues of instability and inefficiency that underlie methods of predefined space partitioning schemes. Our method is general, nonparametric and requires no prior knowledge of the data distribution. A series of experiments demonstrate that our method effectively detects concept drift in single dimension as well as high dimension data, and is also able to explain the change by locating the data points that contribute most to the change. The detection result is guaranteed by statistical tests.

Guo, H, Li, X, He, M, Zhao, X, Liu, G & Xu, G 2016, 'CoSoLoRec: Joint factor model with content, social, location for heterogeneous point-of-interest recommendation', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) International Conference on Knowledge Science, Engineering and Management, International Conference on Knowledge Science, Engineering and Management, Springer, Passau, Germany, pp. 613-627.
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© Springer International Publishing AG 2016.The pervasive use of Location-based Social Networks calls for more precise Point-of-Interest recommendation. The probability of a user’s visit to a target place is influenced by multiple factors. Though there are several fusion models in such fields, heterogeneous information are not considered comprehensively. To this end, we propose a novel probabilistic latent factor model by jointly considering the social correlation, geographical influence and users’ preference. To be specific, a variant of Latent Dirichlet Allocation is leveraged to extract the topics of both user and POI from reviews which is denoted as explicit interest. Then, Probabilistic Latent Factor Model is introduced to depict the implicit interest. Moreover, Kernel Density Estimation and friend-based Collaborative Filtering are leveraged to model user’s geographic allocation and social correlation respectively. Thus, we propose CoSoLoRec, a fusion framework, to ameliorate the recommendation. Experiments on two real-word datasets show the superiority of our approach over the state-of-the-art methods.

Guo, Y, Zhu, J, Lu, H & Lei, G 2016, 'Design considerations of electric motors with soft magnetic composite cores', 2016 IEEE 8th International Power Electronics and Motion Control Conference, IPEMC-ECCE Asia 2016, Power Electronics and Motion Control Conference (IPEMC-ECCE Asia), IEEE, Hefei, China, pp. 3007-3011.
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© 2016 IEEE.Soft magnetic composite (SMC) materials possess many unique properties, which are particularly suitable for development of novel structure electric motors for various electric drive systems. The unique properties of SMC material include three-dimensional (3-D) magnetic and thermal isotropy, very low eddy current loss, and prospect of very low cost mass production. Therefore, the application of SMC materials in electrical appliance, particularly in electric motors, has attracted great interest in research. However, SMC materials also have some drawbacks, e.g. low permeability, high hysteresis loss and low mechanical strength, and hence a direct replacement of electrical steels by SMC would not necessarily lead to satisfaction or improvement of motor performance. To fully explore the application potential of the SMC materials, their unique properties should be fully employed and at the same time the effects of their drawbacks should be avoided or minimized. This paper aims to present some key issues on design of SMC electric motors based on the extensive research in the past two decades by various researchers including the authors of this paper. The key design issues are discussed and some conclusions are drawn for future effort in this area.

Han, B, Tsang, IW & Chen, L 2016, 'On the Convergence of A Family of Robust Losses for Stochastic Gradient Descent', Machine Learning and Knowledge Discovery in Databases - LNCS, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD), Springer, Riva del Garda, Italy.
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The convergence of Stochastic Gradient Descent (SGD) using convex loss functions has been widely studied. However, vanilla SGD methods using convex losses cannot perform well with noisy labels, which adversely affect the update of the primal variable in SGD methods. Unfortunately, noisy labels are ubiquitous in real world applications such as crowdsourcing. To handle noisy labels, in this paper, we present a family of robust losses for SGD methods. By employing our robust losses, SGD methods successfully reduce negative effects caused by noisy labels on each update of the primal variable. We not only reveal the convergence rate of SGD methods using robust losses, but also provide the robustness analysis on two representative robust losses. Comprehensive experimental results on six real-world datasets show that SGD methods using robust losses are obviously more robust than other baseline methods in most situations with fast convergence.

Hao, P, Zhang, G & Lu, J 2016, 'Enhancing Cross Domain Recommendation with Domain Dependent Tags', 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE International Conference on Fuzzy Systems, IEEE, Vancouver, CANADA, pp. 1266-1273.
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One challenge in recommender system is to deal with data sparsity. To handle this issue, social tags are utilized to bring disjoint domains together for knowledge transfer in cross-domain recommendation. The most intuitive way is to use common tags that present in both source and target domains. However, it is difficult to obtain a strong domain connection by exploiting a small amount of common tags, especially when the tagging data in target domain is too scarce to share enough common tags with source domain. In this paper we propose a novel framework, called Enhanced Tag-induced Cross Domain Collaborative Filtering (ETagiCDCF), to integrate the rich information contained in domain dependent tags into recommendation procedure. We perform experiments on two public datasets and compare with several single and cross domain recommendation approaches, the results demonstrate that ETagiCDCF can effectively address data sparseness and improve recommendation performance.

Hazber, MAG, Li, R, Gu, X, Xu, G & Li, Y 2015, 'Semantic SPARQL Query in a Relational Database Based on Ontology Construction', Proceedings - 2015 11th International Conference on Semantics, Knowledge and Grids, SKG 2015, International Conference on Semantics Knowledge and Grid, IEEE, China, pp. 25-32.
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© 2015 IEEE.Constructing an ontology from RDBs and its query through ontologies is a fundamental problem for the development of the semantic web. This paper proposes an approach to extract ontology directly from RDB in the form of OWL/RDF triples, to ensure its availability at semantic web. We automatically construct an OWL ontology from RDB schema using direct mapping rules. The mapping rules provide the basic rules for generating RDF triples from RDB data even for column contents null value, and enable semantic query engines to answer more relevant queries. Then we rewriting SPARQL query from SQL by translating SQL relational algebra into an equivalent SPARQL. The proposed method is demonstrated with examples and the effectiveness of the proposed approach is evaluated by experimental results.

Hazber, MAG, Li, R, Xu, G & Alalayah, KM 2016, 'An approach for automatically generating R2RML-based direct mapping from relational databases', Communications in Computer and Information Science, International Conference of Young Computer Scientists, Engineers and Educators (ICYCSEE), Springer, Harbin, China, pp. 151-169.
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© Springer Science+Business Media Singapore 2016.For integrating relational databases (RDBs) into semantic web applications, the W3C RDB2RDF Working Group recommended two approaches, Direct Mapping (DM) and R2RML. The DM provides a set of mapping rules according to RDB schema, while the R2RML allows users to manually define mappings according to existing target ontology. The major problem to use R2RML is the effort for creating R2RML mapping documents manually. This may lead to appearance of many mistakes in the R2RML documents and requires domain experts. In this paper, we propose and implement an approach to generate an R2RML mapping documents automatically from RDB schema. The R2RML mapping reflects the behavior of the DM specification and allows any R2RML parser to generate a set of RDF triples from relational data. The input of generating approach is DBsInfo class that automatically generated from relational schema. An experimental prototype is developed and shows the effectiveness of our approach algorithms.

Hazber, MAG, Li, R, Zhang, Y & Xu, G 2015, 'An approach for mapping relational database into ontology', Proceedings - 2015 12th Web Information System and Application Conference, WISA 2015, Web Information System and Application Conference (WISA), IEEE, Jinan, China, pp. 120-125.
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© 2015 IEEE.Sharing and reusing the big data in relational databases in a semantic way have become a big challenge. In this paper, we propose a new approach to enable semantic web applications to access relational databases (RDBs) and their contents by semantic methods. Domain ontologies can be used to formulate RDB schema and data in order to simplify the mapping of the underlying data sources. Our method consists of two main phases: building ontology from an RDB schema and the generation of ontology instances from an RDB data automatically. In the first phase, we studied different cases of RDB schema to be mapped into ontology represented in RDF(S)-OWL, while in the second phase, the mapping rules are used to transform RDB data to ontological instances represented in RDF triples. Our approach is demonstrated with examples and validated by ontology validator.

He, K, Weng, J, Liu, JK, Zhou, W & Liu, JN 2016, 'Efficient fine-grained access control for secure personal health records in cloud computing', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 65-79.
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© Springer International Publishing AG 2016. In this paper, we propose an efficient fine-grained access control system for secure Personal Health Records (PHRs) in cloud computing. In this system, the patients have fine-grained access control for their health records. The underlying primitive of this system is a newly designed identity-based conditional proxy re-encryption scheme with chosen-ciphertext security, which is the first of its kind that achieves the highest security level. It is also highly efficient. The public parameters size and also, the private key and ciphertext size are constant and our experimental results indicate that the computational cost does not rely on the message size.

Herron, D, Andalibi, N, Haimson, O, Moncur, W & van den Hoven, E 2016, 'HCI and Sensitive Life Experiences', Proceedings of the 9th Nordic Conference on Human-Computer Interaction, Nordic Conference on Human-Computer Interaction (NordiCHI), ACM, Gothenburg, Sweden, pp. 1-3.
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HCI research has identified a number of life events and life transitions which see individuals in a vulnerable state, such as gender transition, domestic abuse, romantic relationship dissolution, bereavement, and even genocide. Although these life events differ across the human lifespan, considering them as a group of 'sensitive life experiences', and exploring the similarities and differences in how we approach those experiences as researchers could be invaluable in generating a better understanding of them. In this workshop, we aim to identify current opportunities for, and barriers to, the design of social computing systems that support people during sensitive life events and transitions. Participants will take part in activities centred around exploring the similarities and differences between their own and others' research methods and results, drawing on their own experiences in discussions around carrying out research in these sensitive contexts.

Herron, D, Moncur, W & Van Den Hoven, E 2016, 'Digital possessions after a romantic break Up', ACM International Conference Proceeding Series, Nordic Conference on Human-Computer Interaction (NordiCHI), Association Computing Machinery Digital Library, Gothenburg, Sweden.
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© 2016 ACM.With technology becoming more pervasive in everyday life, it is common for individuals to use digital media to support the enactment and maintenance of romantic relationships. Partners in a relationship may create digital possessions frequently. However, after a relationship ends, individuals typically seek to disconnect from their ex-partner. This becomes difficult due to the partners' interwoven digital presence and digital possessions. In this paper, we report on a qualitative study exploring individuals' experiences of relationship break up in a digital context, and discuss their attitudes towards digital possessions from those relationships. Five main themes emerged: digital possessions that sustain relationships, comparing before and after, tainted digital possessions, digital possessions and invasions of privacy, involved and emotional reminiscing. Design opportunities were identified in managing attitudes towards digital possessions, disconnecting and reconnecting, and encouraging awareness of digital possessions.

Holland, S, McPherson, A, Mackay, W, Wanderley, M, Gurevich, M, Mudd, T, O'Modhrain, S, Wilkie, K, Malloch, J, Garcia, J & Johnston, A 2016, 'Music and HCI workshop', Conference on Human Factors in Computing Systems - Proceedings, pp. 3339-3346.
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© 2016 Authors. Music is an evolutionarily deep-rooted, abstract, real-time, complex, non-verbal, social activity. Consequently, interaction design in music can be a valuable source of challenges and new ideas for HCI. This workshop will reflect on the latest research in Music and HCI (Music Interaction for short), with the aim of strengthening the dialogue between the Music Interaction community and the wider HCI community. We will explore recent ideas from Music Interaction that may contribute new perspectives to general HCI practice, and conversely, recent HCI research in non-musical domains with implications for Music Interaction. We will also identify any concerns of Music Interaction that may require unique approaches. Contributors engaged in research in any area of Music Interaction or HCI who would like to contribute to a sustained widening of the dialogue between the distinctive concerns of the Music Interaction community and the wider HCI community will be welcome.

Holland, S, McPherson, AP, Mackay, WE, Wanderley, MM, Gurevich, MD, Mudd, TW, O Modhrain, S, Wilkie, KL, Malloch, JW, Garcia, J & others 2016, 'Music and HCI', Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, ACM, pp. 3339-3346.

Hollmén, J, Spiliopoulou, M, Kane, B, Marshall, A, Soda, P, Antani, S & McGregor, C 2016, 'Preface', Proceedings - IEEE Symposium on Computer-Based Medical Systems, pp. xiii-xiv.
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Huang, W, Zhu, M, Huang, ML & Duh, HBL 2016, 'Evaluating overall quality of dynamic network visualizations', Cooperative Design, Visualization, and Engineering (LNCS), International Conference on Cooperative Design, Visualization, and Engineering (CDVE), Springer, Sydney, NSW, Australia, pp. 157-162.
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© Springer International Publishing AG 2016.Visualizing dynamic networks is a challenging task. One of the challenges we face is how to maintain visual complexity and overall quality of visualizations at a reasonable and sustainable level so that the information about the network embedded in the visualization can be effectively comprehended by the viewer. Many techniques and algorithms have been proposed and developed to facilitate the discovery of changing patterns. Much research has also been done in investigating how visualization should be constructed to be effective. However, how to measure and compare the quality of visualizations of a changing network at different time points has not been well researched. In this paper, we report on a preliminary work towards this direction. In particular, we apply an existing multi-dimensional overall quality measure in a user study data of different networks and found that the measured quality is positively correlated with user task performance regardless of network size.

Hussain, W, Hussain, FK & Hussain, OK 2016, 'Allocating optimized resources in the cloud by a viable SLA model', 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, IEEE International Conference on Fuzzy Systems, IEEE, Vancouver, Canada.
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Hussain, W, Hussain, FK & Hussain, OK 2016, 'QoS prediction methods to avoid SLA violation in post-interaction time phase', 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), IEEE Conference on Industrial Electronics and Applications, IEEE, Hefei, China, pp. 32-37.
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Due to the dynamic nature of cloud computing it is very important for a small to medium scale service providers to optimally assign computing resources and apply accurate prediction methods that enable the best resource management. The choice of an ideal quality of service (QoS) prediction method is one of the key factors in business transactions that help a service provider manage the risk of SLA violations by taking appropriate and immediate action to reduce occurrence, or avoid operations that may cause risk. In this paper we analyze ten prediction methods, including neural network methods, stochastic methods and others to predict time series cloud data and compare their prediction accuracy over five time intervals. We use Cascade Forward Backpropagation, Elman Backpropagation, Generalized Regression, NARX, Simple Exponential Smoothing, Simple Moving Average, Weighted Moving Average, Extrapolation, Holt-Winters Double Exponential Smoothing and ARIMA and predict resource usage at 1, 2, 3, 4 and 5 hours into the future. We use Root Means Square Error and Mean Absolute Deviation as a benchmark for their prediction accuracy. From the prediction results we observed that the ARIMA method provides the most optimal prediction results for all time intervals.

Hussain, W, Hussain, FK & Omar Hussain 2016, 'SLA Management Framework to Avoid Violation in Cloud', Neural Information Processing (LNCS), International Conference on Neural Information Processing, Springer International Publishing, Kyoto, Japan, pp. 309-316.
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Cloud computing is an emerging technology that have a broad scope to offers a wide range of services to revolutionize the existing IT infrastructure. This internet based technology offers a services like – on demand service, shared resources, multitenant architecture, scalability, portability, elasticity and giving an illusion of having an infinite resource by a consumer through virtualization. Because of the elastic nature of a cloud it is very critical of a service provider specially for a small/medium cloud provider to form a viable SLA with a consumer to avoid any service violation. SLA is a key agreement that need to be intelligently form and monitor, and if there is a chance of service violation then a provider should be informed to take necessary remedial action to avoid violation. In this paper we propose our viable SLA management framework that comprise of two time phases – pre-interaction time phase and post-interaction time phase. Our viable SLA framework help a service provider in making a decision of a consumer request, offer the amount of resources to consumer, predict QoS parameters, monitor run time QoS parameters and take an appropriate action to mitigate risks when there is a variation between a predicted and an agreed QoS parameters.

Ihantola, P, Vihavainen, A, Ahadi, A, Butler, M, Börstler, J, Edwards, SH, Isohanni, E, Korhonen, A, Petersen, A, Rivers, K, Rubio, MA, Sheard, J, Skupas, B, Spacco, J, Szabo, C & Toll, D 2015, 'Educational Data Mining and Learning Analytics in Programming: Literature Review and Case Studies', ITiCSE-WGP'15: Proceedings of the 2015 ITiCSE on Working Group Reports, Annual Conference on Innovation and Technology in Computer Science Education, Association for Computing Machinery, Lithunia, pp. 41-63.
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Educational data mining and learning analytics promise better understanding of student behavior and knowledge, as well as new information on the tacit factors that contribute to student actions. This knowledge can be used to inform decisions related to course and tool design and pedagogy, and to further engage students and guide those at risk of failure. This working group report provides an overview of the body of knowledge regarding the use of educational data mining and learning analytics focused on the teaching and learning of programming. In a literature survey on mining students' programming processes for 2005-2015, we observe a significant increase in work related to the field. However, the majority of the studies focus on simplistic metric analysis and are conducted within a single institution and a single course. This indicates the existence of further avenues of research and a critical need for validation and replication to better understand the various contributing factors and the reasons why certain results occur. We introduce a novel taxonomy to analyse replicating studies and discuss the importance of replicating and reproducing previous work. We describe what is the state of the art in collecting and sharing programming data. To better understand the challenges involved in replicating or reproducing existing studies, we report our experiences from three case studies using programming data. Finally, we present a discussion of future directions for the education and research community.

Ikeda, M, Pop, F & Hussain, F 2016, 'Message from CISIS 2016 Program Co-Chairs', Proceedings - 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2016, p. xviii.
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Inibhunu, C & McGregor, C 2016, 'Machine learning model for temporal pattern recognition', 2016 IEEE EMBS International Student Conference: Expanding the Boundaries of Biomedical Engineering and Healthcare, ISC 2016 - Proceedings.
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© 2016 IEEE. Temporal abstraction and data mining are two research fields that have tried to synthesis time oriented data and bring out an understanding on the hidden relationships that may exist between time oriented events. In clinical settings, having the ability to know the hidden relationships on patient data as they unfold could help save a life by aiding in detection of conditions that are not obvious to clinicians and healthcare workers. Understanding the hidden patterns is a huge challenge due to the exponential search space unique to time-series data. In this paper, we propose a temporal pattern recognition model based on dimension reduction and similarity measures thereby maintaining the temporal nature of the raw data.

Ion, A, Frohnhofen, J, Wall, L, Kovacs, R, Alistar, M, Lindsay, J, Lopes, P, Chen, HT & Baudisch, P 2016, 'Metamaterial mechanisms', UIST 2016 - Proceedings of the 29th Annual Symposium on User Interface Software and Technology, ACM Symposium on User Interface Software and Technology, ACM, Tokyo, Japan, pp. 529-539.
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Recently, researchers started to engineer not only the outer shape of objects, but also their internal microstructure. Such objects, typically based on 3D cell grids, are also known as metamaterials. Metamaterials have been used, for example, to create materials with soft and hard regions. So far, metamaterials were understood as materials-we want to think of them as machines. We demonstrate metamaterial objects that perform a mechanical function. Such metamaterial mechanisms consist of a single block of material the cells of which play together in a well-defined way in order to achieve macroscopic movement. Our metamaterial door latch, for example, transforms the rotary movement of its handle into a linear motion of the latch. Our metamaterial Jansen walker consists of a single block of cells-that can walk. The key element behind our metamaterial mechanisms is a specialized type of cell, the only ability of which is to shear. In order to allow users to create metamaterial mechanisms efficiently we implemented a specialized 3D editor. It allows users to place different types of cells, including the shear cell, thereby allowing users to add mechanical functionality to their objects. To help users verify their designs during editing, our editor allows users to apply forces and simulates how the object deforms in response.

Izaddoost, A & McGregor, C 2016, 'Enhance Network Communications in a Cloud-based Real-time Health Analytics Platform Using SDN', 2016 IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS (ICHI), IEEE International Conference on Healthcare Informatics (ICHI), IEEE, Chicago, IL, pp. 388-391.
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Jalali, R, Dauda, A, El-Khatib, K, McGregor, C & Surti, C 2016, 'An architecture for health data collection using off-the-shelf health sensors', 2016 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2016 - Proceedings.
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© 2016 IEEE. Nowadays, many people, and not only the ones with health problems are being more health conscious. With the advent of sensor based technologies, it has become possible to create wearable wireless biometric sensor networks, known as Body Sensor Networks (BSNs) which allow people to collect their health data and send it remotely for further analysis and storage. Research has shown that the use of BSNs enables remote wireless diagnosis of various health conditions. In this paper, we propose a novel layered architecture for smart healthcare system where health community service providers, patients, doctors and hospitals have access to real time data which has been gathered using various sensory mechanisms. An experimental case study has been implemented for evaluation. Early results show benefits of this system in improving the quality of health care.

Jiang, F, Gan, J, Xu, Y & Xu, G 2016, 'Coupled behavioral analysis for user preference-based email spamming', Proceedings of 2016 International Conference on Behavioral, Economic, Socio - Cultural Computing, International Conference on Behavioral, Economic, Socio - Cultural Computing (BESC), IEEE, Durham, NC, pp. 1-5.
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© 2016 IEEE.In this paper, we develop and implement a new email spamming system leveraged by coupled text similarity analysis on user preference and a virtual meta-layer user-based email network, we take the social networks or campus LAN networks as the spam social network scenario. Fewer current practices exploit social networking initiatives to assist in spam filtering. Social network has essentially a large number of accounts features and attributes to be considered. Instead of considering large amount of users accounts features, we construct a new model called meta-layer email network which can reduce these features by only considering individual user's actions as an indicator of user preference, these common user actions are considered to construct a social behavior-based email network. With the further analytic results from text similarity measurements for each individual email contents, the behavior-based virtual email network can be improved with much higher accuracy on user preferences. Further, a coupled selection model is developed for this email network, we are able to consider all relevant factors/features in a whole and recommend the emails practically to the user individually. The experimental results show the new approach can achieve higher precision and accuracy with better email ranking in favor of personalised preference.

Jiang, J, Wen, S, Yu, S, Zhou, W & Qian, Y 2016, 'Analysis of the spreading influence variations for online social users under attacks', 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings.
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© 2016 IEEE. Identifying influential spreaders in online social networks (OSNs) has long been an important but difficult problem to be addressed. Distinguished from previous works that mainly focused on the stationary features of users' influence, we systematically study the variations of users' spreading capability given the fact that influential spreaders are more likely to be the targets of various cyber attacks in real OSNs. In order to rank users' spreading capability, we adopt the k-shell structure which assigns a coreness index, ks, to each user. We find that users' spreading capability can considerably change when attacks occur in specific structures of OSNs. Generally, if the OSN structure is assortative (i.e., large-degree nodes preferably connect to nodes with large degree), users' spreading capability is resilient to attacks. However, if the OSN structure is disassortative (i.e., large-degree nodes preferably connect to nodes with small degree), users' spreading capability decreases significantly under attacks. We further carried out a series of empirical studies in real OSN datasets to disclose the causation of the variations caused by attacks. The research presented in this paper benefit decision makers to protect the propagation in the case of product promotion or prevent the diffusion like rumor.

Johnston, A & Pickrell, M 2016, 'Designing for technicians working in the field: 8 usability heuristics for mobile application design', Proceedings of the 28th Australian Conference on Computer-Human Interaction, Australian Computer Human Interaction Conference, ACM, Launceston, Tasmania, Australia, pp. 494-498.
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Kajdanowicz, T, Michalski, R, Musial, K & Kazienko, P 2016, 'Learning in unlabeled networks - An active learning and inference approach', AI COMMUNICATIONS, IOS PRESS, pp. 123-148.
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Kamaleswaran, R, James, A, Collins, C & McGregor, C 2016, 'CoRAD: Visual Analytics for Cohort Analysis', 2016 IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS (ICHI), IEEE International Conference on Healthcare Informatics (ICHI), IEEE, Chicago, IL, pp. 517-526.
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Kang, G, Li, J & Tao, D 2016, 'Shakeout: A New Regularized Deep Neural Network Training Scheme', Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), AAAI Conference on Artificial Intelligence, AAAI, Phoenix, USA, pp. 1751-1757.
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Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. The invention of effective training techniques largely contributes to this success. The so-called "Dropout" training scheme is one of the most powerful tool to reduce over-fitting. From the statistic point of view, Dropout works by implicitly imposing an L2 regularizer on the weights. In this paper, we present a new training scheme: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, our method randomly chooses to enhance or inverse the contributions of each unit to the next layer. We show that our scheme leads to a combination of L1 regularization and L2 regularization imposed on the weights, which has been proved effective by the Elastic Net models in practice.We have empirically evaluated the Shakeout scheme and demonstrated that sparse network weights are obtained via Shakeout training. Our classification experiments on real-life image datasets MNIST and CIFAR-10 show that Shakeout deals with over-fitting effectively.

Kenning, G & Treadaway, C 2015, 'Art and Design Research for in the Moment Fun and Pleasure', Art of Good Health and Wellbeing, Arts and Health Australia, Sydney.

Kirk, DS, Durrant, A, Wood, G, Leong, TW & Wright, P 2016, 'Understanding the sociality of experience in mobile music listening with Pocketsong', Proceedings of the 2016 ACM Conference on Designing Interactive Systems: Fuse, ACM Conference on Designing Interactive Systems, ACM, Brisbane, Australia, pp. 50-61.
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Music listening practices are changing. Mobile, networked devices now offer rich opportunities not just for personal music consumption but also for personally broadcasting music and for sharing digital meta-data concerning tastes, preferences and general listening habits. However, experiences of music listening and sharing on the move and how this has been impacted by developments in mobile technology remain under-explored. In this paper we present an empirical study of the sociality of mobile music experiences, 'in the wild', using a new location-based mobile music-sharing application (Pocketsong), designed as a technology probe. We report users' experiences of Pocketsong (distilled from interviews), and critically examine the affordances of mobile music applications, the sociality of sharing and 'co-listening', and the relationships between digitally-mediated mobile music consumption and self-expression. Based upon this we reflect upon the interaction design challenges of developing mobile music technologies that work in digitally-mediated social spaces.

Ko, LW, Komarov, SH, Liu, SH, Hsu, WC, König, P, Goeke, P, David Hairston, W, Lin, C & Jung, TP 2016, 'Investigation of brain activity patterns related to the effect of classroom fatigue'.

Ko, LW, Yang, BJ, Singanamalla, SKR, Lin, C, King, JT & Jung, TP 2016, 'A practical neurogaming design based on SSVEP brain computer interface'.

Korhonen, JJ, Lapalme, J, McDavid, D & Gill, AQ 2016, 'Adaptive Enterprise Architecture for the Future: Towards a Reconceptualization of EA', Proceedings - CBI 2016: 18th IEEE Conference on Business Informatics, IEEE Conference on Business Informatics (CBI), IEEE, Paris, France, pp. 272-281.
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© 2016 IEEE.In some conventional definitions, Enterprise Architecture (EA) is conceived as a descriptive overview of the enterprise, while in other views EA is seen as a prescriptive framework of principles and models that helps translate business strategy to enterprise change. The conceptualizations of EA also vary in scope. There is an increasing recognition of EA as a systemic, enterprise-wide capability encompassing all relevant facets of the organization, transcending the traditional IT-centric view. However, we argue that none of the conventional conceptualizations of EA are adaptive in the face of today's complex environment. We view that an adaptive EA must go beyond a single organization and fully appreciate enterprise-in-environment ecosystemic perspective. Drawing on the heritage of Open Socio-Technical Systems Design and adopting the 'three schools of thought' as a meta-paradigmatic backdrop, the paper features four different views of long-time scholar-practitioners, who discuss what an adaptive enterprise architecture would entail. Integration of these views paints a radically reconceptualized picture of enterprise architecture for the future. With this paper, we want to lay a foundation for a debate on the need for alternative conceptualizations, manifestations and research agenda for enterprise architecture.

Krol, LR, Freytag, S-C, Fleck, M, Gramann, K & Zander, TO 2016, 'A Task-Independent Workload Classifier for Neuroadaptive Technology: Preliminary Data', 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, Budapest, HUNGARY, pp. 3171-3174.
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Lai, LB, Qin, L, Lin, XM, Zhang, Y, Chang, LJ & Yang, SY 2016, 'Scalable Distributed Subgraph Enumeration', Proceedings of the VLDB Endowment, International Conference on Very Large Databases, VLDB Endowment, Munich, Germany, pp. 217-228.
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Leinonen, J, Longi, K, Klami, A, Ahadi, A & Vihavainen, A 2016, 'Typing Patterns and Authentication in Practical Programming Exams', ITiCSE’16: Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education, Annual Conference on Innovation and Technology in Computer Science Education, Association for Computing Machinery, Inc, Peru, pp. 160-165.
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In traditional programming courses, students have usually been at least partly graded using pen and paper exams. One of the problems related to such exams is that they only partially connect to the practice conducted within such courses. Testing students in a more practical environment has been constrained due to the limited resources that are needed, for example, for authentication. In this work, we study whether students in a programming course can be identi ed in an exam setting based solely on their typing patterns. We replicate an earlier study that indicated that keystroke analysis can be used for identifying programmers. Then, we examine how a controlled machine examination setting a ects the identi cation accuracy, i.e. if students can be identi ed reliably in a machine exam based on typing pro les built with data from students' programming assignments from a course. Finally, we investigate the identification accuracy in an ncontrolled machine exam,where students can complete the exam at any time using any computer they want. Our results indicate that even though the identi cation accuracy deteriorates when identifying students in an exam, the accuracy is high enough to reliably identify students if the identi cation is not required to be exact, but top k closest matches are regarded as correct.

Leong, T & Robertson, T 2016, 'Voicing values: laying foundations for ageing people to participate in design', PDC '16 Proceedings of the 14th Participatory Design Conference: Full papers - Volume 1, Participatory Design Conference, ACM Press, Aarhus, Denmark.
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Leong, TW & Johnston, B 2016, 'Co-design and robots: A case study of a robot dog for aging people', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Social Robotics (ICSR), Springer, Kansas City, Missouri, United States, pp. 702-711.
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© Springer International Publishing AG 2016.The day-to-day experiences of aging citizens differ significantly from young, technologically savvy engineers. Yet, well-meaning engineers continue to design technologies for aging citizens, informed by skewed stereotypes of aging without deep engagements from these users. This paper describes a co-design project based on the principles of Participatory Design that sought to provide aging people with the capacity to co-design technologies that suit their needs. The project combined the design intuitions of both participants and designers, on equal footing, to produce a companion robot in the form of a networked robotic dog. Besides evaluating a productive approach that empowers aging people in the process of co-designing and evaluating technologies for themselves, this paper presents a viable solution that is playful and meaningful to these elderly people; capable of enhancing their independence, social agency and well-being.

Li, Q, Bian, W, Xu, Y, You, J & Tao, D 2016, 'Random Mixed Field Model for Mixed-Attribute Data Restoration', Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, AAAI, Phoenix, Arizona, USA, pp. 1244-1250.
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Noisy and incomplete data restoration is a critical preprocessing step in developing effective learning algorithms, which targets to reduce the effect of noise and missing values in data. By utilizing attribute correlations and/or instance similarities, various techniques have been developed for data denoising and imputation tasks. However, current existing data restoration methods are either specifically designed for a particular task, or incapable of dealing with mixed-attribute data. In this paper, we develop a new probabilistic model to provide a general and principled method for restoring mixed-attribute data. The main contributions of this study are twofold: a) a unified generative model, utilizing a generic random mixed field (RMF) prior, is designed to exploit mixed-attribute correlations; and b) a structured mean-field variational approach is proposed to solve the challenging inference problem of simultaneous denoising and imputation. We evaluate our method by classification experiments on both synthetic data and real benchmark datasets. Experiments demonstrate, our approach can effectively improve the classification accuracy of noisy and incomplete data by comparing with other data restoration methods.

Li, Q, Qiao, M, Bian, W & Tao, D 2016, 'Conditional graphical Lasso for multi-label image classification', Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Las Vegas, Nevada, United States, pp. 2977-2986.
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Multi-label image classification aims to predict multiple labels for a single image which contains diverse content. By utilizing label correlations, various techniques have been developed to improve classification performance. However, current existing methods either neglect image features when exploiting label correlations or lack the ability to learn image-dependent conditional label structures. In this paper, we develop conditional graphical Lasso (CGL) to handle these challenges. CGL provides a unified Bayesian framework for structure and parameter learning conditioned on image features. We formulate the multi-label prediction as CGL inference problem, which is solved by a mean field variational approach. Meanwhile, CGL learning is efficient due to a tailored proximal gradient procedure by applying the maximum a posterior (MAP) methodology. CGL performs competitively for multi-label image classification on benchmark datasets MULAN scene, PASCAL VOC 2007 and PASCAL VOC 2012, compared with the state-of-the-art multi-label classification algorithms.

Lin, CT, Hsieh, TY, Liu, YT, Wu, SL & Lin, YY 2016, 'A Novel Mechanism to Fuse Various Sub-Aspect Brain-Computer Interface (BCI) Systems with PSO for Motor Imagery Task', Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, IEEE International Conference on Systems, Man and Cybernetics, IEEE, Kowloon, China, pp. 3223-3228.
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© 2015 IEEE. In this study, we develop a novel multi-fusion brain-computer interface (BCI) system based on a fuzzy neural network (FNN) and information fusion approaches to cope with a classification task for identifying right/left hand motor imagery. In the proposed system, we utilize a filter bank and sub-band common spatial pattern (SBCSP) to extract features from raw EEG data. A self-organizing neural fuzzy inference network (SONFIN) is then applied for a recognition task. In order to improve the classification performance, we form a committee of networks and employ fuzzy integral (FI) to attain a joint decision. To further optimize the fusion approaches, a particle swarm optimization (PSO) algorithm is exploited to globally update parameters used in the fusion stage. In consequence, our experimental result shows that the proposed fuzzy fusion system possesses superior performance compared to other comparative models.

Lister, RF 2016, 'Toward a Developmental Epistemology of Computer Programming', WiPSCE '16 Proceedings of the 11th Workshop in Primary and Secondary Computing Education, Workshop in Primary and Secondary Computing Education, ACM, Münster, Germany.
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This paper was written as a companion to my keynote address at the 11th Workshop in Primary and Secondary Computing Education (WiPSCE 2016). The paper outlines my own research on how novices learn to program. Any reader whose interest has been piqued may pursue furher detail in the papers cited. I begin by explaining my philosophical position. In making that explanation, I do not claim that it is the only right position; on the contrary I allude to other philosophical positions that I regard as complimentary to my own. The academic warfare between these positions is pointless and counterproductive --- all the established positions have something positive to offer. Having established my position, I then go on to argue that the work of Jean Piaget, and subsequent neo-Piagetians, offers useful insight into how children learn to program computers.

Liu, A, Zhang, G, Lu, J, Lu, N & Lin, CT 2016, 'An online competence-based concept drift detection algorithm', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Australasian Joint Conference on Artificial Intelligence, Springer, Hobart, TAS, Australia, pp. 416-428.
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© Springer International Publishing AG 2016. The ability to adapt to new learning environments is a vital feature of contemporary case-based reasoning system. It is imperative that decision makers know when and how to discard outdated cases and apply new cases to perform smart maintenance operations. Competencebased empirical distance has been recently proposed as a measurement that can estimate the difference between case sample sets without knowing the actual case distributions. It is reportedly one of the most accurate drift detection algorithms in both synthetic and real-world data sets. However, as the construction of competence models have to retain every case in memory, it is not suitable for online drift detection. In addition, the high computational complexity O(n 2 ) also limits its practical application, especially when dealing with large scale data sets with time constrains. In this paper, therefore, we propose a space-based online case grouping strategy, and a new case group enhanced competence distance (CGCD), to address these issues. The experiment results show that the proposed strategy and related algorithms significantly improve the efficiency of the current leading competence-based drift detection algorithm.

Liu, B, Chen, L, Liu, C, Zhang, C & Qiu, W 2016, 'Mining co-locations from continuously distributed uncertain spatial data', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics):Web Technologies and Applications 18th Asia-Pacific Web Conference, APWeb 2016, Web Technologies and Applications : Asia-Pacific Web Conference (APWeb), Springer, Suzhou, China, pp. 66-78.
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© Springer International Publishing Switzerland 2016.A co-location pattern is a group of spatial features whose instances tend to locate together in geographic space. While traditional co-location mining focuses on discovering co-location patterns from deterministic spatial data sets, in this paper, we study the problem in the context of continuously distributed uncertain data. In particular, we aim to discover co-location patterns from uncertain spatial data where locations of spatial instances are represented as multivariate Gaussian distributions. We first formulate the problem of probabilistic co-location mining based on newly defined prevalence measures. When the locations of instances are represented as continuous variables, the major challenges of probabilistic co-location mining lie in the efficient computation of prevalence measures and the verification of the probabilistic neighborhood relationship between instances. We develop an effective probabilistic co-location mining framework integrated with optimization strategies to address the challenges. Our experiments on multiple datasets demonstrate the effectiveness of the proposed algorithm.

Liu, B, Zhou, W, Jiang, J & Wang, K 2016, 'K-Source: Multiple source selection for traffic offloading in mobile social networks', 2016 8th International Conference on Wireless Communications and Signal Processing, WCSP 2016.
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© 2016 IEEE. The constantly increasing mobile data traffic imposes heavy pressure on the current cellular networks. Traffic offloading which uses complementary network communication technologies to deliver data traffic is a possible solution for this problem. And the efficiency of such opportunistic traffic offloading is highly determined by the selection of initial sources. As an effort towards this issue, we propose a multiple sources selection method to find both the optimal number of initial sources and who are the sources in traffic offloading applications in mobile social networks. Our designed cost function consider both the diffusion time and the cost of data transmission via the cellular networks. Moreover, to accurately estimate the diffusion time, we calculate the diffusion probabilities differently in the dense contact network scenario and the sparse contact network scenario. The parameters in different schemes are analyzed and the proposed scheme is evaluated by extensive simulations.

Liu, C & Chen, L 2016, 'Summarizing Uncertain Transaction Databases by Probabilistic Tiles', Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), IEEE International Joint Conference on Neural Networks, IEEE, Vancouver, pp. 4375-4382.
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Transaction data mining is ubiquitous in various domains and has been researched extensively. In recent years, observing that uncertainty is inherent in many real world applications, uncertain data mining has attracted much research attention. Among the research problems, summarization is important because it produces concise and informative results, which facilitates further analysis. However, there are few works exploring how to effectively summarize uncertain transaction data. In this paper, we formulate the problem of summarizing uncertain transaction data as Minimal Probabilistic Tile Cover Mining, which aims to find a high-quality probabilistic tile set covering an uncertain database with minimal cost. We define the concept of Probabilistic Price and Probabilistic Price Order to evaluate and compare the quality of tiles, and propose a framework to discover the minimal probabilistic tile cover. The bottleneck is to check whether a tile is better than another according to the Probabilistic Price Order, which involves the computation of a joint probability. We prove that it can be decomposed into independent terms and calculated efficiently. Several optimization techniques are devised to further improve the performance. Experimental results on real world datasets demonstrate the conciseness of the produced tiles and the effectiveness and efficiency of our approach

Liu, W 2016, 'Factorization of multiple tensors for supervised feature extraction', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Neural Information Processing, Springer, Kyoto, Japan, pp. 406-414.
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© Springer International Publishing AG 2016.Tensors are effective representations for complex and timevarying networks. The factorization of a tensor provides a high-quality low-rank compact basis for each dimension of the tensor, which facilitates the interpretation of important structures of the represented data. Many existing tensor factorization (TF) methods assume there is one tensor that needs to be decomposed to low-rank factors. However in practice, data are usually generated from different time periods or by different class labels, which are represented by a sequence of multiple tensors associated with different labels. When one needs to analyse and compare multiple tensors, existing TF methods are unsuitable for discovering all potentially useful patterns, as they usually fail to discover either common or unique factors among the tensors: (1) if each tensor is factorized separately, the factor matrices will fail to explicitly capture the common information shared by different tensors, and (2) if tensors are concatenated together to form a larger “overall” tensor and then factorize this concatenated tensor, the intrinsic unique subspaces that are specific to each tensor will be lost. The cause of such an issue is mainly from the fact that existing tensor factorization methods handle data observations in an unsupervised way, considering only features but not labels of the data. To tackle this problem, we design a novel probabilistic tensor factorization model that takes both features and class labels of tensors into account, and produces informative common and unique factors of all tensors simultaneously. Experiment results on feature extraction in classification problems demonstrate the effectiveness of the factors discovered by our method.

Liu, Y, Huang, M, Liang, J & Huang, W 2016, 'Facial Feature Extraction and Recognition for Traditional Chinese Physiognomy', Proceedings - 20th International Conference Information Visualisation, International Conference on Information Visualisation, IEEE Computer Society's Conference Publishing Services (CPS), Lisbon, Portugal, pp. 408-412.
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We propose a novel calculation method of personality based on the Chinese physiognomy. The proposed solution combines the ancient and the modem physiognomy to summarize the corresponding relation between the personality and facial feature and model the baseline to shape the face feature. We compute histogram of image by searching for the threshold values to create a binary image in an adaptive way. The two-pass connected component method indicates the feature region. We encode the binary image to remove the noise point, so that the new connected image can provide a better result. The method was tested on ORL face database.

Liu, YT, Lin, YY, Wu, SL, Hsieh, TY & Lin, CT 2016, 'Assessment of Mental Fatigue: An EEG-Based Forecasting System for Driving Safety', Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, IEEE International Conference on Systems, Man and Cybernetics, IEEE, Kowloon, China, pp. 3233-3238.
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© 2015 IEEE. This study proposes an EEG-based forecasting system based on a functional-link recurrent self-evolving fuzzy neural network (FL-RSEFNN) for assessing mental fatigue during a highway driving task. Drivers' cognitive states significantly affect driving safety, especially for fatigue or drowsy driving which is one of common factors to endanger individuals and the public safety. In this study, a FL-RSEFNN employs an on-line gradient descent (GD) learning rule to address the EEG regression problem in brain dynamics for estimation of driving fatigue. We analyze brain dynamics in a car driving task, which is constructed in a simulated virtual reality (VR) environment. The EEG-based forecasting system is evaluated using the generalized cross-subject approach, and the results indicate that the FLRSEFNN is superior to state-of-The-Art models regardless of the use of recurrent or non-recurrent structures.

Liu, YT, Pal, NR, Wu, SL, Hsieh, TY & Lin, C 2016, 'Adaptive Subspace Sampling for Class Imbalance Processing', Proceedings of the 2016 International Conference on Fuzzy Theory and Its Applications (iFuzzy), International Conference on Fuzzy Theory and Its Applications, IEEE, Taichung, Taiwan, pp. 1-5.
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This paper presents a novel oversampling technique that addresses highly imbalanced data distribution. At present, the imbalanced data that have anomalous class distribution and underrepresented data are difficult to deal with through a variety of conventional machine learning technologies. In order to balance class distributions, an adaptive subspace self-organizing map (ASSOM) that combines the local mapping scheme and globally competitive rule is proposed to artificially generate synthetic samples focusing on minority class samples. The ASSOM is conformed with feature-invariant characteristics, including translation, scaling and rotation, and it retains the independence of basis vectors in each module. Specifically, basis vectors generated via each ASSOM module can avoid generating repeated representative features that offer nothing but heavy computational load. Several experimental results demonstrate that the proposed ASSOM method with supervised learning manner is superior to other existing oversampling techniques

Liu, YT, Wu, SL, Chou, KP, Lin, YY, Lu, J, Zhang, G, Chuang, CH, Lin, WC & Lin, CT 2016, 'A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application', 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, IEEE International Conference on Fuzzy Systems, IEEE, Vancouver, BC, Canada, pp. 2495-2500.
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© 2016 IEEE.A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications.

Liu, YT, Wu, SL, Chou, KP, Lin, YY, Lu, J, Zhang, G, Lin, WC & Lin, CT 2016, 'Driving fatigue prediction with pre-event electroencephalography (EEG) via a recurrent fuzzy neural network', 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, IEEE International Conference on Fuzzy Systems, IEEE, Vancouver, Canada, pp. 2488-2494.
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© 2016 IEEE.We propose an electroencephalography (EEG) prediction system based on a recurrent fuzzy neural network (RFNN) architecture to assess drivers' fatigue degrees during a virtual-reality (VR) dynamic driving environment. Prediction of fatigue degrees is a crucial and arduous biomedical issue for driving safety, which has attracted growing attention of the research community in the recent past. Meanwhile, combined with the benefits of measuring EEG signals facilitates, many EEG-based brain-computer interfaces (BCIs) have been developed for use in real-Time mental assessment. In the literature, EEG signals are severely blended with stochastic noise; therefore, the performance of BCIs is constrained by low resolution in recognition tasks. For this rationale, independent component analysis (ICA) is usually used to find a source mapping from original data that has been blended with unrelated artificial noise. However, the mechanism of ICA cannot be used in real-Time BCI design. To overcome this bottleneck, the proposed system in this paper utilizes a recurrent self-evolving fuzzy neural work (RSEFNN) to increase memory capability for adaptive noise cancellation when assessing drivers' mental states during a car driving task. The experimental results without the use of ICA procedure indicate that the proposed RSEFNN model remains superior performance compared with the state-of-Thearts models.

Lu, H, Zhang, K, Xiao, Y & Wang, C 2016, 'Hybrid model for short-term wind speed forecasting based on non-positive constraint combination theory', Uncertainty Modelling in Knowledge Engineering and Decision Making - Proceedings of the 12th International FLINS Conference, FLINS 2016, The 12th International Conference for Fuzzy Logic and Intelligent Technologies in Nuclear Science, Roubaix, France, pp. 240-245.
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© 2016 by World Scientific Publishing Co. Pte. Ltd. Short-term wind speed forecasting plays an irreplaceable role in efficient management of wind energy systems and accurate forecasting results could provide effective future plans for operators of utilities and wind energy systems. Aiming at improving the accuracy of short-term wind forecasting, this paper presents a new forecasting model based on the non-positive constraint combination theory. In this model, a modified optimization algorithm is used to optimize the weight coefficients of the constituent models based on the non-positive constraint combination theory. The combined model is tested using three sets of 10-min wind speed data from real-world wind farms. The testing results show that the forecasting accuracy of new model is significantly better than the constituent models.

Lumor, T, Chew, EK & Gill, AQ 2016, 'Exploring the Role of Enterprise Architecture in IS-enabled OT: An EA Principles Perspective', http://ieeexplore.ieee.org/document/7584360/, Workshop in conjunction with the IEEE International Enterprise Distributed Object Computing Conference, IEEE, Vienna, Austria, pp. 152-157.
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Although EA principles have received considerable attention in recent years, there is still little known about how EA principles can be used to govern the transformation of the Information Systems enabled organization. In this research-in- progress paper, we communicate our initial step towards answering the sub-question: how do enforcing EA principles contribute to IS-enabled OT? Based on a comprehensive literature review, we initially propose five testable hypotheses and a research model, which is a pre-requisite to developing a data-driven theory for this important area of research. It is anticipated that the ensuing theory will provide a basis for further research studying the impact of EA on IS-enabled OT. The tested research model will also provide guidance to practitioners on how to effectively design and use EA principles in managing transformative changes caused by IS within their organizations and overall industry sectors.

Madi, BMA, Sheng, QZ, Yao, L, Qin, Y & Wang, X 2016, 'PLMwsp: A Probabilistic Latent Model for Web Service QoS Prediction', 2016 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS), IEEE 23rd International Conference on Web Services (ICWS), IEEE, San Francisco, CA, pp. 623-630.
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Manongdo, R & Xu, G 2016, 'Applying client churn prediction modeling on home-based care services industry', Proceedings of the 2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC), International Conference on Behavioral, Economic and Socio-cultural Computing (BESC), IEEE, Durham, USA, pp. 1-6.
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Client churn prediction model is widely acknowledged as an effective way of realizing customer life-time value especially in service-oriented industries and under a competitive business environment. Churn model allows targeting of clients for retention campaigns and is a critical component of customer relationship management(CRM) and business intelligence systems. There are numerous statistical models and techniques applied successfully on data mining projects for various industries. While there is literature for prediction modeling on hospital health care services, non-exist for home-based care services. In this study, logistic regression, random forest and C5.0 decision tree were the models used in building a binary client churn classifier for a home-based care services company based in Australia. All models yielded prediction accuracies over 90% with tree based classifiers marginally higher and C5.0 model found to be suitable for use in this industry. This study also showed that existing client satisfaction measures currently in use by the company does not adequately contribute to churn analysis.

Mao, ZJ, Jung, TP, Lin, C & Huang, YF 2016, 'Predicting EEG Sample Size Required for Classification Calibration', Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience (LNCS), International Conference on Augmented Cognition, Springer, Toronto, Canada, pp. 57-68.
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This study considers an important problem of predicting required calibration sample size for electroencephalogram (EEG)-based classification in brain computer interaction (BCI). We propose an adaptive algorithm based on learning curve fitting to learn the relationship between sample size and classification performance for each individual subject. The algorithm can always provide the predicted result in advance of reaching the baseline performance with an average error of 17.4 %. By comparing the learning curve of different classifiers, the algorithm can also recommend the best classifier for a BCI application. The algorithm also learns a sample size upper bound from the prior datasets and uses it to detect subject outliers that potentially need excessive amount of calibration data. The algorithm is applied to three EEG-based BCI datasets to demonstrate its utility and efficacy. A Matlab package with GUI is also developed and available for downloading at https://github.com/ZijingMao/LearningCurveFittingForSampleSizePrediction. Since few algorithms are yet available to predict performance for BCIs, our algorithm will be an important tool for real-life BCI applications.

McGregor, C & Bonnis, B 2016, 'Big Data Analytics for Resilience Assessment and Development in Tactical Training Serious Games', 2016 IEEE 29TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS), 29th IEEE International Symposium on Computer-Based Medical Systems (CBMS), IEEE, NORTH IRELAND, pp. 158-162.
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McGregor, C, Bonnis, B, Stanfield, B & Stanfield, M 2016, 'Design of the ARAIG haptic garment for enhanced resilience assessment and development in tactical training serious games', IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin, pp. 214-217.
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© 2016 IEEE. First person shooter virtual reality games have begun to be used for serious games for military or civilian tactical training for new approaches for resilience assessment and development as part of new approaches for mental health training. However, sensory stimulation has been largely constrained to visual and auditory sensations with limited tactile feedback through haptic controllers. This paper presents a design for the ARAIG haptic garment for enhanced resilience assessment and development in tactical training serious games.

Mols, I, van den Hoven, E & Eggen, B 2016, 'Informing Design for Reflection: an Overview of Current Everyday Practices', Proceedings of the 9th Nordic Conference on Human-Computer Interaction, Nordic Conference on Human-Computer Interaction (NordCHI), ACM, Gothenburg, Sweden, pp. 1-10.
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There is an increasing interest in HCI in designing to support reflection in users. In this paper, we specifically focus on everyday life reflection, covering and connecting a broad range of topics from someone's life rather than focusing on a very specific aspect. Although many systems aim to support reflection, few are based on an overview of how people currently integrate reflection in everyday life. In this paper, we aim to contribute to this gap through a questionnaire on everyday life reflection practices combining both qualitative and quantitative questions. Findings provide insights in the broad range of people that engage with reflection in different ways. We aim to inform design through four considerations: rumination, timing, initiative and social context.

Mols, I, van den Hoven, E & Eggen, B 2016, 'Technologies for Everyday Life Reflection: Illustrating a Design Space', Proceedings of the TEI '16: Tenth International Conference on Tangible, Embedded, and Embodied Interaction, Tangible, Embedded, and Embodied Interaction, ACM, Eindhoven, The Netherlands, pp. 53-61.
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Reflection gives insight, supports action and can improve wellbeing. People might want to reflect more often for these benefits, but find it difficult to do so in everyday life. Research in HCI has shown the potential of systems to support reflection in different contexts. In this paper we present a design space for supporting everyday life reflection. We produced a workbook with a selection of conceptual design proposals, which show how systems can take different roles in the process of reflection: triggering, supporting and capturing. We describe a design space with two dimensions by combining these roles with strategies found in literature. We contribute to the extensive body of work on reflection by outlining how design for everyday life reflection requires a focus on more holistic reflection, design with openness and integration in everyday life.

Montgomery, J, Reid, M & Drake, BJ 2015, 'Protocols and Structures for Inference: A RESTful API for Machine Learning', Proceedings of The 2nd International Conference on Predictive APIs and Apps, 2nd International Conference on Predictive APIs and Apps, Journal of Machine Learning Research, Sydney, pp. 29-42.
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Diversity in machine learning APIs (in both software toolkits and web services), works against realising machine learning’s full potential, making it difficult to draw on individual algorithms from different products or to compose multiple algorithms to solve complex tasks. This paper introduces the Protocols and Structures for Inference (PSI) service architecture and specification, which presents inferential entities—relations, attributes, learners and predictors—as RESTful web resources that are accessible via a common but flexible and extensible interface. Resources describe the data they ingest or emit using a variant of the JSON schema language, and the API has mechanisms to support non-JSON data and future extension of service features.

Nascimben, M, King, JT & Lin, CT 2016, 'Resting Upper Alpha Can Predict Motor Imagery Performance?'.

Nascimben, M, Yu, Y-H, Lin, C-T, King, J-T, Singh, AK & Chuang, C 2016, 'Effect of a cognitive involving videogame on MI task', Proceedings of the 6th International Brain-Computer Interface Meeting, organized by the BCI Society, Verlag der TU Graz, Graz University of Technology, sponsored by g.tec medical engineering GmbH, pp. 175-175.
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Researcher and developers have to face with performance variation in motor imagery[1] across and within subjects and its fluctuations over time. In addition MI achievement variations within subjects are closely correlated to neurophysiological variables[2]. In our study a MI task was submitted to a group of healthy subjects before and after playing BCIGEM videogame for 90 minutes. Some EEG features were found, suggesting a different pathway of activation inside MU rhythm during Motor Imagery (MI) after a mentally challenging activity like playing a videogame

Nguyen, H, Liu, W, Rivera, P & Chen, F 2016, 'TrafficWatch: Real-time traffic incident detection and monitoring using social media', 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, New Zealand, pp. 540-551.
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© Springer International Publishing Switzerland 2016. Social media has become a valuable source of real-time information. Transport Management Centre (TMC) in Australian state government of New South Wales has been collaborating with us to develop TrafficWatch, a system that leverages Twitter as a channel for transport network monitoring, incident and event managements. This system utilises advanced web technologies and state-of-the-art machine learning algorithms. The crawled tweets are first filtered to show incidents in Australia, and then divided into different groups by online clustering and classification algorithms. Findings from the use of TrafficWatch at TMC demonstrated that it has strong potential to report incidents earlier than other data sources, as well as identifying unreported incidents. TrafficWatch also shows its advantages in improving TMC’s network monitoring capabilities to assess network impacts of incidents and events.

Nguyen, Q, Simoff, S, Qian, Y & Huang, M 2016, 'Deep Exploration of Multidimensional Data with Linkable Scatterplots', ACM Proceeding VINCI '16 Proceedings of the 9th International Symposium on Visual Information Communication and Interaction, Visual Information Communication and Interaction, ACM Press, Dallas, TX, USA, pp. 43-50.
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Clarity, simplicity and visual adjustability to the preference of the analyst are key aspects of the visualization techniques required by visual analytics in broad sense. Scatterplots and scatterplot matrices are commonly used for visually analyzing multidimensional multivariate data. This paper presents a new approach for deep visual exploration of large multi-attribute data using linkable scatterplots. Proposed method overcomes the limitations of the single scatterplot by providing more plot panels for better comparison while it reduces the unnecessary number of panels of the scatterplot matrix method. The panels are fully interactive and linking together where variables can be mapped on axes independently or on common visual attributes such as color, size and shape. We illustrate the effectiveness of proposed linkable scatterplot method on various data sets.

Nguyen, TTS & Lu, H 2016, 'Domain ontology construction using web usage data', Proceedings of AI 2016: Advances in Artificial Intelligence (LNCS), Australasian Joint Conference on Artificial Intelligence, Springer, Hobart, Australia, pp. 338-344.
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© Springer International Publishing AG 2016.Ontologies play an important role in conceptual model design and the development of machine-readable knowledge bases. They can be used to represent various knowledge not only about content concepts, but also explicit and implicit relations. While ontologies exist for many application domains of websites, the implicit relations between domain and accessed Web-pages might be less concerned and unclear. These relations are crucial for Web-page recommendation in recommender systems. This paper presents a novel method developing an ontology of Web-pages mapped to domain knowledge. It will focus on solutions of semi-automating ontology construction using Web usage data. An experiment of Microsoft Web data is implemented and evaluated.

Oberst, S, Zhang, Z, Campbell, G, Morlock, M, Lai, JCS & Hoffmann, N 2016, 'Towards the understanding of hip squeak in total hip arthroplasty using analytical contact models with uncertainty', Proceedings of the 45th International Congress on Noise Control Engineering, Internoise Congress, http://pub.dega-akustik.de/IN2016/data/index.html, Hamburg, Germany, pp. 5539-5549.
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© 2016, German Acoustical Society (DEGA). All rights reserved.Osteoarthritis in hip joints affects patients' quality of life such that often only costly orthopaedic surgeries i.e. total hip arthroplasty (THA) provide relief. Common implant materials are metal alloys, steel or titanium-based, plastics such as ultra-high molecular weight polyethylene, or biocompatible alumina and composite ceramics. Hard-on-hard (HoH) bearing articulations, i.e. ceramic-on-ceramic, or hard-on-soft combinations are used. HoH implants have been known to suffer from squeaking, a phenomenon commonly encountered in friction-induced self-excited vibrations. However, the frictional contact mechanics, its dynamics related to impingement, the effect of socket position, stem configuration, bearing size and patient characteristics are poorly understood. This study gives an overview of the state of the art biomechanical research related to squeaking in THA, with a focus on the effects of friction, stability, related wear and lubrication. An analytical model is proposed to study the onset of friction-induced vibrations in a simplified hemispherical hip stem rubbing in its bearing by varying the contact area. Preliminary results of the complex eigenvalue analysis and stick-slip motion analysis indicate that an increased contact fosters the development of instabilities, even at very small values of the friction coefficient owing to large local contact pressures.

Ojha, S & Williams, MA 2016, 'Ethically-Guided Emotional Responses for Social Robots: Should I Be Angry?', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Social Robotics (ICSR), SPRINGER-VERLAG BERLIN, Kansas City, USA.
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Emotions play a critical role in human-robot interaction. Human-robot interaction in social contexts will be more effective if robots can understand human emotions and express (display) emotions accordingly as a means to communicate their own internal state. In this paper we present a novel computational model of robot emotion generation based on appraisal theory and guided by ethical judgement. There have been recent advances in developing emotion for robots. However, despite the extensive research on robot emotion, it is difficult to say if a particular robot is exhibiting appropriate emotions or even showing that it can empathize with humans by exhibiting similar emotions to humans in the same situation. A key question is - to what extent should a robot direct anger toward a young child or an elderly person for an act that it should show anger towards an ordinary adult to signal danger or stupidity? Realizing the need for an ethically guided approach to emotion expressions in social robots as they interact with people, we present a novel Ethical Emotion Generation System (EEGS) for the expression of the most acceptable emotions in social robots.

Orth, D & van den Hoven, E 2016, '"I wouldn't choose that key ring; it's not me": A Design Study of Cherished Possessions and the Self', Proceedings of the 28th Australian Conference on Human-Computer Interaction (OzCHI’16), Australian Computer Human Interaction Conference, ACM, Launceston, Tasmania.
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We each possess certain objects that are dear to us for a variety of reasons. They can be sentimental to us, bring us delight through their use or empower us. Throughout our lives, we use these cherished possessions to reaffirm who we are, who we were and who we wish to become. To explore this, we conducted a design study that asked ten participants to consider their emotional attachment towards and the identity-relevance of cherished and newly introduced possessions. Participants were then asked to elaborate on their responses in interviews. Through a thematic analysis of these responses, we found that the emotional significance of possessions was reportedly influenced by both their relevance to selfhood and position within a life story. We use these findings to discuss how the design of new products and systems can promote emotional attachment by holding a multitude of emotionally significant meanings to their owners.

Pang, G, Cao, L & Chen, L 2016, 'Outlier detection in complex categorical data by modelling the feature value couplings', Proceedings of the 25th International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence (IJCAI), AAAI Press, New York.
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Pang, G, Cao, L, Chen, L & Liu, H 2016, 'Unsupervised Feature Selection for Outlier Detection by Modelling Hierarchical Value-Feature Couplings', Proceedings - IEEE International Conference on Data Mining, ICDM, IEEE International Conference on Data Mining, IEEE, Barcelona.
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Peppas, P & Williams, MA 2016, 'Kinetic consistency and relevance in belief revision', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Logics in Artificial Intelligence, European Conference, Springer, Larnaca, Cyprus, pp. 401-414.
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© Springer International Publishing AG 2016.A critical aspect of rational belief revision that has been neglected by the classical AGM framework is what we call the principle of kinetic consistency. Loosely speaking, this principle dictates that the revision policies employed by a rational agent at different belief sets, are not independent, but ought to be related in a certain way. We formalise kinetic consistency axiomatically and semantically, and we establish a representation result explicitly connecting the two. We then combine the postulates for kinetic consistency, with Parikh’s postulate for relevant change, and add them to the classical AGM postulates for revision; we call this augmented set the extended AGM postulates. We prove the consistency and demonstrate the scope of the extended AGM postulates by showing that a whole new class of concrete revision operators introduced hererin, called PD operators, satisfies all extended AGM postulates. PD operators are of interest in their own right as they are natural generalisations of Dalal’s revision operator.We conclude the paper with some examples illustrating the strength of the extended AGM postulates, even for iterated revision scenarios.

Pham, P & Huang, M 2016, 'MCquery: interactive visual query of relational data with coordinating context displays', ACSW '16 Proceedings of the Australasian Computer Science Week Multiconference, the Australasian Computer Science Week Multiconference, ACM, Canberra.
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The use of visual queries for relational data retrieval is a well-known approach. Various advanced visualization methods have been proposed to improve the quality of such queries. However, most of current methods focus on constructing queries in a single visualization context that increases user's efforts significantly in visually matching the details with their contexts. The single visualization causes the isolation of data models and query results which could be displayed on a single screen. The challenge is how to visually and synchronously represent all data models, queries and query results in a single screen that will make much convenient for users to interact with the visualization. For dealing with the challenge, this paper proposes a novel method called MCquery. This method allows query specification in the coordinating visual contexts of data models and query results by interaction on node-link graphs of relational data representations. MCquery enables relational data to be analyzed with concrete and relative results from the incremental exploration of data models, queries and query results. The demonstration based on a typical case study shows that MCquery is useful for exploring relational data in a single screen with synchronous visual integration of data models, queries and query results.

Pham, P, Huang, M & Nguyen, Q 2016, 'SumUp: Statistical Visual Query of Multivariate Data with Parallel-Coordinate Geometry', Cooperative Design, Visualization, and Engineering (LNCS), Cooperative Design, Visualization, and Engineering, Springer International Publishing, Sydney, pp. 386-393.
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One of the most noticeable issues of parallel coordinate visualization is how to quantitatively analyze density caused by polyline growth in a limited space on axes. The existing visualization tools only support the comparison among single dimensions and single ranges of polylines, which could face limitation in cases of complicated analytics. This paper proposes a new visual-query technique, named SumUp, for statistical analysis of multiple attributes of dimensions and multiple ranges of polylines. The methodology of SumUp is primarily based on developing dynamic queries using brushing operations to deliver summary stacked bars adaptive with parallel coordinates. Users can easily observe quantitative information from data patterns and compare multiple attributes over the density of polylines in the parallel coordinate visualization. Early experiments show that our proposed technique could potentially enhance the manipulation on parallel coordinates, showing by a typical case study.

Pickrell, M, Bongers, B & Van Den Hoven, E 2016, 'Understanding changes in the motivation of stroke patients undergoing rehabilitation in hospital', Persuasive Technology: Lecture Notes in Computer Science, International Conference on Persuasive Technology, Springer, Salzburg, Austria, pp. 251-262.
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© Springer International Publishing Switzerland 2016. Stroke patient motivation can fluctuate during rehabilitation due to a range of factors. This study reports on qualitative research, consisting of observations of stroke patients undergoing rehabilitation and interviews with patients about the changes in motivation they identified during their time completing rehabilitation in the hospital. We found a range of positive and negative factors which affect motivation. Positive factors include improvements in patient movement and support from other patients and family members. Negative factors include pain and psychological issues such as changes in mood. From this fieldwork, a set of design guidelines has been developed to act as a platform for researchers and designers developing equipment for the rehabilitation of stroke patients.

Pietroni, N, Puppo, E, Marcias, G, Scopigno, R & Cignoni, P 2016, 'Tracing field-coherent quad layouts', Computer Graphics Forum, pp. 485-496.

Popov, A, Fink, W, McGregor, C & Hess, A 2016, 'PHM for astronauts: Elaborating and refining the concept', IEEE Aerospace Conference Proceedings.
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© 2016 IEEE. Clarifying and evolving the PHM for Astronauts concept, introduced in [1], this conceptual paper focuses on particular PHM-based solutions to bring Human Health and Performance (HH&P) technologies to the required technology readiness level (TRL) in order to mitigate the HH&P risks of manned space exploration missions. This paper discusses the particular PHM-based solutions for some HH&P technologies that are, namely by NASA designation, the Autonomous Medical Decision technology and the Integrated Biomedical Informatics technology. Both of the technologies are identified as essential ones in NASA's integrated technology roadmap for the Technology Area 06: Human Health, Life Support, and Habitation Systems. The proposed technology solutions are to bridge PHM, an engineering discipline, to HH&P domain in order to mitigate the risks by focusing on efforts to reduce countermeasure mass and volume and drive the risks down to an acceptable level. The Autonomous Medical Decision technology is based on wireless handheld devices and is a result of a paradigm shift from tele-medicine to that of health support autonomy. The Integrated Biomedical Informatics technology is based on Crew Electronic Health Records (CEHR) system with predictive diagnostics capability developed for crew members rather than for healthcare professionals. The paper explores the proposed PHM-based solutions on crew health maintenance in terms of predictive diagnostics providing early and actionable real-time warnings of impending health problems that otherwise would have gone undetected.

Prior, J, Ferguson, S & Leaney, J 2016, 'Reflection is hard: teaching and learning reflective practice in a software studio', Proceedings of the Australasian Computer Science Week Multiconference, Australasian Computing Education Conference, ACM, Canberra, Australia.
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We have observed that it is a non-trivial exercise for undergraduate students to learn how to reflect. Reflective practice is now recognised as important for software developers and has become a key part of software studios in universities, but there is limited empirical investigation into how best to teach and learn reflection. In the literature on reflection in software studios, there are many papers that claim that reflection in the studio is mandatory. However, there is inadequate guidance about teaching early stage students to reflect in that literature. The essence of the work presented in this paper is a beginning to the consideration of how the teaching of software development can best be combined with teaching reflective practice for early stage software development students. We started on a research programme to understand how to encourage students to learn to reflect. As we were unsure about teaching reflection, and we wished to change our teaching as we progressively understood better what to do, we chose action research as the most suitable approach. Within the action research cycles we used ethnography to understand what was happening with the students when they attempted to reflect. This paper reports on the first 4 semesters of research. We have developed and tested a reflection model and process that provide scaffolding for students beginning to reflect. We have observed three patterns in how our students applied this process in writing their reflections, which we will use to further understand what will help them learn to reflect. We have also identified two themes, namely, motivation and intervention, which highlight where the challenges lie in teaching and learning reflection.

Qiao, M, Bian, W, Da Xu, RY & Tao, D 2016, 'Diversified Hidden Markov Models for Sequential Labeling', 2016 32ND IEEE INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), 32nd IEEE International Conference on Data Engineering (ICDE), IEEE, Helsinki, FINLAND, pp. 1512-+.
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Ramezani, F, Naderpour, M & Lu, J 2016, 'A multi-objective optimization model for virtual machine mapping in cloud data centres', 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE International Conference on Fuzzy Systems, IEEE, Vancouver, BC, Canada, pp. 1259-1265.
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Modern cloud computing environments exploit virtualization for efficient resource management to reduce computational cost and energy budget. Virtual machine (VM) migration is a technique that enables flexible resource allocation and increases the computation power and communication capability within cloud data centers. VM migration helps cloud providers to successfully achieve various resource management objectives such as load balancing, power management, fault tolerance, and system maintenance. However, the VM migration process can affect the performance of applications unless it is supported by smart optimization methods. This paper presents a multi-objective optimization model to address this issue. The objectives are to minimize power consumption, maximize resource utilization (or minimize idle resources), and minimize VM transfer time. Fuzzy particle swarm optimization (PSO), which improves the efficiency of conventional PSO by using fuzzy logic systems, is relied upon to solve the optimization problem. The model is implemented in a cloud simulator to investigate its performance, and the results verify the performance improvement of the proposed model.

Ramos, L, van den Hoven, E & Miller, L 2016, 'Designing for the Other ’Hereafter’: When Older Adults Remember About Forgetting', Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, Conference for Human-Computer Interaction, ACM, San Jose, CA, USA, pp. 721-732.
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Designing to support memory for older individuals is a complex challenge in human-computer interaction (HCI) research. Past literature on human memory has mapped processes for recalling past experiences, learning new things, remembering to carry out future intentions and the importance of attention. However, the understanding of how older adults perceive forgetting in daily life remains limited. This paper narrows this gap through a study with older persons (n=18) living independently using self-reporting and semi-structured focus groups to explore what they forget, how they react, and what mechanisms they put in place to recover from and avoid forgetting. Findings include occurrences of prospective and retrospective memory lapses, conflicting negative and neutral perceptions, and techniques to manage forgetting. Participant responses indicate that an awareness of forgetting fosters internal tensions among older adults, thereby creating opportunities for further design research, e.g., to defuse and normalise these reactions.

Rapp, A, Cena, F, Kay, J, Kummerfeld, B, Hopfgartner, F, Larsen, JE & Van Den Hoven, E 2016, 'FuturePD. the future of personal data: Envisioning new personalized services enabled by Quantified Self technologies', CEUR Workshop Proceedings.
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Quantified Self is rising new challenges for user modeling and personalization. In this workshop we aim at exploring the future of personalized services enabled by Quantified Self technologies.

Rashidi, L, Kan, A, Bailey, J, Chan, J, Leckie, C, Liu, W, Rajasegarar, S & Ramamohanarao, K 2016, 'Node re-ordering as a means of anomaly detection in time-evolving graphs', 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, Riva del Garda, Italy, pp. 162-178.
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© Springer International Publishing AG 2016.Anomaly detection is a vital task for maintaining and improving any dynamic system. In this paper, we address the problem of anomaly detection in time-evolving graphs, where graphs are a natural representation for data in many types of applications. A key challenge in this context is how to process large volumes of streaming graphs. We propose a pre-processing step before running any further analysis on the data, where we permute the rows and columns of the adjacency matrix. This pre-processing step expedites graph mining techniques such as anomaly detection, PageRank, or graph coloring. In this paper, we focus on detecting anomalies in a sequence of graphs based on rank correlations of the reordered nodes. The merits of our approach lie in its simplicity and resilience to challenges such as unsupervised input, large volumes and high velocities of data. We evaluate the scalability and accuracy of our method on real graphs, where our method facilitates graph processing while producing more deterministic orderings. We show that the proposed approach is capable of revealing anomalies in a more efficient manner based on node rankings. Furthermore, our method can produce visual representations of graphs that are useful for graph compression.

Raza, SA, Clark, J & Williams, M-A 2016, 'On Designing Socially Acceptable Reward Shaping', International Conference on Social Robotics, Springer, Cham, pp. 860-869.
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Raza, SA, Johnston, B & Williams, M-A 2016, 'Reward from Demonstration in Interactive Reinforcement Learning', The Twenty-Ninth International Flairs Conference.
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Riaz-Ud-din, F, Doss, R & Zhou, W 2016, 'String matching query verification on cloud-hosted databases', ACM International Conference Proceeding Series.
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© 2016 Copyright held by the owner/author(s). Publication rights licensed to ACM. Database query verification schemes attempt to provide authenticity, completeness, and freshness guarantees for queries executed on untrusted cloud servers. A number of such schemes currently exist in the literature, allowing query verification for queries that are based on matching whole values (such as numbers, dates, etc.) or for queries based on keyword matching. However, there is a notable gap in the research with regard to query verification schemes for patternmatching queries. Our contribution here is to provide such a verification scheme that provides correctness guarantees for pattern-matching queries executed on the cloud. We describe a trivial scheme, âYżand show how it does not provide completeness guarantees, and then proceed to describe our scheme based on efficient primitives such as cryptographic hashing and Merkle hash trees along with suffix arrays. We also provide experimental results based on a working prototype to show the practicality of our scheme.

Romat, H, Williams, M-A, Wang, X, Johnston, B, Bard, H & ACM 2016, 'Natural Human-Robot Interaction Using Social Cues', Proceedings of the 11th ACM/IEEE International Conference on Human Robot Interaction, ACM/IEEE International Conference on Human Robot Interaction (HRI), IEEE, Christchurch, New Zealand, pp. 503-504.
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This paper investigates the problem of how humans understand and control human-robot collaborative action and how to build natural interactions during human-robot collaborative action. We use a "pick and place" experiment to study collaborative activities between a human and a robot. The results show that even if human participants had a good understanding of the maximum reachability of the robot, they consistently take a surprisingly long time to help and assist the robot when a target object is out of its reach. We implemented a number of social cues in the experiment, analysed their effects in order to identify the role they could play to improve the fluency of human-robot collaboration. The experimental results showed that when the robot uses head movements, two hands or a gesture to indicate non-reachability, people react in a more natural way to assist the robot.

Sá, A, Rodriguez-Echavarria, K, Pietroni, N & Cignoni, P 2016, 'State of the art on functional fabrication', Proceedings of the Eurographics Workshop on Graphics for Digital Fabrication, Eurographics Association, pp. 1-9.

Saleheen, S, Lai, W, Huang, X, Huang, W & Huang, M 2016, 'What Next in Designing Personalized Visualization of Web Information', Cooperative Design, Visualization, and Engineering (LNCS), Cooperative Design, Visualization, and Engineering, Springer International Publishing, Sydney, pp. 134-141.
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Current state of the art in personalized visualization of web information is tailored to provide a better view of how the information is resided and connected to each other inside the internet. With the recent enhancement in information and communication technology, users are provided a very large amount of information when they search for a particular information from a specific website. Studies show that, user can perceive the information in a more better way if they are provided the information with visual representation instead of its textual counterpart. However, to be effective to the users, the visual representation should be specific to the need of a particular user. Research is conducted from various viewpoints to make the visual representation (graph-representation of the web information) more user-specific. To achieve this, filtering and clustering techniques have been applied to web information to make large web graphs to compact ones. Besides, user modeling has been applied to infer the user’s need for a specific time and context. These tend to make the navigation of web information easy and effective to the end user. This paper discusses the current progress in graph-based web information visualization and also outlines the scopes of improvements that could benefit the user exploring the desired information from the web space effectively and efficiently.

Schubert, P & Williams, SP 2016, 'The case of uniconnect - the shaping of an academic collaboration platform', Multikonferenz Wirtschaftsinformatik, MKWI 2016, pp. 327-338.
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In this paper we present a study of socio-Technical change and the adoption of an enterprise collaboration system (ECS). Through the use of a longitudinal interpretive case study, we examine the context and dynamics at play in the implementation of an academic collaboration platform. The collaboration platform was envisaged as an information infrastructure for teaching and research collaboration with the objective of supporting Boyer's four scholarships. Using theories of sociotechnical change as both a theoretical and analytical lens and concepts drawn from actor-network theory (ANT) we focus on the shaping of the platform, the translations that occur and the inscriptions that emerge from the aligning and re-Aligning of heterogeneous networks of actors and interests. In the study findings we present the different constellations and concerns that emerged as the project passed through several phases. In its current state the information infrastructure has achieved a degree of durability; the various interests in the actor-network are aligned and, to a large extent, the platform could be described as having achieved irreversibility.

Shu, Q, Guo, H, Liang, J, Che, L, Liu, J & Yuan, X 2016, 'EnsembleGraph: Interactive Visual Analysis of Spatial-Temporal Behavior for Ensemble Data', Pacific Visualization Symposium (PacificVis), 2016 IEEE, IEEE Symposium on Pacific Visualization (PacificVis), IEEE, Taipei, Taiwan.
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This paper presents a novel visual analysis tool, EnsembleGraph, which aims at helping scientists understand spatiotemporal similarities across runs in time-varying ensemble simulation data. We abstract the input data into a graph, where each node represents a region with similar behaviors across runs and nodes in adjacent time frames are linked if their regions overlap spatially. The visualization of this graph, combined with multiple-linked views showing details, enables users to explore, select, and compare the extracted regions that have similar behaviors. The driving application of this paper is the study of regional emission influences over tropospheric ozone, based on the ensemble simulations conducted with different anthropogenic emission absences using MOZART-4. We demonstrate the effectiveness of our method by visualizing the MOZART-4 ensemble simulation data and evaluating the relative regional emission influences on tropospheric ozone concentrations.

Singh, A, Wang, Y-K, Chiu, C-Y, Yu, Y-H, Nascimben, M, King, J-T, Chuang, C-H, Chen, S-A, Ko, L-W, Pal, NR & others 2016, 'Attention in Complex Environment of Brain Computer Interface', Proceedings of the 6th International Brain-Computer Interface Meeting, organized by the BCI Society, Verlag der TU Graz, Graz University of Technology, sponsored by g.tec medical engineering GmbH, pp. 165-165.
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Song, K, Chen, L, Gao, W, Feng, S, Wang, D & Zhang, C 2016, 'PerSentiment: A Personalized Sentiment Classi cation System for Microblog Users', International World Wide Web Conference (WWW), Montreal.

Song, K, Gao, W, Chen, L, Feng, S, Wang, D & Zhang, C 2016, 'Build Emotion Lexicon from the Mood of Crowd via Topic-Assisted Joint Non-negative Matrix Factorization', ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Pisa, Italy.

Sui, Y & Xue, J 2016, 'On-Demand Strong Update Analysis via Value-Flow Refinement', FSE'16: PROCEEDINGS OF THE 2016 24TH ACM SIGSOFT INTERNATIONAL SYMPOSIUM ON FOUNDATIONS OF SOFTWARE ENGINEERING, 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE), ASSOC COMPUTING MACHINERY, Seattle, WA, pp. 460-473.
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Sui, Y & Xue, J 2016, 'SVF: Interprocedural Static Value-Flow Analysis in LLVM', PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON COMPILER CONSTRUCTION (CC 2016), 25th International Conference on Compiler Construction (CC), ASSOC COMPUTING MACHINERY, Barcelona, SPAIN, pp. 265-266.
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Sui, Y, Di, P & Xue, J 2016, 'Sparse Flow-Sensitive Pointer Analysis for Multithreaded Programs', PROCEEDINGS OF CGO 2016: THE 14TH INTERNATIONAL SYMPOSIUM ON CODE GENERATION AND OPTIMIZATION, 14th International Symposium on Code Generation and Optimization (CGO), ASSOC COMPUTING MACHINERY, Barcelona, SPAIN, pp. 160-170.
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Sui, Y, Fan, X, Zhou, H & Xue, J 2016, 'Loop-Oriented Array- and Field-Sensitive Pointer Analysis for Automatic SIMD Vectorization', ACM SIGPLAN NOTICES, ASSOC COMPUTING MACHINERY, pp. 41-51.
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Sutter, D, Tomamichel, M & Harrow, AW 2016, 'Strengthened monotonicity of relative entropy via pinched Petz recovery map', Proceedings IEEE International Symposium on Information Theory (ISIT 2016), IEEE, Barcelona, pp. 760-764.
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Talaei-Khoei, A & Daniel, J 2016, 'Peer Tutoring on Facebook to Engage Students with Flipped Classes: A Correlational Experiment on Learning Outcomes', AMCIS 2016: Surfing the IT Innovation Wave - 22nd Americas Conference on Information Systems, Americas Conference on Information Systems, AMCIS, San Diego, US.
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Tamassia, M, Zambetta, F, Raffe, WL, Mueller, FF & Li, X 2016, 'Dynamic choice of state abstraction in Q-learning', Frontiers in Artificial Intelligence and Applications, European Conference on Artificial Intelligence, IOS Press, The Hague, Holland, pp. 46-54.
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© 2016 The Authors and IOS Press.Q-learning associates states and actions of a Markov Decision Process to expected future reward through online learning. In practice, however, when the state space is large and experience is still limited, the algorithm will not find a match between current state and experience unless some details describing states are ignored. On the other hand, reducing state information affects long term performance because decisions will need to be made on less informative inputs. We propose a variation of Q-learning that gradually enriches state descriptions, after enough experience is accumulated. This is coupled with an ad-hoc exploration strategy that aims at collecting key information that allows the algorithm to enrich state descriptions earlier. Experimental results obtained by applying our algorithm to the arcade game Pac-Man show that our approach significantly outperforms Q-learning during the learning process while not penalizing long-term performance.

Tao, D, yu, X & bian, W 2016, 'Scalable completion of nonnegative matrices with the separable structure', 30th AAAI Conference on Artificial Intelligence, AAAI 2016, AAAI Conference on Artificial Intelligence, AAAI Press, Pheonix, Arizona, United States of America, pp. 2279-2285.
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Tomamichel, M & Hayashi, M 2016, 'Operational interpretation of Rényi conditional mutual information via composite hypothesis testing against Markov distributions', Proceedings IEEE International Symposium on Information Theory (ISIT 2016), IEEE International Symposium on Information Theory, IEEE, Barcelona, Spain, pp. 585-589.
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We revisit the problem of asymmetric binary hypothesis testing against a composite alternative hypothesis. We introduce a general framework to treat such problems when the alternative hypothesis adheres to certain axioms. In this case we find the threshold rate, the optimal error and strong converse exponents (at large deviations from the threshold) and the second-order asymptotics (at small deviations from the threshold). We apply our results to find operational interpretations of Rényi information measures. In particular, in case the alternative hypothesis consists of certain tripartite distributions satisfying the Markov property, we find that the optimal exponents are determined by the Rényi conditional mutual information

Tran, J, Nguyen, Q, Simoff, S & Huang, M 2016, 'Areas of Life Visualisation: Growing Data-Reliance', Lecture Notes in Computer Science, Cooperative Design, Visualization, and Engineering, Springer International Publishing, Sydney, pp. 227-234.
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This paper presents a framework to mine and identify the areas of life and the way they are perceived, understood cognitively, and effectively using visualization and machine learning. We provide an overview of the network of users including their activity and connections as well as zoom and details on demand of each individual areas of life. This research identifies the factors of each area of life which are significant on the user’s social media profile in relation to information associated with each user such as time and location, including dynamic social behaviors. It aims to identify the key psychological factors and salient behaviors in order to find out the psychological factors of the user, and other overheads that can be portrayed in an image.

Treadaway, C, Kenning, G, Prytherch, D & Fennell, J 2016, 'Laugh: designing to enhance positive emotion for people living with dementia', Proceedings - D and E 2016: 10th International Conference on Design and Emotion - Celebration and Contemplation, Design and Emotion, International Design and Emotion Society, Amsterdam.
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Treadaway, C, Prytherch, D, Kenning, G & Fennell, J 2016, 'In the moment: designing for late stage dementia', Design Research Society 2016 (DRS2016), Design Research Society (UK) International Conference, Design Research Society, Brighton UK.
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Van Gennip, DA, Orth, D, Imtiaz, MA, van den Hoven, E & Plimmer, B 2016, 'Tangible Cognition: Bringing Together Tangible Interaction and Cognition in HCI', OzCHI'16: Proceedings of the 28th Australian Conference on Human-Computer Interaction, ACM, Launceston, Tasmania, Australia.
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This workshop will explore the relationship between HCI using tangible user interfaces (TUIs) and cognition. We see exciting opportunities for tangible interaction to address some of the cognitive challenges of concern to the HCI community, in areas such as education, healthcare, games, reminiscing and reflection, and community issues. Drawing together the Australasian community, with those from further afield, we hope to strengthen research and build a local community in this exciting and rapidly developing field. Participation is invited from researchers working in tangible user interfaces or those interested in cognition and interaction. During the workshop the majority of the time will be spent in small group discussions and brainstorming solutions.

Van Gennip, DA, van den Hoven, E & Markopoulos, P 2016, 'The Phenomenology of Remembered Experience: A Repertoire for Design', http://dl.acm.org/citation.cfm?id=2970930, European Conference on Cognitive Ergonomics, ACM Press, Nottingham, United Kingdom, pp. 1-8.
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There is a growing interest in interactive technologies that support remembering by considering functional, experiential, and emotional support to their users. Design driven research benefits from an understanding of how people experience autobiographical remembering. We present a phenomenological study in which twenty-two adults were interviewed using the repertory grid technique; we aimed at soliciting personal constructs that characterize people's remembered experiences. Inductive coding revealed that 77,8% of identified constructs could be reliably coded in five categories referring to contentment, confidence/unease, social interactions, reflection, and intensity. These results align with earlier classifications of personal constructs and models of human emotion. The categorization derived from this study provides an empirically founded characterization of the design space of technologies for supporting remembering. We discuss its potential value as a tool for evaluating interactive systems in relation to personal and social memory talk, and outline future improvements.

Venkata, SK, Keppens, J & Musial, K 2016, 'Agent based simulation to evaluate adaptive caching in distributed databases', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 455-462.
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© Springer International Publishing Switzerland 2016. Caching frequently used data is a common practice to improve query performance in database systems. But traditional algorithms used for cache management prove to be insufficient in distributed environment where groups of users require similar or related data from multiple databases. Repeated data transfers can become a bottleneck leading to long query response time and high resource utilization. Our work focuses on adaptive algorithms to decide on optimal grain of data to be cached and cache refreshment techniques to reduce data transfers. In this paper, we present agent based simulation to investigate and in consequence improve cache management in the distributed database environment. Dynamic grain size and decisions on cache refreshment are made as a result of coordination and interaction between agents. Initial results show better response time and higher data availability compared to traditional caching techniques.

Versteeg, M, van den Hoven, E & Hummels, C 2016, 'Interactive jewellery as mementos: a design exploration', Proceedings of the TEI '16: Tenth International Conference on Tangible, Embedded, and Embodied Interaction, Tangible, Embedded, and Embodied Interaction, ACM, Eindhoven, The Netherlands, pp. 44-52.
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Many current wearables have a technology-driven background: the focus is primarily on functionality, while their possible personal and social-cultural value is underappreciated. We think that developing wearables from a jewellery perspective can compensate for this. The personal and social cultural values embodied by traditional jewellery are often tightly connected to their function as memento. In this paper we reflect from a jewellery perspective, a memory-studies perspective and a TEI-perspective on three design proposals for interactive jewellery. We identify 1) drawing inspiration from interaction with traditional jewellery, 2) using relatively simple technology with high experiential qualities, 3) abstract and poetic data representation and 4) storing data uniquely on the digital jewel as possible design directions.

Vitale, J, Williams, M-A & Johnston, B 2016, 'The face-space duality hypothesis: a computational model', Proceedings of the 38th Annual Conference of the Cognitive Science Society, Annual Conference of the Cognitive Science Society, Cognitive Science Society, Philadelphia, pp. 514-519.
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Wang, D, Deng, S & Xu, G 2016, 'GEMRec: A graph-based emotion-aware music recommendation approach', 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, Shanghai, China, pp. 92-106.
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© Springer International Publishing AG 2016.Music recommendation has gained substantial attention in recent times. As one of the most important context features,user emotion has great potential to improve recommendations,but this has not yet been sufficiently explored due to the difficulty of emotion acquisition and incorporation. This paper proposes a graph-based emotion-aware music recommendation approach (GEMRec) by simultaneously taking a user’s music listening history and emotion into consideration. The proposed approach models the relations between user,music,and emotion as a three-element tuple (user,music,emotion),upon which an Emotion Aware Graph (EAG) is built,and then a relevance propagation algorithm based on random walk is devised to rank the relevance of music items for recommendation. Evaluation experiments are conducted based on a real dataset collected from a Chinese microblog service in comparison to baselines. The results show that the emotional context from a user’s microblogs contributes to improving the performance of music recommendation in terms of hitrate,precision,recall,and F1 score.

Wang, D, Deng, S, Liu, S & Xu, G 2016, 'Improving Music Recommendation Using Distributed Representation', Proceedings of the 25th International Conference Companion on World Wide Web, World Wide Web Conference, ACM, Montreal, Canada, pp. 125-126.
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In this paper, a music recommendation approach based on distributed representation is presented. The proposed approach firstly learns the distributed representations of music pieces and acquires users' preferences from listening records. Then, it recommends appropriate music pieces whose distributed representations are in accordance with target users' preferences. Experiments on a real world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods.

Wang, D, Deng, S, Zhang, X & Xu, G 2016, 'Learning music embedding with metadata for context aware recommendation', Proceedings of the 2016 ACM International Conference on Multimedia Retrieval, ACM International Conference on Multimedia Retrieval, ACM, New York, USA, pp. 249-253.
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© 2016 ACM.Contextual factors can benefit music recommendation and retrieval tasks remarkably. However, how to acquire and utilize the contextual information still need to be studied. In this paper, we propose a context aware music recommendation approach, which can recommend music appropriate for users' contextual preference for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require songs to be described by features beforehand, but it learns music pieces' embeddings (vectors in low-dimensional continuous space) from music playing records and corresponding metadata and infer users' general and contextual preference for music from their playing records with the learned embedding. Then, our approach can recommend appropriate music pieces. Experimental evaluations on a real world dataset show that the proposed approach outperforms baseline methods.

Wang, F, He, YB, Qu, J, Xie, QY, Lin, Q, Ni, XX, Chen, Y, Yu, RH, Lin, CT & Li, Y 2016, 'An Audiovisual BCI System for Assisting Clinical Communication Assessment in Patients with Disorders of Consciousness: A Case Study', Proceedings of the 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, FL, USA, pp. 1536-1539.
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The JFK Coma Recovery Scale-Revised (JFK CRS-R), a behavioral scale, is often used for clinical assessments of patients with disorders of consciousness (DOC), such as patients in a vegetative state. However, there has been a high rate of clinical misdiagnosis with the JFK CRS-R because patients with severe brain injures cannot provide sufficient behavioral responses. It is particularly difficult to evaluate the communication function in DOC patients using the JFK CRS-R because a higher level of behavioral responses is needed for communication assessments than for many other assessments, such as an auditory startle assessment. Brain-computer interfaces (BCIs), which provide control and communication by detecting changes in brain signals, can be used to evaluate patients with DOC without the need of behavioral expressions. In this paper, we proposed an audiovisual BCI system to supplement the JFK CRS-R in assessing the communication ability of patients with DOC. In the graphic user interface of the BCI system, two word buttons (“Yes” and “No” in Chinese) were randomly displayed in the left and right sides and flashed in an alternating manner. When a word button flashed, its corresponding spoken word was broadcast from an ipsilateral headphone. The use of semantically congruent audiovisual stimuli improves the detection performance of the BCI system. Similar to the JFK CRS-R, several situation-orientation questions were presented one by one to patients with DOC. For each question, the patient was required to provide his/her answer by selectively focusing on an audiovisual stimulus (audiovisual “Yes” or “No”). As a case study, we applied our BCI system in a patient with DOC who was clinically diagnosed as being in a minimally conscious state (MCS). According to the JFK CRS-R assessment, this patient was unable to communicate consistently. However, he achieved a high accuracy of 86.5% in our BCI experiment. This result indicates his reliable communication ability and ...

Wang, S, Cheema, MA, Lin, X, Zhang, Y & Liu, D 2016, 'Efficiently computing reverse k furthest neighbors', 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016, IEEE International Conference on Data Engineering, IEEE, Helsinki, Finland, pp. 1110-1121.
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© 2016 IEEE.Given a set of facilities F, a set of users U and a query facility q, a reverse k furthest neighbors (RkFN) query retrieves every user u-U for which q is one of its k-furthest facilities. RkFN query is the natural complement of reverse k-nearest neighbors (RkNN) query that returns every user u for which q is one of its k-nearest facilities. While RkNN query returns the users that are highly influenced by a query q, RkFN query aims at finding the users that are least influenced by a query q. RkFN query has many applications in location-based services, marketing, facility location, clustering, and recommendation systems etc. While there exist several algorithms that answer RkFN query for k = 1, we are the first to propose a solution for arbitrary value of k. Based on several interesting observations, we present an efficient algorithm to process the RkFN queries. We also present a rigorous theoretical analysis to study various important aspects of the problem and our algorithm. An extensive experimental study is conducted using both real and synthetic data sets, demonstrating that our algorithm outperforms the state-of-the-art algorithm even for k = 1. The accuracy of our theoretical analysis is also verified by the experiments.

Wang, S, Liu, W, Wu, J, Cao, L, Meng, Q & Kennedy, PJ 2016, 'Training deep neural networks on imbalanced data sets', Proceedings of the International Joint Conference on Neural Networks, IEEE International Joint Conference on Neural Networks, IEEE, Vancouver, Canada, pp. 4368-4374.
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© 2016 IEEE.Deep learning has become increasingly popular in both academic and industrial areas in the past years. Various domains including pattern recognition, computer vision, and natural language processing have witnessed the great power of deep networks. However, current studies on deep learning mainly focus on data sets with balanced class labels, while its performance on imbalanced data is not well examined. Imbalanced data sets exist widely in real world and they have been providing great challenges for classification tasks. In this paper, we focus on the problem of classification using deep network on imbalanced data sets. Specifically, a novel loss function called mean false error together with its improved version mean squared false error are proposed for the training of deep networks on imbalanced data sets. The proposed method can effectively capture classification errors from both majority class and minority class equally. Experiments and comparisons demonstrate the superiority of the proposed approach compared with conventional methods in classifying imbalanced data sets on deep neural networks.

Wang, W, Huang, M & Nguyen, Q 2016, 'A Space Optimized Scatter Plot Matrix Visualization', Cooperative Design, Visualization, and Engineering (LNCS), Cooperative Design, Visualization, and Engineering, Springer International Publishing, Sydney, pp. 382-385.
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This paper proposed a Space-Optimized Scatter Plot Matrix that used for the presentation of multi-dimensional dataset. This technique achieves the display space utilization in a 2D geometrical space. Our strategy is to maximize the utilization of computer space by optimizing the distribution of the plots in a geometrical plane of a display screen; We also apply interact mechanism, user query and visual cues, to support users’ communication with variables and the discovery of deeper contents.

Wang, W, Yin, H, Sadiq, S, Chen, L, Xie, M & Zhou, X 2016, 'SPORE: A Sequential Personalized Spatial Item Recommender System', Proceedings of the 2016 IEEE 32nd International Conference on Data Engineering (ICDE), IEEE International Conference on Data Engineering, IEEE, Helsinki, pp. 954-965.
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With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important way of helping users discover interesting locations to increase their engagement with location-based services. Although human movement exhibits sequential patterns in LBSNs, most current studies on spatial item recommendations do not consider the sequential influence of locations. Leveraging sequential patterns in spatial item recommendation is, however, very challenging, considering 1) users' check-in data in LBSNs has a low sampling rate in both space and time, which renders existing prediction techniques on GPS trajectories ineffective; 2) the prediction space is extremely large, with millions of distinct locations as the next prediction target, which impedes the application of classical Markov chain models; and 3) there is no existing framework that unifies users' personal interests and the sequential influence in a principled manner. In light of the above challenges, we propose a sequential personalized spatial item recommendation framework (SPORE) which introduces a novel latent variable topic-region to model and fuse sequential influence with personal interests in the latent and exponential space. The advantages of modeling the sequential effect at the topic-region level include a significantly reduced prediction space, an effective alleviation of data sparsity and a direct expression of the semantic meaning of users' spatial activities. Furthermore, we design an asymmetric Locality Sensitive Hashing (ALSH) technique to speed up the online top-k recommendation process by extending the traditional LSH. We evaluate the performance of SPORE on two real datasets and one large-scale synthetic dataset. The results demonstrate a significant improvement in SPORE's ability to recommend spatial items, in terms of both effectiveness and efficiency, compared with the state-of-the-art methods.

Wang, X, Chen, C & Zhang, Y 2016, 'EDMS: A system for efficient processing distance-aware influence maximization', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Australasian Database Conference, Springer, Sydney, Australia, pp. 462-466.
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© Springer International Publishing AG 2016.As a key problem in viral marketing, influence maximization has been widely studied in the literature. It aims to find a set of k users in a social network, which can maximize the influence spread under a certain propagation model. With the proliferation of geo-social networks, location-aware promotion is becoming more and more necessary in real applications. However, the importance of the distance between users and the promoted locations is underestimated in the existing work. For example, when promoting a local store, the owner may prefer to influence more people that are close to the store instead of people that are far away. In this demonstration, we propose EDMS, a centralized system that efficiently processes the distance-aware influence maximization problem. To meet the online requirements, we combine different pruning strategies and the best first search algorithm to significantly reduce the search space. We present a prototype, which provides users with a web interface to issue queries and visualize the search results in real time.

Wang, X, Sheng, QZ, Yao, L, Li, X, Fang, XS, Xu, X & Benatallah, B 2016, 'Empowering Truth Discovery with Multi-Truth Prediction', CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 25th ACM International Conference on Information and Knowledge Management (CIKM), ASSOC COMPUTING MACHINERY, IUPUI, Indianapolis, IN, pp. 881-890.
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Wang, X, Sheng, QZ, Yao, L, Li, X, Fang, XS, Xu, X & Benatallah, B 2016, 'Truth Discovery via Exploiting Implications from Multi-Source Data', CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 25th ACM International Conference on Information and Knowledge Management (CIKM), ASSOC COMPUTING MACHINERY, IUPUI, Indianapolis, IN, pp. 861-870.
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Wang, X, Yang, S & Zhang, Y 2016, 'SKPS: Towards efficient processing of spatial-keyword publish/subscribe system', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - Australasian Database Conference, Australasian Database Conference, Springer, Sydney, Australia, pp. 444-447.
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© Springer International Publishing AG 2016.With the popularity of geo-equipped devices and location based services, spatial-keyword publish/subscribe has emerged as a very important framework to disseminate real-time messages (e.g., geo-tagged e-coupon) to registered subscriptions (e.g., users interested in nearby promotions). While there are several work focusing on improving the efficiency of spatial-keyword publish/subscribe, their techniques fail to consider both the spatial and keyword distributions in a fine manner, thus lacking of scalability when coping with massive subscriptions. In this demonstration, we propose SKPS, a centralized in-memory spatial-keyword publish/ subscribe system, which exploits fully the spatial and keyword distributions of subscription workload during indexing construction, and employs an efficient message matching algorithm to disseminate each incoming message to relevant subscriptions in a real-time manner. We present a prototype of SKPS which provides users with a web-based interface to explore the message dissemination in publish/subscribe system.

Wang, X, Zhang, Y, Zhang, W & Lin, X 2016, 'Distance-aware influence maximization in geo-social network', 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016, IEEE International Conference on Data Engineering, IEEE, Helsinki, Finland, pp. 1-12.
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© 2016 IEEE.Influence maximization is a key problem in viral marketing. Given a social network G and a positive integer k, it aims to identify a seed set of k nodes in G that can maximize the expected influence spread in a certain propagation model. With the proliferation of geo-social networks, location-aware product promotion is becoming more necessary in real applications. However, the importance of the distance between users and the promoted location is underestimated in existing models. For instance, when opening a restaurant in downtown, through online promotion, the owner may expect to influence more customers who are close to the restaurant, instead of people that are far away from it. In this paper, we formally define the distance-aware influence maximization problem, to find a seed set that maximizes the expected influence over users who are more likely to be the potential customers of the promoted location. To efficiently calculate the influence spread, we adopt the maximum influence arborescence (MIA) model for influence approximation. To speed up the search, we propose three pruning strategies to prune unpromising nodes from expensive evaluation and achieve potential early termination in each iteration without sacrificing the final result's approximation ratio. In addition, novel index structures are developed to compute the bounds used in the three pruning strategies. By integrating these pruning strategies, we propose a priority based algorithm which searches users based on their order of influence. The algorithm achieves an approximation ratio of 1 - 1 over e under the MIA model. In the final, comprehensive experiments over real datasets demonstrate the efficiency and effectiveness of the proposed algorithms and pruning strategies.

Wang, X, Zhang, Y, Zhang, W, Lin, X & Huang, Z 2016, 'SKYPE: Top-k spatial-keyword publish/subscribe over sliding window', Proceedings of the VLDB Endowment, International Conference on Very Large Databases, VLDB Endowment, Bangalore, pp. 588-599.
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© 2016 VLDB Endowment 21508097/16/03.As the prevalence of social media and GPS-enabled devices, a massive amount of geo-textual data has been generated in a stream fashion, leading to a variety of applications such as location-based recommendation and information dissemination. In this paper, we investigate a novel real-time top-k monitoring problem over sliding window of streaming data; that is, we continuously maintain the top-k most relevant geo-textual messages (e.g., geo-tagged tweets) for a large number of spatial-keyword subscriptions (e.g., registered users interested in local events) simultaneously. To provide the most recent information under controllable memory cost, sliding window model is employed on the streaming geo-textual data. To the best of our knowledge, this is the first work to study top-k spatial-keyword publish/ subscribe over sliding window. A novel system, called Skype (Top-k Spatial-keyword Publish/Subscribe), is proposed in this paper. In Skype, to continuously maintain top-k results for massive subscriptions, we devise a novel indexing structure upon subscriptions such that each incoming message can be immediately delivered on its arrival. Moreover, to reduce the expensive top-k re-evaluation cost triggered by message expiration, we develop a novel cost-based k-skyband technique to reduce the number of re-evaluations in a costeffective way. Extensive experiments verify the great effciency and effectiveness of our proposed techniques.

Wei, CS, Lin, YP, Wang, YT, Jung, TP, Bigdely-Shamlo, N & Lin, CT 2016, 'Selective Transfer Learning for EEG-Based Drowsiness Detection', Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, IEEE International Conference on Systems, Man and Cybernetics, IEEE, Kowloon, China, pp. 3229-3232.
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© 2015 IEEE. On the pathway from laboratory settings to real world environment, a major challenge on the development of a robust electroencephalogram (EEG)-based brain-computer interface (BCI) is to collect a significant amount of informative training data from each individual, which is labor intensive and time-consuming and thereby significantly hinders the applications of BCIs in real-world settings. A possible remedy for this problem is to leverage existing data from other subjects. However, substantial inter-subject variability of human EEG data could deteriorate more than improve the BCI performance. This study proposes a new transfer learning (TL)-based method that exploits a subject's pilot data to select auxiliary data from other subjects to enhance the performance of an EEG-based BCI for drowsiness detection. This method is based on our previous findings that the EEG correlates of drowsiness were stable within individuals across sessions and an individual's pilot data could be used as calibration/training data to build a robust drowsiness detector. Empirical results of this study suggested that the feasibility of leveraging existing BCI models built by other subjects' data and a relatively small amount of subject-specific pilot data to develop a BCI that can outperform the BCI based solely on the pilot data of the subject.

Wei, CS, Lin, YP, Wang, YT, Lin, C & Jung, TP 2016, 'Transfer Learning with Large-Scale Data in Brain-Computer Interfaces', Proceedings of the 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, FL, USA.
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Human variability in electroencephalogram (EEG) poses significant challenges for developing practical real-world applications of brain-computer interfaces (BCIs). The intuitive solution of collecting sufficient user-specific training/calibration data can be very labor-intensive and time-consuming, hindering the practicability of BCIs. To address this problem, transfer learning (TL), which leverages existing data from other sessions or subjects, has recently been adopted by the BCI community to build a BCI for a new user with limited calibration data. However, current TL approaches still require training/calibration data from each of conditions, which might be difficult or expensive to obtain. This study proposed a novel TL framework that could nearly eliminate requirement of subject-specific calibration data by leveraging large-scale data from other subjects. The efficacy of this method was validated in a passive BCI that was designed to detect neurocognitive lapses during driving. With the help of large-scale data, the proposed TL approach outperformed the within-subject approach while considerably reducing the amount of calibration data required for each individual (~1.5 min of data from each individual as opposed to a 90 min pilot session used in a standard within-subject approach). This demonstration might considerably facilitate the real-world applications of BCIs

Weilemann, E, Brune, P & Gill, AQ 2016, 'Do They Miss the Lectures? – Flipped Classroom Perception by Software Engineering Students', Proceedings of ECSEE 2016 - Flipped Classroom Perception by Software Engineering Students, European Conference of Software Engineering Education, Shaker Verlag, Seeon Monastery, Germany, pp. 245-249.
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Wen, D, Qin, L, Zhang, Y, Lin, X & Yu, JX 2016, 'I/O Efficient Core Graph Decomposition at Web Scale.', CoRR.
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Wen, S, Jiang, J, Yazdi, KM, Xiang, Y & Zhou, W 2016, 'The relation between local and global influence of individuals in scale-free networks', Proceedings - 2015 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2015, pp. 80-84.
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© 2015 IEEE. Large-degree nodes in scale-free networks are normally responsible for large cascades of epidemics. However, recent research shows small-degree nodes can also produce large-scale epidemics in the real world. In this letter, we investigate the relation between local and global influence of individuals in scale-free network in order to theoretically explain this real-world phenomenon. The local influence of an individual corresponds to the node degree, and the global influence of an individual reflects the expected number of individuals directly or indirectly influenced by this individual in epidemics. We formalize the later as the novel epidemic betweenness concept, to mathematically estimate the global influence of individuals. Our analysis shows that the global influence follows power-law distributions in scale-free networks. We also observe that the average global influence of individuals is power-law to the degree of nodes, which well explains the reason why large-degree nodes are more likely to produce large cascades of epidemics. In addition, we discover that some smalldegree nodes also possess large global influence in terms of epidemics betweenness. This well explains the counter-intuitive phenomenon in recent research.

Williams, MA 2016, 'Decision-theoretic human-robot interaction: Designing reasonable and rational robot behavior', Social Robotics (LNCS), International Conference on Social Robotics (ICSR), Springer, Kansas City, USA, pp. 72-82.
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© Springer International Publishing AG 2016.Autonomous robots are moving out of research labs and factory cages into public spaces; people’s homes, workplaces, and lives. A key design challenge in this migration is how to build autonomous robots that people want to use and can safely collaborate with in undertaking complex tasks. In order for people to work closely and productively with robots, robots must behave in way that people can predict and anticipate. robots chose their next action using the classical sensethink- act processing cycle. robotists design actions and action choice mechanisms for robots. This design process determines robot behaviors, and how well people are able to interact with the robot. Crafting how a robot will choose its next action is critical in designing social robots for interaction and collaboration. This paper identifies reasonableness and rationality, two key concepts that are well known in Choice Theory, that can be used to guide the robot design process so that the resulting robot behaviors are easier for humans to predict, and as a result it is more enjoyable for humans to interact and collaborate. Designers can use the notions of reasonableness and rationality to design action selection mechanisms to achieve better robot designs for human-robot interaction. We show how Choice Theory can be used to prove that specific robot behaviors are reasonable and/or rational, thus providing a formal, useful and powerful design guide for developing robot behaviors that people find more intuitive, predictable and fun, resulting in more reliable and safe human-robot interaction and collaboration.

Wu, D, Hussain, F, Zhang, G, Lu, J, Unwin, J & Rance, G 2016, 'A cloud-based comprehensive health information system framework', Uncertainty Modelling in Knowledge Engineering and Decision Making - Proceedings of the 12th International FLINS Conference, FLINS 2016, pp. 612-617.
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© 2016 by World Scientific Publishing Co. Pte. Ltd. Big data appearing in health domain bring great opportunities for the health information system development. To effectively utilize the big health data, three challenges: data heterogeneity, huge data volume and high velocity of data generation, and various kinds of user requirements, need to be dealt with. To solve the problem, this paper proposes a cloud-based comprehensive health information system framework, which uses cloud computing techniques to manage and process the big health data, and provides several data analysis and recommendation services to explore the data and extract values from them.

Wu, D, Lawhern, V, Gordon, S, Lance, B & Lin, C 2016, 'Agreement Rate Initialized Maximum Likelihood Estimator (ARIMLE) for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface', Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE International Conference on Systems, Man and Cybernetics, IEEE, Budapest, Hungary, pp. 724-729.
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Agreement Rate Initialized Maximum Likelihood Estimator (ARIMLE) for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface

Wu, D, Lawhern, V, Gordon, S, Lance, B & Lin, C 2016, 'Offline EEG-based driver drowsiness estimation using enhanced batch-mode active learning (EBMAL) for regression', Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE International Conference on Systems, Man and Cybernetics, IEEE, Budapest, Hungary, pp. 730-736.
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There are many important regression problems in real-world brain-computer interface (BCI) applications, e.g., driver drowsiness estimation from EEG signals. This paper considers offline analysis: given a pool of unlabeled EEG epochs recorded during driving, how do we optimally select a small number of them to label so that an accurate regression model can be built from them to label the rest? Active learning is a promising solution to this problem, but interestingly, to our best knowledge, it has not been used for regression problems in BCI so far. This paper proposes a novel enhanced batch-mode active learning (EBMAL) approach for regression, which improves upon a baseline active learning algorithm by increasing the reliability, representativeness and diversity of the selected samples to achieve better regression performance. We validate its effectiveness using driver drowsiness estimation from EEG signals. However, EBMAL is a general approach that can also be applied to many other offline regression problems beyond BCI.

Wu, D, Lawhern, V, Gordon, S, Lance, B & Lin, C 2016, 'Spectral Meta-Learner for Regression (SMLR) Model Aggregation: Towards Calibrationless Brain-Computer Interface', Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE International Conference on Systems, Man and Cybernetics, IEEE, Budapest, Hungary, pp. 743-749.
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To facilitate the transition of brain-computer interface (BCI) systems from laboratory settings to real-world application, it is very important to minimize or even completely eliminate the subject-specific calibration requirement. There has been active research on calibrationless BCI systems for classification applications, e.g., P300 speller. To our knowledge, there is no literature on calibrationless BCI systems for regression applications, e.g., estimating the continuous drowsiness level of a driver from EEG signals. This paper proposes a novel spectral meta-learner for regression (SMLR) approach, which optimally combines base regression models built from labeled data from auxiliary subjects to label offline EEG data from a new subject. Experiments on driver drowsiness estimation from EEG signals demonstrate that SMLR significantly outperforms three state-of-the-art regression model fusion approaches. Although we introduce SMLR as a regression model fusion in the BCI domain, we believe its applicability is far beyond that.

Wu, W, Li, B, Chen, L & Zhang, C 2016, 'Canonical Consistent Weighted Sampling for Real-Value Weighted Min-Hash', Proceedings of the 2016 IEEE 16th International Conference on Data Mining, IEEE International Conference on Data Mining, IEEE, Barcelona, Spain, pp. 1287-1292.
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Min-Hash, as a member of the Locality Sensitive Hashing (LSH) family for sketching sets, plays an important role in the big data era. It is widely used for efficiently estimating similarities of bag-of-words represented data and has been extended to dealing with multi-sets and real-value weighted sets. Improved Consistent Weighted Sampling (ICWS) has been recognized as the state-of-the-art for real-value weighted Min-Hash. However, the algorithmic implementation of ICWS is flawed because it violates the uniformity of the Min-Hash scheme. In this paper, we propose a Canonical Consistent Weighted Sampling (CCWS) algorithm, which not only retains the same theoretical complexity as ICWS but also strictly complies with the definition of Min-Hash. The experimental results demonstrate that the proposed CCWS algorithm runs faster than the state-of-the-arts while achieving similar classification performance on a number of real-world text data sets.

Wu, W, Li, B, Chen, L & Zhang, C 2016, 'Cross-view feature hashing for image retrieval', 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, Auckland, New Zealand, pp. 203-214.
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© Springer International Publishing Switzerland 2016.Traditional cross-view information retrieval mainly rests on correlating two sets of features in different views. However, features in different views usually have different physical interpretations. It may be inappropriate to map multiple views of data onto a shared feature space and directly compare them. In this paper, we propose a simple yet effective Cross-View Feature Hashing (CVFH) algorithm via a “partition and match” approach. The feature space for each view is bi-partitioned multiple times using B hash functions and the resulting binary codes for all the views can thus be represented in a compatible B-bit Hamming space. To ensure that hashed feature space is effective for supporting generic machine learning and information retrieval functionalities, the hash functions are learned to satisfy two criteria: (1) the neighbors in the original feature spaces should be also close in the Hamming space; and (2) the binary codes for multiple views of the same sample should be similar in the shared Hamming space. We apply CVFH to cross view image retrieval. The experimental results show that CVFH can outperform the Canonical Component Analysis (CCA) based cross-view method.

Xiong, W, Du, B, Zhang, L, Hu, R, Bian, W, Shen, J & Tao, D 2015, 'R2FP: Rich and robust feature pooling for mining visual data', Proceedings - IEEE International Conference on Data Mining, ICDM, IEEE International Conference on Data Mining, IEEE, Atlantic City, NJ, pp. 469-478.
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© 2015 IEEE. The human visual system proves smart in extracting both global and local features. Can we design a similar way for unsupervised feature learning? In this paper, we propose anovel pooling method within an unsupervised feature learningframework, named Rich and Robust Feature Pooling (R2FP), to better explore rich and robust representation from sparsefeature maps of the input data. Both local and global poolingstrategies are further considered to instantiate such a methodand intensively studied. The former selects the most conductivefeatures in the sub-region and summarizes the joint distributionof the selected features, while the latter is utilized to extractmultiple resolutions of features and fuse the features witha feature balancing kernel for rich representation. Extensiveexperiments on several image recognition tasks demonstratethe superiority of the proposed techniques.

Xue, S, Lu, J, Wu, J, Zhang, G & Xiong, L 2016, 'Multi-instance graphical transfer clustering for traffic data learning', Proceedings of the International Joint Conference on Neural Networks, IEEE International Joint Conference on Neural Networks, IEEE, Vancouver, Canada, pp. 4390-4395.
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© 2016 IEEE.In order to better model complex real-world data and to develop robust features that capture relevant information, we usually employ unsupervised feature learning to learn a layer of features representations from unlabeled data. However, developing domain-specific features for each task is expensive, time-consuming and requires expertise of the data. In this paper, we introduce multi-instance clustering and graphical learning to unsupervised transfer learning. For a better clustering efficient, we proposed a set of algorithms on the application of traffic data learning, instance feature representation, distance calculation of multi-instance clustering, multi-instance graphical cluster initialisation, multi-instance multi-cluster update, and graphical multi-instance transfer clustering (GMITC). In the end of this paper, we examine the proposed algorithms on the Eastwest datasets by couples of baselines. The experiment results indicate that our proposed algorithms can get higher clustering accuracy and much higher programming speed.

Yan, H, Sui, Y, Chen, S & Xue, J 2016, 'Automated memory leak fixing on value-flow slices for C programs', Proceedings of the ACM Symposium on Applied Computing, pp. 1386-1393.
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© 2016 ACM. C is the dominant programming language for developing embedded software, operating systems, and device drivers. Unlike programs written in managed languages like Java, C programs rely on explicit memory management and are prone to memory leaks. Existing (static or dynamic) detectors only report leaks, but fixing them often requires considerable manual effort by inspecting a list of reported true and false alarms. How to develop on-demand lightweight techniques for automated leak fixing without introducing new memory errors remains challenging. In this paper, we introduce AUTOFIX, a fully automated leak-fixing approach for C programs by combining static and dynamic program analysis. Given a leaky allocation site reported by a static memory leak detector, AUTOFIX performs a graph reachability analysis to identify leaky paths on the value-flow slices of the program, and then conducts a liveness analysis to locate the program points for inserting fixes (i.e., the missing free calls) on the identified leaky paths. We have implemented AUTOFIX in LLVM-3.5.0 and evaluated it using five SPEC2000 benchmarks and three open-source applications. Experimental results show that AUTOFIX can safely fix all the memory leaks reported by a state-of-theart static memory leak detector with small instrumentation overhead.

Yang, D, Wu, Z, Wang, X, Cao, J & Xu, G 2016, 'Predicting replacement of smartphones with mobile app usage', Web Information Systems Engineering – WISE 2016 (LNCS), International Conference on Web Information Systems Engineering, Springer, Shanghai, China, pp. 343-351.
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© Springer International Publishing AG 2016.To identify right customers who intend to replace the smart phone can help to perform precision marketing and thus bring significant financial gains to cell phone retailers. In this paper,we provide a study of exploiting mobile app usage for predicting users who will change the phone in the future. We first analyze the characteristics of mobile log data and develop the temporal bag-of-apps model,which can transform the raw data to the app usage vectors. We then formularize the prediction problem,present the hazard based prediction model,and derive the inference procedure. Finally,we evaluate both data model and prediction model on real-world data. The experimental results show that the temporal usage data model can effectively capture the unique characteristics of mobile log data,and the hazard based prediction model is thus much more effective than traditional classification methods. Furthermore,the hazard model is explainable,that is,it can easily show how the replacement of smart phones relate to mobile app usage over time.

Yang, J, Zhang, Y, Zhang, W & Lin, X 2016, 'Influence based cost optimization on user preference', 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016, IEEE International Conference on Data Engineering, IEEE, Helsinki, Finland, pp. 709-720.
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© 2016 IEEE.The popularity of e-business and preference learning techniques have contributed a huge amount of product and user preference data. Analyzing the influence of an existing or new product among the users is critical to unlock the great scientific and social-economic value of these data. In this paper, we advocate the problem of influence-based cost optimization for the user preference and product data, which is fundamental in many real applications such as marketing and advertising. Generally, we aim to find a cost optimal position for a new product such that it can attract at least k or a particular percentage of users for the given user preference functions and competitors' products. Although we show the solution space of our problem can be reduced to a finite number of possible positions (points) by utilizing the classical k-level computation techniques, the computation cost is still very expensive due to the nature of the high combinatorial complexity of the k-level problem. To alleviate this issue, we develop efficient pruning and query processing techniques to significantly improve the performance. In particular, our traverse-based 2-dimensional algorithm is very efficient with time complexity O(n) where n is the number of user preference functions. For general multi-dimensional spaces, we develop space partition based algorithm to significantly improve the performance by utilizing cost-based, influence-based and local dominance based pruning techniques. Then, we show that the performance of the partition based algorithm can be further enhanced by utilizing sampling approach, where the problem can be reduced to the classical half-space intersection problem. We demonstrate the efficiency of our techniques with extensive experiments over real and synthetic datasets.

Yao, L, Benatallah, B, Wang, X, Nguyen, KT & Lu, Q 2016, 'Context as a Service: Realizing Internet of Things-Aware Processes for the Independent Living of the Elderly', SERVICE-ORIENTED COMPUTING, (ICSOC 2016), 14th International Conference on Service-Oriented Computing (ICSOC), SPRINGER INTERNATIONAL PUBLISHING AG, Banff, CANADA, pp. 763-779.
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Ye, L, Cao, K, Guo, J, Huang, X, Beadle, P, Argha, A, Piccardi, M, Zhang, G & Su, SW 2016, 'Inertial sensor based post fall analysis for false alarming reduction', Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics, IASTED Telehealth and Assistive Technology, Acta Press, Zurich, Switzerland, pp. 36-43.
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One of the major public health problems among elderly people is falling injury. This study investigates fall detection and prevention by using inertial sensors for which the major existing challenging is how to significantly reduce false alarming in order to enhance the acceptance of elderly users during rehabilitation and daily exercises. Different from most existing approaches in the literature, the behavior after falling will be analyzed in details, which can not only greatly reduce false alarming, but also significantly improves the accuracy of the assessment of the severity of falling injuries.

Ye, T, Hao, Y, Wang, Z, Lai, C, Chen, S, Li, Z, Liang, J & Yuan, X 2016, 'Behavior Analysis through Collaborative Visual Exploration on Trajectory Data', ChinaVis2016, ChinaVis2016, Changsha, China.

Zhang, J & Huang, ML 2016, '2D Approach Measuring Multidimensional Data Pattern in Big Data Visualization', Proceedings of 2016 IEEE International Conference on Big Data Analysis, IEEE International Conference on Big Data Analysis (ICBDA), IEEE, Hangzhou, China, pp. 194-199.
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Big Data, structured and unstructured data, contains millions attributes in multiple dimensions. This has arisen three issues: 1) how to measure the structured and unstructured multidimensional data patterns for Big Data analysis; 2) how to display multidimensional data patterns in normal size of screen; 3) how to optimize the data attributes in Big Data visualization. In this work, we have visual analyzed Big Data variety based on the complexity of multidimensional data. Firstly, we introduce 2D dimension which divided the multidimensional dataset into 2D data pattern subsets, and then establish 2D-Ratio algorithm to measure 2D dimension in multiple data patterns. Second, we create two additional parallel axes by using 2D-Ratio to compare 2D dimensional patterns for visualization. Third, the dimension clustering and shrunk attribute have been introduced in 2D-Ratio parallel coordinates to reduce the data over-crowed. The experiment shows that our model can be efficiently and accurately used for Big Data analysis and visualization.

Zhang, Q, Wu, D, Zhang, G & Lu, J 2016, 'Fuzzy user-interest drift detection based recommender systems', 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, IEEE International Conference on Fuzzy Systems, IEEE, Vancouver, Canada, pp. 1274-1281.
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© 2016 IEEE.Recommender systems aim to provide personalized suggestions to users by modeling user-interests to deal with information overload problem, which is extremely severe in the era of big data. Since user-interests are drifting due to their taste variation on items, recommender systems without considering that will suffer degradation of prediction accuracy. There are two challenges about adapting to user-interest drift in recommender systems: 1) accurately modeling user-interests is not easy since the drift of user-interests may occur in different direction for each user; 2) item features and user-interests are often incomplete and vague, which makes it more difficult to model user-interests. To handle these two issues, this study proposes a fuzzy user-interest drift detection based recommender system that adapts to user-interest drift and improves prediction accuracy. A fuzzy user-interest consistency model is built based on fuzzy set theories, and a user-interest drift detection approach and algorithms are developed based on concept drift techniques to provide guidance to recommendation generation. Empirical experiments are conducted on synthetic and real-world MovieLens datasets. The results show that the proposed approach improves the performance of recommender systems in metric of MAE.

Zhang, Q, Zhang, L, Du, B, Zheng, W, Bian, W & Tao, D 2015, 'MMFE: Multitask multiview feature embedding', Proceedings - IEEE International Conference on Data Mining, ICDM, IEEE International Conference on Data Mining, IEEE, Atlantic City, NJ, pp. 1105-1110.
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© 2015 IEEE. In data mining and pattern recognition area, the learned objects are often represented by the multiple features from various of views. How to learn an efficient and effective feature embedding for the subsequent learning tasks? In this paper, we address this issue by providing a novel multi-task multiview feature embedding (MMFE) framework. The MMFE algorithm is based on the idea of low-rank approximation, which suggests that the observed multiview feature matrix is approximately represented by the low-dimensional feature embedding multiplied by a projection matrix. In order to fully consider the particular role of each view to the multiview feature embedding, we simultaneously suggest the multitask learning scheme and ensemble manifold regularization into the MMFE algorithm to seek the optimal projection. Since the objection function of MMFE is multi-variable and non-convex, we further provide an iterative optimization procedure to find the available solution. Two real world experiments show that the proposed method outperforms single-task-based as well as state-of-the-art multiview feature embedding methods for the classification problem.

Zhang, T, Gu, M, Zhang, G & Lu, J 2016, 'A fast load pattern extraction approach based on dimension reduction and sampling', 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, IEEE International Conference on Fuzzy Systems, IEEE, Vancouver, BC, Canada, pp. 1253-1258.
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© 2016 IEEE.This paper proposes a fast load pattern extraction approach to solve the time consuming problem in using a traditional κ-means clustering method for large volumes of load curves. The approach, based on dimension reduction and sampling, segments and averages sampling characteristic points to reduce the load curve's dimensions, then reduces the overall size of the sample data set using representative random sampling. κ-means clustering algorithm is used to extract load patterns from the representative data set, which will be used to classify the full data set. Reducing the size and dimension of the data set allows use of a less complex algorithm, and thus greatly improves the clustering speed. The validity of the approach is proven by experiments designed to evaluate the trade-off between complexity and consistency.

Zhang, W, Chen, T & Tan, C 2016, 'Demo: A safe low-cost HMD for underwater VR experiences', Proceeding SA '16 SIGGRAPH ASIA 2016 Mobile Graphics and Interactive Applications, International Conference on Computer Graphics and Interactive Techniques in Australasia and Southeast Asia, ACM, Macao.
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Recently, consumer head-mounted VR displays (HMDs) like the Oculus Rift1 and Samsung Gear VR2 have driven much research into interesting applications that include full-body sensory experiences. For example, PaperDude VR [Bolton et al. 2014] uses the Oculus Rift to implement a cycling exergame in order to motivate exercise. Other than using a consumer HMD like the Rift to provide the VR visuals, PaperDude VR includes a real bicycle on a stationary trainer in order to approximate a realistic experience using other non-visual senses. In another work, Birdly [Rheiner 2014] uses the HTC Vive3 as part of a system to simulate flying. In Birdly, the nonvisual elements are slightly harder to simulate, resulting in a rather complex robotic setup which includes sensory-motor coupling and a large fan to provide wind feedback

Zhang, W, Lin, X, Zhang, Y, Zhu, K & Zhu, G 2016, 'Efficient Probabilistic Supergraph Search', 2016 32ND IEEE INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), 32nd IEEE International Conference on Data Engineering (ICDE), IEEE, Helsinki, FINLAND, pp. 1542-1543.
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Zhang, Z, Huang, K, Tan, T, Yang, P & Li, J 2016, 'ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering', Proceedings for the Conference on Computer Vision and Pattern Recognition, IEEE Conference on Computer Vision and Pattern Recognition, IEEE, USA, pp. 752-760.
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For spectral embedding/clustering, it is still an open problem on how to construct an relation graph to reflect the intrinsic structures in data. In this paper, we proposed an approach, named Relation Discovery based Slow Feature Analysis (ReD-SFA), for feature learning and graph construction simultaneously. Given an initial graph with only a few nearest but most reliable pairwise relations, new reliable relations are discovered by an assumption of reliability preservation, i.e., the reliable relations will preserve their reliabilities in the learnt projection subspace. We formulate the idea as a cross entropy (CE) minimization problem to reduce the discrepancy between two Bernoulli distributions parameterized by the updated distances and the existing relation graph respectively. Furthermore, to overcome the imbalanced distribution of samples, a Boosting-like strategy is proposed to balance the discovered relations over all clusters. To evaluate the proposed method, extensive experiments are performed with various trajectory clustering tasks, including motion segmentation, time series clustering and crowd detection. The results demonstrate that ReDSFA can discover reliable intra-cluster relations with high precision, and competitive clustering performance can be achieved in comparison with state-of-the-art

Zhang, Z, Oberst, S & Lai, JCS 2016, 'Influence of contact condition and sliding speed on friction-induced instability', Proceedings of the 23rd International Congress on Sound and Vibration, International Congress on Sound and Vibration: From Ancient to Modern Acoustics (ICSV), International Institute of Acoustics and Vibration, Athens, Greece.
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Brake squeal, defined as audible noise above 1 kHz, is triggered by energy provided in the contact area between the pad and the disc and friction-induced instabilities. Owing to customers' demand of reducing vehicle noise and the increasing use of light composite materials in cars, squealing brakes remain a major concern to the automotive industry because of warranty-related claims. The prediction of disc brake squeal propensity is as challenging as ever. Although friction-induced instabilities are inherently nonlinear and during squeal the brake system's operating and environmental conditions keep changing, mostly linear and steady state methods are used for the analysis of brake squeal propensity. While many different instability mechanisms have been identified, their interactions and the resulting dynamics are not yet fully understood. Linear instability predictions suffer from over- and under-predictions and have to be complemented by extensive noise dynamometer or in vehicle tests. Recent studies indicate that frictional contact is multi-scaled in nature, highly sensitive and inhomogeneous. Very high local pressures and partial contact separations in the contact interface further complicate its numerical modelling. By studying an analytical model of 3 × 3 friction oscillators using three different friction laws (Amonton-Coulomb, the velocity-dependent and the LuGre friction model) in point contact with a sliding rigid plate and incorporating uncertainties in the contact condition, robustly unstable vibration modes have been identified in our previous research. Here, the number and the combination of friction oscillators engaged in contact are randomised to model imperfect contact. In addition, the effect of the variation in the plate's sliding velocity on the in-stability analysis is investigated with randomised friction coefficient of the Amonton-Coulomb friction model. Results of instability prediction and net work calculations are used to illustrate the s...

Zhu, F, Yang, C & Zhang, G 2016, 'SAO-BASED TOPIC MODELING FOR COMPETITIVE TECHNICAL INTELLIGENCE: A CASE STUDY IN GRAPHENE', Proceedings of the 12th International FLINS Conference (FLINS 2016).
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Competitive technical intelligence (CTI) tries to identify key technology, current R&D emphases, and key players for intellectual and policy reasons in academia and industry. Many researches apply Latent Dirichlet Allocation (LDA) to CTI mining based on the assumptions of “bag-of-words”, “bag-of-n-grams” or “bag-of-phrases”, which produce the topics at words/phrases level. However, technological words/phrases are not enough to explore problem & solution patterns hidden in technological documents, which are the most important technology intelligence for solution-oriented CTI mining. In this paper, we propose a Subject-Action-Object (SAO)-based LDA model to identify underlying topics represented by related SAOs and explore the problem & solution patterns embodied in SAO structures. SAO-Based LDA model is built based on the “bag-of-SAO” assumption and perform technology analysis at concept level. The validity and feasibility of the proposed method are tested by a case study in the Graphene technology.

Zhu, X & Xu, G 2016, 'Applying Visual Analytics on Traditional Data Mining Process: Quick Prototype, Simple Expertise Transformation, and Better Interpretation', Proceedings - 4th International Conference on Enterprise Systems: Advances in Enterprise Systems, ES 2016, International Conference on Enterprise Systems: Advances in Enterprise Systems, IEEE, Melbourne, Australia, pp. 208-213.
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© 2016 IEEE. Due to a lack of experience, business might not be confident about the completeness of their proposed data mining (DM) project objectives at early stage. Besides, business domain expertise usually shrinks when delivered to data analysts. This expertise ought to contribute more throughout whole project. In addition, the outcome from DM project might fail to transform into actionable advice as the interpretation for the outcome is hard to understand and, as a result, unconvincing to apply in real. To fill the above three gaps, Visual Analytics (VA) tools are applied in different stages to optimize traditional data analytics process. In my practice, VA tools have offered both an easy access to generate quick insights for evaluating project objective's viability, and a bidirectional channel between data analysts and stakeholders to break the background barrier. Consequently, more applicable outcomes and better client satisfaction are gained.

Ziaee Nejad, M, Lu, Pooyan Asgari, DPAIDGFM & Behbood 2016, 'THE EFFECT OF GOOGLE DRIVE DISTANCE AND DURATION IN RESIDENTIAL PROPERTY IN SYDNEY,AUSTRALIA', International Fuzzy Logic and Intelligent technologies in Nuclear Science Conference, FRANCE.
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Zijlema, AF, Van den hoven, E & Eggen, B 2016, 'Companions: Objects accruing Value and Memories by being a Part of our Lives', Proceedings of the 28th Australian Computer-Human Interaction Conference (OzCHI'16), Australian Computer Human Interaction Conference, ACM Press, Launceston, Australia.
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Cherished utilitarian objects can provide comfort and pleasure through their associations to our personal past and the time and energy we have invested in and with them. In this paper, we present a specific type of object relationship, which we call the companion. They are mundane objects that accrued meaning over time, and evoke tiny pleasureswhen we interact with them. We then draw insights from the HCI research literature on digital possessions and attachment that could be applied to enhance digital products or processes with companion qualities. We argue the importance to design for digital companionship in everyday use products, for example by enabling the accruement of subtle marks of the owners past with the product. We wish to evoke thought and awareness of the role of companions, and how this relationship can be supported in digital products.

Zuo, H, Zhang, G, Behbood, V, Lu, J, Pedrycz, W & Zhang, T 2016, 'FUZZY TRANSFER LEARNING IN DATA-SHORTAGE AND RAPIDLY CHANGING ENVIRONMENTS', UNCERTAINTY MODELLING IN KNOWLEDGE ENGINEERING AND DECISION MAKING, 12th International Conference on Fuzzy Logic and Intelligent Technologies in Nuclear Science (FLINS), WORLD SCIENTIFIC PUBL CO PTE LTD, Roubaix, FRANCE, pp. 175-180.

Kenning, G Art Gallery New South Wales 2016, Art engagement for people with dementia: Independent evaluation of the Art Access Program Art Gallery of New South Wales, Sydney.

Bluff, AJ & Johnston, AJ 2016, 'Creature: A Retelling of Dot and the Kangaroo', Queensland Performing Arts Centre.

Gill, AQ 2016, 'Adaptive Enterprise Architecture Pipeline'.
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Adaptive Enterprise Architecture Pipeline for Managing EA in Higher Education.

Jay, B 2016, 'LamSF repository of proofs in Coq'.

Jay, B 2016, 'Typed-LamSF repository of proofs in Coq'.

Laursen, LF, Chen, H-T, Silva, P, Suehiro, L & Igarashi, T 2016, 'TapDrag: An Alternative Dragging Technique on Medium-Sized MultiTouch Displays Reducing Skin Irritation and Arm Fatigue'.
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Medium-sized touch displays, sized 30 to 50 inches, are becoming more affordable and more widely available. Prolonged use of such displays can result in arm fatigue or skin irritation, especially when multiple long distance drags are involved. To address this issue, we present TapDrag, an alternative dragging technique that complements traditional dragging with a simple tapping gesture on both ends of the intended dragging path. Our experimental evaluation suggests that TapDrag is a viable alternative to traditional dragging with faster task completion times for long distances. Qualitative user feedback indicates that TapDrag helps prevent skin irritation. A reduction in arm fatigue remains unconfirmed.

Li, W, Zhang, Y, Sun, Y, Wang, W, Zhang, W & Lin, X 2016, 'Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)'.
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Approximate Nearest neighbor search (ANNS) is fundamental and essential operation in applications from many domains, such as databases, machine learning, multimedia, and computer vision. Although many algorithms have been continuously proposed in the literature in the above domains each year, there is no comprehensive evaluation and analysis of their performances. In this paper, we conduct a comprehensive experimental evaluation of many state-of-the-art methods for approximate nearest neighbor search. Our study (1) is cross-disciplinary (i.e., including 16 algorithms in different domains, and from practitioners) and (2) has evaluated a diverse range of settings, including 20 datasets, several evaluation metrics, and different query workloads. The experimental results are carefully reported and analyzed to understand the performance results. Furthermore, we propose a new method that achieves both high query efficiency and high recall empirically on majority of the datasets under a wide range of settings.

Wang, X & Williams, M-A 2016, 'PyRIDE: An Interactive Development Environment for PR2 Robot'.
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Python based Robot Interactive Development Environment (PyRIDE) is a software that supports rapid \textit{interactive} programming of robot skills and behaviours on PR2/ROS (Robot Operating System) platform. One of the key features of PyRIDE is its interactive remotely accessible Python console that allows its users to program robots \textit{online} and in \textit{realtime} in the same way as using the standard Python interactive interpreter. It allows programs to be modified while they are running. PyRIDE is also a software integration framework that abstracts and aggregates disparate low level ROS software modules, e.g. arm joint motor controllers, and exposes their functionalities through a unified Python programming interface. PR2 programmers are able to experiment and develop robot behaviours without dealing with specific details of accessing underlying softwares and hardwares. PyRIDE provides a client-server mechanism that allows remote user access of the robot functionalities, e.g. remote robot monitoring and control, access real-time robot camera image data etc. This enables multi-modal human robot interactions using different devices and user interfaces. All these features are seamlessly integrated into one lightweight and portable middleware package. In this paper, we use four real life scenarios to demonstrate PyRIDE key features and illustrate the usefulness of software.