Publications

Demazeau, Y, Gao, J, Xu, G, Kozlak, J, Müller, K, Razzak, I, Chen, H & Gu, Y 2017, Proceedings of 2017 International Conference on Behavioral, Economic, Socio-cultural Computing.
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Diesner, J, Ferrari, E & Xu, G 2017, Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017.
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Gill, A 2017, Adaptive Enterprise Strategy Journey Management Your Journey for Building an Adaptive Business Strategy and Execution Capability in a Rapidly Changing and Increasingly Digital World, Createspace Independent Publishing Platform.
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This book takes you on a journey of the development of a robust, highly scalable and smart method for building adaptive enterprise capabilities that are not achievable using the existing traditional frameworks and methods.

Redshaw, S 2017, In the company of cars: Driving as a social and cultural practice.
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© Sarah Redshaw 2008. It has long been accepted that the social and cultural meanings of the car far exceed the practical need for mobility. This book marks the first attempt to contribute to road safety, considering, in depth, these meanings and the cultures of driving that are shaped by them. In the Company of Cars examines the perspectives that young people have on cars, and explores the broader social and cultural meanings of the car, the potential it is supposed to fulfil, and the anticipated benefits it offers to young drivers. From focus-group research conducted in Australia, the book takes up the views of young people on a range of topics, from media to car use to gender performance. The author looks at the ways in which driving has been defined by articulations of the car that emphasize valued features of the car-driver, such as gender, youthfulness, status, age, power, raciness, sexiness, ruggedness and competitiveness. The book takes a global perspective on mobility, considering the impact of cars and road safety policy on quality of life, and the value and significance of other modes of travel, in a range of countries.

Eggen, B, van den Hoven, E & Terken, J 2017, 'Human-centered design and smart homes: How to study and design for the home experience?' in Handbook of Smart Homes, Health Care and Well-Being, Springer, Germany, pp. 83-92.
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© Springer International Publishing Switzerland 2017.The focus of this chapter is on designing for smart homes. The perspective will be user-driven design research. The chapter starts with a context analysis of the home environment. This analysis shows that, from a user perspective, home is about emotions and not about the physical house with all its smart applications. It is this “home experience” designers have to design for. The core of the chapter consists of the description of three big challenges that modern designers (need to) face when designing or studying smart home environments. These challenges are linked to existing and future design paradigms. The following challenges are addressed: (1) What makes a worthwhile user experience? (2) How to design for user experience? (3) How to design for user experiences that can be seamlessly integrated in everyday life? The chapter concludes with a summary of the main insights that emerge from current design research practice facing these challenges.

Gill, AQ 2017, 'Applying agility and living service systems thinking to enterprise architecture' in Decision Management: Concepts, Methodologies, Tools, and Applications, IGI, USA, pp. 487-502.
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© 2017, IGI Global. All rights reserved. Adaptive enterprise architecture capability plays an important role in enabling complex enterprise transformations. One of the key challenges when establishing an adaptive enterprise architecture capability is identifying the enterprise context and the scope of the enterprise architecture. The objective of this paper is to develop and present an adaptive enterprise service system (AESS) conceptual model, which is a part of The Gill Framework for Adaptive Enterprise Service Systems. This model has been developed using a "Design Research" approach. The AESS conceptual model assimilates agility, service, and living systems thinking (following multi-agent system modelling) for describing and analyzing the enterprise context and scope for establishing an adaptive enterprise architecture capability. The target audience of this AESS model driven approach includes both, enterprise architecture researchers and practitioners.

Macnamara, J & Kenning, G 2017, '“Stoner Sloth”: Lessons from Evaluation of Social Media and Virality' in VanSlyke Turk, J & Valin, J (eds), Public Relations Case Studies from Around the World (2nd Edition), Peter Lang, New York, pp. 147-165.
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In November 2015 the New South Wales (NSW) Department of Premier and Cabinet launched a digital campaign using social media, websites, and paid online search to reduce the incidence of recreational use of Cannabis among 14–18 year olds – the age range identified as the period in which people are most likely to first try recreational drugs. Media metrics and social media tracking of the campaign found that it was perceived to be ‘fun’ but ‘credible’ and was widely shared within the target audience in its first month when messages were highly targeted through special youth sites and paid search. But in mid-December when the videos and GIFs featured in the campaign went ‘viral’ attracting more than 3.5 million views in a few weeks, the campaign received widespread criticism in traditional media and more than 30,000 comments on social media, most of them negative. It was labelled “one of the worst campaigns of 2015” in newspaper headlines and the government terminated the campaign in mid-January 2016 and ordered a detailed independent evaluation . Subsequent in-depth quantitative and qualitative evaluation showed that the ‘edgy’ youth campaign did in fact achieve many of its objectives among 14–18 year olds, causing them to ‘think twice’ about using Cannabis and reducing perceptions that using the drug was ‘cool’. However, the campaign raised major questions about the common view that ‘going viral’ is positive (Godin, 2001; ImagePro, 2016) and provides lessons for planning digital campaigns.

Mathieson, L, Mendes, A, Marsden, J, Pond, J & Moscato, P 2017, 'Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques.' in Bioinformatics, Springer, Germany, pp. 299-325.
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This chapter introduces a new method for knowledge extraction from databases for the purpose of finding a discriminative set of features that is also a robust set for within-class classification. Our method is generic and we introduce it here in the field of breast cancer diagnosis from digital mammography data. The mathematical formalism is based on a generalization of the k-Feature Set problem called (α, β)-k-Feature Set problem, introduced by Cotta and Moscato (J Comput Syst Sci 67(4):686-690, 2003). This method proceeds in two steps: first, an optimal (α, β)-k-feature set of minimum cardinality is identified and then, a set of classification rules using these features is obtained. We obtain the (α, β)-k-feature set in two phases; first a series of extremely powerful reduction techniques, which do not lose the optimal solution, are employed; and second, a metaheuristic search to identify the remaining features to be considered or disregarded. Two algorithms were tested with a public domain digital mammography dataset composed of 71 malignant and 75 benign cases. Based on the results provided by the algorithms, we obtain classification rules that employ only a subset of these features.

Redshaw, SJ 2017, 'Mobile my spaces: Home in cars, working vehicles and contrasting dwelling for backpackers in campervans and homeless car sleepers' in Vasta, E & LLOYD, J (eds), Re-imagining Home in the 21st Century, Edward Elgar Publishing, UK, pp. 87-101.
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Yao, L, Sheng, QZ, Ngu, AHH, Li, X, Benatallah, B & Wang, X 2017, 'Building Entity Graphs for the Web of Things Management' in Managing the Web of Things: Linking the Real World to the Web, pp. 275-303.
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© 2017 Elsevier Inc. All rights reserved. With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web services, physical things are becoming an integral part of the emerging ubiquitous Web. Finding correlations among ubiquitous things is a crucial prerequisite for many important applications such as things search, discovery, classification, recommendation, and composition. This article building an entity graph for Web of Things, whereas we propose a novel graph-based approach for discovering underlying connections of things via mining the rich content embodied in the human-thing interactions in terms of user, temporal and spatial information. We model this various information using two graphs, namely a spatiotemporal graph and a social graph. Then, random walk with restart (RWR) is applied to find proximities among things, a relational graph of things, entity graph, indicating implicit correlations of things is learned. The correlation analysis lays a solid foundation contributing to improved effectiveness in things management and analytics. To demonstrate the utility of the proposed approach, we present two typical applications and a systematic case study in regards to a flexible feature-based classification framework and a unified probabilistic factor based framework, respectively. Our evaluation exhibits the strength and feasibility of approach.

Abdul Hanan, AH, Yazid Idris, M, Kaiwartya, O, Prasad, M & Ratn Shah, R 2017, 'Real traffic-data based evaluation of vehicular traffic environment and state-of-the-art with future issues in location-centric data dissemination for VANETs', Digital Communications and Networks, vol. 3, no. 3, pp. 195-210.
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© 2017 Chongqing University of Posts and Telecommuniocations Extensive investigation has been performed in location-centric or geocast routing protocols for reliable and efficient dissemination of information in Vehicular Adhoc Networks (VANETs). Various location-centric routing protocols have been suggested in literature for road safety ITS applications considering urban and highway traffic environment. This paper characterizes vehicular environments based on real traffic data and investigates the evolution of location-centric data dissemination. The current study is carried out with three main objectives: (i) to analyze the impact of dynamic traffic environment on the design of data dissemination techniques, (ii) to characterize location-centric data dissemination in terms of functional and qualitative behavior of protocols, properties, and strengths and weaknesses, and (iii) to find some future research directions in information dissemination based on location. Vehicular traffic environments have been classified into three categories based on physical characteristics such as speed, inter-vehicular distance, neighborhood stability, traffic volume, etc. Real traffic data is considered to analyze on-road traffic environments based on the measurement of physical parameters and weather conditions. Design issues are identified in incorporating physical parameters and weather conditions into data dissemination. Functional and qualitative characteristics of location-centric techniques are explored considering urban and highway environments. Comparative analysis of location-centric techniques is carried out for both urban and highway environments individually based on some unique and common characteristics of the environments. Finally, some future research directions are identified in the area based on the detailed investigation of traffic environments and location-centric data dissemination techniques.

Ahadi, A, Hellas, A & Lister, R 2017, 'A contingency table derived method for analyzing course data', ACM Transactions on Computing Education, vol. 17, no. 3, pp. 1-19.
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© 2017 ACM. We describe a method for analyzing student data from online programming exercises. Our approach uses contingency tables that combine whether or not a student answered an online exercise correctly with the number of attempts that the student made on that exercise. We use this method to explore the relationship between student performance on online exercises done during semester with subsequent performance on questions in a paper-based exam at the end of semester. We found that it is useful to include data about the number of attempts a student makes on an online exercise.

Ahadi, A, Lister, R & Hellas, A 2017, 'A Contingency Table Derived Methodology for Analyzing Course Data', ACM Transactions on Computing Education, vol. 9, no. 4.
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Ahadi, A, Sablok, G & Hutvagner, G 2017, 'miRTar2GO: a novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA–target interaction data', Nucleic Acids Research, vol. 45, no. 6, pp. 1-10.
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MicroRNAs (miRNAs) are ∼19-22 nucleotides (nt) long regulatory RNAs that regulate gene expression by recognizing and binding to complementary sequences on mRNAs. The key step in revealing the function of a miRNA, is the identification of miRNA target genes. Recent biochemical advances including PAR-CLIP and HITS-CLIP allow for improved miRNA target predictions and are widely used to validate miRNA targets. Here, we present miRTar2GO, which is a model, trained on the common rules of miRNA-target interactions, Argonaute (Ago) CLIP-Seq data and experimentally validated miRNA target interactions. miRTar2GO is designed to predict miRNA target sites using more relaxed miRNA-target binding characteristics. More importantly, miRTar2GO allows for the prediction of cell-type specific miRNA targets. We have evaluated miRTar2GO against other widely used miRNA target prediction algorithms and demonstrated that miRTar2GO produced significantly higher F1 and G scores. Target predictions, binding specifications, results of the pathway analysis and gene ontology enrichment of miRNA targets are freely available at http://www.mirtar2go.org.

Bakshi, A, Talaei-Khoei, A, Ray, P & Solvoll, T 2017, 'PoDMan: Policy deviation management', Australasian Journal of Information Systems, vol. 21.
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© 2017 Bakshi, Talaei-Khoei, Ray & Solvoll. Whenever an unexpected or exceptional situation occurs, complying with the existing policies may not be possible. The main objective of this work is to assist individuals and organizations to decide in the process of deviating from policies and performing a non-complying action. The paper proposes utilizing software agents as supportive tools to provide the best non-complying action while deviating from policies. The article also introduces a process in which the decision on the choice of non-complying action can be made. The work is motivated by a real scenario observed in a hospital in Norway and demonstrated through the same settings.

Banaei, M, Hatami, J, Yazdanfar, A & Gramann, K 2017, 'Walking through architectural spaces: The impact of interior forms on human brain dynamics', Frontiers in Human Neuroscience, vol. 11.
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© 2017 Banaei, Hatami, Yazdanfar and Gramann. Neuroarchitecture uses neuroscientific tools to better understand architectural design and its impact on human perception and subjective experience. The form or shape of the built environment is fundamental to architectural design, but not many studies have shown the impact of different forms on the inhabitants’ emotions. This study investigated the neurophysiological correlates of different interior forms on the perceivers’ affective state and the accompanying brain activity. To understand the impact of naturalistic three-dimensional (3D) architectural forms, it is essential to perceive forms from different perspectives. We computed clusters of form features extracted from pictures of residential interiors and constructed exemplary 3D room models based on and representing different formal clusters. To investigate human brain activity during 3D perception of architectural spaces, we used a mobile brain/body imaging (MoBI) approach recording the electroencephalogram (EEG) of participants while they naturally walk through different interior forms in virtual reality (VR). The results revealed a strong impact of curvature geometries on activity in the anterior cingulate cortex (ACC). Theta band activity in ACC correlated with specific feature types (rs (14) = 0.525, ρ = 0.037) and geometry (rs (14) = –0.579, ρ = 0.019), providing evidence for a role of this structure in processing architectural features beyond their emotional impact. The posterior cingulate cortex and the occipital lobe were involved in the perception of different room perspectives during the stroll through the rooms. This study sheds new light on the use of mobile EEG and VR in architectural studies and provides the opportunity to study human brain dynamics in participants that actively explore and realistically experience architectural spaces.

Bano, M, Zowghi, D & da Rimini, F 2017, 'User Satisfaction and System Success: An Empirical Exploration of User Involvement in Software Development', Empirical Software Engineering: an international journal, vol. 22, no. 5, pp. 23339-2372.
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For over four decades user involvement has been considered intuitively to lead to user satisfaction, which plays a pivotal role in successful outcome of a software project. The objective of this paper is to explore the notion of user satisfaction within the context of the user involvement and system success relationship. We have conducted a longitudinal case study of a software development project and collected qualitative data by means of interviews, observations and document analysis over a period of 3 years. The analysis of our case study data revealed that user satisfaction significantly contributes to the system success even when schedule and budget goals are not met. The case study data analysis also presented additional factors that contribute to the evolution of user satisfaction throughout the project. Users’ satisfaction with their involvement and the resulting system are mutually constituted while the level of user satisfaction evolves throughout the stages of software development process. Effective management strategies and user representation are essential elements of maintaining an acceptable level of user satisfaction throughout software development process.

Berta, M, Fawzi, O & Tomamichel, M 2017, 'On variational expressions for quantum relative entropies', Letters in Mathematical Physics, vol. 107, no. 12, pp. 2239-2265.
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© 2017, Springer Science+Business Media B.V. Distance measures between quantum states like the trace distance and the fidelity can naturally be defined by optimizing a classical distance measure over all measurement statistics that can be obtained from the respective quantum states. In contrast, Petz showed that the measured relative entropy, defined as a maximization of the Kullback–Leibler divergence over projective measurement statistics, is strictly smaller than Umegaki’s quantum relative entropy whenever the states do not commute. We extend this result in two ways. First, we show that Petz’ conclusion remains true if we allow general positive operator-valued measures. Second, we extend the result to Rényi relative entropies and show that for non-commuting states the sandwiched Rényi relative entropy is strictly larger than the measured Rényi relative entropy for α∈(12,∞) and strictly smaller for α∈[0,12). The latter statement provides counterexamples for the data processing inequality of the sandwiched Rényi relative entropy for α<12. Our main tool is a new variational expression for the measured Rényi relative entropy, which we further exploit to show that certain lower bounds on quantum conditional mutual information are superadditive.

Beydoun, G, Hoffmann, A & Gill, A 2017, 'Constructing enhanced default theories incrementally', Complex and Intelligent Systems, vol. 3, no. 2, pp. 83-92.
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The main difference between various formalisms of non-monotonic reasoning is the representation of non-monotonic rules. In default logic, they are represented by special expressions called defaults. In default logic, commonsense knowledge about the world is represented as a set of named defaults. The use of defaults is popular because they reduce the complexity of the representation, and they are sufficient for knowledge representation in many naturally occurring contexts. This paper offers an incremental process to acquire defaults from human experts directly and at the same time it provides added semantics to defaults by adding priorities to defaults and creating additional relations between them. The paper uses an existing incremental framework, NRDR, to generate these defaults. This framework is chosen as it not only enables incremental context driven formulation of defaults, but also allows experts to introduce their own domain terms. In choosing this framework, the paper broadens its utility.

Bluff, A & Johnston, A 2017, 'Creature:Interactions: A Social Mixed-reality Playspace', Leonardo, vol. 50, no. 4, pp. 360-367.
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This paper discusses Creature:Interactions (2015), a large-scale mixed-reality artwork created by the authors that incorporates immersive 360° stereoscopic visuals, interactive technology, and live actor facilitation. The work uses physical simulations to promote an expressive full-bodied interaction as children explore the landscapes and creatures of Ethel C. Pedley's ecologically focused children's novel, Dot and the Kangaroo. The immersive visuals provide a social playspace for up to 90 people and have produced "phantom" sensations of temperature and touch in certain participants.

Bower, M, Wood, LN, Lai, JWM, Howe, C & Lister, R 2017, 'Improving the computational thinking pedagogical capabilities of school teachers', Australian Journal of Teacher Education, vol. 42, no. 3, pp. 53-72.
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The idea of computational thinking as skills and universal competence which every child should possess emerged last decade and has been gaining traction ever since. This raises a number of questions, including how to integrate computational thinking into the curriculum, whether teachers have computational thinking pedagogical capabilities to teach children, and the important professional development and training areas for teachers. The aim of this paper is to address the strategic issues by illustrating a series of computational thinking workshops for Foundation to Year 8 teachers held at an Australian university. Data indicated that teachers' computational thinking understanding, pedagogical capabilities, technological know-how and confidence can be improved in a relatively short period of time through targeted professional learning.

Broekhuijsen, M, van den Hoven, E & Markopoulos, P 2017, 'From PhotoWork to PhotoUse: exploring personal digital photo activities', BEHAVIOUR & INFORMATION TECHNOLOGY, vol. 36, no. 7, pp. 754-767.
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Chaudhuri, BB & Adak, C 2017, 'An approach for detecting and cleaning of struck-out handwritten text', Pattern Recognition, vol. 61, pp. 282-294.
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This paper deals with the identification and processing of struck-out texts in unconstrained offline handwritten document images. If run on the OCR engine, such texts will produce nonsense character-string outputs. Here we present a combined (a) pattern classification and (b) graph-based method for identifying such texts. In case of (a), a feature-based two-class (normal vs. struck-out text) SVM classifier is used to detect moderate-sized struck-out components. In case of (b), skeleton of the text component is considered as a graph and the strike-out stroke is identified using a constrained shortest path algorithm. To identify zigzag or wavy struck-outs, all paths are found and some properties of zigzag and wavy line are utilized. Some other types of strike-out stroke are also detected by modifying the above method. The large sized multi-word and multi-line struck-outs are segmented into smaller components and treated as above. The detected struck-out texts can then be blocked from entering the OCR engine. In another kind of application involving historical documents, page images along with their annotated ground-truth are to be generated. In this case the strike-out strokes can be deleted from the words and then fed to the OCR engine. For this purpose an inpainting-based cleaning approach is employed. We worked on 500 pages of documents and obtained an overall F-Measure of 91.56% (91.06%) in English (Bengali) script for struck-out text detection. Also, for strike-out stroke identification and deletion, the F-Measures obtained were 89.65% (89.31%) and 91.16% (89.29%), respectively.

Chen, H, Zhang, G, Zhu, D & Lu, J 2017, 'Topic-based technological forecasting based on patent data: A case study of Australian patents from 2000 to 2014', Technological Forecasting and Social Change, vol. 119, no. June 2017, pp. 39-52.
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Chen, Z, You, X, Zhong, B, Li, J & Tao, D 2017, 'Dynamically Modulated Mask Sparse Tracking', IEEE Transactions on Cybernetics, vol. 47, no. 11, pp. 3706-3718.
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Visual tracking is a critical task in many computer vision applications such as surveillance and robotics. However, although the robustness to local corruptions has been improved, prevailing trackers are still sensitive to large scale corruptions, such as occlusions and illumination variations. In this paper, we propose a novel robust object tracking technique depends on subspace learning-based appearance model. Our contributions are twofold. First, mask templates produced by frame difference are introduced into our template dictionary. Since the mask templates contain abundant structure information of corruptions, the model could encode information about the corruptions on the object more efficiently. Meanwhile, the robustness of the tracker is further enhanced by adopting system dynamic, which considers the moving tendency of the object. Second, we provide the theoretic guarantee that by adapting the modulated template dictionary system, our new sparse model can be solved by the accelerated proximal gradient algorithm as efficient as in traditional sparse tracking methods. Extensive experimental evaluations demonstrate that our method significantly outperforms 21 other cutting-edge algorithms in both speed and tracking accuracy, especially when there are challenges such as pose variation, occlusion, and illumination changes.

Cheng, HC, Hsieh, MH & Tomamichel, M 2017, 'Exponential decay of matrix Φ-entropies on Markov semigroups with applications to dynamical evolutions of quantum ensembles', Journal of Mathematical Physics, vol. 58, no. 9.
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In this work, we extend the theory of quantum Markov processes on a single quantum state to a broader theory that covers Markovian evolution of an ensemble of quantum states, which generalizes Lindblad's formulation of quantum dynamical semigroups. Our results establish the equivalence between an exponential decrease of the matrix Φ-entropies and the Φ-Sobolev inequalities, which allows us to characterize the dynamical evolution of a quantum ensemble to its equilibrium. In particular, we study the convergence rates of two special semigroups, namely, the depolarizing channel and the phase-damping channel. In the former, since there exists a unique equilibrium state, we show that the matrix Φ-entropy of the resulting quantum ensemble decays exponentially as time goes on. Consequently, we obtain a stronger notion of monotonicity of the Holevo quantity-the Holevo quantity of the quantum ensemble decays exponentially in time and the convergence rate is determined by the modified log-Sobolev inequalities. However, in the latter, the matrix Φ-entropy of the quantum ensemble that undergoes the phase-damping Markovian evolution generally will not decay exponentially. There is no classical analogy for these different equilibrium situations. Finally, we also study a statistical mixing of Markov semigroups on matrix-valued functions. We can explicitly calculate the convergence rate of a Markovian jump process defined on Boolean hypercubes and provide upper bounds to the mixing time. Published by AIP Publishing.

Chubb, CT, Tan, VYF & Tomamichel, M 2017, 'Moderate Deviation Analysis for Classical Communication over Quantum Channels', Communications in Mathematical Physics, vol. 355, no. 3, pp. 1283-1315.
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© 2017, Springer-Verlag GmbH Germany. We analyse families of codes for classical data transmission over quantum channels that have both a vanishing probability of error and a code rate approaching capacity as the code length increases. To characterise the fundamental tradeoff between decoding error, code rate and code length for such codes we introduce a quantum generalisation of the moderate deviation analysis proposed by Altŭg and Wagner as well as Polyanskiy and Verdú. We derive such a tradeoff for classical-quantum (as well as image-additive) channels in terms of the channel capacity and the channel dispersion, giving further evidence that the latter quantity characterises the necessary backoff from capacity when transmitting finite blocks of classical data. To derive these results we also study asymmetric binary quantum hypothesis testing in the moderate deviations regime. Due to the central importance of the latter task, we expect that our techniques will find further applications in the analysis of other quantum information processing tasks.

Coles, PJ, Berta, M, Tomamichel, M & Wehner, S 2017, 'Entropic uncertainty relations and their applications', Reviews of Modern Physics, vol. 89, no. 1, pp. 1-58.
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© 2017 American Physical Society. Heisenberg's uncertainty principle forms a fundamental element of quantum mechanics. Uncertainty relations in terms of entropies were initially proposed to deal with conceptual shortcomings in the original formulation of the uncertainty principle and, hence, play an important role in quantum foundations. More recently, entropic uncertainty relations have emerged as the central ingredient in the security analysis of almost all quantum cryptographic protocols, such as quantum key distribution and two-party quantum cryptography. This review surveys entropic uncertainty relations that capture Heisenberg's idea that the results of incompatible measurements are impossible to predict, covering both finite- and infinite-dimensional measurements. These ideas are then extended to incorporate quantum correlations between the observed object and its environment, allowing for a variety of recent, more general formulations of the uncertainty principle. Finally, various applications are discussed, ranging from entanglement witnessing to wave-particle duality to quantum cryptography.

Corio, E, Laccone, F, Pietroni, N, Cignoni, P & Froli, M 2017, 'Conception and parametric design workflow for a timber large-spanned reversible grid shell to shelter the archaeological site of the Roman Shipwrecks in Pisa', International Journal of Computational Methods and Experimental Measurements, vol. 5, pp. 551-561.

Davis, JJJ, Lin, CT, Gillett, G & Kozma, R 2017, 'An integrative approach to analyze eeg signals and human brain dynamics in different cognitive states', Journal of Artificial Intelligence and Soft Computing Research, vol. 7, no. 4, pp. 287-299.
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© 2017. Electroencephalograph (EEG) data provide insight into the interconnections and relationships between various cognitive states and their corresponding brain dynamics, by demonstrating dynamic connections between brain regions at different frequency bands. While sensory input tends to stimulate neural activity in different frequency bands, peaceful states of being and self-induced meditation tend to produce activity in the mid-range (Alpha). These studies were conducted with the aim of: (a) testing different equipment in order to assess two (2) different EEG technologies together with their benefits and limitations and (b) having an initial impression of different brain states associated with different experimental modalities and tasks, by analyzing the spatial and temporal power spectrum and applying our movie making methodology to engage in qualitative exploration via the art of encephalography. This study complements our previous study of measuring multichannel EEG brain dynamics using MINDO48 equipment associated with three experimental modalities measured both in the laboratory and the natural environment. Together with Hilbert analysis, we conjecture, the results will provide us with the tools to engage in more complex brain dynamics and mental states, such as Meditation, Mathematical Audio Lectures, Music Induced Meditation, and Mental Arithmetic Exercises. This paper focuses on open eye and closed eye conditions, as well as meditation states in laboratory conditions. We assess similarities and differences between experimental modalities and their associated brain states as well as differences between the different tools for analysis and equipment.

De Meo, P, Musial-Gabrys, K, Rosaci, D, Sarne, GML & Aroyo, L 2017, 'Using Centrality Measures to Predict Helpfulness-Based Reputation in Trust Networks', ACM TRANSACTIONS ON INTERNET TECHNOLOGY, vol. 17, no. 1.
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Deng, S, Huang, L, Xu, G, Wu, X & Wu, Z 2017, 'On Deep Learning for Trust-Aware Recommendations in Social Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 5, pp. 1164-1177.
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With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user's trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users' interests and their trusted friends' interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.

Fang, XS, Sheng, QZ, Wang, X, Ngu, AHH & Zhang, Y 2017, 'GrandBase: generating actionable knowledge from Big Data', PSU Research Review, vol. 1, pp. 105-126.
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Purpose This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase. Design/methodology/approach In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed. Findings Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed. Originality/value To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

Gao, L, Luan, TH, Yu, S, Zhou, W & Liu, B 2017, 'FogRoute: DTN-Based Data Dissemination Model in Fog Computing', IEEE Internet of Things Journal, vol. 4, no. 1, pp. 225-235.
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© 2017 IEEE. Fog computing, known as 'cloud closed to ground,' deploys light-weight compute facility, called Fog servers, at the proximity of mobile users. By precatching contents in the Fog servers, an important application of Fog computing is to provide high-quality low-cost data distributions to proximity mobile users, e.g., video/live streaming and ads dissemination, using the single-hop low-latency wireless links. A Fog computing system is of a three tier Mobile-Fog-Cloud structure; mobile user gets service from Fog servers using local wireless connections, and Fog servers update their contents from Cloud using the cellular or wired networks. This, however, may incur high content update cost when the bandwidth between the Fog and Cloud servers is expensive, e.g., using the cellular network, and is therefore inefficient for nonurgent, high volume contents. How to economically utilize the Fog-Cloud bandwidth with guaranteed download performance of users thus represents a fundamental issue in Fog computing. In this paper, we address the issue by proposing a hybrid data dissemination framework which applies software-defined network and delay-tolerable network (DTN) approaches in Fog computing. Specifically, we decompose the Fog computing network with two planes, where the cloud is a control plane to process content update queries and organize data flows, and the geometrically distributed Fog servers form a data plane to disseminate data among Fog servers with a DTN technique. Using extensive simulations, we show that the proposed framework is efficient in terms of data-dissemination success ratio and content convergence time among Fog servers.

Gill, AQ, Braytee, A & Hussain, FK 2017, 'Adaptive service e-contract information management reference architecture', VINE Journal of Information and Knowledge Management Systems, vol. 47, no. 3, pp. 395-410.
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© 2017, © Emerald Publishing Limited. Purpose: The aim of this paper is to report on the adaptive e-contract information management reference architecture using the systematic literature review (SLR) method. Enterprises need to effectively design and implement complex adaptive e-contract information management architecture to support dynamic service interactions or transactions. Design/methodology/approach: The SLR method is three-fold and was adopted as follows. First, a customized literature search with relevant selection criteria was developed, which was then applied to initially identify a set of 1,573 papers. Second, 55 of 1,573 papers were selected for review based on the initial review of each identified paper title and abstract. Finally, based on the second review, 24 papers relevant to this research were selected and reviewed in detail. Findings: This detailed review resulted in the adaptive e-contract information management reference architecture elements including structure, life cycle and supporting technology. Research limitations/implications: The reference architecture elements could serve as a taxonomy for researchers and practitioners to develop context-specific service e-contract information management architecture to support dynamic service interactions for value co-creation. The results are limited to the number of selected databases and papers reviewed in this study. Originality/value: This paper offers a review of the body of knowledge and novel e-contract information management reference architecture, which is important to support the emerging trends of internet of services.

Goodswen, SJ, Kennedy, PJ & Ellis, JT 2017, 'On the application of reverse vaccinology to parasitic diseases: a perspective on feature selection and ranking of vaccine candidates.', International journal for parasitology, vol. 47, no. 12, pp. 779-790.
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Reverse vaccinology has the potential to rapidly advance vaccine development against parasites, but it is unclear which features studied in silico will advance vaccine development. Here we consider Neospora caninum which is a globally distributed protozoan parasite causing significant economic and reproductive loss to cattle industries worldwide. The aim of this study was to use a reverse vaccinology approach to compile a worthy vaccine candidate list for N. caninum, including proteins containing pathogen-associated molecular patterns to act as vaccine carriers. The in silico approach essentially involved collecting a wide range of gene and protein features from public databases or computationally predicting those for every known Neospora protein. This data collection was then analysed using an automated high-throughput process to identify candidates. The final vaccine list compiled was judged to be the optimum within the constraints of available data, current knowledge, and existing bioinformatics programs. We consider and provide some suggestions and experience on how ranking of vaccine candidate lists can be performed. This study is therefore important in that it provides a valuable resource for establishing new directions in vaccine research against neosporosis and other parasitic diseases of economic and medical importance.

Gramann, K, Fairclough, SH, Zander, TO & Ayaz, H 2017, 'Editorial: Trends in Neuroergonomics', FRONTIERS IN HUMAN NEUROSCIENCE, vol. 11.
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Gramann, K, Hoepner, P & Karrer-Gauss, K 2017, 'Modified Navigation Instructions for Spatial Navigation Assistance Systems Lead to Incidental Spatial Learning', FRONTIERS IN PSYCHOLOGY, vol. 8.
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Greeven, CS & Williams, SP 2017, 'Enterprise collaboration systems: Addressing adoption challenges and the shaping of sociotechnical systems', International Journal of Information Systems and Project Management, vol. 5, no. 1, pp. 5-23.
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© 2017, SciKA. This article examines the adoption challenges organizations encounter when they introduce enterprise collaboration systems (ECS) and the measures that can be used, i.e. actions that can be taken, to address these challenges. The aim of the article is to provide an overview of the multitude of different ECS adoption challenges and measures, and based on these, to lay the theoretical and analytical basis for studying the shaping of such complex sociotechnical systems. For this purpose, a qualitative meta-analysis of the academic literature and interviews with companies were conducted, which resulted in a collection of ECS challenges and measures classified and analyzed with regard to their specific spatiotemporal aspects. Drawing on the results of this study, research imperatives, which include the call for studying ECS over multiple time frames and settings, are presented. These will be examined in greater depth as part of our wider, multidisciplinary research program that focuses on enterprise collaboration systems use in the emerging digital workplace.

Grissmann, S, Zander, TO, Faller, J, Broenstrup, J, Kelava, A, Gramann, K & Gerjets, P 2017, 'Affective Aspects of Perceived Loss of Control and Potential Implications for Brain-Computer Interfaces', FRONTIERS IN HUMAN NEUROSCIENCE, vol. 11.
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Grochow, JA & Qiao, Y 2017, 'ALGORITHMS FOR GROUP ISOMORPHISM VIA GROUP EXTENSIONS AND COHOMOLOGY', SIAM JOURNAL ON COMPUTING, vol. 46, no. 4, pp. 1153-1216.
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Han, J, Lu, J & Zhang, G 2017, 'Tri-level decision-making for decentralized vendor-managed inventory', Information Sciences, vol. 421, pp. 85-103.
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© 2017 Elsevier Inc. Vendor-managed inventory (VMI) is a common inventory management policy which allows the vendor to manage the buyer's inventory based on the information shared in the course of supply chain management. One challenge in VMI is that both the vendor and buyer are manufacturers who try to achieve an inventory as small as possible or even a zero inventory; it is therefore difficult to manage inventory coordination between them. This paper considers a decentralized VMI problem in a three-echelon supply chain network in which multiple distributors (third-party logistics companies) are selected to balance the inventory between a vendor (manufacturer) and multiple buyers (manufacturers). To handle this issue, this paper first proposes a tri-level decision model to describe the decentralized VMI problem, which allows us to examine how decision members coordinate with each other in respect of decentralized VMI decision-making in a predetermined sequence. We then turn our attention to the geometry of the solution space and present a vertex enumeration algorithm to solve the resulting tri-level decision model. Lastly, a computational study is developed to illustrate how the proposed tri-level decision model and solution approach can handle the decentralized VMI problem. The results indicate that the proposed tri-level decision-making techniques provide a practical way to design a novel manufacturer-manufacturer (vendor-buyer) VMI system where third-party logistics are involved.

Hatoum, D, Yagoub, D, Ahadi, A, Nassif, NT & McGowan, EM 2017, 'Annexin/S100A protein family regulation through p14ARF-p53 activation: A role in cell survival and predicting treatment outcomes in breast cancer', PLoS ONE, vol. 12, no. 1.
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© 2017 Hatoum et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The annexin family and S100A associated proteins are important regulators of diverse calcium- dependent cellular processes including cell division, growth regulation and apoptosis. Dysfunction of individual annexin and S100A proteins is associated with cancer progression, metastasis and cancer drug resistance. This manuscript describes the novel finding of differential regulation of the annexin and S100A family of proteins by activation of p53 in breast cancer cells. Additionally, the observed differential regulation is found to be beneficial to the survival of breast cancer cells and to influence treatment efficacy. We have used unbiased, quantitative proteomics to determine the proteomic changes occurring post p14ARF-p53 activation in estrogen receptor (ER) breast cancer cells. In this report we identified differential regulation of the annexin/S100A family, through unique peptide recognition at the N-terminal regions, demonstrating p14ARF-p53 is a central orchestrator of the annexin/S100A family of calcium regulators in favor of pro-survival functions in the breast cancer cell. This regulation was found to be cell-type specific. Retrospective human breast cancer studies have demonstrated that tumors with functional wild type p53 (p53wt) respond poorly to some chemotherapy agents compared to tumors with a non-functional p53. Given that modulation of calcium signaling has been demonstrated to change sensitivity of chemotherapeutic agents to apoptotic signals, in principle, we explored the paradigm of how p53 modulation of calcium regulators in ER+ breast cancer patients impacts and influences therapeutic outcomes.

Heng, J, Wang, J, Xiao, L & Lu, H 2017, 'Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting', Applied Energy, vol. 208, pp. 845-866.
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© 2017 Elsevier Ltd Solar radiation forecasting plays a significant role in precisely designing solar energy systems and in the efficient management of solar energy plants. Most research only focuses on accuracy improvements; however, for an effective forecasting model, considering only accuracy or stability is inadequate. To solve this problem, a combined model based on nondominated sorting-based multiobjective bat algorithm (NSMOBA) is developed for the optimization of weight coefficients of each model to achieve high accuracy and stability results simultaneously. In addition, a statistical method and data mining-based approach are used to determine the input variables for constructing the combined model. Monthly average solar radiation and meteorological variables from six datasets in the U.S. collected for case studies were used to assess the comprehensive performance (both in accuracy and stability) of the proposed combined model. The simulation in four experiments demonstrated the following: (a) the proposed combined model is suita ble for providing accurate and stable solar radiation forecasting; (b) the combined model exhibits a more competitive forecasting performance than the individual models by using the advantage of each model; (c) the NSMOBA is an efficient algorithm for providing accurate forecasting results and improving the stability where the single bat algorithm is insufficient.

Hiai, F, König, R & Tomamichel, M 2017, 'Generalized Log-Majorization and Multivariate Trace Inequalities', Annales Henri Poincaré, vol. 18, no. 7, pp. 2499-2521.
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© 2017, Springer International Publishing. We show that recent multivariate generalizations of the Araki–Lieb–Thirring inequality and the Golden–Thompson inequality (Sutter et al. in Commun Math Phys, 2016. doi:10.1007/s00220-016-2778-5) for Schatten norms hold more generally for all unitarily invariant norms and certain variations thereof. The main technical contribution is a generalization of the concept of log-majorization which allows us to treat majorization with regard to logarithmic integral averages of vectors of singular values.

Hou, S, Chen, L, Tao, D, Zhou, S, Liu, W & Zheng, Y 2017, 'Multi-layer multi-view topic model for classifying advertising video', Pattern Recognition, vol. 68, pp. 66-81.
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© 2017 Elsevier Ltd The recent proliferation of advertising (ad) videos has driven the research in multiple applications, ranging from video analysis to video indexing and retrieval. Among them, classifying ad video is a key task because it allows automatic organization of videos according to categories or genres, and this further enables ad video indexing and retrieval. However, classifying ad video is challenging compared to other types of video classification because of its unconstrained content. While many studies focus on embedding ads relevant to videos, to our knowledge, few focus on ad video classification. In order to classify ad video, this paper proposes a novel ad video representation that aims to sufficiently capture the latent semantics of video content from multiple views in an unsupervised manner. In particular, we represent ad videos from four views, including bag-of-feature (BOF), vector of locally aggregated descriptors (VLAD), fisher vector (FV) and object bank (OB). We then devise a multi-layer multi-view topic model, mlmv_LDA, which models the topics of videos from different views. A topical representation for video, supporting category-related task, is finally achieved by the proposed method. Our empirical classification results on 10,111 real-world ad videos demonstrate that the proposed approach effectively differentiate ad videos.

Hu, L, Cao, L, Cao, J, Gu, Z, Xu, G & Wang, J 2017, 'Improving the Quality of Recommendations for Users and Items in the Tail of Distribution', ACM Transactions on Information Systems, vol. 35, no. 3.
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Huang, T, Huang, ML, Nguyen, Q, Zhao, L, Huang, W & Chen, J 2017, 'A Space-Filling Multidimensional Visualization (SFMDVis) for Exploratory Data Analysis', Information Sciences, vol. 390, pp. 32-53.
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The space-filling visualization model was first invented by Ben Shneiderman [28] for maximizing the utilization of display space in relational data (or graph) visualization, especially for tree visualization. It uses the concept of Enclosure which dismisses the “edges” in the graphic representation that are all too frequently used in traditional node-link based graph visualizations. Therefore, the major issue in graph visualization which is the edge crossing can be naturally solved through the adoption of a space filling approach. However in the past, the space-filling concept has not attracted much attention from researchers in the field of multidimensional visualization. Although the problem of ‘edge crossing’ has also occurred among polylines which are used as the basic visual elements in the parallel coordinates visualization, it is problematic if those ‘edge crossings’ among polylines are not evenly distributed on the display plate as visual clutter will occur. This problem could significantly reduce the human readability in terms of reviewing a particular region of the visualization. In this study, we propose a new Space-Filling Multidimensional Data Visualization (SFMDVis) that for the first-time introduces a space-filling approach into multidimensional data visualization. The main contributions are: (1) achieving the maximization of space utilization in multidimensional visualization (i.e. 100% of the display area is fully used), (2) eliminating visual clutter in SFMDVis through the use of the non-classic geometric primitive and (3) improving the quality of visualization for the visual perception of linear correlations among different variables as well as recognizing data patterns. To evaluate the quality of SFMDVis, we have conducted a usability study to measure the performance of SFMDVis in comparison with parallel coordinates and a scatterplot matrix for finding linear correlations and data patterns. The evaluation results have suggested that the acc...

Hussain, W, Hussain, F, Hussain, O, Damiani, E & Chang, E 2017, 'Formulating and managing viable SLAs in Cloud Computing from a small to medium service provider's viewpoint: A state-of-the-art review', Information Systems, vol. 71, pp. 240-259.
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In today's competitive world, service providers need to be customer-focused and proactive in their marketing strategies to create consumer awareness of their services. Cloud computing provides an open and ubiquitous computing feature in which a large random number of consumers can interact with providers and request services. In such an environment, there is a need for intelligent and efficient methods that increase confidence in the successful achievement of business requirements. One such method is the Service Level Agreement (SLA), which is comprised of service objectives, business terms, service relations, obligations and the possible action to be taken in the case of SLA violation. Most of the emphasis in the literature has, until now, been on the formation of meaningful SLAs by service consumers, through which their requirements will be met. However, in an increasingly competitive market based on the cloud environment, service providers too need a framework that will form a viable SLA, predict possible SLA violations before they occur, and generate early warning alarms that flag a potential lack of resources. This is because when a provider and a consumer commit to an SLA, the service provider is bound to reserve the agreed amount of resources for the entire period of that agreement – whether the consumer uses them or not. It is therefore very important for cloud providers to accurately predict the likely resource usage for a particular consumer and to formulate an appropriate SLA before finalizing an agreement. This problem is more important for a small to medium cloud service provider which has limited resources that must be utilized in the best possible way to generate maximum revenue. A viable SLA in cloud computing is one that intelligently helps the service provider to determine the amount of resources to offer to a requesting consumer, and there are number of studies on SLA management in the literature. The aim of this paper is two-fold. First, it pr...

Hussain, W, Hussain, OK, Hussain, FK & Khan MQ 2017, 'Usability Evaluation of English, Local and Plain Languages to Enhance On-Screen Text Readability: A Use Case of Pakistan', Global Journal of Flexible Systems Management, vol. 18, no. 1, pp. 33-49.
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In today’s digital world, information can very easily be accessed and digitally processed anywhere. Devices which are capable of processing digital data range from desktop computers to laptops, mobile phones, tablets, and personal digital assistants. For effective communication, text on a Web site should catch a reader’s attention and should be easy to both read and understand. Different constraints are associated with on-screen text readability and legibility, such as font size, color, and style, as well as foreground and background color contrast, line spacing, text congestion, vocabulary and grammar, but text recognition and comprehension are two of the major problems. In this study, we address the issue of how to enhance text readability for non-native English speakers who have a basic understanding of English language and speak local languages which are not formally taught in academia. We select a use case in Pakistan, a country in which English and Urdu are the official languages, and a number of local languages are spoken in different parts of the country. Due to the wide variety of local languages, no Web site can support the many local language scripts or alphabets and display them on digital devices. When users with only a basic knowledge of English—particularly low-literate users from a local language background—try to read an English text, it is highly challenging for them to understand the meaning of words. In this study, we propose a plain language scheme in which a text is converted into a roman text. A roman text is formed by using the English alphabet and combining letters in such a way that when it is read, it sounds like a local language. To evaluate the applicability of our approach, we conducted a survey of users from different educational backgrounds, using a text written in English, local and plain language from users who speak particular local language. For each survey, we took three to four paragraphs in English on general interest topics...

Ingham, V & Redshaw, S 2017, 'Connecting community organisations for disaster preparedness', International Journal of Safety and Security Engineering, vol. 7, no. 1, pp. 52-64.
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© 2017 WIT Press. After fires swept through the lower Blue Mountains of NSW in October 2013 and destroyed over 200 homes, a research project was initiated and titled ‘Community Connections: Vulnerability and Resilience within the Blue Mountains’. Reported in this article are the results of eight in-depth interviews conducted with local community leaders. They were asked to reflect on their leadership experiences before, during and after the fires. The research clearly demonstrates that prior to the fires there were no formal connections between local emergency services and local community organisations. Each had limited knowledge of the other in terms of skills, capacities, scope and available resources. This article will elaborate on the lessons learned by the community leaders interviewed. Just as collaborative bonds were finally being formed and combined initiatives had begun to bear solid results – reflected in higher levels of householder disaster preparedness, recovery funding ran out. This article highlights the lessons learned, and includes the importance of maintaining a formalised and continuous connection between emergency services and community organisations. The research recommends that disaster preparedness be embraced as a part of ‘core business’ by community organisations, and that multi-stakeholder connections be forged and strengthened through collaborative community engagement initiatives at the level of local disaster planning and preparation. Both recommendations contribute to the paradigm shift anticipated by Australia’s ‘National Strategy for Disaster Resilience’.

Ivanyos, G, Qiao, Y & Subrahmanyam, KV 2017, 'Non-commutative Edmonds’ problem and matrix semi-invariants', Computational Complexity, vol. 26, no. 3, pp. 717-763.
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© 2016, Springer International Publishing. In 1967, J. Edmonds introduced the problem of computing the rank over the rational function field of an n× n matrix T with integral homogeneous linear polynomials. In this paper, we consider the non-commutative version of Edmonds’ problem: compute the rank of T over the free skew field. This problem has been proposed, sometimes in disguise, from several different perspectives in the study of, for example, the free skew field itself (Cohn in J Symbol Log 38(2):309–314, 1973), matrix spaces of low rank (Fortin-Reutenauer in Sémin Lothar Comb 52:B52f 2004), Edmonds’ original problem (Gurvits in J Comput Syst Sci 69(3):448–484, 2004), and more recently, non-commutative arithmetic circuits with divisions (Hrubeš and Wigderson in Theory Comput 11:357-393, 2015. doi:10.4086/toc.2015.v011a014). It is known that this problem relates to the following invariant ring, which we call the F-algebra of matrix semi-invariants, denoted as R(n, m). For a field F, it is the ring of invariant polynomials for the action of SL (n, F) × SL (n, F) on tuples of matrices—(A, C) ∈ SL (n, F) × SL (n, F) sends (B 1 , … , B m ) ∈ M(n, F) ⊕ m to (AB 1 C T , … , AB m C T ). Then those T with non-commutative rank < n correspond to those points in the nullcone of R(n, m). In particular, if the nullcone of R(n, m) is defined by elements of degree ≤ σ, then there follows a poly (n, σ) -time randomized algorithm to decide whether the non-commutative rank of T is full. To our knowledge, previously the best bound for σ was O(n2·4n2) over algebraically closed fields of characteristic 0 (Derksen in Proc Am Math Soc 129(4):955–964, 2001). We now state the main contributions of this paper:We observe that by using an algorithm of Gurvits, and assuming the above bound σ for R(n, m) over Q, deciding whether or not T has non-commutative rank <  n over Q can be done deterministically in time polynomial in the input size and σ.When F is large enough, we devise an a...

Jay, B & Vergara, J 2017, 'Conflicting accounts of λ-definability', Journal of Logical and Algebraic Methods in Programming, vol. 87, pp. 1-3.
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Abstract A function on some domain is -definable if the corresponding function of -terms is so definable. However, the correspondence is parametrized by a representation of the domain. Often there is a natural choice of representation, but when the domain consists of -terms then they can be represented by either themselves or by the Church numeral of their G�del number. This choice determines whether or not all computable functions are -definable.

Jiang, J, Wen, S, Yu, S, Xiang, Y & Zhou, W 2017, 'Identifying Propagation Sources in Networks: State-of-the-Art and Comparative Studies', IEEE Communications Surveys and Tutorials, vol. 19, no. 1, pp. 465-481.
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© 2016 IEEE. It has long been a significant but difficult problem to identify propagation sources based on limited knowledge of network structures and the varying states of network nodes. In practice, real cases can be locating the sources of rumors in online social networks and finding origins of a rolling blackout in smart grids. This paper reviews the state-of-the-art in source identification techniques and discusses the pros and cons of current methods in this field. Furthermore, in order to gain a quantitative understanding of current methods, we provide a series of experiments and comparisons based on various environment settings. Especially, our observation reveals considerable differences in performance by employing different network topologies, various propagation schemes, and diverse propagation probabilities. We therefore reach the following points for future work. First, current methods remain far from practice as their accuracy in terms of error distance (δ) is normally larger than three in most scenarios. Second, the majority of current methods are too time consuming to quickly locate the origins of propagation. In addition, we list five open issues of current methods exposed by the analysis, from the perspectives of topology, number of sources, number of networks, temporal dynamics, and complexity and scalability. Solutions to these open issues are of great academic and practical significance.

Jiang, Y, Lee, H, Li, G & Chung, W-Y 2017, 'A Hybrid Brain-Computer Interface System for Multidimensional Control Using Motor Imagery and Eye Closure', Journal of Medical Imaging and Health Informatics, vol. 7, no. 7, pp. 1580-1588.
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Kaiwartya, O, Prasad, M, Prakash, S, Samadhiya, D, Abdullah, AH & Rahman, SOA 2017, 'An investigation on biometric internet security', International Journal of Network Security, vol. 19, no. 2, pp. 167-176.
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Due to the Internet revolution in the last decade, each and every work area of society are directly or indirectly depending on computers, highly integrated computer networks and communication systems, electronic data storage and high transfer based devices, e-commerce, e-security, e-governance, and e-business. The Internet revolution is also emerged as significant challenge due to the threats of hacking systems and individual accounts, malware, fraud and vulnerabilities of system and networks, etc. In this context, this paper explores E-Security in terms of challenges and measurements. Biometric recognition is also investigated as a key e-security solution. E-Security is precisely described to understand the concept and requirements. The major challenges of e-security; namely, threats, attacks, vulnerabilities are presented in detail. Some measurement are identified and discussed for the challenges. Biometric recognition is discussed in detail wit pros and cons of the approach as a key e-security solution. This investigation helps in clear understating of e-security challenges and possible implementation of the identified measurements for the challenges in wide area of network communications.

Kaur, P, Goyal, M & Lu, J 2017, 'A Comparison of Bidding Strategies for Online Auctions Using Fuzzy Reasoning and Negotiation Decision Functions', IEEE Transactions on Fuzzy Systems, vol. 25, no. 2, pp. 425-438.
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© 1993-2012 IEEE. Bidders often feel challenged when looking for the best bidding strategies to excel in the competitive environment of multiple and simultaneous online auctions for same or similar items. Bidders face complicated issues for deciding which auction to participate in, whether to bid early or late, and how much to bid. In this paper, we present the design of bidding strategies, which aim to forecast the bid amounts for buyers at a particular moment in time based on their bidding behavior and their valuation of an auctioned item. The agent develops a comprehensive methodology for final price estimation, which designs bidding strategies to address buyers' different bidding behaviors using two approaches: Mamdani method with regression analysis and negotiation decision functions. The experimental results show that the agents who follow fuzzy reasoning with a regression approach outperform other existing agents in most settings in terms of their success rate and expected utility.

Kenning, G & Treadaway, C 2017, 'Conversations at the edge of play: media, communication and cultural intersections with dementia', Continuum, pp. 1-13.
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Ko, L-W, Komarov, O, Hairston, WD, Jung, T-P & Lin, C-T 2017, 'Sustained Attention in Real Classroom Settings: An EEG Study.', Frontiers in Human Neuroscience, vol. 11, pp. 1-10.
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Sustained attention is a process that enables the maintenance of response persistence and continuous effort over extended periods of time. Performing attention-related tasks in real life involves the need to ignore a variety of distractions and inhibit attention shifts to irrelevant activities. This study investigates electroencephalography (EEG) spectral changes during a sustained attention task within a real classroom environment. Eighteen healthy students were instructed to recognize as fast as possible special visual targets that were displayed during regular university lectures. Sorting their EEG spectra with respect to response times, which indicated the level of visual alertness to randomly introduced visual stimuli, revealed significant changes in the brain oscillation patterns. The results of power-frequency analysis demonstrated a relationship between variations in the EEG spectral dynamics and impaired performance in the sustained attention task. Across subjects and sessions, prolongation of the response time was preceded by an increase in the delta and theta EEG powers over the occipital region, and decrease in the beta power over the occipital and temporal regions. Meanwhile, implementation of the complex attention task paradigm into a real-world classroom setting makes it possible to investigate specific mutual links between brain activities and factors that cause impaired behavioral performance, such as development and manifestation of classroom mental fatigue. The findings of the study set a basis for developing a system capable of estimating the level of visual attention during real classroom activities by monitoring changes in the EEG spectra.

Lancia, G, Mathieson, L & Moscato, P 2017, 'Separating sets of strings by finding matching patterns is almost always hard', Theoretical Computer Science, vol. 665, pp. 73-86.
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© 2017 Elsevier B.V. We study the complexity of the problem of searching for a set of patterns that separate two given sets of strings. This problem has applications in a wide variety of areas, most notably in data mining, computational biology, and in understanding the complexity of genetic algorithms. We show that the basic problem of finding a small set of patterns that match one set of strings but do not match any string in a second set is difficult (NP-complete, W[2]-hard when parameterized by the size of the pattern set, and APX-hard). We then perform a detailed parameterized analysis of the problem, separating tractable and intractable variants. In particular we show that parameterizing by the size of pattern set and the number of strings, and the size of the alphabet and the number of strings give FPT results, amongst others.

Li, J, Mei, X, Prokhorov, D & Tao, D 2017, 'Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene', IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, vol. 28, no. 3, pp. 690-703.
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Li, J, Meng, Z, Huang, M & Zhang, K 2017, 'An interactive visualization approach to the overview of geoscience data', Journal of Visualization, vol. 20, no. 3, pp. 433-451.
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Geoscience observation data refer to the datasets consisting of time series of multiple parameters generated from the sensors at fixed locations. Although a few works have attempted to visualize such data, none of them views these data as a specific type and attempts to show the overview in all the space, time and attribute aspects. It is important for domain experts to select the subsets of interest from huge amounts of observation data according to the high level patterns shown in the overview. We present a novel approach to visualize geoscience observation data in a compact radial view. Our solution consists of three visual elements. A map showing the spatial aspect is in the center of the visualization, while temporal and attribute aspects are seamlessly combined with the spatial information. Our approach is equipped with interactive mechanisms for highlighting the selected features, adjusting the display range, as well as interactively generating a fisheye view. We demonstrate the usability of our approach with three case studies of different domains. Eye tracking records and user feedbacks obtained in a small experiment also prove the effectiveness of our approach.

Li, Y & Qiao, Y 2017, 'On rank-critical matrix spaces', Differential Geometry and its Application, vol. 55, pp. 68-77.
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© 2017 Elsevier B.V. A matrix space of size m×n is a linear subspace of the linear space of m×n matrices over a field F. The rank of a matrix space is defined as the maximal rank over matrices in this space. A matrix space A is called rank-critical, if any matrix space which properly contains it has rank strictly greater than that of A. In this note, we first exhibit a necessary and sufficient condition for a matrix space A to be rank-critical, when F is large enough. This immediately implies the sufficient condition for a matrix space to be rank-critical by Draisma (2006) [5], albeit requiring the field to be slightly larger. We then study rank-critical spaces in the context of compression and primitive matrix spaces. We first show that every rank-critical matrix space can be decomposed into a rank-critical compression matrix space and a rank-critical primitive matrix space. We then prove, using our necessary and sufficient condition, that the block-diagonal direct sum of two rank-critical matrix spaces is rank-critical if and only if both matrix spaces are primitive, when the field is large enough.

Liang, C, Lin, CT, Yao, SN, Chang, WS, Liu, YC & Chen, SA 2017, 'Visual attention and association: An electroencephalography study in expert designers', Design Studies, vol. 48, pp. 76-95.
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© 2016 Elsevier Ltd Extant research on the visual attention and association of designers is limited, and scientific evidence differentiating among the effects of diverse visual stimuli on design thinking is insufficient. The current study invited 12 healthy expert designers and analysed their experiences of visual attention and association in addition to exploring the differences caused by three types of pictorial representation. The results of this electroencephalography (EEG) experiment indicated that the frontoparietal region was particularly activated when the designers engaged in visual attention tasks, whereas the brainwaves were particularly activated in the distributed prefrontal, frontocentral, and parietooccipital regions during the visual association tasks. In addition, there were no significant differences in the brainwave energy resulting from the three types of pictorial representation applied in this study. The research outcomes linking design studies to cognitive neuroscience establish a concrete foundation for developing future applied research and diverse educational practices.

Lin, C, Liu, Y-T, Wu, S-L, Cao, Z, Wang, Y, Huang, C-S, King, J-T, Chen, S-A, Lu, S-W & Chuang, C 2017, 'EEG-Based Brain-Computer Interfaces: A Novel Neurotechnology and Computational Intelligence Method', IEEE Systems Man and Cybernetics Magazine, vol. 3, no. 4, pp. 16-26.
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This article presents the latest BCI-related research done in our group. Our previous work applied computational intelligence technology in BCIs to inspire detailed investigations of practical issues in real-life applications. Novel EEG devices featuring dry electrodes facilitate and speed up electrode positioning before recording and allow subjects to move freely in operational environments. We also demonstrate the feasibility of applying CCA, RBFNs, effective connectivity measurements, and D-S theory to help BCIs extract informative knowledge from brain signals. Two recent trends in research in the computational and artificial intelligence community, big data and deep learning, are expected to impact the direction and development of BCIs.

Lin, CT, Chuang, CH, Cao, Z, Singh, AK, Hung, CS, Yu, YH, Nascimben, M, Liu, YT, King, JT, Su, TP & Wang, SJ 2017, 'Forehead EEG in Support of Future Feasible Personal Healthcare Solutions: Sleep Management, Headache Prevention, and Depression Treatment', IEEE Access, vol. 5, pp. 10612-10621.
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© 2013 IEEE. There are current limitations in the recording technologies for measuring EEG activity in clinical and experimental applications. Acquisition systems involving wet electrodes are time-consuming and uncomfortable for the user. Furthermore, dehydration of the gel affects the quality of the acquired data and reliability of long-term monitoring. As a result, dry electrodes may be used to facilitate the transition from neuroscience research or clinical practice to real-life applications. EEG signals can be easily obtained using dry electrodes on the forehead, which provides extensive information concerning various cognitive dysfunctions and disorders. This paper presents the usefulness of the forehead EEG with advanced sensing technology and signal processing algorithms to support people with healthcare needs, such as monitoring sleep, predicting headaches, and treating depression. The proposed system for evaluating sleep quality is capable of identifying five sleep stages to track nightly sleep patterns. Additionally, people with episodic migraines can be notified of an imminent migraine headache hours in advance through monitoring forehead EEG dynamics. The depression treatment screening system can predict the efficacy of rapid antidepressant agents. It is evident that frontal EEG activity is critically involved in sleep management, headache prevention, and depression treatment. The use of dry electrodes on the forehead allows for easy and rapid monitoring on an everyday basis. The advances in EEG recording and analysis ensure a promising future in support of personal healthcare solutions.

Liu, C, Zowghi, D, Talaei-Khoei, A & Daniel, J 2017, 'Data Completeness in Healthcare: A Literature Survey', Pacific Asia Journal of the Association for Information Systems, vol. 9, no. 2.
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Liu, S, Li, G, Tran, T & Jiang, Y 2017, 'Preference Relation-based Markov Random Fields for Recommender Systems', Machine Learning, vol. 106, no. 4, pp. 523-546.
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© 2016 The Author(s) A preference relation-based Top-N recommendation approach is proposed to capture both second-order and higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings first, and then inferring the item rankings, based on the assumption of availability of explicit feedback such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed approach drops these assumptions by exploiting preference relations, a more practical user feedback. Furthermore, the proposed approach enjoys the representational power of Markov Random Fields thus side information such as item and user attributes can be easily incorporated. Comparing to related work, the proposed approach has the unique property of modeling both second-order and higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference-relation based methods. Experimental results on public datasets demonstrate that both types of interactions have been properly captured, and significantly improved Top-N recommendation performance has been achieved.

Liu, Y, Huang, M, Liang, J & Huang, W 2017, 'A physiognomy based method for facial feature extraction and recognition', Journal of Visual Languages and Computing, vol. 43, pp. 103-109.
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This paper proposes a novel calculation method of personality based on Chinese physiognomy. The proposed solution combines ancient and modern physiognomy to understand the relationship between personality and facial features and to model a baseline to shape facial features. We compute a histogram of image by searching for threshold values to create a binary image in an adaptive way. The two-pass connected component method indicates the feature's region. We encode the binary image to remove the noise point, so that the new connected image can provide a better result. According to our analysis of contours, we can locate facial features and classify them by means of a calculation method. The number of clusters is decided by a model and the facial feature contours are classified by using the k-means method. The validity of our method was tested on a face database and demonstrated by a comparative experiment.

Liu, YH, Jung, TP, Lin, CT & Liao, LD 2017, 'Editorial Message: Special Issue on Fuzzy Brain–Computer Interface Systems', International Journal of Fuzzy Systems, vol. 19, no. 2, pp. 528-528.
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Liu, YT, Pal, NR, Marathe, AR, Wang, YK & Lin, CT 2017, 'Fuzzy decision-making fuser (FDMF) for integrating human-machine autonomous (HMA) systems with adaptive evidence sources', Frontiers in Neuroscience, vol. 11, pp. 1-10.
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© 2017 Liu, Pal, Marathe, Wang and Lin. A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RS...

Lu, J, Herrera, F & Zhang, G 2017, 'Guest Editorial Special Section on Fuzzy Systems in Data Science', IEEE Transactions on Fuzzy Systems, vol. 25, no. 6, pp. 1373-1375.
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Lu, J, Xuan, J, Zhang, G, Xu, YD & Luo, X 2017, 'Bayesian Nonparametric Relational Topic Model through Dependent Gamma Processes', IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 7, pp. 1357-1369.
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Traditional relational topic models provide a successful way to discover the hidden topics from a document network. Many theoretical and practical tasks, such as dimensional reduction, document clustering, and link prediction, could benefit from this revealed knowledge. However, existing relational topic models are based on an assumption that the number of hidden topics is known a priori, which is impractical in many real-world applications. Therefore, in order to relax this assumption, we propose a nonparametric relational topic model using stochastic processes instead of fixed-dimensional probability distributions in this paper. Specifically, each document is assigned a Gamma process, which represents the topic interest of this document. Although this method provides an elegant solution, it brings additional challenges when mathematically modeling the inherent network structure of typical document network, i.e., two spatially closer documents tend to have more similar topics. Furthermore, we require that the topics are shared by all the documents. In order to resolve these challenges, we use a subsampling strategy to assign each document a different Gamma process from the global Gamma process, and the subsampling probabilities of documents are assigned with a Markov Random Field constraint that inherits the document network structure. Through the designed posterior inference algorithm, we can discover the hidden topics and its number simultaneously. Experimental results on both synthetic and real-world network datasets demonstrate the capabilities of learning the hidden topics and, more importantly, the number of topics.

Lu, M, Lai, C, Ye, T, Liang, J & Yuan, X 2017, 'Visual Analysis of Multiple Route Choices Based on General GPS Trajectories', IEEE Transactions on Big Data, vol. 3, no. 2, pp. 234-247.
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There are often multiple routes between regions. Drivers choose different routes with different considerations. Such considerations, have always been a point of interest in the transportation area. Studies of route choice behaviour are usually based on small range experiments with a group of volunteers. However, the experiment data is quite limited in its spatial and temporal scale as well as the practical reliability. In this work, we explore the possibility of studying route choice behaviour based on general trajectory dataset, which is more realistic in a wider scale. We develop a visual analytic system to help users handle the large-scale trajectory data, compare different route choices, and explore the underlying reasons. Specifically, the system consists of: 1. the interactive trajectory filtering which supports graphical trajectory query; 2. the spatial visualization which gives an overview of all feasible routes extracted from filtered trajectories; 3. the factor visual analytics which provides the exploration and hypothesis construction of different factors' impact on route choice behaviour, and the verification with an integrated route choice model. Applying to real taxi GPS dataset, we report the system's performance and demonstrate its effectiveness with three cases.

Lund, AP, Bremner, MJ & Ralph, TC 2017, 'Quantum sampling problems, BosonSampling and quantum supremacy', npj Quantum Information, vol. 3, no. 1, pp. 1-8.
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© 2017 by the Authors. There is a large body of evidence for the potential of greater computational power using information carriers that are quantum mechanical over those governed by the laws of classical mechanics. But the question of the exact nature of the power contributed by quantum mechanics remains only partially answered. Furthermore, there exists doubt over the practicality of achieving a large enough quantum computation that definitively demonstrates quantum supremacy. Recently the study of computational problems that produce samples from probability distributions has added to both our understanding of the power of quantum algorithms and lowered the requirements for demonstration of fast quantum algorithms. The proposed quantum sampling problems do not require a quantum computer capable of universal operations and also permit physically realistic errors in their operation. This is an encouraging step towards an experimental demonstration of quantum algorithmic supremacy. In this paper, we will review sampling problems and the arguments that have been used to deduce when sampling problems are hard for classical computers to simulate. Two classes of quantum sampling problems that demonstrate the supremacy of quantum algorithms are BosonSampling and Instantaneous Quantum Polynomial-time Sampling. We will present the details of these classes and recent experimental progress towards demonstrating quantum supremacy in BosonSampling.

Lv, L, Fan, S, Huang, M, Huang, W & Yang, G 2017, 'Golden Rectangle Treemap', Journal of Physics : Conference Series, vol. 787, no. 1, pp. 1-6.
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Treemaps, a visualization method of representing hierarchical data sets, are becoming more and more popular for its efficient and compact displays. Several algorithms have been proposed to create more useful display by controlling the aspect ratios of the rectangles that make up a treemap. In this paper, we introduce a new treemap algorithm, generating layout in which the rectangles are easier to select and hierarchy information is easier to obtain. This algorithm generates rectangles which approximate golden rectangles. To prove the effectiveness of our algorithm, at the end of this paper several analyses on golden rectangle treemap have been done on disk file system.

Mans, B & Mathieson, L 2017, 'Incremental Problems in the Parameterized Complexity Setting', Theory of Computing Systems, vol. 60, no. 1, pp. 3-19.
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© 2016, Springer Science+Business Media New York. Dynamic systems are becoming steadily more important with the profusion of mobile and distributed computing devices. Coincidentally incremental computation is a natural approach to deal with ongoing changes. We explore incremental computation in the parameterized complexity setting and show that incrementalization leads to non-trivial complexity classifications. Interestingly, some incremental versions of hard problems become tractable, while others remain hard. Moreover tractability or intractability is not a simple function of the problem’s static complexity, every level of the W-hierarchy exhibits complete problems with both tractable and intractable incrementalizations. For problems that are already tractable in their static form, we also show that incrementalization can lead to interesting algorithms, improving upon the trivial approach of using the static algorithm at each step.

Mao, M, Lu, J, Zhang, G & Zhang, J 2017, 'Multirelational Social Recommendations via Multigraph Ranking', IEEE Transactions on Cybernetics, vol. 47, no. 12, pp. 4049-4061.
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Recommender systems aim to identify relevant items for particular users in large-scale online applications. The historical rating data of users is a valuable input resource for many recommendation models such as collaborative filtering (CF), but these models are known to suffer from the rating sparsity problem when the users or items under consideration have insuf- ficient rating records. With the continued growth of online social networks, the increased user-to-user relationships are reported to be helpful and can alleviate the CF rating sparsity problem. Although researchers have developed a range of social networkbased recommender systems, there is no unified model to handle multirelational social networks. To address this challenge, this paper represents different user relationships in a multigraph and develops a multigraph ranking model to identify and recommend the nearest neighbors of particular users in high-order environments. We conduct empirical experiments on two real-world datasets: 1) Epinions and 2) Last.fm, and the comprehensive comparison with other approaches demonstrates that our model improves recommendation performance in terms of both recommendation coverage and accuracy, especially when the rating data are sparse.

Mathieson, L 2017, 'Graph editing problems with extended regularity constraints', Theoretical Computer Science, vol. 677, pp. 56-68.
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© 2017 Graph editing problems offer an interesting perspective on sub- and supergraph identification problems for a large variety of target properties. They have also attracted significant attention in recent years, particularly in the area of parameterized complexity as the problems have rich parameter ecologies. In this paper we examine generalisations of the notion of editing a graph to obtain a regular subgraph. In particular we extend the notion of regularity to include two variants of edge-regularity along with the unifying constraint of strong regularity. We present a number of results, with the central observation that these problems retain the general complexity profile of their regularity-based inspiration: when the number of edits k and the maximum degree r are taken together as a combined parameter, the problems are tractable (i.e. in FPT), but are otherwise intractable. We also examine variants of the basic editing to obtain a regular subgraph problem from the perspective of parameterizing by the treewidth of the input graph. In this case the treewidth of the input graph essentially becomes a limiting parameter on the natural k+r parameterization.

McGregor, C & Bonnis, B 2017, 'New Approaches for Integration: Integration of Haptic Garments, Big Data Analytics, and Serious Games for Extreme Environments', IEEE Consumer Electronics Magazine, vol. 6, no. 4, pp. 92-96.
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© 2012 IEEE. Haptic garments present new opportunities to increase realism in gaming. As Real As It Gets (ARAIG) is a new form of haptic garment that uses muscle stimulation, vibration, and 7.1 surround sound to provide a new level of realism in gaming. The integration of new haptic garments like ARAIG with big data analytics and serious games presents new opportunities for more realistic virtual training that has application in many domains. In particular, there is great potential to support repeatable virtual training for extreme environments. In 2016, the IEEE Life Sciences Technical Community worked across the IEEE Societies to demonstrate this interdisciplinary nature, with a focus on solving life science problems in extreme environments. This article is based on our keynote address at the International Conference on Consumer Electronics (ICCE)-Berlin in 2016. It provides an example of this interdisciplinary case study research in action.

Meng, Q, Catchpoole, D, Skillicorn, D & Kennedy, PJ 2017, 'DBNorm: Normalizing high-density oligonucleotide microarray data based on distributions', BMC Bioinformatics, vol. 18, no. 1.
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© 2017 The Author(s). Background: Data from patients with rare diseases is often produced using different platforms and probe sets because patients are widely distributed in space and time. Aggregating such data requires a method of normalization that makes patient records comparable. Results: This paper proposed DBNorm, implemented as an R package, is an algorithm that normalizes arbitrarily distributed data to a common, comparable form. Specifically, DBNorm merges data distributions by fitting functions to each of them, and using the probability of each element drawn from the fitted distribution to merge it into a global distribution. DBNorm contains state-of-the-art fitting functions including Polynomial, Fourier and Gaussian distributions, and also allows users to define their own fitting functions if required. Conclusions: The performance of DBNorm is compared with z-score, average difference, quantile normalization and ComBat on a set of datasets, including several that are publically available. The performance of these normalization methods are compared using statistics, visualization, and classification when class labels are known based on a number of self-generated and public microarray datasets. The experimental results show that DBNorm achieves better normalization results than conventional methods. Finally, the approach has the potential to be applicable outside bioinformatics analysis.

Mirtalaie, MA, Hussain, OK, Chang, E & Hussain, FK 2017, 'A decision support framework for identifying novel ideas in new product development from cross-domain analysis', Information Systems, vol. 69, pp. 59-80.
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© 2017 Elsevier Ltd In current competitive times, product manufacturers need not only to retain their existing customer base, but also to increase their market share. One way they can achieve this is by generating new ideas and developing novel products with new features. As highlighted in the literature, in generating new ideas to develop novel and innovative products, it is important that product designers satisfy the needs of both current customers and new customers. However, despite the large number of existing studies that identify novel features in the ideation phase, product designers do not have a systematic framework that utilises additional information relating to products from either far-field or related domains to generate such new ideas in the ideation phase. This paper presents our proposed framework FEATURE which provides just such a systemic framework for product designers in the ideation phase of new product development. FEATURE has three phases. The first phase identifies and recommends to the product designers novel features that can be added to the next version of a reference product. In order to incorporate the customer's voice into the ideation phase, the second phase ascertains the popularity of the proposed features by using social media. The third phase ranks the proposed features based on the designer's decision criteria to select those that should be considered further in the next phases of new product development. We explain the importance of each phase of FEATURE and show the working of its first module in detail.

Mukhopadhyay, P & Qiao, Y 2017, 'Sparse multivariate polynomial interpolation on the basis of Schubert polynomials', Computational Complexity, pp. 1-29.
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© 2016 Springer International PublishingSchubert polynomials were discovered by A. Lascoux and M. Schützenberger in the study of cohomology rings of flag manifolds in 1980s. These polynomials generalize Schur polynomials and form a linear basis of multivariate polynomials. In 2003, Lenart and Sottile introduced skew Schubert polynomials, which generalize skew Schur polynomials and expand in the Schubert basis with the generalized Littlewood–Richardson coefficients. In this paper, we initiate the study of these two families of polynomials from the perspective of computational complexity theory. We first observe that skew Schubert polynomials, and therefore Schubert polynomials, are in #P (when evaluating on nonnegative integral inputs) and VNP. Our main result is a deterministic algorithm that computes the expansion of a polynomial f of degree d in (Formula presented.) on the basis of Schubert polynomials, assuming an oracle computing Schubert polynomials. This algorithm runs in time polynomial in n, d, and the bit size of the expansion. This generalizes, and derandomizes, the sparse interpolation algorithm of symmetric polynomials in the Schur basis by Barvinok and Fomin (Adv Appl Math 18(3):271–285, 1997). In fact, our interpolation algorithm is general enough to accommodate any linear basis satisfying certain natural properties. Applications of the above results include a new algorithm that computes the generalized Littlewood–Richardson coefficients.

Musial-Gabrys, K, Brodka & De Meo 2017, 'Analysis and Applications of Complex Social Networks'.
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Nguyen, H, Liu, W & Chen, F 2017, 'Discovering Congestion Propagation Patterns in Spatio-Temporal Traffic Data', IEEE Transactions on Big Data, vol. 3, no. 2, pp. 169-180.
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Traffic congestion is a condition of a segment in the road network where the traffic demand is greater than the available road capacity. The detection of unusual traffic patterns including congestions is a significant research problem in the data mining and knowledge discovery community. However, to the best of our knowledge, the discovery of propagations, or causal interactions among detected traffic congestions has not been appropriately investigated before. In this research, we introduce algorithms which construct causality trees from congestions and estimate their propagation probabilities based on temporal and spatial information of the congestions. Frequent sub-structures of these causality trees reveal not only recurring interactions among spatio-temporal congestions, but potential bottlenecks or flaws in the designs of existing traffic networks. Our algorithms have been validated by experiments on a travel time data set recorded from an urban road network.

Niazi, M, Mahmood, S, Alshayeb, M, Baqais, AAB & Gill, AQ 2017, 'Motivators for adopting social computing in global software development: An empirical study', Journal of Software: Evolution and Process, vol. 29, no. 8.
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Copyright © 2017 John Wiley & Sons, Ltd. Managing communication in collaborative global software development (GSD) projects is both critical and challenging. While social computing has received much attention from practitioners, social computing adoption is still an emerging research area in GSD. This research paper provides a review of the academic research in social computing and identifies motivators for adopting social computing in the GSD context. We applied the systematic literature review (SLR) and questionnaire survey with 35 software industry experts to address the research objective. Firstly, we implemented a formal SLR approach and identified an initial set of social computing adoption motivators. Secondly, a questionnaire survey was developed based on the SLR and was tested by means of a pilot study. The findings of this combined SLR and questionnaire survey indicate that real-time communication and coordination, knowledge acquisition, expert feedback, and information sharing are the key factors that motivate social computing adoption in GSD projects. The results of t test (ie, t =.558, P =.589) show that there is no significant difference between the findings of SLR and questionnaire. The results of this study suggest the need for developing social computing strategies and policies to guide the strategic adoption of social computing tools in GSD projects.

Oberst, S, Bann, G, Lai, JCS & Evans, TA 2017, 'Cryptic termites avoid predatory ants by eavesdropping on vibrational cues from their footsteps', ECOLOGY LETTERS, vol. 20, no. 2, pp. 212-221.
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Oberst, S, Marburg, S & Hoffmann, N 2017, 'Determining periodic orbits via nonlinear filtering and recurrence spectra in the presence of noise', Procedia Engineering, vol. 199C, pp. 772-777.
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Picard, K, Smith, W, Tran, M, Siwabessy, J & Kennedy, PJ 2017, 'Increased resolution bathymetry in the southeast Indian ocean: MH370 search data', Hydro International, vol. 28/09/2017, pp. 1-5.
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The disappearance of Malaysian Airlines flight MH370 on 8 March 2014 led to a deep ocean search effort of unprecedented scale and detail in the remote southeastern Indian Ocean. Between June 2014 and January 2017, two mapping phases took place: (1) a shipborne bathymetric survey, and (2) a higher-resolution search in areas where accurate mapping of the seafloor was required to guide the detailed underwater search aimed at locating the aircraft wreckage. The latter phase used sidescan, multibeam and synthetic aperture sonar mounted on towed or autonomous underwater vehicles (AUVs). This article describes the mapping of the area where the aircraft was expected to be found.

Pietroni, N, Tarini, M, Vaxman, A, Panozzo, D & Cignoni, P 2017, 'Position-based tensegrity design', ACM Transactions on Graphics (TOG), vol. 36, pp. 172-172.
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Pietroni, N, Tarini, M, Vaxman, A, Panozzo, D & Cignoni, P 2017, 'Position-based tensegrity design', ACM Transactions on Graphics (TOG), vol. 36, pp. 172-172.

Prasad, M, Lin, CT, Li, DL, Hong, CT, Ding, WP & Chang, JY 2017, 'Soft-Boosted Self-Constructing Neural Fuzzy Inference Network', IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 47, no. 3, pp. 584-588.
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© 2013 IEEE. This correspondence paper proposes an improved version of the self-constructing neural fuzzy inference network (SONFIN), called soft-boosted SONFIN (SB-SONFIN). The design softly boosts the learning process of the SONFIN in order to decrease the error rate and enhance the learning speed. The SB-SONFIN boosts the learning power of the SONFIN by taking into account the numbers of fuzzy rules and initial weights which are two important parameters of the SONFIN, SB-SONFIN advances the learning process by: 1) initializing the weights with the width of the fuzzy sets rather than just with random values and 2) improving the parameter learning rates with the number of learned fuzzy rules. The effectiveness of the proposed soft boosting scheme is validated on several real world and benchmark datasets. The experimental results show that the SB-SONFIN possesses the capability to outperform other known methods on various datasets.

Prasad, M, Liu, YT, Li, DL, Lin, CT, Shah, RR & Kaiwartya, OP 2017, 'A new mechanism for data visualization with TSK-type preprocessed collaborative fuzzy rule based system', Journal of Artificial Intelligence and Soft Computing Research, vol. 7, no. 1, pp. 33-46.
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Pratama, M, Lu, J, Lughofer, E, Zhang, G & Er, MJ 2017, 'An Incremental Learning of Concept Drift Using Evolving Type-2 Recurrent Fuzzy Neural Network', IEEE Transactions on Fuzzy Systems, vol. 25, no. 5, pp. 1175-1192.
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The age of online data stream and dynamic environments result in the increasing demand of advanced machine learning techniques to deal with concept drifts in large data streams. Evolving Fuzzy Systems (EFS) are one of recent initiatives from the fuzzy system community to resolve the issue. Existing EFSs are not robust against data uncertainty, temporal system dynamics, and the absence of system order, because vast majority of EFSs are designed in the type-1 feed-forward network architecture. This paper aims to solve the issue of data uncertainty, temporal behaviour, and the absence of system order by developing a novel evolving recurrent fuzzy neural network, called Evolving Type-2 Recurrent Fuzzy Neural Network (eT2RFNN). eT2RFNN is constructed in a new recurrent network architecture, featuring double recurrent layers. The new recurrent network architecture evolves a generalized interval type-2 fuzzy rule, where the rule premise is built upon the interval type-2 multivariate Gaussian function, whereas the rule consequent is crafted by the non-linear wavelet function. The eT2RFNN adopts a holistic concept of evolving systems, where the fuzzy rule can be automatically generated, pruned, merged and recalled in the single pass learning mode. eT2RFNN is capable of coping with the problem of high dimensionality, because it is equipped with online feature selection technology. The efficacy of eT2RFNN was experimentally validated using artificial and real-world data streams and compared with prominent learning algorithms. eT2RFNN produced more reliable predictive accuracy, while retaining lower complexity than its counterparts.

Pratama, M, Zhang, G, Er, MJ & Anavatti, S 2017, 'An Incremental Type-2 Meta-Cognitive Extreme Learning Machine', IEEE Transactions on Cybernetics, vol. 47, no. 2, pp. 339-353.
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© 2016 IEEE. Existing extreme learning algorithm have not taken into account four issues: 1) complexity; 2) uncertainty; 3) concept drift; and 4) high dimensionality. A novel incremental type-2 meta-cognitive extreme learning machine (ELM) called evolving type-2 ELM (eT2ELM) is proposed to cope with the four issues in this paper. The eT2ELM presents three main pillars of human meta-cognition: 1) what-to-learn; 2) how-to-learn; and 3) when-to-learn. The what-to-learn component selects important training samples for model updates by virtue of the online certainty-based active learning method, which renders eT2ELM as a semi-supervised classifier. The how-to-learn element develops a synergy between extreme learning theory and the evolving concept, whereby the hidden nodes can be generated and pruned automatically from data streams with no tuning of hidden nodes. The when-to-learn constituent makes use of the standard sample reserved strategy. A generalized interval type-2 fuzzy neural network is also put forward as a cognitive component, in which a hidden node is built upon the interval type-2 multivariate Gaussian function while exploiting a subset of Chebyshev series in the output node. The efficacy of the proposed eT2ELM is numerically validated in 12 data streams containing various concept drifts. The numerical results are confirmed by thorough statistical tests, where the eT2ELM demonstrates the most encouraging numerical results in delivering reliable prediction, while sustaining low complexity.

Ramezani, F, Lu, J, Taheri, J & Zomaya, AY 2017, 'A Multi-Objective Load Balancing System for Cloud Environments', The Computer Journal, vol. 60, no. 9, pp. 1316-1337.
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Redshaw, S, Ingham, APV, Hicks, PJ & Millynn, J 2017, 'Emergency preparedness through community sector engagement in the Blue Mountains', Australian Journal of Emergency Management, vol. 32, no. 2, pp. 35-40.
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There is an expectation that communities exposed to potential disaster events will make preparations for themselves (COAG 2011). However, communities are frequently underprepared for the onset and results of disaster and a default response is to rely on emergency services organisations. This reliance is exacerbated by the presence within communities of highly vulnerable individuals who, because of age, infirmity or isolation, require additional levels of assistance by responders. Partnerships between community organisations and emergency services organisations can build preparedness by using programs that increase emergency response awareness. This paper provides a study of two partnership programs established by the community and emergency services sectors in the Blue Mountains, New South Wales. These programs successfully raised the level of emergency preparedness and community resilience to disasters.

Redshaw, SJ & Ingham, V 2017, 'Vulnerable voices on fire preparedness: Policy implications for emergency and community services collaboration', Australian Journal of Social Issues, vol. 52, no. 4, pp. 313-330.
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An investigation of household preparedness and community connections was undertaken in the NSW Blue Mountains. The research employed a qualitative approach. Upon receiving ethical approval, interviews and focus groups with a total of 31 vulnerable residents were recorded and transcribed. Data analysis included the manual coding of individual transcripts and key word queries entered into NVivo 10. Fire planning for community resilience within Australia focusses on property preparation and an emergency warning system designed to assist the evacuation decisions of residents. In this article, we report on vulnerable residents and their preparedness for the October 2013 bushfires. Our findings demonstrate that the vulnerable people interviewed did not consider property preservation as a priority, and their knowledge and engagement with the warning system and evacuation procedures was limited. Of practical value, the research found local community services and emergency planning committees should collaboratively plan for vulnerable community members who are unable to take a very active role in preparing themselves or their dependents to face a bushfire or similar disaster. In addition, preparedness and warning communications should be devised and targeted to more clearly assist vulnerable people during the lead up to, and in the midst of, a disaster.

Reitsma, L & van den Hoven, E 2017, 'Wrapping Cultural Values: using Social Embodiment as Stimulus in Designs', The Design Journal: An International Journal for All Aspects of Design, vol. 20, no. sup 1, pp. S401-S410.
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Ritter, SM & Ferguson, S 2017, 'Happy creativity: Listening to happy music facilitates divergent thinking.', PLoS ONE, vol. 12, no. 9, pp. 1-14.
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Creativity can be considered one of the key competencies for the twenty-first century. It provides us with the capacity to deal with the opportunities and challenges that are part of our complex and fast-changing world. The question as to what facilitates creative cognition-the ability to come up with creative ideas, problem solutions and products-is as old as the human sciences, and various means to enhance creative cognition have been studied. Despite earlier scientific studies demonstrating a beneficial effect of music on cognition, the effect of music listening on creative cognition has remained largely unexplored. The current study experimentally tests whether listening to specific types of music (four classical music excerpts systematically varying on valance and arousal), as compared to a silence control condition, facilitates divergent and convergent creativity. Creativity was higher for participants who listened to 'happy music' (i.e., classical music high on arousal and positive mood) while performing the divergent creativity task, than for participants who performed the task in silence. No effect of music was found for convergent creativity. In addition to the scientific contribution, the current findings may have important practical implications. Music listening can be easily integrated into daily life and may provide an innovative means to facilitate creative cognition in an efficient way in various scientific, educational and organizational settings when creative thinking is needed.

Saxena, A, Prasad, M, Gupta, A, Bharill, N, Patel, OP, Tiwari, A, Er, MJ, Ding, W & Lin, CT 2017, 'A review of clustering techniques and developments', Neurocomputing, vol. 267, pp. 664-681.
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© 2017 Elsevier B.V. This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. Clustering is defined as an unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. The approaches used in these methods are discussed with their respective states of art and applicability. The measures of similarity as well as the evaluation criteria, which are the central components of clustering, are also presented in the paper. The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted.

Sharma, G, Gramann, K, Chandra, S, Singh, V & Mittal, AP 2017, 'Brain connectivity during encoding and retrieval of spatial information: individual differences in navigation skills', Brain Informatics, vol. 4, no. 3, pp. 207-217.
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© 2017, The Author(s). Emerging evidence suggests that the variations in the ability to navigate through any real or virtual environment are accompanied by distinct underlying cortical activations in multiple regions of the brain. These activations may appear due to the use of different frame of reference (FOR) for representing an environment. The present study investigated the brain dynamics in the good and bad navigators using Graph Theoretical analysis applied to low-density electroencephalography (EEG) data. Individual navigation skills were rated according to the performance in a virtual reality (VR)-based navigation task and the effect of navigator's proclivity towards a particular FOR on the navigation performance was explored. Participants were introduced to a novel virtual environment that they learned from a first-person or an aerial perspective and were subsequently assessed on the basis of efficiency with which they learnt and recalled. The graph theoretical parameters, path length (PL), global efficiency (GE), and clustering coefficient (CC) were computed for the functional connectivity network in the theta and alpha frequency bands. During acquisition of the spatial information, good navigators were distinguished by a lower degree of dispersion in the functional connectivity compared to the bad navigators. Within the groups of good and bad navigators, better performers were characterised by the formation of multiple hubs at various sites and the percentage of connectivity or small world index. The proclivity towards a specific FOR during exploration of a new environment was not found to have any bearing on the spatial learning. These findings may have wider implications for how the functional connectivity in the good and bad navigators differs during spatial information acquisition and retrieval in the domains of rescue operations and defence systems.

Sharma, S, Puthal, D, Tazeen, S, Prasad, M & Zomaya, AY 2017, 'MSGR: A Mode-Switched Grid-Based Sustainable Routing Protocol for Wireless Sensor Networks', IEEE Access, vol. 5, pp. 19864-19875.
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© 2013 IEEE. A Wireless Sensor Network (WSN) consists of enormous amount of sensor nodes. These sensor nodes sense the changes in physical parameters from the sensing range and forward the information to the sink nodes or the base station. Since sensor nodes are driven with limited power batteries, prolonging the network lifetime is difficult and very expensive, especially for hostile locations. Therefore, routing protocols for WSN must strategically distribute the dissipation of energy, so as to increase the overall lifetime of the system. Current research trends from areas, such as from Internet of Things and fog computing use sensors as the source of data. Therefore, energy-efficient data routing in WSN is still a challenging task for real-Time applications. Hierarchical grid-based routing is an energy-efficient method for routing of data packets. This method divides the sensing area into grids and is advantageous in wireless sensor networks to enhance network lifetime. The network is partitioned into virtual equal-sized grids. The proposed mode-switched grid-based routing protocol for WSN selects one node per grid as the grid head. The routing path to the sink is established using grid heads. Grid heads are switched between active and sleep modes alternately. Therefore, not all grid heads take part in the routing process at the same time. This saves energy in grid heads and improves the network lifetime. The proposed method builds a routing path using each active grid head which leads to the sink. For handling the mobile sink movement, the routing path changes only for some grid head nodes which are nearer to the grid, in which the mobile sink is currently positioned. Data packets generated at any source node are routed directly through the data disseminating grid head nodes on the routing path to the sink.

Sutter, D, Berta, M & Tomamichel, M 2017, 'Multivariate Trace Inequalities', Communications in Mathematical Physics, vol. 352, no. 1, pp. 37-58.
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Toellner, T, Wang, Y, Makeig, S, Mueller, HJ, Jung, T-P & Gramann, K 2017, 'Two Independent Frontal Midline Theta Oscillations during Conflict Detection and Adaptation in a Simon-Type Manual Reaching Task', JOURNAL OF NEUROSCIENCE, vol. 37, no. 9, pp. 2504-2515.
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Tomamichel, M & Leverrier, A 2017, 'A largely self-contained and complete security proof for quantum key distribution', Quantum, vol. 1, pp. 14-52.
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In this work we present a security analysis for quantum key distribution, establishing a rigorous tradeoff between various protocol and security parameters for a class of entanglement-based and prepare-and-measure protocols. The goal of this paper is twofold: 1) to review and clarify the stateof-the-art security analysis based on entropic uncertainty relations, and 2) to provide an accessible resource for researchers interested in a security analysis of quantum cryptographic protocols that takes into account finite resource effects. For this purpose we collect and clarify several arguments spread in the literature on the subject with the goal of making this treatment largely self-contained. More precisely, we focus on a class of prepare-and-measure protocols based on the Bennett-Brassard (BB84) protocol as well as a class of entanglement-based protocols similar to the Bennett-Brassard-Mermin (BBM92) protocol. We carefully formalize the different steps in these protocols, including randomization, measurement, parameter estimation, error correction and privacy amplification, allowing us to be mathematically precise throughout the security analysis. We start from an operational definition of what it means for a quantum key distribution protocol to be secure and derive simple conditions that serve as sufficient condition for secrecy and correctness. We then derive and eventually discuss tradeoff relations between the block length of the classical computation, the noise tolerance, the secret key length and the security parameters for our protocols. Our results significantly improve upon previously reported tradeoffs.

Tomamichel, M, Martinez-Mateo, J, Pacher, C & Elkouss, D 2017, 'Fundamental finite key limits for one-way information reconciliation in quantum key distribution', Quantum Information Processing, vol. 16, no. 11.
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© 2017, Springer Science+Business Media, LLC. The security of quantum key distribution protocols is guaranteed by the laws of quantum mechanics. However, a precise analysis of the security properties requires tools from both classical cryptography and information theory. Here, we employ recent results in non-asymptotic classical information theory to show that one-way information reconciliation imposes fundamental limitations on the amount of secret key that can be extracted in the finite key regime. In particular, we find that an often used approximation for the information leakage during information reconciliation is not generally valid. We propose an improved approximation that takes into account finite key effects and numerically test it against codes for two probability distributions, that we call binary–binary and binary–Gaussian, that typically appear in quantum key distribution protocols.

Tomamichel, M, Wilde, MM & Winter, A 2017, 'Strong Converse Rates for Quantum Communication', IEEE Transactions on Information Theory, vol. 63, no. 1, pp. 715-727.
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© 2016 IEEE. We revisit a fundamental open problem in quantum information theory, namely whether it is possible to transmit quantum information at a rate exceeding the channel capacity if we allow for a non-vanishing probability of decoding error. Here we establish that the Rains information of any quantum channel is a strong converse rate for quantum communication: For any sequence of codes with rate exceeding the Rains information of the channel, we show that the fidelity vanishes exponentially fast as the number of channel uses increases. This remains true even if we consider codes that perform classical post-processing on the transmitted quantum data. As an application of this result, for generalized dephasing channels we show that the Rains information is also achievable, and thereby establish the strong converse property for quantum communication over such channels. Thus we conclusively settle the strong converse question for a class of quantum channels that have a non-trivial quantum capacity.

Tsai, ZR, Chang, YZ, Zhang, HW & Lin, CT 2017, 'Relax the chaos-model-based human behavior by electrical stimulation therapy design', Computers in Human Behavior, vol. 67, pp. 151-160.
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© 2016 Elsevier Ltd The brain's electrical activity is chaotic and unpredictable yet has a hidden order that is attracted to a certain region. There are numerous fractal strange attractors in the brain that change as thinking processes vary. Further, the thinking processes change the human behaviors, especially in schizophrenia or internet addiction. The proposed chaos modelling and control theory may offer useful and relevant information on electrical stimulation therapy design to change this thinking processes through stimulating the brain's electrical activities. The experimental result of relaxing body from a lots of electrotherapy clinics helps mental disorders relax the thinking chaos in mind to replace their chaotic behaviors from the brain's electrical activities. This paper tries to explain the above claim in the aspect of the electrotherapy and control theory to suggest the control signal of electrotherapy based on an assumption for chaos model of patient and its control signals design according to multiple stabilization solutions. In the future, the electrical stimulation therapy will be proof in the Raphael Humanistic Clinic or the other electrotherapy clinics.

Tsai, ZR, Zhang, HW & Lin, CT 2017, 'A study on an energy consumption model correlated to abnormal behavior by contactless method', Computers in Human Behavior, vol. 74, pp. 53-62.
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© 2017 Elsevier Ltd The anxiety disorders, major depressive illness, substance use disorder, false beliefs, confused thinking, reduced social engagement, reduced emotional expression, a lack of motivation, and refusing to accept the wearable medical devices have happened in schizophrenia patients. Of course, a methodological critique of wearable medical devices towards a behavior model is suffering from a refusing action of schizophrenia patient. Hence, a novel real-time and robust application on correlation of stereo vision and abnormal behavior in schizophrenia is proposed in this paper. A robust image process is key to further exploring the behavior of schizophrenia patient by contactless surveillance, and from any view of patient to predict the abnormal sign of patient. This abnormal sign of energies consumption may be caused by inappropriate prescription or other medical negligence. An indicator for this abnormal sign of single specific patient should be designed by comparing with the past normal records of this patient. This study aims to provide a predictive diagnosis of patient. This diagnosis is obtained by this indicator to inform the hospital workers to make the preventing medical treatment. It enhances the secure healthcare, and will be proof in the Chang Bing Show Chwan Memorial hospital since first author has completed training and submitted proof with Institutional Review Board (IRB) in Taiwan in the past.

Walsh, L, Bluff, A & Johnston, A 2017, 'Water, image, gesture and sound: composing and performing an interactive audiovisual work', Digital Creativity, vol. 28, no. 3, pp. 177-195.
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Performing and composing for interactive audiovisual system presents many challenges to the performer. Working with visual, sonic and gestural components requires new skills and new ways of thinking about performance. However, there are few studies that focus on performer experience with interactive systems. We present the work Blue Space for oboe and interactive audiovisual system, highlighting the evolving process of the collaborative development of the work. We consider how musical and technical demands interact in this process, and outline the challenges of performing with interactive systems. Using the development of Blue Space as a self-reflective case study, we examine the role of gestures in interactive audiovisual works and identify new modes of performance.

Wang, H, Wu, J, Pan, S, Zhang, P & Chen, L 2017, 'Towards large-scale social networks with online diffusion provenance detection', Computer Networks, vol. 114, pp. 154-166.
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© 2016 Elsevier B.V.In this paper we study a new problem of online discovering diffusion provenances in large networks. Existing work on network diffusion provenance identification focuses on offline learning where data collected from network detectors are static and a snapshot of the network is available before learning. However, an offline learning model does not meet the need for early warning, real-time awareness, or a real-time response to malicious information spreading in networks. To this end, we propose an online regression model for real-time diffusion provenance identification. Specifically, we first use offline collected network cascades to infer the edge transmission weights, and then use an online l 1 non-convex regression model as the identification model. The proposed methods are empirically evaluated on both synthetic and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.

Wang, H, Zhang, P, Zhu, X, Tsang, IWH, Chen, L, Zhang, C & Wu, X 2017, 'Incremental Subgraph Feature Selection for Graph Classification', IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 1, pp. 128-142.
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Wang, J, Zhang, X, Guo, Z & Lu, H 2017, 'Developing an early-warning system for air quality prediction and assessment of cities in China', Expert Systems with Applications, vol. 84, pp. 102-116.
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© 2017 Elsevier Ltd Air quality has received continuous attention from both environmental managers and citizens. Accordingly, early-warning systems for air pollution are very useful tools to avoid negative health effects and develop effective prevention programs. However, developing robust early-warning systems is very challenging, as well as necessary. This paper develops a reliable and effective early-warning system that consists of air quality prediction and assessment modules. In the prediction module, a hybrid forecasting method is developed for predicting pollutant concentrations that effectively estimates future air quality conditions. In developing this proposed model, we suggest the use of a back propagation neural network algorithm, combined with a probabilistic parameter model and data preprocessing techniques, to address the uncertainties involved in future air quality prediction. Meanwhile, a pre-analysis is implemented, primarily by using optimized distribution functions to examine and analyze statistical characteristics and emission behaviors of air pollutants. The second method, which is developed as part of the second module, is based on fuzzy set theory and the Analytic Hierarchy Process, and it performs air quality assessments to provide a clear and intelligible description of air quality conditions. Using data from the Ministry of Environmental Protection of China and six stages of air quality classification levels, specifically good, moderate, lightly polluted, moderately polluted, heavily polluted and severely polluted, two cities in China, Chengdu and Hangzhou, are used as illustrative examples to verify the effectiveness of the developed early-warning system. The results demonstrate that the proposed methods are effective and reliable for use by environmental supervisors in air pollution monitoring and management.

Wang, W, Yin, H, Chen, L, Sun, Y, Sadiq, S & Zhou, X 2017, 'ST-SAGE: A spatial-Temporal sparse additive generative model for spatial item recommendation', ACM Transactions on Intelligent Systems and Technology, vol. 8, no. 3.
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©c 2017 ACM 2157-6904/2017/04-ART48 15.00. With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important mobile application, especially when users travel away from home. However, this type of recommendation is very challenging compared to traditional recommender systems. A user may visit only a limited number of spatial items, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place, as most of the items visited by a user are usually located within a short distance from the user's home. Moreover, user interests and behavior patterns may vary dramatically across different time and geographical regions. In light of this, we propose ST-SAGE, a spatial-Temporal sparse additive generative model for spatial item recommendation in this article. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns and content of spatial items. To further alleviate the data-sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd's preferences over a well-designed spatial index structure called the spatial pyramid. To speed up the tra ining process of ST-SAGE, we implement a parallel version of themodel inference algorithm on the GraphLab framework.We conduct extensive experiments; the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-The-Art recommender systems in terms of recommendation effectiveness, model training efficiency, and online recommendation efficiency.

Wang, X & Wang, J 2017, 'Detecting glass in Simultaneous Localisation and Mapping', ROBOTICS AND AUTONOMOUS SYSTEMS, vol. 88, pp. 97-103.
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Wang, X, Liu, Y, Zhang, G, Xiong, F & Lu, J 2017, 'Diffusion-based recommendation with trust relations on tripartite graphs', JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT.
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Wang, X, Liu, Y, Zhang, G, Zhang, Y, Chen, H & Lu, J 2017, 'Mixed Similarity Diffusion for Recommendation on Bipartite Networks', IEEE Access, vol. 5, pp. 21029-21038.
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© 2013 IEEE. In recommender systems, collaborative filtering technology is an important method to evaluate user preference through exploiting user feedback data, and has been widely used in industrial areas. Diffusion-based recommendation algorithms inspired by diffusion phenomenon in physical dynamics are a crucial branch of collaborative filtering technology, which use a bipartite network to represent collection behaviors between users and items. However, diffusion-based recommendation algorithms calculate the similarity between users and make recommendations by only considering implicit feedback but neglecting the benefits from explicit feedback data, which would be a significant feature in recommender systems. This paper proposes a mixed similarity diffusion model to integrate both explicit feedback and implicit feedback. First, cosine similarity between users is calculated by explicit feedback, and we integrate it with resource-allocation index calculated by implicit feedback. We further improve the performance of the mixed similarity diffusion model by considering the degrees of users and items at the same time in diffusion processes. Some sophisticated experiments are designed to evaluate our proposed method on three real-world data sets. Experimental results indicate that recommendations given by the mixed similarity diffusion perform better on both the accuracy and the diversity than that of most state-of-the-art algorithms.

Wang, X, Qin, L, Lin, X, Zhang, Y & Chang, L 2017, 'Leveraging set relations in exact set similarity join', Proceedings of the VLDB Endowment, vol. 10, no. 9, pp. 925-936.
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© 2017 VLDB. Exact set similarity join, which finds all the similar set pairs from two collections of sets, is a fundamental problem with a wide range of applications. The existing solutions for set similarity join follow a filtering-verification framework, which generates a list of candidate pairs through scanning indexes in the filtering phase, and reports those similar pairs in the verification phase. Though much research has been conducted on this problem, set relations, which we find out is quite effective on improving the algorithm effciency through computational cost sharing, have never been studied. Therefore, in this paper, instead of considering each set individually, we explore the set relations in different levels to reduce the overall computational costs. First, it has been shown that most of the computational time is spent on the filtering phase, which can be quadratic to the number of sets in the worst case for the existing solutions. Thus we explore index-level set relations to reduce the filtering cost to be linear to the size of the input while keeping the same filtering power. We achieve this by grouping related sets into blocks in the index and skipping useless index probes in joins. Second, we explore answer-level set relations to further improve the algorithm based on the intuition that if two sets are similar, their answers may have a large overlap. We derive an algorithm which incrementally generates the answer of one set from an already computed answer of another similar set rather than compute the answer from scratch to reduce the computational cost. Finally, we conduct extensive performance studies using 21 real datasets with various data properties from a wide range of domains. The experimental results demonstrate that our algorithm outperforms all the existing algorithms across all datasets and can achieve more than an order of magnitude speedup against the stateof-the-art algorithms.

Wang, X, Zhang, W, Zhang, Y, Lin, X & Huang, Z 2017, 'Top-k spatial-keyword publish/subscribe over sliding window', The VLDB Journal, vol. 26, no. 3, pp. 301-326.
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© 2017, Springer-Verlag Berlin Heidelberg. With the prevalence of social media and GPS-enabled devices, a massive amount of geo-textual data have 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 centralized system, called Skype (Top-kSpatial-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. To reduce the expensive top-k re-evaluation cost triggered by message expiration, we develop a novel cost-basedk-skyband technique to reduce the number of re-evaluations in a cost-effective way. Extensive experiments verify the great efficiency and effectiveness of our proposed techniques. Furthermore, to support better scalability and higher throughput, we propose a distributed version of Skype, namely DSkype, on top of Storm, which is a popular distributed stream processing system. With the help of fine-tuned subscription/message distribution mechanisms, DSkype can achieve orders of magnitude speed-up than its centralized version.

Wang, X, Zhang, Y, Zhang, W & Lin, X 2017, 'Efficient Distance-Aware Influence Maximization in Geo-Social Networks', IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 3, pp. 599-612.
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© 2016 IEEE. Given a social network G and a positive integer k, the influence maximization problem aims to identify a set of knodes in G that can maximize the influence spread under a certain propagation model. As the proliferation of geo-social networks, location-aware promotion is becoming more necessary in real applications. In this paper, we study the distance-aware influence maximization (DAIM) problem, which advocates the importance of the distance between users and the promoted location. Unlike the traditional influence maximization problem, DAIM treats users differently based on their distances from the promoted location. In this situation, the knodes selected are different when the promoted location varies. In order to handle the large number of queries and meet the online requirement, we develop two novel index-based approaches, MIA-DA and RIS-DA, by utilizing the information over some pre-sampled query locations. MIA-DA is a heuristic method which adopts the maximum influence arborescence (MIA) model to approximate the influence calculation. In addition, different pruning strategies as well as a priority-based algorithm are proposed to significantly reduce the searching space. To improve the effectiveness, in RIS-DA, we extend the reverse influence sampling (RIS) model and come up with an unbiased estimator for the DAIM problem. Through carefully analyzing the sample size needed for indexing, RIS-DA is able to return a 1 - 1/e - ϵ approximate solution with at least 1 - δ probability for any given query. Finally, we demonstrate the efficiency and effectiveness of proposed methods over real geo-social networks.

Wang, X, Zhang, Y, Zhang, W, Lin, X & Chen, C 2017, 'Bring Order into the Samples: A Novel Scalable Method for Influence Maximization', IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 2, pp. 243-256.
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© 2016 IEEE. As a key problem in viral marketing, influence maximization has been extensively studied in the literature. Given a positive integer k, a social network G and a certain propagation model, it aims to find a set of k nodes that have the largest influence spread. The state-of-the-art method IMM is based on the reverse influence sampling (RIS) framework. By using the martingale technique, it greatly outperforms the previous methods in efficiency. However, IMM still has limitations in scalability due to the high overhead of deciding a tight sample size. In this paper, instead of spending the effort on deciding a tight sample size, we present a novel bottom-k sketch based RIS framework, namely BKRIS, which brings the order of samples into the RIS framework. By applying the sketch technique, we can derive early termination conditions to significantly accelerate the seed set selection procedure. Moreover, we provide a cost-effective method to find a proper sample size to bound the quality of returned result. In addition, we provide several optimization techniques to reduce the cost of generating samples' order and efficiently deal with the worst-case scenario. We demonstrate the efficiency and effectiveness of the proposed method over 10 real world datasets. Compared with the IMM approach, BKRIS can achieve up to two orders of magnitude speedup with almost the same influence spread. In the largest dataset with 1.8 billion edges, BKRIS can return 50 seeds in 1.3 seconds and return 5,000 seeds in 36.6 seconds. It takes IMM 55.32 second and 3,664.97 seconds, respectively.

Wen, D, Qin, L, Zhang, Y, Chang, L & Lin, X 2017, 'Efficient structural graph clustering: An index-based approach', Proceedings of the VLDB Endowment, vol. 11, no. 3, pp. 243-255.
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© 2017 VLDB Endowment. Graph clustering is a fundamental problem widely experienced across many industries. The structural graph clustering (SCAN) method obtains not only clusters but also hubs and outliers. However, the clustering results closely depend on two sensitive parameters, ∈ and μ, while the optimal parameter setting depends on different graph properties and various user requirements. Moreover, all existing SCAN solutions need to scan at least the whole graph, even if only a small number of vertices belong to clusters. In this paper we propose an index-based method for SCAN. Based on our index, we cluster the graph for any ∈ and μ in O( ΣC∈C|EC|) time, where C is the result set of all clusters and |EC| is the number of edges in a specific cluster C. In other words, the time expended to compute structural clustering depends only on the result size, not on the size of the original graph. Our index's space complexity is bounded by O(m), wheremis the number of edges in the graph. To handle dynamic graph updates, we propose algorithms and several optimization techniques for maintaining our index. We conduct extensive experiments to practically evaluate the performance of all our proposed algorithms on 10 real-world networks, one of which contains more than 1 billion edges. The experimental results demonstrate that our approaches significantly outperform existing solutions.

Wilde, MM, Tomamichel, M & Berta, M 2017, 'Converse Bounds for Private Communication Over Quantum Channels', IEEE TRANSACTIONS ON INFORMATION THEORY, vol. 63, no. 3, pp. 1792-1817.
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Wilde, MM, Tomamichel, M, Lloyd, S & Berta, M 2017, 'Gaussian Hypothesis Testing and Quantum Illumination', Physical Review Letters, vol. 119, no. 12.
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© 2017 American Physical Society. Quantum hypothesis testing is one of the most basic tasks in quantum information theory and has fundamental links with quantum communication and estimation theory. In this paper, we establish a formula that characterizes the decay rate of the minimal type-II error probability in a quantum hypothesis test of two Gaussian states given a fixed constraint on the type-I error probability. This formula is a direct function of the mean vectors and covariance matrices of the quantum Gaussian states in question. We give an application to quantum illumination, which is the task of determining whether there is a low-reflectivity object embedded in a target region with a bright thermal-noise bath. For the asymmetric-error setting, we find that a quantum illumination transmitter can achieve an error probability exponent stronger than a coherent-state transmitter of the same mean photon number, and furthermore, that it requires far fewer trials to do so. This occurs when the background thermal noise is either low or bright, which means that a quantum advantage is even easier to witness than in the symmetric-error setting because it occurs for a larger range of parameters. Going forward from here, we expect our formula to have applications in settings well beyond those considered in this paper, especially to quantum communication tasks involving quantum Gaussian channels.

Williams, SP & Hausman, V 2017, 'Categorizing the Business Risks of Social Media', Procedia Computer Science, vol. 121, pp. 266-273.
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© 2017 The Authors. Published by Elsevier B.V. This paper examines the risks arising from the business use of social media and develops a framework for describing and categorizing social media business risks. Using descriptive and axial coding methods an analysis of the academic and professional literatures on social media identified thirty risk types that were grouped into five risk categories. The coding and analysis process revealed further dimensions and issues for social media risk management, including the need to consider the evolutionary nature of risk classification, the existence of risk chains and interdependencies between risks. These are discussed in the context of future work on risk assessment and risk governance.

Wu, D, Lance, BJ, Lawhern, VJ, Gordon, S, Jung, TP & Lin, CT 2017, 'EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 11, pp. 2157-2168.
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© 2001-2011 IEEE. Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first time, it is applied to BCI regression problems, an important category of BCI applications. More specifically, we propose a new feature extraction approach for electroencephalogram (EEG)-based BCI regression problems: a spatial filter is first used to increase the signal quality of the EEG trials and also to reduce the dimensionality of the covariance matrices, and then Riemannian tangent space features are extracted. We validate the performance of the proposed approach in reaction time estimation from EEG signals measured in a large-scale sustained-attention psychomotor vigilance task, and show that compared with the traditional powerband features, the tangent space features can reduce the root mean square estimation error by 4.30%-8.30%, and increase the estimation correlation coefficient by 6.59%-11.13%.

Wu, D, Lawhern, VJ, Gordon, S, Lance, BJ & Lin, C-T 2017, 'Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)', IEEE Transactions on Fuzzy Systems, vol. 25, no. 6, pp. 1522-1535.
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One big challenge that hinders the transition of brain–computer interfaces (BCIs) from laboratory settings to real-life applications is the availability of high-performance and robust learning algorithms that can effectively handle individual differences, i.e., algorithms that can be applied to a new subject with zero or very little subject-specific calibration data. Transfer learning and domain adaptation have been extensively used for this purpose. However, most previous works focused on classification problems. This paper considers an important regression problem in BCI, namely, online driver drowsiness estimation from EEG signals. By integrating fuzzy sets with domain adaptation, we propose a novel online weighted adaptation regularization for regression (OwARR) algorithm to reduce the amount of subject-specific calibration data, and also a source domain selection (SDS) approach to save about half of the computational cost of OwARR. Using a simulated driving dataset with 15 subjects, we show that OwARR and OwARR-SDS can achieve significantly smaller estimation errors than several other approaches. We also provide comprehensive analyses on the robustness of OwARR and OwARR-SDS.

Wu, SL, Liu, YT, Hsieh, TY, Lin, YY, Chen, CY, Chuang, CH & Lin, CT 2017, 'Fuzzy Integral with Particle Swarm Optimization for a Motor-Imagery-Based Brain-Computer Interface', IEEE Transactions on Fuzzy Systems, vol. 25, no. 1, pp. 21-28.
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© 2016 IEEE.A brain-computer interface (BCI) system using electroencephalography signals provides a convenient means of communication between the human brain and a computer. Motor imagery (MI), in which motor actions are mentally rehearsed without engaging in actual physical execution, has been widely used as a major BCI approach. One robust algorithm that can successfully cope with the individual differences in MI-related rhythmic patterns is to create diverse ensemble classifiers using the subband common spatial pattern (SBCSP) method. To aggregate outputs of ensemble members, this study uses fuzzy integral with particle swarm optimization (PSO), which can regulate subject-specific parameters for the assignment of optimal confidence levels for classifiers. The proposed system combining SBCSP, fuzzy integral, and PSO exhibits robust performance for offline single-trial classification of MI and real-time control of a robotic arm using MI. This paper represents the first attempt to utilize fuzzy fusion technique to attack the individual differences problem of MI applications in real-world noisy environments. The results of this study demonstrate the practical feasibility of implementing the proposed method for real-world applications.

Wu, Y, Liao, LD, Pan, HC, He, L, Lin, CT & Tan, MC 2017, 'Fabrication and interfacial characteristics of surface modified Ag nanoparticle based conductive composites', RSC Advances, vol. 7, no. 47, pp. 29702-29712.
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© 2017 The Royal Society of Chemistry. The recent emergence of wearable electronics has driven the advancements of flexible and elastic conductive metal-polymer composites as electrodes and sensors. Surface modification of the conductive metal fillers are required to achieve a good dispersion within the matrix to obtain suitable conductivity and sensing properties. Additionally, it would be critical to ensure that the inclusion of these fillers does not affect the curing of the pre-polymers so as to ensure sufficient filler loading to form functional composites. In this work, a one-step approach is used to modify Ag-PAA nanoparticles via hydrogen bonds to form PAA-PVP complex modified Ag nanoparticles. The interfacial characteristics and thermal stability of these surface-modified Ag nanoparticles were studied to elucidate the underlying chemistries that governed the surface modification process. After surface modification, we successfully improved the dispersion of Ag nanoparticles and enabled curing of PDMS to higher Ag loadings of ∼25 vol%, leading to much lower electrical resistivity of ∼6 Ω cm. Our studies also showed that Ag nanoparticles modified at a PAA/PVP molar ratio of 1:10 resulted in a minimal particle aggregation. In a preliminary testing of our conductive composites as electrodes, clear electrocardiography signals were obtained. The facile surface modification method introduced here can be adapted for other systems to modify the particle interfacial behavior and improve the filler dispersion and loading without adversely affecting the polymer curing chemistry.

Wu, Z, Lei, L, Li, G, Huang, H, Zheng, C, Chen, E & Xu, G 2017, 'A topic modeling based approach to novel document automatic summarization', Expert Systems with Applications, vol. 84, pp. 12-23.
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Wu, Z, Zhu, H, Li, G, Cui, Z, Huang, H, Li, J, Chen, E & Xu, G 2017, 'An efficient Wikipedia semantic matching approach to text document classification', Information Sciences, vol. 393, pp. 15-28.
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© 2017 Elsevier Inc. A traditional classification approach based on keyword matching represents each text document as a set of keywords, without considering the semantic information, thereby, reducing the accuracy of classification. To solve this problem, a new classification approach based on Wikipedia matching was proposed, which represents each document as a concept vector in the Wikipedia semantic space so as to understand the text semantics, and has been demonstrated to improve the accuracy of classification. However, the immense Wikipedia semantic space greatly reduces the generation efficiency of a concept vector, resulting in a negative impact on the availability of the approach in an online environment. In this paper, we propose an efficient Wikipedia semantic matching approach to document classification. First, we define several heuristic selection rules to quickly pick out related concepts for a document from the Wikipedia semantic space, making it no longer necessary to match all the concepts in the semantic space, thus greatly improving the generation efficiency of the concept vector. Second, based on the semantic representation of each text document, we compute the similarity between documents so as to accurately classify the documents. Finally, evaluation experiments demonstrate the effectiveness of our approach, i.e., which can improve the classification efficiency of the Wikipedia matching under the precondition of not compromising the classification accuracy.

Xu, X, Liu, Z, Wang, Z, Sheng, QZ, Yu, J & Wang, X 2017, 'S-ABC: A paradigm of service domain-oriented artificial bee colony algorithms for service selection and composition', Future Generation Computer Systems, vol. 68, pp. 304-319.
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© 2016 Elsevier B.V. With the rapid development of Cloud Computing, Big Data, Social Networks, and the Internet of Things, typical service optimization problems (SOPs) such as service selection, service composition and service resource scheduling in the service computing field have become more and more complicated due to the constant enrichment and dynamic aggregation of large number of services, as well as the unceasing variation of user requirements. Meanwhile, with the long-term development and evolution of business in many application domains, some service domain features (such as priori, correlation and similarity) are usually formed, which have strong influences on solving SOPs. Unfortunately, the existing research efforts on SOPs primarily concentrate on designing general algorithms for specific problems without considering the service domain features. This often leads to undesirable results of SOPs. Therefore, how to design a paradigm of service domain-oriented optimization algorithms with service domain features becomes a challenge for providing optimization strategies and algorithms to solve SOPs effectively. By considering the influences of service domain features on solving SOPs, this paper proposes a set of service domain-oriented artificial bee colony algorithms (S-ABC) based on the optimization mechanism of Artificial Bee Colony (ABC) method. Furthermore, by configuring the items and parameters of the S-ABC paradigm in detail, optimization algorithms for particular SOPs (e.g., service selection and composition) could be derived. In this paper, the superiority of our proposed S-ABC is verified through solving concurrent service selection and service composition problem. By exploiting the artificial bee colony algorithms for the optimization problems in service domains, this work makes novel contributions for solving SOPs, as well as extends the theory of the swarm intelligence optimization.

Xuan, J, Lu, J, Zhang, G, Xu, RYD & Luo, X 2017, 'A Bayesian nonparametric model for multi-label learning', MACHINE LEARNING, vol. 106, no. 11, pp. 1787-1815.
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Xuan, J, Luo, X, Lu, J & Zhang, G 2017, 'Explicitly and implicitly exploiting the hierarchical structure for mining website interests on news events', INFORMATION SCIENCES, vol. 420, pp. 263-277.
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Yang, C, Zhu, D, Wang, X, Zhang, Y, Zhang, G & Lu, J 2017, 'Requirement-oriented core technological components’ identification based on SAO analysis', Scientometrics, vol. 112, no. 3, pp. 1229-1248.
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© 2017, Akadémiai Kiadó, Budapest, Hungary. Technologies play an important role in the survival and development of enterprises. Understanding and monitoring the core technological components (e.g., technology process, operation method, function) of a technology is an important issue for researchers to develop R & D policy and manage product competitiveness. However, it is difficult to identify core technological components from a mass of terms, and we may experience some difficulties with describing complete technical details and understanding the terms-based results. This paper proposes a Subject-Action-Object (SAO)-based method, in which (1) a syntax-based approach is constructed to extract the SAO structures describing the function, relationship and operation in specified topics; (2) a systematic method is built to extract and screen technological components from SAOs; and (3) we propose a “relevance indicator” to calculate the relevance of the technological components to requirements, and finally identify core technological components based on this indicator. Based on the considerations for requirements and novelty, the core technological components identified have great market potential and can be useful in monitoring and forecasting new technologies. An empirical study of graphene is performed to demonstrate the proposed method. The resulting knowledge may hold interest for R & D management and corporate technology strategies in practice.

Yang, H, Jiang, Z & Lu, H 2017, 'A hybridwind speed forecasting system based on a 'decomposition and ensemble' strategy and fuzzy time series', Energies, vol. 10, no. 9, pp. 1-30.
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© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Accurate and stable wind speed forecasting is of critical importance in the wind power industry and has measurable influence on power-system management and the stability of market economics. However, most traditional wind speed forecasting models require a large amount of historical data and face restrictions due to assumptions, such as normality postulates. Additionally, any data volatility leads to increased forecasting instability. Therefore, in this paper, a hybrid forecasting system, which combines the 'decomposition and ensemble' strategy and fuzzy time series forecasting algorithm, is proposed that comprises two modules-data pre-processing and forecasting. Moreover, the statistical model, artificial neural network, and Support Vector Regression model are employed to compare with the proposed hybrid system, which is proven to be very effective in forecasting wind speed data affected by noise and instability. The results of these comparisons demonstrate that the hybrid forecasting system can improve the forecasting accuracy and stability significantly, and supervised discretization methods outperform the unsupervised methods for fuzzy time series in most cases.

Yang, J, Zhang, W, Zhang, Y, Wang, X & Lin, X 2017, 'Categorical top-k spatial influence query', World Wide Web, vol. 20, no. 2, pp. 175-203.
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© 2016 Springer Science+Business Media New York The influence of a spatial facility object depicts the importance of the object in the whole data space. In this paper, we present a novel definition of object influence in applications where objects are of different categories. We study the problem of Spatial Influence Query which considers the contribution of an object in forming functional units consisting of a given set of objects with different categories designated by users. We first show that the problem of spatial influence query is NP-hard with respect to the number of object categories in the functional unit. To tackle the computational hardness, we develop an efficient framework following two main steps, possible participants finding and optimal functional unit computation. Based on this framework, for the first step, novel and efficient pruning techniques are developed based on the nearest neighbor set (NNS) approach. To find the optimal functional unit efficiently, we propose two algorithms, an exact algorithm and an efficient approximate algorithm with performance guarantee. Comprehensive experiments on both real and synthetic datasets demonstrate the effectiveness and efficiency of our techniques.

Yang, S, Cheema, MA, Lin, X, Zhang, Y & Zhang, W 2017, 'Reverse k nearest neighbors queries and spatial reverse top-k queries', The VLDB Journal, vol. 26, no. 2, pp. 151-176.
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© 2016 Springer-Verlag Berlin HeidelbergGiven a set of facilities and a set of users, a reverse k nearest neighbors (RkNN) query q returns every user for which the query facility is one of the k closest facilities. Almost all of the existing techniques to answer RkNN queries adopt a pruning-and-verification framework. Regions-based pruning and half-space pruning are the two most notable pruning strategies. The half-space-based approach prunes a larger area and is generally believed to be superior. Influenced by this perception, almost all existing RkNN algorithms utilize and improve the half-space pruning strategy. We observe the weaknesses and strengths of both strategies and discover that the regions-based pruning has certain strengths that have not been exploited in the past. Motivated by this, we present a new regions-based pruning algorithm called Slice that utilizes the strength of regions-based pruning and overcomes its limitations. We also study spatial reverse top-k (SRTk) queries that return every user u for which the query facility is one of the top-k facilities according to a given linear scoring function. We first extend half-space-based pruning to answer SRTk queries. Then, we propose a novel regions-based pruning algorithm following Slice framework to solve the problem. Our extensive experimental study on synthetic and real data sets demonstrates that Slice is significantly more efficient than all existing RkNN and SRTk algorithms.

Yao, SN, Lin, CT, King, JT, Liu, YC & Liang, C 2017, 'Learning in the visual association of novice and expert designers', Cognitive Systems Research, vol. 43, pp. 76-88.
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© 2017 Elsevier B.V. Designers are adept at determining similarities between previously seen objects and new creations using visual association. However, extant research on the visual association of designers and the differences between expert and novice designers when they engage in the visual association task are scant. Using electroencephalography (EEG), this study attempted to narrow this research gap. Sixteen healthy designers—eight experts and eight novices—were recruited, and asked to perform visual association while EEG signals were acquired, subsequently analysed using independent component analysis. The results indicated that strong connectivity was observed among the prefrontal, frontal, and cingulate cortices, and the default mode network. The experts used both hemispheres and executive functions to support their association tasks, whereas the novices mainly used their right hemisphere and memory retrieval functions. The visual association of experts appeared to be more goal-directed than that of the novices. Accordingly, designing and implementing authentic and goal-directed activities for improving the executive functions of the prefrontal cortex and default mode network are critical for design educators and creativity researchers.

Yin, H, Wang, W, Wang, H, Chen, L & Zhou, X 2017, 'Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 11, pp. 2537-2551.
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© 2017 IEEE. Point-of-interest (POI) recommendation has become an important way to help people discover attractive and interesting places, especially when they travel out of town. However, the extreme sparsity of user-POI matrix and cold-start issues severely hinder the performance of collaborative filtering-based methods. Moreover, user preferences may vary dramatically with respect to the geographical regions due to different urban compositions and cultures. To address these challenges, we stand on recent advances in deep learning and propose a Spatial-Aware Hierarchical Collaborative Deep Learning model (SH-CDL). The model jointly performs deep representation learning for POIs from heterogeneous features and hierarchically additive representation learning for spatial-aware personal preferences. To combat data sparsity in spatial-aware user preference modeling, both the collective preferences of the public in a given target region and the personal preferences of the user in adjacent regions are exploited in the form of social regularization and spatial smoothing. To deal with the multimodal heterogeneous features of the POIs, we introduce a late feature fusion strategy into our SH-CDL model. The extensive experimental analysis shows that our proposed model outperforms the state-of-the-art recommendation models, especially in out-of-town and cold-start recommendation scenarios.

Zander, TO, Andreessen, LM, Berg, A, Bleuel, M, Pawlitzki, J, Zawallich, L, Krol, LR & Gramann, K 2017, 'Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving', FRONTIERS IN HUMAN NEUROSCIENCE, vol. 11.
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Zhang, F, Zhang, W, Zhang, Y, Qin, L & Lin, X 2017, 'OLAK: An Efficient Algorithm to Prevent Unraveling in Social Networks', Proceedings of the VLDB Endowment, vol. 10, no. 6, pp. 649-660.
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In this paper, we study the problem of the anchored k-core. Given a graph G, an integer k and a budget b, we aim to identify b vertices in G so that we can determine the largest induced subgraph J in which every vertex, except the b vertices, has at least k neighbors in J. This problem was introduced by Bhawalkar and Kleinberg et al. in the context of user engagement in social networks, where a user may leave a community if he/she has less than k friends engaged. The problem has been shown to be NP-hard and inapproximable. A polynomial-time algorithm for graphs with bounded tree width has been proposed. However, this assumption usually does not hold in real-life graphs, and their techniques cannot be extended to handle general graphs. Motivated by this, we propose an efficient algorithm, namely onion-layer based anchored k-core (OLAK), for the anchored k-core problem on large scale graphs. To facilitate computation of the anchored k-core, we design an onion layer structure, which is generated by a simple onion-peeling like algorithm against a small set of vertices in the graph. We show that computation of the best anchor can simply be conducted upon the vertices on the onion layers, which significantly reduces the search space. Based on the well organized layer structure, we develop efficient candidates exploration, early termination and pruning techniques to further speed up computation. Comprehensive experiments on 10 real-life graphs demonstrate the effectiveness and efficiency of our proposed methods.

Zhang, F, Zhang, Y, Qin, L, Zhang, W & Lin, X 2017, 'When Engagement Meets Similarity: Efficient (k, r)-Core Computation on Social Networks', Proceedings of the VLDB Endowment, vol. 10, no. 10, pp. 998-1009.
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In this paper, we investigate the problem of (k,r)-core which intends to find cohesive subgraphs on social networks considering both user engagement and similarity perspectives. In particular, we adopt the popular concept of k-core to guarantee the engagement of the users (vertices) in a group (subgraph) where each vertex in a (k,r)-core connects to at least k other vertices. Meanwhile, we consider the pairwise similarity among users based on their attributes. Efficient algorithms are proposed to enumerate all maximal (k,r)-cores and find the maximum (k,r)-core, where both problems are shown to be NP-hard. Effective pruning techniques substantially reduce the search space of two algorithms. A novel (k,k')-core based (k,r)-core size upper bound enhances performance of the maximum (k,r)-core computation. We also devise effective search orders for two mining algorithms where search priorities for vertices are different. Comprehensive experiments on real-life data demonstrate that the maximal/maximum (k,r)-cores enable us to find interesting cohesive subgraphs, and performance of two mining algorithms is effectively improved by proposed techniques.

Zhang, Q, Wu, D, Lu, J, Liu, F & Zhang, G 2017, 'A cross-domain recommender system with consistent information transfer', Decision Support Systems, vol. 104, pp. 49-63.
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© 2017 Elsevier B.V. Recommender systems provide users with personalized online product and service recommendations and are a ubiquitous part of today's online entertainment smorgasbord. However, many suffer from cold-start problems due to a lack of sufficient preference data, and this is hindering their development. Cross-domain recommender systems have been proposed as one possible solution. These systems transfer knowledge from one domain that has adequate preference information to another domain that does not. The outlook for cross-domain recommendation is promising, but existing methods cannot ensure the knowledge extracted from the source domain is consistent with the target domain, which may impact the accuracy of the recommendations. To address this challenging issue, we propose a cross-domain recommender system with consistent information transfer (CIT). Knowledge consistency is based on user and item latent groups, and domain adaptation techniques are used to map and adjust these groups in both domains to maintain consistency during the transfer learning process. Experiments were conducted on five real-world datasets in three categories: movies, books, and music. The results for nine cross-domain recommendation tasks show that CIT outperforms five benchmarks and increases the accuracy of recommendations in the target domain, especially with sparse data. Practically, our proposed method is applied into a telecom product recommender system and a business partner recommender system (Smart BizSeeker) to enhance personalized decision making for both businesses and individual customers.

Zhang, Y, Qian, Y, Huang, Y, Guo, Y, Zhang, G & Lu, J 2017, 'An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation', Scientometrics, vol. 111, no. 3, pp. 1925-1946.
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© 2017, Akadémiai Kiadó, Budapest, Hungary. How to evaluate the value of a patent in technological innovation quantitatively and systematically challenges bibliometrics. Traditional indicator systems and weighting approaches mostly lead to “moderation” results; that is, patents ranked to a top list can have only good-looking values on all indicators rather than distinctive performances in certain individual indicators. Orienting patents authorized by the United States Patent and Trademark Office (USPTO), this paper constructs an entropy-based indicator system to measur e their potential in technological innovation. Shannon’s entropy is introduced to quantitatively weight indicators and a collaborative filtering technique is used to iteratively remove negative patents. What remains is a small set of positive patents with potential in technological innovation as the output. A case study with 28,509 USPTO-authorized patents with Chinese assignees, covering the period from 1976 to 2014, demonstrates the feasibility and reliability of this method.

Zhang, Y, Zhang, G, Zhu, D & Lu, J 2017, 'Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics', Journal of the Association for Information Science and Technology, vol. 68, no. 8.
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Zuo, H, Zhang, G, Pedrycz, W, Behbood, V & Lu, J 2017, 'Fuzzy Regression Transfer Learning in Takagi-Sugeno Fuzzy Models', IEEE Transactions on Fuzzy Systems, vol. 25, no. 6, pp. 1795-1807.
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Data Science is a research field concerned with processes and systems that extract knowledge from massive amounts of data. In some situations, however, data shortage renders existing data-driven methods difficult or even impossible to apply. Transfer learning has recently emerged as a way of exploiting previously acquired knowledge to solve new yet similar problems much more quickly and effectively. In contrast to classical data-driven machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling in the current domain. A significant number of transfer learning methods that address classification tasks have been proposed, but studies on transfer learning in the case of regression problems are still scarce. This study focuses on using transfer learning techniques to handle regression problems in a domain that has insufficient training data. We propose an original fuzzy regression transfer learning method, based on fuzzy rules, to address the problem of estimating the value of the target for regression. A Takagi-Sugeno fuzzy regression model is developed to transfer knowledge from a source domain to a target domain. Experimental results using synthetic data and real world datasets demonstrate that the proposed fuzzy regression transfer learning method significantly improves the performance of existing models when tackling regression problems in the target domain.

Adak, C, Chaudhuri, BB & Blumenstein, M 2017, 'Impact of struck-out text on writer identification', Proceedings of the International Joint Conference on Neural Networks, International Joint Conference on Neural Networks, IEEE, Anchorage, AK, USA, pp. 1465-1471.
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© 2017 IEEE. The presence of struck-out text in handwritten manuscripts may affect the accuracy of automated writer identification. This paper presents a study on such effects of struck-out text. Here we consider offline English and Bengali handwritten document images. At first, the struck-out texts are detected using a hybrid classifier of a CNN (Convolutional Neural Network) and an SVM (Support Vector Machine). Then the writer identification process is activated on normal and struck-out text separately, to ascertain the impact of struck-out texts. For writer identification, we use two methods: (a) a hand-crafted feature-based SVM classifier, and (b) CNN-extracted auto-derived features with a recurrent neural model. For the experimental analysis, we have generated a database from 100 English and 100 Bengali writers. The performance of our system is very encouraging.

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 2017, 'Writer identification by training on one script but testing on another', Proceedings - International Conference on Pattern Recognition, 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.

Agrawal, S & Williams, MA 2017, 'Robot authority and human obedience: A study of human behaviour using a robot security guard', ACM/IEEE International Conference on Human-Robot Interaction, 2017 ACM/IEEE International Conference on Human-Robot Interaction, Vienna, Austria, pp. 57-58.
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© 2017 Authors. There has been much debate, sci-fi movie scenes, and several scientific studies exploring the concept of robot authority. Some of the key research questions include: when should humans follow/question robot instructions; how can a robot increase its ability to convince humans to follow their instructions or to change their behaviour. In this paper, we describe a recent experiment designed to explore the notions of robot authority and human obedience. We set up a robot in a publicly accessible building to act as a security guard that issued instructions to specific humans. We identified and analysed the factors that affected a human's decisions to follow the robot's instruction. The four key factors were: perceived aggression, responsiveness, anthropomorphism, level of safety and intelligence in the robot's behaviour. We implemented various social cues to exhibit and convey authority and aggressiveness in the robot's behaviour. The results suggest that the degree of aggression that different people perceived in the robot's behaviour did not have a significant impact in their decision to follow the robot's instruction. Although, the people who disobeyed the robot, perceived the robot's behaviour to be more unsafe and less human-like than the people who followed the robot's instructions and also found the robot to be more responsive.

Ahadi, A, Lal, S, Leinonen, J, Hellas, A & Lister, R 2017, 'Performance and Consistency in Learning to Program', Proceedings of the Nineteenth Australasian Computing Education Conference, Australasian Computing Education Conference, ACM, Geelong.
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Performance and consistency play a large role in learning. Decreasing the effort that one invests into course work may have short-term benefits such as reduced stress. However, as courses progress, neglected work accumulates and may cause challenges with learning the course content at hand. In this work, we analyze students' performance and consistency with programming assignments in an introductory programming course. We study how performance, when measured through progress in course assignments, evolves throughout the course, study weekly fluctuations in students' work consistency, and contrast this with students' performance in the course final exam. Our results indicate that whilst fluctuations in students' weekly performance do not distinguish poor performing students from well performing students with a high accuracy, more accurate results can be achieved when focusing on the performance of students on individual assignments which could be used for identifying struggling students who are at risk of dropping out of their studies.

Alkalbani, AM, Gadhvi, L, Patel, B, Hussain, FK, Ghamry, AM & Hussain, OK 2017, 'Analysing cloud services reviews using opining mining', Proceedings - International Conference on Advanced Information Networking and Applications, AINA, pp. 1124-1129.
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© 2017 IEEE. There is increasing interest in sharing the experience of products and services on the web platform, and social media has opened a way for product and service providers to understand their consumers needs and expectations. This paper explores reviews by cloud consumers that reflect consumers experiences with cloud services. The reviews of around 6,000 cloud service users were analysed using sentiment analysis to identify the attitude of each review, and to determine whether the opinion expressed was positive, negative, or neutral. The analysis used two data mining tools, KNIME and RapidMiner, and the results were compared. We developed four prediction models in this study to predict the sentiment of users reviews. The proposed model is based on four supervised machine learning algorithms: K-Nearest Neighbour (k-NN), Nave Bayes, Random Tree, and Random Forest. The results show that the Random Forest predictions achieve 97.06% accuracy, which makes this model a better prediction model than the other three.

Anshar, M & Williams, M-A 2017, 'Evolving Artificial Pain from Fault Detection through Pattern Data Analysis', 2017 IEEE International Conference on Real-time Computing and Robotics (RCAR), IEEE International Conference on Real-time Computing and Robotics, IEEE, Okinawa, Japan, pp. 694-699.
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Fault detection is a classical area of study in robotics which mainly considered as a stimulus in robot motion planning. Earlier studies, reported in [1]–[3] provide the foundation for the importance of incorporating failure detection into robot planning mechanisms. Various aspects of robot motion planning are investigated in [4]–[8] and extensions the scope to multiple robot planning [9], [10]. All of these studies assume that the robot is fully functional. In practice, however, robots fail and their failure can affect not only the plans but also put resources and people at risk. As the use of robots grow, such as in human-robot interaction or robot-to robot interaction,a new and growing field of research, robots are required to develop more sophisticated social skills. Understanding the concept of pain in humans themselves is critical for planning and tasks that require human-robot interaction. However, this raises an issue on how a robot can develop a proper concept of pain which relies on the robot being aware of its own body machinery aspects. [11] proposes a damage recovery approach where robots are aware of their body hardware failure, such as one or few of robot joints suffer from malfunctions. The study reports that the robot successfully discovers a new qualitative behavior of hexapod gaits. However, when this type of fault is detected, it merely functions as a stimulus to activate the new motion plan or new motion behaviour generation. Unlike in robots, in human mechanism, any machinery of body failure will generate internal states where humans experience what is called ‘pain’ [12]. In fact, if the faults in robots themselves are associated with not only stimulus but also specific meaningful magnitude, it will be beneficial for robots to incorporate them as part of their experience. This paper intends to derive machinery faults of the robots, detected from robot proprioceptive sensors, into an appropriate representation of pain by introducing an artifi...

Aravanis, T, Peppas, P & Williams, MA 2017, 'Epistemic-entrenchment characterization of Parikh's axiom', IJCAI International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization, Melbourne, Australia, pp. 772-778.
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In this article, we provide the epistemicentrenchment characterization of the weak version of Parikh's relevance-sensitive axiom for belief revision - known as axiom (P) - for the general case of incomplete theories. Loosely speaking, axiom (P) states that, if a belief set K can be divided into two disjoint compartments, and the new information φ relates only to the first compartment, then the second compartment should not be affected by the revision of K by φ. The above-mentioned characterization, essentially, constitutes additional constraints on epistemicentrenchment preorders, that induce AGM revision functions, satisfying the weak version of Parikh's axiom (P).

Aravanis, TI & Peppas, P 2017, 'Belief revision in answer set programming', Proceedings of the 21st Pan-Hellenic Conference on Informatics, Pan-Hellenic Conference on Informatics, ACM, Larissa, Greece, pp. 1-5.
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© 2017 Association for Computing Machinery. Answer Set Programming is a declarative problem solving approach, initially tailored to modeling problems in the area of Knowledge Representation and Reasoning. In this article,we provide a logic program, in the context of Answer Set Programming framework, that implements the AGM belief revision process, constructed by means of faithful preorders. The above-mentioned approach constitutes a representative implementation of the Answer Set Programming's modeling methodology, as well as a practical method/construction, bringing us a step closer to the successful development of an AGM belief revision system, for real-world applications.

Balaji, S, Patil, M & McGregor, C 2017, 'A Cloud Based Big Data Based Online Health Analytics for Rural NICUs and PICUs in India: Opportunities and Challenges', Proceedings - IEEE Symposium on Computer-Based Medical Systems, pp. 385-390.
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© 2017 IEEE. High frequency physiological data has great potential to provide new insights for many conditions patients can develop in critical care when utilized by Big Data Analytics based Clinical Decision Support Systems, such as Artemis. Artemis was deployed in NICU at SickKids Hospital in Toronto in August 2009. It employs all the potentiality of big data. Both original data together with newly generated analytics is stored in the data persistence component of Artemis. Real-time analytics is performed in the Online Analytics component. The knowledge extraction component of the system takes care of data mining which is enabled to support clinical research for various conditions. Artemis to date has been utilized in three different implementations. However the use of Artemis still holds many challenges for lower resource settings. This research demonstrates the challenges and opportunities to use Artemis cloud as a cloud computing based Health Analytics-as-a-Service approach for the provision of remote real-time patient monitoring for low resource settings. We present case study research to demonstrate the implications, opportunities and challenges of utilizing Artemis in a low resource setting for small and remote pediatric critical care units viz NICU/PICU in India. Utilizing potentiality of big data within pediatric intensive care units has great potential to improve healthcare in these low resource settings.

Bano, M, Zowghi, D & Kearney, M 2017, 'Feature Based Sentiment Analysis for Evaluating the Mobile Pedagogical Affordances of Apps', Tomorrow's Learning: Involving Everyone. Learning with and about Technologies and Computing, IFIP TC 3 World Conference on Computers in Education, Springer, Dublin, Ireland, pp. 281-291.
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The launch of millions of apps has made it challenging for teachers to select the most suitable m-learning app for their teaching purposes. Several evaluation frameworks have been proposed in the research literature to assist teachers in selecting the right apps for their needs. This paper presents an innovative technique for evaluating educational mobile apps by analysing the feedback of past app users through the lens of a mobile pedagogical perspective. We have utilized a sentiment analysis tool to assess the opinions of the app users against the criteria offered by a rigorous mobile learning pedagogical framework. The investigation has provided initial confirmation of the powerful utility of the feature based sentiment analysis technique for evaluating the mobile pedagogical affordances of learning apps.

Bashir, MR & Gill, AQ 2017, 'IoT Enabled Smart Buildings: A Systematic Review', 2017 Intelligent Systems Conference (IntelliSys), Intelligent Systems Conference, IEEE, London, UK, pp. 151-159.
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There is an increasing interest in the Internet of Things (IoT) enabled smart buildings. The main question is: what are the key challenges, which must be addressed to effectively manage and analyze the big data for IoT enabled smart buildings. There is a need for the systematic literature review to understand the challenges and the solutions to overcome such challenges. Using the SLR approach, 22 relevant studies were identified and reviewed in this paper. The data from these selected studies were extracted to identify the challenges and relevant solutions. The findings from this research paper will serve as a knowledge base for researchers and practitioners for conducting further research and development in this important area.

Bei, X, Qiao, Y & Zhang, S 2017, 'Networked fairness in cake cutting', IJCAI International Joint Conference on Artificial Intelligence, pp. 3632-3638.
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We introduce a graphical framework for fair division in cake cutting, where comparisons between agents are limited by an underlying network structure. We generalize the classical fairness notions of envy-freeness and proportionality to this graphical setting. Given a simple undirected graph G, an allocation is envy-free on G if no agent envies any of her neighbor's share, and is proportional on G if every agent values her own share no less than the average among her neighbors, with respect to her own measure. These generalizations open new research directions in developing simple and efficient algorithms that can produce fair allocations under specific graph structures. On the algorithmic frontier, we first propose a moving-knife algorithm that outputs an envy-free allocation on trees. The algorithm is significantly simpler than the discrete and bounded envy-free algorithm recently designed in [Aziz and Mackenzie, 2016a] for complete graphs. Next, we give a discrete and bounded algorithm for computing a proportional allocation on descendant graphs, a class of graphs by taking a rooted tree and connecting all its ancestor-descendant pairs.

Bell, JA & Leong, T 2017, 'Collaborative Futures: A Technology Design Approach to Support Living Well with Dementia', 29th Australian Conference on Human-Computer Interaction, Brisbane, QLD, Australia, November 2017, Australian Computer Human Interaction Conference, ACM New York, NY, USA, Brisbane, Australia, pp. 397-401.
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This paper presents insights into Younger Onset Dementia (YOD) offering clear differentiation in the circumstances, needs and challenges of people with YOD from those with late onset dementia. We point to opportunities for the potential role of digital technology to improve the experiences of people living with YOD. This is important because while HCI has long engaged with dementia, these efforts have been predominantly focused on designing technologies for elderly people experiencing dementia. In particular, this paper highlights concerns raised by people with YOD which have significant impact for HCI researchers when engaging people with YOD in research and in technology design. As such, this paper argues for a broadening of HCI research to include YOD and to rethink current research and design methods in ‘dementia and technology’ settings.

Belovs, A, Ivanyos, G, Qiao, Y, Santha, M & Yang, S 2017, 'On the polynomial parity argument complexity of the combinatorial nullstellensatz', Leibniz International Proceedings in Informatics, LIPIcs.
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© Aleksandrs Belovs, Gabor Ivanyos, Youming Qiao, Miklos Santha, and Siyi Yang. The complexity class PPA consists of NP-search problems which are reducible to the parity principle in undirected graphs. It contains a wide variety of interesting problems from graph theory, combinatorics, algebra and number theory, but only a few of these are known to be complete in the class. Before this work, the known complete problems were all discretizations or combinatorial analogues of topological fixed point theorems. Here we prove the PPA-completeness of two problems of radically different style. They are PPA-Circuit CNSS and PPA-Circuit Chevalley, related respectively to the Combinatorial Nullstellensatz and to the Chevalley-Warning Theorem over the two elements field F2. The input of these problems contain PPA-circuits which are arithmetic circuits with special symmetric properties that assure that the polynomials computed by them have always an even number of zeros. In the proof of the result we relate the multilinear degree of the polynomials to the parity of the maximal parse subcircuits that compute monomials with maximal multilinear degree, and we show that the maximal parse subcircuits of a PPA-circuit can be paired in polynomial time.

Berry, DM, Cleland-Huang, J, Ferrari, A, Maalej, W, Mylopoulos, J & Zowghi, D 2017, 'Panel: Context-Dependent Evaluation of Tools for NL RE Tasks: Recall vs. Precision, and beyond', Proceedings - 2017 IEEE 25th International Requirements Engineering Conference, RE 2017, IEEE 25th International Requirements Engineering Conference (RE), IEEE, Lisbon, Portugal, pp. 570-573.
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© 2017 IEEE. Context and Motivation Natural language processing has been used since the 1980s to construct tools for performing natural language (NL) requirements engineering (RE) tasks. The RE field has often adopted information retrieval (IR) algorithms for use in implementing these NL RE tools. Problem Traditionally, the methods for evaluating an NL RE tool have been inherited from the IR field without adapting them to the requirements of the RE context in which the NL RE tool is used. Principal Ideas This panel discusses the problem and considers the evaluation of tools for a number of NL RE tasks in a number of contexts. Contribution The discussion is aimed at helping the RE field begin to consistently evaluate each of its tools according to the requirements of the tool's task.

Billingsley, R, Billingsley, J, Gärdenfors, P, Peppas, P, Prade, H, Skillicorn, D & Williams, MA 2017, 'The altruistic robot: Do what i want, not just what i say', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Scalable Uncertainty Management, Granada, Spain, pp. 149-162.
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© Springer International Publishing AG 2017. As autonomous robots expand their application beyond research labs and production lines, they must work in more flexible and less well defined environments. To escape the requirement for exhaustive instruction and stipulated preference ordering, a robot’s operation must involve choices between alternative actions, guided by goals. We describe a robot that learns these goals from humans by considering the timeliness and context of instructions and rewards as evidence of the contours and gradients of an unknown human utility function. In turn, this underlies a choice-theory based rational preference relationship. We examine how the timing of requests, and contexts in which they arise, can lead to actions that pre-empt requests using methods we term contemporaneous entropy learning and context sensitive learning. We provide experiments on these two methods to demonstrate their usefulness in guiding a robot’s actions.

Billingsley, R, Prade, H, Richard, G & Williams, MA 2017, 'Towards analogy-based decision - A proposal', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12th International Conference Flexible Query Answering Systems, London UK, pp. 28-35.
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© Springer International Publishing AG 2017. This short paper outlines an analogy-based decision method. It takes advantage of analogical proportions between situations, i.e., a is to b as c is to d, for proposing plausibly good decisions that may be appropriate for a new situation at hand. It goes beyond case-based decision where the idea of graded similarity may hide some small but crucial differences between situations. The method relies on triples of known cases rather than on individual cases for making a prediction on the appropriateness of a potential decision, or for proposing a way of adapting a decision according to situations. The approach may be of interest in a variety of problems ranging from flexible querying systems to cooperative artificial agents.

Bluff, A & Johnston, A 2017, 'Storytelling with Interactive Physical Theatre: A case study of Dot and the Kangaroo', Proceedings of the 4th International Conference on Movement Computing, International Conference on Movement Computing, ACM, London, United Kingdom, pp. 1-8.
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This paper examines the way movement based interactive visuals were used as a storytelling device in the physical theatre production of Creature: Dot and the Kangaroo. A number of performers and artists involved in the production were interviewed and their perceptions of the interactive technology have been contrasted against a similar study into abstract dance. The animated backgrounds and interactive animal graphics projected onto the stage were found to reduce the density of script by describing the location of action and spirit of the character, reducing the necessity for this to be spoken. Peak moments of the show were identified by those interviewed and a scene analysis revealed that the most successful scenes featured a more integrated storytelling where the interaction between performers and the digital projections portrayed a key narrative message.

Bou Ghantous, G & Gill, A 2017, 'DevOps: Concepts, Practices, Tools, Benefits andChallenges', PACIS2017; Proceedings 96, PACIS2017, AIS Electronic Library (AISeL), Malaysia.
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Braytee, A, Liu, W & Kennedy, PJ 2017, 'Supervised context-aware non-negative matrix factorization to handle high-dimensional high-correlated imbalanced biomedical data', Proceedings of the International Joint Conference on Neural Networks, 2017 International Joint Conference on Neural Networks, Anchorage, AK, USA, pp. 4512-4519.
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© 2017 IEEE. Traditional feature selection techniques are used to identify a subset of the most useful features, and consider the rest as unimportant, redundant or noisy. In the presence of highly correlated features, many variable selection methods consider correlated features as redundant and need to be removed. In this paper, a novel supervised feature selection algorithm SCANMF is proposed by jointly integrating correlation analysis and structural analysis of the balanced supervised non-negative matrix factorization (NMF). Furthermore, ℓ2,1-norm minimization constraint is incorporated into the objective function to guarantee sparsity in the feature matrix rows and reduce noisy features. Our algorithm exploits the discriminative information, feature combinations, and the original features in the context of a supervised NMF method which can be beneficial for both classification and interpretation. An efficient iterative algorithm is designed to solve the constrained optimization problem with guaranteed convergence. Finally, a series of extensive experiments are conducted on 8 complex datasets. Promising results using multiple classifiers demonstrate the effectiveness and efficiency of our algorithm over state-of-the-art methods.

Braytee, A, Liu, W, Catchpoole, DR & Kennedy, PJ 2017, 'Multi-label feature selection using correlation information', International Conference on Information and Knowledge Management, Proceedings, ACM on Conference on Information and Knowledge Management, ACM, Singapore, Singapore, pp. 1649-1656.
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© 2017 ACM. High-dimensional multi-labeled data contain instances, where each instance is associated with a set of class labels and has a large number of noisy and irrelevant features. Feature selection has been shown to have great benefits in improving the classification performance in machine learning. In multi-label learning, to select the discriminative features among multiple labels, several challenges should be considered: interdependent labels, different instances may share different label correlations, correlated features, and missing and .awed labels. This work is part of a project at .e Children's Hospital at Westmead (TB-CHW), Australia to explore the genomics of childhood leukaemia. In this paper, we propose a CMFS (Correlated-and Multi-label Feature Selection method), based on non-negative matrix factorization (NMF) for simultaneously performing feature selection and addressing the aforementioned challenges. Significantly, a major advantage of our research is to exploit the correlation information contained in features, labels and instances to select the relevant features among multiple labels. Furthermore, l2;1-norm regularization is incorporated in the objective function to undertake feature selection by imposing sparsity on the feature matrix rows. We employ CMFS to decompose the data and multi-label matrices into a low-dimensional space. To solve the objective function, an efficient iterative optimization algorithm is proposed with guaranteed convergence. Finally, extensive experiments are conducted on high-dimensional multi-labeled datasets. The experimental results demonstrate that our method significantly outperforms state-of-the-art multi-label feature selection methods.

Broekhuijsen, M, van den Hoven, E & Markopoulos, P 2017, 'Design Directions for Media-Supported Collocated Remembering Practices', Proceedings of the TEI’17: Eleventh International Conference on Tangible, Embedded, and Embodied Interaction, International Conference on Tangible, Embedded, and Embodied Interaction, ACM, New York, Yokohama, Japan, pp. 21-30.
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Since the widespread adoption of digital photography, people create many digital photos, often with the intention to use them for shared remembering. Practices around digital photography have changed along with advances in media sharing technologies such as smartphones, social media, and mobile connectivity. Although much research was done at the start of digital photography, commercially available tools for media-supported shared remembering still have many limitations. The objective of our research is to explore spatial and material design directions to better support the use of personal photos for collocated shared remembering. In this paper, we present seven design requirements that resulted from a redesign workshop with fifteen participants, and four design concepts (two spatial, two material) that we developed based on those requirements. By reflecting on the requirements and designs we conclude with challenges for interaction designers to support collocated remembering practices.

Buchan, J, Bano, M, Zowghi, D, MacDonell, S & Schinde, A 2017, 'Alignment of Stakeholder Expectations about User Involvement in Agile Software Development', Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering, International Conference on Evaluation and Assessment in Software Engineering, ACM, Karlskrona, Sweden, pp. 334-343.
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Context: User involvement is generally considered to contributing to user satisfaction and project success and is central to Agile software development. In theory, the expectations about user involvement, such as the PO’s, are quite demanding in this Agile way of working. But what are the expectations seen in practice, and are the expectations of user involvement aligned among the development team and users? Any misalignment could contribute to conflict and miscommunication among stakeholders that may result in ineffective user involvement. Objective: Our aim is to compare and contrast the expectations of two stakeholder groups (software development team, and software users) about user involvement in order to understand the expectations and assess their alignment. Method: We have conducted an exploratory case study of expectations about user involvement in an Agile software development. Qualitative data was collected through interviews to design a novel method for the assessing the alignment of expectations about user involvement by applying Repertory Grids (RG). Results: By aggregating the results from the interviews and RGs, varying degrees of expectation alignments were observed between the development team and user representatives. Conclusion: Alignment of expectations can be assessed in practice using the proposed RG instrument and can reveal misalignment between user roles and activities they participate in Agile software development projects. Although we used RG instrument retrospectively in this study, we posit that it could also be applied from the start of a project, or proactively as a diagnostic tool throughout a project to assess and ensure that expectations are aligned.

Cao, Z, Prasad, M & Lin, CT 2017, 'Estimation of SSVEP-based EEG complexity using inherent fuzzy entropy', IEEE International Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, Naples, Italy.
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© 2017 IEEE. This study considers the dynamic changes of complexity feature by fuzzy entropy measurement and repetitive steady-state visual evoked potential (SSVEP) stimulus. Since brain complexity reflects the ability of the brain to adapt to changing situations, we suppose such adaptation is closely related to the habituation, a form of learning in which an organism decreases or increases to respond to a stimulus after repeated presentations. By a wearable electroencephalograph (EEG) with Fpz and Oz electrodes, EEG signals were collected from 20 healthy participants in one resting and five-times 15 Hz SSVEP sessions. Moreover, EEG complexity feature was extracted by multi-scale Inherent Fuzzy Entropy (IFE) algorithm, and relative complexity (RC) was defined the difference between resting and SSVEP. Our results showed the enhanced frontal and occipital RC was accompanied with increased stimulus times. Compared with the 1st SSVEP session, the RC was significantly higher than the 5th SSVEP session at frontal and occipital areas (p < 0.05). It suggested that brain has adapted to changes in stimulus influence, and possibly connected with the habituation. In conclusion, effective evaluation of IFE has a potential EEG signature of complexity in the SSEVP-based experiment.

Castro-Wunsch, K, Ahadi, A & Peterson, A 2017, 'Evaluating Neural Networks as a Method for Identifying Students in Need of Assistance', Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, ACM SIGCSE Technical Symposium on Computer Science Education, ACM, Washington, USA, pp. 111-1116.
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Course instructors need to be able to students in need of assistance as early in the course as possible. Recent work has suggested that machine learning approaches applied to snapshots of small programming exercises may be an effective solution to this problem. However, these results have been obtained using data from a single institution, and prior work using features extracted from student code has been highly sensitive to differences in context. This work provides two contributions: first, a partial reproduction of previously published results, but in a different context, and second, an exploration of the efficacy of neural networks in solving this problem. Our findings confirm the importance of two features (the number of steps required to solve a problem and the correctness of key problems), indicate that machine learning techniques are relatively stable across contexts (both across terms in a single course and across courses), and suggest that neural network based approaches are as effective as the best Bayesian and decision tree methods. Furthermore, neural networks can be tuned to be reliably pessimistic, so they may serve a complementary role in solving the problem of identifying students who need assistance.

Chang, YC, Wang, YK, Wu, D & Lin, CT 2017, 'Generating a fuzzy rule-based brain-state-drift detector by riemann-metric-based clustering', 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, IEEE International Conference on Systems, Man, and Cybernetics, IEEE, Banff, AB, Canada, pp. 1220-1225.
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© 2017 IEEE. Brain-state drifts could significantly impact on the performance of machine-learning algorithms in brain computer interface (BCI). However, less is understood with regard to how brain transition states influence a model and how it can be represented for a system. Herein we are interested in the hidden information of brain state-drift occurring in both simulated and real-world human-system interaction. This research introduced the Riemann metric to categorize EEG data, and visualized the clustering result so that the distribution of the data can be observable. Moreover, to defeat subjective uncertainty of electroencephalography (EEG) signals, fuzzy theory was employed. In this study, we built a fuzzy rule-based brain-statedrift detector to observe the brain state and imported data from different subjects to testify the performance. The result of the detection is acceptable and shown in this paper. In the future, we expect that brain-state drifting can be connected with human behaviors via the proposed fuzzy rule-based classification. We also will develop a new structure for a fuzzy rule-based brain-statedrift detector to improve the detection accuracy.

Chantamit-O-Pas, P & Goyal, M 2017, 'Prediction of stroke using deep learning model', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Neural Information Processing, Springer, China, pp. 774-781.
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© Springer International Publishing AG 2017. Many predictive techniques have been widely applied in clinical decision making such as predicting occurrence of a disease or diagnosis, evaluating prognosis or outcome of diseases and assisting clinicians to recommend treatment of diseases. However, the conventional predictive models or techniques are still not effective enough in capturing the underlying knowledge because it is incapable of simulating the complexity on feature representation of the medical problem domains. This research reports predictive analytical techniques for stroke using deep learning model applied on heart disease dataset. The atrial fibrillation symptoms in heart patients are a major risk factor of stroke and share common variables to predict stroke. The outcomes of this research are more accurate than medical scoring systems currently in use for warning heart patients if they are likely to develop stroke.

Chen, S, Chen, S, Lin, L, Yuan, X, Liang, J & Zhang, X 2017, 'E-Map: A Visual Analytics Approach for Exploring Significant Event Evolutions in Social Media', Proceedings of IEEE Symposium on Visual Analytics Science and Technology (VAST'17), IEEE Conference on Visual Analytics Science and Technology, IEEE, Phoenix, Arizona, USA, pp. 2720-2729.
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Significant events are often discussed and spread through social media, involving many people. Reposting activities and opinions expressed in social media offer good opportunities to understand the evolution of events. However, the dynamics of reposting activities and the diversity of user comments pose challenges to understand event-related social media data. We propose E-Map, a visual analytics approach that uses map-like visualization tools to help multi-faceted analysis of social media data on a significant event and in-depth understanding of the development of the event. E-Map transforms extracted keywords, messages, and reposting behaviors into map features such as cities, towns, and rivers to build a structured and semantic space for users to explore. It also visualizes complex posting and reposting behaviors as simple trajectories and connections that can be easily followed. By supporting multi-level spatial temporal exploration, E-Map helps to reveal the patterns of event development and key players in an event, disclosing the ways they shape and affect the development of the event. Two cases analysing real-world events confirm the capacities of E-Map in facilitating the analysis of event evolution with social media data

Chen, Z, Li, J, Chen, Z & You, X 2017, 'Generic pixel level object tracker using bi-channel fully convolutional network', Neural Information Processing (LNCS), International Conference on Neural Information Processing, Springer, Guangzhou, China, pp. 666-676.
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© Springer International Publishing AG 2017. As most of the object tracking algorithms predict bounding boxes to cover the target, pixel-level tracking methods provide a better description of the target. However, it remains challenging for a tracker to precisely identify detailed foreground areas of the target. In this work, we propose a novel bi-channel fully convolutional neural network to tackle the generic pixel-level object tracking problem. By capturing and fusing both low-level and high-level temporal information, our network is able to produce pixel-level foreground mask of the target accurately. In particular, our model neither updates parameters to fit the tracked target nor requires prior knowledge about the category of the target. Experimental results show that the proposed network achieves compelling performance on challenging videos in comparison with competitive tracking algorithms.

Cheng, EJ, Prasad, M, Puthal, D, Sharma, N, Prasad, OK, Chin, PH, Lin, CT & Blumenstein, M 2017, 'Deep Learning Based Face Recognition with Sparse Representation Classification', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 665-674.
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© 2017, Springer International Publishing AG. Feature extraction is an essential step in solving real-world pattern recognition and classification problems. The accuracy of face recognition highly depends on the extracted features to represent a face. The traditional algorithms uses geometric techniques, comprising feature values including distance and angle between geometric points (eyes corners, mouth extremities, and nostrils). These features are sensitive to the elements such as illumination, variation of poses, various expressions, to mention a few. Recently, deep learning techniques have been very effective for feature extraction, and deep features have considerable tolerance for various conditions and unconstrained environment. This paper proposes a two layer deep convolutional neural network (CNN) for face feature extraction and applied sparse representation for face identification. The sparsity and selectivity of deep features can strengthen sparseness for the solution of sparse representation, which generally improves the recognition rate. The proposed method outperforms other feature extraction and classification methods in terms of recognition accuracy.

Cheng, HC, Hsieh, MH & Tomamichel, M 2017, 'Sphere-packing bound for classical-quantum channels', IEEE International Symposium on Information Theory - Proceedings, IEEE Information Theory Workshop, IEEE, Kaohsiung, Taiwan, pp. 479-483.
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© 2017 IEEE. We study lower bounds on the optimal error probability in channel coding at rates below capacity, commonly termed sphere-packing bounds. In this work, we establish a sphere-packing bound for classical-quantum channels, which significantly improves previous prefactor from the order of subexponential to polynomial. Furthermore, the gap between the obtained error exponent for constant composition codes and the best known classical random coding exponent vanishes in the order of o(log n/n), indicating our sphere-packing bound is almost exact in the high rate regime. The main technical contributions are two converse Hoeffding bounds for quantum hypothesis testing and the saddle-point properties of error exponent functions.

Cheng, HC, Hsieh, MH & Tomamichel, M 2017, 'Sphere-packing bound for symmetric classical-quantum channels', IEEE International Symposium on Information Theory - Proceedings, IEEE International Symposium on Information Theory, IEEE, Aachen, Germany, pp. 286-290.
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© 2017 IEEE. "To be considered for the 2017 IEEE Jack Keil Wolf ISIT Student Paper Award." We provide a sphere-packing lower bound for the optimal error probability in finite blocklengths when coding over a symmetric classical-quantum channel. Our result shows that the pre-factor can be significantly improved from the order of the subexponential to the polynomial, This established pre-factor is arguably optimal because it matches the best known random coding upper bound in the classical case. Our approaches rely on a sharp concentration inequality in strong large deviation theory and crucial properties of the error-exponent function.

Chiu, CY, Singh, AK, Wang, YK, King, JT & Lin, CT 2017, 'A wireless steady state visually evoked potential-based BCI eating assistive system', Proceedings of the International Joint Conference on Neural Networks, International Joint Conference on Neural Networks, IEEE, Anchorage, AK, USA, pp. 3003-3007.
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© 2017 IEEE. Brain-Computer interface (BCI) which aims at enabling users to perform tasks through their brain waves has been a feasible and worth developing solution for growing demand of healthcare. Current proposed BCI systems are often with lower applicability and do not provide much help for reducing burdens of users because of the time-consuming preparation required by adopted wet sensors and the shortage of provided interactive functions. Here, by integrating a state visually evoked potential (SSVEP)-based BCI system and a robotic eating assistive system, we propose a non-invasive wireless steady state visually evoked potential (SSVEP)-based BCI eating assistive system that enables users with physical disabilities to have meals independently. The analysis compared different methods of classification and indicated the best method. The applicability of the integrated eating assistive system was tested by an Amyotrophic Lateral Sclerosis (ALS) patient, and a questionnaire reply and some suggestion are provided. Fifteen healthy subjects engaged the experiment, and an average accuracy of 91.35%, and information transfer rate (ITR) of 20.69 bit per min are achieved. For online performance evaluation, the ALS patient gave basic affirmation and provided suggestions for further improvement. In summary, we proposed a usable SSVEP-based BCI system enabling users to have meals independently. With additional adjustment of movement design of the robotic arm and classification algorithm, the system may offer users with physical disabilities a new way to take care of themselves.

Chivukula, AS & Liu, W 2017, 'Adversarial learning games with deep learning models', Proceedings of the International Joint Conference on Neural Networks, International Joint Conference on Neural Networks, IEEE, Anchorage, AK, USA, pp. 2758-2767.
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© 2017 IEEE. Deep learning has been found to be vulnerable to changes in the data distribution. This means that inputs that have an imperceptibly and immeasurably small difference from training data correspond to a completely different class label in deep learning. Thus an existing deep learning network like a Convolutional Neural Network (CNN) is vulnerable to adversarial examples. We design an adversarial learning algorithm for supervised learning in general and CNNs in particular. Adversarial examples are generated by a game theoretic formulation on the performance of deep learning. In the game, the interaction between an intelligent adversary and deep learning model is a two-person sequential noncooperative Stackelberg game with stochastic payoff functions. The Stackelberg game is solved by the Nash equilibrium which is a pair of strategies (learner weights and genetic operations) from which there is no incentive for either learner or adversary to deviate. The algorithm performance is evaluated under different strategy spaces on MNIST handwritten digits data. We show that the Nash equilibrium leads to solutions robust to subsequent adversarial data manipulations. Results suggest that game theory and stochastic optimization algorithms can be used to study performance vulnerabilities in deep learning models.

Choi, Y & McGregor, C 2017, 'A Flexible Parental Engaged Consent Model for the Secondary Use of Their Infant's Physiological Data in the Neonatal Intensive Care Context', Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017, IEEE International Conference on Healthcare Informatics, IEEE, Park City, UT, USA, pp. 502-507.
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© 2017 IEEE. The secondary use of health data, especially the use of physiological data for research holds many opportunities for improving the current understanding of neonatal conditions. As a neonate is unable to provide their consent regarding participation in research studies, a substitute decision maker (SDM) must provide parental or legal guardian consent. However it has been well documented that there are many emotional, mental and physical challenges associated with the parental consent process in the neonatal intensive care unit (NICU). It is proposed that a flexible parental engaged consent model could help alleviate some of these issues by providing parents with the ability to choose and change their clinical engagement level preference for their infant's participation in research at their convenience at any point in time. In this paper, an extension to Service based Multidimensional Temporal Data Mining Framework (STDMn0) to allow for the functionality of flexible patient or surrogate consent is presented based on the use of a flexible consent model initially proposed by Heath [1]. This functionality is demonstrated via an example implementation for a generic retrospective research study in the NICU setting.

Chou, KP, Li, DL, Prasad, M, Lin, CT & Lin, WC 2017, 'A method to enhance the deep learning in an aerial image', 2017 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017 - Proceedings, International Symposium on Intelligent Signal Processing and Communication Systems, IEEE, Xiamen, China, pp. 724-728.
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© 2017 IEEE. In this paper, we propose a kind of pre-processing method which can be applied to the depth learning method for the characteristics of aerial image. This method combines the color and spatial information to do the quick background filtering. In addition to increase execution speed, but also to reduce the rate of false positives.

Chou, KP, Li, DL, Prasad, M, Pratama, M, Su, SY, Lu, H, Lin, CT & Lin, WC 2017, 'Robust Facial Alignment for Face Recognition', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017 International Conference on Neural Information Processing, Guangzhou, China, pp. 497-504.
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© 2017, Springer International Publishing AG. This paper proposes a robust real-time face recognition system that utilizes regression tree based method to locate the facial feature points. The proposed system finds the face region which is suitable to perform the recognition task by geometrically analyses of the facial expression of the target face image. In real-world facial recognition systems, the face is often cropped based on the face detection techniques. The misalignment is inevitably occurred due to facial pose, noise, occlusion, and so on. However misalignment affects the recognition rate due to sensitive nature of the face classifier. The performance of the proposed approach is evaluated with four benchmark databases. The experiment results show the robustness of the proposed approach with significant improvement in the facial recognition system on the various size and resolution of given face images.

Chou, KP, Prasad, M, Gupta, D, Sankar, S, Xu, TW, Sundaram, S, Lin, CT & Lin, WC 2017, 'Block-based feature extraction model for early fire detection', 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, Honolulu, HI, USA, pp. 1-6.
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© 2017 IEEE. Every year the fire disaster always causes a lot of casualties and property damage. Many researchers are involved in the study of related disaster prevention. Early warning systems and stable fire can significantly reduce the damage caused by fire. Many existing image-based early warning systems can perform well in a particular field. In this paper, we propose a general framework that can be applied in most realistic environments. The proposed system is based on a block-based feature extraction method, which analyses local information in separate regions leading to a reduction in computing data. Local features of fire block are extracted from the detailed characteristics of fire objects, which include fire color, fire source immobility, and disorder. Each local feature has high detection rate and filter out different false-positive cases. Global analysis with fire texture and non-moving properties are applied to further reduce false alarm rate. The proposed system is composed of algorithms with low computation. Through a series of experiments, it can be observed that Experimental results show that the proposed system has higher detection rate and low false alarm rate under various environment.

Chou, KP, Prasad, M, Li, DL, Bharill, N, Lin, YF, Hussain, F, Lin, CT & Lin, WC 2017, 'Automatic Multi-view Action Recognition with Robust Features', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Neural Information Processing, Springer, Guangzhou, China, pp. 554-563.
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© 2017, Springer International Publishing AG. This paper proposes view-invariant features to address multi-view action recognition for different actions performed in different views. The view-invariant features are obtained from clouds of varying temporal scale by extracting holistic features, which are modeled to explicitly take advantage of the global, spatial and temporal distribution of interest points. The proposed view-invariant features are highly discriminative and robust for recognizing actions as the view changes. This paper proposes a mechanism for real world application which can follow the actions of a person in a video based on image sequences and can separate these actions according to given training data. Using the proposed mechanism, the beginning and ending of an action sequence can be labeled automatically without the need for manual setting. It is not necessary in the proposed approach to re-train the system if there are changes in scenario, which means the trained database can be applied in a wide variety of environments. The experiment results show that the proposed approach outperforms existing methods on KTH and WEIZMANN datasets.

Chou, KP, Prasad, M, Puthal, D, Chen, PH, Vishwakarma, DK, Sundarami, S, Lin, CT & Lin, WC 2017, 'Fast Deformable Model for Pedestrian Detection with Haar-like features', 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, IEEE Symposium Series on Computational Intelligence, IEEE, Honolulu, HI, USA, pp. 1-8.
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© 2017 IEEE. This paper proposes a novel Fast Deformable Model for Pedestrian Detection (FDMPD) to detect the pedestrians efficiently and accurately in the crowded environment. Despite of multiple detection methods available, detection becomes difficult due to variety of human postures and perspectives. The proposed study is divided into two parts. First part trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. Second part uses these six response feature maps with full-body model to produce spatial deep features. The combined deep features are used as an input to SVM to judge the existence of pedestrian. As per the experiments conducted on the INRIA person dataset, the proposed FDMPD approach shows greater than 44.75 % improvement compared to other state-of-the-art methods in terms of efficiency and robustness.

Chu, C, Brownlow, J, Meng, Q, Fu, B, Culbert, B, Zhu, M, Xu, G & He, X 2017, 'Combining heterogeneous features for time series prediction', 2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC), International Conference on Behavioral, Economic, Socio-cultural Computing, IEEE, Krakow, Poland.
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Time series prediction is a challenging task in reality, and various methods have been proposed for it. However, only the historical series of values are exploited in most of existing methods. Therefore, the predictive models might be not effective in some cases, due to: (1) the historical series of values is not sufficient usually, and (2) features from heterogeneous sources such as the intrinsic features of data samples themselves, which could be very useful, are not take into consideration. To address these issues, we proposed a novel method in this paper which learns the predictive model based on the combination of dynamic features extracted from series of historical values and static features of data samples. To evaluate the performance of our proposed method, we compare it with linear regression and boosted trees, and the experimental results validate our method's superiority.

Coluccia, A, Ghenescu, M, Piatrik, T, De Cubber, G, Schumann, A, Sommer, L, Klatte, J, Schuchert, T, Beyerer, J, Farhadi, M, Amandi, R, Aker, C, Kalkan, S, Saqib, M, Sharma, N, Makkah, SDK & Blumenstein, M 2017, 'Drone-vs-Bird detection challenge at IEEE AVSS2017', Proceedings of the 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017, IEEE International Conference on Advanced Video and Signal Based Surveillance, IEEE, Lecce, Italy, pp. 1-6.
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© 2017 IEEE. Small drones are a rising threat due to their possible misuse for illegal activities, in particular smuggling and terrorism. The project SafeShore, funded by the European Commission under the Horizon 2020 program, has launched the 'drone-vs-bird detection challenge' to address one of the many technical issues arising in this context. The goal is to detect a drone appearing at some point in a video where birds may be also present: the algorithm should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds. This paper reports on the challenge proposal, evaluation, and results1.

Dasgupta, A & Gill, AQ 2017, 'Fog Computing Challenges: A Systematic Review', Australasian Conference on Information Systems, Hobart, Australia.
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Demediuk, S, Tamassia, M, Raffe, WL, Zambetta, F, Li, X & Mueller, F 2017, 'Monte Carlo tree search based algorithms for dynamic difficulty adjustment', 2017 IEEE Conference on Computational Intelligence and Games (CIG), Computational Intelligence and Games, IEEE, New York, NY, USA.
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Maintaining player immersion is a crucial step in making an enjoyable video game. One aspect of player immersion is the level of challenge the game presents to the player. To avoid a mismatch between a player's skill and the challenge of a game, which can result from traditional manual difficulty selection mechanisms (e.g. easy, medium, hard), Dynamic Dif­ficulty Adjustment (DDA) has previously been proposed as a means of automatically detecting a player's skill and adjusting the level of challenge the game presents accordingly. This work contributes to the field of DDA by proposing a novel approach to artificially intelligent agents for opponent control. Specifically, we propose four new DDA Artificially Intelligent (AI) agents: Reactive Outcome Sensitive Action Selection (Reactive OSAS), Proactive OSAS, and their "True" variants. These agents provide the player with an level of difficulty tailored to their skill in real-time by altering the action selection policy and the heuristic playout evaluation of Monte Carlo Tree Search. The DDA AI agents are tested within the FightingICE engine, which has been used in the past as an environment for AI agent competitions. The results of the experiments against other AI agents and human players show that these novel DDA AI agents can adjust the level of difficulty in real-time, by targeting a zero health difference as the outcome of the fighting game. This work also demonstrates the trade-off existing between targeting the outcome exactly (Reactive OSAS) and introducing proactive behaviour (i.e., the DDA AI agent fights even if the health difference is zero) to increase the agents believability (Proactive OSAS).

Diesner, J, Ferrari, E & Xu, G 2017, 'Welcome from the ASONAM 2017 program chairs', Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, p. xviii.
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Do, Q, Liu, W & Chen, F 2017, 'Discovering both explicit and implicit similarities for cross-domain recommendation', Advances in Knowledge Discovery and Data Mining (LNAI), Pacific Asia Conference on Advances in Knowledge Discovery and Data Mining, Springer, Jeju, South Korea, pp. 618-630.
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© 2017, Springer International Publishing AG. Recommender System has become one of the most important techniques for businesses today. Improving its performance requires a thorough understanding of latent similarities among users and items. This issue is addressable given recent abundance of datasets across domains. However, the question of how to utilize this cross-domain rich information to improve recommendation performance is still an open problem. In this paper, we propose a cross-domain recommender as the first algorithm utilizing both explicit and implicit similarities between datasets across sources for performance improvement. Validated on real-world datasets, our proposed idea outperforms the current cross-domain recommendation methods by more than 2 times. Yet, the more interesting observation is that both explicit and implicit similarities between datasets help to better suggest unknown information from cross-domain sources.

Do, QNT, Hussain, FK & Nguyen, BT 2017, 'A fuzzy approach to detect spammer groups', IEEE International Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, Naples, Italy, pp. 1-6.
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© 2017 IEEE. Cloud computing has been advancing at an impressive rate in recent years and is likely to increase more and more in the near future. New services are being developed constantly, such as cloud infrastructure, security and platform as a service, to name just a few. Due to the vast pool of available services, review websites have been created to help customers make decisions for their business. This leads to some reviewers taking advantage of these tools to promote the providers that hire them or to discredit competitors. These reviewers can either act individually or cooperate with each other. When reviewers collude to promote one product or defame another, they are called spammer groups. In this paper, we present an approach to identify spammer groups. First, a network-based method is used to identify individual spam reviewers. Then, a fuzzy k-means clustering algorithm is used to find the group that they belong to. A case study that suggests which group an incorrect review belongs to is provided to further understand the new method.

Dong, F, Lu, J, Li, K & Zhang, G 2017, 'Concept Drift Region Identification via Competence-based Discrepancy Distribution Estimation', 12th International Conference on Intelligent Systems and Knowledge and Engineering, Nanjing, China.
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ElShaweesh, O, Hussain, FK, Lu, H, Al-Hassan, M & Kharazmi, S 2017, 'Personalized Web Search Based on Ontological User Profile in Transportation Domain', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 24th International Conference on Neural Information Processing 2017, Springer, Guangzhou, China, pp. 239-248.
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© 2017, Springer International Publishing AG. Current conventional search engines deliver similar results to all users for the same query. Because of the variety of user interests and preferences, personalized search engines, based on semantics, hold the promise of providing more efficient information that better reflects users’ needs. The main feature of building a personalized web search is to represent user interests in terms of user profiles. This paper proposes a personalized search approach using an ontology-based user profile. The aim of this approach is to build user profiles based on user browsing behavior and semantic knowledge of specific domain ontology to enhance the quality of the search results. The proposed approach utilizes a re-ranked algorithm to sort the results returned by the search engine to provide a search result that best relates to the user query. This algorithm evaluates the similarity between a user query, the retrieved search results and the ontological concepts. This similarity is computed by taking into account a user’s explicit browsing behavior, semantic knowledge of concepts, and synonyms of term-based vectors extracted from the WordNet API. A set of experiments using a case study from a transport service domain validates the effectiveness of the proposed approach and demonstrates promising results.

Fan, X, Sui, Y, Liao, X & Xue, J 2017, 'Boosting the precision of virtual call integrity protection with partial pointer analysis for C++', ISSTA 2017 - Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis, ACM SIGSOFT International Symposium on Software Testing and Analysis, ACM, Santa Barbara, CA, USA, pp. 329-340.
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© 2017 Association for Computing Machinery. We present, Vip, an approach to boosting the precision of Virtual call Integrity Protection for large-scale real-world C++ programs (e.g., Chrome) by using pointer analysis for the first time. Vip introduces two new techniques: (1) a sound and scalable partial pointer analysis for discovering statically the sets of legitimate targets at virtual callsites from separately compiled C++ modules and (2) a lightweight instrumentation technique for performing (virtual call) integrity checks at runtime. Vip raises the bar against vtable hijacking attacks by providing stronger security guarantees than the CHA-based approach with comparable performance overhead. Vip is implemented in LLVM-3.8.0 and evaluated using SPEC programs and Chrome. Statically, Vip protects virtual calls more effectively than CHA by significantly reducing the sets of legitimate targets permitted at 20.3% of the virtual callsites per program, on average. Dynamically, Vip incurs an average (maximum) instrumentation overhead of 0.7% (3.3%), making it practically deployable as part of a compiler tool chain.

Fang, XS, Sheng, QZ, Wang, X & Ngu, AHH 2017, 'Value Veracity Estimation for Multi-Truth Objects via a Graph-Based Approach', Proceedings of the 26th International Conference on World Wide Web Companion, International World Wide Web Conferences Steering Committee, pp. 777-778.
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Fang, XS, Sheng, QZ, Wang, X, Barhamgi, M, Yao, L & Ngu, AHH 2017, 'SourceVote: Fusing multi-valued data via inter-source agreements', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 164-172.
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© Springer International Publishing AG 2017. Data fusion is a fundamental research problem of identifying true values of data items of interest from conflicting multi-sourced data. Although considerable research efforts have been conducted on this topic, existing approaches generally assume every data item has exactly one true value, which fails to reflect the real world where data items with multiple true values widely exist. In this paper, we propose a novel approach, SourceVote, to estimate value veracity for multi-valued data items. SourceVote models the endorsement relations among sources by quantifying their two-sided inter-source agreements. In particular, two graphs are constructed to model inter-source relations. Then two aspects of source reliability are derived from these graphs and are used for estimating value veracity and initializing existing data fusion methods. Empirical studies on two large real-world datasets demonstrate the effectiveness of our approach.

Ferguson, SJ 2017, 'Creative Coding for the Raspberry Pi using the HappyBrackets Platform', ACM SIGCHI Conference on Creativity and Cognition, ACM, Singapore, Singapore.
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This workshop will introduce creative coding audio for the Raspberry Pi, using the 'beads' platform for audio programming, and the 'HappyBrackets' platform for inter-device communication and sensor data acquisition. We will demonstrate methods to allow each self-contained battery-powered device to acquire sensor data about its surroundings and the way it is being interacted with, as well as methods for designing systems where groups of these devices wirelessly communicate their state, allowing new interaction possibilities and approaches.

Ferguson, SJ, Bown, O, Rowe, A, Birtles, L & Bennewith, C 2017, 'Networked Pixels: Strategies for Building Visual and Auditory Images with Distributed Independent Devices', Proceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition, ACM Creativity and Cognition, ACM, Singapore, pp. 299-299.
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This paper describes the development of the hardware and software for Bloom, a light installation installed at Kew Gardens, London in December of 2016. The system is made up of a set of nearly 1000 distributed pixel devices each with LEDs, GPS sensor, and sound hardware, networked together with WiFi to form a display system. Media design for this system required consideration of the distributed nature of the devices. We outline the software and hardware designed for this system, and describe two approaches to the software and media design, one whereby we employ the distributed devices themselves for computation purposes (the approach we ultimately selected), and another whereby the devices are controlled from a central server that is performing most of the computation necessary. We then review these approaches and outline possibilities for future research.

Ferguson, SJ, Rowe, A, Bown, O, Birtles, L & Bennewith, C 2017, 'Sound Design for a System of 1000 Distributed Independent Audio-Visual Devices', Proceedings of the international conference on new interfaces for musical expression, Copenhagen, Denmark, 2017, New Interfaces for Musical Expression 2017, NIME, Copenhagen, Denmark, pp. 245-250.
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This paper describes the sound design for Bloom, a light and sound installation made up of 1000 distributed independent audio-visual pixel devices, each with RGB LEDs, Wifi, Accelerometer, GPS sensor, and sound hardware. These types of systems have been explored previously, but only a few systems have exceeded 30-50 devices and very few have included sound capability, and therefore the sound design possibilities for large systems of distributed audio devices are not yet well understood. In this article we describe the hardware and software implementation of sound synthesis for this system, and the implications for design of media for this context.

Fernando, KES, Mcgregor, C & James, AG 2017, 'CRISP-TDM0for standardized knowledge discovery from physiological data streams: Retinopathy of prematurity and blood oxygen saturation case study', Proceedings of the 2017 IEEE Life Sciences Conference, LSC 2017, IEEE Life Sciences Conference, IEEE, Sydney, NSW, Australia, pp. 226-229.
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© 2017 IEEE. The CRoss Industry Standard Process for Temporal Data Mining (CRISP-TDM) that supports physiological stream temporal data mining and CRISP-DM0 that supports null hypothesis driven confirmatory data mining in combination was proposed by prior research. This combined CRISP-TDM0 is utilised as the standardised approach to managing, reporting and performing retrospective clinical research and is designed to solve the limitation in knowledge discovery amongst physiological data streams [1]. The temporal abstractions (TA) of high fidelity blood oxygenation saturation (SpO2) levels of nine premature neonates are analysed using data collected by the Artemis Platform that complies with the Big Data concept [2] and correlated with Retinopathy of Prematurity (ROP) data. The hourly SpO2, TA pattern visualisation manifested three clusters and this is further supported by mathematical review of time percentage spent in target, below and over oxygenation. Clustering based on ROP stage and gestational age identified probable association within these three clusters. However known risk factors showed no association with ROP.

Ferrari, A, Spoletini, P, Donati, B, Zowghi, D & Gnesi, S 2017, 'Interview Review: Detecting Latent Ambiguities to Improve the Requirements Elicitation Process', Proceedings of the 2017 IEEE 25th International Requirements Engineering Conference, IEEE International Requirements Engineering Conference, IEEE Computer Society Publications, Lisbon, pp. 400-405.
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The review of software process artifacts, which include requirements as well as source code [1], is an effective practice to improve the quality of products [2]–[3][4][5]. In particular, the benefits of requirements reviews have been highlighted by several studies, especially for what concerns the identification of defects in requirements specifications [3], [6], [7]. Nevertheless, despite the usage of requirements reviews dates back at least 40 years [6], challenges exist for their widespread application in the software industry [8], [9]. Among the challenges, Salger highlights that “Software requirements are based on flawed ‘upstream’ requirements and reviews on requirements specifications are thus in vain” [8]. This observation poses an emphasis on the need to ameliorate early requirements elicitation activities, especially to improve the completeness of the specifications, a quality attribute that is recognised to be hard to assess by means of reviews [10].

Gao, F, Musial, K & Gabrys, B 2017, 'A community bridge boosting social network link prediction model', Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, pp. 683-689.
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© 2017 Association for Computing Machinery. Link prediction in social networks is a very challenging research problem. The majority of existing approaches are based on the assumption that a given network evolves following a single phenomenon, e.g.”rich get richer” or”friend of my friend is my friend”. However, dynamics of network dynamic changes over time and different parts of the network evolve in different manner. Because of that, we hypothesise that the prediction accuracy can be improved by providing different treatment to different nodes and links. Building on that assumption, we propose a Community Bridge Boosting Prediction Model (CBBPM) that treats certain bridge nodes differently depending on their structural position. For such bridge nodes their similarity score obtained using traditional link-based prediction methods is boosted. By doing so the importance of these nodes is increased and at the same time ensuring that the CBBPM can be used with any existing link prediction method. Our experimental results show that such bridge node similarity boosting mechanism can improve the accuracy of traditional link prediction methods.

Ghamry, AM, Alkalbani, AM, Tran, V, Tsai, YC, Hoang, ML & Hussain, FK 2017, 'Towards a public cloud services registry', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 18th International Conference Web Information Systems Engineering, Puschino, Russia, pp. 290-295.
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© 2017, Springer International Publishing AG. Cloud services registry is a cloud services datadase which contains thousands of records of cloud consumers’ reviews and cloud services, such as Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). The data set is harvested from a web portal called www.serchen.com. Each record holds detail information about the service such as service name, service description, categories, key features, service provider link and review list. Each review contains reviewer name, review date and review content. This work is an extension of our previous work Blue Pages data set [6]. The data set is valuable for future research in cloud service identification, discovery, comparison and selection.

Gill, AQ, Behbood, V, Ramadan-Jradi, R & Beydoun, G 2017, 'IoT architectural concerns: a systematic review', Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing, International Conference on Internet of things and Cloud Computing, ACM, Cambridge, United Kingdom, pp. 1-9.
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There is increasing interest in studying and applying Internet of Things (IoT) within the overall context of digital-physical ecosystems. Most recently, much has been published on the benefits and applications of IoT. The main question is: what are the key IoT architectural concerns, which must be addressed to effectively develop and implement an IoT architecture? There is a need to systematically review and synthesize the literature on IoT architectural challenges or concerns. Using the SLR approach and applying customised search criteria derived from the research question, 22 relevant studies were identified and reviewed in this paper. The data from these papers were extracted to identify the IoT architectural challenges and relevant solutions. These results were organised into to 9 major challenge and 7 solution categories. The results of this research will serve as a resource for practitioners and researchers for the effective adoption, and setting future research priorities and directions in this emerging area of IoT architecture.

Gupta, D, Borah, P & Prasad, M 2017, 'A fuzzy based Lagrangian twin parametric-margin support vector machine (FLTPMSVM)', 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, IEEE Symposium Series on Computational Intelligence, IEEE, Honolulu, HI, USA, pp. 1-7.
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© 2017 IEEE. In the spirit of twin parametric-margin support vector machine (TPMSVM) and support vector machine based on fuzzy membership values (FSVM), a new method termed as fuzzy based Lagrangian twin parametric-margin support vector machine (FLTPMSVM) is proposed in this paper to reduce the effect of the outliers. In FLTPMSVM, we assign the weights to each data samples on the basis of fuzzy membership values to reduce the effect of outliers. Also, we consider the square of the 2-norm of slack variables to make the objective function strongly convex and find the solution of the proposed FLTPMSVM by solving simple linearly convergent iterative schemes instead of solving a pair of quadratic programming problems as in case of SVM, TWSVM, FTSVM and TPMSVM. No need of external toolbox is required for FLTPMSVM. The numerical experiments are performed on artificial as well as well known real-world datasets which show that our proposed FLTPMSVM is having better generalization performance and less training cost in comparison to support vector machine, twin support vector machine, fuzzy twin support vector machine and twin parametric-margin support vector machine.

Han, J, Wan, S, Lu, J & Zhang, G 2017, 'Tri-level multi-follower decision-making in a partial-cooperative situation', Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017, International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Nanjing, China, pp. 1-6.
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© 2017 IEEE. Tri-level decision-making addresses compromises between interactive decision entities that are distributed throughout a three-level hierarchy. These decision entities are respectively termed as the top-level leader, the middle-level follower and the bottom-level follower. This paper considers a tri-level multi-follower (TLMF) decision problem where both cooperative and uncooperative relationships coexist between multiple followers at the same level. In this situation, followers share some decision variables with their counterparts and also control individual decision variables to achieve their respective goals; this is also known as partial-cooperative TLMF decision-making. To support this category of decision-making, this paper first presents a linear model to characterize the partial-cooperative TLMF decision-making process. It then develops a vertex enumeration algorithm to obtain a solution to the resulting model. Lastly, we apply the proposed TLMF decision techniques to handle an inventory management problem in applications.

Haque, MN, Mathieson, L & Moscato, P 2017, 'A Memetic Algorithm for community detection by maximising the Connected Cohesion', Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, IEEE Symposium Series on Computational Intelligence, IEEE, Honolulu, Hawaii, USA, pp. 1-8.
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Community detection is an exciting field of research which has attracted the interest of many researchers during the last decade. While many algorithms and heuristics have been proposed to scale existing approaches a relatively smaller number of studies have looked at exploring different measures of quality of the detected community. Recently, a new score called ‘cohesion’ was introduced in the computing literature. The cohesion score is based comparing the number of triangles in a given group of vertices to the number of triangles only partly in that group. In this contribution, we propose a memetic algorithm that aims to find a subset of the vertices of an undirected graph that maximizes the cohesion score. The associated combinatorial optimisation problem is known to be NP-Hard and we also prove it to be W[1]-hard when parameterized by the score. We used a Local Search individual improvement heuristic to expand the putative solution. Then we removed all vertices from the group which are not a part of any triangle and expand the neighbourhood by adding triangles which have at least two nodes already in the group. Finally we compute the maximum connected component of this group. The highest quality solutions of the memetic algorithm have been obtained for four real-world network scenarios and we compare our results with ground-truth information about the graphs. We also compare the results to those obtained with eight other community detection algorithms via interrater agreement measures. Our results give a new lower bound on the parameterized complexity of this problem and give novel insights on its poten

Henderson, H & Leong, TW 2017, 'Lessons Learned: A Study on User Difficulties with Parking Meters', Proceedings of the 29th Australian Conference on Computer-Human Interaction, ACM, pp. 533-537.
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Herron, D, Moncur, W & van den Hoven, E 2017, 'Digital Decoupling and Disentangling: Towards Design for Romantic Break Up', Proceedings of the 2017 Conference on Designing Interactive Systems, Designing Interactive Systems, ACM International Conference Proceeding Series, Edinburgh, United Kingdom.
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Romantic relationships are often facilitated through digital technologies, such as social networking sites and communication services. They are also facilitated through "digital possessions", such as messages sent to mobile devices and photos shared through social media. When individuals break up, digitally disconnecting can be facilitated by using those digital technologies and managing or curating these digital possessions. This research explores the break up stories of 13 individuals aged between 18 and 52. The aim of this work is to inform the design of systems focused on supporting individuals to decouple and disentangle digitally in the wake of a break up. Four areas of interest emerged from the data: communication, using digital possessions, managing digital possessions, and experiences of technology. Opportunities for design were identified in decoupling and disentangling, and designing around guilt.

Hobbs, B, Oberst, S, Niven, R & Ord, A 2017, 'Mineralising systems as nonlinear dynamic systems', Gold2017@Rotorua, Rotorua, New Zealand, pp. 49-51.
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The purpose of this paper is to emphasise that hydrothermal mineralising systems are giant chemical reactors that operate as nonlinear dynamical systems held far from equilibrium by the infux of energy and mass. Te nonlinear behaviour leaves its mark as apparently stochastic distributions of alteration assemblages, mineralisation and structures are deterministic and contain all the information required to understand why the ore body is small or large and whether it is well or poorly endowed.

Hobbs, B, Ord, A, Oberst, S & Niven, RK 2017, 'Nonlinear episodic and chaotic behaviour of orogenic gold systems', 18th Annual Conference of the International Association for Mathematical Geosciences, Freemantle, Australia, pp. 1-1.

Hu, L, Cao, L, Wang, S, Xu, G, Cao, J & Gu, Z 2017, 'Diversifying personalized recommendation with user-session context', IJCAI International Joint Conference on Artificial Intelligence, International Joint Conferences on Artifical Intelligence, International Joint Conferences on Artificial Intelligence Organization, Melbourne, Australia, pp. 1858-1864.
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Recommender systems (RS) have become an integral part of our daily life. However, most current RS often repeatedly recommend items to users with similar profiles. We argue that recommendation should be diversified by leveraging session contexts with personalized user profiles. For this, current session-based RS (SBRS) often assume a rigidly ordered sequence over data which does not fit in many real-world cases. Moreover, personalization is often omitted in current SBRS. Accordingly, a personalized SBRS over relaxedly ordered user-session contexts is more pragmatic. In doing so, deep-structured models tend to be too complex to serve for online SBRS owing to the large number of users and items. Therefore, we design an efficient SBRS with shallow wide-in-wide-out networks, inspired by the successful experience in modern language modelings. The experiments on a real-world e-commerce dataset show the superiority of our model over the state-of-the-art methods.

Hua, J, Huang, M & Wang, G 2017, 'Performance Comparisons between Force-directed Algorithms on Structured Data Analysis', Proceedings of ICVRV 2017, International Conference on Virtual Reality and Visualization, IEEE, Zhengzhou, China.
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Force-directed algorithms have been widely applied in practical data visualization area due to their capabilities of producing good layouts, which follows metrics for graph drawing aesthetics, yet evaluation the performance of relevant algorithms is still a challenge, since layout quality is largely relying on personal judgement and/or methods’ input parameters, and most aesthetics criteria conflict with each other. This study evaluates the performance measurements of four algorithms in terms of seven commonly applied aesthetic criteria and demonstrates the experimental framework.

Huang, C, Yao, L, Wang, X, Benatallah, B & Sheng, QZ 2017, 'Expert as a Service: Software Expert Recommendation via Knowledge Domain Embeddings in Stack Overflow', Proceedings - 2017 IEEE 24th International Conference on Web Services, ICWS 2017, pp. 317-324.
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© 2017 IEEE. Question answering (Q&A) communities have gained momentum recently as an effective means of knowledge sharing over the crowds, where many users are experts in the real-world and can make quality contributions in certain domains or technologies. Although the massive user-generated Q&A data present a valuable source of human knowledge, a related challenging issue is how to find those expert users effectively. In this paper, we propose a framework for finding such experts in a collaborative network. Accredited with recent works on distributed word representations, we are able to summarize text chunks from the semantics perspective and infer knowledge domains by clustering pre-trained word vectors. In particular, we exploit a graph-based clustering method for knowledge domain extraction and discern the shared latent factors using matrix factorization techniques. The proposed clustering method features requiring no post-processing of clustering indicators and the matrix factorization method is combined with the semantic similarity of the historical answers to conduct expertise ranking of users given a query. We use Stack Overflow, a website with a large group of users and a large number of posts on topics related to computer programming, to evaluate the proposed approach and conduct extensively experiments to show the effectiveness of our approach.

Huang, Z, Lin, X, Zhang, W & Zhang, Y 2017, 'Efficient matrix sketching over distributed data', Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, ACM, Chicago, Illinois, pp. 347-359.
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© 2017 ACM. A sketch or synopsis of a large dataset captures vital properties of the original data while typically occupying much less space. In this paper, we consider the problem of computing a sketch of a massive data matrix A ϵ ℝn×d, which is distributed across a large number of s servers. Our goal is to output a matrix B ϵ ℝℓ × dwhich is significantly smaller than but still approximates A well in terms of covariance error, i.e., ||ATA - BTB||2. Here, for a matrix A, ||A||2 is the spectral norm of A, which is defined as the largest singular value of A. Following previous works, we call B a covariance sketch of A. We are mainly focused on minimizing the communication cost, which is arguably the most valuable resource in distributed computations. We show a gap between deterministic and randomized communication complexity for computing a covariance sketch. More specifically, we first prove a tight deterministic lower bound, then show how to bypass this lower bound using randomization. In Principle Component Analysis (PCA), the goal is to find a low-dimensional subspace that captures as much of the variance of a dataset as possible. Based on a well-known connection between covariance sketch and PCA, we give a new algorithm for distributed PCA with improved communication cost. Moreover, in our algorithms, each server only needs to make one pass over the data with limited working space.

Hung, YC, Wang, YK, Prasad, M & Lin, CT 2017, 'Brain dynamic states analysis based on 3D convolutional neural network', Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, IEEE International Conference on Systems, Man, and Cybernetics, IEEE, Banff, AB, Canada, pp. 222-227.
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© 2017 IEEE. Drowsiness driving is one major factor of traffic accident. Monitoring the changes of brain signals provides an effective and direct way for drowsiness detection. One 3D convolutional neural network (3D CNN)-based forecasting system has been proposed to monitor electroencephalography (EEG) signals and predict fatigue level during driving. The limited weight sharing and channel-wise convolution were both applied to extract the significant phenomenon in various frequency bands of brain signals and the spatial information of EEG channel location, respectively. The proposed 3D CNN with limited weight sharing and channel-wise convolution has been demonstrated to predict reaction time (RT) of driving with low root mean square error (RMSE) through the brain dynamics. This proposed approach outperforms with the state-of-the-art algorithms, such as traditional CNN, Neural Network (NN), and support vector regression (SVR). Compared with traditional CNN and Artificial Neural Network, the RMSE of 3D CNN-based RT prediction has been improved 9.5% (RMSE from 0.6322 to 0.5720) and 8% (RMSE from 0.6217 to 0.5720), respectively. We envision that this study might open a new branch between deep learning application in neuro-cognitive analysis and real world application.

Hussain, W, Farookh Hussain & Omar Hussain 2016, 'Risk Management Framework to Avoid SLA Violation in Cloud from a Provider’s Perspective', Advances on P2P, Parallel, Grid, Cloud and Internet Computing, Advances on P2P, Parallel, Grid, Cloud and Internet Computing, Springer International Publishing, Asan, Korea, pp. 233-241.
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Managing risk is an important issue for a service provider to avoid SLA violation in any business. The elastic nature of cloud allows consumers to use a number of resources depending on their business needs. Therefore, it is crucial for service providers; particularly SMEs to first form viable SLAs and then manage them. When a provider and a consumer execute an agreed SLA, the next step is monitoring and, if a violation is predicted, appropriate action should be taken to manage that risk. In this paper we propose a Risk Management Framework to avoid SLA violation (RMF-SLA) that assists cloud service providers to manage the risk of service violation. Our framework uses a Fuzzy Inference System (FIS) and considers inputs such as the reliability of a consumer; the attitude towards risk of the provider; and the predicted trajectory of consumer behavior to calculate the amount of risk and the appropriate action to manage it. The framework will help small-to-medium sized service providers manage the risk of service violation in an optimal way.

Inibhunu, C, Schauer, A, Redwood, O, Clifford, P & Mcgregor, C 2017, 'Predicting hospital admissions and emergency room visits using remote home monitoring data', 2017 IEEE Life Sciences Conference, LSC 2017, IEEE Life Sciences Conference, pp. 282-285.
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© 2017 IEEE. The costs of lengthy hospital admissions (HA) and multiple emergency room visits (ER Visits) from patients with conditions such as heart failure (HF) and chronic obstructive pulmonary disease (COPD) can place a significant burden on healthcare systems. Understanding the various factors contributing to hospitalization and ER visits could aid cost-effective management in the delivery of services leading to potential improvement on quality of life for patients. This can be facilitated by collecting data using remoting patient monitoring (RPM) services and using analytics to discover important factors about patients. This paper presents our research that utilizes predictive modeling to determine key factors that are significant determinant to hospitalization and multiple ER Visits. The results shows that gender, past medical history and vital status are key factors to hospital admissions and ER Visits. Additionally, when a factor to indicate the period before, during and after an ER Visits was included, the resulting model shows a very high likelihood ratio and improved p values on all vital status. Our results shows that more research is needed to fully understand the temporal patterns among variables during hospitalization or ER visit.

Inibhunu, C, Schauer, A, Redwood, O, Clifford, P & Mcgregor, C 2017, 'The impact of gender, medical history and vital status on emergency visits and hospital admissions: A remote patient monitoring case study', 2017 IEEE Life Sciences Conference, LSC 2017, IEEE Life Sciences Conference, IEEE, Sydney, NSW, Australia, pp. 278-281.
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© 2017 IEEE. Remote Program Monitoring (RPM) is considered to have potential to improve the quality of life on patients diagnosed with cardiac conditions such as heart failure (HF) and chronic obstructive pulmonary disease (COPD). Remote collection and analysis of patients data could aid in effective decision making on necessary care needed by patients monitored. This could lead to reduction on healthcare costs as well as improved outcomes for the patients. As a component of our predictive analytics research, this paper presents results of remote patient monitoring study of patients from the Cardiac Clinic of Southlake Regional Health Centre who were referred to WeCare for home based monitoring. Results indicate statistically significant evidence on impact of gender, medical history and vital status as risk factors for subsequent hospitalization and multiple emergency room visits.

Ivanyos, G, Qiao, Y & Venkata Subrahmanyam, K 2017, 'Constructive non-commutative rank computation is in deterministic polynomial time', Leibniz International Proceedings in Informatics, LIPIcs, Innovations in Theoretical Computer Science Conference, Schloss Dagstuhl, Berkeley, CA, USA, pp. 1-18.
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Let B be a linear space of matrices over a field F spanned by n × n matrices B1, . . . ,Bm. The non-commutative rank of B is the minimum r € N such that there exists U < Fnsatisfying dim(U) - dim(B(U)) n - r, where B(U) := span(Ui2€[m]Bi(U)). Computing the non-commutative rank generalizes some well-known problems including the bipartite graph maximum matching problem and the linear matroid intersection problem. In this paper we give a deterministic polynomial-Time algorithm to compute the non-commutative rank over any field F. Prior to our work, such an algorithm was only known over the rational number field Q, a result due to Garg et al, [20]. Our algorithm is constructive and produces a witness certifying the non-commutative rank, a feature that is missing in the algorithm from [20]. Our result is built on techniques which we developed in a previous paper [24], with a new reduction procedure that helps to keep the blow-up parameter small. There are two ways to realize this reduction. The first involves constructivizing a key result of Derksen and Makam [12] which they developed in order to prove that the null cone of matrix semi-invariants is cut out by generators whose degree is polynomial in the size of the matrices involved. We also give a second, simpler method to achieve this. This gives another proof of the polynomial upper bound on the degree of the generators cutting out the null cone of matrix semi-invariants. Both the invariant-Theoretic result and the algorithmic result rely crucially on the regularity lemma proved in [24]. In this paper we improve on the constructive version of the regularity lemma from [24] by removing a technical coprime condition that was assumed there.

Jiahui, G, Bin, Y, Xu, G, Zhenglu, Y & Jin-Mao, W 2017, 'An Enhanced Convolutional Neural Network Model for Answer Selection', Proceeding WWW '17 Companion Proceedings of the 26th International Conference on World Wide Web Companion, International Conference on World Wide Web, ACM, Perth, Western Australia, pp. 789-790.
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Answer selection is an important task in question answering (QA) from the Web. To address the intrinsic difficulty in encoding sentences with semantic meanings, we introduce a general framework, i.e., Lexical Semantic Feature based Skip Convolution Neural Network (LSF-SCNN), with several op- timization strategies. The intuitive idea is that the granular representations with more semantic features of sentences are deliberately designed and estimated to capture the similar- ity between question-answer pairwise sentences. The experi- mental results demonstrate the effectiveness of the proposed strategies and our model outperforms the state-of-the-art ones by up to 3.5% on the metrics of MAP and MRR.

Jiang, S, Zhang, C, Zhang, Y, Zhang, W, Lin, X, Cheema, MA & Wang, X 2017, 'DSKQ: A system for efficient processing of diversified spatial-keyword query', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Australasian Database Conference, Springer, Brisbane, QLD, Australia, pp. 280-284.
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© 2017, Springer International Publishing AG. With the rapid development of mobile portable devices and location positioning technologies, massive amount of geo-textual data are being generated by a huge number of web users on various social platforms, such as Facebook and Twitter. Meanwhile, spatial-textual objects that represent Point-of-interests (POIs, e.g., shops, cinema, hotel or restaurant) are increasing pervasively. Consequently, how to retrieve a set of objects that best matches the user’s submitted spatial keyword query (SKQ) has been intensively studied by the research communities and commercial organisations. Existing works only focus on returning the nearest matching objects, although we observe that many real-life applications are now using diversification to enhance the quality of the query results. Thus, existing methods fail to solve the problem of diversified SKQ efficiently. In this demonstration, we introduce DSKQ, a diversified in-memory spatial-keyword query system, which considers both the textual relevance and the spatial diversity of the results processing on road network. We present a prototype of DSKQ which provides users with an application-based interface to explore the diversified spatial-keyword query system.

Kenning, G 2017, 'Evaluating Art and Design projects for Dementia'.

Kovacs, R, Seufert, A, Wall, L, Chen, HT, Meinel, F, Müller, W, You, S, Brehm, M, Striebel, J, Kommana, Y, Popiak, A, Bläsius, T & Baudisch, P 2017, 'TrussFab: Fabricating sturdy large-scale structureson desktop 3D printers', Conference on Human Factors in Computing Systems - Proceedings, pp. 2606-2616.
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© 2017 ACM. We present TrussFab, an integrated end-to-end system that allows users to fabricate large scale structures that are sturdy enough to carry human weight. TrussFab achieves the large scale by complementing 3D print with plastic bottles. It does not use these bottles as "bricks" though, but as beams that form structurally sound node-link structures, also known as trusses, allowing it to handle the forces resulting from scale and load. TrussFab embodies the required engineering knowledge, allowing non-engineers to design such structures and to validate their design using integrated structural analysis. We have used TrussFab to design and fabricate tables and chairs, a 2.5 m long bridge strong enough to carry a human, a functional boat that seats two, and a 5 m diameter dome.

Kovacs, R, Seufert, A, Wall, LW, Chen, HT, Meinel, F, Müller, W, You, S, Kommana, Y & Baudisch, P 2017, 'Demonstrating trussFab: Fabricating sturdy large-scale structures on desktop 3D printers', Conference on Human Factors in Computing Systems - Proceedings, Conference on Human Factors in Computing Systems, Denver, Colarado, pp. 445-448.
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Copyright © 2017 by the Association for Computing Machinery, Inc. (ACM). We demonstrate TrussFab, an end-to-end system that allows users to fabricate large-scale structures that are sturdy enough to carry human weight. TrussFab achieves the large scale by complementing 3D print with plastic bottles. It does not use these bottles as "bricks" but as beams that form structurally sound structures, also known as trusses, allowing it to handle the forces resulting from scale and load.

Kovacs, R, Wall, L, Seufert, A, Chen, HT, Müller, W, Meinel, F, Kommana, Y, Bläsius, T, Schneider, O, Roumen, T & Baudisch, P 2017, 'Demonstrating TrussFab's editor: Designing sturdy large-scale structures', UIST 2017 Adjunct - Adjunct Publication of the 30th Annual ACM Symposium on User Interface Software and Technology, pp. 43-45.
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Copyright © 2017 is held by the owner/author(s). We demonstrate TrussFab's editor for creating large-scale structures that are sturdy enough to carry human weight. TrussFab achieves the large scale by using plastic bottles as beams that form structurally sound node-link structures, also known as trusses, allowing it to handle the forces resulting from scale and load. During this hands-on demo at UIST, attendees will use the TrussFab software to design their own structures, validate their design using integrated structural analysis, and export their designs for 3D printing.

Lai, JCS, Oberst, S & Evans, TA 2017, 'Termites thrive by using vibrations', International Congress on Sound and Vibration 2017, Curran Associates, Inc., London, UK, pp. 1-6.
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Termites are notoriously cryptic: infestations in houses are often discovered when an apparently intact timber object collapses, the archetypal 'falling through the floor boards'. This behaviour is adaptive because remaining undiscovered is the termite's primary defence against predators. However, being cryptic severely limits the ability of termites to explore their environment and assess potential food sources. Despite being blind and hidden, termites can assess a piece of wood swiftly after contacting only a small part of it (sometimes as little as a few square millimetres). Although termite soldiers have been known to produce vibratory alarm signals to warn conspecific workers, it is not until our sustained research over the last 10 years that termites have been shown to use vibrations as a principal tool for communications, making foraging decisions and detection. In this paper, some of our recent discoveries on the use of vibrations by termites will be discussed. These include the discovery of (a) two drywood termite species, Cryptotermes (Cr.) domesticus and Cr. secundus, using vibration signals produced as a by-product of their feeding to assess food size; (b) the ability of Cr. secundus and Coptotermes (Co.) acinaciformis to discriminate material properties based on vibration signals; and (c) the amazing ability of the subterranean termites Co. acinaciformis to distinguish unloaded wood from loaded wood. Food size is just one factor termites consider when foraging; competitors and predators are also important factors. Our results show that Cr. secundus worker termites are able to discriminate their own species from the subterranean species, Co. acinaciformis, by eavesdropping on their competitors, while Co. acinaciformis can detect and avoid one of their main predators, ants of the species Iridomyrmex pupuereus, using only vibrations caused by ants walking. (PDF) Termites thrive by vibrations. Available from: https://www.researchgate.net/publication/31...

Lawrence, C, Leong, TW, Gay, V, Woods, A & Wadley, G 2017, '#thismymob: Digital land rights and reconnecting indigenous communities', ACM International Conference Proceeding Series, OZICHI Workshop, Brisbane, pp. 646-647.
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© 2017 Association for Computing Machinery. All rights reserved. We propose to hold a one-day workshop on developing projects relating to #thismymob: Digital Land Rights and Reconnecting Indigenous Communities at OzCHI 2017 Brisbane. See http://www.arc.gov.au/newsmedia/ news/thismymob-digital-land-rights-andreconnecting-indigenous-communities.

Leong, T & Feng, VX 2017, 'Digital meaning: exploring and understanding the motivations and experiences of virtual volunteers', Proceedings of the 29th Australian Conference on Computer-Human Interaction, Australian Conference on Computer-Human Interaction, ACM, Brisbane, pp. 366-370.
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Li, G, Zhang, Z & Wang, G 2017, 'Emotion recognition based on low-cost in-ear EEG', 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS), IEEE.
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Li, Y & Qiao, Y 2017, 'Linear algebraic analogues of the graph isomorphism problem and the erds-rényi model', Annual Symposium on Foundations of Computer Science - Proceedings, IEEE Annual Symposium on Foundations of Computer Science, IEEE, Berkeley, CA, USA, pp. 463-474.
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© 2017 IEEE. A classical difficult isomorphism testing problem is to test isomorphism of p-groups of class 2 and exponent p in time polynomial in the group order. It is known that this problem can be reduced to solving the alternating matrix space isometry problem over a finite field in time polynomial in the underlying vector space size. We propose a venue of attack for the latter problem by viewing it as a linear algebraic analogue of the graph isomorphism problem. This viewpointleads us to explore the possibility of transferring techniques for graph isomorphism to this long-believed bottleneck case of group isomorphism.In 1970s, Babai, Erds, and Selkow presented the first average-case efficient graph isomorphism testing algorithm (SIAM J Computing, 1980). Inspired by that algorithm, we devise an average-case efficient algorithm for the alternating matrix space isometry problem over a key range of parameters, in a random model of alternating matrix spaces in vein of the Erdos-R4;enyi model of random graphs. For this, we develop a linear algebraic analogue of the classical individualisation technique, a technique belonging to a set of combinatorial techniques that has been critical for the progress on the worst-case time complexity for graph isomorphism, but was missing in the group isomorphism context. This algorithm also enables us to improve Higmans 57-year-old lower bound on the number of p-groups (Proc. of the LMS, 1960). We finally show that Luks dynamic programming technique for graph isomorphism (STOC 1999) can be adapted to slightly improve the worst-case time complexity of the alternating matrix space isometry problem in a certain range of parameters.Most notable progress on the worst-case time complexity of graph isomorphism, including Babais recent breakthrough (STOC 2016) and Babai and Luks previous record (STOC 1983), has relied on both group theoretic and combinatorial techniques. By developing a linear algebraic analogue of the individualisation ...

Liu, A, Song, Y, Zhang, G & Lu, J 2017, 'Regional concept drift detection and density synchronized drift adaptation', IJCAI International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence Orgqanization, Melbourne, Australia, pp. 2280-2286.
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In data stream mining, the emergence of new patterns or a pattern ceasing to exist is called concept drift. Concept drift makes the learning process complicated because of the inconsistency between existing data and upcoming data. Since concept drift was first proposed, numerous articles have been published to address this issue in terms of distribution analysis. However, most distributionbased drift detection methods assume that a drift happens at an exact time point, and the data arrived before that time point is considered not important. Thus, if a drift only occurs in a small region of the entire feature space, the other non-drifted regions may also be suspended, thereby reducing the learning efficiency of models. To retrieve nondrifted information from suspended historical data, we propose a local drift degree (LDD) measurement that can continuously monitor regional density changes. Instead of suspending all historical data after a drift, we synchronize the regional density discrepancies according to LDD. Experimental evaluations on three benchmark data sets show that our concept drift adaptation algorithm improves accuracy compared to other methods.

Liu, A, Zhang, G & Lu, J 2017, 'Fuzzy time windowing for gradual concept drift adaptation', IEEE International Conference on Fuzzy Systems, International Conference on Fuzzy Systems, IEEE, Naples, Italy, pp. 1-6.
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© 2017 IEEE. The aim of machine learning is to find hidden insights into historical data, and then apply them to forecast the future data or trends. Machine learning algorithms optimize learning models for lowest error rate based on the assumption that the historical data and the data to be predicted conform to the same knowledge pattern (data distribution). However, if the historical data is not enough, or the knowledge pattern keeps changing (data uncertainty), this assumption will become invalid. In data stream mining, this phenomenon of knowledge pattern changing is called concept drift. To address this issue, we propose a novel fuzzy windowing concept drift adaptation (FW-DA) method. Compared to conventional windowing-based drift adaptation algorithms, FW-DA achieves higher accuracy by allowing the sliding windows to keep an overlapping period so that the data instances belonging to different concepts can be determined more precisely. In addition, FW-DA statistically guarantees that the upcoming data conforms to the inferred knowledge pattern with a certain confidence level. To evaluate FW-DA, four experiments were conducted using both synthetic and real-world data sets. The experiment results show that FW-DA outperforms the other windowing-based methods including state-of-the-art drift adaptation methods.

Liu, B, Chen, L, Zhu, X, Zhang, Y & Zhang, C 2017, 'Protecting Location Privacy in Spatial Crowdsourcing using Encrypted Data', 20th International Conference on Extending Database Technology (EDBT), International Conference on Extending Database Technology, Open Proceedings, Venice, Italy.
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In spatial crowdsourcing, spatial tasks are outsourced to a set of workers in proximity of the task locations for efficient assignment. It usually requires workers to disclose their locations, which inevitably raises security concerns about the privacy of the workers’ locations. In this paper, we propose a secure SC framework based on encryption, which ensures that workers’ location information is never released to any party, yet the system can still assign tasks to workers situated in proximity of each task’s location. We solve the challenge of assigning tasks based on encrypted data using homomorphic encryption. Moreover, to overcome the efficiency issue, we propose a novel secure indexing technique with a newly devised SKD-tree to index encrypted worker locations. Experiments on real-world data evaluate various aspects of the performance of the proposed SC platform.

Liu, F, Zhang, G & Lu, J 2017, 'Heterogeneous unsupervised domain adaptation based on fuzzy feature fusion', IEEE International Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, Naples, Italy, pp. 1-6.
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© 2017 IEEE. Domain adaptation is a transfer learning approach that has been widely studied in the last decade. However, existing works still have two limitations: 1) the feature spaces of the domains are homogeneous, and 2) the target domain has at least a few labeled instances. Both limitations significantly restrict the domain adaptation approach when knowledge is transferred across domains, especially in the current era of big data. To address both issues, this paper proposes a novel fuzzy-based heterogeneous unsupervised domain adaptation approach. This approach maps the feature spaces of the source and target domains onto the same latent space constructed by fuzzy features. In the new feature space, the label spaces of two domains are maintained to reduce the probability of negative transfer occurring. The proposed approach delivers superior performance over current benchmarks, and the heterogeneous unsupervised domain adaptation (HeUDA) method provides a promising means of giving a learning system the associative ability to judge unknown things using related knowledge.

Liu, Q, Huang, H, Lu, J, Gao, Y & Zhang, G 2017, 'Enhanced word embedding similarity measures using fuzzy rules for query expansion', IEEE International Conference on Fuzzy Systems.
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© 2017 IEEE. Query expansion has been widely used to select additional words that are related to the original query words in the field of information retrieval. In this paper, we present a novel query expansion method that jointly uses fuzzy rules and a word embedding similarity calculation. The expansion words are generated using a word embedding method and selected according to their semantic similarity to the original query. Fuzzy rules are used to enhance the word similarity calculations and reweight expansion words. When measuring and ranking the relevance of a retrieved document, the original query and the expansion words with their weights are considered. We conduct experiments on the query expansion in document ranking tasks. Experimental results from the document ranking task show that the proposed method is able to significantly outperform state-of-the-art baseline methods.

Liu, S, Pang, N, Xu, G & Liu, H 2017, 'Collaborative Filtering via Different Preference Structures', International Conference on Knowledge Science, Engineering and Management, Springer, Melbourne, Australia, pp. 309-321.
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Recently, social network websites start to provide third-parity sign-in options via the OAuth 2.0 protocol. For example, users can login Netflix website using their Facebook accounts. By using this service, accounts of the same user are linked together, and so does their information. This fact provides an opportunity of creating more complete profiles of users, leading to improved recommender systems. However, user opinions distributed over different platforms are in different preference structures, such as ratings, rankings, pairwise comparisons, voting, etc. As existing collaborative filtering techniques assume the homogeneity of preference structure, it remains a challenge task of how to learn from different preference structures simultaneously. In this paper, we propose a fuzzy preference relation-based approach to enable collaborative filtering via different preference structures. Experiment results on public datasets demonstrate that our approach can effectively learn from different preference structures, and show strong resistance to noises and biases introduced by cross-structure preference learning.

Liu, S, Xu, G, Zhu, X & Zhou, Z 2017, 'Towards Simplified Insurance Application via Sparse Questionnaire Optimization', 2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC), International Conference on Behavioral, Economic, and Socio-Cultural Computing, IEEE, Poland.
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Life insurance application requires in-person meetings with underwriters, tedious paperwork, and an average waiting period of six weeks before an offer can be made. This outdated process has become a barrier for broader consumer adoption, resulting large coverage gap. In this work, we aim to closing this gap by leveraging data mining techniques to optimize the insurance questionnaire form. Our experiment on 10 years of insurance application data has identified that only ~2% of all questions have shown high relevancy to determining the risks of applicants, resulting a significantly simplified questionnaire.

Liu, W, Chang, X, Chen, L & Yang, Y 2017, 'Early Active Learning with Pairwise Constraint for Person Re-identification', ECML PKDD 2017: Machine Learning and Knowledge Discovery in Databases, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, Skopje, Macedonia, pp. 103-118.
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Research on person re-identification (re-id) has attached much attention in the machine learning field in recent years. With sufficient labeled training data, supervised re-id algorithm can obtain promising performance. However, producing labeled data for training supervised re-id models is an extremely challenging and time-consuming task because it requires every pair of images across no-overlapping camera views to be labeled. Moreover, in the early stage of experiments, when labor resources are limited, only a small number of data can be labeled. Thus, it is essential to design an effective algorithm to select the most representative samples. This is referred as early active learning or early stage experimental design problem. The pairwise relationship plays a vital role in the re-id problem, but most of the existing early active learning algorithms fail to consider this relationship. To overcome this limitation, we propose a novel and efficient early active learning algorithm with a pairwise constraint for person re-identification in this paper. By introducing the pairwise constraint, the closeness of similar representations of instances is enforced in active learning. This benefits the performance of active learning for re-id. Extensive experimental results on four benchmark datasets confirm the superiority of the proposed algorithm.

Lu, H, Heng, J & Wang, C 2017, 'An AI-Based Hybrid Forecasting Model for Wind Speed Forecasting', Neural Information Processing Part IV (LNCS), International Conference on Neural Information Processing, Springer, Guangzhou, China, pp. 221-230.
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© 2017, Springer International Publishing AG. Forecasting of wind speed plays an important role in wind power prediction for management of wind energy. Due to intermittent nature of wind, accurately forecasting of wind speed has been a long standing research challenge. Artificial neural networks (ANNs) is one of promising approaches to predict wind speed. However, since the results of ANN-based models are strongly dependent on the initial weights and thresholds values which are usually randomly generated, the stability of forecasting results is not always satisfactory. This paper presents a new hybrid model for short term forecasting of wind speed with high accuracy and strong stability by optimizing the parameters in a generalized regression neural network (GRNN) using a multi-objective firefly algorithm (MOFA). To evaluate the effectiveness of this hybrid algorithm, we apply it for short-term forecasting of wind speed from four wind power stations in Penglai, China, along with four typical ANN-based models, which are back propagation neural network (BPNN), radical basis function neural network (RBFNN), wavelet neural network (WNN) and GRNN. The comparison results clearly show that this hybrid model can significantly reduce the impact of randomness of initialization on the forecasting results and achieve good accuracy and stability.

Lu, M, Liang, J, Zhang, Y, Li, G, Chen, S, Li, Z & Yuan, X 2017, 'Interaction+: Interaction Enhancement for Web-based Visualizations', Proceedings of IEEE Pacific Visualization Symposium (PacificVis 2017), IEEE Pacific Visualization Symposium, IEEE, Seoul, Korea, pp. 61-70.
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In this work, we present Interaction+, a tool that enhances the interactive capability of existing web-based visualizations. Different from the toolkits for authoring interactions during the visualization construction, Interaction+ takes existing visualizations as input, analyzes the visual objects, and provides users with a suite of interactions to facilitate the visual exploration, including selection, aggregation, arrangement, comparison, filtering, and annotation. Without accessing the underlying data or process how the visualization is constructed, Interaction+ is application-independent and can be

Lucassen, G, Dalpiaz, F, E.M. van der Werf, JM, Brinkkemper, S & Zowghi, D 2017, 'Behavior-Driven Requirements Traceability via Automated Acceptance Tests', Proceedings of the 2017 IEEE 25th International Requirements Engineering Conference Workshops, International Requirements Engineering Conference Workshops, IEEE, Lisbon, pp. 431-434.
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Although information retrieval advances significantly improved automated traceability tools, their accuracy is still far from 100% and therefore they still need human intervention. Furthermore, despite the demonstrated benefits of traceability, many practitioners find the overhead for its creation and maintenance too high. We propose the Behavior Driven Traceability Method (BDT) that takes a different standpoint on automated traceability: we establish ubiquitous traceability between user story requirements and source code by taking advantage of the automated acceptance tests that are created as part of the Behavior Driven Development process.

Luo, L, Liu, W, Koprinska, I & Chen, F 2015, 'DAAR: A discrimination-aware association rule classifier for decision support', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Conference on Big Data Analytics and Knowledge Discovery (DAWAK), Springer, Spain, pp. 47-68.
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© Springer-Verlag GmbH Germany 2017. Undesirable correlations between sensitive attributes (such as race, gender or personal status) and the class label (such as recruitment decision and approval of credit card), may lead to biased decision in data analytics. In this paper, we investigate how to build discrimination-aware models even when the available training set is intrinsically discriminating based on the sensitive attributes. We propose a new classification method called Discrimination-Aware Association Rule classifier (DAAR), which integrates a new discrimination-aware measure and an association rule mining algorithm. We evaluate the performance of DAAR on three real datasets from different domains and compare DAAR with two non-discrimination-aware classifiers (a standard association rule classification algorithm and the state-of-the-art association rule algorithm SPARCCC), and also with a recently proposed discrimination-aware decision tree method. Our comprehensive evaluation is based on three measures: predictive accuracy, discrimination score and inclusion score. The results show that DAAR is able to effectively filter out the discriminatory rules and decrease the discrimination severity on all datasets with insignificant impact on the predictive accuracy. We also find that DAAR generates a small set of rules that are easy to understand and applied by users, to help them make discrimination-free decisions.

Madhisetty, S & Williams, MA 2017, 'Framework for privacy in photos and videos when using social media', ICEIS 2017 - Proceedings of the 19th International Conference on Enterprise Information Systems, International Conference on Enterprise Information Systems, Science and Technology Publications, Porto, Portugal, pp. 331-336.
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Privacy is a social construct. Having said that, how can it be contextualised and studied scientifically? This research contributes by investigating how to manage privacy better in the context of sharing and storing photos and videos using social media. Social media such as Facebook, Twitter, WhatsApp and many more applications are becoming popular. The instant sharing of tacit information via photos and videos makes the problem of privacy even more critical.The main problem was, nobody could define the actual meaning of privacy. Though there are definitions about privacy and Acts to protect it, there is no clear consensus as to what it actually means. I asked myself a question, how do I manage something when I don't know what it means exactly? I then decided to do this research by asking questions about privacy in particular categories of photos so that I could arrive at a general consensus. The data has been processed using the principles of Grounded Theory (GT) to develop a framework which assists in the effective management of privacy in photos and videos.

Maggs, CA & Musial-Gabrys, K 2017, 'LINNEAN SYSTEMATICS IN THE AGE OF BIG DATA', PHYCOLOGIA, INT PHYCOLOGICAL SOC, pp. 123-124.

McGregor, C, Bonnis, B, Stanfield, B & Stanfield, M 2017, 'Integrating Big Data analytics, virtual reality, and ARAIG to support resilience assessment and development in tactical training', 2017 IEEE 5th International Conference on Serious Games and Applications for Health, SeGAH 2017.
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© 2017 IEEE. Combat tactical training activities utilising virtual reality environments are being used increasingly to create training scenarios to promote resilience against stressors and to enable standardized training scenarios to allow trainees to learn techniques for various stressors. Resilience is an important component for mental health. However, assessment of the trainees' response to these training activities has either been limited to various pre and post training assessment metrics or collected in parallel during experiments and analysed after collection rather than in real-time. New Big Data approaches have the potential to provide real-time analytics. We have created a Big Data analytics platform, Athena, that in real-time acquires data from a first person shooter military combat simulation game, ArmA 3, as well as the data ArmA 3 sends to the muscle stimulation component of a multisensory garment, ARAIG that provides on the body feedback to the wearer for communications, weapon fire and being hit and integrates that data with physiological response data such as heart rate, breathing behaviour and blood oxygen saturation. We present results from our initial pilot study from an ethics approved equipment integration study. Our approach is equally applicable for Virtual Reality Graded Exposure Therapy with physiological monitoring.

McGregor, C, Orlov, O, Baevsky, R, Chernikova, A & Rusanov, V 2917, 'Big data analytics for continuous assessment of astronaut health risk and its application to human-in-the-loop (HITL) related aerospace', Proceedings of the 19th AIAA Non-Deterministic Approaches Conference, 2017, AIAA Non-Deterministic Approaches Conference, ARC, Grapevine, Texas.
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© 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. The man-instrumentation-equipment-vehicle-environment ecosystem is complex in aerospace missions. Health status of the individual has important implications on decision making and performance that should be factored into assessments for probability of success/risk of failure both in offline and real-time models. To date probabilistic models have not considered the dynamic nature of health status. Big Data analytics is enabling new forms of analytics to assess health status in real-time. There is great potential to integrate dynamic health status information with platforms assessing risk and the probability of success for dynamic individualized real-time probabilistic predictive risk assessment. In this research we present an approach utilizing Big Data analytics to enable continuous assessment of astronaut health risk and show its implications for integration with HITL related aerospace mission.

Meng, Q, Catchpoole, D, Skillicom, D & Kennedy, PJ 2019, 'Relational autoencoder for feature extraction', Proceedings of the International Joint Conference on Neural Networks, International Joint Conference on Neural Networks, IEEE, Anchorage, AK, USA, pp. 364-371.
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© 2017 IEEE. Feature extraction becomes increasingly important as data grows high dimensional. Autoencoder as a neural network based feature extraction method achieves great success in generating abstract features of high dimensional data. However, it fails to consider the relationships of data samples which may affect experimental results of using original and new features. In this paper, we propose a Relation Autoencoder model considering both data features and their relationships. We also extend it to work with other major autoencoder models including Sparse Autoencoder, Denoising Autoencoder and Variational Autoencoder. The proposed relational autoencoder models are evaluated on a set of benchmark datasets and the experimental results show that considering data relationships can generate more robust features which achieve lower construction loss and then lower error rate in further classification compared to the other variants of autoencoders.

Meng, Q, Wu, J, Ellis, J & Kennedy, PJ 2017, 'Dynamic island model based on spectral clustering in genetic algorithm', Proceedings of the International Joint Conference on Neural Networks, International Joint Conference on Neural Networks, IEEE, Anchorage, AK, USA, pp. 1724-1731.
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© 2017 IEEE. How to maintain relative high diversity is important to avoid premature convergence in population-based optimization methods. Island model is widely considered as a major approach to achieve this because of its flexibility and high efficiency. The model maintains a group of sub-populations on different islands and allows sub-populations to interact with each other via predefined migration policies. However, current island model has some drawbacks. One is that after a certain number of generations, different islands may retain quite similar, converged sub-populations thereby losing diversity and decreasing efficiency. Another drawback is that determining the number of islands to maintain is also very challenging. Meanwhile initializing many sub-populations increases the randomness of island model. To address these issues, we proposed a dynamic island model (DIM-SP) which can force each island to maintain different sub-populations, control the number of islands dynamically and starts with one sub-population. The proposed island model outperforms the other three state-of-the-art island models in three baseline optimization problems including job shop scheduler, travelling salesmen, and quadratic multiple knapsack.

Mols, I, Van Den Hoven, E & Eggen, B 2017, 'Balance, cogito and dott: Exploring media modalities for everyday-life reflection', TEI 2017 - Proceedings of the 11th International Conference on Tangible, Embedded, and Embodied Interaction, Tangible, Embedded, and Embodied Interaction, ACM, Yokohama, Japan, pp. 31-35.
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© 2017 ACM. Reflection in and on everyday life can provide selfinsight, increase gratitude and have a positive effect on well-being. To integrate reflection in everyday life, media technologies can provide support. In this paper, we explore how both media creation & use in different modalities can support reflection. We present the ongoing work of designing and building Balance, Cogito, and Dott, focusing on media in audible, textual and visual form. We discuss our research-Through-design process and address the differences between modalities in terms of interaction, tangibility, and the integration in everyday life.

Murray-Leslie, A & Johnston, A 2017, 'The Liberation of the Feet: Demaking the High Heeled Shoe For Theatrical Audio-Visual Expression', Proceedings of the International Conference on New Interfaces for Musical Expression, New Interfaces for Musical Expression, Aalborg University Copenhagen, Copenhagen, Denmark, pp. 296-301.
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This paper describes a series of fashionable sounding shoe and foot based appendages made between 2007-2017. The research attempts to demake the physical high-heeled shoe through the iterative design and fabrication of new foot based musical instruments. This process of demaking also changes the usual purpose of shoes and associated stereotypes of high heeled shoe wear. Through turning high heeled shoes into wearable musical instruments for theatrical audio visual expressivity we question why so many musical instruments are made for the hands and not the feet? With this creative work we explore ways to redress the imbalance and consider what a genuinely “foot based” expressivity could be.

Naik, T, Mcgregor, C & James, A 2017, 'Automated partial premature infant pain profile scoring using big data analytics', 2017 IEEE Life Sciences Conference, LSC 2017, IEEE Life Sciences Conference, IEEE, Sydney, NSW, Australia, pp. 246-249.
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© 2017 IEEE. Lack of valid and reliable pain assessment in the neonatal population has become a significant challenge in the Neonatal Intensive Care Unit (NICU). In an attempt to forego the manual pain scoring system, this paper presents an initial framework to automate a partial pain score for newborn infants using big data analytics that automates the analysis of high speed physiological data. An ethically approved retrospective clinical research study was performed to calculate Artemis Premature Infant Pain Profile (APIPP) scores from premature infant data collected from the Artemis platform. Using the Premature Infant Pain Profile (PIPP) as the gold standard scale, scoring techniques were automated to create data abstractions from gestational age and the physiological streams of Heart Rate (HR) and Oxygen Saturation (SpO2). These were then brought together to compute an automated partial pain score. APIPP was retrospectively compared with the PIPP which was manually scored by nursing staff at The Hospital for Sick Children, Toronto. Differences within both the scales were evaluated and analysed by creating a data model. Future research will focus on the clinical validation of this work by implementing this work into a clinical decision support system (CDSS) named Artemis.

Nascimben, M, Wang, YK, Singh, AK, King, JT & Lin, CT 2017, 'Influence of EEG tonic changes on Motor Imagery performance', International IEEE/EMBS Conference on Neural Engineering, NER, pp. 46-49.
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© 2017 IEEE. In Motor Imagery literature, performance predictors are commonly divided in four categories: personal, psychological, anatomical and neurophysiological. However these predictors are limited to inter-subjects changes. To overcome this limitation and evaluate intra-subjects performance, we tried to combine two groups of these measures: psychological and neurophysiological. As neurophysiological variables tonic changes in resting EEG theta and alpha sub-bands were considered. As psychological parameter we analyzed internalized attention and its correlates in lower alpha. We found that when internalized attention doesn't decrease, Motor Imagery performance outcome can be correctly predicted by resting EEG tonic variations.

Nassir, S & Leong, TW 2017, 'Traversing Boundaries: Understanding the Experiences of Ageing Saudis', Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Specialist Interest Group on Conference on Computer Human Interactions, Association for Computing Machinery (ACM), Denver, Colorado, USA, pp. 6386-6386.
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This is a methods paper that draws from our fieldwork conducted in Saudi Arabia to understand ageing people's experiences. This paper focuses on insights gained when using qualitative methods to understand the experiences of ageing Saudis. The aim is to highlight some of the cultural considerations, opportunities, challenges, and issues that influenced our approach and deployment of interviews and probes. Influences of social-cultural practices and religion led to interesting challenges for recruitment, conducting cross-gender communications, and how participants reported their experiences. This paper offers methodological considerations that include the influences of local culture, gender, religion, etc. We also discuss how we shaped our fieldwork tools based upon considerations of local cultural and religious contexts. In particular, we highlight the usefulness of probes in traversing cultural boundaries.

Ning, X, Yao, L, Wang, X & Benatallah, B 2017, 'Calling for response: Automatically distinguishing situation-aware tweets during crises', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 195-208.
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© Springer International Publishing AG 2017. Recent years have witnessed the prevalence and use of social media during crises, such as Twitter, which has been becoming a valuable information source for offering better responses to crisis and emergency situations by the authorities. However, the sheer amount of information of tweets can’t be directly used. In such context, distinguishing the most important and informative tweets is crucial to enhance emergency situation awareness. In this paper, we design a convolutional neural network based model to automatically detect crisis-related tweets. We explore the twitter-specific linguistic, sentimental and emotional analysis along with statistical topic modeling to identify a set of quality features. We then incorporate them to into a convolutional neural network model to identify crisis-related tweets. Experiments on real-world Twitter dataset demonstrate the effectiveness of our proposed model.

Novianto, R & Williams, MA 3015, 'Emotion in robot decision making', Advances in Intelligent Systems and Computing, International Conference on Robot Intelligence Technology and Applications, Springer, Bucheon, Korea, pp. 221-232.
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© Springer international publishing switzerland 2017. Social robots are expected to behave in a socially acceptablemanner. They have to accommodate emotions in their decision-makings when dealing with people in social environments. In this paper, we present a novel emotion mechanism that influences decision making and behaviors through attention. We describe its implementation in a cognitive architecture and demonstrate its capability in a robot companion experiment. Results show that the robot can successfully bias its behaviors in order to make users happy. Our proposed emotion mechanism can be used in social robots to predict emotions and bias behaviors in order to improve their performances.

Oberst, S, Lai, JCS & Evans, TA 2017, 'Extracting critical information from ant and termite substrate vibrations', Invertebrate Sound and Vibration 2017, Ebsdorfergrund-Rauischholzhausen, Germany.

Oberst, S, Lester, D, Niven, RK, Ord, A, Hobbs, BE & Hoffmann, NP 2017, 'Application of recurrence plot quantification to mineralising systems in geology', 7th International Symposium on Recurrence Plots, São Paulo.
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Oberst, S, Niven, R, Ord, A, Hobbs, B & Lester, D 2017, 'Application of recurrence plots to orebody exploration data', Target 2017, At University Club, University of Western Australia.
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A recurring question in mineral exploration is to predict the spatial distribution of ore-bearing minerals, to determine both (i) the economic value of a prospective deposit, and (ii) the optimal locations for mineral discovery (Jébrak M, 1997). In hydrothermal gold deposits such as the Imperial deposit, in the Yilgarn of Western Australia, the gold exhibits a highly variable spatial distribution, causing great difficulties for ore recovery and mine planning; the hypothesis is that such variability results from orebody formation by a non-equilibrium open chemical reactor process with complex chemical-flow-heat-mechanical couplings (Ord et al., 2012, 2016; Lester et al., 2012). With this insight, an important goal is to develop new computational methods to analyse drill-core data to extract key features of the underlying dynamical system, so as to shed light on the orebody formation mechanism(s). In order to discover whether prediction is possible in such systems we need to establish whether the patterns of alteration and mineralisation are intrinsically random or have an underlying deterministic origin.

Ojha, S & Williams, M-A 2017, 'Emotional Appraisal : A Computational Perspective', Website proceedings of the Fifth Annual Conference on Advances in Cognitive Systems, Fifth Annual Conference on Advances in Cognitive Systems, ACS, Troy, USA, pp. 1-15.
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Research on computational modelling of emotions has received significant attention in the last few decades. As such, several computational models of emotions have been proposed which have provided an unprecedented insight into the implications of the emotion theories emerging from cognitive psychology studies. Yet the existing computational models of emotion have distinct limitations namely:(i) low replicability - difficult to implement the given computational model by reading the description of the model, (ii) domain dependence - model only applicable in one or more predefined scenarios or domains, (iii) low scalability and integrability - difficult to use the system in larger or different domains and difficult to integrate the model in wide range of other intelligent systems. In this paper, we propose a completely domain-independent mathematical representation for computational modelling of emotion that provides better replicability and integrability. The implementation of our model is inspired by appraisal theory - an emotion theory which assumes that emotions result from the cognitive evaluation of a situation.

Ojha, S, Vitale, J & Williams, M-A 2017, 'A Domain-Independent Approach of Cognitive Appraisal Augmented by Higher Cognitive Layer of Ethical Reasoning', Proceedingsof the 39th Annual Meeting of the Cognitive Science Society, Annual Meeting of the Cognitive Science Society, Cognitive Science Society, London, pp. 2833-2838.
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According to cognitive appraisal theory, emotion in an individual is the result of how a situation/event is evaluated by the individual. This evaluation has different outcomes among people and it is often suggested to be operationalised by a set of rules or beliefs acquired by the subject throughout development. Unfortunately, this view is particularly detrimental for computational applications of emotion appraisal. In fact, it requires providing a knowledge base that is particularly difficult to establish and manage, especially in systems designed for highly complex scenarios, such as social robots. In addition, according to appraisal theory, an individual might elicit more than one emotion at a time in reaction to an event. Hence, determining which emotional state should be attributed in relationship to a specific event is another critical issue not yet fully addressed by the available literature. In this work, we show that: (i) the cognitive appraisal process can be realised without a complex set of rules; instead, we propose that this process can be operationalised by knowing only the positive or negative perceived effect the event has on the subject, thus facilitating extensibility and integrability of the emotional system; (ii) the final emotional state to attribute in relation to a specific situation is better explained by ethical reasoning mechanisms. These hypotheses are supported by our experimental results. Therefore, this contribution is particularly significant to provide a more simple and generalisable explanation of cognitive appraisal theory and to promote the integration between theories of emotion and ethics studies, currently often neglected by the available literature.

Ord, A, Hobbs, B, Munro, M, Oberst, S & Niven, RK 2017, 'Evidence-based geological models: how to comprehend big data', 18th Annual Conference of the International Association for Mathematical Geosciences, Freemantle, Australia, pp. 1-1.

Ord, A, Oberst, S, Niven, R & Hobbs, B 2017, 'What do we do with all these data? Ore Exploration Using Modern Technology', AIG Bulletin, Gold17@Rotorua, Australian Institute of Geoscientists, Rotorua, New Zealand., pp. 66-69.
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Orlov, O, McGregor, C, Baevsky, R, Chernikova, A, Prysyazhnyuk, A & Rusanov, V 2017, 'Perspective use of the technologies for big data analysis in manned space flights on the international space station', Proceedings of the International Astronautical Congress, IAC, pp. 1951-1960.
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Copyright © 2017 by the International Astronautical Federation (IAF). All rights reserved. Recent technologies in the area of Big Data analytics which provide fast and effective review of various and diverse files of information arriving from different sources are being developed increasingly. Various new software are being proposed to provide useful results in this area. Such technologies are the important stimulus of modern scientific and technical progress, in particular in the field of development of piloted space flights. In this publication we present the prospects of the use of Big Data analytics technology in a system of medical control of crews of the International space station (ISS). Today there is an active accumulation of experience of piloted space flights on ISS where the international scientific and technical cooperation actively develops. An important step withm this direction is the organisation of a new joint Russian-Canadian space experiment "Cosmocard 2018" It will build on the Russian experiment "Cosmocard" which is currently being carried out on the ISS since September, 2014. In this project we have begun work for the modernisation of the software for the onboard computer which will enable the estimation in real-time of a mode of state of health of members of the crew. The Artemis platform, a Big Data analytics platform proposed by McGregor for the analysis of great volumes of physiological and other environmental data, will be used for this purpose. We have begun to reengrneer algorithms for definition of a functional condition of an organism and risk of development of diseases developed previously by the Institute of Biomedical Problems of the Russian Academy of Sciences to run in real-time within the structure of the new software for the onboard computer that is based on Artemis. These new algorithms will be tested, in the beginning, during simulation experiments with long isolation using the same "Cosmocard" physiological monitoring dev...

Padmanabha A G, A, Appaji M, A, Prasad, M, Lu, H & Joshi, S 2017, 'Classification of diabetic retinopathy using textural features in retinal color fundus image', Intelligent Systems and Knowledge Engineering (ISKE), 2017 12th International Conference on, International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Nanjing, China, pp. 1-5.
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Early, diagnosis is essential for diabetic patients to avoid partial or complete blindness. This work presents a new analysis method of texture features for classification of Diabetic Retinopathy (DR). The proposed method masks the blood vessels and optic disk segmented and directly extracts the textural features from the remaining retinal region. The proposed method is much simpler with comparison of the other methods that detect the defective regions first and then extract the required features for classification. The Haralick texture measures calculated are used for classification of DR. The proposed method is evaluated through a classification of DR using both Support Vector Machine (SVM) and Artificial Neural Network (ANN). The results of SVM have a better accuracy (87.5%) over ANN (79%).The performance of the proposed method is presented also in terms of sensitivity and specificity.

Pan, P, Feng, J, Chen, L & Yang, Y 2017, 'Online compressed robust PCA', Proceedings of the International Joint Conference on Neural Networks, International Joint Conference on Neural Networks, IEEE, Anchorage, AK, USA, pp. 1041-1048.
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© 2017 IEEE. In this work, we consider the problem of robust principal component analysis (RPCA) for streaming noisy data that has been highly compressed. This problem is prominent when one deals with high-dimensional and large-scale data and data compression is necessary. To solve this problem, we propose an online compressed RPCA algorithm to efficiently recover the low-rank components of raw data. Though data compression incurs severe information loss, we provide deep analysis on the proposed algorithm and prove that the low-rank component can be asymptotically recovered under mild conditions. Compared with other recent works on compressed RPCA, our algorithm reduces the memory cost significantly by processing data in an online fashion and reduces the communication cost by accepting sequential compressed data as input.

Pang, G, Cao, L, Chen, L & Liu, H 2017, 'Learning homophily couplings from non-IID data for joint feature selection and noise-resilient outlier detection', IJCAI International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence Organization, Melbourne, Australia, pp. 2585-2591.
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This paper introduces a novel wrapper-based outlier detection framework (WrapperOD) and its instance (HOUR) for identifying outliers in noisy data (i.e., data with noisy features) with strong couplings between outlying behaviors. Existing subspace or feature selection-based methods are significantly challenged by such data, as their search of feature subset(s) is independent of outlier scoring and thus can be misled by noisy features. In contrast, HOUR takes a wrapper approach to iteratively optimize the feature subset selection and outlier scoring using a top-κ outlier ranking evaluation measure as its objective function. HOUR learns homophily couplings between outlying behaviors (i.e., abnormal behaviors are not independent - they bond together) in constructing a noise-resilient outlier scoring function to produce a reliable outlier ranking in each iteration. We show that HOUR (i) retains a 2-approximation outlier ranking to the optimal one; and (ii) significantly outperforms five stateof-the-art competitors on 15 real-world data sets with different noise levels in terms of AUC and/or P@n. The source code of HOUR is available at https://sites.google.com/site/gspangsite/sourcecode.

Pang, N, Zhu, D, Li, G & Liu, S 2017, 'WarnFi: Non-Invasive WiFi-based Abnormal Activity Sensing Using Non-parametric Model', IEEE Military Communications Conference, IEEE, Baltimore, MD, USA.
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Pham, PG, Huang, M & Nguyen, Q 2017, 'Interactive Data Exploration through Multiple Visual Contexts with Different Data Models and Dimensions', Proccdings of 21st International Conference Information Visualisation, 2017 21st International Conference Information Visualisation, IEEE CPS, London, UK, pp. 84-89.
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Visual analytics plays a key role in bringing insights to audiences who are interested and dedicated in data exploration. In the area of relational data, many advanced visualization tools and frameworks are proposed in order to dealing with such data features. However, the majority of those have not greatly considered the whole process from data-model mining to query utilizing on dimensions and data values, which might cause interruption to exploration activities. This paper presents a new interactive exploration framework for relational data analysis through automatic interconnection of data models, data dimensions and data values. The basic idea is to construct a relative and switchable chain of those context representations by integrating our previous techniques on node-link, parallel coordinate and scatterplot graphics. This approach enables users to flexibly make relative queries on desired contexts at any stage of exploration for deep data understanding. The result from a typical case study for the framework demonstration indicates that our approach is able to handle the addressed challenge.

Pickrell, M, van den Hoven, E & Bongers, A 2017, 'Exploring in-hospital rehabilitation exercises for stroke patients- informing interaction design', Proceeding OZCHI '17 Proceedings of the 29th Australian Conference on Computer-Human Interaction, OzCHI, ACM, Brisbane, Australia, pp. 228-237.
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Rehabilitation exercises following stroke are by necessity repetitive and consequently can be tedious for patients. Hospitals are set up with equipment such as clothes pegs, wooden blocks and mechanical hand counters, which patients use to re-learn how to manipulate objects. The aim of this study is to understand the context of stroke patients rehabilitation as well as which types of feedback are most appropriate for patients when performing their rehabilitation exercises. Over 60 hours were spent observing stroke patients undergoing rehabilitation. Fourteen stroke patients who had attended a balance class were interviewed about their experiences and the feedback they received. From this fieldwork, a set of design guidelines has been developed to guide researchers and designers developing computer-based equipment for stroke patient rehabilitation.

Prysyazhnyuk, A, Baevsky, R, Berseneva, A, Chernikova, A, Luchitskaya, E, Rusanov, V & Mcgregor, C 2017, 'Big data analytics for enhanced clinical decision support systems during spaceflight', 2017 IEEE Life Sciences Conference, LSC 2017, IEEE Life Sciences Conference, IEEE, Sydney, NSW, Australia, pp. 296-299.
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© 2017 IEEE. Recent advancements in the field of space medicine and technology have extended the boundaries of space travel, presenting humankind with the ability to explore undiscovered habitats. As humans embark on long range missions, adaptation mechanisms will be put to the test, challenging provision of medical care in space. To date, a vast amount of knowledge has been accumulated through a series of experiments, both in terrestrial simulation environments and space missions on the ISS. As a result, functional health state algorithm has been developed and validated by IBMP, to identify transitional states between health and disease. Significant limitations on provision of medical care in space are imposed due to retrospective data processing and analysis techniques. Some of these limitations can be addressed by the proposed instantiation of the functional state algorithm within the Online Analytics component of the Artemis platform, to enhance clinical decision support systems during spaceflight.

Qiao, M, Yu, J, Bian, W, Li, Q & Tao, D 2017, 'Improving Stochastic block models by incorporating power-law degree characteristic', IJCAI International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence Organization, Melbourne, Australia, pp. 2620-2626.
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Stochastic block models (SBMs) provide a statistical way modeling network data, especially in representing clusters or community structures. However, most block models do not consider complex characteristics of networks such as scale-free feature, making them incapable of handling degree variation of vertices, which is ubiquitous in real networks. To address this issue, we introduce degree decay variables into SBM, termed power-law degree SBM (PLD-SBM), to model the varying probability of connections between node pairs. The scale-free feature is approximated by a power-law degree characteristic. Such a property allows PLD-SBM to correct the distortion of degree distribution in SBM, and thus improves the performance of cluster prediction. Experiments on both simulated networks and two real-world networks including the Adolescent Health Data and the political blogs network demonstrate the validity of the motivation of PLD-SBM, and its practical superiority.

Qin, M, Jin, D, He, D, Gabrys, B & Musial, K 2017, 'Adaptive community detection incorporating topology and content in social networks', Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, pp. 675-682.
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© 2017 Association for Computing Machinery. In social network analysis, community detection is a basic step to understand the structure, function and semantics of networks. Some conventional community detection methods may have limited performance because they merely focus on topological structure of networks. In addition to topology, content information is another significant aspect of social networks. Some state-of-the-art methods started to combine these two aspects of information, but they often assume that topology and content share the same characteristics. However, for some examples of social networks, content may mismatch with topological structure. In order to better cope with such situations, we introduce a novel community detection method under the framework of nonnegative matrix factorization (NMF). Our proposed method integrates topology and content of networks, and introduces a novel adaptive parameter for controlling the contribution of content with respect to the identified mismatch degree between the topological and content information. The case study using real social networks show that our new method can simultaneously obtain community partition and the corresponding semantic descriptions. Experiments on both artificial networks and real social networks further indicate that our method outperforms some state-of-the-art methods while exhibiting more robust behaviour when the mismatch topological and content information is observed.

Qiu, X, Cen, W, Qian, Z, Peng, Y, Zhang, Y, Lin, X & Zhou, J 2017, 'Real-time constrained cycle detection in large dynamic graphs', Proceedings of the VLDB Endowment, pp. 1876-1888.
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© 2018 VLDB Endowment. As graph data is prevalent for an increasing number of Internet applications, continuously monitoring structural patterns in dynamic graphs in order to generate real-time alerts and trigger prompt actions becomes critical for many applications. In this paper, we present a new system GraphS to efficiently detect constrained cycles in a dynamic graph, which is changing constantly, and return the satisfying cycles in real-time. A hot point based index is built and efficiently maintained for each query so as to greatly speed-up query time and achieve high system throughput. The GraphS system is developed at Alibaba to actively monitor various online fraudulent activities based on cycle detection. For a dynamic graph with hundreds of millions of edges and vertices, the system is capable to cope with a peak rate of tens of thousands of edge updates per second and find all the cycles with predefined constraints with a 99.9% latency of 20 milliseconds.

R. Kridalukmana, H. Y. Lu & M. Naderpour 2017, 'An object oriented Bayesian network approach for unsafe driving maneuvers prevention system', 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Nanjing, China, pp. 1-6.
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As the main contributor to the traffic accidents, unsafe driving maneuvers have taken attentions from automobile industries. Although driving feedback systems have been developed in effort of dangerous driving reduction, it lacks of drivers awareness development. Therefore, those systems are not preventive in nature. To cover this weakness, this paper presents an approach to develop drivers awareness to prevent dangerous driving maneuvers. The approach uses Object-Oriented Bayesian Network to model hazardous situations. The result of the model can truthfully reflect a driving environment based upon situation analysis, data generated from sensors, and maneuvers detectors. In addition, it also alerts drivers when a driving situation that has high probability to cause unsafe maneuver to be detected. This model then is used to design a system, which can raise drivers awareness and prevent unsafe driving maneuvers.

Ramezani, F & Naderpour, M 2017, 'A fuzzy virtual machine workload prediction method for cloud environments', IEEE International Conference on Fuzzy Systems, IEEE, Naples, Italy.
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Due to the dynamic nature of cloud environments, the workload of virtual machines (VMs) fluctuates leading to imbalanced loads and utilization of virtual and physical cloud resources. It is, therefore, essential that cloud providers accurately forecast VM performance and resource utilization so they can appropriately manage their assets to deliver better quality cloud services on demand. Current workload and resource prediction methods forecast the workload or CPU utilization pattern of the given web-based applications based on their historical data. This gives cloud providers an indication of the required number of resources (VMs or CPUs) for these applications to optimize resource allocation for software as a service (SaaS) or platform as a service (PaaS), reducing their service costs. However, historical data cannot be used as the only data source for VM workload predictions as it may not be available in every situation. Nor can historical data provide information about sudden and unexpected peaks in user demand. To solve these issues, we have developed a fuzzy workload prediction method that monitors both historical and current VM CPU utilization and workload to predict VMs that are likely to be performing poorly. This model can also predict the utilization of physical machine (PM) resources for virtual resource discovery.

Raza, SA & Williams, M-A 2017, 'Potential Based Reward Shaping Using Learning to Rank', Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, ACM, pp. 261-262.
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Raza, SA & Williams, M-A 2017, 'Unconventional Formats of Background Knowledge from Human Teacher in Reward Shaping', Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, ACM, pp. 373-374.
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Reis, MDL, Fermé, E & Peppas, P 2017, 'Construction of system of spheres-based transitively relational partial meet multiple contractions: An impossibility result', IJCAI International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence, Melbourne, Australia, pp. 5045-5049.
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In this paper we show that, contrary to what is the case in what concerns contractions by a single sentence, there is not a system of spheres-based construction of multiple contractions which generates each and every transitively relational partial meet multiple contraction. Furthermore, we propose two system of spheresbased constructions of multiple contractions which generate (only) transitively relational partial meet multiple contractions.

Run-ze, W, Xu, G, Enhong, C, Qi, L & Ng, W 2017, 'Knowledge or Gaming? Cognitive Modelling Based on Multiple-Attempt Response', Proceedings of the 26th International Conference on World Wide Web Companion, International World Wide Web Conference Companion, ACM, Perth, Western Australia, pp. 321-329.
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Recent decades have witnessed the rapid growth of intelli- gent tutoring systems (ITS), in which personalized adaptive techniques are successfully employed to improve the learning of each individual student. However, the problem of using cognitive analysis to distill the knowledge and gaming factor from students learning history is still underexplored. To this end, we propose a Knowledge Plus Gaming Response Model (KPGRM) based on multiple-attempt responses. Specifi- cally, we first measure the explicit gaming factor in each multiple-attempt response. Next, we utilize collaborative filtering methods to infer the implicit gaming factor of one- attempt responses. Then we model student learning cog- nitively by considering both gaming and knowledge factors simultaneously based on a signal detection model. Extensive experiments on two real-world datasets prove that KPGRM can model student learning more effectively as well as obtain a more reasonable analysis.

Salama, U, Yao, L, Wang, X, Paik, HY & Beheshti, A 2017, 'Multi-Level Privacy-Preserving Access Control as a Service for Personal Healthcare Monitoring', Proceedings - 2017 IEEE 24th International Conference on Web Services, ICWS 2017, pp. 878-881.
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© 2017 IEEE. The Internet of Things (IoT) changes many sectors of our lives. In the healthcare domain, IoT presents as mobile medical applications over various sensors that update healthcare professionals on patients' health information. However, IoT-based healthcare systems also face major challenges in protecting patients' privacy via an effective access control system. This paper presents an ambient home solution framework for privacy-preserving monitoring of patients' health status. We focus on two major points: 1) how to use the data collected from ambient and biometric sensors, to perform the high-level task of activity recognition, and 2) how to secure the collected healthcare data via effective access control. We achieve multi-level access control by using Public Key Infrastructure (PKI) for authentication and Attribute-Based Access Control (ABAC) for authorisation. Our access control system regulates access to healthcare data by classification over healthcare professionals and data. Our system provides guidelines to define data classes and healthcare professional groups and specifies security policies to control access to the data classes. The system is flexible and can incorporate more policy rules, professionals, and data classes.

Saqib, M, Daud Khan, S, Sharma, N & Blumenstein, M 2017, 'A study on detecting drones using deep convolutional neural networks', Proceedings of the 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017, IEEE International Conference on Advanced Video and Signal Based Surveillance, IEEE, Lecce, Italy.
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© 2017 IEEE. The object detection is a challenging problem in computer vision with various potential real-world applications. The objective of this study is to evaluate the deep learning based object detection techniques for detecting drones. In this paper, we have conducted experiments with different Convolutional Neural Network (CNN) based network architectures namely Zeiler and Fergus (ZF), Visual Geometry Group (VGG16) etc. Due to sparse data available for training, networks are trained with pre-trained models using transfer learning. The snapshot of trained models is saved at regular interval during training. The best models having high mean Average Precision (mAP) for each network architecture are used for evaluation on the test dataset. The experimental results show that VGG16 with Faster R-CNN perform better than other architectures on the training dataset. Visual analysis of the test dataset is also presented.

Scopigno, R, Cignoni, P, Pietroni, N, Callieri, M & Dellepiane, M 2017, 'Digital fabrication techniques for cultural heritage: a survey', Computer Graphics Forum, pp. 6-21.

Shang, D, Zhang, G & Lu, J 2017, 'Fast concept drift detection using singular vector decomposition', Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017, International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Nanjing, China, pp. 1-6.
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© 2017 IEEE. Data stream mining is widely used in online applications such as sensor networks, financial transactions, etc. Such systems generate data at high velocity and their underlying distributions may change over time. This is referred to as concept drift problem and it is considered to be the root cause of performance degradation of online machine learning models. To tackle this problem, a reliable and fast drift detection method is required to achieve real time responsiveness to the drifts. This paper presents a fast and accurate drift detection method, namely KS-SVD test - KSSVD, to monitor the distribution changes of the data stream. Our method employs the SVD technique to first check the direction change of the data, followed by a KS test on each direction to detect the univariate distribution changes. Experiments show that our method is efficient and accurate, especially in high dimension situation.

Singh, J, Prasad, M, Daraghmi, YA, Tiwari, P, Yadav, P, Bharill, N, Pratama, M & Saxena, A 2017, 'Fuzzy LogicHybrid model with semantic filtering approach for pseudo relevance feedback-based query expansion', 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, Symposium Series on Computational Intelligence, IEEE, Honolulu, HI, USA, pp. 1-7.
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© 2017 IEEE. Individual query expansion term selection methods have been widely investigated in an attempt to improve their performance. Each expansion term selection method has its own weaknesses and strengths. To overcome the weaknesses and utilize the strengths of individual methods, this paper combined multiple term selection methods. In this paper, initially the possibility of improving the overall performance using individual query expansion (QE) term selection methods are explored. Secondly, some well-known rank aggregation approaches are used for combining multiple QE term selection methods. Thirdly, a new fuzzy logic-based QE approach that considers the relevance score produced by different rank aggregation approaches is proposed. The proposed fuzzy logic approach combines different weights of each term using fuzzy rules to infer the weights of the additional query terms. Finally, Word2vec approach is used to filter semantically irrelevant terms obtained after applying the fuzzy logic approach. The experimental results demonstrate that the proposed approaches achieve significant improvements over each individual term selection method, aggregated method and related state-of-the-art method.

Sohaib, O, Lu, H & Hussain, W 2017, 'Internet of Things (IoT) in E-commerce: For People with Disabilities', 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE Conference on Industrial Electronics and Applications, IEEE, Cambodia.
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Song, Y, Zhang, G, Lu, J & Lu, H 2017, 'A fuzzy kernel c-means clustering model for handling concept drift in regression', IEEE International Conference on Fuzzy Systems, International Conference on Fuzzy Systems, IEEE, Naples, Italy, pp. 1-6.
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© 2017 IEEE. Concept drift, given the huge volume of high-speed data streams, requires traditional machine learning models to be self-adaptive. Techniques to handle drift are especially needed in regression cases for a wide range of applications in the real world. There is, however, a shortage of research on drift adaptation for regression cases in the literature. One of the main obstacles to further research is the resulting model complexity when regression methods and drift handling techniques are combined. This paper proposes a self-adaptive algorithm, based on a fuzzy kernel c-means clustering approach and a lazy learning algorithm, called FKLL, to handle drift in regression learning. Using FKLL, drift adaptation first updates the learning set using lazy learning, then fuzzy kernel c-means clustering is used to determine the most relevant learning set. Experiments show that the FKLL algorithm is better able to respond to drift as soon as the learning sets are updated, and is also suitable for dealing with reoccurring drift, when compared to the original lazy learning algorithm and other state-of-the-art regression methods.

Soro, A, Brereton, M, Roe, P, Wyeth, P, Johnson, D, Ambe, AH, Morrison, A, Bardzell, S, Leong, T, Ju, W, Lindtner, S, Rogers, Y & Burr, J 2017, 'Designing the Social Internet of Things', http://dl.acm.org/citation.cfm?id=3027069&CFID=761603206&CFTOKEN=70033412, CHI Conference Extended Abstracts on Human Factors in Computing Systems, Denver, Colorado, USA.
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Stender, M, Oberst, S & Hoffmann, N 2017, 'Why mechanical machines should be treated as complex systems', Recurrence Plot Symposium, Sāo Paulo, Brazil.
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Nonlinear time and recurrence analysis have proven to be highly successful for a very diverse field of nonlinear dynamical processes. Typically, this data driven method is applied to systems that cannot be modelled in a bottom-up manner (i.e. model-based) due to complexities or uncertainties involved but can be quantified via measurements. Well-known examples range from natural sciences, medicine and biology (physiology) to finance and earth science to only mention a few. Focus is on analyzing the output, i.e. observations instead of trying to mathematically detail the systems. Friction brakes, turbo machinery and many other highly sophisticated mechanical machines reveal a plethora of unwanted and possibly safety critical noise and vibration phenomena, which today’s engineers can only partly relate to operational conditions or system configurations. Analysis approaches are mostly limited to the concept of assembling models of little parts: geometrical, material and dynamical properties of each part are determined, from which a mathematical model of the complete machine is created following the pure form of linearity (superposition principle). This bottom-up approach has been applied successfully in many disciplines with the aid of advanced material models, discretization strategies, model order reduction methods and high performance computing. However, system inherent nonlinearities and their interaction, multi-physics, different time and length scales and unknown operational conditions often cause large discrepancies between experimental results and the mathematical models for numerous engineering structures, making high-fidelity modelling of a synthesized machine an ambitious task. Considering those modeling challenges in machine dynamics and the limited modeling improvements made in recent years, we pose the provocative question: why do we not as engineers look at a machine and its dynamics as if it was a brain, a stock market or a planet’s climate system, i....

Sutter, D, Berta, M & Tomamichel, M 2017, 'Quantum Markov chains and logarithmic trace inequalities', IEEE International Symposium on Information Theory - Proceedings, IEEE International Symposium on Information Theory, IEEE, Aachen, Germany, pp. 1988-1992.
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© 2017 IEEE. A Markov chain is a tripartite quantum state ρABCwhere there exists a recovery map RB→BCsuch that ρABC= RB→BC(ρAB). More generally, an approximate Markov chain ρABCis a state whose distance to the closest recovered state RB→BC(ρAB) is small. Recently it has been shown that this distance can be bounded from above by the conditional mutual information I(A: C|B)ρof the state. We improve on this connection by deriving the first bound that is tight in the commutative case and features an explicit recovery map that only depends on the reduced state pBC. The key tool in our proof is a multivariate extension of the Golden-Thompson inequality, which allows us to extend logarithmic trace inequalities from two to arbitrarily many matrices.

Talaei-Khoei, A, Lewis, L, Kaul, M, Daniel, J & Sharma, R 2017, 'Use of Lean Robotic Communication to Improve Social Response of Children with Autism', AMCIS 2017 - America's Conference on Information Systems: A Tradition of Innovation, Americas Conference on Information Systems 2017, AIS Electronic Library (AISeL), Boston, US.
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Talaei-Khoei, A, Safaei, F & Kaul, M 2017, 'Engaging students with flipped classes using 3D video collaboration technology', AMCIS 2017 - America's Conference on Information Systems: A Tradition of Innovation.
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© 2017 AIS/ICIS Administrative Office. All Rights Reserved. The objective of this paper is to demonstrate the potential of 3D video collaboration technologies to engage students with the learning materials prior flipped classes. The study uses iSee designed for 3D video collaborative classes and compared it with online learning management systems. An experiment has been reported on 273 students in an undergraduate Information System course. It was revealed that the correlation between either the students’ engagement on the online quizzes or their engagement on iSee discussions with their learning outcomes were high. However, our statistical analysis showed that the relationship between iSee engagement and students’ learning outcomes was stronger. The qualitative observations during this experiment are also discussed. The results have been discussed in the lens of Theory of Peer Learning and the future research have been suggested. This study motivates teaching practitioners in Information Systems to use 3D video collaboration technologies in flipped classes.

Tamassia, M, Raffe, W, Sifa, R, Drachen, A, Zambetta, F & Hitchens, M 2016, 'Predicting player churn in destiny: A Hidden Markov models approach to predicting player departure in a major online game', IEEE Conference on Computatonal Intelligence and Games, CIG, IEEE Conference on Computational Intelligence and Games, Santorini, Greece.
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© 2016 IEEE. Destiny is, to date, the most expensive digital game ever released with a total operating budget of over half a billion US dollars. It stands as one of the main examples of AAA titles, the term used for the largest and most heavily marketed game productions in the games industry. Destiny is a blend of a shooter game and massively multi-player online game, and has attracted dozens of millions of players. As a persistent game title, predicting retention and churn in Destiny is crucial to the running operations of the game, but prediction has not been attempted for this type of game in the past. In this paper, we present a discussion of the challenge of predicting churn in Destiny, evaluate the area under curve (ROC) of behavioral features, and use Hidden Markov Models to develop a churn prediction model for the game.

Tomamichel, M, Chubb, CT & Tan, V 2018, 'Moderate deviation analysis for classical communication over quantum channels', IEEE International Symposium on Information Theory - Proceedings, Vail, CO, USA, pp. 1544-1548.
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Tonkin, M, Vitale, J, Ojha, S, Clark, J, Pfeiffer, S, Judge, W, Wang, X & Williams, M 2017, 'Embodiment, Privacy and Social Robots: May I Remember You?', Social Robotics: 9th International Conference, ICSR 2017, International Conference on Social Robotics, Springer International Publishing, Tsukuba, Japan, pp. 506-515.
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As social robots move from the laboratory into public settings the possibility of unwanted intrusion into a user's personal privacy is magnified. The actual social interaction between human and robot may involve anthropomorphising of the robot by the user, and this may prompt the user to disclose private or sensitive information. To comprehend possible impacts we conducted an exploratory study with a novel privacy measure to understand changes to users' privacy considerations when interacting with an embodied robotic system vs a disembodied system. In this paper we measure the difference in personal information provided to such systems, and discuss the idea that embodiment may increase users' risk tolerance and reduce their privacy concerns.

Tonkin, M, Vitale, J, Ojha, S, Williams, M-A, Fuller, P, Judge, W & Wang, X 2017, 'Would You Like to Sample? Robot Engagement in a Shopping Centre', 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), International Symposium on Robot and Human Interactive Communication, IEEE, Lisbon, Portugal, pp. 42-49.
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Nowadays, robots are gradually appearing in public spaces such as libraries, train stations, airports and shopping centres. Only a limited percentage of research literature explores robot applications in public spaces. Studying robot applications in the wild is particularly important for designing commercially viable applications able to meet a specific goal. Therefore, in this paper we conduct an experiment to test a robot application in a shopping centre, aiming to provide results relevant for today’s technological capability and market. We compared the performance of a robot and a human in promoting food samples in a shopping centre, a well known commercial application, and then analysed the effects of the type of engagement used to achieve this goal. Our results show that the robot is able to engage customers similarly to a human as expected. However unexpectedly, while an actively engaging human was able to perform better than a passively engaging human, we found the opposite effect for the robot. In this paper we investigate this phenomenon, with possible explanation ready to be explored and tested in subsequent research.

Treadaway, C, Prytherch, D, Kenning, G & Fennell, J 2017, 'In the moment: designing for late stage dementia', Design Issues, Massachusetts Institute of Technology Press (MIT Press).
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Tsai, WC, Orth, D & van den Hoven, E 2017, 'Designing Memory Probes to Inform Dialogue', Proceedings of the 2017 Conference on Designing Interactive Systems, Conference on Designing Interactive Systems, ACM, Edinburgh, United Kingdom, pp. 889-901.
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To investigate the phenomenon that occurs during interactions between used objects and autobiographical memories, which are both ever-changing and imbedded with personal significance, an adapted probing method capable of managing these complex qualities is needed. This pictorial is our attempt to find a nuanced indication of how probes could go beyond common usage to facilitate complex felt experience, and how probes can be used in less prescriptive ways to instead promote reminiscent dialogues that are rich and open to interpretation for both participants and researchers. It illustrates our exploration into potential Memory Probes and how this might be done that reflects the value we see in creating restrictions or limitations in technology mediated interactions to encourage active participation by users in social acts such as memory creation and remembrance.

van Rijmenam, M, Erekhinskaya, T, Schweitzer, J & Williams, MA 2017, 'How Big Data Analytics Changes the Practice of Strategy When Navigating Times of Disruptive Innovation', Transforming Entrepreneurial - Thinking into Dynamic Capabilities, Strategic Management Society (SMS) Special Conference, Banff, Alberta, Canada.
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van Rijmenam, M, Schweitzer, J & Williams, MA 2017, 'A Distributed Future:How Blockchain Affects Strategic Management, Organisation Design & Governance', At the Interface, Academy of Management 2017 Annual Meeting, Academy of Management, Atlanta, Georia.
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Blockchain is a new technology that transforms strategic management, organisational design and governance due to its decentralised and distributed characteristics. It is a database technology, a distributed ledger, that records and maintains indefinitely an ever-growing list of data records, which cannot be altered or tampered with. The usage of smart contracts on blockchains, affects strategic management as the process of developing, executing and evaluating decisions will become automated and irreversible. This will result in new, disruptive, organisation design, including that of a Decentralised Autonomous Organisation (DAO). These are organisations that establish governance without managers or employees, run completely by autonomous computer software, where trust among actors is established cryptographically. However, organisations that want to move to the Blockchain face numerous business and technical challenges. In this conceptual paper, we provide an overview of the Blockchain, how it affects strategic management, changes organisational design and requires a new form of corporate governance

Venkata, SK, Musial, K, Mahmoud, S & Keppens, J 2017, 'Demonstration: Multi-agent system for distributed cache maintenance', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 364-368.
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© Springer International Publishing AG 2017. Innovations in science and technology is increasing the demand on huge data transfers and hence number of data caches. In this paper, we consider the community caching solution, CommCache, where many groups of users are working together on related projects distributed all over the world. We demonstrate the use of proactive caches for data placement problem with the help of multi-agent coordination.

Venkata, SK, Musial, K, Mahmoud, S & Keppens, J 2017, 'Multi-Agent System for Distributed Cache Maintenance', ADVANCES IN PRACTICAL APPLICATIONS OF CYBER-PHYSICAL MULTI-AGENT SYSTEMS: THE PAAMS COLLECTION, PAAMS 2017, 15th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS), SPRINGER INTERNATIONAL PUBLISHING AG, Porto, PORTUGAL, pp. 157-169.
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Verma, S, Liu, W, Wang, C & Zhu, L 2017, 'Extracting highly effective features for supervised learning via simultaneous tensor factorization', Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, AAAI, San Francisco, USA, pp. 4995-4996.
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Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Real world data is usually generated over multiple time periods associated with multiple labels, which can be represented as multiple labeled tensor sequences. These sequences are linked together, sharing some common features while exhibiting their own unique features. Conventional tensor factorization techniques are limited to extract either common or unique features, but not both simultaneously. However, both types of these features are important in many machine learning systems as they inherently affect the systems' performance. In this paper, we propose a novel supervised tensor factorization technique which simultaneously extracts ordered common and unique features. Classification results using features extracted by our method on CIFAR-10 database achieves significantly better performance over other factorization methods, illustrating the effectiveness of the proposed technique.

Vitale, J, Johnston, B & Williams, MA 2017, 'Facial Motor Information is Sufficient for Identity Recognition', Proceedings of the 39th Annual Meeting of the Cognitive Science Society, The 39th Annual Meeting of the Cognitive Science Society, Cognitive Science Society, London, pp. 3447-3452.
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The face is a central communication channel providing information about the identities of our interaction partners and their potential mental states expressed by motor configurations. Although it is well known that infants ability to recognise people follows a developmental process, it is still an open question how face identity recognition skills can develop and, in particular, how facial expression and identity processing potentially interact during this developmental process. We propose that by acquiring information of the facial motor configuration observed from face stimuli encountered throughout development would be sufficient to develop a face-space representation. This representation encodes the observed face stimuli as points of a multidimensional psychological space able to assist facial identity and expression recognition. We validate our hypothesis through computational simulations and we suggest potential implications of this understanding with respect to the available findings in face processing.

Vo, NNY & Xu, G 2017, 'The volatility of Bitcoin returns and its correlation to financial markets', Proceedings of 4th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2017), Krakow, Poland, 16-18 October 2017, International Conference on Behavioral, Economic, Socio-cultural Computing, IEEE, Krakow, Poland.
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The 2008 financial crisis had scattered incredulity around the globe regarding traditional financial systems, which made investors and non-financial customers turn to other alternative such as digital banking systems. The existence and development of blockchain technology make cryptocurrency in recent years believably become a complete alternative to traditional ones. Bitcoin is the world's first peer-to-peer and decentralized digital cash system initiated by Nakamoto [1]. Though being the most prominent cryptocurrency, Bitcoin has not been a legal trading currency in various countries. Its exchange rate has appeared to be an exceptionally high-risk portfolio with extreme volatility, which requires a more detailed evaluation before making any decision. This paper utilizes knowledge of statistics for financial time series and machine learning to (i) fit the parametric distribution and (ii) model and forecast the volatility of Bitcoin returns, and (iii) analyze its correlation to other financial market indicators. The fitted parametric time series model significantly outperforms other standard models in explaining the stylized facts and statistical variances in the behavior of Bitcoin returns. The model forecast also outperforms some machine learning methodologies, which would benefit policy makers, banks and financial investors in trading activities for both long-term and short-term strategies.

Wang, D, Xu, G & Deng, S 2017, 'Music recommendation via heterogeneous information graph embedding', Proceedings of the 2017 International Joint Conference on Neural Networks, International Joint Conference on Neural Networks, IEEE, Anchorage, Alaska, USA, pp. 596-603.
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Traditional music recommendation techniques suffer from limited performance due to the sparsity of user-music interaction data, which is addressed by incorporating auxiliary information. In this paper, we study the problem of personalized music recommendation that takes different kinds of auxiliary information into consideration. To achieve this goal, a Heterogeneous Information Graph (HIG) is first constructed to encode different kinds of heterogeneous information, including the interactions between users and music pieces, music playing sequences, and the metadata of music pieces. Based on HIG, a Heterogeneous Information Graph Embedding method (HIGE) is proposed to learn the latent low-dimensional representations of music pieces. Then, we further develop a context-aware music recommendation method. Extensive experiments have been conducted on real-world datasets to compare the proposed method with other state-of-the-art recommendation methods. The results demonstrate that the proposed method significantly outperforms those baselines, especially on sparse datasets.

Wang, G, Zhang, G, Choi, KS, Lam, KM & Lu, J 2017, 'An output-based knowledge transfer approach and its application in bladder cancer prediction', Proceedings of the International Joint Conference on Neural Networks, International Joint Conference on Neural Networks, IEEE, Anchorage, AK, USA, pp. 356-363.
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© 2017 IEEE. Many medical applications face a situation that the on-hand data cannot fully fit an existing predictive model or on-line tool, since these models or tools only use the most common predictors and the other valuable features collected in the current scenario are not considered altogether. On the other hand, the training data in the current scenario is not sufficient to learn a predictive model effectively yet. In order to overcome these problems and construct an efficient classifier, for these real situations in medical fields, in this work we present an approach based on the least squares support vector machine (LS-SVM), which utilizes a transfer learning framework to make maximum use of the data and guarantee its enhanced generalization capability. The proposed approach is capable of effectively learning a target domain with limited samples by relying on the probabilistic outputs from the other previously learned model using a heterogeneous method in the source domain. Moreover, it autonomously and quickly decides how much output knowledge to transfer from source domain to the target one using a fast leave-one-out cross validation strategy. This approach is applied on a real-world clinical dataset to predict 5-year mortality of bladder cancer patients after radical cystectomy, and the experimental results indicate that the proposed method can achieve better performances compared to traditional machine learning methods, consistently showing the potential of the proposed method under the circumstances with insufficient data.

Wilde, MM, Tomamichel, M & Berta, M 2017, 'A meta-converse for private communication over quantum channels', IEEE International Symposium on Information Theory - Proceedings, IEEE International Symposium on Information Theory, IEEE, Aachen, Germany, pp. 291-295.
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© 2017 IEEE. We establish a converse bounds on the private transmission capabilities of a quantum channel. The main conceptual development builds firmly on the notion of a private state, which is a powerful, uniquely quantum method for simplifying the tripartite picture of privacy involving local operations and public classical communication to a bipartite picture of quantum privacy involving local operations and classical communication. This approach has previously led to some of the strongest upper bounds on secret key rates, including the squashed entanglement and the relative entropy of entanglement. Here we use this approach along with a "privacy test" to establish a general meta-converse bound for private communication.

Wu, D, Sharma, N & Blumenstein, M 2017, 'Recent Advances in Video Based Human Action Recognition Using Deep Learning: A Review', Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), International Joint Conference on Neural Networks, IEEE, Anchorage, Alaska, USA, pp. 2865-2872.
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In this work, we consider the problem of robust principal component analysis (RPCA) for streaming noisy data that has been highly compressed. This problem is prominent when one deals with high-dimensional and large-scale data and data compression is necessary. To solve this problem, we propose an online compressed RPCA algorithm to efficiently recover the low-rank components of raw data. Though data compression incurs severe information loss, we provide deep analysis on the proposed algorithm and prove that the low-rank component can be asymptotically recovered under mild conditions. Compared with other recent works on compressed RPCA, our algorithm reduces the memory cost significantly by processing data in an online fashion and reduces the communication cost by accepting sequential compressed data as input.

Wu, W, Li, B, Chen, L & Zhang, C 2017, 'Consistent Weighted Sampling Made More Practical', Proceedings of the 26th International Conference on World Wide Web, International World Wide Web Conference, ACM DL, Perth, Australia.
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Min-Hash, which is widely used for efficiently estimating similarities of bag-of-words represented data, plays an increasingly important role in the era of big data. It has been extended to deal with real-value weighted sets -- Improved Consistent Weighted Sampling (ICWS) is considered as the state-of-the-art for this problem. In this paper, we propose a Practical CWS (PCWS) algorithm. We first transform the original form of ICWS into an equivalent expression, based on which we find some interesting properties that inspire us to make the ICWS algorithm simpler and more efficient in both space and time complexities. PCWS is not only mathematically equivalent to ICWS and preserves the same theoretical properties, but also saves 20% memory footprint and substantial computational cost compared to ICWS. The experimental results on a number of real-world text data sets demonstrate that PCWS obtains the same (even better) classification and retrieval performance as ICWS with 1/5~1/3 reduced empirical runtime.

Xu, G, He, X & Butler, A 2017, 'What comes first the co-Authorship network or the citation?', 4th Multidisciplinary International Social Networks Conference, Multidisciplinary International Social Networks Conference, Association for Computing Machinery, Bangkok, Thailand.
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For many decades citation counting has been used as the way to quantify the nebulous notion of research "quality". Indeed, in conversation the terms "research quality", "impact" or "excellence in research" are simply a reference to a scientific document’s citation count. Moreover, the commonly used journal "impact" factors are simply manipulated forms of citation counting. In recent times, the word "impact" has morphed into the new ’mot du jour’. This paper investigates and discusses the association between co-Authorship networks and citations of institutions within an arbitrary, but defined, subject area. The data examined is readily available and the analytical techniques employed are deliberately simple. The simplicity of this analysis is driven by the desire to show that citation counts are not explicitly related to the quality of research but that citations are a result of multifaceted author networks that are inherent in scientific endeavor. The paper presents an argument that the improved ability to conduct effective network analysis and related research shows that the notion of high citations being the same as "research quality" has run its course. Citation performance is more likely to be a result of co-Authorship network dynamics rather than any perceived notion of "quality". Moreover, it is time the folly of citation counting is put to rest and that if one wants know what "impact" one is having that you need look no further than your co-Authorship network and the reach it has across whatever subject area you are interested in. The discussion and results herein highlight that rather than counting citations, the "impact" of research is driven by connections through networks of people. © 2017 ACM.

Xu, Y, Gu, Y, Do, DMQ, Xie, H & Qing, L 2017, 'A Faster without Scarifying Accuracy Online Decomposition Approach for Higher-Order Tensors', Proceedings - 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017, International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, IEEE, Nanjing, China, pp. 159-166.
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© 2017 IEEE. Tensors could be very suitable for representing multidimensional data. In recent years, CANDECOMP/PARAFAC (CP) decomposition which is one of the most popular methods for Multidimensional Data Analysis has been widely studied and extensively applied. However, today's datasets will often change dynamically, and the amount of data is showing a trend of exponential growth. It is a very necessary and difficult task to perform a CP decomposition on a dynamically changing tensor with very large scale growth. The traditional and classic methods, such as Alternating Least Squares (ALS) algorithm, cannot be directly used to the dynamical tensor due to their huge consumption of time and memory. In addition, the existing online CP methods can only partially solve this problem and can only be applied to thirdorder tensor. Based on the online CP method, we proposed a simplified online CP decomposition algorithm that can be a good solution to these problems. It not only has the similar decomposition accuracy rate with ALS algorithm but also the decomposition speed faster than the ALS algorithm hundreds of thousands of times. Comparing with other state-of-theart online CP methods, it has better decomposition quality and decomposition speed. The experimental results of four methods show that, our approach reduces computational time significantly without scarifying accuracy. our approach has a similar accuracy rate, and the speed has increased by tens times than online CP decomposition. Even in some datasets, the speed and accuracy of our approach are both better than the other approach.

Yan, H, Sui, Y, Chen, S & Xue, J 2017, 'Machine-learning-guided typestate analysis for static use-After-free detection', Proceedings of the 33rd Annual Computer Security Applications Conference, Annual Computer Security Applications Conference, ACM, Orlando, FL, USA, pp. 42-54.
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Typestate analysis relies on pointer analysis for detecting temporal memory safety errors, such as use-After-free (UAF). For large programs, scalable pointer analysis is usually imprecise in analyzing their hard "corner cases", such as infeasible paths, recursion cycles, loops, arrays, and linked lists. Due to a sound over-Approximation of the points-To information, a large number of spurious aliases will be reported conservatively, causing the corresponding typestate analysis to report a large number of false alarms. Thus, the usefulness of typestate analysis for heap-intensive clients, like UAF detection, becomes rather limited, in practice. We introduce Tac, a static UAF detector that bridges the gap between typestate and pointer analyses by machine learning. Tac learns the correlations between program features and UAF-related aliases by using a Support Vector Machine (SVM) and applies this knowledge to further disambiguate the UAF-related aliases reported imprecisely by the pointer analysis so that only the ones validated by its SVM classifier are further investigated by the typestate analysis. Despite its unsoundness, Tac represents a practical typestate analysis approach for UAF detection. We have implemented Tac in LLVM-3.8.0 and evaluated it using a set of eight open-source C/C++ programs. The results show that Tac is effective (in terms of finding 5 known CVE vulnerabilities, 1 known bug, and 8 new bugs with a low false alarm rate) and scalable (in terms of analyzing a large codebase with 2,098 KLOC in just over 4 hours).

Yang, G, Dai, Y, Zhao, H, Hirota, K & Lu, H 2017, 'Intelligent web-based experiment management system using multi-agent concept', Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, Annual Conference of the IEEE Industrial Electronics Society, IEEE, Beijing, China, pp. 8508-8514.
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© 2017 IEEE. Many web-based online learning systems focus more on textual and/or image based content delivery without including experiment systems, or if included they are usually operated under pre-defined conditions, such as fixed scenarios and pre-determined delivery orders. These limitations hinder personalized learning and collaboration between students and discourage student engagement. To circumvent these limitations, an Intelligent Web-based Experiment Management System (IWEMS) using multi-agent concept is presented. In the system, three kinds of software agents are used: (i) Student-Agent, responsible for assessing the knowledge levels of students. A fuzzy set based algorithm is used and the results are plotted through a dynamic polar chart; (ii) Teacher-Agent, responsible for tracking experiment progress of each student and recommending personalized the next-to-do experiment to him or her; and (iii) Co-Agent, responsible for group formation based on similar knowledge levels to facilitate collaborative learning between students. A prototype of this system is developed using a Java Agent Development Framework(JADE), where a client/server architecture and a MySQL database are used. It demonstrates the validity of the design and effectiveness of this system's functionality, achieves the personalization recommendation of next-to-do experiment and collaborative learning environment.

Yang, J, Zhang, W, Yang, S, Zhang, Y & Lin, X 2017, 'TT-Join: Efficient set containment join', Proceedings - International Conference on Data Engineering, International Conference on Data Engineering, IEEE, San Diego, CA, USA, pp. 509-520.
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© 2017 IEEE. In this paper, we study the problem of set containment join. Given two collections R and S of records, the set containment join R ⊆ S retrieves all record pairs {(r, s)} ∈ R × S such that r ⊆ s. This problem has been extensively studied in the literature and has many important applications in commercial and scientific fields. Recent research focuses on the in-memory set containment join algorithms, and several techniques have been developed following intersectionoriented or union-oriented computing paradigms. Nevertheless, we observe that two computing paradigms have their limits due to the nature of the intersection and union operators. Particularly, intersection-oriented method relies on the intersection of the relevant inverted lists built on the elements of S. A nice property of the intersection-oriented method is that the join computation is verification free. However, the number of records explored during the join process may be large because there are multiple replicas for each record in S. On the other hand, the unionoriented method generates a signature for each record in R and the candidate pairs are obtained by the union of the inverted lists of the relevant signatures. The candidate size of the union-oriented method is usually small because each record contributes only one replica in the index. Unfortunately, unionoriented method needs to verify the candidate pairs, which may be cost expensive especially when the join result size is large. As a matter of fact, the state-of-The-Art union-oriented solution is not competitive compared to the intersection-oriented ones. In this paper, we propose a new union-oriented method, namely TT-Join, which not only enhances the advantage of the previous unionoriented methods but also integrates the goodness of intersectionoriented methods by imposing a variant of prefix tree structure. We conduct extensive experiments on 20 real-life datasets by comparing our method with 7 existing methods. The experime...

Yi, C, Yu, D, Sun, Y & Liang, J 2017, 'McVA: A Multi-comparison Visual Analysis System for Maximum Residue Limit Standard in Food Safety', Proceedings of ChinaVis 2017, ChinaVis 2017, QingDao, China.
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Yin, R, Li, K, Zhang, G & Lu, J 2017, 'Detecting overlapping protein complexes in dynamic protein-protein interaction networks by developing a fuzzy clustering algorithm', IEEE International Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, Naples, Italy.
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© 2017 IEEE. Protein complexes play important roles in proteinprotein interaction networks. Recent studies reveal that many proteins have multiple functions and belong to more than one different complexes. To get better complex division, we need to consider time-dependent information of networks. However, only few studies can be found to concentrate on detecting overlapping clusters in time-dependent networks. To solve this problem, we propose integrated model of time-dependent network (IM-TDN) to describe time-dependent networks. On the base of this model, we propose similarity based dynamic fuzzy clustering (SDFC) algorithm to detect overlapping clusters. We apply the algorithm to synthetic data and real world protein-protein interaction network dataset. The results showed that our algorithm by using the model which we proposed achieved better results over the state-of-the-art baseline algorithms.

Yu, H, Lu, J & Zhang, G 2017, 'Learning a fuzzy decision tree from uncertain data', Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017, International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Piscataway, USA, pp. 1-7.
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© 2017 IEEE. Uncertainty in data exists when the value of a data item is not a precise value, but rather by an interval data with a probability distribution function, or a probability distribution of multiple values. Since there are intrinsic differences between uncertain and certain data, it is difficult to deal with uncertain data using traditional classification algorithms. Therefore, in this paper, we propose a fuzzy decision tree algorithm based on a classical ID3 algorithm, it integrates fuzzy set theory and ID3 to overcome the uncertain data classification problem. Besides, we propose a discretization algorithm that enables our proposed Fuzzy-ID3 algorithm to handle the interval data. Experimental results show that our Fuzzy-ID3 algorithm is a practical and robust solution to the problem of uncertain data classification and that it performs better than some of the existing algorithms.

Yueqing, S, Lin, L, Zhongwei, X, Qing, X, Xin, L & Xu, G 2017, 'Co-training an Improved Recurrent Neural Network with Probability Statistic Models for Named Entity Recognition', Database Systems for Advanced Applications (LNCS), International Conference on Database Systems for Advanced Applications, Springer, Suzhou, China, pp. 545-555.
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Named Entity Recognition (NER) is a subtask of information extraction in Natural Language Processing (NLP) field and thus being wildly studied. Currently Recurrent Neural Network (RNN) has become a popular way to do NER task, but it needs a lot of train data. The lack of labeled train data is one of the hard problems and traditional co-training strategy is a way to alleviate it. In this paper, we consider this situation and focus on doing NER with co-training using RNN and two probability statistic models i.e. Hidden Markov Model (HMM) and Conditional Random Field (CRF). We proposed a modified RNN model by redefining its activation function. Compared to traditional sigmoid function, our new function avoids saturation to some degree and makes its output scope very close to [0, 1], thus improving recognition accuracy. Our experiments are conducted ATIS benchmark. First, supervised learning using those models are compared when using different train data size. The experimental results show that it is not necessary to use whole data, even small part of train data can also get good performance. Then, we compare the results of our modified RNN with original RNN. 0.5% improvement is obtained. Last, we compare the co-training results. HMM and CRF get higher improvement than RNN after co-training. Moreover, using our modified RNN in co-training, their performances are improved further.

Yuqiang, H, Wan, Y, Liang, C, Xu, G & Jian, W 2017, 'Exploiting Geographical Location for Team Formation in Social Coding Sites', PAKDD 2017: Advances in Knowledge Discovery and Data Mining, Pacific-Asia Conference on Knowledge Discovery and Data Mining, SpringerLink, Jeju Island, Korea, pp. 499-510.
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Social coding sites (SCSs) such as GitHub and BitBucket are collaborative platforms where developers from different background (e.g., culture, language, location, skills) form a team to contribute to a shared project collaboratively. One essential task of such collaborative development is how to form a optimal team where each member makes his/her greatest contribution, which may have a great effect on the effi- ciency of collaboration. To the best of knowledge, all existing related works model the team formation problem as minimizing the commu- nication cost among developers or taking the workload of individuals into account, ignoring the impact of geographical location of each devel- oper. In this paper, we aims to exploit the geographical proximity factor to improve the performance of team formation in social coding sites. Specifically, we incorporate the communication cost and geographical proximity into a unified objective function and propose a genetic algo- rithm to optimize it. Comprehensive experiments on a real-world dataset (e.g., GitHub) demonstrate the performance of the proposed model with the comparison of some state-of-the-art ones.

Zekveld, J, Bakker, S, Zijlema, A & Van Den Hoven, E 2017, 'Wobble: Shaping unobtrusive reminders for prospective memories in the home context', TEI 2017 - Proceedings of the 11th International Conference on Tangible, Embedded, and Embodied Interaction, International Conference on Tangible, Embedded and Embodied Interactions, Yokohama, pp. 31-35.
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© 2017 ACM. Reminders are designed to support remembering actions or intentions to be performed later in time. Most technologies that have a reminding functionality do so by asking attention (e.g., by using auditory alerts or vibration patterns) from users at a certain point in time or location. Because of their obtrusive nature, the reminders of many (digital) prospective memory AIDS we use on a daily basis are hard to ignore, regardless of our ability and motivation to perform the reminded action or intention. In this paper, we present Wobble: An interactive cone-shaped artefact for reminding in the home environment. Wobble was designed to investigate peripheral reminders. Our results imply that wobble is best suitable for reminding intentions that do not require direct action but can be carried out over a period of time, which is a type of reminding currently not met by most electronic memory AIDS.

Zhang, F, Zhang, Y, Qin, L, Zhang, W & Lin, X 2017, 'Finding Critical Users for Social Network Engagement: The Collapsed k-Core Problem', Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, AAAI, San Francisco, USA, pp. 245-251.
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In social networks, the leave of critical users may significantly break network engagement, i.e., lead a large number of other users to drop out. A popular model to measure social network engagement is k-core, the maximal induced subgraph in which every vertex has at least k neighbors. To identify critical users for social network engagement, we propose the collapsed kcore problem: given a graph G, a positive integer k and a budget b, we aim to find b vertices in G such that the deletion of the b vertices leads to the smallest k-core. We prove the problem is NP-hard. Then, an efficient algorithm is proposed, which significantly reduces the number of candidate vertices to speed up the computation. Our comprehensive experiments on 9 real-life social networks demonstrate the effectiveness and efficiency of our proposed method.

Zhang, J, Sui, Y & Xue, J 2017, 'Incremental analysis for probabilistic programs', Static Analysis (LNCS), International Symposium on Static Analysis, Springer, New York, NY, USA, pp. 450-472.
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© 2017, Springer International Publishing AG. This paper presents Icpp, a new data-flow-based InCremental analysis for Probabilistic Programs, to infer their posterior probability distributions in response to small yet frequent changes to probabilistic knowledge, i.e., prior probability distributions and observations. Unlike incremental analyses for usual programs, which emphasize code changes, such as statement additions and deletions, Icpp focuses on changes made to probabilistic knowledge, the key feature in probabilistic programming. The novelty of Icpp lies in capturing the correlation between prior and posterior probability distributions by reasoning about the probabilistic dependence of each data-flow fact, so that any posterior probability affected by newly changed probabilistic knowledge can be incrementally updated in a sparse manner without recomputing it from scratch, thereby allowing the previously computed results to be reused. We have evaluated Icpp with a set of probabilistic programs. Our results show that Icpp is an order of magnitude faster than the state-of-the-art data-flow-based inference in analyzing probabilistic programs under small yet frequent changes to probabilistic knowledge, with an average analysis overhead of around 0.1 s in response to a single change.

Zhang, X, Yao, L, Huang, C, Sheng, QZ & Wang, X 2017, 'Intent Recognition in Smart Living Through Deep Recurrent Neural Networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 748-758.
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© 2017, Springer International Publishing AG. Electroencephalography (EEG) signal based intent recognition has recently attracted much attention in both academia and industries, due to helping the elderly or motor-disabled people controlling smart devices to communicate with outer world. However, the utilization of EEG signals is challenged by low accuracy, arduous and time-consuming feature extraction. This paper proposes a 7-layer deep learning model to classify raw EEG signals with the aim of recognizing subjects’ intents, to avoid the time consumed in pre-processing and feature extraction. The hyper-parameters are selected by an Orthogonal Array experiment method for efficiency. Our model is applied to an open EEG dataset provided by PhysioNet and achieves the accuracy of 0.9553 on the intent recognition. The applicability of our proposed model is further demonstrated by two use cases of smart living (assisted living with robotics and home automation).

Zhang, X, Yao, L, Zhang, D, Wang, X, Sheng, QZ & Gu, T 2017, 'Multi-person brain activity recognition via comprehensive EEG signal analysis', ACM International Conference Proceeding Series, pp. 28-37.
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© 2017 Association for Computing Machinery. An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. Existing techniques mostly focus on binary brain activity recognition for a single person, which limits their deployment in wider and complex practical scenarios. Therefore, multi-person and multi-class brain activity recognition has obtained popularity recently. Another challenge faced by brain activity recognition is the low recognition accuracy due to the massive noises and the low signal-to-noise ratio in EEG signals. Moreover, the feature engineering in EEG processing is time-consuming and highly relies on the expert experience. In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition. Specifically, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. Then, we adopt an Autoencoder layer to automatically refine the raw EEG signals by eliminating various artifacts. We evaluate our approach on both a public and a local EEG datasets and conduct extensive experiments to explore the effect of several factors (such as normalization methods, training data size, and Autoencoder hidden neuron size) on the recognition results. The experimental results show that our approach achieves a high accuracy comparing to competitive state-of-the-art methods, indicating its potential in promoting future research on multi-person EEG recognition.

Zhang, Y, Huang, Y, Porter, AL, Zhang, G & Lu, J 2017, 'Discovering interactions in big data research: A learning-enhanced bibliometric study', PICMET 2017 - Portland International Conference on Management of Engineering and Technology: Technology Management for the Interconnected World, Proceedings, Portland International Conference on Management of Engineering and Technology, IEEE, Portland, OR, USA, pp. 1-12.
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© 2017 PICMET. As one of the most representative emerging technologies, big data analytics and its related applications are rapidly leading the development of information technologies and are significantly shaping thinking and behavior in today's interconnected world. Exploring the technological evolution of big data research is an effective way to enhance technology management and create value for research and development strategies for both government and industry. This paper uses a learning-enhanced bibliometric study to discover interactions in big data research by detecting and visualizing its evolutionary pathways. Concentrating on a set of 5840 articles derived from Web of Science covering the period between 2000 and 2015, text mining and bibliometric techniques are combined to profile the hotspots in big data research and its core constituents. A learning process is used to enhance the ability to identify the interactive relationships between topics in sequential time slices, revealing technological evolution and death. The outputs include a landscape of interactions within big data research from 2000 to 2015 with a detailed map of the evolutionary pathways of specific technologies. Empirical insights for related studies in science policy, innovation management, and entrepreneurship are also provided.

Zhang, Y, Yu, JX, Zhang, Y & Qin, L 2017, 'A fast order-based approach for core maintenance', Proceedings - International Conference on Data Engineering, International Conference on Data Engineering, IEEE, Piscataway, USA, pp. 337-348.
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© 2017 IEEE. Graphs have been widely used in many applications such as social networks, collaboration networks, and biological networks. One important graph analytics is to explore cohesive subgraphs in a large graph. Among several cohesive subgraphs studied, k-core is one that can be computed in linear time for a static graph. Since graphs are evolving in real applications, in this paper, we study core maintenance which is to reduce the computational cost to compute k-cores for a graph when graphs are updated from time to time dynamically. We identify drawbacks of the existing efficient algorithm, which needs a large search space to find the vertices that need to be updated, and has high overhead to maintain the index built, when a graph is updated. We propose a new order-based approach to maintain an order, called k-order, among vertices, while a graph is updated. Our new algorithm can significantly outperform the state-of-Theart algorithm up to 3 orders of magnitude for the 11 large real graphs tested. We report our findings in this paper.

Zhang, Z, Oberst, S & Lai, J 2017, 'Uncertainty Analysis for the Prediction of Disc Brake Squeal Propensity', Internoise 2017, Hong Kong, China.
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Zhou, Z, Xu, G, Zhu, X & Liu, S 2017, 'Latent Factor Analysis for Low-dimensional Implicit Preference Prediction', International Conference on Behavioral, Economic, Socio-cultural Computing, IEEE, Poland, pp. 1-2.
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User preference prediction aims to predict a users future preferences on a large number of items according to his/her preference history. To achieve this goal, many models have been proposed, but mainly for explicit preference data, such as 5-star ratings. Nevertheless, real-world data are often in implicit format, such as purchase action, and the number of items is not always large. In this paper, we demonstrate the use of latent factor models for solving the task of predicting user preferences on implicit and low-dimensional dataset.

Zhou, Z, Xu, GUANGDONG, Zhu, W, Li, J & Zhang, W 2017, 'Structure embedding for knowledge base completion and analytics.', International Joint Conference on Neural Networks IJCNN 2017, IEEE, Anchorage, Alaska, USA.
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To explore the latent information of Human Knowledge, the analysis for Knowledge Bases (KBs) (e.g. WordNet, Freebase) is essential. Some previous KB element embedding frameworks are used for KBs structure analysis and completion. These embedding frameworks use low-dimensional vector space representation for large scale of entities and relations in KB. Based on that, the vector space representation of entities and relations which are not contained by KB can be measured. The embedding idea is reasonable, while the current embedding methods have some issues to get proper embeddings for KB elements. The embedding methods use entity-relation-entity triplet, contained by most of current KB, as training data to output the embedding representation of entities and relations. To measure the truth of one triplet (The knowledge represented by triplet is true or false), some current embedding methods such as Structured Embedding (SE) project entity vectors into subspace, the meaning of such subspace is not clear for knowledge reasoning. Some other embedding methods such as TransE use simple linear vector transform to represent relation (such as vector add or minus), which can't deal with the multiple relations match or multiple entities match problem. For example, there are multiple relations between two entities, or there are multiple entities have same relation with one entity. Insipred by previous KB element structured embedding methods, we propose a new method, Bipartite Graph Network Structured Embedding (BGNSE). BGNSE combines the current KB embedding methods with bipartite graph network model, which is widely used in many fields including image data compression, collaborative filtering. BGNSE embeds each entity-relation-entity KB triplet into a bipartite graph network structure model, represents each entity by one bipartite graph layer, represents relation by link weights matrix of bipartite graph network. Based on bipartite graph model, our proposed method has followi...

Zhu, D, Pang, N, Li, G & Liu, S 2017, 'NotiFi: Non-Invasive Abnormal Activity Detection Using Fine-grained WiFi Signals', Proceedings of the International Joint Conference on Neural Networks (IJCNN), International Joint Conference on Neural Networks, IEEE, Anchorage, Alaska, USA, pp. 1766-1773.
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We build an ubiquitous abnormal activity detection system, namely NotiFi, for accurately detecting the abnormal activities on commercial off-the-shelf (COTS) IEEE 802.11 devices. In contrast to the traditional wearable sensor based and computer vision based systems which require additional sensors or enough lighting in line-of-sight (LoS) scenario, we proceed directly with abnormal activity characterization and activity modeling at the WiFi signal level based on Channel State Information (CSI). The intuition of NotiFi is that whenever the human body occludes the wireless signal transmitting from the access point to the receiver, the phase and the amplitude information of Channel State Information (CSI) will change sensitively. By creating a multiple hierarchical Dirichlet processes, NotiFi automatically learns the number of human body activity categories for abnormal detection. Experimental results in three typical indoor environments indicate that NotiFi can achieve satisfactory performance in accuracy, robustness and stability.

Zhu, D, Pang, N, Li, G & Liu, S 2016, 'WiseFi: Activity Localization and Recognition on Commodity Off-the-shelf WiFi Devices', IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems, IEEE, Sydney, Australia.
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Zhu, F, Zhang, G, Lu, J & Zhu, D 2017, 'First-order causal process for causal modelling with instantaneous and cross-temporal relations', Proceedings of the International Joint Conference on Neural Networks, International Joint Conference on Neural Networks, IEEE, Anchorage, USA, pp. 380-387.
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© 2017 IEEE. Motivated by the real damped simple harmonic oscillator (SHO) system, in this paper, we propose a process interpretation of causality and the first-order causal process (FoCP) model for temporal causal modelling. Compared with existing causal models that are able to model feedbacks, such as the structural equation model (SEM) and the structure vector autoregressive (SVAR) model, the FoCP model entails a novel 2-stage evolution semantic for instantaneous and cross-temporal causal relations existing in many real world dynamic systems. Graphical representations are developed to illustrate the causal structure compactly. Useful properties of the new model are identified and used to develop a conditional independence based algorithm for learning the causal structure from a multivariate time series dataset. Experiments on both simulated and real data validate the feasibility of the method to discover simple while meaningful causal structures of dynamic systems.

Zijlema, AF, Van den Hoven, E & Eggen, B 2017, 'Preserving objects, preserving memories: repair professionals and object owners on the relation between memories and traces on personal possessions', PLATE 2017 Conference Proceedings, Product Lifetimes And The Environment 2017, Delft University of Technology and IOS Press, Delft, the Netherlands, pp. 453-457.
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Traces of ageing and use on the material of products, and memories associated with products, have been found to contribute to product attachment and can stimulate product longevity. We present findings of a qualitative study that focused on the relation between traces of ageing and use on personal possessions and memories and the effects of repair on objects. With this research, we intended to increase our understanding of the role of traces on personal possessions and memories. We interviewed five professionals at their workplace who worked as a restorer or did repairs of personal possessions, and five owners of a repaired or restored possession. The motivations for bringing an object for repair were not only related to the deteriorating condition of the object but were also triggered by situational events or circumstances, such as passing on ownership or knowing someone who could repair the object. We found five different categories of traces among the possessions of the interviewed object owners: Traces of use, traces of ageing, traces of repair, traces of accidents and alterations. We found that objects gained meaning after the repair. When object owners or repair professionals decided not to repair traces, it was often for aesthetical and reminding reasons, but also because it may be how the owner remembered the object. Traces can cue associations to their use in the past, and also to the (imagined) history of the objects. These findings indicate that repair can enhance the cueing of memories and that preservation of meaningful traces may contribute to attachment.

Zijlema, AF, Van den Hoven, E & Eggen, B 2017, 'What comes to mind when being triggered by personal items in the home? A qualitative exploration of cuing responses', Conference of Society for Applied Research in Memory and Cognition, Sydney, Australia.
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We investigated how personal holiday-items affect the retrieval of autobiographical memories. People often keep souvenirs, photos, and other acquired items from their holidays in their home. But what do the holiday-items evoke when people encounter them? We interviewed nine participants during a ‘home tour’, discussing holiday-items from one particular holiday while walking with the participant through their homes. Qualitative analysis resulted in four types of cuing responses: ‘no-memory’ responses, ‘know’ responses, ‘memory evoked think or feel’ responses, and ‘remember’ responses. For each of these responses, we discuss the item types and their characteristics, giving a peek into everyday life remembering.

Zuo, H, Zhang, G, Lu, J & Pedrycz, W 2017, 'Fuzzy Rule-based Transfer Learning for Label Space Adaptation', 2017 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Naples, ITALY.
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Kenning, G University of Technology Sydney 2017, Making It Together: Reciprocal Design to promote positive wellbeing for people living with dementia, Sydney.

Vitale, J, Tonkin, M, Ojha, S, Williams, M-A, Wang, X & Judge, W 2017, Privacy by Design in Machine Learning Data Collection: A User Experience Experimentation.
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Aggarwal, D, Brennen, GK, Lee, T, Santha, M & Tomamichel, M 2017, 'Quantum attacks on Bitcoin, and how to protect against them'.
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The key cryptographic protocols used to secure the internet and financial transactions of today are all susceptible to attack by the development of a sufficiently large quantum computer. One particular area at risk are cryptocurrencies, a market currently worth over 150 billion USD. We investigate the risk of Bitcoin, and other cryptocurrencies, to attacks by quantum computers. We find that the proof-of-work used by Bitcoin is relatively resistant to substantial speedup by quantum computers in the next 10 years, mainly because specialized ASIC miners are extremely fast compared to the estimated clock speed of near-term quantum computers. On the other hand, the elliptic curve signature scheme used by Bitcoin is much more at risk, and could be completely broken by a quantum computer as early as 2027, by the most optimistic estimates. We analyze an alternative proof-of-work called Momentum, based on finding collisions in a hash function, that is even more resistant to speedup by a quantum computer. We also review the available post-quantum signature schemes to see which one would best meet the security and efficiency requirements of blockchain applications.

Dickson, A & Gill, AQ 2017, 'Aquatic Sciences Data Reference Model'.
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Context: To inform effective decision making, aquatic ecosystem scientists are required to integrate and interpret information from a variety of sources and domains such as biology, chemistry, hydrology, geology, meteorology, climate science and geophysics. Problem: Preparation, analysis, interpretation and communication of data from disparate sources in different formats is a challenging task. Data from different domains can often be stored differently and is subject to multiple interpretations. This suggests a need to define a taxonomy of aquatic ecosystem data. Solutions: To address the problem in hand, this paper proposes the Data Reference Model (DRM) to facilitate the understanding of data entities, topics and relationships of data within the Aquatic Science domain. Research Method: A series of brainstorming exercises with experienced aquatic ecologists identified a range of data entities and an analysis of existing data standards provided clarity to the entity selection process. Adherence to the Open Geospatial Consortium (OGC) WaterML-WQ Best Practice and the data standards of the Atlas of Living Australia provided guidance to the development of the Aquatic Sciences DRM. The DRM is documented as a tree structure diagram and presented as a poster of data entity taxonomy, to enhance communication. Finally, it is applied to a ten-year aquatic ecosystem monitoring dataset, to demonstrate implementation. Impact: The DRM is aimed to be an important tool for the facilitation of communication between practitioners of aquatic ecosystem science and information systems specialists. It establishes a vocabulary that the two, almost opposing parties, can comprehend and provides a structured and tested approach to data interpretation and governance. Conclusion: The DRM is not intended to be a static document that covers the entirety of its subject matter, but rather an evolving model that, through ongoing collaboration, will facilitate communication and understandi...

Gill, AQ 2017, 'Adaptive Analytics and Information Architecture'.
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The 24th Annual ACS Canberra Conference

Gill, AQ 2017, 'Adaptive Strategy: Digital Transformation in Higher Education'.
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EduTech – International Congress & Expo 2017

Gill, AQ 2017, 'Building adaptive architecture as a strategic platform for digital transformation'.
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2nd annual Digital Government Transformation Conference 2017

Gill, AQ 2017, 'Building an Adaptive Enterprise Architecture Capability for our Rapidly Evolving Digital World'.
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CFO Edge Conference 2017

Gudi, SLKC, ojha, S, clark, J, johnston, B & williams, M-A 2017, 'Fog Robotics: An Introduction'.
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Cloud Robotics (CR) is an emerging and successful approach to robotics. The number of robots or other IoT devices may increase drastically in the future which might need enormous bandwidth and there might be security concerns. If robots in CR are not secured then robots can even become surveillance bot by hackers. Moreover, if an internet connection is lost due to network hitches then in that crucial moment robot may not be available to complete its given task. For example, a robot assisting a person can stop working unexpectedly or work with the instructions from hacker. In order to address such problems, we propose a new approach to robotics - Fog Robotics (FR) in this paper, so a network of robots can be used more securely and efficiently as compared to CR.

Hobbs, B, Ord, A, Oberst, S & Niven, RK 2017, 'Nonlinear episodic and chaotic behaviour of orogenic gold systems'.

Jay, B 2017, 'A Combinatory Account of Substitution'.

Jay, B 2017, 'Beyond Lambda-Calculus: Intensional Computation'.

Jay, B 2017, 'Deconstructing Lambda-Calculus'.

Jay, B 2017, 'Intensional-computation, repository of proofs in Coq'.

Jay, B 2017, 'Repository of proofs for L-calculus in Coq'.

Jay, B 2017, 'SF repository of proofs in Coq'.

Jay, B 2017, 'Typed LambdaFactor Calculus repository of proofs in Coq'.

Mann, RL & Bremner, MJ 2017, 'On the Complexity of Random Quantum Computations and the Jones Polynomial'.
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There is a natural relationship between Jones polynomials and quantum computation. We use this relationship to show that the complexity of evaluating relative-error approximations of Jones polynomials can be used to bound the classical complexity of approximately simulating random quantum computations. We prove that random quantum computations cannot be classically simulated up to a constant total variation distance, under the assumption that (1) the Polynomial Hierarchy does not collapse and (2) the average-case complexity of relative-error approximations of the Jones polynomial matches the worst-case complexity over a constant fraction of random links. Our results provide a straightforward relationship between the approximation of Jones polynomials and the complexity of random quantum computations.

Singh, AK, Chen, H-T, King, J-T & Lin, C-T 2017, 'Measuring Cognitive Conflict in Virtual Reality with Feedback-Related Negativity'.
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As virtual reality (VR) emerges as a mainstream platform, designers have started to experiment new interaction techniques to enhance the user experience. This is a challenging task because designers not only strive to provide designs with good performance but also carefully ensure not to disrupt users' immersive experience. There is a dire need for a new evaluation tool that extends beyond traditional quantitative measurements to assist designers in the design process. We propose an EEG-based experiment framework that evaluates interaction techniques in VR by measuring intentionally elicited cognitive conflict. Through the analysis of the feedback-related negativity (FRN) as well as other quantitative measurements, this framework allows designers to evaluate the effect of the variables of interest. We studied the framework by applying it to the fundamental task of 3D object selection using direct 3D input, i.e. tracked hand in VR. The cognitive conflict is intentionally elicited by manipulating the selection radius of the target object. Our first behavior experiment validated the framework in line with the findings of conflict-induced behavior adjustments like those reported in other classical psychology experiment paradigms. Our second EEG-based experiment examines the effect of the appearance of virtual hands. We found that the amplitude of FRN correlates with the level of realism of the virtual hands, which concurs with the Uncanny Valley theory.

Xuan, J, Lu, J, Zhang, G & Xu, RYD 2017, 'Cooperative Hierarchical Dirichlet Processes: Superposition vs. Maximization'.
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The cooperative hierarchical structure is a common and significant data structure observed in, or adopted by, many research areas, such as: text mining (author-paper-word) and multi-label classification (label-instance-feature). Renowned Bayesian approaches for cooperative hierarchical structure modeling are mostly based on topic models. However, these approaches suffer from a serious issue in that the number of hidden topics/factors needs to be fixed in advance and an inappropriate number may lead to overfitting or underfitting. One elegant way to resolve this issue is Bayesian nonparametric learning, but existing work in this area still cannot be applied to cooperative hierarchical structure modeling. In this paper, we propose a cooperative hierarchical Dirichlet process (CHDP) to fill this gap. Each node in a cooperative hierarchical structure is assigned a Dirichlet process to model its weights on the infinite hidden factors/topics. Together with measure inheritance from hierarchical Dirichlet process, two kinds of measure cooperation, i.e., superposition and maximization, are defined to capture the many-to-many relationships in the cooperative hierarchical structure. Furthermore, two constructive representations for CHDP, i.e., stick-breaking and international restaurant process, are designed to facilitate the model inference. Experiments on synthetic and real-world data with cooperative hierarchical structures demonstrate the properties and the ability of CHDP for cooperative hierarchical structure modeling and its potential for practical application scenarios.

Zijlema, AF, Van den Hoven, E & Eggen, B 2017, 'Memory cue evolvement with personal objects and media: The development of the item-memory relation over time'.
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Personal items that remind us of our past have not always been reminders; at some point they started cuing autobiographical memories, and its function and meaning may have changed over time. We are interested in the item-memory relationship and how this relationship evolves over time. Therefore, we set up a study with 19 participants, who filled in cards with questions about the memories the item cued and the interaction with the item, over a time period of eight months with three personal items for each participant. In this poster presentation we will discuss factors that contributed to the item-memory relationship.