Publications

Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Closing the Loop: Placing Ontology Usage Analysis in the Ontology Development and Deployment Lifecycle' in Studies in Computational Intelligence, pp. 23-47.
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© 2018, Springer International Publishing AG, part of Springer Nature. The aim of this book is to present a framework to measure and analyse the usage of ontologies. Usage and ontology are the two key words that signify the focus of this work.

Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Evaluation of U Ontology' in Studies in Computational Intelligence, pp. 243-268.
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© 2018, Springer International Publishing AG, part of Springer Nature. The utilization of the OUSAF and its computational components were presented in the previous chapter. The U Ontology, which conceptualizes the Ontology Usage Analysis domain, was used to obtain ontology usage-related information from the OUSAF. In this chapter, the U Ontology will be evaluated using an ontology evaluation methodology to measure the quality of the developed ontology. For an ontology to be of good quality and remain useful for its users, it needs to conform to a set of good practices. These practices are analysed using evaluation techniques which assess an ontology based on specific criteria to ensure that the developed ontology meets the user’s expectations.

Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Identification Phase: Ontology Usage Network Analysis Framework (OUN-AF)' in Studies in Computational Intelligence, pp. 63-101.
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© 2018, Springer International Publishing AG, part of Springer Nature. Analysing ontology usage, as we have noted in the previous chapter, is comprised of four phases: identification, investigation, representation and utilization. The identification phase, which is the focus of this chapter, is responsible for identifying different ontologies that are being used in a particular application area or in a given dataset for further analysis. The ontologies to be analysed fall into two categories: the domain ontology to be analysed for usage is knownthe domain ontology to be analysed for usage needs to be identified according to application-specific requirements.

Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Investigation Phase: Empirical Analysis of Domain Ontology Usage (EMP-AF)' in Studies in Computational Intelligence, pp. 103-140.
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© 2018, Springer International Publishing AG, part of Springer Nature. As highlighted in the previous chapters, in order to make effective and efficient use of an ontology, it is important to understand how a given ontology is being used by the users and its level of adoption. The next phase of the OUSAF, after the identification phase, is therefore the investigation phase, in which the analysis of the use of domain ontologies on the Web takes place.

Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Investigation Phase: Quantitative Analysis of Domain Ontology Usage (QUA-AF)' in Studies in Computational Intelligence, pp. 141-169.
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© 2018, Springer International Publishing AG, part of Springer Nature. In the previous chapter, the EMP-AF was proposed to perform an empirical analysis of domain ontology usage. The empirical analysis, through its observed factors such as the relationship between different ontologies based on an entity’s semantic description, ontology component usage, contextual description, and provision and availability of knowledge patterns, helps us to understand the uptake and adoption of domain ontologies on the Web. In other words, it gives a comprehensive analysis of the “usage” aspect of a domain ontology and its components. While the insights obtained through EMP-AF highlight the key aspects of the usage dimension, two other dimensions warrant consideration to fully realize the perceived benefits of Ontology Usage Analysis (OUA), as mentioned in Chap. 3, and to enable the quantitative analysis of OUA. These are the “technology” and “business” dimensions, which also have a direct relationship with ontology adoption and usage.

Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Measuring and Analysing the Use of Ontologies A Semantic Framework for Measuring Ontology Usage' in Studies in Computational Intelligence, pp. 3-26.

Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Motivation' in Studies in Computational Intelligence, pp. 1-21.
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© 2018, Springer International Publishing AG, part of Springer Nature. Since its inception, the internet has transformed the way we communicate, interact and do business across the globe. Described and dubbed as information highway, the internet has provided an unprecedented seamless infrastructure to assimilate and dissimilate information with an ease and speed never before witnessed by mankind. Today, as a result of this, 51.7% of the world’s population is using the internet. Various recent initiatives such as promotion and adoptions of internet in remote regions and under privileged areas has increased the actual internet usage than the reported figures. Capitalizing on such intrinsic properties of the internet as simplicity, ubiquity and scalability, Tim Berners-Lee introduced the World Wide Web in 1989 (Berners-Lee, Fischetti, Weaving the web: the original design and ultimate destiny of the World Wide Web by its inventor, HarperSanFrancisco, San Francisco, 1999, [25]) as a platform for publishing and consuming information on a universal scale. The World Wide Web (also known as the WWW or Web), which without a doubt is one of the most significant computational phenomena to date, has revolutionized information sharing by providing a decentralized information platform which has enabled and empowered users to be more interactive and participative, turning each user of the Web into a potential publisher (Fig. 1.1). Being able to publish information which is accessible to anyone in the world with access to the Web for a low cost has resulted in the proliferation of approximately 4.62 billion indexed web documents containing information on a variety of topics, and creating a huge amount of diversified information commonly referred as Big Data.

Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'New Research Directions' in Studies in Computational Intelligence, pp. 269-271.
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© 2018, Springer International Publishing AG, part of Springer Nature. As discussed in the previous chapters, numerous domain ontologies developed in different areas have resulted in a huge growth of structured data on the Web. While this has led to an increase in the number of ontologies published on the Web, at the same time there is no formal approach to evaluate, measure, and analyse the use of those ontologies.

Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Ontology Usage Analysis Framework (OUSAF)' in Studies in Computational Intelligence, pp. 49-62.
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© 2018, Springer International Publishing AG, part of Springer Nature. As emphasized in the previous chapters, web ontologies are being developed and deployed to describe information in a semantically rich fashion, but to benefit from the deployment of ontologies, it is important to understand which components of an ontology are being used and how they are being used. Such understanding can improve the utilization of Semantic Web data and allow its potential benefits to be realized (Baker and Herman, Semantic web case studies and use cases, 2009, [13]). In the previous chapter, we discussed the existing literature, which measures “usage” with a greater focus on understanding RDF data in general than from the perspective of analysing how ontologies are being used to represent that RDF data. To address that drawback, we present our proposed Ontology USage Analysis Framework (OUSAF) in this chapter. Section 3.2 presents the notations used in this book. In Sect. 3.3, ‘Ontology Usage Analysis’ is defined and key terms used in the definition are discussed in detail. Section 3.4 describes the different phases of Ontology Usage Analysis framework and the purpose of each phase. In Sect. 3.5, each phase of the framework is explained in detail. The conclusion of the chapter is presented in Sect. 3.6.

Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Preface', pp. ix-xii.

Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Representation Phase: Ontology Usage Ontology (U Ontology)' in Studies in Computational Intelligence, pp. 171-203.
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© 2018, Springer International Publishing AG, part of Springer Nature. The huge increase in the use of ontologies and Semantic Web data has increased the need for usage-related information to assist stakeholders (or users) to make effective use of currently available semantic information. As noted in earlier chapters, stakeholders can be different groups of users such as ontology developers, domain experts, application developers and data publishers, each of whom will have a view-specific requirement of the same information. The identification and investigation phases of the OUSAF, discussed in Chaps. 4 – 6, help us to identify and measure usage-related information. However, once the usage-related information has been determined, it needs to be presented to stakeholders in a structured format; providing granular access to ontology usage-related information therefore meets the needs of each stakeholder. This is done in the Representation phase of the OUSAF, in which an ontology usage ontology (the U Ontology) is developed to represent ontology usage analysis-related information. In this chapter, the conceptual framework of the U Ontology is presented.

Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Utilization Phase: Utilization of OUSAF' in Studies in Computational Intelligence, pp. 205-242.
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© 2018, Springer International Publishing AG, part of Springer Nature. To demonstrate the utilization of the OUSAF, in this chapter, a methodological approach is adopted which provides a systematic flow of activities and the interaction between different components to analyse the utilization. This methodological approach is presented in Sect. 8.2. Section 8.3 presents details on the construction of the dataset that is used to demonstrate the utilization phase. In Sects. 8.4–8.6, the utilization of the different phases of the OUSAF is presented. Section 8.7 summarizes the achievements of the utilization phase. Section 8.8 concludes the chapter.

Johnston, A & Bluff, A 2018, 'Collaborative Creation in Interactive Theatre' in Candy, L, Edmonds, E & Poltronieri, F (eds), Explorations in Art and Technology, Springer, London, pp. 341-351.

Kenning, G 2018, 'Visual stimulation and visual analysis in design: Participatory and co-design approaches for people living with dementia'.

Kenning, G & Law, J 2018, 'Knitting and Crochet as Experiment: Exploring Social and Material Practices of Computation and Craft' in Luckman, S (ed), The Craft Economy: Making, Materiality and Meaning, Bloomsbury (UK).
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Lee, T, Ghapanchi, AH, Talaei-Khoei, A & Ray, P 2018, 'Strategic information system planning in healthcare organizations' in Global Business Expansion: Concepts, Methodologies, Tools, and Appl., pp. 1048-1077.
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© 2018, IGI Global. The healthcare industry is a critical and growing part of economies worldwide. To provide better quality of care, and value for money, billions of dollars are being spent on bettering information systems in healthcare organizations. Strategic Information System Planning (SISP) is instrumental in making informed decisions to achieve the health organizations' goals and objectives. This paper undertakes a systematic review to gain insight into existing studies on SISP in healthcare organizations. Our systematic review of papers on SISP from 1985 to 2011 examines the background and trend of research into SISP in the healthcare industry, classification of topics in SISP, as well as sets of tools and guidelines to aid practitioners and the research community alike.

Li, J, Chen, Z & Ma, Z 2018, 'Learning Colours from Textures by Sparse Manifold Embedding' in Yurish, SY (ed), Advances in Signal Processing: Reviews, ISFA.
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Liu, S & Li, G 2018, 'Personalized Hotel Recommendation based on Social Networks' in Gursoy, D (ed), Routledge Handbook of Hospitality Marketing, Routledge, UK.
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Recommender systems have become an important tool for users to identify interesting items as well as for businesses to promote their products to the right users. With the rapid development of social networks, travelers have started to seek recommendations and advice from web services such as TripAdvisor and Yelp. Although the initial purpose of travelers is to share their opinions on social networks, this provides an opportunity for hospitality businesses to learn about their customers’ preferences. Given these data on preferences, recent advances in data science research have made it possible to build automatic recommender systems that can generate hotel recommendations tailored to each traveler. This chapter introduces the basic concepts and tools for creating hotel recommender systems

Liu, W & Quan, D 2018, 'Scalable Multimodal Factorization for Learning from Very Big Data' in Seng, KP, Ang, L-M, Gao, J & Liew, AW-C (eds), Multimodal Analytics for Next-Generation Big Data Technologies and Applications, Spinger, Switzerland.
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Recent technology advances in data acquisition bring to re- search communities new opportunities as well as new challenges. They enable researchers to acquire multiple modes of information about the real world. This multimodal data can be naturally and e ciently represented by a multi-way structure, so-called tensors, which can be analyzed to ex- tract the underlying core patterns of the observed data. Multiple datasets obtained from di erent acquisition methods and sensors are increasingly available. The increasing availability of multiple modalities, captured in correlated tensors, provides a complete picture of the whole data patterns. Given large-scale datasets, existing distributed methods for joint analy- sis of multi-dimensional data generated from multiple sources decompose them on several computing nodes following Map-Reduce paradigm. How to improve the performance of Map-Reduce based factorization algorithms as observed data gets bigger is still an open problem. This requires an even more e cient solution that not only reduces communication overhead but also optimizes factors faster. In this book chapter, we provide readers knowledge about Tensor Factor- ization and joint analysis of several correlated Tensors. We propose a Scalable Multimodal Factorization (SMF) algorithm for analyzing corre- lated big multimodal data. It has two key features to enable big multimodal data analysis. Firstly, SMF's design, based on Apache Spark, enables it to have the smallest communication cost. Secondly, its optimized solver converges faster. These key advantages reduce factorization's time com- plexity. As a result, SMF's performance is extremely e cient as the data increases. Con rmed by our experiments with 1 billion known entries, SMF outperforms the currently fastest Coupled Tensor Factorization and Tensor Factorization by 17.8 and 3.8 times, respectively. Compellingly, SMF achieves this speed with the highest accuracy.

Mathieson, L, Cotta, C & Moscato, P 2018, 'Memetic Algorithms' in Resende, MGC, Marti, R & Pardalos, PM (eds), Handbook of Heuristics, Springer.
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Song, K, Feng, S, Gao, W, Wang, D, Chen, L & Zhang, C 2018, 'Build emotion lexicon from microblogs by combining effects of seed words and emoticons in a heterogeneous graph' in Social Media Content Analysis: Natural Language Processing and Beyond, World Scientific, Singapore, pp. 171-195.
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© 2018 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. As an indispensable resource for emotion analysis, emotion lexicons have attracted increasing attention in recent years. Most existing methods focus on capturing the single emotional effect of words rather than the emotion distributions which are helpful to model multiple complex emotions in a subjective text. Meanwhile, automatic lexicon building methods are overly dependent on seed words but neglect the effect of emoticons which are natural graphical labels of fine-grained emotion. In this chapter, we describe a novel emotion lexicon building framework that leverages both seed words and emoticons simultaneously to capture emotion distributions of candidate words more accurately. Our method overcomes the weakness of existing methods by combining the effects of both seed words and emoticons in a unified three-layer heterogeneous graph, in which a multi-label random walk (MLRW) algorithm is performed to strengthen the emotion distribution estimation. Experimental results on real-world data reveal that our constructed emotion lexicon achieves promising results for emotion classification compared to the state-of-the-art lexicons.

van den Hoven, EA, Broekhuijsen, M & Mols, I 2018, 'Design Applications for Social Remembering' in Meade, ML, Harris, CB, Van Bergen, P, Sutton, J & Barnier, A (eds), Collaborative remembering:  Theories, research, and applications, Oxford University Press, Oxford, UK, pp. 386-403.
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Za'in, C, Pratama, M, Prasad, M, Puthal, D, Lim, CP & Seera, M 2018, 'Motor fault detection and diagnosis based on a meta-cognitive random vector functional link network' in Fault Diagnosis of Hybrid Dynamic and Complex Systems, pp. 15-44.
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Alderighi, T, Malomo, L, Giorgi, D, Pietroni, N, Bickel, B & Cignoni, P 2018, 'Metamolds: computational design of silicone molds', ACM Transactions on Graphics (TOG), vol. 37, no. 4, pp. 136-136.
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Alshehri, MD & Hussain, FK 2018, 'A fuzzy security protocol for trust management in the internet of things (Fuzzy-IoT)', Computing.
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© 2018, Springer-Verlag GmbH Austria, ein Teil von Springer Nature. Recently, the Internet of things (IoT) has received a lot of attention from both industry and academia. A reliable and secure IoT connection and communication is essential for the proper working of the IoT network as a whole. One of the ways to achieve robust security in an IoT network is to enable and build trusted communication among the things (nodes). In this area, the existing IoT literature faces many critical issues, such as the lack of intelligent cluster-based trust approaches for IoT networks and the detection of attacks on the IoT trust system from malicious nodes, such as bad service providers. The existing literature either does not address these issues or only addresses them partially. Our proposed solution can firstly detect on-off attacks using the proposed fuzzy-logic based approach, and it can detect contradictory behaviour attacks and other malicious nodes. Secondly, we develop a fuzzy logic-based approach to detect malicious nodes involved in bad service provisioning. Finally, to maintain the security of the IoT network, we develop a secure messaging system that enables secure communication between nodes. This messaging system uses hexadecimal values with a structure similar to serial communication. We carried out extensive experimentation under varying network sizes to validate the working of our proposed solution and also to test the efficiency of the proposed methods in relation to various types of malicious behavior. The experiment results demonstrate the effectiveness of our approach under various conditions.

Alshehri, MD, Hussain, F & Hussain, O 2018, 'Clustering-Driven Intelligent Trust Management Methodologyfor the Internet of Things (CITM-IoT)', Mobile Networks and Applications, vol. 23, no. 3, pp. 419-431.
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The growth and adoption of the Internet of Things (IoT) is increasing day by day. The large number of IoT devices increases the risk of security threats such as (but not limited to) viruses or cyber-attacks. One possible approach to achieve IoT security is to enable a trustworthy IoT environment in IoT wherein the interactions are based on the trust value of the communicating nodes. Trust management and trust assessment has been extensively studied in distributed networks in general and the IoT in particular, but there are still outstanding pressing issues such as bad-mouthing of trust values which prevent them from being used in practical IoT applications. Furthermore, there is no research in ensuring that the developed IoT trust solutions are scalable across billions of IoT nodes. To address the above-mentioned issues, we propose a methodology for scalable trust management solution in the IoT. The methodology addresses practical and pressing issues related to IoT trust management such as trust-based IoT clustering, intelligent methods for countering bad-mouthing attacks on trust systems, issues of memory-efficient trust computation and trust-based migration of IoT nodes from one cluster to another. Experimental results demonstrate the effectiveness of the proposed approaches.

Alzoubi, Y, Gill, AQ & Moulton, B 2018, 'A measurement model to analyze the effect of agile enterprise architecture on geographically distributed agile development', Journal of Software Engineering Research and Development, vol. 6, no. 4.
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Anshar, M & Williams, MA 2018, 'Evolving robot empathy towards humans with motor disabilities through artificial pain generation', AIMS Neuroscience, vol. 5, no. 1, pp. 56-73.
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© 2018 the Author(s). In contact assistive robots, a prolonged physical engagement between robots and humans with motor disabilities due to shoulder injuries, for instance, may at times lead humans to experience pain. In this situation, robots will require sophisticated capabilities, such as the ability to recognize human pain in advance and generate counter-responses as follow up emphatic action. Hence, it is important for robots to acquire an appropriate pain concept that allows them to develop these capabilities. This paper conceptualizes empathy generation through the realization of synthetic pain classes integrated into a robot's self-awareness framework, and the implementation of fault detection on the robot body serves as a primary source of pain activation. Projection of human shoulder motion into the robot arm motion acts as a fusion process, which is used as a medium to gather information for analyses then to generate corresponding synthetic pain and emphatic responses. An experiment is designed to mirror a human peer's shoulder motion into an observer robot. The results demonstrate that the fusion takes place accurately whenever unified internal states are achieved, allowing accurate classification of synthetic pain categories and generation of empathy responses in a timely fashion. Future works will consider a pain activation mechanism development.

Babar, A, Bunker, D & Gill, AQ 2018, 'Investigating the Relationship between Business Analysts’ Competency and IS Requirements Elicitation: A Thematic-analysis Approach', Communications of the Association for Information Systems, vol. 42, no. 1, pp. 334-362.
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Bai, L, Wang, J, Ma, X & Lu, H 2018, 'Air Pollution Forecasts: An Overview.', International journal of environmental research and public health, vol. 15, no. 4.
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Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

Bano, M, Zowghi, D & da Rimini, F 2018, 'User Involvement in Software Development: The Good, the Bad, and the Ugly', IEEE Software, vol. 35, no. 6, pp. 8-11.
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Bano, M, Zowghi, D, Kearney, M, Schuck, S & Aubusson, P 2018, 'Mobile learning for science and mathematics school education: A systematic review of empirical evidence', Computers and Education, vol. 121, pp. 30-58.
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The ubiquity, flexibility, ease of access and diverse capabilities of mobile technologies make them valuable and a necessity in current times. However, they are under-utilized assets in mathematics and science school education. This article analyses the high quality empirical evidence on mobile learning in secondary school science and mathematics education. Our study employed a Systematic Literature Review (SLR) using well-accepted and robust guidelines. The SLR resulted in the detailed analysis of 49 studies (60 papers) published during 2003–2016. Content and thematic analyses were used to ascertain pedagogical approaches, methodological designs, foci, and intended and achieved outcomes of the studies. The apps and technologies used in these studies were further classified for domain, type and context of use. The review has highlighted gaps in existing literature on the topic and has provided insights that have implications for future research.

Berta, M, Scholz, VB & Tomamichel, M 2018, 'Rényi Divergences as Weighted Non-commutative Vector-Valued Lp-Spaces', Annales Henri Poincaré, vol. 19, no. 6, pp. 1843-1867.
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© 2018, Springer International Publishing AG, part of Springer Nature. We show that Araki and Masuda’s weighted non-commutative vector-valued Lp-spaces (Araki and Masuda in Publ Res Inst Math Sci Kyoto Univ 18:339–411, 1982) correspond to an algebraic generalization of the sandwiched Rényi divergences with parameter α=p2. Using complex interpolation theory, we prove various fundamental properties of these divergences in the setup of von Neumann algebras, including a data-processing inequality and monotonicity in α. We thereby also give new proofs for the corresponding finite-dimensional properties. We discuss the limiting cases α→{12,1,∞} leading to minus the logarithm of Uhlmann’s fidelity, Umegaki’s relative entropy, and the max-relative entropy, respectively. As a contribution that might be of independent interest, we derive a Riesz–Thorin theorem for Araki–Masuda Lp-spaces and an Araki–Lieb–Thirring inequality for states on von Neumann algebras.

Bickel, B, Cignoni, P, Malomo, L & Pietroni, N 2018, 'State of the Art on Stylized Fabrication', Computer Graphics Forum, vol. 37, pp. 325-342.
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Bluff, A, Johnston, A & Clarkson, D 2018, 'Interaction, Narrative and Animation in Live Theatre', IEEE COMPUTER GRAPHICS AND APPLICATIONS, vol. 38, no. 2, pp. 8-14.
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Bo Han, Tsang, IW, Ling Chen, Yu, CP & Sai-Fu Fung 2018, 'Progressive Stochastic Learning for Noisy Labels.', IEEE transactions on neural networks and learning systems, vol. 29, no. 10, pp. 5136-5148.
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Large-scale learning problems require a plethora of labels that can be efficiently collected from crowdsourcing services at low cost. However, labels annotated by crowdsourced workers are often noisy, which inevitably degrades the performance of large-scale optimizations including the prevalent stochastic gradient descent (SGD). Specifically, these noisy labels adversely affect updates of the primal variable in conventional SGD. To solve this challenge, we propose a robust SGD mechanism called progressive stochastic learning (POSTAL), which naturally integrates the learning regime of curriculum learning (CL) with the update process of vanilla SGD. Our inspiration comes from the progressive learning process of CL, namely learning from "easy" tasks to "complex" tasks. Through the robust learning process of CL, POSTAL aims to yield robust updates of the primal variable on an ordered label sequence, namely, from "reliable" labels to "noisy" labels. To realize POSTAL mechanism, we design a cluster of "screening losses," which sorts all labels from the reliable region to the noisy region. To sum up, POSTAL using screening losses ensures robust updates of the primal variable on reliable labels first, then on noisy labels incrementally until convergence. In theory, we derive the convergence rate of POSTAL realized by screening losses. Meanwhile, we provide the robustness analysis of representative screening losses. Experimental results on UCI1 simulated and Amazon Mechanical Turk crowdsourcing data sets show that the POSTAL using screening losses is more effective and robust than several existing baselines.1UCI is the abbreviation of University of California Irvine.

Boixo, S, Isakov, SV, Smelyanskiy, VN, Babbush, R, Ding, N, Jiang, Z, Bremner, MJ, Martinis, JM & Neven, H 2018, 'Characterizing Quantum Supremacy in Near-Term Devices', Nature Physics, vol. 14, pp. 595-600.
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A critical question for the field of quantum computing in the near future is whether quantum devices without error correction can perform a well-defined computational task beyond the capabilities of state-of-the-art classical computers, achieving so-called quantum supremacy. We study the task of sampling from the output distributions of (pseudo-)random quantum circuits, a natural task for benchmarking quantum computers. Crucially, sampling this distribution classically requires a direct numerical simulation of the circuit, with computational cost exponential in the number of qubits. This requirement is typical of chaotic systems. We extend previous results in computational complexity to argue more formally that this sampling task must take exponential time in a classical computer. We study the convergence to the chaotic regime using extensive supercomputer simulations, modeling circuits with up to 42 qubits - the largest quantum circuits simulated to date for a computational task that approaches quantum supremacy. We argue that while chaotic states are extremely sensitive to errors, quantum supremacy can be achieved in the near-term with approximately fifty superconducting qubits. We introduce cross entropy as a useful benchmark of quantum circuits which approximates the circuit fidelity. We show that the cross entropy can be efficiently measured when circuit simulations are available. Beyond the classically tractable regime, the cross entropy can be extrapolated and compared with theoretical estimates of circuit fidelity to define a practical quantum supremacy test.

Bown, O & Ferguson, S 2018, 'Creative Media + the Internet of Things = Media Multiplicities', Leonardo, vol. 51, no. 1, pp. 53-54.
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This paper proposes the term "media multiplicities" to describe contemporary media artworks that create multiples of "internet of things" devices. It discusses the properties that distinguish media multiplicities from other forms of media artwork, provides parameters for categorizing media multiplicities, and discusses aesthetic and creative factors in the production of media multiplicities.

Bown, O & Ferguson, SJ 2018, 'Understanding media multiplicities', Entertainment Computing, vol. 25, pp. 62-70.
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Internet of Things (IoT) technologies enable new forms of media artworks. ‘Media multiplicities’ are defined here as creative media experiences made up of multiples of interacting and coordinated devices. In this paper, we review the state of the art of multiplicitous media artworks and provide a systematic analysis of the novel affordances and different forms such artworks can take, specifically that they are spatial, scalable, scatterable and sensing. We consider the analysis of media multiplicities from the point of view of both user experience and creative production. We offer three primary axes through which a categorisation of multiplicitous media forms can be framed: substrate versus object; composed versus self-organised, and homogeneous versus heterogeneous. We also analyse how the number of elements in the multiplicities (from tens to tens of thousands and beyond) affects the qualities of the experience.

Cao, Z & Lin, CT 2018, 'Inherent Fuzzy Entropy for the Improvement of EEG Complexity Evaluation', IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, pp. 1032-1035.
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© 2017 IEEE. In recent years, the concept of entropy has been widely used to measure the dynamic complexity of signals. Since the state of complexity of human beings is significantly affected by their health state, developing accurate complexity evaluation algorithms is a crucial and urgent area of study. This paper proposes using inherent fuzzy entropy (Inherent FuzzyEn) and its multiscale version, which employs empirical mode decomposition and fuzzy membership function (exponential function) to address the dynamic complexity in electroencephalogram (EEG) data. In the literature, the reliability of entropy-based complexity evaluations has been limited by superimposed trends in signals and a lack of multiple time scales. Our proposed method represents the first attempt to use the Inherent FuzzyEn algorithm to increase the reliability of complexity evaluation in realistic EEG applications. We recorded the EEG signals of several subjects under resting condition, and the EEG complexity was evaluated using approximate entropy, sample entropy, FuzzyEn, and Inherent FuzzyEn, respectively. The results indicate that Inherent FuzzyEn is superior to other competing models regardless of the use of fuzzy or nonfuzzy structures, and has the most stable complexity and smallest root mean square deviation.

Cao, Z, Lai, K-L, Lin, C-T, Chuang, C-H, Chou, C-C & Wang, S-J 2018, 'Exploring resting-state EEG complexity before migraine attacks.', Cephalalgia: an international journal of headache, vol. 38, no. 7, pp. 1296-1306.
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Objective Entropy-based approaches to understanding the temporal dynamics of complexity have revealed novel insights into various brain activities. Herein, electroencephalogram complexity before migraine attacks was examined using an inherent fuzzy entropy approach, allowing the development of an electroencephalogram-based classification model to recognize the difference between interictal and preictal phases. Methods Forty patients with migraine without aura and 40 age-matched normal control subjects were recruited, and the resting-state electroencephalogram signals of their prefrontal and occipital areas were prospectively collected. The migraine phases were defined based on the headache diary, and the preictal phase was defined as within 72 hours before a migraine attack. Results The electroencephalogram complexity of patients in the preictal phase, which resembled that of normal control subjects, was significantly higher than that of patients in the interictal phase in the prefrontal area (FDR-adjusted p < 0.05) but not in the occipital area. The measurement of test-retest reliability (n = 8) using the intra-class correlation coefficient was good with r1 = 0.73 ( p = 0.01). Furthermore, the classification model, support vector machine, showed the highest accuracy (76 ± 4%) for classifying interictal and preictal phases using the prefrontal electroencephalogram complexity. Conclusion Entropy-based analytical methods identified enhancement or "normalization" of frontal electroencephalogram complexity during the preictal phase compared with the interictal phase. This classification model, using this complexity feature, may have the potential to provide a preictal alert to migraine without aura patients.

Castro, J, Lu, J, Zhang, G, Dong, Y & Martinez, L 2018, 'Opinion Dynamics-Based Group Recommender Systems', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 12, pp. 2394-2406.
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With the accessibility to information, users often face the problem of selecting one item (a product or a service) from a huge search space. This problem is known as information overload. Recommender systems (RSs) personalize content to a user's interests to help them select the right item in information overload scenarios. Group RSs (GRSs) recommend items to a group of users. In GRSs, a recommendation is usually computed by a simple aggregation method for individual information. However, the aggregations are rigid and overlook certain group features, such as the relationships between the group members' preferences. In this paper, it is proposed a GRS based on opinion dynamics that considers these relationships using a smart weights matrix to drive the process. In some groups, opinions do not agree, hence the weights matrix is modified to reach a consensus value. The impact of ensuring agreed recommendations is evaluated through a set of experiments. Additionally, a sensitivity analysis studies its behavior. Compared to existing group recommendation models and frameworks, the proposal based on opinion dynamics would have the following advantages: 1) flexible aggregation method; 2) member relationships; and 3) agreed recommendations.

Chapman, RJ, Karim, A, Huang, Z, Flammia, ST, Tomamichel, M & Peruzzo, A 2018, 'Beating the classical limits of information transmission using a quantum decoder', Physical Review A, vol. 97, no. 1.
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© 2018 American Physical Society. Encoding schemes and error-correcting codes are widely used in information technology to improve the reliability of data transmission over real-world communication channels. Quantum information protocols can further enhance the performance in data transmission by encoding a message in quantum states; however, most proposals to date have focused on the regime of a large number of uses of the noisy channel, which is unfeasible with current quantum technology. We experimentally demonstrate quantum enhanced communication over an amplitude damping noisy channel with only two uses of the channel per bit and a single entangling gate at the decoder. By simulating the channel using a photonic interferometric setup, we experimentally increase the reliability of transmitting a data bit by greater than 20% for a certain damping range over classically sending the message twice. We show how our methodology can be extended to larger systems by simulating the transmission of a single bit with up to eight uses of the channel and a two-bit message with three uses of the channel, predicting a quantum enhancement in all cases.

Chen, J, Tian, Z, Cui, X, Yin, L & Wang, X 2018, 'Trust architecture and reputation evaluation for internet of things', Journal of Ambient Intelligence and Humanized Computing, pp. 1-9.

Chen, S, Chen, S, Wang, Z, Liang, J, Wu, Y & Yuan, X 2018, 'D-MAP+: Interactive visual analysis and exploration of ego-centric and event-centric information diffusion patterns in social media', ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 1.
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© 2018 Association for Computing Machinery. Information diffusion analysis is important in social media. In this work, we present a coherent ego-centric and event-centric model to investigate diffusion patterns and user behaviors. Applying the model, we propose Diffusion Map+ (D-Maps+), a novel visualization method to support exploration and analysis of user behaviors and diffusion patterns through a map metaphor. For ego-centric analysis, users who participated in reposting (i.e., resending a message initially posted by others) one central user's posts (i.e., a series of original tweets) are collected. Event-centric analysis focuses on multiple central users discussing a specific event, with all the people participating and reposting messages about it. Social media users are mapped to a hexagonal grid based on their behavior similarities and in the chronological order of repostings. With the additional interactions and linkings, D-Map+ is capable of providing visual profiling of influential users, describing their social behaviors and analyzing the evolution of significant events in social media. A comprehensive visual analysis system is developed to support interactive exploration with D-Map+. We evaluate our work with real-world social media data and find interesting patterns among users and events. We also perform evaluations including user studies and expert feedback to certify the capabilities of our method.

Chen, S, Wang, Z, Yuan, X & Liang, J 2018, 'Uncertainty-aware Visual Analytics for Exploring Human Behaviors from Heterogeneous Spatial Temporal Data', Journal of Visual Languages and Computing, vol. 48, pp. 187-198.
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Chen, Y, Dong, Y, Sun, Y & Liang, J 2018, 'A Multi-comparable visual analytic approach for complex hierarchical data', Journal of Visual Languages and Computing, vol. 47, pp. 19-30.
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© 2018 Elsevier Ltd Maximum residue limit (MRL) standard which specifies the highest level of every pesticide residue in different agricultural products plays a critical role in food safety. However, such standards which related to the characteristics of pesticides and the classification of agricultural products which organized into a hierarchical structure are complex and vary widely across different regions or countries. So it is a big challenge to compare multi-regional MRL standard data comprehensively. In this paper, we present a multi-comparable visual analytic approach for complex hierarchical data and a visual analytics system (McVA) to support multiple comparison and evaluation of MRL standard. With a cooperative multi-view visual design, our proposed approach links the hierarchies of MRL datasets and provides the capacity for comparison at different levels and dimensions. We also introduce a metric model for evaluating the completeness and strictness of MRL standards quantitatively. The case study of real problems and the positive feedback from domain experts demonstrate the effectiveness of this approach.

Chi, L, Li, B, Zhu, X, Pan, S & Chen, L 2018, 'Hashing for Adaptive Real-Time Graph Stream Classification With Concept Drifts.', IEEE Transactions on Cybernetics, vol. 48, no. 5, pp. 1591-1604.
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Many applications involve processing networked streaming data in a timely manner. Graph stream classification aims to learn a classification model from a stream of graphs with only one-pass of data, requiring real-time processing in training and prediction. This is a nontrivial task, as many existing methods require multipass of the graph stream to extract subgraph structures as features for graph classification which does not simultaneously satisfy "one-pass" and "real-time" requirements. In this paper, we propose an adaptive real-time graph stream classification method to address this challenge. We partition the unbounded graph stream data into consecutive graph chunks, each consisting of a fixed number of graphs and delivering a corresponding chunk-level classifier. We employ a random hashing function to compress the original node set of graphs in each chunk for fast feature detection when training chunk-level classifiers. Furthermore, a differential hashing strategy is applied to map unlimited increasing features (i.e., cliques) into a fixed-size feature space which is then used as a feature vector for stochastic learning. Finally, the chunk-level classifiers are weighted in an ensemble learning model for graph classification. The proposed method substantially speeds up the graph feature extraction and avoids unbounded graph feature growth. Moreover, it effectively offsets concept drifts in graph stream classification. Experiments on real-world and synthetic graph streams demonstrate that our method significantly outperforms existing methods in both classification accuracy and learning efficiency.

Chikara, RK, Chang, EC, Lu, YC, Lin, DS, Lin, CT & Ko, LW 2018, 'Monetary reward and punishment to response inhibition modulate activation and synchronization within the inhibitory brain network', Frontiers in Human Neuroscience, vol. 12.
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© 2018 Chikara, Chang, Lu, Lin, Lin and Ko. A reward or punishment can modulate motivation and emotions, which in turn affect cognitive processing. The present simultaneous functional magnetic resonance imaging-electroencephalography study examines neural mechanisms of response inhibition under the influence of a monetary reward or punishment by implementing a modified stop-signal task in a virtual battlefield scenario. The participants were instructed to play as snipers who open fire at a terrorist target but withhold shooting in the presence of a hostage. The participants performed the task under three different feedback conditions in counterbalanced order: a reward condition where each successfully withheld response added a bonus (i.e., positive feedback) to the startup credit, a punishment condition where each failure in stopping deduced a penalty (i.e., negative feedback), and a no-feedback condition where response outcome had no consequences and served as a control setting. Behaviorally both reward and punishment conditions led to significantly down-regulated inhibitory function in terms of the critical stop-signal delay. As for the neuroimaging results, increased activities were found for the no-feedback condition in regions previously reported to be associated with response inhibition, including the right inferior frontal gyrus and the pre-supplementary motor area. Moreover, higher activation of the lingual gyrus, posterior cingulate gyrus (PCG) and inferior parietal lobule were found in the reward condition, while stronger activation of the precuneus gyrus was found in the punishment condition. The positive feedback was also associated with stronger changes of delta, theta, and alpha synchronization in the PCG than were the negative or no-feedback conditions. These findings depicted the intertwining relationship between response inhibition and motivation networks.

Chotipant, S, Hussain, FK & Hussain, OK 2018, 'SERNOTATE: An automated approach for business service description annotation for efficient service retrieval and composition', Concurrency and Computation: Practice and Experience, vol. 30, no. 1.
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Copyright © 2017 John Wiley & Sons, Ltd. Business service advertisements are today published online to convey essential information about services to customers. However, current Web search engines are unable to search and combine online service advertisements. Semantic service annotation is important for its ability to enable machines to understand the meaning of services and support in effective service retrieval and service composition. Existing research in the area of semantic service annotation has focused on the annotation of Web services in a semi-automated approach. It cannot be applied to business service information as it is not in the form of Web Services Description Language but in free text format. Moreover, semi-automated approaches are inappropriate for annotating a large amount of online service information which changes dynamically and they are therefore not suitable for the timely dissemination of service information to customers. To solve these issues, we propose SERNOTATE, which is an automated approach for business service description annotation for efficient service retrieval and composition. We propose new semantic-based linking approaches, namely, Extended Case-based Reasoning, vector-based, and classification-based, that automatically annotate business services to relevant service concepts. Each approach assists in the single-label and multi-label annotation of service terms to concept terms to provide a better representation of services. The experimental results test and validate the applicability of the proposed approaches to the automatic annotation of business service descriptions to service concepts on a real-world dataset.

Chou, K-P, Prasad, M, Wu, D, Sharma, N, Li, D-L, Lin, Y-F, Blumenstein, M, Lin, W-C & Lin, C-T 2018, 'Robust Feature-Based Automated Multi-View Human Action Recognition System', IEEE ACCESS, vol. 6, pp. 15283-15296.
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Chuang, C-H, Cao, Z, King, J-T, Wu, B-S, Wang, Y-K & Lin, C-T 2018, 'Brain Electrodynamic and Hemodynamic Signatures Against Fatigue During Driving.', Frontiers in Neuroscience, vol. 12, pp. 1-12.
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Fatigue is likely to be gradually cumulated in a prolonged and attention-demanding task that may adversely affect task performance. To address the brain dynamics during a driving task, this study recruited 16 subjects to participate in an event-related lane-departure driving experiment. Each subject was instructed to maintain attention and task performance throughout an hour-long driving experiment. The subjects' brain electrodynamics and hemodynamics were simultaneously recorded via 32-channel electroencephalography (EEG) and 8-source/16-detector functional near-infrared spectroscopy (fNIRS). The behavior performance demonstrated that all subjects were able to promptly respond to lane-deviation events, even if the sign of fatigue arose in the brain, which suggests that the subjects were fighting fatigue during the driving experiment. The EEG event-related analysis showed strengthening alpha suppression in the occipital cortex, a common brain region of fatigue. Furthermore, we noted increasing oxygenated hemoglobin (HbO) of the brain to fight driving fatigue in the frontal cortex, primary motor cortex, parieto-occipital cortex and supplementary motor area. In conclusion, the increasing neural activity and cortical activations were aimed at maintaining driving performance when fatigue emerged. The electrodynamic and hemodynamic signatures of fatigue fighting contribute to our understanding of the brain dynamics of driving fatigue and address driving safety issues through the maintenance of attention and behavioral performance.

Cruz, CG, Naderpour, M & Ramezani, F 2018, 'Water resource selection and optimisation for shale gas developments in Australia: A combinatorial approach', Computers & Industrial Engineering, vol. 124, pp. 1-11.
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Daniel, J, Naderpour, M & Lin, CT 2018, 'A Fuzzy Multi-Layer Assessment Method for EFQM', IEEE Transactions on Fuzzy Systems.
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IEEE Although the European Foundation for Quality Management (EFQM) is one of the best-known business excellence frameworks, its inherent self-assessment approaches have several limitations. A critical review of self-assessment models reveals that most models are ambiguous and limited to precise data. In addition, the impact of expert knowledge on scoring is overly subjective, and most methodologies assume the relationships between variables are linear. This paper presents a new fuzzy multi-layer assessment method that relies on fuzzy inference systems (FISs) to accommodate imprecise data and varying assessor experiences to overcome uncertainty and complexity in the EFQM model. The method was implemented, tested, and verified under real conditions in a regional electricity company. The case was assessed by internal company experts and external assessors from an EFQM business excellence organization, and the model was implemented using Matlab software. When comparing the classical model with the new model, assessors and experts favored outputs from the new model.

Delgrande, JP, Peppas, P & Woltran, S 2018, 'General belief revision', Journal of the ACM, vol. 65, no. 5.
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© 2018 ACM. In artificial intelligence, a key question concerns how an agent may rationally revise its beliefs in light of new information. The standard (AGM) approach to belief revision assumes that the underlying logic contains classical propositional logic. This is a significant limitation, since many representation schemes in AI don't subsume propositional logic. In this article, we consider the question of what the minimal requirements are on a logic, such that the AGM approach to revision may be formulated. We show that AGM-style revision can be obtained even when extremely little is assumed of the underlying language and its semantics; in fact, one requires little more than a language with sentences that are satisfied at models, or possible worlds. The classical AGM postulates are expressed in this framework and a representation result is established between the postulate set and certain preorders on possible worlds. To obtain the representation result, we add a new postulate to the AGM postulates, and we add a constraint to preorders on worlds. Crucially, both of these additions are redundant in the original AGM framework, and so we extend, rather than modify, the AGM approach. As well, iterated revision is addressed and the Darwiche/Pearl postulates are shown to be compatible with our approach. Various examples are given to illustrate the approach, including Horn clause revision, revision in extended logic programs, and belief revision in a very basic logic called literal revision.

Deng, Z, He, T, Ding, W & Cao, Z 2018, 'A Multimodel Fusion Engine for Filtering Webpages', IEEE Access, vol. 6.
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OAPA Fusing multiple existing models for filtering webpages can mitigate the shortcomings of individual filtering models. To provide an engine for such fusion, we propose a multimodel fusion engine for filtering webpages (MMFEFWP) for the extraction of target webpages. This engine can handle large datasets of webpages crawled from websites and supports five individual filtering models and the fusion of any two of them. There are two possible fusion methods: one is to simultaneously satisfy the conditions of both individual models, and the other is to satisfy the conditions of one of the two individual models. We present the functions, architecture, and software design of the proposed engine. We use recall ratio (RR) and precision ratio (PR) as the evaluation indices of the filtering models and propose rules describing how PR and RR change when individual models are fused. We use 200,000 webpages collected by crawling the popular online shopping website "www.jd.com" as the experimental dataset to verify these rules. The experimental results show that two-model fusion can improve either PR or RR. Thus, the proposed engine has good practical value for engineering applications.

Ding, W, Lin, CT & Cao, Z 2018, 'Shared Nearest-Neighbor Quantum Game-Based Attribute Reduction With Hierarchical Coevolutionary Spark and Its Application in Consistent Segmentation of Neonatal Cerebral Cortical Surfaces', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE The unprecedented increase in data volume has become a severe challenge for conventional patterns of data mining and learning systems tasked with handling big data. The recently introduced Spark platform is a new processing method for big data analysis and related learning systems, which has attracted increasing attention from both the scientific community and industry. In this paper, we propose a shared nearest-neighbor quantum game-based attribute reduction (SNNQGAR) algorithm that incorporates the hierarchical coevolutionary Spark model. We first present a shared coevolutionary nearest-neighbor hierarchy with self-evolving compensation that considers the features of nearest-neighborhood attribute subsets and calculates the similarity between attribute subsets according to the shared neighbor information of attribute sample points. We then present a novel attribute weight tensor model to generate ranking vectors of attributes and apply them to balance the relative contributions of different neighborhood attribute subsets. To optimize the model, we propose an embedded quantum equilibrium game paradigm (QEGP) to ensure that noisy attributes do not degrade the big data reduction results. A combination of the hierarchical coevolutionary Spark model and an improved MapReduce framework is then constructed that it can better parallelize the SNNQGAR to efficiently determine the preferred reduction solutions of the distributed attribute subsets. The experimental comparisons demonstrate the superior performance of the SNNQGAR, which outperforms most of the state-of-the-art attribute reduction algorithms. Moreover, the results indicate that the SNNQGAR can be successfully applied to segment overlapping and interdependent fuzzy cerebral tissues, and it exhibits a stable and consistent segmentation performance for neonatal cerebral cortical surfaces.

Ding, W, Lin, CT & Prasad, M 2018, 'Hierarchical co-evolutionary clustering tree-based rough feature game equilibrium selection and its application in neonatal cerebral cortex MRI', Expert Systems with Applications, vol. 101, pp. 243-257.
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© 2018 Elsevier Ltd A wide variety of feature selection methods have been developed as promising solutions to find the classification pattern inside increasing applications. But the exploring efficient, flexible and robust feature selection method to handle the rising big data is still an exciting challenge. This paper presents a novel hierarchical co-evolutionary clustering tree-based rough feature game equilibrium selection algorithm (CTFGES). It aims to select out the high-quality feature subsets, which can enrich the research of feature selection and classification in the heterogeneous big data. Firstly, we construct a flexible hierarchical co-evolutionary clustering tree model to speed up the process of feature selection, which can effectively extract the features from the parent and children branches of four-layer co-evolutionary clustering tree. Secondly, we design a mixed co-evolutionary game equilibrium scheme with adaptive dynamics to guide parent and children branch subtrees to approach the optimal equilibrium regions, and enable their feature sets to converge stably to the Nash equilibrium. So both noisy heterogeneous features and non-identified redundant ones can be further eliminated. Finally, the extensive experiments on various big datasets are conducted to demonstrate the more excellent performance of CTFGES, in terms of accuracy, efficiency and robustness, compared with the representative feature selection algorithms. In addition, the proposed CTFGES algorithm has been successfully applied into the feature segmentation of large-scale neonatal cerebral cortex MRI with varying noise ratios and intensity non-uniformity levels. The results indicate that it can be adaptive to derive from the cortical folding surfaces and achieves the satisfying consistency with medical experts, which will be potential significance for successfully assessing the impact of aberrant brain growth on the neurodevelopment of neonatal cerebrum.

Ding, W, Lin, CT, Chen, S, Zhang, X & Hu, B 2018, 'Multiagent-consensus-MapReduce-based attribute reduction using co-evolutionary quantum PSO for big data applications', Neurocomputing, vol. 272, pp. 136-153.
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© 2017 Elsevier B.V. The attribute reduction for big data applications has become an urgent challenge in pattern recognition, machine learning and data mining. In this paper, we introduce the multi-agent consensus MapReduce optimization model and co-evolutionary quantum PSO with self-adaptive memeplexes for designing the attribute reduction method, and propose a multiagent-consensus-MapReduce-based attribute reduction algorithm (MCMAR). Firstly, the co-evolutionary quantum PSO with self-adaptive memeplexes is designed for grouping particles into different memeplexes, which aims to explore the search space and locate the global best region during the attribute reduction of big datasets. Secondly, the four layers neighborhood radius framework with compensatory scheme is constructed to partition big attribute sets by exploiting the interdependency among multiple-relevant-attribute sets. Thirdly, a novel multi-agent consensus MapReduce optimization model is adopted to perform the multiple-relevance-attribute reduction, in which five kinds of agents are used to conduct the ensemble co-evolutionary optimization. So the uniform reduction framework of different agents' co-evolutionary game under the bounded rationality is further refined. Fourthly, the approximation MapReduce parallelism mechanism is permitted to formalize to the multi-agent co-evolutionary consensus structure, interaction and adaptation, which enhances different agents to share their solutions. Finally, extensive experimental studies substantiate the effectiveness and accuracy of MCMAR on some well-known benchmark datasets. Moreover, successful applications in big medical datasets are expected to dramatically scaling up MCMAR for complex infant brain MRI in terms of efficiency and feasibility.

Ding, W, Lin, CT, Prasad, M, Cao, Z & Wang, JD 2018, 'A Layered-Coevolution-Based Attribute-Boosted Reduction Using Adaptive Quantum Behavior PSO and Its Consistent Segmentation for Neonates Brain Tissue', IEEE Transactions on Fuzzy Systems, vol. 26, no. 3.
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IEEE The main challenge of attribute reduction in large data applications is to develop a new algorithm to deal with large, noisy, and uncertain large data linking multiple relevant data sources, structured or unstructured. This paper proposes a new and efficient layered-coevolution-based attribute-boosted reduction algorithm (LCQ-ABR*) using adaptive quantum behavior particle swarm optimization (PSO). First, the quantum rotation angle of an evolutionary particle is updated by a dynamic change of self-adapting step size. Second, a self-adaptive partitioning strategy is employed to group particles into different memeplexes, and the quantum-behavior mechanism with the particles & #x0027; states depicted by the wave function cooperates to achieve superior performance in their respective memeplexes. Third, a new layered co-evolutionary model with multi-agent interaction is constructed to decompose a complex attribute set, and it can self-adapt the attribute sizes among different layers and produce the reasonable decompositions by exploiting any interdependency among multiple relevant attribute subsets. Fourth, the decomposed attribute subsets are evolved to compute the positive region and discernibility matrix by using their best quantum particles, and the global optimal reduction set is induced successfully. Finally, extensive comparative experiments are provided to illustrate that LCQ-ABR* has better feasibility and effectiveness of attribute reduction on large-scale and uncertain dataset problems with complex noise, compared with representative algorithms. Moreover, LCQ-ABR* can be successfully applied in the consistent segmentation for neonatal brain 3D-MRI, and the consistent segmentation results further demonstrate its stronger applicability.

Dong, F, Lu, J, Zhang, G & Li, K 2018, 'Active fuzzy weighting ensemble for dealing with concept drift', International Journal of Computational Intelligence Systems, vol. 11, no. 1, pp. 438-450.
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© 2018, the Authors. The concept drift problem is a pervasive phenomenon in real-world data stream applications. It makes well-trained static learning models lose accuracy and become outdated as time goes by. The existence of different types of concept drift makes it more difficult for learning algorithms to track. This paper proposes a novel adaptive ensemble algorithm, the Active Fuzzy Weighting Ensemble, to handle data streams involving concept drift. During the processing of data instances in the data streams, our algorithm first identifies whether or not a drift occurs. Once a drift is confirmed, it uses data instances accumulated by the drift detection method to create a new base classifier. Then, it applies fuzzy instance weighting and a dynamic voting strategy to organize all the existing base classifiers to construct an ensemble learning model. Experimental evaluations on seven datasets show that our proposed algorithm can shorten the recovery time of accuracy drop when concept drift occurs, adapt to different types of concept drift, and obtain better performance with less computation costs than the other adaptive ensembles.

Dong, F, Zhang, G, Lu, J & Li, K 2018, 'Fuzzy competence model drift detection for data-driven decision support systems', Knowledge-Based Systems, vol. 143, pp. 284-294.
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© 2017 Elsevier B.V. This paper focuses on concept drift in business intelligence and data-driven decision support systems (DSSs). The assumption of a fixed distribution in the data renders conventional static DSSs inaccurate and unable to make correct decisions when concept drift occurs. However, it is important to know when, how, and where concept drift occurs so a DSS can adjust its decision processing knowledge to adapt to an ever-changing environment at the appropriate time. This paper presents a data distribution-based concept drift detection method called fuzzy competence model drift detection (FCM-DD). By introducing fuzzy sets theory and replacing crisp boundaries with fuzzy ones, we have improved the competence model to provide a better, more refined empirical distribution of the data stream. FCM-DD requires no prior knowledge of the underlying distribution and provides statistical guarantee of the reliability of the detected drift, based on the theory of bootstrapping. A series of experiments show that our proposed FCM-DD method can detect drift more accurately, has good sensitivity, and is robust.

Dong, M, Yao, L, Wang, X, Benatallah, B, Huang, C & Ning, X 2018, 'Opinion fraud detection via neural autoencoder decision forest', Pattern Recognition Letters.
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© 2018 Online reviews play an important role in influencing buyers’ daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users’ genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore, it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods.

Dyson, LE & Frawley, JK 2018, 'A student-generated video careers project: Understanding the learning processes in and out of the classroom', International Journal of Mobile and Blended Learning, vol. 10, no. 4, pp. 32-51.
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Copyright © 2018, IGI Global. This article describes how in recent years, the multimedia recording capabilities of mobile devices have been used increasingly to create a more active, learner-centred educational experience. Despite the proven value of student-generated multimedia projects, there are still gaps in our understanding of how students learn during them. This article reports on a project in which first-year information technology students interviewed IT professionals in their workplace and video-recorded the interview to enable sharing with their peers. In order to understand the statistically significant increases found in students' learning, student diaries and reflections were analyzed qualitatively. Factors found to contribute to learning included: the iterative nature of student activities; the multiple, evolving representations of knowledge as students proceeded through the project; the importance of the workplace context in engaging students and enhancing learning; the affordance of mobile technology for capturing and sharing this context; and the collaborative and metacognitive processes fostered by the project.

El-Sayed, H, Sankar, S, Daraghmi, Y-A, Tiwari, P, Rattagan, E, Mohanty, M, Puthal, D & Prasad, M 2018, 'Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier.', Sensors (Basel, Switzerland), vol. 18, no. 6.
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Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.

El-Sayed, H, Sankar, S, Prasad, M, Puthal, D, Gupta, A, Mohanty, M & Lin, CT 2018, 'Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment', IEEE Access, vol. 6, pp. 1706-1717.
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© 2013 IEEE. A centralized infrastructure system carries out existing data analytics and decision-making processes from our current highly virtualized platform of wireless networks and the Internet of Things (IoT) applications. There is a high possibility that these existing methods will encounter more challenges and issues in relation to network dynamics, resulting in a high overhead in the network response time, leading to latency and traffic. In order to avoid these problems in the network and achieve an optimum level of resource utilization, a new paradigm called edge computing (EC) is proposed to pave the way for the evolution of new age applications and services. With the integration of EC, the processing capabilities are pushed to the edge of network de vices such as smart phones, sensor nodes, wearables, and on-board units, where data analytics and knowledge generation are performed which removes the necessity for a centralized system. Many IoT applications, such as smart cities, the smart grid, smart traffic lights, and smart vehicles, are rapidly upgrading their applications with EC, significantly improving response time as well as conserving network resources. Irrespective of the fact that EC shifts the workload from a centralized cloud to the edge, the analogy between EC and the cloud pertaining to factors such as resource management and computation optimization are still open to research studies. Hence, this paper aims to validate the efficiency and resourcefulness of EC. We extensively survey the edge systems and present a comparative study of cloud computing systems. After analyzing the different network properties in the system, the results show that EC systems perform better than cloud computing systems. Finally, the research challenges in implementing an EC system and future research directions are discussed.

Fan, X, Zhao, J, Ren, F, Wang, Y, Feng, Y, Ding, L, Zhao, L, Shang, Y, Li, J, Ni, J, Jia, B, Liu, Y & Chang, Z 2018, 'Dimerization of p15RS mediated by a leucine zipper–like motif is critical for its inhibitory role on Wnt signaling', Journal of Biological Chemistry, vol. 293, no. 20, pp. 7618-7628.
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© 2018 Fan et al. We previously demonstrated that p15RS, a newly discovered tumor suppressor, inhibits Wnt/-catenin signaling by interrupting the formation of -cateninTCF4 complex. However, it remains unclear how p15RS helps exert such an inhibitory effect on Wnt signaling based on its molecular structure. In this study, we reported that dimerization of p15RS is required for its inhibition on the transcription regulation of Wnt-targeted genes. We found that p15RS forms a dimer through a highly conserved leucine zipper–like motif in the coiled-coil terminus domain. In particular, residues Leu-248 and Leu-255 were identified as being responsible for p15RS dimerization, as mutation of these two leucines into prolines disrupted the homodimer formation of p15RS and weakened its suppression of Wnt signaling. Functional studies further confirmed that mutations of p15RS at these residues results in diminishment of its inhibition on cell proliferation and tumor formation. We therefore concluded that dimerization of p15RS governed by the leucine zipper–like motif is critical for its inhibition of Wnt/-catenin signaling and tumorigenesis.

Fang, XS, Sheng, QZ, Wang, X, Chu, D & Ngu, AHH 2018, 'SmartVote: a full-fledged graph-based model for multi-valued truth discovery', World Wide Web.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. In the era of Big Data, truth discovery has emerged as a fundamental research topic, which estimates data veracity by determining the reliability of multiple, often conflicting data sources. Although considerable research efforts have been conducted on this topic, most current approaches assume only one true value for each object. In reality, objects with multiple true values widely exist and the existing approaches that cope with multi-valued objects still lack accuracy. In this paper, we propose a full-fledged graph-based model, SmartVote, which models two types of source relations with additional quantification to precisely estimate source reliability for effective multi-valued truth discovery. Two graphs are constructed and further used to derive different aspects of source reliability (i.e., positive precision and negative precision) via random walk computations. Our model incorporates four important implications, including two types of source relations, object popularity, loose mutual exclusion, and long-tail phenomenon on source coverage, to pursue better accuracy in truth discovery. Empirical studies on two large real-world datasets demonstrate the effectiveness of our approach.

Feng, X, Chang, L, Lin, X, Qin, L, Zhang, W & Yuan, L 2018, 'Distributed computing connected components with linear communication cost', DISTRIBUTED AND PARALLEL DATABASES, vol. 36, no. 3, pp. 555-592.
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Fonseca, A, Kerick, S, King, J-T, Lin, C-T & Jung, T-P 2018, 'Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data.', Frontiers in human neuroscience, vol. 12.
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The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices.

Frawley, J & Dyson, LE 2018, 'Literacies and learning in motion: Meaning making and transformation in a community mobile storytelling project', International Journal of Mobile and Blended Learning, vol. 10, no. 4, pp. 52-72.
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Copyright © 2018, IGI Global. Mobile and participatory cultures have led to widespread change in the way we communicate; emphasizing user generated content and digital multimedia. In this environment, informal learning may occur through digital and networked activities, with literacy no longer limited to alphabetic and character-based texts. This article explores adult learners' new literacies within the context of a digital mobile storytelling project. A qualitative approach is used to explore the artifacts and practices of nine adult participants who comprise the study. Participants created a range of fiction, non-fiction, poetry and diary-style content in a variety of modes and media. Outcomes from content analysis, interview and survey methods depict mobile digital literacies as characteristically situated, experiential and multimodal. The mobile and participatory nature of this project was catalytic to participants' imaginative re-interpretation of the world around them as sources for meaning making and transformation. This paper contributes a case example of mobile learning with adults in a community setting.

Gheisari, S, Catchpoole, DR, Charlton, A & Kennedy, PJ 2018, 'Convolutional Deep Belief Network with Feature Encoding for Classification of Neuroblastoma Histological Images.', Journal of Pathology Informatics, vol. 9, pp. 1-28.
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Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification.We apply a combination of convolutional deep belief network (CDBN) with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier.We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors.The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods.The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images.

Gheisari, S, Catchpoole, DR, Charlton, A, Melegh, Z, Gradhand, E & Kennedy, PJ 2018, 'Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding.', Diagnostics, vol. 8, no. 3, pp. 1-18.
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Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathology study is considered the gold standard for classification of neuroblastoma histological images, computers can help to extract many more features some of which may not be recognizable by human eyes. This paper, proposes a combination of Scale Invariant Feature Transform with feature encoding algorithm to extract highly discriminative features. Then, distinctive image features are classified by Support Vector Machine classifier into five clinically relevant classes. The advantage of our model is extracting features which are more robust to scale variation compared to the Patched Completed Local Binary Pattern and Completed Local Binary Pattern methods. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our approach identified features that outperformed the state-of-the-art on both our neuroblastoma dataset and a benchmark breast cancer dataset. Our method shows promise for classification of neuroblastoma histological images.

Gill, AQ, Henderson-Sellers, B & Niazi, M 2018, 'Scaling for agility: A reference model for hybrid traditional-agile software development methodologies', Information Systems Frontiers: a journal of research and innovation, vol. 20, no. 2, pp. 315-341.
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The adoption of agility at a large scale often requires the integration of agile and non-agile development elements for architecting a hybrid adaptive methodology. The challenge is ”which elements or components (agile or non-agile) are relevant to develop the context-aware hybrid adaptive methodology reference architecture?” This paper addresses this important challenge and develops a hybrid adaptive methodology reference architecture model using a qualitative constructive empirical research approach. In this way, we have uncovered the agility, abstraction, business value, business policy, rules, legal, context and facility elements or components that have not been explicitly modelled or discussed in International Standards (IS) such as the ISO/IEC 24744 metamodel. It is anticipated that a context-aware hybrid adaptive methodology can be architected by using the proposed context-aware hybrid adaptive methodology reference architecture elements for a particular situation when using a situational method engineering approach.

Goodswen, SJ, Kennedy, PJ & Ellis, JT 2018, 'A Gene-Based Positive Selection Detection Approach to Identify Vaccine Candidates Using Toxoplasma gondii as a Test Case Protozoan Pathogen.', Frontiers in Genetics, vol. 9.
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Over the last two decades, various in silico approaches have been developed and refined that attempt to identify protein and/or peptide vaccines candidates from informative signals encoded in protein sequences of a target pathogen. As to date, no signal has been identified that clearly indicates a protein will effectively contribute to a protective immune response in a host. The premise for this study is that proteins under positive selection from the immune system are more likely suitable vaccine candidates than proteins exposed to other selection pressures. Furthermore, our expectation is that protein sequence regions encoding major histocompatibility complexes (MHC) binding peptides will contain consecutive positive selection sites. Using freely available data and bioinformatic tools, we present a high-throughput approach through a pipeline that predicts positive selection sites, protein subcellular locations, and sequence locations of medium to high T-Cell MHC class I binding peptides. Positive selection sites are estimated from a sequence alignment by comparing rates of synonymous (dS) and non-synonymous (dN) substitutions among protein coding sequences of orthologous genes in a phylogeny. The main pipeline output is a list of protein vaccine candidates predicted to be naturally exposed to the immune system and containing sites under positive selection. Candidates are ranked with respect to the number of consecutive sites located on protein sequence regions encoding MHCI-binding peptides. Results are constrained by the reliability of prediction programs and quality of input data. Protein sequences from Toxoplasma gondii ME49 strain (TGME49) were used as a case study. Surface antigen (SAG), dense granules (GRA), microneme (MIC), and rhoptry (ROP) proteins are considered worthy T. gondii candidates. Given 8263 TGME49 protein sequences processed anonymously, the top 10 predicted candidates were all worthy candidates. In particular, the top ten included ROP5 and...

Graham, C, Smith, W, Moncur, W & van den Hoven, E 2018, 'Introduction: Mortality in design', Design Issues, vol. 34, no. 1, pp. 3-14.
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Gu, Y, Gu, M, Long, Y, Xu, G, Yang, Z, Zhou, J & Qu, W 2018, 'An enhanced short text categorization model with deep abundant representation', World Wide Web, vol. 21, no. 6, pp. 1705-1719.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Short text categorization is a crucial issue to many applications, e.g., Information Retrieval, Question-Answering System, MRI Database Construction and so forth. Many researches focus on data sparsity and ambiguity issues in short text categorization. To tackle these issues, we propose a novel short text categorization strategy based on abundant representation, which utilizes Bi-directional Recurrent Neural Network(Bi-RNN) with Long Short-Term Memory(LSTM) and topic model to catch more contextual and semantic information. Bi-RNN enriches contextual information, and topic model discovers more latent semantic information for abundant text representation of short text. Experimental results demonstrate that the proposed model is comparable to state-of-the-art neural network models and method proposed is effective.

Guo, J, Ren, W, Ren, Y & Zhu, T 2018, 'A watermark-based in-situ access control model for image big data', Future Internet, vol. 10, no. 8.
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© 2018 by the authors. When large images are used for big data analysis, they impose new challenges in protecting image privacy. For example, a geographic image may consist of several sensitive areas or layers. When it is uploaded into servers, the image will be accessed by diverse subjects. Traditional access control methods regulate access privileges to a single image, and their access control strategies are stored in servers, which imposes two shortcomings: (1) fine-grained access control is not guaranteed for areas/layers in a single image that need to maintain secret for different roles; and (2) access control policies that are stored in servers suffers from multiple attacks (e.g., transferring attacks). In this paper, we propose a novel watermark-based access control model in which access control policies are associated with objects being accessed (called an in-situ model). The proposed model integrates access control policies as watermarks within images, without relying on the availability of servers or connecting networks. The access control for images is still maintained even though images are redistributed again to further subjects. Therefore, access control policies can be delivered together with the big data of images. Moreover, we propose a hierarchical key-role-area model for fine-grained encryption, especially for large size images such as geographic maps. The extensive analysis justifies the security and performance of the proposed model.

He, Q, Wang, J & Lu, H 2018, 'A hybrid system for short-term wind speed forecasting', Applied Energy, vol. 226, pp. 756-771.
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© 2018 Elsevier Ltd Wind speed forecasting is important for high-efficiency utilization of wind energy. Correspondingly, numerous researchers have always focused on the development of reliable forecasting models of wind speed, which is often noisy, unstable and irregular. Current approaches could adapt to various wind speed data. However, many of these usually ignore the importance of the selection of the modeling sample, which often results in poor forecasting performance. In this study, a hybrid forecasting system is proposed that contains three modules: data preprocessing, data clustering, and forecasting modules. In this system, the decomposing technique is applied to reduce the influence of noise within the raw data series to obtain a more stable sequence that is conducive to extract traits from the original data. To extract the characteristic of similarity within wind speed data, a kernel-based fuzzy c-means clustering algorithm is used in data clustering module. In the forecasting module, a sample with a highly similar fluctuation pattern is selected as training dataset, and which could reduce the training requirement of model to improve the forecasting accuracy. The experimental results indicate that the developed system outperforms the discussed traditional forecasting models with respect to forecasting accuracy.

He, T, Cai, L, Meng, T, Chen, L, Deng, Z & Cao, Z 2018, 'Parallel Community Detection Based on Distance Dynamics for Large-Scale Network', IEEE Access, vol. 6, pp. 42775-42789.
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© 2013 IEEE. Data mining task is a challenge on finding a high-quality community structure from large-scale networks. The distance dynamics model was proved to be active on regular-size network community, but it is difficult to discover the community structure effectively from the large-scale network (0.1-1 billion edges), due to the limit of machine hardware and high time complexity. In this paper, we proposed a parallel community detection algorithm based on the distance dynamics model called P-Attractor, which is capable of handling the detection problem of large networks community. Our algorithm first developed a graph partitioning method to divide large network into lots of sub-networks, yet maintaining the complete neighbor structure of the original network. Then, the traditional distance dynamics model was improved by the dynamic interaction process to simulate the distance evolution of each sub-network. Finally, we discovered the real community structure by removing all external edges after evolution process. In our extensive experiments on multiple synthetic networks and real-world networks, the results showed the effectiveness and efficiency of P-Attractor, and the execution time on 4 threads and 32 threads are around 10 and 2 h, respectively. Our proposed algorithm is potential to discover community from a billion-scale network, such as Uk-2007.

Hou, Z, Wang, Y, Sui, Y, Gu, J, Zhao, T & Zhou, X 2018, 'Managing high-performance computing applications as an on-demand service on federated clouds', Computers and Electrical Engineering, vol. 67, pp. 579-595.
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© 2018 Elsevier Ltd There are several challenges (e.g., imbalance between supply and demand of hardware resources and software licenses, and usability) under modern High-Performance Computing (HPC) environment. As a means of providing an on-demand service for end users, we propose a Software-as-a-Service (SaaS) approach for managing commercial HPC applications as a Web-based service deployed on top of federated clouds. Some inter-trusted private or public clouds are federated to create a unified service platform with a large amount of hardware resources. In addition, an on-demand, pay-per-use model for Web-service-enabled HPC applications is proposed. Further, we provide an economic analysis of the proposed approach from the perspective of end users, cloud service providers, and Independent Software Vendors (ISVs). We conduct a simulation using two HPC application services on three federated clouds. A combined Quality of Service (QoS) and economic evaluation demonstrates a better effect of the proposed approach comparing with existing HPC platforms.

Hu, C, Lu, J, Liu, X & Zhang, G 2018, 'Robust vehicle routing problem with hard time windows under demand and travel time uncertainty', COMPUTERS & OPERATIONS RESEARCH, vol. 94, pp. 139-153.
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Hua, J, Huang, M & Wang, G 2018, 'Graph Layout Performance Comparisons of Force-directed Algorithms', International Journal of Performability Engineering, vol. 14, no. 1, pp. 67-76.
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Due to force-directed algorithms’ capabilities of producing aesthetically pleasing graph layouts, which follow metrics for graph drawing aesthetics, these layouts have become the most common methods in the practical data visualization area. However, evaluating the performance of relevant algorithms remains a challenge, since graph layout quality is largely relying on aspects such as human intuition, personal judgment and methods’ pre-setting parameters. In addition, most aesthetics criteria of graph drawing conflict with each other. This study evaluated the performance measurements of four force-directed algorithms in terms of seven commonly applied aesthetic criteria based on practical raw data collected, and demonstrated the experimental framework. The early outcomes compared twenty final graph layouts and gave empirical evidences; the study may assist with future detailed force-directed algorithms selection based on users’ specific requirements.

Hua, J, Huang, M, Wang, G & Zreika, M 2018, 'Applying Data Visualization Techniques for Stock Relationship Analysis', Filomat, vol. 32, no. 5, pp. 1931-1936.
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Decision making in stock investment is often made based on current events in the market and the analysis of historical data on specific stocks. Besides, similar rates of price changing over a long-term period on different stocks may indicate potential connections between those listed corporations. The proposed methodology applies the force-directed algorithm and time-series chart to offer stakeholders capability to gain deeper insights initiative on potential relationships between stocks comes with less human interventions. Hence to assist in future decision making on stock investment via graph layouts.

Huang, C, Yao, L, Wang, X, Benatallah, B & Sheng, QZ 2018, 'Software Expert Discovery via Knowledge Domain Embeddings in a Collaborative Network', Pattern Recognition Letters.
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Hussain, W & Hussain, F 2018, 'Comparing time series with machine learning-based prediction approaches for violation management in cloud SLAs', Future Generation Computer Systems.
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In cloud computing, service level agreements (SLAs) are legal agreements between a service provider and consumer that contain a list of obligations and commitments which need to be satisfied by both parties during the transaction. From a service provider’s perspective, a violation of such a commitment leads to penalties in terms of money and reputation and thus has to be effectively managed. In the literature, this problem has been studied under the domain of cloud service management. One aspect required to manage cloud services after the formation of SLAs is to predict the future Quality of Service (QoS) of cloud parameters to ascertain if they lead to violations. Various approaches in the literature perform this task using different prediction approaches however none of them study the accuracy of each. However, it is important to do this as the results of each prediction approach vary according to the pattern of the input data and selecting an incorrect choice of a prediction algorithm could lead to service violation and penalties. In this paper, we test and report the accuracy of time series and machine learning-based prediction approaches. In each category, we test many different techniques and rank them according to their order of accuracy in predicting future QoS. Our analysis helps the cloud service provider to choose an appropriate prediction approach (whether time series or machine learning based) and further to utilize the best method depending on input data patterns to obtain an accurate prediction result and better manage their SLAs to avoid violation penalties.

Hussain, W, Hussain, FK, Hussain, O, Bagia, R & Chang, E 2018, 'Risk-based framework for SLA violation abatement from the cloud service provider’s perspective', The Computer Journal, vol. 61, no. 9, pp. 1306-1322.
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The constant increase in the growth of the cloud market creates new challenges for cloud service providers. One such challenge is the need to avoid possible service level agreement (SLA) violations and their consequences through good SLA management. Researchers have proposed various frameworks and have made significant advances in managing SLAs from the perspective of both cloud users and providers. However, none of these approaches guides the service provider on the necessary steps to take for SLA violation abatement; that is, the prediction of possible SLA violations, the process to follow when the system identifies the threat of SLA violation, and the recommended action to take to avoid SLA violation. In this paper, we approach this process of SLA violation detection and abatement from a risk management perspective. We propose a Risk Management-based Framework for SLA violation abatement (RMF-SLA) following the formation of an SLA which comprises SLA monitoring, violation prediction and decision recommendation. Through experiments, we validate and demonstrate the suitability of the proposed framework for assisting cloud providers to minimize possible service violations and penalties.

Ivanyos, G, Kulkarni, R, Qiao, Y, Santha, M & Sundaram, A 2018, 'On the complexity of trial and error for constraint satisfaction problems', Journal of Computer and System Sciences, vol. 92, pp. 48-64.
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© 2017 Elsevier Inc. In 2013 Bei, Chen and Zhang introduced a trial and error model of computing, and applied to some constraint satisfaction problems. In this model the input is hidden by an oracle which, for a candidate assignment, reveals some information about a violated constraint if the assignment is not satisfying. In this paper we initiate a systematic study of constraint satisfaction problems in the trial and error model, by adopting a formal framework for CSPs, and defining several types of revealing oracles. Our main contribution is to develop a transfer theorem for each type of the revealing oracle. To any hidden CSP with a specific type of revealing oracle, the transfer theorem associates another CSP in the normal setting, such that their complexities are polynomial-time equivalent. This in principle transfers the study of a large class of hidden CSPs to the study of normal CSPs. We apply the transfer theorems to get polynomial-time algorithms or hardness results for several families of concrete problems.

Jay, B 2018, 'Self-Quotation in a Typed, Intensional Lambda-Calculus', Electronic Notes in Theoretical Computer Science, vol. 336, pp. 207-222.
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Intensional lambda-calculus adds intensional combinators to lambda-calculus to facilitate analysis. In particular, they are used to factorise data structures and programs into their components, which can be used to convert abstractions into combinators. This paper shows how to type an intensional lambda-calculus using no more than the types of System F. Even the quotation function used to access program syntax can be defined and typed, as can its inverse: the calculus supports typed self-quotation. Thus, one may freely alternate between program analysis and program execution. Proofs of all results have been verified in Coq.

Jhang, JY, Lin, CJ, Lin, CT & Young, KY 2018, 'Navigation Control of Mobile Robots Using an Interval Type-2 Fuzzy Controller Based on Dynamic-group Particle Swarm Optimization', International Journal of Control, Automation and Systems, vol. 16, no. 5, pp. 2446-2457.
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© 2018, Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature. This paper presents an effective navigation control method for mobile robots in an unknown environment. The proposed behavior manager (BM) switches between two behavioral control patterns, wall-following behavior (WFB) and toward-goal behavior (TGB), based on the relationship between the mobile robot and the unknown environment. An interval type-2 fuzzy neural controller with a dynamic-group particle swarm optimization (DGPSO) algorithm is proposed to provide WFB control and obstacle avoidance for mobile robots. In the WFB learning process, the input signal of a controller is the distance between the wall and the sonar sensors, and its output signal is the speed of two wheels of a mobile robot. A fitness function, which operates on the total distance traveled by the mobile robot, distance from the side wall, angle to the side wall, and moving speed, evaluates the WFB performance of the mobile robot. In addition, an escape mechanism is proposed to avoid a dead cycle. Experimental results reveal that the proposed DGPSO is superior to other methods in WFB and navigation control.

Ji, K, Chen, Z, Sun, R, Ma, K, Yuan, Z & Xu, G 2018, 'GIST: A generative model with individual and subgroup-based topics for group recommendation', Expert Systems with Applications, vol. 94, pp. 81-93.
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© 2017 Elsevier Ltd In this paper, a Topic-based probabilistic model named GIST is proposed to infer group activities, and make group recommendations. Compared with existing individual-based aggregation methods, it not only considers individual members’ interest, but also consider some subgroups’ interest. Intuition might seem that when a group of users want to take part in an activity, not every group member is decisive, instead, more likely the subgroups of members having close relationships lead to the final activity decision. That motivates our study on jointly considering individual members’ choices and subgroups’ choices for group recommendations. Based on this, our model uses two kinds of unshared topics to model individual members’ interest and subgroups’ interest separately, and then make final recommendations according to the choices from the two aspects with a weight-based scheme. Moreover, the link information in the graph topology of the groups can be used to optimize the weights of our model. The experimental results on real-life data show that the recommendation accuracy is significantly improved by GIST comparing with the state-of-the-art methods.

Jordaan, J, Punzet, S, Melnikov, A, Sanches, A, Oberst, S, Marburg, S & Powell, DA 2018, 'Measuring monopole and dipole polarizability of acoustic meta-atoms', Applied Physics Letters, vol. 113, no. 22.
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© 2018 Author(s). We present a method to extract monopole and dipole polarizability from experimental measurements of two-dimensional acoustic meta-atoms. In contrast to extraction from numerical results, this enables all second-order effects and uncertainties in material properties to be accounted for. We apply the technique to 3D-printed labyrinthine meta-atoms of a variety of geometries. We show that the polarizability of structures with a shorter acoustic path length agrees well with numerical results. However, those with longer path lengths suffer strong additional damping, which we attribute to the strong viscous and thermal losses in narrow channels.

Kaiwartya, O, Abdullah, AH, Cao, Y, Lloret, J, Kumar, S, Shah, RR, Prasad, M & Prakash, S 2018, 'Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things', IEEE Internet of Things Journal, vol. 5, no. 2, pp. 571-580.
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IEEE Recently, virtualization in wireless sensor networks (WSNs) has witnessed significant attention due to the growing service domain for IoT. Related literature on virtualization in WSNs explored resource optimization without considering communication failure in WSNs environments. The failure of a communication link in WSNs impacts many virtual networks running IoT services. In this context, this paper proposes a framework for optimizing fault tolerance in virtualization in WSNs, focusing on heterogeneous networks for service-oriented IoT applications. An optimization problem is formulated considering fault tolerance and communication delay as two conflicting objectives. An adapted non-dominated sorting based genetic algorithm (A-NSGA) is developed to solve the optimization problem. The major components of A-NSGA include chromosome representation, fault tolerance and delay computation, crossover and mutation, and non-dominance based sorting. Analytical and simulation based comparative performance evaluation has been carried out. From the analysis of results, it is evident that the framework effectively optimizes fault tolerance for virtualization in WSNs.

Kang, G, Li, J & Tao, D 2018, 'Shakeout: A New Approach to Regularized Deep Neural Network Training', IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 40, no. 5, pp. 1245-1258.
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Kaur, P & Goyal, ML 2018, 'A Clustering Based approach to End Price prediction', International Journal of Computer Science and Information Security, vol. 16, no. 9, pp. 125-141.
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Kendall, S, Redshaw, S, Ward, S, Wayland, S & Sullivan, E 2018, 'Systematic review of qualitative evaluations of reentry programs addressing problematic drug use and mental health disorders amongst people transitioning from prison to communities.', Health & justice, vol. 6.
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The paper presents a systematic review and metasynthesis of findings from qualitative evaluations of community reentry programs. The programs sought to engage recently released adult prison inmates with either problematic drug use or a mental health disorder.Seven biomedical and social science databases, Cinahl, Pubmed, Scopus, Proquest, Medline, Sociological abstracts and Web of Science and publisher database Taylor and Francis were searched in 2016 resulting in 2373 potential papers. Abstract reviews left 140 papers of which 8 were included after detailed review. Major themes and subthemes were identified through grounded theory inductive analysis of results from the eight papers. Of the final eight papers the majority (6) were from the United States. In total, the papers covered 405 interviews and included 121 (30%) females and 284 (70%) males.Findings suggest that the interpersonal skills of case workers; access to social support and housing; and continuity of case worker relationships throughout the pre-release and post-release period are key social and structural factors in program success.Evaluation of community reentry programs requires qualitative data to contextualize statistical findings and identify social and structural factors that impact on reducing incarceration and improving participant health. These aspects of program efficacy have implications for reentry program development and staff training and broader social and health policy and services.

Kendrick, L, Musial, K & Gabrys, B 2018, 'Change point detection in social networksCritical review with experiments', Computer Science Review, vol. 29, pp. 1-13.
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© 2018 Elsevier Inc. Change point detection in social networks is an important element in developing the understanding of dynamic systems. This complex and growing area of research has no clear guidelines on what methods to use or in which circumstances. This paper critically discusses several possible network metrics to be used for a change point detection problem and conducts an experimental, comparative analysis using the Enron and MIT networks. Bayesian change point detection analysis is conducted on different global graph metrics (Size, Density, Average Clustering Coefficient, Average Shortest Path) as well as metrics derived from the Hierarchical and Block models (Entropy, Edge Probability, No. of Communities, Hierarchy Level Membership). The results produced the posterior probability of a change point at weekly time intervals that were analysed against ground truth change points using precision and recall measures. Results suggest that computationally heavy generative models offer only slightly better results compared to some of the global graph metrics. The simplest metrics used in the experiments, i.e. nodes and links numbers, are the recommended choice for detecting overall structural changes.

Kenning, G 2018, 'LAUGH: co-design for people living with advanced dementia', Australian Journal Of Dementia Care.

Kenning, G 2018, 'Reciprocal design: inclusive design approaches for people with late stage dementia', Design for Health, vol. 2, no. 1, pp. 142-162.
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Kenning, G & Treadaway, C 2018, 'Designing for dementia: iterative grief and transitional objects', Mortality and Design in Design Issues, vol. 34, no. 1.
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Kok, VC, Zhang, H-W, Lin, C-T, Huang, S-C & Wu, M-F 2018, 'Positive association between hypertension and urinary bladder cancer: epidemiologic evidence involving 79,236 propensity score-matched individuals.', Upsala journal of medical sciences, vol. 123, no. 2, pp. 109-115.
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INTRODUCTION:We hypothesized that hypertensive patients harbor a higher risk of urinary bladder (UB) cancer. MATERIAL AND METHODS:We performed a population-based cohort study on adults using a National Health Insurance Research Database (NHIRD) dataset. Hypertension and comparison non-hypertensive (COMP) groups comprising 39,618 patients each were propensity score-matched by age, sex, index date, and medical comorbidities. The outcome was incident UB cancer validated using procedure codes. We constructed multivariable Cox models to derive adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs). Cumulative incidence was compared using a log-rank test. RESULTS:During a total follow-up duration of 380,525 and 372,020 person-years in the hypertension and COMP groups, 248 and 186 patients developed UB cancer, respectively, representing a 32% increase in the risk (aHR, 1.32; 95% CI, 1.09-1.60). Hypertensive women harbored a significantly increased risk of UB cancer (aHR, 1.55; 95% CI, 1.12-2.13) compared with non-hypertensive women, whereas men with hypertension had a statistically non-significant increased risk (aHR, 1.22; 95% CI, 0.96-1.55). The sensitivity analysis demonstrated that the increased risk was sustained throughout different follow-up durations for the entire cohort; a statistical increase in the risk was also noted among hypertensive men. CONCLUSION:This nationwide population-based propensity score-matched cohort study supports a positive association between hypertension and subsequent UB cancer development.

Krol, LR, Pawlitzki, J, Lotte, F, Gramann, K & Zander, TO 2018, 'SEREEGA: Simulating Event-Related EEG Activity'.
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Electroencephalography (EEG) is a popular method to monitor brain activity, but it can be difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings, ensuring that it is known beforehand which effects are present in the data. As such, simulated data can be used, among other things, to assess or compare signal processing and machine learning algorithms, to model EEG variabilities, and to design source reconstruction methods. In this paper, we present SEREEGA, short for Simulating Event-Related EEG Activity. SEREEGA is a MATLAB-based open-source toolbox dedicated to the generation of simulated epochs of EEG data. It is modular and extensible, at initial release supporting five different publicly available head models and capable of simulating multiple different types of signals mimicking brain activity. This paper presents the architecture and general workflow of this toolbox, as well as a simulated data set demonstrating some of its functions.

Krol, LR, Pawlitzki, J, Lotte, F, Gramann, K & Zander, TO 2018, 'SEREEGA: Simulating event-related EEG activity', JOURNAL OF NEUROSCIENCE METHODS, vol. 309, pp. 13-24.
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Lenka, RK, Rath, AK, Tan, Z, Sharma, S, Puthal, D, Simha, NVR, Prasad, M, Raja, R & Tripathi, SS 2018, 'Building Scalable Cyber-Physical-Social Networking Infrastructure Using IoT and Low Power Sensors', IEEE Access, vol. 6, pp. 30162-30173.
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© 2013 IEEE. Wireless sensors are an important component to develop the Internet of Things (IoT) Sensing infrastructure. There are enormous numbers of sensors connected with each other to form a network (well known as wireless sensor networks) to complete the IoT Infrastructure. These deployed wireless sensors are with limited energy and processing capabilities. The IoT infrastructure becomes a key factor to building cyber-physical-social networking infrastructure, where all these sensing devices transmit data toward the cloud data center. Data routing toward cloud data center using such low power sensor is still a challenging task. In order to prolong the lifetime of the IoT sensing infrastructure and building scalable cyber infrastructure, there is the requirement of sensing optimization and energy efficient data routing. Toward addressing these issues of IoT sensing, this paper proposes a novel rendezvous data routing protocol for low-power sensors. The proposed method divides the sensing area into a number of clusters to lessen the energy consumption with data accumulation and aggregation. As a result, there will be less amount of data stream to the network. Another major reason to select cluster-based data routing is to reduce the control overhead. Finally, the simulation of the proposed method is done in the Castalia simulator to observe the performance. It has been concluded that the proposed method is energy efficient and it prolongs the networks lifetime for scalable IoT infrastructure.

Leong, TW 2018, 'A book to inspire the pursuit of mystery and enchantment in HCI', Interactions, vol. 25, no. 6, pp. 25-25.
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Li, G & Chung, W-Y 2018, 'Combined EEG-Gyroscope-tDCS Brain Machine Interface System for Early Management of Driver Drowsiness', IEEE Transactions on Human-Machine Systems, vol. 48, no. 1, pp. 50-62.
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Li, H, Wang, J, Lu, H & Guo, Z 2018, 'Research and application of a combined model based on variable weight for short term wind speed forecasting', RENEWABLE ENERGY, vol. 116, pp. 669-684.
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Li, S, Ren, W, Zhu, T & Choo, KKR 2018, 'Lico: A Lightweight Access Control Model for Inter-Networking Linkages', IEEE Access, vol. 6, pp. 51748-51755.
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© 2013 IEEE. Processes in operating systems are assigned different privileges to access different resources. A process may invoke other processes whose privileges are different; thus, its privileges are expanded (or escalated) due to such improper 'inheritance.' Inter-networking can also occur between processes, either transitively or iteratively. This complicates the monitoring of inappropriate privilege assignment/escalation, which can result in information leakage. Such information leakage occurs due to privilege transitivity and inheritance and can be defined as a general access control problem for inter-networking linkages. This is also a topic that is generally less studied in existing access control models. Specifically, in this paper, we propose a lightweight directed graph-based model, LiCo, which is designed to facilitate the authorization of privileges among inter-networking processes. To the best of our knowledge, this is the first general access control model for inter-invoking processes and general inter-networking linkages.

Li, X, Nie, L, Xu, H & Wang, X 2018, 'Collaborative Fall Detection Using Smart Phone and Kinect', MOBILE NETWORKS & APPLICATIONS, vol. 23, no. 4, pp. 775-788.
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Li, Y, Qiao, Y, Wang, X & Duan, R 2018, 'Tripartite-to-Bipartite Entanglement Transformation by Stochastic Local Operations and Classical Communication and the Structure of Matrix Spaces', Communications in Mathematical Physics, vol. 358, no. 2, pp. 791-814.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. We study the problem of transforming a tripartite pure state to a bipartite one using stochastic local operations and classical communication (SLOCC). It is known that the tripartite-to-bipartite SLOCC convertibility is characterized by the maximal Schmidt rank of the given tripartite state, i.e. the largest Schmidt rank over those bipartite states lying in the support of the reduced density operator. In this paper, we further study this problem and exhibit novel results in both multi-copy and asymptotic settings, utilizing powerful results from the structure of matrix spaces. In the multi-copy regime, we observe that the maximal Schmidt rank is strictly super-multiplicative, i.e. the maximal Schmidt rank of the tensor product of two tripartite pure states can be strictly larger than the product of their maximal Schmidt ranks. We then provide a full characterization of those tripartite states whose maximal Schmidt rank is strictly super-multiplicative when taking tensor product with itself. Notice that such tripartite states admit strict advantages in tripartite-to-bipartite SLOCC transformation when multiple copies are provided. In the asymptotic setting, we focus on determining the tripartite-to-bipartite SLOCC entanglement transformation rate. Computing this rate turns out to be equivalent to computing the asymptotic maximal Schmidt rank of the tripartite state, defined as the regularization of its maximal Schmidt rank. Despite the difficulty caused by the super-multiplicative property, we provide explicit formulas for evaluating the asymptotic maximal Schmidt ranks of two important families of tripartite pure states by resorting to certain results of the structure of matrix spaces, including the study of matrix semi-invariants. These formulas turn out to be powerful enough to give a sufficient and necessary condition to determine whether a given tripartite pure state can be transformed to the bipa...

Lin, C-T, Chiu, T-C, Wang, Y-K, Chuang, C-H & Gramann, K 2018, 'Granger causal connectivity dissociates navigation networks that subserve allocentric and egocentric path integration.', Brain Research, vol. 1679, pp. 91-100.
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Studies on spatial navigation demonstrate a significant role of the retrosplenial complex (RSC) in the transformation of egocentric and allocentric information into complementary spatial reference frames (SRFs). The tight anatomical connections of the RSC with a wide range of other cortical regions processing spatial information support its vital role within the human navigation network. To better understand how different areas of the navigational network interact, we investigated the dynamic causal interactions of brain regions involved in solving a virtual navigation task. EEG signals were decomposed by independent component analysis (ICA) and subsequently examined for information flow between clusters of independent components (ICs) using direct short-time directed transfer function (sdDTF). The results revealed information flow between the anterior cingulate cortex and the left prefrontal cortex in the theta (4-7 Hz) frequency band and between the prefrontal, motor, parietal, and occipital cortices as well as the RSC in the alpha (8-13 Hz) frequency band. When participants prefered to use distinct reference frames (egocentric vs. allocentric) during navigation was considered, a dominant occipito-parieto-RSC network was identified in allocentric navigators. These results are in line with the assumption that the RSC, parietal, and occipital cortices are involved in transforming egocentric visual-spatial information into an allocentric reference frame. Moreover, the RSC demonstrated the strongest causal flow during changes in orientation, suggesting that this structure directly provides information on heading changes in humans.

Lin, CT, Hsieh, TY, Liu, YT, Lin, YY, Fang, CN, Wang, YK, Yen, G, Pal, NR & Chuang, CH 2018, 'Minority Oversampling in Kernel Adaptive Subspaces for Class Imbalanced Datasets', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 5, pp. 950-962.
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© 1989-2012 I EEE. The class imbalance problem in machine learning occurs when certain classes are underrepresented relative to the others, leading to a learning bias toward the majority classes. To cope with the skewed class distribution, many learning methods featuring minority oversampling have been proposed, which are proved to be effective. To reduce information loss during feature space projection, this study proposes a novel oversampling algorithm, named minority oversampling in kernel adaptive subspaces (MOKAS), which exploits the invariant feature extraction capability of a kernel version of the adaptive subspace self-organizing maps. The synthetic instances are generated from well-trained subspaces and then their pre-images are reconstructed in the input space. Additionally, these instances characterize nonlinear structures present in the minority class data distribution and help the learning algorithms to counterbalance the skewed class distribution in a desirable manner. Experimental results on both real and synthetic data show that the proposed MOKAS is capable of modeling complex data distribution and outperforms a set of state-of-the-art oversampling algorithms.

Lin, CT, King, JT, Fan, JW, Appaji, A & Prasad, M 2018, 'The Influence of Acute Stress on Brain Dynamics during Task Switching Activities', IEEE Access, vol. 6, pp. 3249-3255.
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© 2013 IEEE. Task switching is a common method to investigate executive functions such as working memory and attention. This paper investigates the effect of acute stress on brain activity using task switching. Surprisingly few studies have been conducted in this area. There is behavioral and physiological evidence to indicate that acute stress makes the participants more tense which results in a better performance. In this current study, under stressful conditions, the participants gave quick responses with high accuracy. However, unexpected results were found in relation to salivary cortisol. Furthermore, the electroencephalogram results showed that acute stress was pronounced at the frontal and parietal midline cortex, especially on the theta, alpha, and gamma bands. One possible explanation for these results may be that the participants changed their strategy in relation to executive functions during stressful conditions by paying more attention which resulted in a higher working memory capacity which enhanced performance during the task switching.

Lin, CT, King, JT, Singh, AK, Prasad, M, Ma, Z, Lin, JW, Machado, AMC, Appaji, A & Gupta, A 2018, 'Voice Navigation Effects on Real-World Lane Change Driving Analysis using an Electroencephalogram', IEEE Access, vol. 6.
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OAPA Improving the degree of assistance given by in-car navigation systems is an important issue for the safety of both drivers and passengers. There is a vast body of research that assesses the usability and interfaces of the existing navigation systems but very few investigations study the impact on the brain activity based on navigation-based driving. In this study, a real-world experiment is designed to acquire the electroencephalography (EEG) and in-car information to analyze the dynamic brain activity while the driver is performing the lane-changing task based on the auditory instructions from an in-car navigation system. The results show that auditory cues can influence the speed and increase the frontal EEG delta and beta power which is related to motor preparation and decision making during a lane change. However, there were no significant results on the alpha power. A better lane-change assessment can be obtained using specific vehicle information (lateral acceleration and heading angle) with EEG features for future naturalized driving study.

Lin, CT, Nascimben, M, King, JT & Wang, YK 2018, 'Task-related EEG and HRV entropy factors under different real-world fatigue scenarios', Neurocomputing, vol. 311, pp. 24-31.
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© 2018 Elsevier B.V. We classified the alertness levels of 17 subjects in different experimental sessions in a six-month longitudinal study based on a daily sampling system and related alertness to performance on a psychomotor vigilance task (PVT). As to our best knowledge, this is the first EEG-based longitudinal study for real-world fatigue. Alertness and PVT performance showed a monotonically increasing relationship. Moreover, we identified two measures in the entropy domain from electroencephalography (EEG) and heart rate variability (HRV) signals that were able to identify the extreme classes of PVT performers. Wiener entropy on selected leads from the frontal-parietal axis was able to discriminate the group of best performers. Sample entropy from the HRV signal was able to identify the worst performers. This joint EEG-HRV quantification provides complementary indexes to indicate more reliable human performance.

Lin, CT, Prasad, M, Chung, CH, Puthal, D, El-Sayed, H, Sankar, S, Wang, YK, Singh, J & Sangaiah, AK 2018, 'IoT-Based Wireless Polysomnography Intelligent System for Sleep Monitoring', IEEE Access, vol. 6, pp. 405-414.
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Polysomnography (PSG) is considered the gold standard in the diagnosis of obstructive sleep apnea (OSA). The diagnosis of OSA requires an overnight sleep experiment in a laboratory. However, due to limitations in relation to the number of labs and beds available, patients often need to wait a long time before being diagnosed and eventually treated. In addition, the unfamiliar environment and restricted mobility when a patient is being tested with a polysomnogram may disturb their sleep, resulting in an incomplete or corrupted test. Therefore, it is posed that a PSG conducted in the patient's home would be more reliable and convenient. The Internet of Things (IoT) plays a vital role in the e-Health system. In this paper, we implement an IoT-based wireless polysomnography system for sleep monitoring, which utilizes a battery-powered, miniature, wireless, portable, and multipurpose recorder. A Java-based PSG recording program in the personal computer is designed to save several bio-signals and transfer them into the European data format. These PSG records can be used to determine a patient's sleep stages and diagnose OSA. This system is portable, lightweight, and has low power-consumption. To demonstrate the feasibility of the proposed PSG system, a comparison was made between the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system. Several healthy volunteer patients participated in the PSG experiment and were monitored by both the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system simultaneously, under the supervision of specialists at the Sleep Laboratory in Taipei Veteran General Hospital. A comparison of the results of the time-domain waveform and sleep stage of the two systems shows that the proposed system is reliable and can be applied in practice. The proposed system can facilitate the long-Term tracing and research of personal sleep monitoring at home.

Liu, A, Lu, J, Liu, F & Zhang, G 2018, 'Accumulating regional density dissimilarity for concept drift detection in data streams', Pattern Recognition, vol. 76, pp. 256-272.
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© 2017 Elsevier Ltd In a non-stationary environment, newly received data may have different knowledge patterns from the data used to train learning models. As time passes, a learning model's performance may become increasingly unreliable. This problem is known as concept drift and is a common issue in real-world domains. Concept drift detection has attracted increasing attention in recent years. However, very few existing methods pay attention to small regional drifts, and their accuracy may vary due to differing statistical significance tests. This paper presents a novel concept drift detection method, based on regional-density estimation, named nearest neighbor-based density variation identification (NN-DVI). It consists of three components. The first is a k-nearest neighbor-based space-partitioning schema (NNPS), which transforms unmeasurable discrete da ta instances into a set of shared subspaces for density estimation. The second is a distance function that accumulates the density discrepancies in these subspaces and quantifies the overall differences. The third component is a tailored statistical significance test by which the confidence interval of a concept drift can be accurately determined. The distance applied in NN-DVI is sensitive to regional drift and has been proven to follow a normal distribution. As a result, the NN-DVI's accuracy and false-alarm rate are statistically guaranteed. Additionally, several benchmarks have been used to evaluate the method, including both synthetic and real-world datasets. The overall results show that NN-DVI has better performance in terms of addressing problems related to concept drift-detection.

Liu, F, Lu, J & Zhang, G 2018, 'Unsupervised Heterogeneous Domain Adaptation via Shared Fuzzy Equivalence Relations', IEEE Transactions on Fuzzy Systems, vol. 26, no. 6, pp. 3555-3568.
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Liu, Q, Wu, R, Chen, E, Xu, G, Su, Y, Chen, Z & Hu, G 2018, 'Fuzzy cognitive diagnosis for modelling examinee performance', ACM Transactions on Intelligent Systems and Technology, vol. 9, no. 4.
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© 2018 ACM. Recent decades have witnessed the rapid growth of educational data mining (EDM), which aims at automatically extracting valuable information from large repositories of data generated by or related to people's learning activities in educational settings. One of the key EDM tasks is cognitive modelling with examination data, and cognitive modelling tries to profile examinees by discovering their latent knowledge state and cognitive level (e.g. the proficiency of specific skills). However, to the best of our knowledge, the problem of extracting information from both objective and subjective examination problems to achieve more precise and interpretable cognitive analysis remains underexplored. To this end, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees' cognitive modelling with both objective and subjective problems. Specifically, to handle the partially correct responses on subjective problems, we first fuzzify the skill proficiency of examinees. Then we combine fuzzy set theory and educational hypotheses to model the examinees' mastery on the problems based on their skill proficiency. Finally, we simulate the generation of examination score on each problem by considering slip and guess factors. In this way, the whole diagnosis framework is built. For further comprehensive verification, we apply our FuzzyCDF to three classical cognitive assessment tasks, i.e., predicting examinee performance, slip and guess detection, and cognitive diagnosis visualization. Extensive experiments on three real-world datasets for these assessment tasks prove that FuzzyCDF can reveal the knowledge states and cognitive level of the examinees effectively and interpretatively.

Liu, YT, Pal, NR, Marathe, AR & Lin, CT 2018, 'Weighted Fuzzy Dempster-Shafer Framework for Multimodal Information Integration', IEEE Transactions on Fuzzy Systems, vol. 26, no. 1, pp. 338-352.
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© 1993-2012 IEEE. This study proposes an architecture based on a weighted fuzzy Dempster-Shafer framework (WFDSF), which can adjust weights associated with inconsistent evidence obtained by different classification approaches, to realize a fusion system for integrating multimodal information. The Dempster-Shafer theory (D-S theory) of evidence enables us to integrate heterogeneous information from multiple sources to obtain collaborative inferences for a given problem. To conquer various uncertainties associated with the collected information, our system assigns beliefs and plausibilities to possible hypotheses of each decision maker and uses a combination rule to fuse multimodal information. For information fusion, an important step in D-S aggregation is to find an appropriate basic probability assignment scheme for allocating support to each possible hypothesis/class, which remains an arduous and unsolved problem. Here, we propose a mathematical structure to aggregate weighted evidence extracted from two different types of approaches: fuzzy Naïve Bayes and nearest mean classification rule. Further, an intuitionistic belief assignment is employed to address uncertainties between hypotheses/classes. Finally, 12 benchmark problems from the UCI machine learning repository for classification are employed to validate the proposed WFDSF-based scheme. In addition, an application of WFDSF to a practical brain-computer interface problem involving multimodal data fusion is demonstrated in this study. The experimental results show that the WFDSF is superior to several existing methods.

Lu, J, Xuan, J, Zhang, G & Luo, X 2018, 'Structural property-aware multilayer network embedding for latent factor analysis', Pattern Recognition, vol. 76, pp. 228-241.
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© 2017 Elsevier Ltd Multilayer network is a structure commonly used to describe and model the complex interaction between sets of entities/nodes. A three-layer example is the author-paper-word structure in which authors are linked by co-author relation, papers are linked by citation relation, and words are linked by semantic relation. Network embedding, which aims to project the nodes in the network into a relatively low-dimensional space for latent factor analysis, has recently emerged as an effective method for a variety of network-based tasks, such as collaborative filtering and link prediction. However, existing studies of network embedding both focus on the single-layer network and overlook the structural properties of the network, e.g., the degree distribution and communities, which are significant for node characterization, such as the preferences of users in a social network. In this paper, we propose four multilayer network embedding algorithms based on Nonnegative Matrix Factorization (NMF) with consideration given to four structural properties: whole network (NNMF), community (CNMF), degree distribution (DNMF), and max spanning tree (TNMF). Experiments on synthetic data show that the proposed algorithms are able to preserve the desired structural properties as designed. Experiments on real-world data show that multilayer network embedding improves the accuracy of document clustering and recommendation, and the four embedding algorithms corresponding to the four structural properties demonstrate the differences in performance on these two tasks. These results can be directly used in document clustering and recommendation systems.

Malomo, L, Pérez, J, Iarussi, E, Pietroni, N, Miguel, E, Cignoni, P & Bickel, B 2018, 'FlexMaps: Computational design of flat flexible shells for shaping 3D objects', ACM Transactions on Graphics, vol. 37, no. 6.
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© 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM. We propose FlexMaps, a novel framework for fabricating smooth shapes out of flat, flexible panels with tailored mechanical properties. We start by mapping the 3D surface onto a 2D domain as in traditional UV mapping to design a set of deformable flat panels called FlexMaps. For these panels, we design and obtain specific mechanical properties such that, once they are assembled, the static equilibrium configuration matches the desired 3D shape. FlexMaps can be fabricated from an almost rigid material, such as wood or plastic, and are made flexible in a controlled way by using computationally designed spiraling microstructures.

Meng, T, Cai, L, He, T, Chen, L, Deng, Z, Ding, W & Cao, Z 2018, 'A Modified Distance Dynamics Model for Improvement of Community Detection', IEEE Access, vol. 6, pp. 63934-63947.
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© 2018 IEEE. Community detection is a key technique for identifying the intrinsic community structures of complex networks. The distance dynamics model has been proven effective in finding communities with arbitrary size and shape and identifying outliers. However, to simulate distance dynamics, the model requires manual parameter specification and is sensitive to the cohesion threshold parameter, which is difficult to determine. Furthermore, it has difficulty handling rough outliers and ignores hubs (nodes that bridge communities). In this paper, we propose a robust distance dynamics model, namely, Attractor++, which uses a dynamic membership degree. In Attractor++, the dynamic membership degree is used to determine the influence of exclusive neighbors on the distance instead of setting the cohesion threshold. In addition, considering its inefficiency and low accuracy in handling outliers and identifying hubs, we design an outlier optimization model that is based on triangle adjacency. By using optimization rules, a postprocessing method further judges whether a singleton node should be merged into the same community as its triangles or regarded as a hub or an outlier. Extensive experiments on both real-world and synthetic networks demonstrate that our algorithm more accurately identifies nodes that have special roles (hubs and outliers) and more effectively identifies community structures.

Ming, Y, Pelusi, D, Fang, CN, Prasad, M, Wang, YK, Wu, D & Lin, CT 2018, 'EEG data analysis with stacked differentiable neural computers', Neural Computing and Applications.
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© 2018, Springer-Verlag London Ltd., part of Springer Nature. Differentiable neural computer (DNC) has demonstrated remarkable capabilities in solving complex problems. In this paper, we propose to stack an enhanced version of differentiable neural computer together to extend its learning capabilities. Firstly, we give an intuitive interpretation of DNC to explain the architectural essence and demonstrate the stacking feasibility by contrasting it with the conventional recurrent neural network. Secondly, the architecture of stacked DNCs is proposed and modified for electroencephalogram (EEG) data analysis. We substitute the original Long Short-Term Memory network controller by a recurrent convolutional network controller and adjust the memory accessing structures for processing EEG topographic data. Thirdly, the practicability of our proposed model is verified by an open-sourced EEG dataset with the highest average accuracy achieved; then after fine-tuning the parameters, we show the minimal mean error obtained on a proprietary EEG dataset. Finally, by analyzing the behavioral characteristics of the trained stacked DNCs model, we highlight the suitableness and potential of utilizing stacked DNCs in EEG signal processing.

Mirtalaie, MA, Hussain, OK, Chang, E & Hussain, FK 2018, 'Extracting sentiment knowledge from pros/cons product reviews: Discovering features along with the polarity strength of their associated opinions', Expert Systems with Applications, vol. 114, pp. 267-288.
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© 2018 Elsevier Ltd Sentiment knowledge extraction is a growing area of research in the literature. It helps in analyzing users’ opinions about different entities or events, which can then be utilized by analysts for various purposes. Particularly, feature-based sentiment analysis is one of the challenging research areas that analyzes users’ opinions on various features of a product or service. Of the three formats for the product reviews, our focus in this paper is limited to analyzing the pros/cons type. Due to the nature of pros/cons reviews, they are mostly concise and follow a different structure from other review types. Therefore, specialized techniques are needed to analyze these reviews and extract the customers’ discussed product features along with their personal attitudes. In this paper, we propose the Pros/Cons Sentiment Analyzer (PCSA) framework that exploits dependency relations in extracting sentiment knowledge from pros/cons reviews. We also utilize two different lexicons to ascertain the polarity strength of the extracted features based on the customers’ opinions. Several experiments are conducted to evaluate the performance of PCSA in its different phases.

Mueller, F, Andres, J, Svanæs, D, Schraefel, MC, Gerling, K, Tholander, J, Martin-Niedecken, AL, Marquez Segura, E, van den Hoven, E, Graham, N, Höök, K & Sas, C 2018, 'Body-centric computing: results from a weeklong Dagstuhl seminar in a German castle', Interactions, vol. 25, no. 4, pp. 34-39.
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Nawaz, F, Janjua, NK, Hussain, OK, Hussain, FK, Chang, E & Saberi, M 2018, 'Event-driven approach for predictive and proactive management of SLA violations in the Cloud of Things', Future Generation Computer Systems, vol. 84, pp. 78-97.
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© 2018 Elsevier B.V. In a dynamic environment such as the cloud-of-things, one of the most critical factors for successful service delivery is the QoS under defined constraints. Even though guarantees in the form of service level agreements (SLAs) are provided to users, many services exhibit dynamic Quality of Service (QoS) variations. This QoS variation as well as changes in the behavior and state of the service is caused by some internal events (such as varying loads) and external events (such as location and weather), which results in frequent SLA violations. Most of the existing violation prediction approaches use historic data to predict future QoS values. They do not consider dynamic changes and the events that cause these changes in QoS attributes. In this paper, we propose an event-driven-based proactive approach for predicting SLA violations by combining logic-based reasoning and probabilistic inferencing. The results show that our proposed approach is efficient and proactively identifies SLA violations under uncertain QoS observations.

Nguyen, Q, Arness, D, Sanderson, C, Simoff, S & Huang, M 2018, 'Enabling Effective Tree Exploration Using Visual Cues', Journal of Visual Languages and Computing, vol. 47, pp. 44-61.
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This article presents a new interactive visualization for exploring large hierarchical structures by providing visual cues on a node link tree visualization. Our technique provides topological previews of hidden substructures with three types of visual cues including simple cues, tree cues and treemap cues. We demonstrate the visual cues on Degree-of-Interest Tree (DOITree) due to its familiar mapping, its capability of providing multiple focused nodes, and its dynamic rescaling of substructures to fit the available space. We conducted a usability study with 28 participants that measured completion time and accuracy across five different topology search tasks. The simple cues had the fastest completion time across three of the node identification tasks. The treemap cues had the highest rate of correct answers on four of the five tasks, although only reaching statistical significance for two of these. As predicted, user ratings demonstrated a preference for the easy to understand tree cues followed by the simple cue, despite this not consistently reflected in performance results.

Niazi, M, Mishra, A & Gill, AQ 2018, 'What Do Software Practitioners Really Think About Software Process Improvement Project Success? An Exploratory Study', Arabian Journal for Science and Engineering, vol. 43, no. 12, pp. 7719-7735.
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© 2018, King Fahd University of Petroleum & Minerals. Software practitioners have always shown a significant interest in implementing software process improvement (SPI) initiatives to ensure the delivery of quality products. Software industry and SPI methodologies have evolved over a period of time; however, still many SPI initiatives have not been successful. There is a need to understand software practitioners’ perspectives on SPI success which can be helpful for tailoring or improving effective situation-specific SPI methodologies. This research presents an exploratory study of Turkish software development organizations. The main research question is: What software practitioners’ really think about SPI project success. This study was conducted with 27 Turkish software development organizations to identity and analyse important SPI factors that contribute to the success of SPI projects. The results reveal that professional growth, increased professional recognition, project planning, monitoring of project risks, providing technical support, adoption of current technologies, strong leadership and commitment are among the highest ranked factors that contribute towards the success of SPI initiatives. The findings of this research provide a foundation for further work in tailoring and improving situation-specific SPI methodologies for software project environments.

Ning, X, Yac, L, Wang, X, Benatallah, B, Dong, M & Zhang, S 2018, 'Rating prediction via generative convolutional neural networks based regression', Pattern Recognition Letters.
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Ratings are an essential criterion for evaluating the quality of movies and a critical indicator of whether a customer would watch a movie. Therefore, an important related research challenge is to predict the rating of a movie before it is released in cinema or even before it is produced. Many existing approaches fail to address this challenge because they predict movie ratings based on post-production factors such as review comments from social media. Consequently, they are generally inapplicable until a movie has been released for a certain period of time when a sufficient number of review comments have become available. In this paper, we propose a regression model based on generative convolutional neural networks for movie rating prediction. Instead of post-production factors widely used by previous work, this model learns from movies’ intrinsic pillars such as genres, budget, cast, director and plot information, which are obtainable before the production of movies. In particular, the model explores the correlations between the rating of a movie and its intrinsic attributes to predict its rating. The results can serve as a reference for investors and movie studios to determine an optimal portfolio for movie production and a guidance to the interested users to choose the movie to watch. Extensive experiments on a real dataset are benchmarked against a set of baselines and state of the art approaches. The results demonstrate the effectiveness of our approach. The proposed model is also general to be extended to handle other prediction tasks.

Niu, T, Wang, J, Lu, H & Du, P 2018, 'Uncertainty modeling for chaotic time series based on optimal multi-input multi-output architecture: Application to offshore wind speed', Energy Conversion and Management, vol. 156, pp. 597-617.
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© 2017 Elsevier Ltd Wind energy is attracting more attention with the growing demand for energy. However, the efficient development and utilization of wind energy are restricted due to the intermittency and randomness of wind speed. Although abundant investigations concerning wind speed forecasting have been conducted by numerous researchers, most of the studies merely attach importance to point forecasts, which cannot quantitatively characterize the uncertainties as developing intervals. In this study, a novel interval prediction architecture has been designed, aiming at constructing effective prediction intervals for a wind speed series, composed of a preprocessing module, a feature selection module, an optimization module, a forecast module and an evaluation module. The feature selection module, in cooperation with the preprocessing module, is developed to determine the optimal model input. Furthermore, the forecast module optimized by the optimization module is considered a predictor for giving prediction intervals. The experimental results shed light on the architecture that not only outperforms the benchmark models considered, but also has great potential for application to wind power systems.

Oberst, S & Tuttle, S 2018, 'Nonlinear dynamics of thin-walled elastic structures for applications in space', Mechanical Systems and Signal Processing, vol. 110, pp. 469-484.
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Driven by the need for multi-functionality and increasing demands for low mass and compact-stowing, unfolding, self-deploying or –morphing smart mechanical structures have become popular space engineering designs for flexible appendages. Extensive research has been conducted on the use of tape springs as hinge deployment mechanisms for space booms, solar sails, or optical membranes or directly for used as antennas. However, the vibrational behaviour of tape springs and its related dynamics have rarely been addressed in detail, even though missions are underway with similarly flexible appendages installed. By conducting quasi-static bending tests on a tape spring antenna, we evidence hysteresis behaviours in both the opposite- and equal sense bending directions. Apart from the well-known snap-through buckling, the structure exhibits torsional buckling in the equal sense bending direction before collapsing. Micro-vibrational excitation triggers nonlinear jump phenomena and the period-doubling route to chaos. Using a computational tape spring model and simplified environmental loads similar to those encountered in near-Earth orbits, coupling between the first bending and torsional modes generates a dynamic instability which is predicted by a complex eigenvalue analysis step. The current study highlights that high perturbation sensitivity and system-inherent nonlinearities can lead to stability issues. In the course of designing a spacecraft with thin-walled appendages, system-level trade-offs are routinely performed. Since it is unclear how severely the vibrations of flexible appendages might affect their proper functioning or the control of the spacecraft, it is of paramount importance to validate experimentally thin-walled structures thoroughly for their dynamic and stability behaviours.

Oberst, S, Baetz, J, Campbell, G, Lampe, F, Lai, JCS, Hoffmann, N & Morlock, MM 2018, 'Vibro-acoustic and nonlinear analysis of cadavric femoral bone impaction in cavity preparations', International Journal of Mechanical Sciences, vol. 144, pp. 739-745.
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Oberst, S, Lai, J & Evans, TA 2018, 'Key physical wood properties in termite foraging decisions', Journal of the Royal Society Interface, vol. 15.
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Oberst, S, Niven, RK, Lester, DR, Ord, A, Hobbs, B & Hoffmann, N 2018, 'Detection of unstable periodic orbits in mineralising geological systems', Chaos, vol. 28, no. 8, pp. 085711-085721.
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Worldwide, mineral exploration is suffering from rising capital costs, due to the depletion of readily recoverable reserves and the need to discover and assess more inaccessible or geologically complex deposits. For gold exploration, this problem is particular acute. We propose an innovative approach to mineral exploration and orebody characterisation, based on the analysis of geological core data as a spatial dynamical system, using the mathematical tools of dynamical systems analysis. This approach is highly relevant for orogenic gold deposits, which - in contrast to systems formed at chemical equilibrium - exhibit many features of nonlinear dynamical systems, including episodic fluctuations on various length and time scales. Feedback relationships between thermo-chemical and deformation processes produce recurrent fluid temperatures and pressures, and the deposition of vein-filling minerals such as pyrite and gold. We therefore relax the typical assumption of chemical equilibrium and analyse the underlying processes as aseismic, non-adiabatic and inherent to a hydrothermal, nonlinear dynamical open-flow chemical reactor. These processes are approximated using the Gray-Scott model (GSM) of reaction-diffusion as complex toy system, which captures some of the features of the underlying mineralisation processes, including the spatio-temporal Turing patterns of unsteady chemical reactions. By use of this analysis, we demonstrate the capability of recurrence plots, recurrence power spectra and recurrence time probabilities to detect underlying unstable periodic orbits, as one sign of deterministic dynamics, and their robustness for the analysis of data contaminated by noise. Recurrence plot based quantification is then applied to three mineral concentrations in core data from the Sunrise Dam gold deposit in the Yilgarn region of Western Australia. Using a moving window, we reveal the episodic recurring low-dimensional dynamic structures and the period doubling rout...

Oberst, S, Tuttle, S, Griffith, D, Lambert, A & Boyce, R 2018, 'Experimental validation of tape springs to be used as thin-walled space structures', Journal of Sound and Vibration, vol. 419, pp. 558-570.
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With the advent of standardised launch geometries and off-the-shelf payloads, space programs utilising nano-satellite platforms are growing worldwide. Thin-walled, flexible and self-deployable structures are commonly used for antennae, instrument booms or solar panels owing to their lightweight, ideal packaging characteristics and near zero energy consumption. However their behaviour in space, in particular in Low Earth Orbits with continually changing environmental conditions, raises many questions. Accurate numerical models, which are often not available due to the difficulty of experimental testing under 1g-conditions, are needed to answer these questions. In this study, we present on-earth experimental validations, as a starting point to study the response of a tape spring as a representative of thin-walled flexible structures under static and vibrational loading. Material parameters of tape springs in a singly (straight, open cylinder) and a doubly curved design, are compared to each other by combining finite element calculations, with experimental laser vibrometry within a single and multi-stage model updating approach. While the determination of the Young's modulus is unproblematic, the damping is found to be inversely proportional to deployment length. With updated material properties the buckling instability margin is calculated using different slenderness ratios. Results indicate a high sensitivity of thin-walled structures to miniscule perturbations, which makes proper experimental testing a key requirement for stability prediction on thin-elastic space structures. The doubly curved tape spring provides closer agreement with experimental results than a straight tape spring design.

Ojha, S, Williams, MA & Johnston, B 2018, 'The Essence of Ethical Reasoning in Robot-Emotion Processing', International Journal of Social Robotics, vol. 10, no. 2, pp. 211-223.
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© 2017, Springer Science+Business Media B.V., part of Springer Nature. As social robots become more and more intelligent and autonomous in operation, it is extremely important to ensure that such robots act in socially acceptable manner. More specifically, if such an autonomous robot is capable of generating and expressing emotions of its own, it should also have an ability to reason if it is ethical to exhibit a particular emotional state in response to a surrounding event. Most existing computational models of emotion for social robots have focused on achieving a certain level of believability of the emotions expressed. We argue that believability of a robot’s emotions, although crucially necessary, is not a sufficient quality to elicit socially acceptable emotions. Thus, we stress on the need of higher level of cognition in emotion processing mechanism which empowers social robots with an ability to decide if it is socially appropriate to express a particular emotion in a given context or it is better to inhibit such an experience. In this paper, we present the detailed mathematical explanation of the ethical reasoning mechanism in our computational model, EEGS, that helps a social robot to reach to the most socially acceptable emotional state when more than one emotions are elicited by an event. Experimental results show that ethical reasoning in EEGS helps in the generation of believable as well as socially acceptable emotions.

Orth, D, Thurgood, C & van den Hoven, E 2018, 'Designing Objects with Meaningful Associations', International Journal of Design, vol. 12, no. 2, pp. 91-104.
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Objects often become cherished for their ties to beliefs, experiences, memories, people, places or values that are significant to their owner. These ties can reflect the ways in which we as humans use objects to characterise, communicate and develop our sense of self. This paper outlines our approach to applying product attachment theory to design practices. We created six artefacts that were inspired by interviews conducted with three individuals who discussed details of their life stories. We then evaluated the associations that came to mind for our participants when interacting with these newly designed artefacts to determine whether these links brought meaning to them. Our findings highlight the potential of design to bring emotional value to products by embodying significant aspects of a person’s self-identity. To do so, designers must consider both the importance and authenticity of the associations formed between an object and an individual.

Orth, D, Thurgood, C & van den Hoven, E 2018, 'Designing Objects with Meaningful Associations', INTERNATIONAL JOURNAL OF DESIGN, vol. 12, no. 2, pp. 91-104.
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Pfister, C, Rol, MA, Mantri, A, Tomamichel, M & Wehner, S 2018, 'Capacity estimation and verification of quantum channels with arbitrarily correlated errors.', Nature communications, vol. 9, no. 1, pp. 27-27.
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The central figure of merit for quantum memories and quantum communication devices is their capacity to store and transmit quantum information. Here, we present a protocol that estimates a lower bound on a channel's quantum capacity, even when there are arbitrarily correlated errors. One application of these protocols is to test the performance of quantum repeaters for transmitting quantum information. Our protocol is easy to implement and comes in two versions. The first estimates the one-shot quantum capacity by preparing and measuring in two different bases, where all involved qubits are used as test qubits. The second verifies on-the-fly that a channel's one-shot quantum capacity exceeds a minimal tolerated value while storing or communicating data. We discuss the performance using simple examples, such as the dephasing channel for which our method is asymptotically optimal. Finally, we apply our method to a superconducting qubit in experiment.

Pham, P & Huang, M 2018, 'Quantitative Approach on Parallel Coordinates andScatter Plots for Multidimensional-Data Visual Analytics', Journal of computers, vol. 13, no. 4, pp. 407-416.
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Parallel coordinates and scatter plots are two well-known visualization techniques for multidimensional data analytics and often employed cooperatively for flexibility increase in exploration of such data. Existing approaches approximately consider qualitative issues and single attribute comparison, which might face statistic challenges in case of quantitative requirement. This paper introduces a new quantitative approach for visual enhancement of parallel coordinates and scatter plots in term of multiple attribute comparison. The method is based on the visual integration of interactive stacked bars and visual queries on parallel axes and scatter charts. The parallel coordinates play the role of a context view while the scatter charts are for focus details. Using the technique, users could not only quantitatively analyze multivariate data, but also flexibly compare multiple target attributes. Moreover, further investigation is enabled for deep understanding of desired information. The characteristics and usefulness of our approach are demonstrated via a case study with two typical use cases.

Ploderer, B & Leong, TW 2018, 'Manual engagement and automation in amateur photography', Media International Australia, vol. 166, no. 1, pp. 44-56.
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© 2017, © The Author(s) 2017. Automation has been central to the development of modern photography and, in the age of digital and smartphone photography, now largely defines everyday experience of the photographic process. In this article, we question the acceptance of automation as the default position for photography, arguing that discussions of automation need to move beyond binary concerns of whether to automate or not and, instead, to consider what is being automated and the degree of automation couched within the particularities of people’s practices. We base this upon findings from ethnographic fieldwork with people engaging manually with film-based photography. While automation liberates people from having to interact with various processes of photography, participants in our study reported a greater sense of control, richer experiences and opportunities for experimentation when they were able to engage manually with photographic processes.

Protzak, J & Gramann, K 2018, 'Investigating Established EEG Parameter During Real-World Driving', FRONTIERS IN PSYCHOLOGY, vol. 9.
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Puthal, D, Obaidat, MS, Nanda, P, Prasad, M, Mohanty, SP & Zomaya, AY 2018, 'Secure and Sustainable Load Balancing of Edge Data Centers in Fog Computing', IEEE Communications Magazine, vol. 56, no. 5, pp. 60-65.
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Fog computing is a recent research trend to bring cloud computing services to network edges. EDCs are deployed to decrease the latency and network congestion by processing data streams and user requests in near real time. EDC deployment is distributed in nature and positioned between cloud data centers and data sources. Load balancing is the process of redistributing the work load among EDCs to improve both resource utilization and job response time. Load balancing also avoids a situation where some EDCs are heavily loaded while others are in idle state or doing little data processing. In such scenarios, load balancing between the EDCs plays a vital role for user response and real-time event detection. As the EDCs are deployed in an unattended environment, secure authentication of EDCs is an important issue to address before performing load balancing. This article proposes a novel load balancing technique to authenticate the EDCs and find less loaded EDCs for task allocation. The proposed load balancing technique is more efficient than other existing approaches in finding less loaded EDCs for task allocation. The proposed approach not only improves efficiency of load balancing; it also strengthens the security by authenticating the destination EDCs.

Qin, M, Lei, K, Gabrys, B & Musial-Gabrys, K 2018, 'Adaptive community detection incorporating topology and content in social networks^✰', Knowledge-Based Systems, vol. 161, pp. 342-356.
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© 2018 In social network analysis, community detection is a basic step to understand the structure and function of networks. Some conventional community detection methods may have limited performance because they merely focus on the networks’ topological structure. Besides topology, content information is another significant aspect of social networks. Although some state-of-the-art methods started to combine these two aspects of information for the sake of the improvement of community partitioning, they often assume that topology and content carry similar information. In fact, for some examples of social networks, the hidden characteristics of content may unexpectedly mismatch with topology. To better cope with such situations, we introduce a novel community detection method under the framework of non-negative matrix factorization (NMF). Our proposed method integrates topology as well as content of networks and has an adaptive parameter (with two variations) to effectively control the contribution of content with respect to the identified mismatch degree. Based on the disjoint community partition result, we also introduce an additional overlapping community discovery algorithm, so that our new method can meet the application requirements of both disjoint and overlapping community detection. The case study using real social networks shows that our new method can simultaneously obtain the community structures and their corresponding semantic description, which is helpful to understand the semantics of communities. Related performance evaluations on both artificial and real networks further indicate that our method outperforms some state-of-the-art methods while exhibiting more robust behavior when the mismatch between topology and content is observed.

Qu, Y, Yu, S, Gao, L, Zhou, W & Peng, S 2018, 'A hybrid privacy protection scheme in cyber-physical social networks', IEEE Transactions on Computational Social Systems, vol. 5, no. 3, pp. 773-784.
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© 2014 IEEE. The rapid proliferation of smart mobile devices has significantly enhanced the popularization of the cyber-physical social network, where users actively publish data with sensitive information. Adversaries can easily obtain these data and launch continuous attacks to breach privacy. However, existing works only focus on either location privacy or identity privacy with a static adversary. This results in privacy leakage and possible further damage. Motivated by this, we propose a hybrid privacy-preserving scheme, which considers both location and identity privacy against a dynamic adversary. We study the privacy protection problem as the tradeoff between the users aiming at maximizing data utility with high-level privacy protection while adversaries possessing the opposite goal. We first establish a game-based Markov decision process model, in which the user and the adversary are regarded as two players in a dynamic multistage zero-sum game. To acquire the best strategy for users, we employ a modified state-action-reward-state-action reinforcement learning algorithm. Iteration times decrease because of cardinality reduction from n to 2, which accelerates the convergence process. Our extensive experiments on real-world data sets demonstrate the efficiency and feasibility of the propose method.

Qu, Y, Yu, S, Zhou, W, Peng, S, Wang, G & Xiao, K 2018, 'Privacy of Things: Emerging Challenges and Opportunities in Wireless Internet of Things', IEEE Wireless Communications, vol. 25, no. 6, pp. 91-97.
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© 2002-2012 IEEE. The proliferation of wireless devices and appliances is facilitating the rapid development of the Internet of Things (IoT). Numerous state-of-the-art applications are being used in, for example, smart cities, autonomous vehicles, and biocomputing. With the popularization of IoT, new challenges are emerging with respect to privacy issues. In this article, we first summarize privacy constraints and primary attacks based on new features of IoT. Then we present three case studies to demonstrate principal vulnerabilities and classify existing protection schemes. Built on this analysis, we identify three key challenges: a lack of theoretical foundation, the trade-off optimization between privacy and data utility, and system isomerism over-complexity and high scalability. Finally, we illustrate possible promising future directions and potential solutions to the emerging challenges facing wireless IoT scenarios. We aim to assist interested readers in investigating the unexplored parts of this promising domain.

Qumer Gill, A, Loumish, A, Riyat, I & Han, S 2018, 'DevOps for information management systems', VINE Journal of Information and Knowledge Management Systems, vol. 48, no. 1, pp. 122-139.
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© 2018, Emerald Publishing Limited. Purpose: Development and operations (DevOps) is complex in nature. Organizations are unsure how to effectively establish a DevOps capability for the continuous delivery of information management systems. This paper aims to compile and analyze DevOps by applying the well-known systematic literature review (SLR) approach. This review is intended to provide a knowledge base to support the informed, effective and less risky adoption of DevOps for information management systems. Design/methodology/approach: In this qualitative research study, the SLR method was applied to identify 3,790 papers, of which, 32 relevant papers were selected and reviewed. Findings: The results are organized using the well-known ISO/IEC 24744 metamodel elements: people (roles), process, technology and artifacts. In total 11 major roles, 6 processes, 23 technologies, 5 artifacts and 7 challenges (including 6 corresponding solutions) were found. DevOps engineer is becoming a newly identified role. Continuous delivery pipeline and continuous improvement are the most highlighted major DevOps processes. Build system technology is becoming the key focus of DevOps. Finally, major challenges are around people and culture and the misunderstanding of DevOps. Potential research areas are: DevOps analytics, artifacts and tool–chain integration. Research limitations/implications: The research findings will serve as a resource for both practitioners and researchers who have interest in the research and adoption of DevOps for information management systems. Originality/value: This paper provides a comprehensive systematic review of the body of knowledge to support the ongoing research and adoption of emerging trends of DevOps for information management systems.

Redshaw, S 2018, 'Combustion, hydraulic, and other forms of masculinity: An essay exploring dominant values and representations of the driver in driverless technology', Transfers, vol. 8, no. 1, pp. 86-103.
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© Transfers. This article presents two representations of masculinity based on media images found in television and online promotion related to motor vehicles. The dominant image in much advertising is the bursting, thrusting power of what I refer to as "combustion" masculinity, identified as active engagement and connected with significant road trauma. The less visible, fluid power found in professional driving that I refer to as "hydraulic" masculinity draws on precision and awareness of the surroundings rather than aggressive force. Social analysis of electric and driverless vehicle promotion and media discussion indicate that moving to electric and fully automated driving requires overcoming the essential contradiction of combustion power, as it is associated with cars and freedom. Alternative modes and images of being mobile must be highlighted in order to challenge the combustion image. Fundamentally, activity should be ascribed to all mobile persons, and policy and mobility systems should be designed to maximize mobility for all.

Redshaw, S, Ingham, V, McCutcheon, M, Hicks, J & Burmeister, O 2018, 'Assessing the impact of vulnerability on perceptions of social cohesion in the context of community resilience to disaster in the Blue Mountains.', Australian Journal of Rural Health, vol. 26, no. 1, pp. 14-19.
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To assess the impact of network communications, community participation and elements of vulnerability on the perception of social cohesiveness in the Blue Mountains local government area (Blue Mountains LGA).A questionnaire was administered to residents of the Blue Mountains LGA. Econometric analysis of the resulting data was undertaken.Blue Mountains LGA, Australia.One thousand one hundred and three residents of the Blue Mountains LGA responded to the questionnaire.The responses enabled the construction of variables measuring individual perceptions of community cohesiveness, their network communications and community participation. Demographic data and data on the vulnerabilities of individuals were also collected.The data were used in an econometric model which identified that network communications and community participation impacted positively on perceptions of social cohesiveness while vulnerability factors had a negative impact.Remedial action to build community cohesiveness and network communications can be expected to have a positive impact on social cohesiveness. In developing strategies to build community cohesiveness and network communication, particular care needs to be taken to ensure the inclusion of those members of society who are regarded as the most vulnerable.

Redshaw, SJ & Ingham, V 2018, '‘Neighbourhood is if they come out and talk to you’: neighbourly connections and bonding social capital as weak or strong ties', Journal of Sociology.
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Neighbourly relations have been theorised as ‘friendly distance’ in contrast to connections which are theorised as strong or intensive ties. The article explores the neighbourly relationships between residents of a peri-urban regional area outside Sydney in Australia. Strong interview themes emerged regarding the ways in which residents who were well connected within their locality talked about their neighbours, and this was in direct contrast to those living with a chronic condition – these people expressed a lack of connection with their neighbours. The major theme, ‘not in each other’s pockets’ reflects the negotiated nature of neighbour interactions, while the theme ‘neighbourhood is if they come out and talk to you’ speaks of isolation. The interactions of neighbours may in many cases constitute bonding capital as important weak or casual ties. These may not be available to the chronically ill or socially isolated or adequate without linking and bridging capital.

Saberi, M, Theobald, M, Hussain, OK, Chang, E & Hussain, FK 2018, 'Interactive feature selection for efficient customer recognition in contact centers: Dealing with common names', Expert Systems with Applications, vol. 113, pp. 356-376.
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© 2018 Elsevier Ltd We propose an interactive decision-making framework to assist a Customer Service Representative (CSR) in the efficient and effective recognition of customer records in a database with many ambiguous entries. Our proposed framework consists of three integrated modules. The first module focuses on the detection and resolution of duplicate records to improve effectiveness and efficiency in customer recognition. The second module determines the level of ambiguity in recognizing an individual customer when there are multiple records with the same name. The third module recommends the series of feature-related questions that the CSR should ask the customer to enable rapid recognition, based on that level of ambiguity. In the first module, the F-Swoosh approach for duplicate detection is used, and in the second module a dynamic programming-based technique is used to determine the level of ambiguity within the customer database for a given name. In the third module, Levenshtein edit distance is used for feature selection in combination with weights based on the Inverse Document Frequency (IDF) of terms. The algorithm that requires the minimum number of questions to be put to the customer to achieve recognition is the algorithm that is chosen. We evaluate the proposed framework on a synthetic dataset and demonstrate how it assists the CSR to rapidly recognize the correct customer.

Samaras, E & Johnston, A 2018, 'Fleeting Film: Using Story to Seek Archival Permanence in the Transitory and Globalized Digital Visual Effects Industry', Preservation, Digital Technology and Culture, vol. 47, no. 1, pp. 12-22.
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© 2018 Walter de Gruyter GmbH, Berlin/Boston. Archiving is a long-standing vocation, founded on principles such as provenance, original order, truth, evidence, preservation and permanence. A far cry from the visual spectacle and movable feast of film visual effects (VFX) - a transitory and globalized industry of disposable firms, ever-advancing technologies and a roving workforce which craft digital animations and seamless effects for the big screen. In this paper we utilize the concept of "story" as a premise to bring together the seemingly different vocations of archival science and film VFX. Through an exploration of digital film production and archival practice under the context of storytelling, we aim to highlight the need for archivists to work with the VFX industry to ensure evidence of this culturally significant aspect of filmmaking and cinema discourse is preserved into the future. As well present the argument that archives are more than collections of historical evidence. Archives are story - and archivists are storytellers.

Shahsavari, M, Golpayegani, AH, Saberi, M & Hussain, FK 2018, 'Recruiting the K-most influential prospective workers for crowdsourcing platforms', Service Oriented Computing and Applications, vol. 12, no. 3-4, pp. 247-257.
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© 2018, Springer-Verlag London Ltd., part of Springer Nature. Viral marketing is widely used by businesses to achieve their marketing objectives using social media. In this work, we propose a customized crowdsourcing approach for viral marketing which aims at efficient marketing based on information propagation through a social network. We term this approach the social community-based crowdsourcing platform and integrate it with an information diffusion model to find the most efficient crowd workers. We propose an intelligent viral marketing framework (IVMF) comprising two modules to achieve this end. The first module identifies the K-most influential users in a given social network for the platform using a novel linear threshold diffusion model. The proposed model considers the different propagation behaviors of the network users in relation to different contexts. Being able to consider multiple topics in the information propagation model as opposed to only one topic makes our model more applicable to a diverse population base. Additionally, the proposed content-based improved greedy (CBIG) algorithm enhances the basic greedy algorithm by decreasing the total amount of computations required in the greedy algorithm (the total influence propagation of a unique node in any step of the greedy algorithm). The highest computational cost of the basic greedy algorithm is incurred on computing the total influence propagation of each node. The results of the experiments reveal that the number of iterations in our CBIG algorithm is much less than the basic greedy algorithm, while the precision in choosing the K influential nodes in a social network is close to the greedy algorithm. The second module of the IVMF framework, the multi-objective integer optimization model, is used to determine which social network should be targeted for viral marketing, taking into account the marketing budget. The overall IVMF framework can be used to select a social network and recruit the K...

Singh, AK 2018, 'Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering', Journal of Healthcare Engineering, vol. 2018.
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Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.

Singh, AK, Chen, HT, Cheng, YF, King, JT, Ko, LW, Gramann, K & Lin, CT 2018, 'Visual Appearance Modulates Prediction Error in Virtual Reality', IEEE Access, vol. 6, pp. 24617-24624.
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© 2013 IEEE. Different rendering styles induce different levels of agency and user behaviors in virtual reality environments. We applied an electroencephalogram-based approach to investigate how the rendering style of the users' hands affects behavioral and cognitive responses. To this end, we introduced prediction errors due to cognitive conflicts during a 3-D object selection task by manipulating the selection distance of the target object. The results showed that, for participants with high behavioral inhibition scores, the amplitude of the negative event-related potential at approximately 50-250 ms correlated with the realism of the virtual hands. Concurring with the uncanny valley theory, these findings suggest that the more realistic the representation of the user's hand is, the more sensitive the user becomes toward subtle errors, such as tracking inaccuracies.

Song, J, Wang, J & Lu, H 2018, 'A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting', Applied Energy, vol. 215, pp. 643-658.
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© 2018 Elsevier Ltd Short-term wind speed forecasting has a significant influence on enhancing the operation efficiency and increasing the economic benefits of wind power generation systems. A substantial number of wind speed forecasting models, which are aimed at improving the forecasting performance, have been proposed. However, some conventional forecasting models do not consider the necessity and importance of data preprocessing. Moreover, they neglect the limitations of individual forecasting models, leading to poor forecasting accuracy. In this study, a novel model combining a data preprocessing technique, forecasting algorithms, an advanced optimization algorithm, and no negative constraint theory is developed. This combined model successfully overcomes some limitations of the individual forecasting models and effectively improves the forecasting accuracy. To estimate the effectiveness of the proposed combined model, 10-min wind speed data from the wind farm in Peng Lai, China are used as case studies. The experiment results demonstrate that the developed combined model is definitely superior compared to all other conventional models. Furthermore, it can be used as an effective technique for smart grid planning.

Stender, M, Tiedemann, M, Hoffmann, N & Oberst, S 2018, 'Impact of an irregular friction formulation on dynamics of a minimal model for brake squeal', Mechanical Systems and Signal Processing, vol. 107, pp. 439-451.
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Friction-induced vibrations are of major concern in the design of reliable, efficient and comfortable technical systems. Well-known examples for systems susceptible to self-excitation can be found in fluid structure interaction, disk brake squeal, rotor dynamics, hip implants noise and many more. While damping elements and amplitude reduction are well-understood in linear systems, nonlinear systems and especially self-excited dynamics still constitute a challenge for damping element design. Additionally, complex dynamical systems exhibit deterministic chaotic cores which add severe sensitivity to initial conditions to the system response. Especially the complex friction interface dynamics remain a challenging task for measurements and modeling. Today, mostly simple and regular friction models are investigated in the field of self-excited brake system vibrations. This work aims at investigating the effect of high-frequency irregular interface dynamics on the nonlinear dynamical response of a self-excited structure. Special focus is put on the characterization of the system response time series. A low-dimensional minimal model is studied which features self-excitation, gyroscopic effects and friction-induced damping. Additionally, the employed friction formulation exhibits temperature as inner variable and superposed chaotic fluctuations governed by a Lorenz attractor. The time scale of the irregular fluctuations is chosen one order smaller than the overall system dynamics. The influence of those fluctuations on the structural response is studied in various ways, i.e. in time domain and by means of recurrence analysis. The separate time scales are studied in detail and regimes of dynamic interactions are identified. The results of the irregular friction formulation indicate dynamic interactions on multiple time scales, which trigger larger vibration amplitudes as compared to regular friction formulations conventionally studied in the field of friction-induced vibr...

Sui, Y & Xue, J 2018, 'Value-Flow-Based Demand-Driven Pointer Analysis for C and C++', IEEE Transactions on Software Engineering, vol. 14, no. 8.
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IEEE We present SUPA, a value-flow-based demand-driven flow- and context-sensitive pointer analysis with strong updates for C and C++ programs. SUPA enables computing points-to information via value-flow refinement, in environments with small time and memory budgets. We formulate SUPA by solving a graph-reachability problem on an inter-procedural value-flow graph representing a program's def-use chains, which are pre-computed efficiently but over-approximately. To answer a client query (a request for a variable's points-to set), SUPA reasons about the flow of values along the pre-computed def-use chains sparsely (rather than across all program points), by performing only the work necessary for the query (rather than analyzing the whole program). In particular, strong updates are performed to filter out spurious def-use chains through value-flow refinement as long as the total budget is not exhausted.

Sui, Y, Fan, X, Zhou, H & Xue, J 2018, 'Loop-Oriented Pointer Analysis for Automatic SIMD Vectorization', ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, vol. 17, no. 2.
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Sui, Y, Yan, H, Zheng, Z, Zhang, Y & Xue, J 2018, 'Parallel construction of interprocedural memory SSA form', Journal of Systems and Software, vol. 146, pp. 186-195.
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© 2018 Elsevier Inc. Interprocedural memory SSA form, which provides a sparse data-flow representation for indirect memory operations, paves the way for many advanced program analyses. Any performance improvement for memory SSA construction benefits for a wide range of clients (e.g., bug detection and compiler optimisations). However, its construction is much more expensive than that for scalar-based SSA form. The memory objects distinguished at a pointer dereference significantly increases the number of variables that need to be put on SSA form, resulting in considerable analysis overhead when analyzing large programs (e.g., millions of lines of code). This paper presents PARSSA, a fully parameterised approach for parallel construction of interprocedural memory SSA form by utilising multi-core computing resources. PARSSA partitions whole-program memory objects into uniquely identified memory regions. The indirect memory accesses in a function are fully parameterised using partitioned memory regions, so that the memory SSA construction of a parameterised function is readily parallelised. We implemented PARSSA in LLVM using Intel Threading Building Block (TBB) for creating parallel tasks. We evaluated PARSSA using 15 large applications. PARSSA achieves up to 6.9 × speedup against the sequential version on an 8-core machine.

Talaei-Khoei, A & Daniel, J 2018, 'How younger elderly realize usefulness of cognitive training video games to maintain their independent living', International Journal of Information Management, vol. 42, pp. 1-12.
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© 2018 The objective of this paper is to understand the perception that younger elderly persons have towards the usefulness of playing Xbox Kinect video games as an assistive technology that is designed to maintain their cognitive abilities. Available literature highlights two kinds of assistive technologies; the first being Supportive Technologies that provide aid for already-declined functional abilities (such as hearing aids), and the second being Empowering Technologies that maintain functional abilities which have not yet declined (such as Xbox Kinect cognitive games). The difference in the nature between supportive and empowering technologies plays an important role in perceiving their benefits. For instance, while hearing aids as a supportive technology are perceived as useful through the improvement of hearing abilities, cognitive training games as an empowering technology have a long-term usefulness for cognitive abilities. This study conducts twenty-one qualitative interviews (range 65–87 years; mean = 71; SD = 3.81) and introduces perceived transfer effect. This effect allows the elderly to perceive the usefulness of playing cognitive training video games, which are designed to cultivate the cognitive abilities. In addition, this study found that the elderly value their independent living, and through cognitive video games, the elderly may remain capable of living independently.

Talaei-Khoei, A & Wilson, JM 2018, 'Identifying people at risk of developing type 2 diabetes: A comparison of predictive analytics techniques and predictor variables', INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, vol. 119, pp. 22-38.
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Tamassia, M, Zambetta, F, Raffe, WL, Mueller, FF & Li, X 2018, 'Learning Options From Demonstrations: A Pac-Man Case Study', IEEE TRANSACTIONS ON GAMES, vol. 10, no. 1, pp. 91-96.
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Tomamichel, M & Hayashi, M 2018, 'Operational interpretation of Rényi information measures via composite hypothesis testing against product and markov distributions', IEEE Transactions on Information Theory, vol. 64, no. 2, pp. 1064-1082.
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© 1963-2012 IEEE. We revisit the problem of asymmetric binary hypothesis testing against a composite alternative hypothesis. We introduce a general framework to treat such problems when the alternative hypothesis adheres to certain axioms. In this case, we find the threshold rate, the optimal error and strong converse exponents (at large deviations from the threshold), and the second order asymptotics (at small deviations from the threshold). We apply our results to find the operational interpretations of various Rényi information measures. In case the alternative hypothesis is comprised of bipartite product distributions, we find that the optimal error and strong converse exponents are determined by the variations of Rényi mutual information. In case the alternative hypothesis consists of tripartite distributions satisfying the Markov property, we find that the optimal exponents are determined by the variations of Rényi conditional mutual information. In either case, the relevant notion of Rényi mutual information depends on the precise choice of the alternative hypothesis. As such, this paper also strengthens the view that different definitions of Rényi mutual information, conditional entropy, and conditional mutual information are adequate depending on the context in which the measures are used.

Tsai, W-C & van den Hoven, E 2018, 'Memory Probes: Exploring Retrospective User Experience Through Traces of Use on Cherished Objects', INTERNATIONAL JOURNAL OF DESIGN, vol. 12, no. 3, pp. 57-72.
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Vichitvanichphong, S, Talaei-Khoei, A, Kerr, D & Ghapanchi, AH 2018, 'Assistive technologies for aged care: Comparative literature survey on the effectiveness of theories for supportive and empowering technologies', Information Technology and People, vol. 31, no. 2, pp. 405-427.
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© 2018, Emerald Publishing Limited. Purpose: One may categorize assistive technologies for aged care into two types, namely, supportive (helping the elderly with their everyday activities) and empowering (obtaining physical or educational training to help seniors to maintain their capabilities). This paper looks at the impact of this perspective in the adoption of technologies that are used to aid already declined functions in comparison with technologies that are used to empower an elderly person’s capabilities. The purpose of this paper is to extract the factors that influence adoption of assistive technologies among seniors and the theories used in this context. Design/methodology/approach: A systematic literature review was conducted on relevant peer reviewed papers. A preliminary exploratory search was conducted to identify keywords and online databases for the search. A total of 104 papers published since 2000 were analyzed after title, abstract and full text filtering. Findings: The study summarizes and categorizes the factors impacting the adoption of assistive technologies among seniors. The paper analyses the theoretical support utilized by literature and maps the empirical evidence for supportive and empowering technologies. Research limitations/implications: This research like any other literature review is limited to the search keys. However, the keys have brought to this work from a preliminary search. Practical implications: The work informs nursing professionals on the process to adopt technologies among seniors. The paper also helps technology developers to design technological products that are easier to adopt for older adults. Originality/value: It was found that existing adoption theories are able to handle supportive technology adoption mainly because of the direct link between usage and improved functionalities. However due to the indirect effect of empowering technologies, elderlies lag in benefit realization for empowering technologies. Th...

Wakefield, J, Frawley, JK, Tyler, J & Dyson, LE 2018, 'The impact of an iPad-supported annotation and sharing technology on university students' learning', COMPUTERS & EDUCATION, vol. 122, pp. 243-259.
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Wan, Y, Chen, L, Xu, G, Zhao, Z, Tang, J & Wu, J 2018, 'SCSMiner: mining social coding sites for software developer recommendation with relevance propagation', World Wide Web, vol. 21, no. 6, pp. 1523-1543.
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With the advent of social coding sites, software development has entered a new era of collaborative work. Social coding sites (e.g., GitHub) can integrate social networking and distributed version control in a unified platform to facilitate collaborative developments over the world. One unique characteristic of such sites is that the past development experiences of developers provided on the sites convey the implicit metrics of developer’s programming capability and expertise, which can be applied in many areas, such as software developer recruitment for IT corporations. Motivated by this intuition, we aim to develop a framework to effectively locate the developers with right coding skills. To achieve this goal, we devise a generativ e probabilistic expert ranking model upon which a consistency among projects is incorporated as graph regularization to enhance the expert ranking and a perspective of relevance propagation illustration is introduced. For evaluation, StackOverflow is leveraged to complement the ground truth of expert. Finally, a prototype system, SCSMiner, which provides expert search service based on a real-world dataset crawled from GitHub is implemented and demonstrated.

Wan, Y, Xu, G, Chen, L, Zhao, Z & Wu, J 2018, 'Exploiting cross-source knowledge for warming up community question answering services', Neurocomputing, vol. 320, pp. 25-34.
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© 2018 Elsevier B.V. Community Question Answering (CQA) services such as Yahoo! Answers, Quora and StackOverflow are collaborative platforms where users can share and exchange their knowledge explicitly by asking and answering questions. One essential task in CQA is learning topical expertise of users, which may benefit many applications such as question routing and best answers identification. One limitation of existing related works is that they only consider the warm-start users who have posted many questions or answers, while ignoring cold-start users who have few posts. In this paper, we aim to exploit knowledge from cross sources such as GitHub and StackOverflow to build up the richer views of expertise for better CQA. Inspired by the idea of Bayesian co-training, we propose a topical expertise model from the perspective of multi-view learning. Specifically, we incorporate the consistency existing among multiple views into a unified probabilistic graphic model. Comprehensive experiments on two real-world datasets demonstrate the performance of our proposed model with the comparison of some state-of-the-art ones.

Wang, D, Deng, S & Xu, G 2018, 'Sequence-based context-aware music recommendation', Information Retrieval Journal, vol. 21, no. 2-3, pp. 230-252.
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© 2017, Springer Science+Business Media, LLC. Contextual factors greatly affect users’ preferences for music, so they can benefit music recommendation and music retrieval. However, how to acquire and utilize the contextual information is still facing challenges. This paper proposes a novel approach for context-aware music recommendation, which infers users’ preferences for music, and then recommends music pieces that fit their real-time requirements. Specifically, the proposed approach first learns the low dimensional representations of music pieces from users’ music listening sequences using neural network models. Based on the learned representations, it then infers and models users’ general and contextual preferences for music from users’ historical listening records. Finally, music pieces in accordance with user’s preferences are recommended to the target user. Extensive experiments are 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 data.

Wang, D, Deng, S, Zhang, X & Xu, G 2018, 'Learning to embed music and metadata for context-aware music recommendation', World Wide Web, vol. 21, pp. 1399-1423.
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© 2017 Springer Science+Business Media, LLC, part of Springer Nature Contextual factors greatly influence users’ musical preferences, so they are beneficial remarkably to music recommendation and retrieval tasks. However, it still needs to be studied how to obtain and utilize the contextual information. In this paper, we propose a context-aware music recommendation approach, which can recommend music pieces appropriate for users’ contextual preferences for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require music pieces to be represented by features ahead, but it can learn the representations from users’ historical listening records. Specifically, the proposed approach first learns music pieces’ embeddings (feature vectors in low-dimension continuous space) from music listening records and corresponding metadata. Then it infers and models users’ global and contextual preferences for music from their listening records with the learned embeddings. Finally, it recommends appropriate music pieces according to the target user’s preferences to satisfy her/his real-time requirements. Experimental evaluations on a real-world dataset show that the proposed approach outperforms baseline methods in terms of precision, recall, F1 score, and hitrate. Especially, our approach has better performance on sparse datasets.

Wang, J, Du, P, Lu, H, Yang, W & Niu, T 2018, 'An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting', Applied Soft Computing Journal, vol. 72, pp. 321-337.
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© 2018 Elsevier B.V. Accurate and stable annual electricity consumption forecasting play vital role in modern social and economic development through providing effective planning and guaranteeing a reliable supply of sustainable electricity. However, establishing a robust method to improve prediction accuracy and stability simultaneously of electricity consumption forecasting has been proven to be a highly challenging task. Most previous researches only pay more attention to enhance prediction accuracy, which usually ignore the significant of forecasting stability, despite its importance to the effectiveness of forecasting models. Considering the characteristics of annual power consumption data as well as one criterion i.e. accuracy or stability is insufficient, in this study a novel hybrid forecasting model based on an improved grey forecasting mode optimized by multi-objective ant lion optimization algorithm is successfully developed, which can not only be utilized to dynamic choose the best input training sets, but also obtain satisfactory forecasting results with high accuracy and strong ability. Case studies of annual power consumption datasets from several regions in China are utilized as illustrative examples to estimate the effectiveness and efficiency of the proposed hybrid forecasting model. Finally, experimental results indicated that the proposed forecasting model is superior to the comparison models.

Wang, J, Li, H & Lu, H 2018, 'Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China', Applied Soft Computing Journal, vol. 71, pp. 783-799.
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© 2018 Elsevier B.V. With atmospheric environmental pollution becoming increasingly serious, developing an early warning system for air quality forecasting is vital to monitoring and controlling air quality. However, considering the large fluctuations in the concentration of pollutants, most previous studies have focused on enhancing accuracy, while few have addressed the stability and uncertainty analysis, which may lead to insufficient results. Therefore, a novel early warning system based on fuzzy time series was successfully developed that includes three modules: deterministic prediction module, uncertainty analysis module, and assessment module. In this system, a hybrid model combining the fuzzy time series forecasting technique and data reprocessing approaches was constructed to forecast the major air pollutants. Moreover, an uncertainty analysis was generated to further analyze and explore the uncertainties involved in future air quality forecasting. Finally, an assessment module proved the effectiveness of the developed model. The experimental results reveal that the proposed model outperforms the comparison models and baselines, and both the accuracy and the stability of the developed system are remarkable. Therefore, fuzzy logic is a better option in air quality forecasting and the developed system will be a useful tool for analyzing and monitoring air pollution.

Wang, J, Niu, T, Lu, H, Guo, Z, Yang, W & Du, P 2018, 'An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms', Applied Energy, vol. 211, pp. 492-512.
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© 2017 Elsevier Ltd The uncertainty analysis and modeling of wind speed, which has an essential influence on wind power systems, is consistently considered a challenging task. However, most investigations thus far were focused mainly on point forecasts, which in reality cannot facilitate quantitative characterization of the endogenous uncertainty involved. An analysis-forecast system that includes an analysis module and a forecast module and can provide appropriate scenarios for the dispatching and scheduling of a power system is devised in this study; this system superior to those presented in previous studies. In order to qualitatively and quantitatively investigate the uncertainty of wind speed, recurrence analysis techniques are effectively developed for application in the analysis module. Furthermore, in order to quantify the uncertainty accurately, a novel architecture aimed at uncertainty mining is devised for the forecast module, where a non-parametric model optimized by an improved multi-objective water cycle algorithm is considered a predictor for producing intervals for each mode component after feature selection. The results of extensive in-depth experiments show that the devised system is not only superior to the considered benchmark models, but also has good potential practical applications in wind power systems.

Wang, T, Lu, J & Zhang, G 2018, 'Two-Stage Fuzzy Multiple Kernel Learning Based on Hilbert-Schmidt Independence Criterion', IEEE Transactions on Fuzzy Systems, vol. 26, no. 6, pp. 3703-3714.
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© 1993-2012 IEEE. Multiple kernel learning (MKL) is a principled approach to kernel combination and selection for a variety of learning tasks, such as classification, clustering, and dimensionality reduction. In this paper, we develop a novel fuzzy multiple kernel learning model based on the Hilbert-Schmidt independence criterion (HSIC) for classification, which we call HSIC-FMKL. In this model, we first propose an HSIC Lasso-based MKL formulation, which not only has a clear statistical interpretation that minimum redundant kernels with maximum dependence on output labels are found and combined, but also enables the global optimal solution to be computed efficiently by solving a Lasso optimization problem. Since the traditional support vector machine (SVM) is sensitive to outliers or noises in the dataset, fuzzy SVM (FSVM) is used to select the prediction hypothesis once the optimal kernel has been obtained. The main advantage of FSVM is that we can associate a fuzzy membership with each data point such that these data points can have different effects on the training of the learning machine. We propose a new fuzzy membership function using a heuristic strategy based on the HSIC. The proposed HSIC-FMKL is a two-stage kernel learning approach and the HSIC is applied in both stages. We perform extensive experiments on real-world datasets from the UCI benchmark repository and the application domain of computational biology which validate the superiority of the proposed model in terms of prediction accuracy.

Wang, X, Huang, C, Yao, L, Benatallah, B & Dong, M 2018, 'A Survey on Expert Recommendation in Community Question Answering', Journal of Computer Science and Technology, vol. 33, no. 4, pp. 625-653.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time; on the other hand, an answerer might face numerous new questions without being able to identify the questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.

Wang, X, Xu, X, Sheng, QZ, Wang, Z & Yao, L 2018, 'Novel Artificial Bee Colony Algorithms for QoS-Aware Service Selection', IEEE Transactions on Services Computing, pp. 1-1.
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Wang, Y, Feng, C, Chen, L, Yin, H, Guo, C & Chu, Y 2018, 'User identity linkage across social networks via linked heterogeneous network embedding', World Wide Web, vol. 2018, pp. 1-22.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature User identity linkage has important implications in many cross-network applications, such as user profile modeling, recommendation and link prediction across social networks. To discover accurate cross-network user correspondences, it is a critical prerequisite to find effective user representations. While structural and content information describe users from different perspectives, there is a correlation between the two aspects of information. For example, a user who follows a celebrity tends to post content about the celebrity as well. Therefore, the projections of structural and content information of a user should be as close to each other as possible, which inspires us to fuse the two aspects of information in a unified space. However, owing to the information heterogeneity, most existing methods extract features from content and structural information respectively, instead of describing them in a unified way. In this paper, we propose a Linked Heterogeneous Network Embedding model (LHNE) to learn the comprehensive representations of users by collectively leveraging structural and content information in a unified framework. We first model the topics of user interests from content information to filter out noise. Next, cross-network structural and content information are embedded into a unified space by jointly capturing the friend-based and interest-based user co-occurrence in intra-network and inter-network, respectively. Meanwhile, LHNE learns user transfer and topic transfer for enhancing information exchange across networks. Empirical results show LHNE outperforms the state-of-the-art methods on both real social network and synthetic datasets and can work well even with little or no structural information.

Wang, Y-K, Jung, T-P & Lin, C-T 2018, 'Theta and Alpha Oscillations in Attentional Interaction during Distracted Driving.', Frontiers in Behavioral Neuroscience, vol. 12, pp. 3-3.
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Performing multiple tasks simultaneously usually affects the behavioral performance as compared with executing the single task. Moreover, processing multiple tasks simultaneously often involve more cognitive demands. Two visual tasks, lane-keeping task and mental calculation, were utilized to assess the brain dynamics through 32-channel electroencephalogram (EEG) recorded from 14 participants. A 400-ms stimulus onset asynchrony (SOA) factor was used to induce distinct levels of attentional requirements. In the dual-task conditions, the deteriorated behavior reflected the divided attention and the overlapping brain resources used. The frontal, parietal and occipital components were decomposed by independent component analysis (ICA) algorithm. The event- and response-related theta and alpha oscillations in selected brain regions were investigated first. The increased theta oscillation in frontal component and decreased alpha oscillations in parietal and occipital components reflect the cognitive demands and attentional requirements as executing the designed tasks. Furthermore, time-varying interactive over-additive (O-Add), additive (Add) and under-additive (U-Add) activations were explored and summarized through the comparison between the summation of the elicited spectral perturbations in two single-task conditions and the spectral perturbations in the dual task. Add and U-Add activations were observed while executing the dual tasks. U-Add theta and alpha activations dominated the posterior region in dual-task situations. Our results show that both deteriorated behaviors and interactive brain activations should be comprehensively considered for evaluating workload or attentional interaction precisely.

Wei, C-S, Lin, Y-P, Wang, Y-T, Lin, C-T & Jung, T-P 2018, 'A subject-transfer framework for obviating inter- and intra-subject variability in EEG-based drowsiness detection.', NeuroImage, vol. 174, pp. 407-419.
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Inter- and intra-subject variability pose a major challenge to decoding human brain activity in brain-computer interfaces (BCIs) based on non-invasive electroencephalogram (EEG). Conventionally, a time-consuming and laborious training procedure is performed on each new user to collect sufficient individualized data, hindering the applications of BCIs on monitoring brain states (e.g. drowsiness) in real-world settings. This study proposes applying hierarchical clustering to assess the inter- and intra-subject variability within a large-scale dataset of EEG collected in a simulated driving task, and validates the feasibility of transferring EEG-based drowsiness-detection models across subjects. A subject-transfer framework is thus developed for detecting drowsiness based on a large-scale model pool from other subjects and a small amount of alert baseline calibration data from a new user. The model pool ensures the availability of positive model transferring, whereas the alert baseline data serve as a selector of decoding models in the pool. Compared with the conventional within-subject approach, the proposed framework remarkably reduced the required calibration time for a new user by 90% (18.00 min-1.72 ± 0.36 min) without compromising performance (p = 0.0910) when sufficient existing data are available. These findings suggest a practical pathway toward plug-and-play drowsiness detection and can ignite numerous real-world BCI applications.

Wei, C-S, Wang, Y-T, Lin, C-T & Jung, T-P 2018, 'Toward Drowsiness Detection Using Non-hair-Bearing EEG-Based Brain-Computer Interfaces.', IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, vol. 26, no. 2, pp. 400-406.
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Drowsy driving is one of the major causes that lead to fatal accidents worldwide. For the past two decades, many studies have explored the feasibility and practicality of drowsiness detection using electroencephalogram (EEG)-based brain-computer interface (BCI) systems. However, on the pathway of transitioning laboratory-oriented BCI into real-world environments, one chief challenge is to obtain high-quality EEG with convenience and long-term wearing comfort. Recently, acquiring EEG from non-hair-bearing (NHB) scalp areas has been proposed as an alternative solution to avoid many of the technical limitations resulted from the interference of hair between electrodes and the skin. Furthermore, our pilot study has shown that informative drowsiness-related EEG features are accessible from the NHB areas. This study extends the previous work to quantitatively evaluate the performance of drowsiness detection using cross-session validation with widely studied machine-learning classifiers. The offline results showed no significant difference between the accuracy of drowsiness detection using the NHB EEG and the whole-scalp EEG across all subjects ( ). The findings of this study demonstrate the efficacy and practicality of the NHB EEG for drowsiness detection and could catalyze explorations and developments of many other real-world BCI applications.

Wu, D, King, JT, Chuang, CH, Lin, CT & Jung, TP 2018, 'Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)', IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, pp. 771-781.
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© 1993-2012 IEEE. Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and k-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by 10.02-19.77\%, and at the same time increase the correlation to the true response speed by 19.39-86.47\%.

Wu, W, Li, B, Chen, L, Zhu, X & Zhang, C 2018, 'K-Ary Tree Hashing for Fast Graph Classification', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 5, pp. 936-949.
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IEEE Existing graph classification usually relies on an exhaustive enumeration of substructure patterns, where the number of substructures expands exponentially w.r.t. with the size of the graph set. Recently, the Weisfeiler-Lehman (WL) graph kernel has achieved the best performance in terms of both accuracy and efficiency among state-of-the-art methods. However, it is still time-consuming, especially for large-scale graph classification tasks. In this paper, we present a < formula > < tex > $k$ < /tex > < /formula > -Ary Tree based Hashing (KATH) algorithm, which is able to obtain competitive accuracy with a very fast runtime. The main idea of KATH is to construct a traversal table to quickly approximate the subtree patterns in WL using < formula > < tex > $k$ < /tex > < /formula > -ary trees. Based on the traversal table, KATH employs a recursive indexing process that performs only < formula > < tex > $r$ < /tex > < /formula > times of matrix indexing to generate all < formula > < tex > $(r-1)$ < /tex > < /formula > -depth < formula > < tex > $k$ < /tex > < /formula > -ary trees, where the leaf node labels of a tree can uniquely specify the pattern. After that, the MinHash scheme is used to fingerprint the acquired subtree patterns for a graph. Our experimental results on both real world and synthetic datasets show that KATH runs significantly faster than state-of-the-art methods while achieving competitive or better accuracy.

Wu, Z, Li, G, Liu, Q, Xu, G & Chen, E 2018, 'Covering the Sensitive Subjects to Protect Personal Privacy in Personalized Recommendation', IEEE Transactions on Services Computing, pp. 493-508.
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Wu, Z, Xu, G, Lu, C, Chen, E, Jiang, F & Li, G 2018, 'An effective approach for the protection of privacy text data in the CloudDB', World Wide Web, vol. 21, pp. 915-938.
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© 2017 Springer Science+Business Media, LLC Due to the advantages of pay-on-demand, expand-on-demand and high availability, cloud databases (CloudDB) have been widely used in information systems. However, since a CloudDB is distributed on an untrusted cloud side, it is an important problem how to effectively protect massive private information in the CloudDB. Although traditional security strategies (such as identity authentication and access control) can prevent illegal users from accessing unauthorized data, they cannot prevent internal users at the cloud side from accessing and exposing personal privacy information. In this paper, we propose a client-based approach to protect personal privacy in a CloudDB. In the approach, privacy data before being stored into the cloud side, would be encrypted using a traditional encryption algorithm, so as to ensure the security of privacy data. To execute various kinds of query operations over the encrypted data efficiently, the encrypted data would be also augmented with additional feature index, so that as much of each query operation as possible can be processed on the cloud side without the need to decrypt the data. To this end, we explore how the feature index of privacy data is constructed, and how a query operation over privacy data is transformed into a new query operation over the index data so that it can be executed on the cloud side correctly. The effectiveness of the approach is demonstrated by theoretical analysis and experimental evaluation. The results show that the approach has good performance in terms of security, usability and efficiency, thus effective to protect personal privacy in the CloudDB.

Wu, Z, Zheng, C, Xiejian, J, Zhou, Z, Xu, G & Chen, E 2018, 'An approach for the protection of users’ book browsing preference privacy in a digital library', The Electronic Library, vol. 36, no. 6, pp. 1154-1166.
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Wunderlich, A & Gramann, K 2018, 'Electrocortical Evidence for Long-Term Incidental Spatial Learning Through Modified Navigation Instructions'.
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The use of Navigation Assistance Systems for spatial orienting has become increasingly popular. Such automated navigation support, however, comes with a reduced processing of the surrounding environment and often with a decline of spatial orienting ability. To prevent such deskilling and to support spatial learning, the present study investigated incidental spatial learning by comparing standard navigation instructions with two modified navigation instruction conditions. The first modified instruction condition highlighted landmarks and provided additional redundant information regarding the landmark (contrast condition), while the second highlighted landmarks and included personal relevant information regarding the landmark (personal-reference condition). Participants' spatial knowledge of the previously unknown virtual city was tested three weeks later. Behavioral and electroencephalographic (EEG) data demonstrated enhanced memory performance for participants in the modified navigation instruction conditions without further differentiating between modified instructions. Recognition performance of landmarks was better and the late positive complex of the event-related potential (ERP) revealed amplitude differences reflecting an increased amount of recollected information for modified navigation instructions. The results indicate a significant long-term spatial learning effect when landmarks are highlighted during navigation instructions.

Xiang, L, Zhao, G, Li, Q, Hao, W & Li, F 2018, 'TUMK-ELM: A fast unsupervised heterogeneous data learning approach', IEEE Access, vol. 6, pp. 35305-35315.
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© 2013 IEEE. Advanced unsupervised learning techniques are an emerging challenge in the big data era due to the increasing requirements of extracting knowledge from a large amount of unlabeled heterogeneous data. Recently, many efforts of unsupervised learning have been done to effectively capture information from heterogeneous data. However, most of them are with huge time consumption, which obstructs their further application in the big data analytics scenarios, where an enormous amount of heterogeneous data are provided but real-time learning are strongly demanded. In this paper, we address this problem by proposing a fast unsupervised heterogeneous data learning algorithm, namely two-stage unsupervised multiple kernel extreme learning machine (TUMK-ELM). TUMK-ELM alternatively extracts information from multiple sources and learns the heterogeneous data representation with closed-form solutions, which enables its extremely fast speed. As justified by theoretical evidence, TUMK-ELM has low computational complexity at each stage, and the iteration of its two stages can be converged within finite steps. As experimentally demonstrated on 13 real-life data sets, TUMK-ELM gains a large efficiency improvement compared with three state-of-the-art unsupervised heterogeneous data learning methods (up to 140 000 times) while it achieves a comparable performance in terms of effectiveness.

Xu, D, Tsang, IW & Zhang, Y 2018, 'Online Product Quantization', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 11, pp. 2185-2198.
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© 1989-2012 IEEE. Approximate nearest neighbor (ANN) search has achieved great success in many tasks. However, existing popular methods for ANN search, such as hashing and quantization methods, are designed for static databases only. They cannot handle well the database with data distribution evolving dynamically, due to the high computational effort for retraining the model based on the new database. In this paper, we address the problem by developing an online product quantization (online PQ) model and incrementally updating the quantization codebook that accommodates to the incoming streaming data. Moreover, to further alleviate the issue of large scale computation for the online PQ update, we design two budget constraints for the model to update partial PQ codebook instead of all. We derive a loss bound which guarantees the performance of our online PQ model. Furthermore, we develop an online PQ model over a sliding window with both data insertion and deletion supported, to reflect the real-time behavior of the data. The experiments demonstrate that our online PQ model is both time-efficient and effective for ANN search in dynamic large scale databases compared with baseline methods and the idea of partial PQ codebook update further reduces the update cost.

Xu, G 2018, 'Unsupervised Keyword Extraction from Microblog Posts via Hashtags', Journal of Web Engineering, vol. 17 (1&2), pp. 93-120.
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Xu, X, Motta, G, Tu, Z, Xu, H, Wang, Z & Wang, X 2018, 'A new paradigm of software service engineering in big data and big service era', Computing, vol. 100, no. 4, pp. 353-368.
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© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. In the big data era, servitization becomes one of the important development trends of the IT world. More and more software resources are developed and existed in the format as services on the Internet. These services from multi-domains and multi-networks are converged as a huge complicated service network or ecosystem, which can be called as Big Service. How to reuse the abundant open service resources to rapidly develop the new applications or comprehensive service solutions to meet massive individualized customer requirements is a key issue in the big data and big service ecosystem. Based on analyzing the ecosystem of big service, this paper presents a new paradigm of software service engineering, Requirement-Engineering Two-Phase of Service Engineering Paradigm (RE2SEP), which includes service oriented requirement engineering, domain oriented service engineering, and the development approach of software services. By means of the RE2SEP approach, the adaptive service solutions can be efficiently designed and implemented to match the requirement propositions of massive individualized customers in Big Service ecosystem. A case study of the RE2SEP applications, which is a project on citizens mobility service in smart city environment, is also given in this paper. The RE2SEP paradigm will change the way of traditional life-cycle oriented software engineering, and lead a new approach of software service engineering.

Xuan, J, Lu, J, Yan, Z & Zhang, G 2018, 'Bayesian Deep Reinforcement Learning via Deep Kernel Learning', INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol. 12, no. 1, pp. 164-171.
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Xuan, J, Lu, J, Zhang, G, Xu, RYD & Luo, X 2018, 'Doubly Nonparametric Sparse Nonnegative Matrix Factorization Based on Dependent Indian Buffet Processes', IEEE Transactions on Neural Networks and Learning Systems, pp. 1835-1849.
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Sparse nonnegative matrix factorization (SNMF) aims to factorize a data matrix into two optimized nonnegative sparse factor matrices, which could benefit many tasks, such as document-word co-clustering. However, the traditional SNMF typically assumes the number of latent factors (i.e., dimensionality of the factor matrices) to be fixed. This assumption makes it inflexible in practice. In this paper, we propose a doubly sparse nonparametric NMF framework to mitigate this issue by using dependent Indian buffet processes (dIBP). We apply a correlation function for the generation of two stick weights associated with each column pair of factor matrices while still maintaining their respective marginal distribution specified by IBP. As a consequence, the generation of two factor matrices will be columnwise correlated. Under this framework, two classes of correlation function are proposed: 1) using bivariate Beta distribution and 2) using Copula function. Compared with the single IBP-based NMF, this paper jointly makes two factor matrices nonparametric and sparse, which could be applied to broader scenarios, such as co-clustering. This paper is seen to be much more flexible than Gaussian process-based and hierarchial Beta process-based dIBPs in terms of allowing the two corresponding binary matrix columns to have greater variations in their nonzero entries. Our experiments on synthetic data show the merits of this paper compared with the state-of-the-art models in respect of factorization efficiency, sparsity, and flexibility. Experiments on real-world data sets demonstrate the efficiency of this paper in document-word co-clustering tasks.

Yang, J, Zhang, W, Yang, S, Zhang, Y, Lin, X & Yuan, L 2018, 'Efficient set containment join', VLDB Journal, vol. 27, no. 4, pp. 471-495.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. 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 intersection-oriented 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 union-oriented 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, union-oriented 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 union-oriented methods but also integrates the goodness of intersection-oriented methods by imposing a variant of prefix tree structure. We conduct extensive experiments on 20 real-life datasets and sy...

Yang, LX, Li, P, Yang, X, Xiang, Y & Zhou, W 2018, 'A differential game approach to patch injection', IEEE Access, vol. 6, pp. 58924-58938.
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© 2018 IEEE. To fight against the evolving computer viruses, we must constantly inject new virus patches into the computer networks. This paper addresses the patch injection problem, i.e., the problem of developing a patch injection strategy to mitigate the negative impact of virus attacks. As the impact of an attack depends on not only the patch injection strategy but the unknown virus injection strategy, the patch injection problem is very complicated. This paper initiates the study of the patch injection problem by means of security economics and differential game theory. First, based on a novel virus-patch mixed propagation model, we model the original problem as a differential game. Second, we develop a method for finding a candidate for the Nash equilibrium of the game, examine the structure of the candidate, and give some examples of the candidate. Furthermore, we demonstrate through comparative experiments that the candidate is better in terms of the Nash equilibrium solution concept. Therefore, we recommend the patch injection strategy in the candidate. Finally, we examine the effects of some factors on the performance of the recommended patch injection strategy. Overall, these findings undoubtedly have guiding significance to defense against virus infections.

Yang, M, Zhu, T, Liu, B, Xiang, Y & Zhou, W 2018, 'Differential Private POI Queries via Johnson-Lindenstrauss Transform', IEEE Access, vol. 6, pp. 29685-29699.
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© 2013 IEEE. The growing popularity of location-based services is giving untrusted servers relatively free reign to collect huge amounts of location information from mobile users. This information can reveal far more than just a user's locations but other sensitive information, such as the user's interests or daily routines, which raises strong privacy concerns. Differential privacy is a well-acknowledged privacy notion that has become an important standard for the preservation of privacy. Unfortunately, existing privacy preservation methods based on differential privacy protect user location privacy at the cost of utility, aspects of which have to be sacrificed to ensure that privacy is maintained. To solve this problem, we present a new privacy framework that includes a semi-trusted third party. Under our privacy framework, both the server and the third party only hold a part of the user's location information. Neither the server nor the third party knows the exact location of the user. In addition, the proposed perturbation method based on the Johnson Lindenstrauss transform satisfies the differential privacy. Two popular point of interest queries, -NN and Range, are used to evaluate the method on two real-world data sets. Extensive comparisons against two representative differential privacy-based methods show that the proposed method not only provides a strict privacy guarantee but also significantly improves performance.

Yao, L, Sheng, QZ, Benatallah, B, Dustdar, S, Wang, X, Shemshadi, A & Kanhere, SS 2018, 'WITS: an IoT-endowed computational framework for activity recognition in personalized smart homes', Computing, vol. 100, no. 4, pp. 369-385.
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© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. Over the past few years, activity recognition techniques have attracted unprecedented attentions. Along with the recent prevalence of pervasive e-Health in various applications such as smart homes, automatic activity recognition is being implemented increasingly for rehabilitation systems, chronic disease management, and monitoring the elderly for their personal well-being. In this paper, we present WITS, an end-to-end web-based in-home monitoring system for convenient and efficient care delivery. The system unifies the data- and knowledge-driven techniques to enable a real-time multi-level activity monitoring in a personalized smart home. The core components consist of a novel shared-structure dictionary learning approach combined with rule-based reasoning for continuous daily activity tracking and abnormal activities detection. WITS also exploits an Internet of Things middleware for the scalable and seamless management and learning of the information produced by ambient sensors. We further develop a user-friendly interface, which runs on both iOS and Andriod, as well as in Chrome, for the efficient customization of WITS monitoring services without programming efforts. This paper presents the architectural design of WITS, the core algorithms, along with our solutions to the technical challenges in the system implementation.

Yao, L, Sheng, QZ, Li, X, Gu, T, Tan, M, Wang, X, Wang, S & Ruan, W 2018, 'Compressive Representation for Device-Free Activity Recognition with Passive RFID Signal Strength', IEEE Transactions on Mobile Computing, vol. 17, no. 2, pp. 293-306.
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© 2017 IEEE. Understanding and recognizing human activities is a fundamental research topic for a wide range of important applications such as fall detection and remote health monitoring and intervention. Despite active research in human activity recognition over the past years, existing approaches based on computer vision or wearable sensor technologies present several significant issues such as privacy (e.g., using video camera to monitor the elderly at home) and practicality (e.g., not possible for an older person with dementia to remember wearing devices). In this paper, we present a low-cost, unobtrusive, and robust system that supports independent living of older people. The system interprets what a person is doing by deciphering signal fluctuations using radio-frequency identification (RFID) technology and machine learning algorithms. To deal with noisy, streaming, and unstable RFID signals, we develop a compressive sensing, dictionary-based approach that can learn a set of compact and informative dictionaries of activities using an unsupervised subspace decomposition. In particular, we devise a number of approaches to explore the properties of sparse coefficients of the learned dictionaries for fully utilizing the embodied discriminative information on the activity recognition task. Our approach achieves efficient and robust activity recognition via a more compact and robust representation of activities. Extensive experiments conducted in a real-life residential environment demonstrate that our proposed system offers a good overall performance and shows the promising practical potential to underpin the applications for the independent living of the elderly.

Yao, L, Sheng, QZ, Wang, X, Wang, S, Li, X & Wang, S 2018, 'Collaborative text categorization via exploiting sparse coefficients', World Wide Web, vol. 21, no. 2, pp. 373-394.
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© 2017, Springer Science+Business Media New York. Text categorization is widely characterized as a multi-label classification problem. Robust modeling of the semantic similarity between a query text and training texts is essential to construct an effective and accurate classifier. In this paper, we systematically investigate the Web page/text classification problem via integrating sparse representation with random measurements. In particular, we first adopt a very sparse data-independent random measurement matrix to map the original high dimensional text feature space to a lower dimensional space without loss of key information. We then propose a generic sparse representation method to obtain the sparse solution by decoding the semantic correlations between the query text and entire training samples. Based on the above method, we also design and examine a series of rules by taking advantage of the sparse coefficients to propagate multiple labels for the given query texts. We have conducted extensive experiments using real-world datasets to examine our proposed approach, and the results show the effectiveness of the proposed approach.

Yao, L, Sheng, QZ, Wang, X, Zhang, WE & Qin, Y 2018, 'Collaborative location recommendation by integrating multi-dimensional contextual information', ACM Transactions on Internet Technology, vol. 18, no. 3.
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© 2018 ACM. Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks and services in recent years. Compared with traditional recommendation tasks, POI recommendation focuses more on making personalized and context-aware recommendations to improve user experience. Traditionally, the most commonly used contextual information includes geographical and social context information. However, the increasing availability of check-in data makes it possible to design more effective location recommendation applications by modeling and integrating comprehensive types of contextual information, especially the temporal information. In this article, we propose a collaborative filtering method based on Tensor Factorization, a generalization of the Matrix Factorization approach, tomodel themulti-dimensional contextual information. Tensor Factorization naturally extendsMatrix Factorization by increasing the dimensionality of concerns, within which the three-dimensional model is the one most popularly used. Our method exploits a high-order tensor to fuse heterogeneous contextual information about users' check-ins instead of the traditional two-dimensional user-location matrix. The factorization of this tensor leads to a more compact model of the data that is naturally suitable for integrating contextual information to make POI recommendations. Based on the model, we further improve the recommendation accuracy by utilizing the internal relations within users and locations to regularize the latent factors. Experimental results on a large real-world dataset demonstrate the effectiveness of our approach.

Yao, L, Wang, X, Sheng, QZ, Benatallah, B & Huang, C 2018, 'Mashup Recommendation by Regularizing Matrix Factorization with API Co-Invocations', IEEE Transactions on Services Computing.
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IEEE Mashup is a dominant approach for building data-centric applications, especially mobile applications, in recent years. Since mashups are predominantly based on public data sources and existing APIs, it requires no sophisticated programming knowledge of people to develop mashup applications. The recent prevalence of open APIs and open data sources in the Big Data era has provided new opportunities for mashup development, but at the same time increase the difficulty of selecting the right services for a given mashup task. The API recommendation for mashup differs from traditional service recommendation tasks in lacking the specific QoS information and formal semantic specification of the APIs, which limits the adoption of many existing methods. Although there are a significant number of service recommendation approaches, most of them focus on improving the recommendation accuracy and few work pays attention to the diversity of the recommendation results. Another challenge comes from the existence of both explicit and implicit correlations among the different APIs generally neglected by existing recommendation methods. In this paper, we address the above deficiencies of existing approaches by exploring API recommendation for mashups in the reusable composition context, with the goal of helping developers identify the most appropriate APIs for composition task

Yasin, A, Liu, L, Li, T, Wang, J & Zowghi, D 2018, 'Design and preliminary evaluation of a cyber Security Requirements Education Game (SREG)', Information and Software Technology, vol. 95, pp. 179-200.
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© 2017 Elsevier B.V. Context: Security, in digitally connected organizational environments of today, involves many different perspectives, including social, physical, and technical factors. In order to understand the interactions among these correlated aspects and elicit potential threats geared towards a given organization, different security requirements analysis approaches are proposed in the literature. However, the body of knowledge is yet to unleash its full potential due to the complex nature of security problems, and inadequate ways to improve security awareness of key players in the organization. Objective: Objective(s) of the research study is to improve the security awareness of players utilizing serious games via: (i) Know-how of security concepts and security protection; (ii) guided process of identifying valuable assets and vulnerabilities in a given organizational setting; (iii) guided process of defining successful security attacks to the organization. Method: Important methods used to address the above objectives include: (i) a comprehensive review of the literature to better understand security and game design elements; (ii) designing a serious game using cyber security knowledge and game-based techniques combined with security requirements engineering concepts; (iii) using empirical evaluation (observation and survey) to verify the effectiveness of the proposed game design. Result: The solution proposed is a serious game for security requirements education, which: (i) can be an effective and fun way of learning security related concepts; (ii) mimics a real life problem setting in a presentable and understandable way; (iii) motivates players to learn more about security related concepts in future. Conclusion: From this study, we conclude that the proposed Security Requirement Education Game (SREG) has positive results and is helpful for players of the game to get an understanding of security attacks and vulnerabilities.

Ye, D, He, Q, Wang, Y & Yang, Y 2018, 'An agent-based service adaptation approach in distributed multi-tenant service-based systems', Journal of Parallel and Distributed Computing, vol. 122, pp. 11-25.
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Service adaptation aims to alleviate the runtime quality management problem of distributed service-based systems (SBSs). Most of the existing service adaptation approaches are designed for single-tenant SBSs. However, modern distributed SBSs must achieve multi-tenancy due to simultaneous existence of multiple tenants. Thus, it is vital to study service adaptation in distributed multi-tenant SBSs. Currently, service adaptation has not been properly addressed in distributed multi-tenant SBSs. Existing approaches for service adaptation in multi-tenant SBSs are centralised which are not very efficient if the SBSs are large and distributed. Some decentralised service adaptation approaches, which are developed in single-tenant SBSs, may be extended to accommodate multi-tenant SBSs. These approaches, however, either incur significant communication overhead to obtain required information, or simply assume that some specific global information is already known, which is not realistic in large and distributed SBSs. To overcome the limitations of existing approaches, in this paper, a novel agent-based hybrid service adaptation approach for distributed multi-tenant SBSs is proposed, which is based on the multi-agent coalition formation technique. Our hybrid approach combines the advantages of both centralised and decentralised approaches while avoiding their disadvantages. The experimental results demonstrate the effectiveness of the proposed approach.

Yin, Z, Wang, F, Liu, W & Chawla, S 2018, 'Sparse Feature Attacks in Adversarial Learning', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 6, pp. 1164-1177.
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© 2018 IEEE. Adversarial learning is the study of machine learning techniques deployed in non-benign environments. Example applications include classification for detecting spam, network intrusion detection, and credit card scoring. In fact, as the use of machine learning grows in diverse application domains, the possibility for adversarial behavior is likely to increase. When adversarial learning is modelled in a game-theoretic setup, the standard assumption about the adversary (player) behavior is the ability to change all features of the classifiers (the opponent player) at will. The adversary pays a cost proportional to the size of the 'attack'. We refer to this form of adversarial behavior as a dense feature attack. However, the aim of an adversary is not just to subvert a classifier but carry out data transformation in a way such that spam continues to remain effective. We demonstrate that an adversary could potentially achieve this objective by carrying out a sparse feature attack. We design an algorithm to show how a classifier should be designed to be robust against sparse adversarial attacks. Our main insight is that sparse feature attacks are best defended by designing classifiers which use ℓ1 regularizers.

Ying, H, Wu, J, Xu, G, Liu, Y, Liang, T, Zhang, X & Xiong, H 2018, 'Time-aware metric embedding with asymmetric projection for successive POI recommendation', World Wide Web, pp. 1-16.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature Successive Point-of-Interest (POI) recommendation aims to recommend next POIs for a given user based on this user’s current location. Indeed, with the rapid growth of Location-based Social Networks (LBSNs), successive POI recommendation has become an important and challenging task, since it can help to meet users’ dynamic interests based on their recent check-in behaviors. While some efforts have been made for this task, most of them do not capture the following properties: 1) The transition between consecutive POIs in user check-in sequences presents asymmetric property, however existing approaches usually assume the forward and backward transition probabilities between a POI pair are symmetric. 2) Users usually prefer different successive POIs at different time, but most existing studies do not consider this dynamic factor. To this end, in this paper, we propose a time-aware metric embedding approach with asymmetric projection (referred to as MEAP-T) for successive POI recommendation, which takes the above two properties into consideration. In addition, we exploit three latent Euclidean spaces to project the POI-POI, POI-user, and POI-time relationships. Finally, the experimental results on two real-world datasets show MEAP-T outperforms the state-of-the-art methods in terms of both precision and recall.

Yu, D, Fu, B, Xu, G & Qin, A 2018, 'Constrained nonnegative matrix factorization-based semi-supervised multi-label learning', International Journal of Machine Learning and Cybernetics.
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Yu, D, Xu, Z, Kao, Y & Lin, C-T 2018, 'The Structure and Citation Landscape of IEEE Transactions on Fuzzy Systems (1994–2015)', IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, pp. 430-442.
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Zhang, B, Zhu, T, Hu, C & Zhao, C 2018, 'Cryptanalysis of a Lightweight Certificateless Signature Scheme for IIOT Environments', IEEE Access, vol. 6, pp. 73885-73894.
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Zhang, H, Xu, G, Liang, X, Xu, G, Li, F, Fu, K, Wang, L & Huang, T 2018, 'An Attention-Based Word-Level Interaction Model for Knowledge Base Relation Detection', IEEE Access, vol. 6, pp. 75429-75441.
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OAPA Relation detection is a challenging task because of the high variance of relation expression in real-world questions, and it plays a crucial role in Knowledge Base Question Answering (KBQA). Traditional relation detection models based on deep learning follow an encoding-comparing paradigm, where the question and the candidate relation are represented as vectors to compare their semantic similarity. Max- or average- pooling operation, which is used to compress the sequence of words into fixed-dimensional vectors, becomes the bottleneck of information flow. In this paper, we propose an Attention-Based Wordlevel Interaction Model (ABWIM) to alleviate the information loss issue caused by aggregating the sequence into a fixed-dimensional vector before the comparison. Firstly, attention mechanism is adopted to learn the soft alignments between words from the question and the relation. Then fine-grained comparisons are performed on the aligned words. Finally, the comparison results are merged with a simple recurrent layer to estimate the semantic similarity. Besides, a dynamic sample selection strategy is proposed to accelerate the training procedure without decreasing the performance. Experimental results of relation detection on both SimpleQuestions and WebQuestions datasets show that ABWIM achieves state-of-the-art accuracy, demonstrating its effectiveness.

Zhang, J, McBurney, P & Musial, K 2018, 'Convergence of trading strategies in continuous double auction markets with boundedly-rational networked traders', Review of Quantitative Finance and Accounting, vol. 50, no. 1, pp. 301-352.
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© 2017, Springer Science+Business Media New York. This paper considers the convergence of trading strategies among artificial traders connected to one another in a social network and trading in a continuous double auction financial marketplace. Convergence is studied by means of an agent-based simulation model called the Social Network Artificial stoCk marKet model. Six different canonical network topologies (including no-network) are used to represent the possible connections between artificial traders. Traders learn from the trading experiences of their connected neighbours by means of reinforcement learning. The results show that the proportions of traders using particular trading strategies are eventually stable. Which strategies dominate in these stable states depends to some extent on the particular network topology of trader connections and the types of traders.

Zhang, Q, Lu, J, Wu, D & Zhang, G 2018, 'A Cross-Domain Recommender System With Kernel-Induced Knowledge Transfer for Overlapping Entities', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE The aim of recommender systems is to automatically identify user preferences within collected data, then use those preferences to make recommendations that help with decisions. However, recommender systems suffer from data sparsity problem, which is particularly prevalent in newly launched systems that have not yet had enough time to amass sufficient data. As a solution, cross-domain recommender systems transfer knowledge from a source domain with relatively rich data to assist recommendations in the target domain. These systems usually assume that the entities either fully overlap or do not overlap at all. In practice, it is more common for the entities in the two domains to partially overlap. Moreover, overlapping entities may have different expressions in each domain. Neglecting these two issues reduces prediction accuracy of cross-domain recommender systems in the target domain. To fully exploit partially overlapping entities and improve the accuracy of predictions, this paper presents a cross-domain recommender system based on kernel-induced knowledge transfer, called KerKT. Domain adaptation is used to adjust the feature spaces of overlapping entities, while diffusion kernel completion is used to correlate the non-overlapping entities between the two domains. With this approach, knowledge is effectively transferred through the overlapping entities, thus alleviating data sparsity issues. Experiments conducted on four data sets, each with three sparsity ratios, show that KerKT has 1.13%-20% better prediction accuracy compared with six benchmarks. In addition, the results indicate that transferring knowledge from the source domain to the target domain is both possible and beneficial with even small overlaps.

Zhang, Y, Lu, J, Liu, F, Liu, Q, Porter, A, Chen, H & Zhang, G 2018, 'Does deep learning help topic extraction? A kernel k-means clustering method with word embedding', Journal of Informetrics, vol. 12, no. 4, pp. 1099-1117.
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© 2018 All rights reserved. Topic extraction presents challenges for the bibliometric community, and its performance still depends on human intervention and its practical areas. This paper proposes a novel kernel k-means clustering method incorporated with a word embedding model to create a solution that effectively extracts topics from bibliometric data. The experimental results of a comparison of this method with four clustering baselines (i.e., k-means, fuzzy c-means, principal component analysis, and topic models) on two bibliometric datasets demonstrate its effectiveness across either a relatively broad range of disciplines or a given domain. An empirical study on bibliometric topic extraction from articles published by three top-tier bibliometric journals between 2000 and 2017, supported by expert knowledge-based evaluations, provides supplemental evidence of the method's ability on topic extraction. Additionally, this empirical analysis reveals insights into both overlapping and diverse research interests among the three journals that would benefit journal publishers, editorial boards, and research communities.

Zhang, Y, Lu, X & Li, J 2018, 'Single-sample face recognition under varying lighting conditions based on logarithmic total variation', Signal, Image and Video Processing.
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© 2018, Springer-Verlag London Ltd., part of Springer Nature. The logarithmic total variation (LTV) algorithm is a classical algorithm that is proposed to address the illumination interference in face recognition. Some state-of-the-art techniques based on LTV assume that the illumination component mainly lies in the low-frequency features among face images. However, these techniques adopt unsuitable methods to process low-frequency features, resulting in final unsatisfactory recognition rates. In this paper, we propose an improved illumination normalization method based on the LTV method, called the RETINA&TH-LTV algorithm. In this algorithm, the retina model is utilized to eliminate most of the illumination component in low-frequency features. Then, an advanced contrast-limited adaptive histogram equalization technique is proposed to remove the residual lighting component. At the same time, through realizing threshold-value filtering on high-frequency features, the enhancement of facial features is achieved. Finally, the processed frequency features are combined to form a robust holistic feature image, which is then utilized for recognition. Insufficient training images in face recognition are also taken into consideration in this research. Comparative experiments for single-sample face recognition are conducted on YALE B, CMU PIE and our self-built driver databases. The nearest neighbor classifier and extended sparse representation classifier are employed as classification methods. The results indicate that the RETINA&TH-LTV algorithm has promising performance, especially in serious illumination and insufficient training sample conditions.

Zheng, Y, Zhang, G, Zhang, Z & Lu, J 2018, 'A reducibility method for the weak linear bilevel programming problems and a case study in principal-agent', Information Sciences, vol. 454-455, pp. 46-58.
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© 2018 A weak linear bilevel programming (WLBP) problem often models problems involving hierarchy structure in expert and intelligent systems under the pessimistic point. In the paper, we deal with such a problem. Using the duality theory of linear programming, the WLBP problem is first equivalently transformed into a jointly constrained bilinear programming problem. Then, we show that the resolution of the jointly constrained bilinear programming problem is equivalent to the resolution of a disjoint bilinear programming problem under appropriate assumptions. This may give a possibility to solve the WLBP problem via a single-level disjoint bilinear programming problem. Furthermore, some examples illustrate the solution process and feasibility of the proposed method. Finally, the WLBP problem models a principal-agent problem under the pessimistic point that is also compared with a principal-agent problem under the optimistic point.

Zhong, S, Ren, W, Zhu, T, Ren, Y & Choo, KKR 2018, 'Performance and Security Evaluations of Identity-and Pairing-Based Digital Signature Algorithms on Windows, Android, and Linux Platforms: Revisiting the Algorithms of Cha and Cheon, Hess, Barreto, Libert, Mccullagh and Quisquater, and Paterson and Schuldt', IEEE Access, vol. 6, pp. 37850-37857.
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© 2013 IEEE. Bilinear pairing, an essential tool to construct-efficient digital signatures, has applications in mobile devices and other applications. One particular research challenge is to design cross-platform security protocols (e.g. Windows, Linux, and other popular mobile operating systems) while achieving an optimal security-performance tradeoff. That is, how to choose the right digital signature algorithm, for example, on mobile devices while considering the limitations on both computation capacity and battery life. In this paper, we examine the security-performance tradeoff of four popular digital signature algorithms, namely: CC (proposed by Cha and Cheon in 2003), Hess (proposed by Hess in 2002), BLMQ (proposed by Barreto et al. in 2005), and PS (proposed by Paterson and Schuldt in 2006), on various platforms. We empirically evaluate their performance using experiments on Windows, Android, and Linux platforms, and find that BLMQ algorithm has the highest computational efficiency and communication efficiency. We also study their security properties under the random oracle model and assuming the intractability of the CDH problem, we reveal that the BLMQ digital signature scheme satisfies the property of existential unforgeable on adaptively chosen message and ID attack. The efficiency of PS algorithm is lower, but it is secure under the standard model.

Zuo, H, Zhang, G, Pedrycz, W, Behbood, V & Lu, J 2018, 'Granular Fuzzy Regression Domain Adaptation in Takagi-Sugeno Fuzzy Models', IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, pp. 847-858.
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© 1993-2012 IEEE. In classical data-driven machine learning methods, massive amounts of labeled data are required to build a high-performance prediction model. However, the amount of labeled data in many real-world applications is insufficient, so establishing a prediction model is impossible. Transfer learning has recently emerged as a solution to this problem. It exploits the knowledge accumulated in auxiliary domains to help construct prediction models in a target domain with inadequate training data. Most existing transfer learning methods solve classification tasks; only a few are devoted to regression problems. In addition, the current methods ignore the inherent phenomenon of information granularity in transfer learning. In this study, granular computing techniques are applied to transfer learning. Three granular fuzzy regression domain adaptation methods to determine the estimated values for a regression target are proposed to address three challenging cases in domain adaptation. The proposed granular fuzzy regression domain adaptation methods change the input and/or output space of the source domain's model using space transformation, so that the fuzzy rules are more compatible with the target data. Experiments on synthetic and real-world datasets validate the effectiveness of the proposed methods.

Abdulkareem, SA, Augustijn, EW, Musial, K, Mustafa, YT & Filatova, T 2018, 'The impact of social versus individual learning for agents' risk perception during epidemics', Proceedings - IEEE 14th International Conference on eScience, e-Science 2018, pp. 297-298.
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© 2018 IEEE. Epidemics have always been a source of concern to people, both at the individual and government level. To fight outbreaks effectively, we need advanced tools that enable us to understand the factors that influence the spread of life-threatening diseases.

Abou Maroun, E, Daniel, J, Zowghi, D & Talaei-Khoei, A 2018, 'Blockchain in Supply Chain Management: Australian Manufacturer Case Study', Seventh Australasian Symposium on Service Research and Innovation, Sydney, Australia.

Abu Ul Fazal, M, Ferguson, S & Johnston, A 2018, 'Investigating concurrent speech-based designs for information communication', ACM International Conference Proceeding Series.
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© 2018 Association for Computing Machinery. Speech-based information is usually communicated to users in a sequential manner, but users are capable of obtaining information from multiple voices concurrently. This fact implies that the sequential approach is possibly under-utilizing human perception capabilities to some extent and restricting users to perform optimally in an immersive environment. This paper reports on an experiment that aimed to test different speech-based designs for concurrent information communication. Two audio streams from two types of content were played concurrently to 34 users, in both a continuous or intermittent form, with the manipulation of a variety of spatial configurations (i.e. Diotic, Diotic-Monotic, and Dichotic). In total, 12 concurrent speech-based design configurations were tested with each user. The results showed that the concurrent speech-based information designs involving intermittent form and the spatial difference in information streams produce comprehensibility equal to the level achieved in sequential information communication.

Adak, C, Chaudhuri, BB & Blumenstein, M 2018, 'A study on idiosyncratic handwriting with impact on writer identification', Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR, International Conference on Frontiers in Handwriting Recognition, Niagara Falls, NY, USA, pp. 193-198.
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© 2018 IEEE. In this paper, we study handwriting idiosyncrasy in terms of its structural eccentricity. In this study, our approach is to find idiosyncratic handwritten text components and model the idiosyncrasy analysis task as a machine learning problem supervised by human cognition. We employ the Inception network for this purpose. The experiments are performed on two publicly available databases and an in-house database of Bengali offline handwritten samples. On these samples, subjective opinion scores of handwriting idiosyncrasy are collected from handwriting experts. We have analyzed the handwriting idiosyncrasy on this corpus which comprises the perceptive ground-truth opinion. We also investigate the effect of idiosyncratic text on writer identification by using the SqueezeNet. The performance of our system is promising.

Adak, C, Chaudhuri, BB & Blumenstein, M 2018, 'Cognitive Analysis for Reading and Writing of Bengali Conjuncts', Proceedings of the International Joint Conference on Neural Networks.
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© 2018 IEEE. In this paper, we study the difficulties arising in reading and writing of Bengali conjunct characters by human-beings. Such difficulties appear when the human cognitive system faces certain obstructions in effortlessly reading/writing. In our computer-based investigation, we consider the reading/writing difficulty analysis task as a machine learning problem supervised by human perception. To this end, we employ two distinct models: (a) an auto-derived feature-based Inception network and (b) a hand-crafted feature-based SVM (Support Vector Machine). Two commonly used Bengali printed fonts and three contemporary handwritten databases are used for collecting subjective opinion scores from human readers/writers. On this corpus, which contains the perceptive ground-truth opinion of reading/writing complications, we have undertaken to conduct the experiments. The experimental results obtained on various types of conjunct characters are promising.

Adak, C, Chaudhuri, BB & Blumenstein, M 2017, 'Legibility and Aesthetic Analysis of Handwriting', Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, IAPR International Conference on Document Analysis and Recognition, IEEE, Kyoto, Japan, pp. 175-182.
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© 2017 IEEE. This paper deals with computer-based cognitive analysis towards legibility and aesthetics of a handwritten document. The legible text creates a human perception that the writing can be read effortlessly because of its orthographic clarity. The aesthetic property relates to the beautiful appearance of a handwritten document. In this study, we deal with these properties on offline Bengali handwriting. We formulate both legibility and aesthetic analysis tasks as machine learning problems supervised by the human cognitive system. We employ automatically derived feature-based recurrent neural networks to investigate writing legibility. For aesthetics evaluation, we employ hand-crafted feature-based support vector machines (SVMs). We have collected contemporary Bengali handwritings, on which the subjective legibility and aesthetic scores are provided by human readers. On this corpus containing legibility and aesthetic ground-Truth information, we executed our experiments. The experimental results obtained on various handwritings are encouraging.

Adak, C, Marinai, S, Chaudhuri, BB & Blumenstein, M 2018, 'Offline Bengali writer verification by PDF-CNN and siamese net', Proceedings - 13th IAPR International Workshop on Document Analysis Systems, DAS 2018, pp. 381-386.
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© 2018 IEEE. Automated handwriting analysis is a popular area of research owing to the variation of writing patterns. In this research area, writer verification is one of the most challenging branches, having direct impact on biometrics and forensics. In this paper, we deal with offline writer verification on complex handwriting patterns. Therefore, we choose a relatively complex script, i.e., Indic Abugida script Bengali (or, Bangla) containing more than 250 compound characters. From a handwritten sample, the probability distribution functions (PDFs) of some handcrafted features are obtained and input to a convolutional neural network (CNN). For such a CNN architecture, we coin the term 'PDFCNN', where handcrafted feature PDFs are hybridized with auto-derived CNN features. Such hybrid features are then fed into a Siamese neural network for writer verification. The experiments are performed on a Bengali offline handwritten dataset of 100 writers. Our system achieves encouraging results, which sometimes exceed the results of state-of-The-Art techniques on writer verification.

Agrawal, S & Williams, MA 2018, 'Would You Obey an Aggressive Robot: A Human-Robot Interaction Field Study', RO-MAN 2018 - 27th IEEE International Symposium on Robot and Human Interactive Communication, pp. 240-246.
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© 2018 IEEE. Social Robots have the potential to be of tremendous utility in healthcare, search and rescue, surveillance, transport, and military applications. In many of these applications, social robots need to advise and direct humans to follow important instructions. In this paper, we present the results of a Human-Robot Interaction field experiment conducted using a PR2 robot to explore key factors involved in obedience of humans to social robots. This paper focuses on studying how the human degree of obedience to a robot's instructions is related to the perceived aggression and authority of the robot's behavior. We implemented several social cues to exhibit and convey both authority and aggressiveness in the robot's behavior. In addition to this, we also analyzed the impact of other factors such as perceived anthropomorphism, safety, intelligence and responsibility of the robot's behavior on participants' compliance with the robot's instructions. The results suggest that the degree of perceived aggression in the robot's behavior by different participants did not have a significant impact on their decision to follow the robot's instruction. We have provided possible explanations for our findings and identified new research questions that will help to understand the role of robot authority in human-robot interaction, and that can help to guide the design of robots that are required to provide advice and instructions.

Ahadi, A, Lal, S, Lister, R & Hellas, A 2018, 'Learning Programming, Syntax Errors and Institution-specific Factors', ACM International Conference Proceeding Series, pp. 90-96.
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Learning programming is a road that is paved with mistakes. Initially, novices are bound to write code with syntactic mistakes, but after a while semantic mistakes take a larger role in the novice programmers’ lives. Researchers who wish to understand that road are increasingly using data recorded from students’ programming processes. Such data can be used to draw inferences on the typical errors, and on how students approach fixing them. At the same time, if the lens that is used to analyze such data is used only from one angle, the view is likely to be narrow. In this work, we replicate a previous multi-institutional study by Brown et al. [5]. That study used a large scale programming process data repository to analyze mistakes that novices make while learning programming. In our single institution replication of that study, we use data collected from approximately 800 students. We investigate the frequency, time required to fix, and the development of mistakes through the semester. We contrast our findings from our single institution with the multi-institutional study, and show that whilst the data collection tools and the research methodology are the same, the results can differ solely due to how the course is conducted.

Ahadi, A, Lister, R & Mathieson, L 2018, 'Syntax Error Based Quantification of the Learning Progress of the Novice Programmer', 23rd Conference on Innovation and Technology in Computer Science Education, Innovation and Technology in Computer Science Education, The Association for Computing Machinery, Inc., Larnaca, Cyprus, pp. 10-14.
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Recent data-driven research has produced metrics for quantifying a novice programmer's error profile, such as Jadud's error quotient. However, these metrics tend to be context dependent and contain free parameters. This paper reviews the caveats of such metrics and proposes a more general approach to developing a metric. The online implementation of the proposed metric is publicly available at http://online-analysis-demo.herokuapp.com/.

Almasoud, AS, Eljazzar, MM & Hussain, F 2018, 'Toward a Self-Learned Smart Contracts', Proceedings - 2018 IEEE 15th International Conference on e-Business Engineering, ICEBE 2018, pp. 269-273.
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© 2018 IEEE. In recent years, Blockchain technology has been highly valued and disruptive. Several researches have presented a merge between blockchain and current application i.e. Medical, supply chain, and e-commerce. Although Blockchain architecture does not have a standard yet, IBM, MS, AWS offer BaaS (Blockchain as a Service). In addition to the current public chains i.e. Ethereum, NEO, and Cardeno; there are some differences between several public ledgers in terms of development and architecture. This paper introduces the main factors that affect integration of Artificial Intelligence with Blockchain. As well as, how it could be integrated for forecasting and automating; building self-regulated chain.

Alshehri, MD & Hussain, F 2017, 'A Centralized Trust Management Mechanism for the Internet of Things (CTM-IoT)', Advances on Broad-Band Wireless Computing, Communication and Applications, International Conference on Broad-Band Wireless Computing, Communication and Applications, Springer, Barcelona, Spain, pp. 533-543.
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The Internet of Things (IoT) is an extended network that allows all devices to be connected to one another over the Internet. This new network faces numerous challenges, but mainly security issues. One such issue is how the IoT’s nodes can trust each other when they are connected over the Internet. There is a lack of studies that address the issue of trust management in IoT, or that provide a fully trustworthy framework. This paper proposes and delivers a centralized trust management mechanism for IoT by adding trust modules as a feature of the central trust manager, the Super Node (SN). To deliver a comprehensive approach, the SN includes other modules which are integrated with the whole IoT Trust Management framework to provide trustworthy communication between all nodes.

Angelini, L, Mugellini, E, Abou Khaled, O, Couture, N, van den Hoven, EA & Bakker, S 2018, 'Internet of Tangibles: Exploring the Interaction-Attention Continuum', the Twelfth International Conference on Tangible, Embedded, and Embodied Interaction, ACM, Stockholm, Sweden, pp. 740-743.
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Aravanis, TI, Peppas, P & Williams, MA 2018, 'Iterated belief revision and dalal⇔s operator', ACM International Conference Proceeding Series.
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© 2018 Association for Computing Machinery. One of the main shortcomings of the early AGM paradigm is its lack of any guidelines for iterated revision; it formalizes only one-step rational belief revision. Darwiche and Pearl, subsequently, addressed this problem by introducing four additional postulates (the DP postulates), supplementing the AGM ones. Despite the popularity of the DP approach, there are still controversies surrounding the DP postulates. In this article, we prove a conflict between each one of the latter, and one of the most popular and intuitive “off the shelf” revision functions, that is Dalal’s revision operator.

Badarinath, D, Chaitra, S, Bharill, N, Tanveer, M, Prasad, M, Suma, HN, Abhishek Appaji, M & Vinekar, A 2018, 'Study of Clinical Staging and Classification of Retinal Images for Retinopathy of Prematurity (ROP) Screening', Proceedings of the International Joint Conference on Neural Networks.
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© 2018 IEEE. Retinopathy of Prematurity (ROP) is a disease which requires immediate precautionary measures to prevent blindness in the infants, and this condition is prevalent in premature babies in all the underdeveloped, developing, and in the developed countries as well. This paper proposes a tool by which the stage and zones of Retinopathy of Prematurity in infants can be diagnosed easily. This tool takes the input from the Retcam and detects the stage, zone, and gives a rating of 1 to 9 for classifying the severity of the disease in the infants. This is achieved by extracting the optic disc, marking the ridge, and the distance of the optic nerve. This tool can be easily used by nurses and paramedics, unlike the existing technologies which require the guidance of a specialist to come to a conclusion.

Bajwa, MA, Prior, J, Leaney, J & Ferguson, S 2018, 'Enterprise IT Governance impact on Agile Software Development Project Success', Designing Digitalization (ISD2018 Proceedings), International Conference on Information Systems Development, AIS, Lund, Sweden, pp. 1-3.
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Enterprise IT (EIT) governance has become the primary approach in leveraging the IT function to achieve business objectives. We found in previously published work that decision making is the core of EIT governance. We collected quantitative data from professionals on decision making in Agile Software Development (ASD) projects, which we analyzed using Spearman’s Ranked Correlation Coefficient. Decision-making clarity in implementation and decision-making distribution in the organization layers positively impact ASD project success. However, our finding that tailoring the decision-making process does not impact ASD project success was most surprising. We conclude that the impact of decision-making factors in an ASD project’s success needs to be explored more deeply.

Bano, M & Zowghi, D 2017, 'Crowd Vigilante Detecting Sabotage in Crowdsourcing', Requirements Engineering for Internet of Things, 4th Asia Pacific Requirements Engineering Symposium 2017, Springer, Melaka, Malaysia, pp. 114-120.
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Crowdsourcing is a complex and sociotechnical problem solving approach for collaboration of geographically distributed volunteer crowd to contribute to the achievement of a common task. One of the major issues faced by crowdsourced projects is the trustworthiness of the crowd. This paper presents a vision to develop a framework with supporting methods and tools for early detection of the malicious acts of sabotage in crowdsourced projects by utilizing and scaling digital forensic techniques. The idea is to utilize the crowd to build the digital evidence of sabotage with systematic collection and analysis of data from the same crowdsourced project where the threat is situated. The proposed framework aims to improve the security of the crowdsourced projects and their outcomes by building confidence about the trustworthiness of the workers.

Bano, M & Zowghi, D 2018, 'Crowd vigilante: Detecting sabotage in crowdsourcing', Communications in Computer and Information Science, pp. 114-120.
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© Springer Nature Singapore Pte Ltd. 2018. Crowdsourcing is a complex and sociotechnical problem solving approach for collaboration of geographically distributed volunteer crowd to contribute to the achievement of a common task. One of the major issues faced by crowdsourced projects is the trustworthiness of the crowd. This paper presents a vision to develop a framework with supporting methods and tools for early detection of the malicious acts of sabotage in crowdsourced projects by utilizing and scaling digital forensic techniques. The idea is to utilize the crowd to build the digital evidence of sabotage with systematic collection and analysis of data from the same crowdsourced project where the threat is situated. The proposed framework aims to improve the security of the crowdsourced projects and their outcomes by building confidence about the trustworthiness of the workers.

Bano, M, Zowghi, D & da Rimini, F 2018, 'Power and Politics of User Involvement in Software Development', ACM, International Conference on Evaluation and Assessment in Software Engineering, ACM, Christchurch, New Zealand, pp. 157-162.
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[CONTEXT] Involving users in software development is a complex and multi-faceted concept. Empirical research that studies power and politics of user involvement in software development is scarce. [OBJECTIVE] In this paper, we present the results from a case study of a software development project, where organizational politics was explored in context of user involvement in software development. [METHOD] We collected data through 30 interviews with 20 participants, attending workshops, observing project meetings, and analysing projects documents. The qualitative data was rigorously and iteratively analyzed. [RESULTS] The results indicate that the politics was a significant factor used to exert power and influence in decision-making processes. Communication channels were exploited for political purposes. These contributed to the users' dissatisfaction with their involvement thus impacting on the project outcome. [CONCLUSION] Having multiple teams of stakeholders with different levels of power in decision-making, the politics is inevitable and inescapable. Without careful attention, the political aspect of user involvement in software development can contribute to unsuccessful project.

Bano, M, Zowghi, D, Ferrari, A, Spoletini, P & Donati, B 2018, 'Learning from Mistakes: An Empirical Study of Elicitation Interviews performed by Novices', 2018 IEEE 26th International Requirements Engineering Conference (RE), International Requirements Engineering Conference, IEEEE, Banff, Alberta, Canada, pp. 182-193.
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[Context] Interviews are the most widely used elici-tation technique in requirements engineering. However, conduct-ing effective requirements elicitation interviews is challenging, due to the combination of technical and soft skills that requirements analysts often acquire after a long period of professional prac-tice. Empirical evidence about training the novices on conducting effective requirements elicitation interviews is scarce. [Objectives] We present a list of most common mistakes that novices make in requirements elicitation interviews. The objective is to assist the educators in teaching interviewing skills to student analysts. [Re-search Method] We conducted an empirical study involving role-playing and authentic assessment with 110 students, teamed up in 28 groups, to conduct interviews with a customer. One researcher made observation notes during the interview while two research-ers reviewed the recordings. We qualitatively analyzed the data to identify the themes and classify the mistakes. [Results and conclu-sion] We identified 34 unique mistakes classified into 7 high level themes. We also give examples of the mistakes made by the novices in each theme, to assist the educationists and trainers. Our re-search design is a novel combination of well-known pedagogical approaches described in sufficient details to make it repeatable for future requirements engineering education and training research.

Barbar, M, Sui, Y, Zhang, H, Chen, S & Xue, J 2018, 'Live path CFI against control flow hijacking attacks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 768-779.
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© Springer International Publishing AG, part of Springer Nature 2018. Through memory vulnerabilities, control flow hijacking allows an attacker to force a running program to execute other than what the programmer has intended. Control Flow Integrity (CFI) aims to prevent the adversarial effects of these attacks. CFI attempts to enforce the programmer’s intent by ensuring that a program only runs according to a control flow graph (CFG) of the program. The enforced CFG can be built statically or dynamically, and Per-Input Control Flow Integrity (PICFI) represents a recent advance in dynamic CFI techniques. PICFI begins execution with the empty CFG of a program and lazily adds edges to the CFG during execution according to concrete inputs. However, this CFG grows monotonically, i.e., edges are never removed when corresponding control flow transfers become illegal. This paper presents LPCFI, Live Path Control Flow Integrity, to more precisely enforce forward edge CFI using a dynamically computed CFG by both adding and removing edges for all indirect control flow transfers from indirect callsites, thereby raising the bar against control flow hijacking attacks.

Barbar, M, Sui, Y, Zhang, H, Chen, S & Xue, J 2018, 'Live path control flow integrity', Proceedings - International Conference on Software Engineering, International Conference on Software Engineering, Gothenburg, Sweden, pp. 195-196.
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© 2018 Authors. Per-Input Control Flow Integrity (PICFI) represents a recent advance in dynamic CFI techniques. PICFI starts with the empty CFG of a program and lazily adds edges to the CFG during execution according to concrete inputs. However, this CFG grows monotonically, i.e., invalid edges are never removed when corresponding control flow transfers (via indirect calls) become illegal (i.e., will never be executed again). This paper presents LPCFI, Live Path Control Flow Integrity, to more precisely enforce forward edge CFI using a dynamically computed CFG by both adding and removing edges for all indirect control flow transfers from function pointer calls, thereby raising the bar against control flow hijacking attacks.

Biddle, R, Liu, S, Tilocca, P & Xu, G 2018, 'Automated Underwriting in Life Insurance: Predictions and Optimisation', Australasian Database Conference, Springer, Gold Coast, QLD, Australia.
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Underwriting is an important stage in the life insurance process and is concerned with accepting individuals into an insurance fund and on what terms. It is a tedious and labour-intensive process for both the applicant and the underwriting team. An applicant must fill out a large survey containing thousands of questions about their life. The underwriting team must then process this application and assess the risks posed by the applicant and offer them insurance products as a result. Our work implements and evaluates classical data mining techniques to help automate some aspects of the process to ease the burden on the underwriting team as well as optimise the survey to improve the applicant experience. Logistic Regression, XGBoost and Recursive Feature Elimination are proposed as techniques for the prediction of underwriting outcomes. We conduct experiments on a dataset provided by a leading Australian life insurer and show that our early-stage results are promising and serve as a foundation for further work in this space.

Bou Ghantous, G & Gill, AQ 2018, 'DevOps Reference Architecture for Multi-Cloud IOT Applications', International Conference on Business Informatics, IEEE, Vienna, Austria..
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There is a growing interest among organizations in adopting DevOps approach for IoT (Internet of Things) applications. However, the challenge is: how to apply DevOps when a multi-cloud heterogeneous environment is required for IoT application. This paper aims to addresses this important challenge and proposes a DevOps Reference Architecture (DRA) to deploy IoT-applications on multi-cloud. The proposed architecture is evaluated by the means of a case study, which involves deploying an IoT application on the chosen set of clouds. The results of this initial evaluation indicate that the proposed architecture would help practitioners and researchers to understand the usefulness and applicability of DevOps approach on multi-cloud platform for automating IoT application deployment.

Braytee, A, Anaissi, A & Kennedy, PJ 2018, 'Sparse feature learning using ensemble model for highly-correlated high-dimensional data', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 423-434.
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© Springer Nature Switzerland AG 2018. High-dimensional highly correlated data exist in several domains such as genomics. Many feature selection techniques consider correlated features as redundant and therefore need to be removed. Several studies investigate the interpretation of the correlated features in domains such as genomics, but investigating the classification capabilities of the correlated feature groups is a point of interest in several domains. In this paper, a novel method is proposed by integrating the ensemble feature ranking and co-expression networks to identify the optimal features for classification. The main advantage of the proposed method lies in the fact, that it does not consider the correlated features as redundant. But, it shows the importance of the selected correlated features to improve the performance of classification. A series of experiments on five high dimensional highly correlated datasets with different levels of imbalance ratios show that the proposed method outperformed the state-of-the-art methods.

Brownlow, J, Chu, C, Fu, B, Xu, G, Culbert, B & Meng, Q 2018, 'Cost-sensitive churn prediction in fund management services', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 776-788.
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© Springer International Publishing AG, part of Springer Nature 2018. Churn prediction is vital to companies as to identify potential churners and prevent losses in advance. Although it has been addressed as a classification task and a variety of models have been employed in practice, fund management services have presented several special challenges. One is that financial data is extremely imbalanced since only a tiny proportion of customers leave every year. Another is a unique cost-sensitive learning problem, i.e., costs of wrong predictions for churners should be related to their account balances, while costs of wrong predictions for non-churners should be the same. To address these issues, this paper proposes a new churn prediction model based on ensemble learning. In our model, multiple classifiers are built using sampled datasets to tackle the imbalanced data issue while exploiting data fully. Moreover, a novel sampling strategy is proposed to deal with the unique cost-sensitive issue. This model has been deployed in one of the leading fund management institutions in Australia, and its effectiveness has been fully validated in real applications.

Brownlow, J, Chu, C, Fu, B, Xu, G, Culbert, B & Meng, Q 2018, 'Cost-sensitive Churn Prediction in Fund Management Services - Industrial Track', the 23rd International Conference on Database Systems for Advanced Applications, Australia.
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Brownlow, J, Chu, C, Xu, G, Culbert, B, Fu, B & Meng, Q 2018, 'A Multiple Source based Transfer Learning Framework for Marketing Campaigns', the 2018 International Joint Conference on Neural Networks, Rio De Janeiro, Brasil.
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Buchan, J, Bano, M, Zowghi, D & Volabouth, P 2018, 'Semi-automated Extraction of New Requirements from Online Reviews for Software Product Evolution', Proceedings of the 25th Australasian Software Engineering Conference, Australian Software Engineering Conference, IEEE, Adelaide, Australia, pp. 31-40.
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In order to improve and increase their utility, software products must evolve continually and incrementally to meet the new requirements of current and future users. Online reviews from users of the software provide a rich and readily available resource for discovering candidate new features for future software releases. However, it is challenging to manually analyze a large volume of potentially unstructured and noisy data to extract useful information to support software release planning decisions. This paper investigates machine learning techniques to automatically identify text that represents users’ ideas for new features from their online reviews. A binary classification approach to categorize extracted text as either a feature or non-feature was evaluated experimentally. Three machine learning algorithms were evaluated in the experiments: Naïve Bayes (with multinomial and Bernoulli variants), Support Vector Machines (with linear and multinomial variants) and Logistic Regression. Variations on the configurations of k-fold cross validation, the use of n-grams and review sentiment were also experimentally evaluated. Based on binary classification of over a thousand separate reviews of two products, Trello and Jira, linear Support Vector Machines with review sentiment as an input, using n-gram (1,4) together with k-fold 10 cross validation gave the best performance. The results have confirmed the feasibility and accuracy of semi-automated extraction of candidate requirements from a large volume of unstructured and noisy online user reviews. The next steps planned are to experiment with machine supported grouping, prioritizing and visualizing the extracted features to best support release planners’ work, as well as extending the sources of candidate requirements.

Butler, A, Xu, G & Musial-Gabrys, K 2018, 'Research Performance Reporting is Fallacious', The 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing, Taiwan.
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Chantamit-O-Pas, P & Goyal, M 2018, 'A Case-Based Reasoning Framework for Prediction of Stroke', Advances in Intelligent Systems and Computing, pp. 219-227.
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© Springer Nature Singapore Pte Ltd. 2018. Case-based reasoning (CBR) has been a popular method in health care sector from the last two decades. It is used for analysis, prediction, diagnosis and recommending treatment for patients. This research purposes a conceptual CBR framework for stroke disease prediction that uses previous case-based knowledge. The outcomes of this approach not only assist in stroke disease decision-making, but also will be very useful for prevention and early treatment of patients.

Chantamit-o-Pas, P & Goyal, M 2018, 'Long Short-Term Memory Recurrent Neural Network for Stroke Prediction', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 312-323.
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© 2018, Springer International Publishing AG, part of Springer Nature. Electronic Healthcare Records (EHRs) describe the details about a patient’s physical and mental health, diagnosis, lab results, treatments or patient care plan and so forth. Currently, the International Classification of Diseases, 10th Revision or ICD-10 code is used for representing each patient record. The huge amount of information in these records provides insights about the diagnosis and prediction of various diseases. Various data mining techniques are used for the analysis of data deriving from these patient records. Recurrent Neural Network (RNN) is a powerful and widely used technique in machine learning and bioinformatics. This research aims at the investigation of RNN with Long Short-Term Memory (LSTM) hidden units. The empirical research is intended to evaluate the ability of LSTMs to recognize patterns in multi-label classification of cerebrovascular symptoms or stroke. First, we integrated ICD-10 code into health record, as well as other potential risk factors within EHRs into the pattern and model for prediction. Next, we modelled the effectiveness of LSTMs for prediction of stroke based on healthcare records. The results show several strong baselines that include accuracy, recall, and F1 measure score.

Chen, J, Lin, Z, Liu, X, Deng, Z & Wang, X 2018, 'Reputation-based framework for internet of things', Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, pp. 592-597.
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© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018. Internet of Things (IoT) is going to create a world where physical objects are integrated into traditional networks in order to provide intelligent services for human-beings. Trust plays an important role in communications and interactions of objects in IoT. Two vital tasks of trust management are trust model design and reputation evaluation. However, current literature cannot be simply and directly applied to the IoT due to smart node hardware constraints, very limited computing and energy resources. Therefore a general and flexible model is needed to meet the special requirements for IoT. In this paper, we firstly design LTrust, a layered trust model for IoT. Then, a Reputation Evaluation Scheme for the Node (RES-N) has been presented. The proposed trust model and reputation evaluation scheme provide a general framework for the study of trust management for IoT. The efficiency of RES-N is validated by the simulation results.

Chen, K, Yao, L, Wang, X, Zhang, D, Gu, T, Yu, Z & Yang, Z 2018, 'Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling', Proceedings of the International Joint Conference on Neural Networks.
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© 2018 IEEE. Multimodal features play a key role in wearable sensor based human activity recognition (HAR). Selecting the most salient features adaptively is a promising way to maximize the effectiveness of multimodal sensor data. In this regard, we propose a 'collect fully and select wisely' principle as well as an interpretable parallel recurrent model with convolutional attentions to improve the recognition performance. We first collect modality features and the relations between each pair of features to generate activity frames, and then introduce an attention mechanism to select the most prominent regions from activity frames precisely. The selected frames not only maximize the utilization of valid features but also reduce the number of features to be computed effectively. We further analyze the accuracy and interpretability of the proposed model based on extensive experiments. The results show that our model achieves competitive performance on two benchmarked datasets and works well in real life scenarios.

Chen, Z, You, X & Li, J 2018, 'Learning to focus for object proposals', 2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017, pp. 439-444.
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© 2017 IEEE. Object proposal generators address the wasteful exhaustive search of the sliding window scheme in visual object detection and have been shown effective. However, the number of candidate windows is still large in order to ensure full coverage of potential objects. This paper presents a complementary technique that aims to work with any proposal generating system, amending the workflow from 'propose-assess' to 'propose-adjust-assess'. The adjustment serves as an auto-focus mechanism for the system and reduces the number of object proposals to be processed. The auto-focus is realized by two learning-based transformation models, one translating and the other deforming the windows towards better alignments of the objects, which are trained for identifying generic objects using image cues. Experiments on reallife image data sets show that the proposed technique can reduce the number of proposals without loss of performance.

Chennafi, M, Khan, MA, Li, G, Lian, Y & Wang, G 2018, 'Study of Music Effect on Mental Stress Relief Based on Heart Rate Variability', 2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), 2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), IEEE.
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Chivukula, AS, Li, J & Liu, W 2018, 'Discovering granger-causal features from deep learning networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 692-705.
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© Springer Nature Switzerland AG 2018. In this research, we propose deep networks that discover Granger causes from multivariate temporal data generated in financial markets. We introduce a Deep Neural Network (DNN) and a Recurrent Neural Network (RNN) that discover Granger-causal features for bivariate regression on bivariate time series data distributions. These features are subsequently used to discover Granger-causal graphs for multivariate regression on multivariate time series data distributions. Our supervised feature learning process in proposed deep regression networks has favourable F-tests for feature selection and t-tests for model comparisons. The experiments, minimizing root mean squared errors in the regression analysis on real stock market data obtained from Yahoo Finance, demonstrate that our causal features significantly improve the existing deep learning regression models.

Culbert, B, Fu, B, Brownlow, J, Chu, C, Meng, Q & Xu, G 2018, 'Customer Churn Prediction in Superannuation: A Sequential Pattern Mining Approach', Australasian Database Conference.
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Demediuk, S, Murrin, A, Bulger, D, Hitchens, M, Drachen, A, Raffe, WL & Tamassia, M 2018, 'Player retention in league of legends: A study using survival analysis', ACM International Conference Proceeding Series.
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© 2018 ACM. Multi-player online esports games are designed for extended durations of play, requiring substantial experience to master. Furthermore, esports game revenues are increasingly driven by in-game purchases. For esports companies, the trends in players leaving their games therefore not only provide information about potential problems in the user experience, but also impacts revenue. Being able to predict when players are about to leave the game-churn prediction-is therefore an important solution for companies in the rapidly growing esports sector, as this allows them to take action to remedy churn problems. The objective of the work presented here is to understand the impact of specific behavioral characteristics on the likelihood of a player continuing to play the esports title League of Legends. Here, a solution to the problem is presented based on the application of survival analysis, using Mixed Effects Cox Regression, to predict player churn. Survival Analysis forms a useful approach for the churn prediction problem as it provides rates as well as an assessment of the characteristics of players who are at risk of leaving the game. Hazard rates are also presented for the leading indicators, with results showing that duration between matches played is a strong indicator of potential churn.

Demediuk, S, Tamassia, M, Raffe, WL, Zambetta, F, Mueller, FF & Li, X 2018, 'Measuring player skill using dynamic difficulty adjustment', ACM International Conference Proceeding Series.
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© 2018 ACM. Video games have a long history of use for educational and training purposes, as they provided increased motivation and learning for players. One of the limitations of using video games in this manner is, players still need to be tested outside of the game environment to test their learning outcomes. Traditionally, determining a player's skill level in a competitive game, requires players to compete directly with each other. Through the application of the Adaptive Training Framework, this work presents a novel method to determine the skill level of the player after each interaction with the video game. This is done by measuring the effort of a Dynamic Difficult Adjustment agent, without the need for direct competition between players. The experiments conducted in this research show that by measuring the players Heuristic Value Average, we can obtain the same ranking of players as state-of-the-art ranking systems, without the need for direct competition.

Derix, EC & Leong, TW 2018, 'Days of our lives', Proceedings of the 30th Australian Conference on Computer-Human Interaction - OzCHI '18, Australian Conference on Computer-Human Interaction, ACM Press, Melbourne, Australia.
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Dong, M, Yao, L, Wang, X, Benatallah, B, Sheng, QZ & Huang, H 2018, 'DUAL: A Deep Unified Attention Model with Latent Relation Representations for Fake News Detection', Web Information Systems Engineering – WISE 2018, Springer International Publishing, pp. 199-209.
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The prevalence of online social media has enabled news to spread wider and faster than traditional publication channels. The easiness of creating and spreading the news, however, has also facilitated the massive generation and dissemination of fake news. It, therefore, becomes especially important to detect fake news so as to minimize its adverse impact such as misleading people. Despite active efforts to address this issue, most existing works focus on mining news’ content or context information from individuals but neglect the use of clues from multiple resources. In this paper, we consider clues from both news’ content and side information and propose a hybrid attention model to leverage these clues. In particular, we use an attention-based bi-directional Gated Recurrent Units (GRU) to extract features from news content and a deep model to extract hidden representations of the side information. We combine the two hidden vectors resulted from the above extractions into an attention matrix and learn an attention distribution over the vectors. Finally, the distribution is used to facilitate better fake news detection. Our experimental results on two real-world benchmark datasets show our approach outperforms multiple baselines in the accuracy of detecting fake news.

Fang, K, Wang, X, Tomamichel, M & Berta, M 2018, 'Quantum Channel Simulation and the Channel's Smooth Max-Information', International Symposium on Information Theory, Vail, CO, USA.
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We study the general framework of quantum channel simulation, that is, the ability of a quantum channel to simulate another one using different classes of codes. First, we show that the minimum error of simulation and the one-shot quantum simulation cost under no-signalling assisted codes are given by semidefinite programs. Second, we introduce the channel's smooth max-information, which can be seen as a one-shot generalization of the mutual information of a quantum channel. We provide an exact operational interpretation of the channel's smooth max-information as the one-shot quantum simulation cost under no-signalling assisted codes. Third, we derive the asymptotic equipartition property of the channel's smooth max-information, i.e., it converges to the quantum mutual information of the channel in the independent and identically distributed asymptotic limit. This implies the quantum reverse Shannon theorem in the presence of no-signalling correlations. Finally, we explore the simulation cost of various quantum channels.

Fu, L, Li, J, Zhou, L, Ma, Z, Liu, S, Lin, Z & Prasad, M 2018, 'Utilizing Information from Task-Independent Aspects via GAN-Assisted Knowledge Transfer', Proceedings of the International Joint Conference on Neural Networks, International Joint Conference on Neural Networks, IEEE, Rio de Janeiro, Brazil.
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© 2018 IEEE. Observed data often have multiple labels with respect to different aspects. For example, a picture can have one label specifying the contents in terms of the object category such as aeroplane, building, cat, etc. And in the meanwhile have another label describing the image style such as photo-realistic or artistic. The central idea of this work is that any annotation of the data contains precious knowledge and is not to be foregone: An analytic task focusing on one aspect of the data can benefit from the knowledge transferred from the other aspects. We propose a passive knowledge transfer scheme for deep neural network training based on the generative adversarial nets (GANs). The adversarial training scheme encourages the nets to encode data into representations that are both discriminative for the target aspect and invariant with respect to the irrelevant aspects. We show that the scheme mixes the conditional distributions of the encoded data on the irrelevant aspects, by the theory on the link between the GAN framework and the Wasserstein metric in distribution spaces. Moreover, we empirically verified the method by i) classifying images despite influence by geometric transform and ii) recognizing the movements (geometric transform) regardless the image contents.

Gao, Q, Ma, S, Shao, S, Sui, Y, Zhao, G, Ma, L, Ma, X, Duan, F, Deng, X, Zhang, S & Chen, X 2018, 'CoBOT: Static C/C++ bug detection in the presence of incomplete code', Proceedings - International Conference on Software Engineering, pp. 385-388.
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© 2018 Authors. To obtain precise and sound results, most of existing static analyzers require whole program analysis with complete source code. However, in reality, the source code of an application always interacts with many third-party libraries, which are often not easily accessible to static analyzers. Worse still, more than 30% of legacy projects [1] cannot be compiled easily due to complicated configuration environments (e.g., third-party libraries, compiler options and macros), making ideal "whole-program analysis" unavailable in practice. This paper presents CoBOT [2], a static analysis tool that can detect bugs in the presence of incomplete code. It analyzes function APIs unavailable in application code by either using function summarization or automatically downloading and analyzing the corresponding library code as inferred from the application code and its configuration files. The experiments show that CoBOT is not only easy to use, but also effective in detecting bugs in real-world programs with incomplete code. Our demonstration video is at: https://youtu.be/bhjJp3e7LPM.

Garcia, JA 2018, 'Assessing the validity of in-game stepping performance data from a kinect-based fall prevention exergame', 2018 IEEE 6th International Conference on Serious Games and Applications for Health (SeGAH), 2018 IEEE 6th International Conference on Serious Games and Applications for Health (SeGAH), IEEE.
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Garcia, JA, Raffe, WL & Navarro, KF 2018, 'Assessing user engagement with a fall prevention game as an unsupervised exercise program for older people', ACM International Conference Proceeding Series.
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© 2018 ACM. Falling is, unfortunately, a leading cause of injury and death in the global elderly population. However, it has previously been shown that increased physical and cognitive activity can decrease the occurrence of falls in the elderly. This paper investigates the potential for a long-term, unsupervised fall prevention training tool in the form of the StepKinnection game, which was designed to exercise both reflex times and movement speed while also providing entertainment. Specifically, this game was used in a three month user study consisting of 10 participants over the age of 65. Adherence to the training program, enjoyment of the game, and ease of use of the game were investigated using a custom usability questionnaire, four established usability scales, heuristic evaluation of gameplay data, and semi-structured interviews. Results show that participants generally had positive attitudes towards the game, they felt that they would engage with this training program more than there current exercises, and that the game was easy to use without guidance or supervision beyond the initial set up support and instructions provided at the start of the experiment period.

Gardenfors, P, Williams, M-A, Johnston, B, Billingsley, R, Vitale, J, Peppas, P & Clark, J 2018, 'Event boards as tools for holistic AI', International Workshop on Artificial Intelligence and Cognition 2018, Palermo, Italy.
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Gehrke, L, Iversen, JR, Makeig, S & Gramann, K 2018, 'The invisible maze task (IMT): Interactive exploration of sparse virtual environments to investigate action-driven formation of spatial representations', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 293-310.
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© Springer Nature Switzerland AG 2018. The neuroscientific study of human navigation has been constrained by the prerequisite of traditional brain imaging studies that require participants to remain stationary. Such imaging approaches neglect a central component that characterizes navigation - the multisensory experience of self-movement. Navigation by active movement through space combines multisensory perception with internally generated self-motion cues. We investigated the spatial microgenesis during free ambulatory exploration of interactive sparse virtual environments using motion capture synchronized to high resolution electroencephalographic (EEG) data as well AS psychometric and self-report measures. In such environments, map-like allocentric representations must be constructed out of transient, egocentric first-person perspective 3-D spatial information. Considering individual differences of spatial learning ability, we studied if changes in exploration behavior coincide with spatial learning of an environment. To this end, we analyzed the quality of sketch maps (a description of spatial learning) that were produced after repeated learning trials for differently complex maze environments. We observed significant changes in active exploration behavior from the first to the last exploration of a maze: a decrease in time spent in the maze predicted an increase in subsequent sketch map quality. Furthermore, individual differences in spatial abilities as well as differences in the level of experienced immersion had an impact on the quality of spatial learning. Our results demonstrate converging evidence of observable behavioral changes associated with spatial learning in a framework that allows the study of cortical dynamics of navigation.

Ghafouri, M, Wang, X, Yuan, L, Zhang, Y & Lin, X 2018, 'Maintaining boolean Top-K spatial temporal results in publish-subscribe systems', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 147-160.
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© Springer International Publishing AG, part of Springer Nature 2018. Nowadays many devices and applications in social networks and location-based services are producing, storing and using description, location and occurrence time of objects. Given a massive number of boolean top-k spatial-temporal queries and the spatial-textual message streams, in this paper we study the problem of continuously updating top-k messages with the highest ranks, each of which contains all the requested keywords when rank of a message is calculated by its location and freshness. Decreasing the ranks of existing top-k results over time and producing new incoming messages, cause continuously computing and maintaining the best results. To the best of our knowledge, there is no prior work that can exactly solve this problem. We propose two indexing and matching methods, then conduct an experimental evaluation to show the impact of parameters and analyse the models.

Ghapanchi, AH & Talaei-Khoei, A 2018, 'Rethinking technology acceptance: Towards a theory of technology utilization', Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018.
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© 2018 Association for Information Systems. All rights reserved. The current paper aims at introducing a new approach in technology acceptance called "Technology Utilization". There is a body of research in failure of technology acceptance theories to support the empirical studies. The present research suggests technology utilization; that is last phase of acceptance where the main focus is on how the technology is being used. The paper reviews the literature on technology acceptance and illustrates the need for a new approach that highlights the use of technology as opposed to its uptake. The article demonstrates technology utilization concepts through an open source software project. The paper extends the literature for theoretical supports of technology acceptance by introducing the concept of technology utilization, for further development and investigation. The paper also informs practitioners that adoption of a technology is not only about its uptake but also relates to its use.

Gheisari, S, Catchpoole, DR, Charlton, A & Kennedy, PJ 2018, 'Patched completed local binary pattern is an effective method for neuroblastoma histological image classification', Communications in Computer and Information Science, Australasian Conference on Data Mining, Bathurst, NSW, Australia, pp. 57-71.
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© Springer Nature Singapore Pte Ltd. 2018. Neuroblastoma is the most common extra cranial solid tumour in children. The histology of neuroblastoma has high intra-class variation, which misleads existing computer-aided histological image classification methods that use global features. To tackle this problem, we propose a new Patched Completed Local Binary Pattern (PCLBP) method combining Sign Binary Pattern (SBP) and Magnitude Binary Pattern (MBP) within local patches to build feature vectors which are classified by k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) classifiers. The advantage of our method is extracting local features which are more robust to intra-class variation compared to global ones. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our experiments show the proposed method improves the weighted average F-measure by 1.89% and 0.81% with k-NN and SVM classifiers, respectively.

Gong, S, Wang, Z & Oberst, S 2018, 'Non-linear analysis of vibrating flip-flow screens', MATEC Web of Conferences, International Conference on Design and Manufacturing Engineering, EDP Sciences, Monash University, Melbourne, Australia, pp. 04007-04010.
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Vibrating flip-flow screens provide an effective solution for the screening of highly viscous or fine materials. Apart from other factors, the vibration characteristics of the main and floating screen frames are largely responsible for the flip-flow screen’s sifting performance and its processing capacity. In this paper, the vibration characteristics of a vibrating flip-flow screen with linear and nonlinear springs are compared. Analytical results highlight that increasing the relative amplitude and avoiding undesirable resonances of the main and the floating screen frames can be realised to improve the screen’s performance. The materials on the screen panel have less an effect on the vibration characteristics of the vibrating flip-flow screen with nonlinear springs than using linear springs. Other design parameters which influence the performance of vibrating flip-flow screens are discussed.

Gong, Y, Li, Z, Zhang, J, Liu, W, Zheng, Y & Kirsch, C 2018, 'Network-wide Crowd Flow Prediction of Sydney Trains via customized Online Non-negative Matrix Factorization', The 27th ACM International Conference on Information and Knowledge Management, Turin, Italy.
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Gui, M, Zhang, Z, Yang, Z, Gu, Y & Xu, G 2018, 'An Effective Joint Framework for Document Summarization', Companion of the The Web Conference 2018 on The Web Conference 2018.
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Hayati, H, Walker, P, Brown, T, Kennedy, P & Eager, D 2018, 'A simple spring-loaded inverted pendulum (SLIP) model of a bio-inspired quadrupedal robot over compliant terrains', ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE).
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Copyright © 2018 ASME. To study the impact of compliant terrains on the biomechanics of rapid legged movements, a well-known spring loaded inverted pendulum (SLIP) model is deployed. The model is a three-degrees-of-freedom system (3 DOF), inspired by galloping greyhounds competing in a racing condition. A single support phase of hind-leg stance in a galloping gait is taken into consideration due to its primary function in powering the greyhounds locomotion and higher rate of musculoskeletal injuries. To obtain and solve the nonlinear second-order differential equation of motions, the Lagrangian method and MATLABb R2017b (ode45 solver), which is based on the Runge-Kutta method, has been used, respectively. To get the viscoelastic behavior of compliant terrains, a Clegg hammer test was developed and performed five times on each sample. The effective spring and damping coefficients of each sample were then determined from the hysteresis curves. The results showed that galloping on the synthetic rubber requires more muscle force compared with wet sand. However, according to the Clegg hammer test, wet sand had a higher impact force than synthetic rubber which can be a risk factor for bone fracture, particularly hock fracture, in greyhounds. The results reported in this paper are not only useful for identifying optimum terrain properties and injury thresholds of an athletic track, but also can be used to design control methods and shock impedances for legged robots performing on compliant terrains

Henderson, H, Tomitsch, M & Leong, TW 2018, 'Tools to think with', Proceedings of the 30th Australian Conference on Computer-Human Interaction - OzCHI '18, Australian Conference on Computer-Human Interaction, ACM Press, Melbourne, Australia.
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Herron, D, Moncur, W, Marija, C, Grubisic, D, Vistica, O & van den Hoven, E 2018, 'Digital Possessions in the Museum of Broken Relationships', CHI EA '18 Extended Abstracts, Conference on Human Factors in Computing Systems, Montreal QC, Canada.
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Herse, S, Vitale, J, Ebrahimian, D, Tonkin, M, Ojha, S, Sidra, S, Johnston, B, Phillips, S, Gudi, SLKC, Clark, J, Judge, W & Williams, MA 2018, 'Bon Appetit! Robot Persuasion for Food Recommendation', ACM/IEEE International Conference on Human-Robot Interaction, pp. 125-126.
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© 2018 Authors. The integration of social robots within service industries requires social robots to be persuasive. We conducted a vignette experiment to investigate the persuasiveness of a human, robot, and an information kiosk when offering consumers a restaurant recommendation. We found that embodiment type significantly affects the persuasiveness of the agent, but only when using a specific recommendation sentence. These preliminary results suggest that human-like features of an agent may serve to boost persuasion in recommendation systems. However, the extent of the effect is determined by the nature of the given recommendation.

Herse, S, Vitale, J, Tonkin, M, Ebrahimian, D, Ojha, S, Johnston, B, Judge, W & Williams, MA 2018, 'Do You Trust Me, Blindly? Factors Influencing Trust Towards a Robot Recommender System', RO-MAN 2018 - 27th IEEE International Symposium on Robot and Human Interactive Communication, pp. 7-14.
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© 2018 IEEE. When robots and human users collaborate, trust is essential for user acceptance and engagement. In this paper, we investigated two factors thought to influence user trust towards a robot: preference elicitation (a combination of user involvement and explanation) and embodiment. We set our experiment in the application domain of a restaurant recommender system, assessing trust via user decision making and perceived source credibility. Previous research in this area uses simulated environments and recommender systems that present the user with the best choice from a pool of options. This experiment builds on past work in two ways: first, we strengthened the ecological validity of our experimental paradigm by incorporating perceived risk during decision making; and second, we used a system that recommends a nonoptimal choice to the user. While no effect of embodiment is found for trust, the inclusion of preference elicitation features significantly increases user trust towards the robot recommender system. These findings have implications for marketing and health promotion in relation to Human-Robot Interaction and call for further investigation into the development and maintenance of trust between robot and user.

Huang, C, Yao, L, Wang, X, Benatallah, B, Zhang, S & Dong, M 2018, 'Expert recommendation via tensor factorization with regularizing hierarchical topical relationships', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 373-387.
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© Springer Nature Switzerland AG 2018. Knowledge acquisition and exchange are generally crucial yet costly for both businesses and individuals, especially when the knowledge concerns various areas. Question Answering Communities offer an opportunity for sharing knowledge at a low cost, where communities users, many of whom are domain experts, can potentially provide high-quality solutions to a given problem. In this paper, we propose a framework for finding experts across multiple collaborative networks. We employ the recent techniques of tree-guided learning (via tensor decomposition), and matrix factorization to explore user expertise from past voted posts. Tensor decomposition enables to leverage the latent expertise of users, and the posts and related tags help identify the related areas. The final result is an expertise score for every user on every knowledge area. We experiment on Stack Exchange Networks, a set of question answering websites on different topics with a huge group of users and posts. Experiments show our proposed approach produces steady and premium outputs.

Hussain, F, Mirtalaie, MA, Hussain, OK & Chang, E 2017, 'Sentiment Analysis of Specific Product's Features Using Product Tree for Application in New Product Development', 9th International Conference on Intelligent Networking and Collaborative Systems, International Conference on Intelligent Networking and Collaborative Systems, Toronto, CANADA, pp. 82-95.
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New Product Development (NPD) is a multi-step process by which novel products are introduced in the market. Sentiment analysis, which ascertains the popularity of each new feature added to the product, is one of the key steps in this process. In this paper we present an approach by which product designers analyze users’ reviews from social media platforms to determine the popularity of a specific product’s feature in order to make a decision about adding it to the product’s next generation. Our proposed approach utilizes a product tree generated from a product specification document to facilitate forming an efficient link between features mentioned in the users’ reviews and those of the product designer’s interest. Furthermore, it captures the links/interactions between a feature of interest and its other related features in a product to ascertain its polarity.

Inibhunu, C & McGregor, C 2018, 'Fusing dimension reduction and classification for mining interesting frequent patterns in patients data', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 1-15.
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© Springer International Publishing AG, part of Springer Nature 2018. Vast amounts of data are collected about elderly patients diagnosed with chronic conditions and receiving care in telehealth services. The potential to discover hidden patterns in the collected data can be crucial in making effective decisions on dissemination of services and lead to improved quality of care for patients. In this research, we investigate a knowledge discovery method that applies a fusion of dimension reduction and classification algorithms to discover interesting patterns in patient data. The research premise is that discovery of such patterns could help explain unique features about patients who are likely or unlikely to have an adverse event. This is a unique and innovative technique that utilizes the best of probability, rules, random trees and association algorithms for; (a) feature selection, (b) predictive modelling and (c) frequent pattern mining. The proposed method has been applied in a case study context to discover interesting patterns and features in patients participating in telehealth services. The results of the models developed shows that identification of best feature set can lead to accurate predictors of adverse events as well as effective in generation of frequent patterns and discovery of interesting features in varying patient cohort.

Ivanyos, G & Qiao, Y 2018, 'Algorithms based on ∗-algebras, and their applications to isomorphism of polynomials with one secret, group isomorphism, and polynomial identity testing', Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 2357-2376.
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© Copyright 2018 by SIAM. We consider two basic algorithmic problems concerning tuples of (skew-)symmetric matrices. The first problem asks to decide, given two tuples of (skew-)symmetric matrices (B1; : : : ;Bm) and (C1; : : : ;Cm), whether there exists an invertible matrix A such that for every i 2 f1; : : : ;mg, AtBiA = Ci. We show that this problem can be solved in randomized polynomial time over finite fields of odd size, the reals, and the complex numbers. The second problem asks to decide, given a tuple of square matrices (B1; : : : ;Bm), whether there exist invertible matrices A and D, such that for every i 2 f1; : : : ;mg, ABiD is (skew-)symmetric. We show that this problem can be solved in deterministic polynomial time over fields of characteristic not 2. For both problems we exploit the structure of the underlying α-algebras (algebras with an involutive antiautomorphism), and utilize results and methods from the module isomorphism problem. Applications of our results range from multivariate cryptography, group isomorphism, to polynomial identity testing. Specifically, these results imply efficient algorithms for the following problems. (1) Test isomorphism of quadratic forms with one secret over a finite field of odd size. This problem belongs to a family of problems that serves as the security basis of certain authentication schemes proposed by Patarin (Eurocrypt 1996). (2) Test isomorphism of p-groups of class 2 and exponent p (p odd) with order p' in time polynomial in the group order, when the commutator subgroup is of order pO( p '). (3) Deterministically reveal two families of singularity witnesses caused by the skew-symmetric structure. This represents a natural next step for the polynomial identity testing problem, in the direction set up by the recent resolution of the non-commutative rank problem (Garg-Gurvits-Oliveira-Wigderson, FOCS 2016; Ivanyos-Qiao-Subrahmanyam, ITCS 2017).

Jay, B 2018, 'Recursive Programs in Normal Form (Short Paper)', Proceedings of the ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation, ACM, pp. 67-73.

Jhang, JY, Lee, CL, Lin, CJ, Lin, CT & Young, KY 2017, 'Using AdaBoost-based Multiple Functional Neural Fuzzy Classifiers Fusion for Classification Applications', MATEC Web of Conferences, International Conference on Inventions, Sun Moon Lake, Taiwan.
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© The Authors, published by EDP Sciences, 2018. In this study, two intelligent classifiers, the AdaBoost-based incremental functional neural fuzzy classifier (AIFNFC) and the AdaBoost-based fixed functional neural fuzzy classifier (AFFNFC), are proposed for solving the classification problems. The AIFNFC approach will increase the amount of functional neural fuzzy classifiers based on the corresponding error during the training phase; while the AFNFC approach is equipped with a fixed amount of functional neural fuzzy classifiers. Then, the weights of AdaBoost procedure are assigned for classifiers. The proposed methods are applied to different classification benchmarks. Results of this study demonstrate the effectiveness of the proposed AIFNFC and AFFNFC methods.

Jin, D, Liu, Z, He, D, Gabrys, B & Musial, K 2018, 'Robust detection of communities with multi-semantics in large attributed networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Knowledge Science, Engineering and Management, Changchun, China, pp. 362-376.
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© 2018, Springer Nature Switzerland AG. In this paper, we are interested in how to explore and utilize the relationship between network communities and semantic topics in order to find the strong explanatory communities robustly. First, the relationship between communities and topics displays different situations. For example, from the viewpoint of semantic mapping, their relationship can be one-to-one, one-to-many or many-to-one. But from the standpoint of underlying community structures, the relationship can be consistent, partially consistent or completely inconsistent. Second, it will be helpful to not only find communities more precise but also reveal the communities’ semantics that shows the relationship between communities and topics. To better describe this relationship, we introduce the transition probability which is an important concept in Markov chain into a well-designed nonnegative matrix factorization framework. This new transition probability matrix with a suitable prior which plays the role of depicting the relationship between communities and topics can perform well in this task. To illustrate the effectiveness of the proposed new approach, we conduct some experiments on both synthetic and real networks. The results show that our new method is superior to baselines in accuracy. We finally conduct a case study analysis to validate the new method’s strong interpretability to detected communities.

Kenning, G, Treadaway, C, Fennell, J & Prytherch, D 2018, 'Evaluation or ‘anecdote’? Understanding the impact of design', Proceedings of the 5th European International Conference on Design4Health, Sheffield, UK, 4th – 6th September 2018. Sheffield: Sheffield Hallam University., 5th International Conference on Design 4 Health, Sheffield Hallam University, Sheffield, England.
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Designers are increasingly engaging in participatory co-design approaches to understand the needs and wants of the people who will engage with the artefacts produced. These approaches are particularly relevant for design that focuses on social change, work undertaken with vulnerable groups who may not be articulate, and for designers working across disciplines, engaging with, for example, health related issues. While these projects may have clear goals in terms of outputs, often outcomes are not as clearly defined. So, how do we measure, evaluate and understand what has taken place, and reflect and report the findings? This paper reports on a UK-funded research project designing for people living with advanced dementia using mixed methodology research and evaluation. It explores issues around how designers self-evaluate and report on outputs and outcomes, and how robust evaluation can be undertaken throughout projects that use design research and thinking, or action research approaches. While, informal feedback in design projects can sometimes be dismissed as ‘anecdotal’, it suggests that unsolicited responses provide rich and valuable data to inform the overall evaluation and findings of a project. While designers may resist evaluation, feel that evaluation is imposed on them, or are not sure how to evaluate their projects, this paper suggests that by evaluating appropriately they can produce evidence to show the value and importance of their work.

Kerr, D, Talaei-Khoei, A & Ghapanchi, AH 2018, 'A paradigm shift for bring your own device (BYOD)', Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018, Americas Conference on Information Systems, New Orleans, LA.
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© 2018 Association for Information Systems. All rights reserved. The present paper aims to understand the effectiveness of the current adoption models in newly-built concept of Bring Your Own Device (BYOD) and also the role of IT departments in adopting BYOD in organizations. The paper extends the literature on adoption and combines them with the body of work on BYOD. The article compares these two concepts to build a theoretical framework in which BYOD can be examined. The paper presents the lessons learned from four different case studies in Australia, United Kingdom and Denmark. It was found that BYOD requires adaptation as opposed to adoption of technologies. Adaptation is a process in which an employee customises software in a way that allows them to do their task on their own device, at their own place and in their own time. The paper also reports that the adaptation process dictated by BYOD can lead to either innovation or errors and difficulties made by inconsistent use of technologies. The article informs IS researchers that there is a need to study the phenomena of technology adaption further. The research also rises awareness among practitioners on the significant changes that need to be implemented to run BYOD practices.

Khan, MA, Chennafi, M, Li, G & Wang, G 2018, 'Electroencephalogram-Based Comparative Study of Music Effect on Mental Stress Relief', 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), IEEE.
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Krishna Chand Gudi, SL, Ojha, S, Johnston, B, Clark, J & Williams, MA 2018, 'Fog robotics for efficient, fluent and robust human-robot interaction', NCA 2018 - 2018 IEEE 17th International Symposium on Network Computing and Applications.
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© 2018 IEEE. Active communication between robots and humans is essential for effective human-robot interaction. To accomplish this objective, Cloud Robotics (CR) was introduced to make robots enhance their capabilities. It enables robots to perform extensive computations in the cloud by sharing their outcomes. Outcomes include maps, images, processing power, data, activities, and other robot resources. But due to the colossal growth of data and traffic, CR suffers from serious latency issues. Therefore, it is unlikely to scale a large number of robots particularly in human-robot interaction scenarios, where responsiveness is paramount. Furthermore, other issues related to security such as privacy breaches and ransomware attacks can increase. To address these problems, in this paper, we have envisioned the next generation of social robotic architectures based on Fog Robotics (FR) that inherits the strengths of Fog Computing to augment the future social robotic systems. These new architectures can escalate the dexterity of robots by shoving the data closer to the robot. Additionally, they can ensure that human-robot interaction is more responsive by resolving the problems of CR. Moreover, experimental results are further discussed by considering a scenario of FR and latency as a primary factor comparing to CR models.

Lai, JCS, Oberst, S & Evans, TA 2018, 'Termites use vibrations to eavesdrop on predatory ants', Internoise 2018, Chicago, Illinois, USA..
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Lazar, A, Toombs, AL, Morrissey, K, Kenning, G, Boger, J & Brankaert, R 2018, 'CHI 2018 Engage with CHI', CHI 2018 Engage with CHI, Montreal, Canada.

Lazar, A, Toombs, AL, Morrissey, K, Kenning, G, Boger, J & Brankaert, R 2018, 'HCIxdementia workshop: Engaging people living with dementia', Conference on Human Factors in Computing Systems - Proceedings.
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© 2018 Copyright is held by the owner/author(s). Increasingly, HCI researchers are recognizing the challenges and opportunities of designing with and for people living with dementia. Recent critiques have highlighted the limited ways people with dementia are engaged in the research and design process. The second CHI HCIxDementia workshop will focus on engagement with and by people living with dementia. Through interactions with local community organizations and people living with dementia, workshop attendees will explore design possibilities. Building on open questions from the CHI 2017 workshop, this workshop will address how HCI researchers can support people living with dementia in engaging as leaders and with research, industry, and the community.

Li, M, Puthal, D, Yang, C, Luo, Y, Zhang, J & Li, J 2018, 'Stock market analysis using social networks', ACM International Conference Proceeding Series.
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© 2018 ACM. Nowadays, the use of social media has reached unprecedented levels. Among all social media, with its popular micro-blogging service, Twitter enables users to share short messages in real time about events or express their own opinions. In this paper, we examine the effectiveness of various machine learning techniques on retrieved tweet corpus. A machine learning model is deployed to predict tweet sentiment, as well as gain an insight into the correlation between twitter sentiment and stock prices. Specifically, that correlation is acquired by mining tweets using Twitter's search API and process it for further analysis. To determine tweet sentiment, two types of machine learning techniques are adopted including Naïve Bayes classification and Support vector machines. By evaluating each model, we discover that support vector machine gives higher accuracy through cross validation. After predicting tweet sentiment, we mine historical stock data using Yahoo finance API, while the designed feature matrix for stock market prediction includes positive, negative, neutral and total sentiment score and stock price for each day. In order to capturing the correlation situation between tweet opinions and stock market prices, hence, evaluating the direct correlation between tweet sentiments and stock market prices, the same machine learning algorithm is implemented for conducting our empirical study.

Li, M, Zhang, Y, Sun, Y, Wang, W, Tsang, IW & Lin, X 2018, 'An efficient exact nearest neighbor search by compounded embedding', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 37-54.
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© Springer International Publishing AG, part of Springer Nature 2018. Nearest neighbor search (NNS) in high dimensional space is a fundamental and essential operation in applications from many domains, such as machine learning, databases, multimedia and computer vision. In this paper, we first propose a novel and effective distance lower bound computation technique for Euclidean distance by using the combination of linear and non-linear embedding methods. As such, each point in a high dimensional space can be embedded into a low dimensional space such that the distance between two embedded points lower bounds their distance in the original space. Following the filter-and-verify paradigm, we develop an efficient exact NNS algorithm by pruning candidates using the new lower bounding technique and hence reducing the cost of expensive distance computation in high dimensional space. Our comprehensive experiments on 10 real-life and diverse datasets, including image, video, audio and text data, demonstrate that our new algorithm can significantly outperform the state-of-the-art exact NNS techniques.

Li, W, Qiao, M, Qin, L, Zhang, Y, Chang, L & Lin, X 2018, 'Exacting Eccentricity for Small-World Networks', 2018 IEEE 34th International Conference on Data Engineering (ICDE), International Conference on Data Engineering, IEEE, Paris, France, pp. 785-796.
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This paper studies the efficiency issue on computing the exact eccentricity-distribution of a small-world network. Eccentricity-distribution reflects the importance of each node in a graph, which is beneficial for graph analysis. Moreover, it is key to computing two fundamental graph characters: diameter and radius. Existing eccentricity computation algorithms, however, are either inefficient in handling large-scale networks emerging nowadays in practice or approximate algorithms that are inappropriate to small-world networks. We propose an efficient approach for exact eccentricity computation. Our approach is based on a plethora of insights on the bottleneck of the existing algorithms - one-node eccentricity computation and the upper/lower bounds update. Extensive experiments demonstrate that our approach outperforms the state-of-the-art up to three orders of magnitude on real large small-world networks.

Lin, A, Li, J, Zhang, L, Ma, Z & Luo, W 2018, 'Multiple-task learning and knowledge transfer using generative adversarial capsule nets', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 669-680.
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© Springer Nature Switzerland AG 2018. It is common that practical data has multiple attributes of interest. For example, a picture can be characterized in terms of its content, e.g. the categories of the objects in the picture, and in the meanwhile the image style such as photo-realistic or artistic is also relevant. This work is motivated by taking advantage of all available sources of information about the data, including those not directly related to the target of analytics. We propose an explicit and effective knowledge representation and transfer architecture for image analytics by employing Capsules for deep neural network training based on the generative adversarial nets (GAN). The adversarial scheme help discover capsule-representation of data with different semantic meanings in respective dimensions of the capsules. The data representation includes one subset of variables that are particularly specialized for the target task – by eliminating information about the irrelevant aspects. We theoretically show the elimination by mixing conditional distributions of the represented data. Empirical evaluations show the propose method is effective for both standard transfer-domain recognition tasks and zero-shot transfer.

Lin, A, Li, J, Zhang, L, Shi, L & Ma, Z 2018, 'A new family of generative adversarial nets using heterogeneous noise to model complex distributions', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Australasian Joint Conference on Artificial Intelligence, Springer, Wellington, New Zealand, pp. 706-717.
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© Springer Nature Switzerland AG 2018. Generative adversarial nets (GANs) are effective framework for constructing data models and enjoys desirable theoretical justification. On the other hand, realizing GANs for practical complex data distribution often requires careful configuration of the generator, discriminator, objective function and training method and can involve much non-trivial effort. We propose an novel family of generative adversarial nets (GANs), where we employ both continuous noise and random binary codes in the generating process. The binary codes in the new GAN model (named BGANs) play the role of categorical latent variables helps improve the model capability and training stability when dealing with complex data distributions. BGAN has been evaluated and compared with existing GANs trained with the state-of-the-art method on both synthetic and practical data. The empirical evaluation shows effectiveness of BGAN.

Lingayat, A, Singh, A, Naik, V, Badre, RR & Gupta, AK 2018, 'Horizon, a Web-Based User Interface for Managing Services in OpenStack: An Introspection', 2018 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2018.
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© 2018 IEEE. Cloud computing, an emerging technology in today's world is making a significant impact on sharing resources. An open-source platform OpenStack used for provisioning cloud environment is gaining tremendous popularity. OpenStack introduces independent projects developed for providing specific services in the cloud. These services interact with each other to act as one cloud environment. The services are managed using commands and require knowledge of their usage. To make the management of the services more convenient OpenStack has designed and developed a user interface based project called Horizon. It is a web-based interface which interacts with other services to make them configurable and manageable through a dashboard. In this paper, we will discuss the present scenario of the services interacting with Horizon.

Liu, C, Chen, L, Tsang, I & Yin, H 2018, 'Towards the Learning of Weighted Multi-label Associative Classifiers', Proceedings of the International Joint Conference on Neural Networks.
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© 2018 IEEE. Because of the ability to capture the correlation between features and labels, association rules have been applied to multi-label classification. However, existing multi-label associative classification algorithms usually exploit association rules using heuristic strategies. Moreover, only the covering association rules whose feature set is a subset of the testing instance are considered. Discarding any mined rules may diminish the performance of the classifier, especially when some rules only differ from the testing instance by a few insignificant features. In this paper we propose Weighted Multi-label Associative Classifiers (WMAC) that leverage an extended set of association rules with overlapping features with the testing instance to learn a universal weight vector for features. For this purpose, we embed the set of rules into a linear model and weigh the association rules by its confidence. Empirical results on diversified datasets clearly demonstrate that WMAC outperforms other well-established multi-label classification algorithms.

Liu, C, Talaei-Khoei, A & Zowghi, D 2018, 'Theoretical Support for Enhancing Data Quality: Application in Electronic Medical Records', 2018 Americas Conference on Information Systems, Americas Conference on Information Systems, New Orleans.
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This paper aims at reviewing the existing theoretical support to enhance data quality and utilizing the findings of the review in the context of electronic medical records (EMRs). For this to happen, we first conducted a survey of publications that have a focus on an empirical investigation of factors influencing data quality in the conceptual models. By using a well-established taxonomy development method from the discipline of information systems, we then proposed 3 dimensions for studying factors influencing data quality and constructing the conceptual model for enhancing data quality: breadth, depth, and interaction, within 9 characteristics under different dimensions. Last, we compared related studies using the proposed dimensions and utilized the findings of the review in enhancing EMRs quality to disclose the limitations and possibilities of new areas for further study.

Liu, C, Zowghi, D, Talaei-Khoei, A & Daniel, J 2018, 'Achieving Data Completeness in Electronic Medical Records: A Conceptual Model and Hypotheses Development', Proceedings of the 51st Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, -, Hilton Waikoloa Village, Hawaii, USA, pp. 2824-2833.
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This paper aims at proposing a conceptual model of achieving data completeness in electronic medical records (EMR). For this to happen, firstly, we draw on the model of factors influencing data quality management to construct our conceptual model. Secondly, we develop hypotheses of relationships between influencing factors for data completeness and mediators for achieving data completeness in EMR based on the literature. Our conceptual model extends the prior model for factors influencing data quality management by adding a new factor and exploring the relationships between the influencing factors within the context of data completeness in EMR. The proposed conceptual model and the presented hypotheses once empirically validated will be the basis for the development of tools and techniques for achieving data completeness in EMR.

Liu, F, Zhang, G & Lu, J 2018, 'Unconstrained fuzzy feature fusion for heterogeneous unsupervised domain adaptation', 2018 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), International Conference on Fuzzy Systems, IEEE, Rio de Janeiro, BRAZIL.
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Liu, J, Ma, J, Zhou, W, Xiang, Y & Huang, X 2018, 'Dissemination of authenticated tree-structured data with privacy protection and fine-grained control in outsourced databases', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 167-186.
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© 2018, Springer Nature Switzerland AG. The advent of cloud computing has inspired an increasing number of users outsourcing their data to remote servers to enjoy flexible and affordable data management services. However, storing data in a remote cloud server raises data privacy and security concerns, i.e., the integrity and origin of the query results. Although some solutions have been proposed to address these issues, none of them consider the arbitrary dissemination control of authenticated tree-structured data while disseminating to other users. To address the above concerns, in this paper, we first propose a novel and efficient redactable signature scheme which features editable homomorphic operation and redaction control on tree-structured data. Subsequently, we prove the security properties of our scheme and conduct extensive theoretical and experimental analyses. The experimental results show that our scheme outperforms the existing solutions in disseminating of authenticated tree-structured data with privacy protection and dissemination control in outsourced database (ODB) model.

Liu, Q, Huang, H, Zhang, G, Gao, Y, Xuan, J & Lu, J 2018, 'Semantic structure-based word embedding by incorporating concept convergence and word divergence', 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, pp. 5261-5268.
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Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Representing the semantics of words is a fundamental task in text processing. Several research studies have shown that text and knowledge bases (KBs) are complementary sources for word embedding learning. Most existing methods only consider relationships within word-pairs in the usage of KBs. We argue that the structural information of well-organized words within the KBs is able to convey more effective and stable knowledge in capturing semantics of words. In this paper, we propose a semantic structure-based word embedding method, and introduce concept convergence and word divergence to reveal semantic structures in the word embedding learning process. To assess the effectiveness of our method, we use WordNet for training and conduct extensive experiments on word similarity, word analogy, text classification and query expansion. The experimental results show that our method outperforms state-of-the-art methods, including the methods trained solely on the corpus, and others trained on the corpus and the KBs.

Liu, W & Chivukula, A 2018, 'Discovering Granger-causal Features from Deep Learning Networks', The 31st Australasian Joint Conference on Artificial Intelligence, Springer, Wellington, New Zealand, pp. 692-692.
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Liu, W, Chang, X, Chen, L & Yang, Y 2018, 'Semi-supervised Bayesian attribute learning for person re-identification'.
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Liu, W, Chang, X, Chen, L & Yang, Y 2018, 'Semi-supervised Joint Learning of Representation and Relation for Person Re-identification', AAAI Conference on Artificial Intelligence, Louisiana, USA.

Loke, L, Bown, O, Ferguson, S, Bray, L, Fraietta, A & Packham, K 2018, 'Your move sounds so predictable!', CHI PLAY 2018 - Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts, pp. 121-125.
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© 2018 Copyright is held by the owner/author(s). Your Move Sounds So Predictable is a semi-improvised two-player movement and sound game, based around a pair of bespoke motion-sensing sonic balls. Players pull a card and follow the instruction on where to place the ball in relation to their body. The sonic behavior of each ball has been programmed to exhibit a moderately complex and hard-to-predict set of responses to the user input that challenge the user’s expectation and the experience of autonomy and causality. The balls also communicate with each other, adding additional causal flows. Each player explores this relationship between movement and sound through play, whilst at the same time attending to the emergent sonic composition created by the group. Chaos or harmony will ensue.

LU, S, Oberst, S, Zhang, G & Luo, Z 2018, 'Comparing complex dynamics using machine learning-reconstructed attracting sets', Colloquium on Irregular Engineering Oscillations and Signal Processing, TUHH, Hamburg, Germany.

LU, S, Oberst, S, Zhang, G & Luo, Z 2018, 'Order patterns recurrence plots and new quantifications to unveil nonlinear dynamics from stochastic systems', International Conference on Time Series and Forecasting 2018, Granada, Spain.
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Mahdavi, F, Hossain, MI, Hayati, H, Eager, D & Kennedy, P 2018, 'Track Shape, Resulting Dynamics and Injury Rates of Greyhounds', Volume 13: Design, Reliability, Safety, and Risk, International Mechanical Engineering Congress and Exposition, ASME, Pittsburgh, Pennsylvania, USA.
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Mehar, AM, Gill, AQ & Matawie, K 2018, 'Analytical Model for Residential Predicting Energy Consumption', International Conference on Business Informatics, Vienna, Austria..

Melnikov, A, Quann, L, Alú, A, Oberst, S, Marburg, S & Powell, D 2018, 'Theory for Willis coupling prediction of acoustic meta-atoms', Symposium on Acoustic Metamaterials, Xatavia, Spain.

Ming, Y, Yu-Kai, W, Mukesh, P, Dongrui, W & Chin-Teng, L 2018, 'Sustained Attention Driving Task Analysis based on Recurrent Residual Neural Network using EEG Data', IEEE International Conference on Fuzzy Systems.
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Musial-Gabrys, K, Wahid-Ul-Ashraf, A & Budka, M 2017, 'Newton’s Gravitational Law for Link Prediction in Social Networks', Studies in Computational Intelligence, International Conference on Complex Networks and their Applications, Springer Verlag, Lyon, France, pp. 93-104.
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Link prediction is an important research area in network science due to a wide range of real-world application. There are a number of link prediction methods. In the area of social networks, these methods are mostly inspired by social theory, such as having more mutual friends between two people in a social network platform entails higher probability of those two people becoming friends in the future. In this paper we take our inspiration from a different area, which is Newton’s law of universal gravitation. Although this law deals with physical bodies, based on our intuition and empirical results we found that this could also work in networks, and especially in social networks. In order to apply this law, we had to endow nodes with the notion of mass and distance. While node importance could be considered as mass, the shortest path, path count, or inverse similarity (AdamicAdar, Katz score etc.) could be considered as distance. In our analysis, we have primarily used degree centrality to denote the mass of the nodes, while the lengths of shortest paths between them have been used as distances. In this study we compare the proposed link prediction approach to 7 other methods on 4 datasets from various domains. To this end, we use the ROC curves and the AUC measure to compare the methods. As the results show that our approach outperforms the other 7 methods on 2 out of the 4 datasets, we also discuss the potential reasons of the observed behaviour.

Nanda, P, Liu, M, Zhang, X, Yang, C, Yu, S & Li, J 2018, 'Asymmetric Commutative Encryption Scheme Based Efficient Solution to the Millionaires’ Problem', IEEE Computer Society, IEEE International Conference On Trust, Security And Privacy In Computing And Communications (IEEE TrustCom-18), IEEE, New York, NY, USA.
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Secure multiparty computation (SMC) is an important scheme in cryptography and can be applied in various real-life problems. The first SMC problem is the millionaires' problem which involves two-party secure computation. Because the efficiency of public key encryption scheme appears less than symmetric encryption scheme, most existing solutions based on public key cryptography to this problem is inefficient. Thus, a solution based on the symmetric encryption scheme has been proposed. Although it is claimed that this approach can be efficient and practical, we discover that there exist several severe security flaws in this solution. In this paper, we analyze the vulnerability of existing solutions, and propose a new scheme based on the Decisional Diffie-Hellman hypothesis (DDH). Our solution also uses two special encodings (0-encoding and 1-encoding) generated by our modified encoding method to reduce the computation cost of modular multiplications. Extensive experiments are conducted to evaluate the efficiency of our solution, and the experimental results show that our solution can be much more efficient and be approximately 8000 times faster than the solution based on symmetric encryption scheme for a 32-bit input and short-term security. Moreover, our solution is also more efficient than the state-of-the-art solution.

Nassir, SA & Leong, TW 2018, 'Conducting Qualitative Fieldwork with Ageing Saudis: A Visual Diary', DIS'18 Proceedings of the 2018 Designing Interactive Systems Conference, Designing Interactive Systems (DIS'18), ACM, Hong Kong.
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This pictorial offers a visual diary of our qualitative fieldwork to understand ageing people's experiences in Saudi Arabia. It provides insights gained through conducting qualitative fieldwork with ageing Saudis. We present a range of cultural considerations that shaped the design of the fieldwork and highlight opportunities, challenges, and issues that we faced when conducting interviews and deploying research probes. In particular, we highlight the power and effectiveness of using probes to elicit participants' values, views and desires when working within the sociocultural norms of Saudi Arabia.

Nejad, MZ, Lu, J & Behbood, V 2017, 'Applying dynamic Bayesian tree in property sales price estimation', 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, JiangSu, China, pp. 1-6.
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© 2017 IEEE. Accurate prediction of Residential Property Sale Price is very important and significant in the operation of the real estate market. Property sellers and buyers/Investors wish to know a fair value for their properties in particular at the time of the sales transaction. The main reason to build an Automated Valuation Model is to be accurate enough to replace human. To select a most suitable model for the property sale price prediction, this paper examined seven Tree-based machine learning models including Dynamic Bayesian Tree (online learning method), Random Forest, Stochastic Gradient Boosting, CART, Bagged CART, Tree Bagged Ensembles and Boosted Tree (batch learning methods) by comparing their RMSE and MAE performances. The performance of these models are tested on 1967 records of unit sales from 19 suburbs of Sydney, Australia. The main purpose of this study is to compare the performance of batch models with the online model. The results demonstrated that Dynamic Bayesian Tree as an online model stands in the middle of batch models based on the root mean square error (RMSE) and mean absolute error (MAE). It shows using online model for estimating the property sale price is reasonable for real world application.

Nguyen, Q, Lau, CW, Qu, Z, Simoff, SJ, Huang, M & Catchpoole, DR 2018, 'A Mobile Tool for Interactive Visualisation of Genomics Data', Proceedings of ITME 2018, International Conference on Information Technology in Medicine and Education, IEEE Computer Society CPS, Hangzhou, Zhejiang, China, pp. 688-697.
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Advancement in genomic research and technology has significantly improved our understandings of biology, health, and medicine. Genomics data are very complex and contain genotype and phenotype information. Health researchers have long known that many diseases such as cancer are hereditary. Gaining insight and understanding of such data would enable a better understanding of the correlation between genes and diseases, which could facilitate personalised treatment for the patients. Visualisations have been increasingly used to break the complexity of genomics data to guide better decisions. Unfortunately, research works on interactive visualisations of the genomics data on mobile devices and immersive platforms are still limited. This paper presents a new interactive visualisation and navigation of genomics data on the mobile platform. The visualisation provides an overview of the entire patient cohort in a 3D similarity-space environment as well as 2D detail views of genes of interests. We introduce a new algorithm that enables effective touch-based interaction and exploration of large number of items on small mobile screens. We illustrate the effectiveness of our platform through a childhood cancer dataset, B-cell Acute Lymphoblastic Leukaemia (ALL) as well as a pilot qualitative study with the domain experts.

Ning, X, Yao, L, Wang, X, Benatallah, B, Salim, F & Haghighi, PD 2018, 'Predicting Citywide Passenger Demand via Reinforcement Learning from Spatio-Temporal Dynamics', Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, ACM, pp. 19-28.
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Ning, X, Yao, L, Wang, X, Benatallah, B, Zhang, S & Zhang, X 2018, 'Data-Augmented Regression with Generative Convolutional Network', Web Information Systems Engineering – WISE 2018, Springer International Publishing, pp. 301-311.
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Generative adversarial networks (GAN)-based approaches have been extensively investigated whereas GAN-inspired regression (i.e., numeric prediction) has rarely been studied in image and video processing domains. The lack of sufficient labeled data in many real-world cases poses great challenges to regression methods, which generally require sufficient labeled samples for their training. In this regard, we propose a unified framework that combines a robust autoencoder and a generative convolutional neural network (GCNN)-based regression model to address the regression problem. Our model is able to generate high-quality artificial samples via augmenting the size of a small number of training samples for better training effects. Extensive experiments are conducted on two real-world datasets and the results show that our proposed model consistently outperforms a set of advanced techniques under various evaluation metrics.

Nobakht, M, Sui, Y, Seneviratne, A & Hu, W 2018, 'Permission Analysis of Health and Fitness Apps in IoT Programming Frameworks', Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018, pp. 533-538.
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© 2018 IEEE. Popular IoT programming frameworks, such as Google Fit, enable third-party developers to build apps to store and retrieve user data from a variety of data sources (e.g., wearables). The problem of overprivilege stands out due to the diversity and complexity of IoT apps, and developers rushing to release apps to meet the high demand in the IoT market. Any incorrect API usage of the IoT frameworks by third-party developers can lead to security risks, especially in health and fitness apps. Protecting sensitive user information is critically important to prevent financial and psychological harms. This paper presents PGFIT, a static permission analysis tool that precisely and efficiently identifies overprivilege issues in third-party apps built on top of a popular IoT programming framework, Google Fit. PGFIT extracts the set of requested permission scopes and the set of used data types in Google Fitenabled apps to determine whether the requested permission scopes are actually necessary. In this way, PGFIT serves as a quality assurance tool for developers and a privacy checker for app users. We used PGFIT to perform overprivilege analysis on a set of 20 Google Fit-enabled apps and with manual inspection, we found that 6 (30%) of them are overprivileged.

Oberst, S 2018, 'Nonlinear Dynamics: Towards a paradigm change via evidence-based complex dynamics modelling', Noise and Vibrations Emerging Methods, Ibiza, Spain.
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Oberst, S, Baetz, J, Campbell, G, Lampe, F, Lai, JCS, Hoffmann, N & Morlock, M 2018, 'Vibro-acoustic and nonlinear analysis of cadavric femoral bone impaction for cavity preparation in hip implants', MATEC Web of Conferences, EDP Sciences.
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Oberst, S, Baetz, J, Campbell, G, Lampe, F, Lai, JCS, Hoffmann, N & Morlock, MM 2017, 'Vibro-acoustic and nonlinear analysis of cadavric femoral bone impactionin cavity preparations', MATEC Web of Conferences, International Congress on Engineering Vibration, EDP Sciences, Sofia, Bulgaria, pp. 1-6.
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Owing to an ageing population, the impact of unhealthy lifestyle, or simply congenital or gender specific issues (dysplasia), degenerative bone and joint disease (osteoarthritis) at the hip pose an increasing problem in many countries. Osteoarthritis is painful and causes mobility restrictions; amelioration is often only achieved by replacing the complete hip joint in a total hip arthroplasty (THA). Despite significant orthopaedic progress related to THA, the success of the surgical process relies heavily on the judgement, experience, skills and techniques used of the surgeon. One common way of implanting the stem into the femur is press fitting uncemented stem designs into a prepared cavity. By using a range of compaction broaches, which are impacted into the femur, the cavity for the implant is formed. However, the surgeon decides whether to change the size of the broach, how hard and fast it is impacted or when to stop the excavation process, merely based on acoustic, haptic or visual cues which are subjective. It is known that non-ideal cavity preparations increase the risk of peri-prosthetic fractures especially in elderly people. This study reports on a simulated hip replacement surgery on a cadaver and the analysis of impaction forces and the microphone signals during compaction. The recorded transient signals of impaction forces and acoustic pressures (≈ 80 µs - 2 ms) are statistically analysed for their trend, which shows increasing heteroscedasticity in the force-pressure relationship between broach sizes. TIKHONOV regularisation, as inverse deconvolution technique, is applied to calculate the acoustic transfer functions from the acoustic responses and their mechanical impacts. The extracted spectra highlight that system characteristics altered during the cavity preparation process: in the high-frequency range the number of resonances increased with impacts and broach size. By applying nonlinear time series analysis the system dynamics increase in compl...

Oberst, S, Lai, JCS & Evans, TA 2018, 'Excitation signal extraction of ant walking pattern under the influence of noise using a biomechanical bipedal mathematical model', 2nd International Symposium on Biotremology, San Michele all’Adige, Italy.

Oberst, S, Lim, S, Romão, AC, Lai, JCS, Stender, M, Hoffmann, NP & Evans, TA 2018, 'A coupled mono-bipedal biomechanical surrogate model to mimic ants walking and running gait analysed using recurrence plot quantification analysis', Colloquium on Irregular Oscillations and Signal Processing,, Hamburg, Germany.

Ojha, S, Vitale, J, Raza, SA, Billingsley, R & Williams, M-A 2018, 'Implementing the Dynamic Role of Mood and Personality in Emotion Processing of Cognitive Agents', Annual Conference on Advances in Cognitive Systems.
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Ouyang, D, Qin, L, Chang, L, Lin, X, Zhang, Y & Zhu, Q 2018, 'When Hierarchy meets 2-hop-labeling: Effiicient shortest distance ?eries on road networks', Proceedings of the ACM SIGMOD International Conference on Management of Data, International Conference on Management of Data, AMC, Houston, TX, USA, pp. 709-724.
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© 2018 Association for Computing Machinery. Computing the shortest distance between two vertices is a fundamental problem in road networks. Since a direct search using the Dijkstra's algorithm results in a large search space, researchers resort to indexing-based approaches. State-of-the-art indexing-based solutions can be categorized into hierarchy-based solutions and hopbased solutions. However, the hierarchy-based solutions require a large search space for long-distance queries while the hop-based solutions result in a high computational waste for short-distance queries. To overcome the drawbacks of both solutions, in this paper, we propose a novel hierarchical 2-hop index (H2H-Index) which assigns a label for each vertex and at the same time preserves a hierarchy among all vertices. With the H2H-Index, we design an e?cient query processing algorithm with performance guarantees by visiting part of the labels for the source and destination based on the vertex hierarchy. We also propose an algorithm to construct the H2H-Index based on distance preserved graphs. The algorithm is further optimized by computing the labels based on the partially computed labels of other vertices. We conducted extensive performance studies using large real road networks including the whole USA road network. The experimental results demonstrate that our approach can achieve a speedup of an order of magnitude in query processing compared to the state-of-the-art while consuming comparable indexing time and index size.

Pan, J, Li, J, Han, X & Jia, K 2018, 'Residual MeshNet: Learning to deform meshes for single-view 3D reconstruction', Proceedings - 2018 International Conference on 3D Vision, 3DV 2018, International Conference on 3D Vision, IEEE, Verona, Italy, pp. 719-727.
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© 2018 IEEE. This work presents a novel architecture of deep neural networks to generate meshes approximating the surface of a 3D object from a single image. Compared to existing learning-based 3D reconstruction models, our architecture is characterized by (1) deep mesh deformation stacks with residual network design, where a simple mesh is transformed to approximate the target surface and undergoes multiple deformation steps to progressively refine the result and reduce the residuals, and (2) parallel paths per deformation step, which can exponentially enrich the generated meshes using deeper structure and more model parameters. We also propose novel regularization scheme that encourages the meshes to be both globally complementary to cover the target surface and locally consistent with each other. Empirical evaluation on benchmark datasets show advantage of the proposed architecture over existing methods.

Pang, G, Cao, L, Chen, L, Liu, H & Lian, D 2018, 'Sparse Modeling-based Sequential Ensemble Learning for Effective Outlier Detection in High-dimensional Numeric Data.', AAAI Conference on Artificial Intelligence.
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Pang, G, Chen, L, Cao, L & Liu, H 2018, 'Learning representations of ultrahigh-dimensional data for random distance-based outlier detection', Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2041-2050.
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© 2018 Association for Computing Machinery. Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods to address the curse of dimensionality. However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i.e., outliers). This paper introduces a ranking model-based framework, called RAMODO, to address this issue. RAMODO unifies representation learning and outlier detection to learn low-dimensional representations that are tailored for a state-of-the-art outlier detection approach - the random distance-based approach. This customized learning yields more optimal and stable representations for the targeted outlier detectors. Additionally, RAMODO can leverage little labeled data as prior knowledge to learn more expressive and application-relevant representations. We instantiate RAMODO to an efficient method called REPEN to demonstrate the performance of RAMODO. Extensive empirical results on eight real-world ultrahigh dimensional data sets show that REPEN (i) enables a random distance-based detector to obtain significantly better AUC performance and two orders of magnitude speedup; (ii) performs substantially better and more stably than four state-of-the-art representation learning methods; and (iii) leverages less than 1% labeled data to achieve up to 32% AUC improvement.

Peng, Y, Zhang, Y, Zhang, W, Lin, X & Qin, L 2018, 'Efficient probabilistic k-core computation on uncertain graphs', Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018, pp. 1204-1207.
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© 2018 IEEE. As uncertainty is inherent in a wide spectrum of graph applications such as social network and brain network, it is highly demanded to re-visit classical graph problems in the context of uncertain graphs. Driven by real-Applications, in this paper, we study the problem of k-core computation on uncertain graphs and propose a new model, namely (k,θ )-core, which consists of nodes with probability at least θ to be kcore member in the uncertain graph. We show the computation of (k,θ )-core is NP-hard, and hence resort to sampling based methods. Effective and efficient pruning techniques are proposed to significantly reduce the candidate size. To further reduce the cost of k-core computation on multiple sampled graphs, we design a k-core membership check algorithm following a novel expansion-based search paradigm. Extensive experiments on reallife graphs demonstrate the effectiveness and efficiency of our proposed techniques.

Peppas, P & Williams, M 2018, 'Parametrised Difference Revision', Principles of Knowledge Representation and Reasoning.

Prasad, M, Anh, N, Srikanth, N & Sundaram, S 2018, 'Wave Forecasting using Meta-cognitive Interval Type-2 Fuzzy Inference System', Procedia Computer Science, International Neural Network Society Conference on Big Data and Deep Learning, Elsevier, Bali, Indonesia, pp. 33-41.
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Renewable energy is fast becoming a mainstay in today’s energy scenario. One of the important sources of renewable energy is the wave energy, in addition to wind, solar, tidal, etc. Wave prediction/forecasting is consequently essential in coastal and ocean engineering studies. However, it is difficult to predict wave parameters in long term and even in the short term due to its intermittent nature. This study aims to propose a solution to handle the issue using Interval type-2 fuzzy inference system, or IT2FIS. IT2FIS has been shown to be capable of handling uncertainty associated with the data. The proposed IT2FIS is a fuzzy neural network realizing Takagi-Sugeno-Kang inference mechanism employing meta-cognitive learning algorithm. The algorithm monitors knowledge in a sample to decide an appropriate learning strategy. Performance of the system is evaluated by studying significant wave heights obtained from buoys located in Singapore. The results compared with existing state-of-the art fuzzy inference system approaches clearly indicate the advantage of IT2FIS based wave prediction.

Prasad, M, Anh, N, Srikanth, N & Sundaram, S 2018, 'Wind Speed Intervals Prediction using Meta-cognitive Approach', Procedia Computer Science, International Neural Network Society Conference on Big Data and Deep Learning, Elsevier, Bali, Indonesia, pp. 23-32.
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In this paper, an interval type-2 neural fuzzy inference system and its meta-cognitive learning algorithm for wind speed prediction is proposed. Interval type-2 neuro-fuzzy system is capable of handling uncertainty associated with the data and meta-cognition employs self-regulation mechanism for learning. The proposed system realizes Takagi-Sugeno-Kang inference mechanism and adopts a fast data-driven interval-reduction method. Meta-cognitive learning enables the network structure to evolve automatically based on the knowledge in data. The parameters are updated based on an extended Kalman filter algorithm. In addition, the proposed network is able to construct prediction intervals to quantify uncertainty associated with forecasts. For performance evaluation, a real-world wind speed prediction problem is utilized. Using historical data, the model provides short-term prediction intervals of wind speed. The performance of proposed algorithm is compared with existing state-of-the art fuzzy inference system approaches and the results clearly indicate its advantages in forecasting problems.

Prasad, M, Chang, LC, Gupta, D, Pratama, M, Sundaram, S & Lin, CT 2018, 'Online video streaming for human tracking based on weighted resampling particle filter', Procedia Computer Science, pp. 2-12.
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© 2018 The Authors. Published by Elsevier Ltd. This paper proposes a weighted resampling method for particle filter which is applied for human tracking on active camera. The proposed system consists of three major parts which are human detection, human tracking, and camera control. The codebook matching algorithm is used for extracting human region in human detection system, and the particle filter algorithm estimates the position of the human in every input image. The proposed system in this paper selects the particles with highly weighted value in resampling, because it provides higher accurate tracking features. Moreover, a proportional-integral-derivative controller (PID controller) controls the active camera by minimizing difference between center of image and the position of object obtained from particle filter. The proposed system also converts the position difference into pan-tilt speed to drive the active camera and keep the human in the field of view (FOV) camera. The intensity of image changes overtime while tracking human therefore the proposed system uses the Gaussian mixture model (GMM) to update the human feature model. As regards, the temporal occlusion problem is solved by feature similarity and the resampling particles. Also, the particle filter estimates the position of human in every input frames, thus the active camera drives smoothly. The robustness of the accurate tracking of the proposed system can be seen in the experimental results.

Prasad, M, Liu, CL, Li, DL, Jha, C & Lin, CT 2018, 'Multi-view Vehicle Detection based on Part Model with Active Learning', Proceedings of the International Joint Conference on Neural Networks.
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© 2018 IEEE. Nowadays, most ofthe vehicle detection methods aim to detect only single-view vehicles, and the performance is easily affected by partial occlusion. Therefore, a novel multi-view vehicle detection system is proposed to solve the problem of partial occlusion. The proposed system is divided into two steps: Background filtering and part model. Background filtering step is used to filter out trees, sky and other road background objects. In the part model step, each of the part models is trained by samples collected by using the proposed active learning algorithm. This paper validates the performance of the background filtering method and the part model algorithm in multi-view car detection. The performance of the proposed method outperforms previously proposed methods.

Prasad, M, Za’in, C, Pratama, M, Lughofer, E, Ferdaus, M & Cai, Q 2018, 'Big Data Analytics based on PANFIS MapReduce', Procedia Computer Science, 3rd International Neural Network Society Conference on Big Data and Deep Learning, Elsevier, Bali, Indonesia, pp. 140-152.
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In this paper, a big data analytic framework is introduced for processing high-frequency data stream. This framework architecture is developed by combining an advanced evolving learning algorithm namely Parsimonious Network Fuzzy Inference System (PANFIS) with MapReduce parallel computation, where PANFIS has the capability of processing data stream in large volume. Big datasets are learnt chunk by chunk by processors in MapReduce environment and the results are fused by rule merging method, that reduces the complexity of the rules. The performance measurement has been conducted, and the results are showing that the MapReduce framework along with PANFIS evolving system helps to reduce the processing time around 22 percent in average in comparison with the PANFIS algorithm without reducing performance in accuracy.

Prasad, M, Zheng, DR, Mery, D, Puthal, D, Sundaram, S & Lin, CT 2018, 'A fast and self-adaptive on-line learning detection system', Procedia Computer Science, pp. 13-22.
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© 2018 The Authors. Published by Elsevier Ltd. This paper proposes a method to allow users to select target species for detection, generate an initial detection model by selecting a small piece of image sample and as the movie plays, continue training this detection model automatically. This method has noticeable detection results for several types of objects. The framework of this study is divided into two parts: the initial detection model and the online learning section. The detection model initialization phase use a sample size based on the proportion of users of the Haar-like features to generate a pool of features, which is used to train and select effective classifiers. Then, as the movie plays, the detecting model detects the new sample using the NN Classifier with positive and negative samples and the similarity model calculates new samples based on the fusion background model to calculate a new sample and detect the relative similarity to the target. From this relative similarity-based conservative classification of new samples, the conserved positive and negative samples classified by the video player are used for automatic online learning and training to continuously update the classifier. In this paper, the results of the test for different types of objects show the ability to detect the target by choosing a small number of samples and performing automatic online learning, effectively reducing the manpower needed to collect a large number of image samples and a large amount of time for training. The Experimental results also reveal good detection capability.

Prysyazhnyuk, A, Mcgregor, C, Bersenev, E & Slonov, AV 2018, 'Investigation of adaptation mechanisms during five-day dry immersion utilizing big-data analytics', 2018 IEEE Life Sciences Conference, LSC 2018, pp. 247-250.
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© 2018 IEEE. Emerging technology continues to redefine the concept of health and human capacity to adapt to various extreme environments on Earth, as well as in space, while preserving performance and alleviating adverse effects on the human body. Technological advancements enable effective modeling of extreme environmental conditions in terrestrial facilities, demonstrating great potential for scientific discovery, modernization of available countermeasure systems and development of comprehensive software tools for clinical decision support. To date, a vast amount of knowledge has been accumulated on physiological deconditioning in response to spaceflight environment. The underlying conditions are often closely associated with maladaptation, supported by changes in heart rate variability parameters. However, existing methods do not support real-time data acquisition, processing and analytics, thereby limiting the usability of physiological data to inform clinical decision making and timely introduction of countermeasure systems. The proposed extension of Artemis, big data analytics platform and modernization of the wellness algorithm, demonstrate great potential to address limitations of existing methods, while significantly improve the provision of medical care in space or in terrestrial environments for individuals working and/or living under conditions of chronic stress. Current study demonstrates application of the proposed big-data analytics framework in a 5-day dry immersion experiment.

Raffe, WL & Garcia, JA 2018, 'Combining skeletal tracking and virtual reality for game-based fall prevention training for the elderly', 2018 IEEE 6th International Conference on Serious Games and Applications for Health, SeGAH 2018, pp. 1-7.
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© 2018 IEEE. This paper provides a preliminary appraisal of combining commercial skeletal tracking and virtual reality technologies for the purposes of innovative gameplay interfaces in fall prevention exergames for the elderly. This work uses the previously published StepKinnection game, which used skeletal tracking with a flat screen monitor, as a primary point of comparison for the proposed combination of these interaction modalities. Here, a Microsoft Kinect is used to track the player's skeleton and represent it as an avatar in the virtual environment while the HTC Vive is used for head tracking and virtual reality visualization. Multiple avatar positioning modes are trialled and discussed via a small self-reflective study (with the authors as participants) to examine their ability to allow accurate stepping motions, maintain physical comfort, and encourage self-identification or empathy with the avatar. While this is just an initial study, it highlights promising opportunities for designing engaging step training games with this integrated interface but also highlights its limitations, especially in the context of an unsupervised exercise program of older people in independent living situations.

Rajora, S, Li, D-L, Jha, C, Bharill, N, Patel, OP, Joshi, S, Puthal, D & Prasad, M 2018, 'A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance', 2018 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI), 8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI), IEEE, Bengaluru, INDIA, pp. 1958-1963.
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Razzak, I, Saris, RA & Xu, G 2018, 'Robust 2D Joint Sparse Principle Component Analysis with F-norm Minimization for Sparse Modelling: 2D-RJSPCA', the 2018 International Joint Conference on Neural Networks, Rio De Janeiro.
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Roberts, AGK, Catchpoole, DR & Kennedy, PJ 2018, 'Variance-based Feature Selection for Classification of Cancer Subtypes Using Gene Expression Data', Proceedings of the International Joint Conference on Neural Networks, International Joint Conference on Neural Networks, IEEE, Rio de Janeiro, Brazil.
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© 2018 IEEE. Classification in cancer has traditionally relied on feature selection by differential expression as a first step, where genes are selected according to the strength of evidence for a consistent difference in expression level between classes. However, recent work has shown that many genes also differ in the variance of their gene expression between disease states, and in particular between cancers of different types, prognosis, or stages of development. Features selected based on increased variance in cancer or differences in variance between tumours of differing prognosis have been used to successfully predict tumour progression or prognosis within the same cancer type, and to classify cancer subtypes in cases where there is an overall increase in variance in one class over the other. Here, we apply feature selection by differential variance to the more general problem of classification of cancer subtypes. We show that classifiers using features selected by differential variance are able to distinguish between clinically relevant cancer subtypes, that these classifiers perform as well as classifiers based on features selected by differential expression, and that combining the two approaches often gives better classification results than either feature selection method alone.

Saeed, Z, Razzak, I, Abbasi, R & Xu, G 2018, 'Text Stream to Temporal Network - A Dynamic Heartbeat Graph to Detect Emerging Events on Twitter', The 22nd Pacific-Asia Conference on Knowledge Discovery and Data, Australia.
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Saqib, M, Daud Khan, S, Sharma, N & Blumenstein, M 2018, 'Extracting descriptive motion information from crowd scenes', International Conference Image and Vision Computing New Zealand, pp. 1-6.
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© 2017 IEEE. An important contribution that automated analysis tools can generate for management of pedestrians and crowd safety is the detection of conflicting large pedestrian flows: this kind of movement pattern, in fact, may lead to dangerous situations and potential threats to pedestrian's safety. For this reason, detecting dominant motion patterns and summarizing motion information from the scene are inevitable for crowd management. In this paper, we develop a framework that extracts motion information from the scene by generating point trajectories using particle advection approach. The trajectories obtained are then clustered by using unsupervised hierarchical clustering algorithm, where the similarity is measured by the Longest Common Sub-sequence (LCS) metric. The achieved motions patterns in the scene are summarized and represented by using color-coded arrows, where speeds of the different flows are encoded with colors, the width of an arrow represents the density (number of people belonging to a particular motion pattern) while the arrowhead represents the direction. This novel representation of crowded scene provides a clutter free visualization which helps the crowd managers in understanding the scene. Experimental results show that our method outperforms state-of-the-art methods.

Saqib, M, Khan, SD, Sharma, N & Blumenstein, M 2018, 'Person Head Detection in Multiple Scales Using Deep Convolutional Neural Networks', Proceedings of the International Joint Conference on Neural Networks.
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© 2018 IEEE. Person detection is an important problem in computer vision with many real-world applications. The detection of a person is still a challenging task due to variations in pose, occlusions and lighting conditions. The purpose of this study is to detect human heads in natural scenes acquired from a publicly available dataset of Hollywood movies. In this work, we have used state-of-the-art object detectors based on deep convolutional neural networks. These object detectors include region-based convolutional neural networks using region proposals for detections. Also, object detectors that detect objects in the single-shot by looking at the image only once for detections. We have used transfer learning for fine-tuning the network already trained on a massive amount of data. During the fine-tuning process, the models having high mean Average Precision (mAP) are used for evaluation of the test dataset. Experimental results show that Faster R-CNN [18] and SSD MultiBox [13] with VGG16 [21] perform better than YOLO [17] and also demonstrate significant improvements against several baseline approaches.

Schmidt, S, Ehrenbrink, P, Weiss, B, Voigt-Antons, J-N, Kojic, T, Johnston, A & Moller, S 2018, 'Impact of Virtual Environments on Motivation and Engagement During Exergames', 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX), 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX), IEEE.
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Schraefel, MC, Andres, J & Van Den Hoven, E 2018, 'The body as starting point: Exploring inside and around body boundaries for body-centric computing design', Conference on Human Factors in Computing Systems - Proceedings.
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© 2018 Copyright is held by the owner/author(s). More HCI designs and devices are embracing what is being dubbed “body centric computing,” where designs both deliberately engage the body as the locus of interest, whether to move the body into play or relaxation, or to track and monitor its performance, or to use it as a surface for interaction. Most HCI researchers are engaging in these designs, however, with little direct knowledge of how the body itself works either as a set of complex internal systems or as sets of internal and external systems that interact dynamically. The science of how our body interacts with the microbiome around us also increasingly demonstrates that our presumed boundaries between what is inside and outside us may be misleading if not considered harmful. Developing both (1) introductory knowledge and (2) design practice of how these in-bodied and circum-bodied systems work with our understanding of the em-bodied self, and how this gnosis/praxis may lead to innovative new body-centric computing designs is the topic of this workshop.

Shakeri Hossein Abad, Z, Rahman, M, Cheema, A, Gervasi, V, Zowghi, D & Barker, K 2018, 'Dynamic Visual Analytics for Elicitation Meetings with ELICA', IEEE, 2018 IEEE 26th International Requirements Engineering Conference (RE), IEEE Computer Society, Banff, Canada, pp. 492 - 493-492 - 493.
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Requirements elicitation can be very challenging in projects that require deep domain knowledge about the system at hand. As analysts have the full control over the elicitation process, their lack of knowledge about the system under study inhibits them from asking related questions and reduces the accuracy of requirements provided by stakeholders. We present ELICA, a generic interactive visual analytics tool to assist analysts during requirements elicitation process. ELICA uses a novel information extraction algorithm based on a combination of Weighted Finite State Transducers (WFSTs)(generative model) and SVMs (discriminative model). ELICA presents the extracted relevant information in an interactive GUI (including zooming, panning, and pinching) that allows analysts to explore which parts of the ongoing conversation (or specification document) match with the extracted information. In this demonstration, we show that ELICA is usable and effective in practice, and is able to extract the related information in real-time. We also demonstrate how carefully designed features in ELICA facilitate the interactive and dynamic process of information extraction.

Shakeri, Z, Gervasi, V, Zowghi, D & Barker, K 2018, 'ELICA: An Automated Tool for Dynamic Extraction of Requirements Relevant Information', International Workshop on Artificial Intelligence for Requirements Engineering, IEEE, Banff, Canada.
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Requirements elicitation requires extensive knowledge and deep understanding of the problem domain where the final system will be situated. However, in many software development projects, analysts are required to elicit the requirements from an unfamiliar domain, which often causes communication barriers between analysts and stakeholders. In this paper, we propose a requirements ELICitation Aid tool (ELICA) to help analysts better understand the target application domain by dynamic extraction and labeling of requirements-relevant knowledge. To extract the relevant terms, we leverage the flexibility and power of Weighted Finite State Transducers (WFSTs) in dynamic modeling of natural language processing tasks. In addition to the information conveyed through text, ELICA captures and processes non-linguistic information about the intention of speakers such as their confidence level, analytical tone, and emotions. The extracted information is made available to the analysts as a set of labeled snippets with highlighted relevant terms which can also be exported as an artifact of the Requirements Engineering (RE) process. The application and usefulness of ELICA are demonstrated through a case study. This study shows how pre-existing relevant information about the application domain and the information captured during an elicitation meeting, such as the conversation and stakeholders' intentions, can be captured and used to support analysts achieving their tasks.

Shakeri, Z, Noaeen, M, Zowghi, D, Far, B & Barker, K 2018, 'Two Sides of the Same Coin: Software Developers' Perceptions of Task Switching and Task Interruption', ACM, Evaluation and Assessment in Software Engineering, ACM, Christchurch, New Zealand, pp. 175-180.
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In the constantly evolving world of software development, switching back and forth between tasks has become the norm. While task switching often allows developers to perform tasks effectively and may increase creativity via the flexible pathway, there are also consequences to frequent task-switching. For high-momentum tasks like software development, "flow", the highly productive state of concentration, is paramount. Each switch distracts the developers' flow, requiring them to switch mental state and an additional immersion period to get back into the flow. However, the wasted time due to time fragmentation caused by task switching is largely invisible and unnoticed by developers and managers. We conducted a survey with 141 software developers to investigate their perceptions of differences between task switching and task interruption and to explore whether they perceive task switchings as disruptive as interruptions. We found that practitioners perceive considerable similarities between the disruptiveness of task switching (either planned or unplanned) and random interruptions. The high level of cognitive cost and low performance are the main consequences of task switching articulated by our respondents. Our findings broaden the understanding of flow change among software practitioners in terms of the characteristics and categories of disruptive switches as well as the consequences of interruptions caused by daily meetings.

Sharma, N, Mandal, R, Sharma, R, Pal, U & Blumenstein, M 2018, 'Signature and logo detection using deep CNN for document image retrieval', Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR, pp. 416-422.
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© 2018 IEEE. Signature and logo as a query are important for content-based document image retrieval from a scanned document repository. This paper deals with signature and logo detection from a repository of scanned documents, which can be used for document retrieval using signature or logo information. A large intra-category variance among signature and logo samples poses challenges to traditional hand-crafted feature extraction-based approaches. Hence, the potential of deep learning-based object detectors namely, Faster R-CNN and YOLOv2 were examined for automatic detection of signatures and logos from scanned administrative documents. Four different network models namely ZF, VGG16, VGG-M, and YOLOv2 were considered for analysis and identifying their potential in document image retrieval. The experiments were conducted on the publicly available 'Tobacco-800' dataset. The proposed approach detects Signatures and Logos simultaneously. The results obtained from the experiments are promising and at par with the existing methods.

Sharma, N, Scully-Power, P & Blumenstein, M 2018, 'Shark detection from aerial imagery using region-based CNN, a study', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 224-236.
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© Springer Nature Switzerland AG 2018. Shark attacks have been a very sensitive issue for Australians and many other countries. Thus, providing safety and security around beaches is very fundamental in the current climate. Safety for both human beings and underwater creatures (sharks, whales, etc.) in general is essential while people continue to visit and use the beaches heavily for recreation and sports. Hence, an efficient, automated and real-time monitoring approach on beaches for detecting various objects (e.g. human activities, large fish, sharks, whales, surfers, etc.) is necessary to avoid unexpected casualties and accidents. The use of technologies such as drones and machine learning techniques are promising directions in such challenging circumstances. This paper investigates the potential of Region-based Convolutional Neural Networks (R-CNN) for detecting various marine objects, and Sharks in particular. Three network architectures namely Zeiler and Fergus (ZF), Visual Geometry Group (VGG16), and VGG_M were considered for analysis and identifying their potential. A dataset consisting of 3957 video frames were used for experiments. VGG16 architecture with faster-R-CNN performed better than others, with an average precision of 0.904 for detecting Sharks.

Sharma, N, Sengupta, A, Sharma, R, Pal, U & Blumenstein, M 2018, 'Pincode detection using deep CNN for postal automation', International Conference Image and Vision Computing New Zealand, pp. 1-6.
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© 2017 IEEE. Postal automation has been a topic of research over a decade. The challenges and complexity involved in developing a postal automation system for a multi-lingual and multi-script country like India are many-fold. The characteristics of Indian postal documents include: multi-lingual behaviour, unconstrained handwritten addresses, structured/unstructured envelopes and postcards, being among the most challenging aspects. This paper examines the state-of-the-art Deep CNN architectures for detecting pin-code in both structured and unstructured postal envelopes and documents. Region-based Convolutional Neural Networks (RCNN) are used for detecting the various significant regions, namely Pin-code blocks/regions, destination address block, seal and stamp in a postal document. Three network architectures, namely Zeiler and Fergus (ZF), Visual Geometry Group (VGG16), and VGG M were considered for analysis and identifying their potential. A dataset consisting of 2300 multilingual Indian postal documents of three different categories was developed and used for experiments. The VGG-M architecture with Faster-RCNN performed better than others and promising results were obtained.

Song, Y, Zhang, G, Lu, H & Lu, J 2018, 'A Self-adaptive Fuzzy Network for Prediction in Non-stationary Environments', 2018 IEEE International Conference on Fuzzy Systems, International Conference on Fuzzy Systems, Rio de Janeiro, Brazil, pp. 1-8.
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Spoletini, P, Ferrari, A, Bano, M, Zowghi, D & Gnesi, S 2018, 'Interview Review: an Empirical Study on Detecting Ambiguities in Requirements Elicitation Interviews', Requirements Engineering: Foundation for Software Quality, International Working Conference on Requirements Engineering: Foundation for Software Quality, Springer, Ultrecht, Netherlands, pp. 101-118.
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[Context and Motivation] Ambiguities identified during requirements elicitation interviews can be used by the requirements analyst as triggers for additional questions and, consequently, for disclosing further – possibly tacit – knowledge. Therefore, every unidentified ambiguity may be a missed opportunity to collect additional information. [Question/problem] Ambiguities are not always easy to recognize, especially during highly interactive activities such as requirements elicitation interviews. Moreover, since different persons can perceive ambiguous situations differently, the unique perspective of the analyst in the interview might not be enough to identify all ambiguities. [Principal idea/results] To maximize the number of ambiguities recognized in interviews, this paper proposes a protocol to conduct reviews of requirements elicitation interviews. In the proposed protocol, the interviews are audio recorded and the recordings are inspected by both the analyst who performed the interview and another reviewer. The idea is to use the identified cases of ambiguity to create questions for the follow-up interviews. Our empirical evaluation of this protocol involves 42 students from Kennesaw State University and University of Technology Sydney. The study shows that, during the review, the analyst and the other reviewer identify 68% of the total number of ambiguities discovered, while 32% were identified during the interviews. Furthermore, the ambiguities identified by analysts and other reviewers during the review significantly differ from each other. [Contribution] Our results indicate that interview reviews allow the identification of a considerable number of undetected ambiguities, and can potentially be highly beneficial to discover unexpressed information in future interviews.

Stender, M, Oberst, S & Hoffmann, NP 2018, 'Reconstruction of differential equations from time-series data for feature engineering and model identification', Colloquium on Irregular Oscillations and Signal Processing, Hamburg, Germany.
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Talaei-Khoei, A, Daniel, J & Dokhanchi, M 2018, 'Students' learning experience in flipped classes using social media', Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018, Americas Conference on Information Systems, New Orleans, USA.
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© 2018 Association for Information Systems. All rights reserved. The current work aims at deploying Facebook as a social media site to improve students' learning experience with the flipped learning materials through implementation of a socially enabled peer and self-learning environment. Provided the body of literature on poor students' experience in flipped classes, the paper implements an experiment comparing the online quizzes and Facebook to improve students' experience with the online materials in flipped classes. The study looks at the students' perceptions. The paper provides recommendations to the instructors on how to use Facebook to improve students' experience with the flipped materials. This study also motivates teaching practitioners in Information Systems to improve flipped learning by using social networking sites in their courses.

Talaei-Khoei, A, Kerr, D & Motiwalla, LF 2018, 'Lessons learned from using gamification for teaching business informatics to first year undergraduate students', Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018, Americas Conference on Information Systems, New Orleans, USA.
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© 2018 Association for Information Systems. All rights reserved. The paper aims at utilizing the learner-centered framework in game-based learning. While there has been a body of research on the application of games in teaching, the question, "how can games be integrated into learning theories to improve students' experience?" has largely been ignored. Therefore, there exist conflicting findings on the effectiveness of games for students' learning in the literature. The present article has reported the use of the Beer Game to teach basic concepts of business informatics to undergraduate students and provide insights on lessons learned for the successful deployment of game-based learning in classrooms. The paper describes an experiment from four perspectives of the learner-centered framework; namely cognition, motivation, social skills and assessment. The study found that although game-based learning is relatively motivational, to provide a sufficient level of cognition to learning outcomes, there should be a direct and descriptive link between the theoretical perspectives and the application of the game. This can be achieved by involving games in the assessment tasks. In addition, games that can be played in collaborative and/or competitive environments can also enhance student understanding of the learning objectives of the assignment.

Talaei-Khoei, A, Motiwalla, LF & Kazemi, SF 2018, 'A Predictive Method to Determine Incomplete Electronic Medical Records', SIGMIS-CPR'18: PROCEEDINGS OF THE 2018 ACM SIGMIS CONFERENCE ON COMPUTERS AND PEOPLE RESEARCH, Annual ACM SIGMIS Conference on Computers and People Research (ACM SIGMIS-CPR), ASSOC COMPUTING MACHINERY, NY, pp. 99-106.
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Tiwary, M, Sharma, S, Mishra, P, El-Sayed, H, Prasad, M & Puthal, D 2018, 'Building Scalable Mobile Edge Computing by Enhancing Quality of Services', PROCEEDINGS OF THE 2018 13TH INTERNATIONAL CONFERENCE ON INNOVATIONS IN INFORMATION TECHNOLOGY (IIT), International Conference on Innovations in Information Technology, IEEE, Al Ain, United Arab Emirates, pp. 141-146.
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Tonkin, M, Vitale, J, Herse, S, Williams, M-A, Judge, W & Wang, X 2018, 'Design Methodology for the UX of HRI: A Field Study of a Commercial Social Robot at an Airport', HRI '18: PROCEEDINGS OF THE 2018 ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION, 13th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI), ASSOC COMPUTING MACHINERY, Chicago, IL, pp. 407-415.
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Ul Fazal, MA, Ferguson, S, Karim, MS & Johnston, A 2018, 'Concurrent voice-based multiple information communication: A study report of profile-based users' interaction', 145th Audio Engineering Society International Convention, AES 2018.
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© 2018 KASHYAP. This paper reports a study conducted with 10 blind and 8 sighted participants using a prototype system for communicating multiple information streams concurrently, using two methods of presentation. The prototype system in method one played two continuous voice-based articles diotically, differing by voice gender and content. In the second method, prototype communicated one continuous article in the female voice and three headlines as interval-based short interruptions in a male voice dichotically. In this investigation the continuous method remained more effective in communicating multiple information compared to the interval-based interruption method, and also the users who possessed at least tertiary qualification performed better in comprehending the multiple concurrent information than the non-tertiary qualified users.

Verma, S, Liu, W, Wang, C & Zhu, L 2018, 'Hybrid networks: Improving deep learning networks via integrating two views of images', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 46-58.
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© 2018, Springer Nature Switzerland AG. The principal component analysis network (PCANet) is an unsupervised parsimonious deep network, utilizing principal components as filters in the layers. It creates an amalgamated view of the data by transforming it into column vectors which destroys its spatial structure while obtaining the principal components. In this research, we first propose a tensor-factorization based method referred as the Tensor Factorization Networks (TFNet). The TFNet retains the spatial structure of the data by preserving its individual modes. This presentation provides a minutiae view of the data while extracting matrix factors. However, the above methods are restricted to extract a single representation and thus incurs information loss. To alleviate this information loss with the above methods we propose Hybrid Network (HybridNet) to simultaneously learn filters from both the views of the data. Comprehensive results on multiple benchmark datasets validate the superiority of integrating both the views of the data in our proposed HybridNet.

Vishwa, A & Hussain, FK 2018, 'A Blockchain based approach for multimedia privacy protection and provenance', 2018 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI), 8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI), IEEE, Bengaluru, INDIA, pp. 1941-1945.

Vitale, J, Tonkin, M, Herse, S, Ojha, S, Clark, J, Williams, M, Wang, X & Judge, W 2018, 'Be More Transparent and Users Will Like You: A Robot Privacy and User Experience Design Experiment', Proceedings of 2018 ACM/IEEE International Conference on Human- Robot Interaction, International Conference on Human-Robot Interaction, ACM, Chicago, IL, USA, pp. 379-387.
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Vo, NNY, Liu, S, He, X & Xu, G 2018, 'Multimodal Mixture Density Boosting Network for Personality Mining', Advances in Knowledge Discovery and Data Mining (LNCS), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Melbourne, Australia, pp. 644-655.
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Knowing people’s personalities is useful in various real-world applications, such as personnel selection. Traditionally, we have to rely on qualitative methodologies, e.g. surveys or psychology tests to determine a person’s traits. However, recent advances in machine learning have it possible to automate this process by inferring personalities from textual data. Despite of its success, text-based method ignores the facial expression and the way people speak, which can also carry important information about human characteristics. In this work, a personality mining framework is proposed to exploit all the information from videos, including visual, auditory, and textual perspectives. Using a state-of-art cascade network built on advanced gradient boosting algorithms, the result produced by our proposed methodology can achieve lower the prediction errors than most current machine learning algorithms. Our multimodal mixture density boosting network especially perform well with small sample size datasets, which is useful for learning problems in psychology fields where big data is often not available.

Vo, NNY, Xu, G, Liu, S, Brownlow, EJ, Culbert, B & Chu, C 2018, 'Client Churn Prediction with Call Log Analysis', Database Systems for Advanced Applications, International Conference on Database Systems for Advanced Applications, Springer, Gold Coast, Australia, pp. 752-763.
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Wahid-Ul-Ashraf, A, Budka, M & Musial, K 2018, 'Netsim - The framework for complex network generator', Procedia Computer Science, pp. 547-556.
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© 2018 The Author(s). Networks are everywhere and their many types, including social networks, the Internet, food webs etc., have been studied for the last few decadeS. However, in real-world networks, it's hard to find examples that can be easily comparable, i.e. have the same density or even number of nodes and edgeS. We propose a flexible and extensible Netsim framework to understand how properties in different types of networks change with varying number of edges and verticeS. Our approach enables to simulate three classical network models (random, small-world and scale-free) with easily adjustable model parameters and network size. To be able to compare different networks, for a single experimental setup we kept the number of edges and vertices fixed across the modelS. To understand how they change depending on the number of nodes and edges we ran over 30,000 simulations and analysed different network characteristics that cannot be derived analytically. Two of the main findings from the analysis are that the average shortest path does not change with the density of the scale-free network but changes for small-world and random networks; the apparent difference in mean betweenness centrality of the scale-free network compared with random and small-world networkS.

Wan, Y, Zhao, Z, Yang, M, Xu, G, Ying, H, Wu, J & Yu, PS 2018, 'Improving Automatic Source Code Summarization via Deep Reinforcement Learning', The 33rd IEEE/ACM International Conference on Automated Software Engineering, Corum, Montpellier, France.
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Wang, B, Yan, Z, Lu, J & Zhang, G 2018, 'Explore Uncertainty in Residual Networks forCrowds Flow Prediction', International Joint Conference on Neural Networks, Rio de Janeiro, Brazil.
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Wang, B, Yan, Z, Lu, J, Zhang, G & Li, T 2018, 'Deep multi-task learning for air quality prediction', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 93-103.
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© 2018, Springer Nature Switzerland AG. Predicting the concentration of air pollution particles has been an important task of urban computing. Accurately measuring and estimating makes the citizen and governments can behave with suitable decisions. In order to predict the concentration of several air pollutants at multiple monitoring stations throughout the city region, we proposed a novel deep multi-task learning framework based on residual Gated Recurrent Unit (GRU). The experimental results on the real world data from London region substantiate that the proposed deep model has manifest superiority than shallow models and outperforms 9 baselines.

Wang, H, Chen, J, Wang, X, Liu, X & Na, Z 2018, 'Privacy protection for location sharing services in social networks', Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, pp. 97-102.
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© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018. Recently, there is an increase interest in location sharing services in social networks. Behind the convenience brought by location sharing, there comes an indispensable security risk of privacy. Though many efforts have been made to protect user’s privacy for location sharing, they are not suitable for social network. Most importantly, little research so far can support user relationship privacy and identity privacy. Thus, we propose a new privacy protection protocol for location sharing in social networks. Different from previous work, the proposed protocol can provide perfect privacy for location sharing services. Simulation results validate the feasibility and efficiency of the proposed protocol.

Wang, J, Chen, L, Qin, L & Wu, X 2018, 'ASTM: An Attentional Segmentation Based Topic Model for Short Texts', Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 577-586.
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© 2018 IEEE. To address the data sparsity problem in short text understanding, various alternative topic models leveraging word embeddings as background knowledge have been developed recently. However, existing models combine auxiliary information and topic modeling in a straightforward way without considering human reading habits. In contrast, extensive studies have proven that it is full of potential in textual analysis by taking into account human attention. Therefore, we propose a novel model, Attentional Segmentation based Topic Model (ASTM), to integrate both word embeddings as supplementary information and an attention mechanism that segments short text documents into fragments of adjacent words receiving similar attention. Each segment is assigned to a topic and each document can have multiple topics. We evaluate the performance of our model on three real-world short text datasets. The experimental results demonstrate that our model outperforms the state-of-the-art in terms of both topic coherence and text classification.

Wang, S, Hu, L, Cao, L, Huang, X & Liu, W 2018, 'Attention-based transactional context embedding for next-item recommendation', Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 32nd AAAI Conference on Artificial Intelligence 2018, New Orleans, United States, pp. 2532-2539.
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To recommend the next item to a user in a transactional context is practical yet challenging in applications such as marketing campaigns. Transactional context refers to the items that are observable in a transaction. Most existing transaction-based recommender systems (TBRSs) make recommendations by mainly considering recently occurring items instead of all the ones observed in the current context. Moreover, they often assume a rigid order between items within a transaction, which is not always practical. More importantly, a long transaction often contains many items irreverent to the next choice, which tends to overwhelm the influence of a few truely relevant ones. Therefore, we posit that a good TBRS should not only consider all the observed items in the current transaction but also weight them with different relevance to build an attentive context that outputs the proper next item with a high probability. To this end, we design an effective attention-based transaction embedding model (ATEM) for context embedding to weight each observed item in a transaction without assuming order. The empirical study on real-world transaction datasets proves that ATEM significantly outperforms the state-of-the-art methods in terms of both accuracy and novelty.

Wang, S, Zhang, Y, Lin, X & Cheema, MA 2018, 'Maximize spatial influence of facility bundle considering reverse k nearest neighbors', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 684-700.
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© Springer International Publishing AG, part of Springer Nature 2018. Consider a two dimensional Euclidean space, let F be a set of points representing facilities and U be a set of points representing users. Spatial influence of a facility is the number of users who have this facility as one of their k nearest facilities. This is because that users normally prefer to go to their nearby facilities and are naturally influenced the most by their nearest facilities. Given a facility bundle of size t, spatial influence of the bundle is the number of distinct users influenced by any one of them. Existing works on facility selection problem find out top-t facilities with the highest spatial influence. However, the literature lacks study on this problem when the t facilities have the highest spatial influence as a bundle. We are the first to study the problem of Maximizing Bundled Reverse k Nearest Neighbors (MB-RkNN), where the spatial influence of a facility bundle of size t is maximized. We prove its NP-hardness, and propose a branch-and-bound best first search algorithm that greedily select the currently best facility until we get t facilities. We introduce the concept of kNN region such that a group of users have their kNN facilities all belong to the same kNN region. This sharing property of kNN region allows us to avoid redundant calculation with dynamic programming technique. We conduct experiments on real data sets and show that our algorithm is orders of magnitudes better than our baseline algorithm both in terms of CPU time and IO cost.

Wang, X, Fang, K & Tomamichel, M 2018, 'On Finite Blocklength Converse Bounds for Classical Communication over Quantum Channels', IEEE International Symposium on Information Theory (ISIT), Vail, CO, USA, pp. 2157-2161.
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Wu, Q, Li, X, Do, Q, Fan, J, Ge, R & Wang, J 2018, 'CTF-PSF: Coupled Tensor Factorization with Partially Shared Factors', Proceedings of the International Joint Conference on Neural Networks.
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© 2018 IEEE. Coupled matrix-tensor factorization has been suc- cessfully applied in various fields in the processing of coupled data. However, the unshared components between coupled data tend to make the joint decomposition inaccurate. In order to solve this problem, in this work, we propose a method to improve the traditional method by combining individual decomposition and coupled decomposition to analyze the shared and unshared components. Numerical experiments are given to illustrate the advantages of the proposed method compared to the existing approaches.

Wu, W, Li, B, Chen, L & Zhang, C 2018, 'Efficient attributed network embedding via recursive randomized hashing', IJCAI International Joint Conference on Artificial Intelligence, pp. 2861-2867.
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© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Attributed network embedding aims to learn a low-dimensional representation for each node of a network, considering both attributes and structure information of the node. However, the learning based methods usually involve substantial cost in time, which makes them impractical without the help of a powerful workhorse. In this paper, we propose a simple yet effective algorithm, named NetHash, to solve this problem only with moderate computing capacity. NetHash employs the randomized hashing technique to encode shallow trees, each of which is rooted at a node of the network. The main idea is to efficiently encode both attributes and structure information of each node by recursively sketching the corresponding rooted tree from bottom (i.e., the predefined highest-order neighboring nodes) to top (i.e., the root node), and particularly, to preserve as much information closer to the root node as possible. Our extensive experimental results show that the proposed algorithm, which does not need learning, runs significantly faster than the state-of-the-art learning-based network embedding methods while achieving competitive or even better performance in accuracy.

Wunderlich, A & Gramann, K 2018, 'Electrocortical evidence for long-term incidental spatial learning through modified navigation instructions', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 261-278.
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© Springer Nature Switzerland AG 2018. The use of Navigation Assistance Systems for spatial orienting has become increasingly popular. Such automated navigation support, however, comes with a reduced processing of the surrounding environment and often with a decline of spatial orienting ability. To prevent such deskilling and to support spatial learning, the present study investigated incidental spatial learning by comparing standard navigation instructions with two modified navigation instruction conditions. The first modified instruction condition highlighted landmarks and provided additional redundant information regarding the landmark (contrast condition), while the second highlighted landmarks and included information of personal interest to the participant (personal-reference condition). Participants’ spatial knowledge of the previously unknown virtual city was tested three weeks later. Behavioral and electroencephalographic (EEG) data demonstrated enhanced spatial memory performance for participants in the modified navigation instruction conditions without further differentiating between modified instructions. Recognition performance of landmarks was better and the late positive complex of the event-related potential (ERP) revealed amplitude differences reflecting an increased amount of recollected information for modified navigation instructions. The results indicate a significant long-term spatial learning effect when landmarks are highlighted during navigation instructions.

Xu, G, Wu, H & Shi, H 2018, 'Workshop Proposal on Knowledge Discovery from Digital Libraries', Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, Joint Conference on Digital Libraries, ACM, Texas, USA, pp. 429-430.
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© 2018 Authors. The workshop is with the ACM/IEEE Joint Conference on Digital Libraries in 2018 (JCDL 2018) which will be held in Fort Worth, Texas, USA on June 3 - 7, 2018. The Joint Conference on Digital Libraries (JCDL) is a major international forum focusing on digital libraries and associated technical, practical, and social issues.

Xue, S, Lu, J, Zhang, G & Xiong, L 2018, 'A Framework of Transferring Structures Across Large-scale Information Networks', Proceedings of the International Joint Conference on Neural Networks.
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© 2018 IEEE. The existing domain-specific methods for mining information networks in machine learning aims to represent the nodes of an information network into a vector format. However, the real-world large-scale information network cannot make well network representations by one network. When the information of the network structure transferred from one network to another network, the performance of network representation might decrease sharply. To achieve these ends, we propose a novel framework to transfer useful information across relational large-scale information networks (FTLSIN). The framework consists of a 2-layer random walks to measure the relations between two networks and predict links across them. Experiments on real-world datasets demonstrate the effectiveness of the proposed model.

Yan, H, Sui, Y, Chen, S & Xue, J 2018, 'Spatio-temporal context reduction: a pointer-analysis-based static approach for detecting use-after-free vulnerabilities', 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE), IEEE, pp. 327-337.
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Yan, Z, Lu, J & Zhang, G 2018, 'Distributed model predictive control of linear systems with coupled constraints based on collective neurodynamic optimization', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 318-328.
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© Springer Nature Switzerland AG 2018. Distributed model predictive control explores an array of local predictive controllers that synthesize the control of subsystems independently yet they communicate to efficiently cooperate in achieving the closed-loop control performance. Distributed model predictive control problems naturally result in sequential distributed optimization problems that require real-time solution. This paper presents a collective neurodynamic approach to design and implement the distributed model predictive control of linear systems in the presence of globally coupled constraints. For each subsystem, a neurodynamic model minimizes its cost function using local information only. According to the communication topology of the network, neurodynamic models share information to their neighbours to reach consensus on the optimal control actions to be carried out. The collective neurodynamic models are proven to guarantee the global optimality of the model predictive control system.

Yang, H, Pan, S, Zhang, P, Chen, L, Lian, D & Zhang, C 2018, 'Binarized attributed network embedding', Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 1476-1481.
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© 2018 IEEE. Attributed network embedding enables joint representation learning of node links and attributes. Existing attributed network embedding models are designed in continuous Euclidean spaces which often introduce data redundancy and impose challenges to storage and computation costs. To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation. Specifically, we define a new Weisfeiler-Lehman proximity matrix to capture data dependence between node links and attributes by aggregating the information of node attributes and links from neighboring nodes to a given target node in a layer-wise manner. Based on the Weisfeiler-Lehman proximity matrix, we formulate a new Weisfiler-Lehman matrix factorization learning function under the binary node representation constraint. The learning problem is a mixed integer optimization and an efficient cyclic coordinate descent (CCD) algorithm is used as the solution. Node classification and link prediction experiments on real-world datasets show that the proposed BANE model outperforms the state-of-the-art network embedding methods.

Yin, J, Zhou, Z, Liu, S, Wu, Z & Xu, G 2018, 'Social Spammer Detection: A Multi-Relational Embedding Approach', The 22nd Pacific-Asia Conference on Knowledge Discovery and Data, Australia.
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Ying, H, Zhuang, F, Zhang, F, Liu, Y, Xu, G, Xie, X, Xiong, H & Wu, J 2018, 'Sequential Recommender System based on Hierarchical Attention Networks', the 27th International Joint Conference on Artificial Intelligence.
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Yu, H, Lu, J & Zhang, G 2018, 'An incremental dual nu-support vector regression algorithm', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Melbourne, VIC, Australia, pp. 522-533.
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© 2018, Springer International Publishing AG, part of Springer Nature. Support vector regression (SVR) has been a hot research topic for several years as it is an effective regression learning algorithm. Early studies on SVR mostly focus on solving large-scale problems. Nowadays, an increasing number of researchers are focusing on incremental SVR algorithms. However, these incremental SVR algorithms cannot handle uncertain data, which are very common in real life because the data in the training example must be precise. Therefore, to handle the incremental regression problem with uncertain data, an incremental dual nu-support vector regression algorithm (dual-v-SVR) is proposed. In the algorithm, a dual-v-SVR formulation is designed to handle the uncertain data at first, then we design two special adjustments to enable the dual-v-SVR model to learn incrementally: incremental adjustment and decremental adjustment. Finally, the experiment results demonstrate that the incremental dual-v-SVR algorithm is an efficient incremental algorithm which is not only capable of solving the incremental regression problem with uncertain data, it is also faster than batch or other incremental SVR algorithms.

Yu, H, Lu, J, Zhang, G & Wu, D 2018, 'A Dual Neural Network Based On Confidence Intervals For Fuzzy Random Regression Problems', 2018 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Rio de Janeiro, BRAZIL.
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Yue, L, Wen, D, Cui, L, Qin, L & Zheng, Y 2018, 'K-connected cores computation in large dual networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 169-186.
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© Springer International Publishing AG, part of Springer Nature 2018. Computing k-cores is a fundamental and important graph problem, which can be applied in many areas, such as community detection, network visualization, and network topology analysis. Due to the complex relationship between different entities, dual graph widely exists in the applications. A dual graph contains a physical graph and a conceptual graph, both of which have the same vertex set. Given that there exist no previous studies on the k-core in dual graphs, we formulate a k-connected core (k-CCO) model in dual graphs. A k-CCO is a k-core in the conceptual graph, and also connected in the physical graph. Given a dual graph and an integer k, we propose a polynomial time algorithm for computing all k-CCOs. We also propose three algorithms for computing all maximum-connected cores (MCCO), which are the existing k-CCOs such that a (k +1)-CCO does not exist. We conduct extensive experiments on six real-world datasets and several synthetic datasets. The experimental results demonstrate the effectiveness and efficiency of our proposed algorithms.

Zhang, F, Yuan, L, Zhang, Y, Qin, L, Lin, X & Zhou, A 2018, 'Discovering strong communities with user engagement and tie strength', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 425-441.
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© Springer International Publishing AG, part of Springer Nature 2018. In this paper, we propose and study a novel cohesive subgraph model, named (k,s)-core, which requires each user to have at least k familiars or friends (not just acquaintances) in the subgraph. The model considers both user engagement and tie strength to discover strong communities. We compare the (k,s)-core model with k-core and k-truss theoretically and experimentally. We propose efficient algorithms to compute the (k,s)-core and decompose the graph by a particular sub-model k-fami. Extensive experiments show (1) our (k,s)-core and k-fami are effective cohesive subgraph models and (2) the (k,s)-core computation and k-fami decomposition are efficient on various real-life social networks.

Zhang, L, Li, J, Huang, T, Ma, Z, Lin, Z & Prasad, M 2018, 'GAN2C: Information Completion GAN with Dual Consistency Constraints', Proceedings of the International Joint Conference on Neural Networks.
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© 2018 IEEE. This paper proposes an information completion technique, GAN2C, by imposing dual consistency constraints (2C) to a closed loop encoder-decoder architecture based on the generative adversarial nets (GAN). When adopting deep neural networks as function approximators, GAN2C enables highly effective multi-modality image conversion with sparse observation in the target modes. For empirical demonstration and model evaluation, we show that trained deep neural networks in GAN2C can infer colors for grayscale images, as well as estimate rich 3D information of a scene by densely predicting the depths. The results of the experiments show that in both tasks GAN2C as a generic framework has been comparable to or advanced the state-of-the-art performance which are achieved by highly specialized systems. Code is available at https://github.com/AdalinZhang/GAN2C.

Zhang, Q, Lu, J, Wu, D & Zhang, G 2018, 'Cross-domain recommendation with consistent knowledge transfer by subspace alignment', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 67-82.
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© Springer Nature Switzerland AG 2018. Recommender systems have drawn great attention from both academic area and practical websites. One challenging and common problem in many recommendation methods is data sparsity, due to the limited number of observed user interaction with the products/services. Cross-domain recommender systems are developed to tackle this problem through transferring knowledge from a source domain with relatively abundant data to the target domain with scarce data. Existing cross-domain recommendation methods assume that similar user groups have similar tastes on similar item groups but ignore the divergence between the source and target domains, resulting in decrease in accuracy. In this paper, we propose a cross-domain recommendation method transferring consistent group-level knowledge through aligning the source subspace with the target one. Through subspace alignment, the discrepancy caused by the domain-shift is reduced and the knowledge shared local top-n recommendation via refined item-user bi-clustering two domains is ensured to be consistent. Experiments are conducted on five real-world datasets in three categories: movies, books and music. The results for nine cross-domain recommendation tasks show that our proposed method has improved the accuracy compared with five benchmarks.

Zhang, Q, Wu, D, Lu, J & Zhang, G 2018, 'Cross-domain recommendation with probabilistic knowledge transfer', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 208-219.
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© Springer Nature Switzerland AG 2018. Recommender systems have drawn great attention from both academic and practical area. One challenging and common problem in many recommendation methods is data sparsity, due to the limited number of observed user interaction with the products/services. To alleviate the data sparsity problem, cross-domain recommendation methods are developed to share group-level knowledge in several domains so that recommendation in the domain with scarce data can benefit from domains with relatively abundant data. However, divergence exists in the data of similar domains so that the extracted group-level knowledge is not always suitable to be applied in the target domain, thus recommendation accuracy in the target domain is impaired. In this paper, we propose a cross-domain recommendation method with probabilistic knowledge transfer. The proposed method maintain two sets of group-level knowledge, profiling both domain-shared and domain-specific characteristics of the data. In this way users’ mixed preferences can be profiled comprehensively thus improves the performance of the cross-domain recommender systems. Experiments are conducted on five real-world datasets in three categories: movies, books and music. The results for nine cross-domain recommendation tasks show that our proposed method has improved the accuracy compared with five benchmarks.

Zhang, Y, Hu, C, Lu, X & Li, J 2018, 'A novel illumination normalization method in face recognition based on logarithmic total variation', Proceedings of SPIE - The International Society for Optical Engineering, International Conference on Digital Image Processing, SPIE, Shanghai, China.
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© 2018 SPIE. Varying illumination is a tricky issue in face recognition. In this paper, we make improvement on the logarithmic total variation (LTV) algorithm to handle the varying illumination in face image. First of all, logarithmic total variation (LTV) is adopt to separate the face image into high-frequency and low-frequency features. Then, a novel illumination normalization method is proposed to handle low-frequency feature, which is founded on the advanced contrast limited adaptive histogram equalization (CLAHE). Furthermore, threshold-value filtering is utilized to realize enhancement on high-frequency feature. Finally, the normalized face image can take shape through the normalized high-frequency feature and enhanced low-frequency feature. We make comparative experiments on YALE B databases, including three types of techniques. The finnal results show that CLA&TH-LTV algorithm owns excellent recognition performance compared to other state-of-art algorithms.

Zhang, Y, Ren, W, Zhu, T & Bi, W 2018, 'MoSa: A modeling and sentiment analysis system for mobile application big data', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 582-595.
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© Springer Nature Switzerland AG 2018. A large amount of data about ending users are generated in the interaction over mobile applications, which becomes a valuable data source for sensing human behaviors and public sentiment trends on some topics. Existing works concentrate on traditional feedback data from web sites, which usually come from desktops instead of from mobile terminals. Few studies have been conducted on interactive data from mobile applications such as news aggregation and recommendation applications. In this paper, we propose a system that can model feedback behaviors of mobile users, and can analyze sentiment trends in mobile feedbacks. The testing data are authentic and are dumped from the most frequently used mobile application in China called Toutiao. We propose several analysis methods on sentiment of comments, and modeling algorithms on feedback behaviors. We build a system called MoSa and by using the system, we discover several implicit behavior models and hidden sentiment trends as follows: During news spreading stage, the number of comments grow linearly per month with slope of 3 in 3 months; The dynamics of replying comments are positively correlated with personal daily routines in 24 h; Replying comment behaviors are much more rare than clicking agreement behaviors in mobile applications; The standard deviation of sentiment values in comments are highly influenced by timing stages. Our system and modeling methods provide empirical results for guiding interaction design in mobile Internet, social networks, and blockchain-based crowdsourcing.

Zhang, Y, Sui, Y & Xue, J 2018, 'Launch-mode-aware Context-sensitive Activity Transition Analysis', Proceedings of the 40th International Conference on Software Engineering, ACM, pp. 598-608.
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Zhang, Y, Wang, W, Xuan, J, Lu, J, Zhang, G & Lin, H 2018, 'Map-based medical practice behavior analysis: Methodology and a case study on Australia’s medical practices', the 13th International FLINS Conference, pp. 1323-1330.
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Zhang, Y, Wang, X, Zhang, G & Lu, J 2018, 'Predicting the dynamics of scientific activities: A diffusion-based network analytic methodology', the 81th Annual Meeting of the Association for Information Science and Technology, Vancouver.
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Zhang, Z, Oberst, S & Lai, JCS 2018, 'Instability analysis of brake squeal with uncertain contact conditions', International Congress on Sound and Vibration, Hiroshima, Japan.
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Zhou, A, Zhang, F, Yuan, L, Zhang, Y & Lin, X 2018, 'Finding maximal stable cores in social networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 224-235.
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© Springer International Publishing AG, part of Springer Nature 2018. Maximal Stable Cores are a cohesive subgraph on a social network which use both engagement and similarity to identify stable groups of users. The problem is, when given a query user and a similarity threshold, to find all Maximal Stable Cores relative to the user. We propose a baseline algorithm and as the problem is NP-Hard, an improved heuristic algorithm which utilises linear time k-core decomposition. Experiments how that when the two algorithms differ, the improved algorithm significantly outperforms the baseline.

Zhou, Z, Liu, S, Xu, G, Xie, X, Yin, J, Li, Y & Zhang, W 2018, 'Knowledge-based Recommendation with Hierarchical Collaborative Embedding', The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Melbourne, Australia.
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Zhu, F, Lin, A, Zhang, G & Lu, J 2018, 'Counterfactual Inference with Hidden Confounders Using Implicit Generative Models', AI 2018: Advances in Artificial Intelligence, Australasian Joint Conference on Artificial Intelligence, Springer, Wellington, New Zealand, pp. 519-530.
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In observational studies, a key problem is to estimate the causal effect of a treatment on some outcome. Counterfactual inference tries to handle it by directly learning the treatment exposure surfaces. One of the biggest challenges in counterfactual inference is the existence of unobserved confounders, which are latent variables that affect both the treatment and outcome variables. Building on recent advances in latent variable modelling and efficient Bayesian inference techniques, deep latent variable models, such as variational auto-encoders (VAEs), have been used to ease the challenge by learning the latent confounders from the observations. However, for the sake of tractability, the posterior of latent variables used in existing methods is assumed to be Gaussian with diagonal covariance matrix. This specification is quite restrictive and even contradictory with the underlying truth, limiting the quality of the resulting generative models and the causal effect estimation. In this paper, we propose to take advantage of implicit generative models to detour this limitation by using black-box inference models. To make inference for the implicit generative model with intractable likelihood, we adopt recent implicit variational inference based on adversary training to obtain a close approximation to the true posterior. Experiments on simulated and real data show the proposed method matches the state-of-art.

Zowghi, D, Bano, M, Ferrari, A, Spoletini, P & Gnesi, S 2018, 'Interview Review: an Empirical Study on Detecting Ambiguities in Requirements Elicitation Interviews', Lecture Notes in Computer Science, International Working Conference on Requirements Engineering: Foundation for Software Quality, Springer Verlag, Utrecht, The Netherlands, pp. 101-118.
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[Context and Motivation] Ambiguities identified during requirements elicitation interviews can be used by the requirements analyst as triggers for additional questions and, consequently, for disclosing further – possibly tacit – knowledge. Therefore, every unidentified ambiguity may be a missed opportunity to collect additional information. [Question/problem] Ambiguities are not always easy to recognize, especially during highly interactive activities such as requirements elicitation interviews. Moreover, since different persons can perceive ambiguous situations differently, the unique perspective of the analyst in the interview might not be enough to identify all ambiguities. [Principal idea/results] To maximize the number of ambiguities recognized in interviews, this paper proposes a protocol to conduct reviews of requirements elicitation interviews. In the proposed protocol, the interviews are audio recorded and the recordings are inspected by both the analyst who performed the interview and another reviewer. The idea is to use the identified cases of ambiguity to create questions for the follow-up interviews. Our empirical evaluation of this protocol involves 42 students from Kennesaw State University and University of Technology Sydney. The study shows that, during the review, the analyst and the other reviewer identify 68% of the total number of ambiguities discovered, while 32% were identified during the interviews. Furthermore, the ambiguities identified by analysts and other reviewers during the review significantly differ from each other. [Contribution] Our results indicate that interview reviews allow the identification of a considerable number of undetected ambiguities, and can potentially be highly beneficial to discover unexpressed information in future interviews.

Zuo, H, Zhang, G & Lu, J 2018, 'Fuzzy domain adaptation using unlabeled target data', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 242-250.
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© Springer Nature Switzerland AG 2018. Transfer learning has been emerging recently and gaining more attention because of its ability to deal with “small labeled data” issue in new markets and for new products. It addresses the problem of leveraging knowledge acquired from previous domain (a source domain with a large amount of labeled data) to improve the accuracy of tasks in the current domain (a target domain with little labeled data). Fuzzy rule-based transfer learning methods are developed due to the ability to dealing with the uncertainty in domain adaptation scenarios. Although some effort is made to develop the fuzzy methods, they only apply the knowledge of the labeled data in the target domain to assist the model’s construction. This work develops a new method that explores and utilizes the information contained in the unlabeled target data to improve the performance of the new constructed model. The experiments on both synthetic datasets and real-world datasets illustrate the effectiveness of our method, and also give the application scope of applying it.

van den Hoven, E, Kenning, G & Van Gennip, D 2018, Materialising Memories: Design Research to Support Remembering, pp. 1-28, Sydney.
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Anshu, A, Berta, M, Jain, R & Tomamichel, M 2018, 'Partially smoothed information measures'.
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Smooth entropies are a tool for quantifying resource trade-offs in (quantum) information theory and cryptography. In typical bi- and multi-partite problems, however, some of the sub-systems are often left unchanged and this is not reflected by the standard smoothing of information measures over a ball of close states. We propose to smooth instead only over a ball of close states which also have some of the reduced states on the relevant sub-systems fixed. This partial smoothing of information measures naturally allows to give more refined characterizations of various information-theoretic problems in the one-shot setting. In particular, we immediately get asymptotic second-order characterizations for tasks such as privacy amplification against classical side information or classical state splitting. For quantum problems like state merging the general resource trade-off is tightly characterized by partially smoothed information measures as well. However, for quantum systems we can so far only give the asymptotic first-order expansion of these quantities.

Gill, AQ 2018, 'SECURE INFORMATION ARCHITECTURE: SECURITY BY DESIGN'.
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ACS & DAMA Joint Professional Development event 2018

Huber, S, Koenig, R & Tomamichel, M 2018, 'Jointly constrained semidefinite bilinear programming with an application to Dobrushin curves'.
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We propose a branch-and-bound algorithm for minimizing a bilinear functional of the form \[ f(X,Y) = \mathrm{tr}((X\otimes Y)Q)+\mathrm{tr}(AX)+\mathrm{tr}(BY) , \] of pairs of Hermitian matrices $(X,Y)$ restricted by joint semidefinite programming constraints. The functional is parametrized by self-adjoint matrices $Q$, $A$ and $B$. This problem generalizes that of a bilinear program, where $X$ and $Y$ belong to polyhedra. The algorithm converges to a global optimum and yields upper and lower bounds on its value in every step. Various problems in quantum information theory can be expressed in this form. As an example application, we compute Dobrushin curves of quantum channels, giving upper bounds on classical coding with energy constraints.

Jay, B 2018, 'Intensional Computation: three draft chapters'.

LU, S, Zhang, G, Luo, Z & Oberst, S 2018, 'Order pattern recurrence plots unveiling determinism buried in noise'.

Mann, RL & Bremner, MJ 2018, 'Approximation Algorithms for Complex-Valued Ising Models on Bounded Degree Graphs'.
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We study the problem of approximating the Ising model partition function with complex parameters on bounded degree graphs. We establish a deterministic polynomial-time approximation scheme for the partition function when the interactions and external fields are absolutely bounded close to zero. Furthermore, we prove that for this class of Ising models the partition function does not vanish. Our algorithm is based on an approach due to Barvinok for approximating evaluations of a polynomial based on the location of the complex zeros and a technique due to Patel and Regts for efficiently computing the leading coefficients of graph polynomials on bounded degree graphs. Finally, we show how our algorithm can be extended to approximate certain output probability amplitudes of quantum circuits.

Oberst, S, Stender, M, Baetz, J, Campbell, G, Lampe, F, Lai, JCS, Morlock, M & Hoffmann, N 2018, 'Extracting differential equations from measured vibro-acoustic impulse responses in cavity preparation of total hip arthroplasty'.

Raza, SA 2018, 'Computational reinforcement learning using rewards from human feedback'.

Taranto, P, Pollock, FA, Milz, S, Tomamichel, M & Modi, K 2018, 'Quantum Markov Order'.
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We formally extend the notion of Markov order to open quantum processes by accounting for the instruments used to probe the system of interest at different times. Our description recovers the classical Markov order property in the appropriate limit: when the stochastic process is classical and the instruments are non-invasive, i.e., restricted to orthogonal projective measurements. We then prove that there do not exist non-Markovian quantum processes that have finite Markov order with respect to all possible instruments; the same process exhibits distinct memory effects with respect to different probing instruments. This naturally leads to a relaxed definition of quantum Markov order with respect to specified sequences of instruments. The memory effects captured by different choices of instruments vary dramatically, providing a rich landscape for future exploration.

van Gennip, DAP 2018, 'Bringing up the past : interaction design for serendipitous reminiscing'.
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People reminisce to entertain themselves, to connect with others, and to increase self-awareness. Photographs have proven a great resource to support reminiscing. However, with a move towards digital capture and storage, people have more photos that end up undervalued and underused. This thesis explores how interaction design can support reminiscing in everyday life, in particular through the use of digital photos. We focus on serendipitous reminiscing: the casual recollection and reliving of past experiences, brought about by chance encounters with things that remind of one’s past. These encounters are easily afforded to physical things but not to digital photos. Based on a diary study on involuntary reminiscing, a repertory grid study on remembering as experience, and two research-through-design studies on interactive photo displays, this thesis explores how people relate to memory cues in everyday life. We confirm that encounters with personal media and other things that remind one of one’s past are welcome, and that the value of photo displays stems from making photos present in everyday life (rather than their interactive features). The findings contribute to design research by furthering the understanding of remembering as experience and the development of several novel concepts that facilitate serendipitous reminiscing.

Youssry, A, Ferrie, C & Tomamichel, M 2018, 'Efficient online quantum state estimation using a matrix-exponentiated gradient method'.
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In this paper, we explore an efficient online algorithm for quantum state estimation based on a matrix-exponentiated gradient method previously used in the context of machine learning. The state update is governed by a learning rate that determines how much weight is given to the new measurement results obtained in each step. We show convergence of the running state estimate in probability to the true state for both noiseless and noisy measurements. We find that in the latter case the learning rate has to be chosen adaptively and decreasing to guarantee convergence beyond the noise threshold. As a practical alternative we then propose to use running averages of the measurement statistics and a constant learning rate to overcome the noise problem. The proposed algorithm is numerically compared with batch maximum-likelihood and least-squares estimators. The results show a superior performance of the new algorithm in terms of accuracy and runtime complexity.