Aguirre-Ollinger, G, Ramezani, N & Wang, X 2015, 'Automated generation of rhythmic task sequences in 3-D space using dynamic movement primitives'.
Al-Hassan, M, Lu, H & Lu, J 2015, 'A semantic enhanced hybrid recommendation approach: A case study of e-Government tourism service recommendation system', DECISION SUPPORT SYSTEMS, vol. 72, pp. 97-109.
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Alzoubi, YI, Gill, AQ & Al-Ani, A 2015, 'Distributed Agile Development Communication: An Agile Architecture Driven Framework', Journal of Software, vol. 10, no. 6, pp. 681-694.
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Agile methods depend on active communication and effective knowledge sharing among team
members for producing high quality working software systems in short releases and iterations. However,
effective communication in Distributed Agile Development (DAD) can be challenging due to a number of
different factors, such as physical locations, multi-cultures and time-zones. The agile body of knowledge
mainly discusses some technology and non-technology solutions and strategies to mitigate the DAD
communication challenges from a project management perspective. Nevertheless, it has recently been
argued that there is a need to understand and analyze DAD communication from other related but different
perspectives, such as enterprise strategy, enterprise architecture and service management. Due to the fact
that agile EA provides a holistic view and blueprint of the whole environment in which a number of projects
are developed and managed, we attempt in this study to explore the effect of agile Enterprise Architecture
(EA) on DAD communication. Particularly, we propose the development of an agile EA driven approach from
the architecture body of knowledge for handling the DAD communication challenges that have not been
thoroughly investigated before
Anaissi, A, Goyal, M, Catchpoole, DR, Braytee, A & Kennedy, PJ 2015, 'Case-based retrieval framework for gene expression data.', Cancer Informatics, vol. 14, pp. 21-31.
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BACKGROUND: The process of retrieving similar cases in a case-based reasoning system is considered a big challenge for gene expression data sets. The huge number of gene expression values generated by microarray technology leads to complex data sets and similarity measures for high-dimensional data are problematic. Hence, gene expression similarity measurements require numerous machine-learning and data-mining techniques, such as feature selection and dimensionality reduction, to be incorporated into the retrieval process. METHODS: This article proposes a case-based retrieval framework that uses a k-nearest-neighbor classifier with a weighted-feature-based similarity to retrieve previously treated patients based on their gene expression profiles. RESULTS: The herein-proposed methodology is validated on several data sets: a childhood leukemia data set collected from The Children's Hospital at Westmead, as well as the Colon cancer, the National Cancer Institute (NCI), and the Prostate cancer data sets. Results obtained by the proposed framework in retrieving patients of the data sets who are similar to new patients are as follows: 96% accuracy on the childhood leukemia data set, 95% on the NCI data set, 93% on the Colon cancer data set, and 98% on the Prostate cancer data set. CONCLUSION: The designed case-based retrieval framework is an appropriate choice for retrieving previous patients who are similar to a new patient, on the basis of their gene expression data, for better diagnosis and treatment of childhood leukemia. Moreover, this framework can be applied to other gene expression data sets using some or all of its steps.
Angelini, L, Lalanne, D, van den Hoven, E, Khaled, O & Mugellini, E 2015, 'Move, Hold and Touch: A Framework for Tangible Gesture Interactive Systems', Machines, vol. 3, no. 3, pp. 173-207.
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Technology is spreading in our everyday world, and digital interaction beyond the screen, with real objects, allows taking advantage of our natural manipulative and communicative skills. Tangible gesture interaction takes advantage of these skills by bridging two popular domains in Human-Computer Interaction, tangible interaction and gestural interaction. In this paper, we present the Tangible Gesture Interaction Framework (TGIF) for classifying and guiding works in this field. We propose a classification of gestures according to three relationships with objects: move, hold and touch. Following this classification, we analyzed previous work in the literature to obtain guidelines and common practices for designing and building new tangible gesture interactive systems. We describe four interactive systems as application examples of the TGIF guidelines and we discuss the descriptive, evaluative and generative power of TGIF.
Ashraf, J, Chang, E, Hussain, OK & Hussain, FK 2015, 'Ontology usage analysis in the ontology lifecycle: A state-of-the-art review', KNOWLEDGE-BASED SYSTEMS, vol. 80, pp. 34-47.
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Azadeh, A, Zia, NP, Saberi, M, Hussain, FK, Yoon, JH, Hussain, OK & Sadri, S 2015, 'A trust-based performance measurement modeling using t-norm and t-conorm operators', APPLIED SOFT COMPUTING, vol. 30, pp. 491-500.
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Bakker, S, Hausen, D, van den Hoven, E & Selker, T 2015, 'Preface: Designing for peripheral interaction: Seamlessly integrating interactive technology in everyday life', Interaction Design and Architecture(s), vol. 26, no. 1, pp. 3-5.
Bakker, S, van den Hoven, E & Eggen, B 2015, 'Evaluating Peripheral Interaction Design', Human-Computer Interaction, vol. 30, no. 6, pp. 473-506.
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Many actions in the physical world take place in the background or periphery of peoples attention. However interactions with computing technologies usually require focused attention. This paper explores the concept of peripheral interaction: physical interaction with technology that takes place outside the focus of attention. A peripheral interaction design (called FireFlies), which supports primary school teachers in their everyday routine through open-ended light-objects on the childrens desks, was deployed in four classrooms for six weeks. Results of interviews and video analysis indicate that the six participating teachers were able to physically interact with the FireFlies interactive artefact quickly and frequently without disturbing ongoing tasks. In the final weeks of the study, the teachers seemed able to easily shift their focus of attention between their main task and the interactive system. We therefore conclude that, even though it is difficult to measure peoples attention, a longitudinal approach seemed effective to find indicators for peripheral interaction.
Bakker, S, van den Hoven, E & Eggen, B 2015, 'Peripheral Interaction: Characteristics and Considerations', Personal and Ubiquitous Computing, vol. 19, no. 1, pp. 239-254.
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In everyday life, we are able to perceive information and perform physical actions in the background or periphery of attention. Inspired by this observation, several researchers have studied interactive systems that display digital information in the periphery of attention. To broaden the scope of this research direction, a few recent studies have focused on interactive systems that cannot only be perceived in the background, but also enable users to physically interact with digital information in their periphery. Such peripheral interaction designs can support computing technology to fluently embed in, and become a meaningful part of peoples everyday routines. With the increasing ubiquity of technology in our everyday environment, we believe that this direction is highly relevant nowadays. This paper presents an in-depth analysis of three case studies on peripheral interaction. These case studies involved the design and development of peripheral interactive systems and deployment of these systems in the real context of use for a number of weeks. Based on the insights gained through these case studies, we discuss generalized characteristics and considerations for peripheral interaction design and evaluation. The aim of the work presented in this paper is to support interaction design researchers and practitioners in anticipating and facilitating peripheral interaction with the designs they are evaluating or developing.
Behbood, V, Lu, J, Zhang, G & Pedrycz, W 2015, 'Multi-Step Fuzzy Bridged Refinement Domain Adaptation Algorithm and Its Application to Bank Failure Prediction', IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, pp. 1917-1935.
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Machine learning plays an important role in data classification and data-based prediction. In some real world applications, however, the training data (coming from the source domain) and test data (from the target domain) come from different domains or time periods, and this may result in the different distributions of some features. Moreover, the values of the features and/or labels of the data sets might be non-numeric and involve vague values. Traditional learning-based prediction and classification methods cannot handle these two issues. In this study, we propose a multi-step fuzzy bridged refinement domain adaptation algorithm, which offers an effective way to deal with both issues. It utilizes a concept of similarity to modify the labels of the target instances that were initially predicted by a shift-unaware model. It then refines the labels using instances that are most similar to a given target instance. These instances are extracted from mixture domains composed of source and target domains. The proposed algorithm is built on a basis of some data and refines the labels, thus performing completely independently of the shift-unaware prediction model. The algorithm uses a fuzzy set-based approach to deal with the vague values of the features and labels. Four different data sets are used in the experiments to validate the proposed algorithm. The results, which are compared with those generated by the existing domain adaptation methods, demonstrate a significant improvement in prediction accuracy in both the above-mentioned data sets.
Beliakov, G, Li, G & Liu, S 2015, 'Parallel bucket sorting on graphics processing units based on convex optimization', OPTIMIZATION, vol. 64, no. 4, pp. 1033-1055.
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Chen, C, Zhang, J, Xie, Y, Xiang, Y, Zhou, W, Hassan, MM, Alelaiwi, A & Alrubaian, M 2015, 'A Performance Evaluation of Machine Learning-Based Streaming Spam Tweets Detection', IEEE Transactions on Computational Social Systems, vol. 2, no. 3, pp. 65-76.
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© 2014 IEEE. The popularity of Twitter attracts more and more spammers. Spammers send unwanted tweets to Twitter users to promote websites or services, which are harmful to normal users. In order to stop spammers, researchers have proposed a number of mechanisms. The focus of recent works is on the application of machine learning techniques into Twitter spam detection. However, tweets are retrieved in a streaming way, and Twitter provides the Streaming API for developers and researchers to access public tweets in real time. There lacks a performance evaluation of existing machine learning-based streaming spam detection methods. In this paper, we bridged the gap by carrying out a performance evaluation, which was from three different aspects of data, feature, and model. A big ground-truth of over 600 million public tweets was created by using a commercial URL-based security tool. For real-time spam detection, we further extracted 12 lightweight features for tweet representation. Spam detection was then transformed to a binary classification problem in the feature space and can be solved by conventional machine learning algorithms. We evaluated the impact of different factors to the spam detection performance, which included spam to nonspam ratio, feature discretization, training data size, data sampling, time-related data, and machine learning algorithms. The results show the streaming spam tweet detection is still a big challenge and a robust detection technique should take into account the three aspects of data, feature, and model.
Chen, H, Zhang, G, Zhu, D & Lu, J 2015, 'A patent time series processing component for technology intelligence by trend identification functionality', Neural Computing and Applications, vol. 26, no. 2, pp. 345-353.
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Chen, Y, Zhang, X, Feng, Y, Liang, J & Chen, H 2015, 'Sunburst with ordered nodes based on hierarchical clustering: a visual analyzing method for associated hierarchical pesticide residue data', Journal of Visualization, vol. 18, no. 2, pp. 237-254.
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According to the characteristics of pesticide residue data and analyzing requirements in food safety fields, we presented a visual analyzing method for associated hierarchical data, called sunburst with ordered nodes based on hierarchical clustering (SONHC). SONHC arranged the leaf nodes in sunburst in order using hierarchical clustering algorithm, put the associated dataset as a node in center of the sunburst, and connected it with the associated leaf nodes in sunburst using colored lines. So, it can present not only two hierarchical structures but also the relationships between them. Based on SONHC and some interaction techniques (clicking, contraction and expansion, etc) we developed an associated visual analyzing system (AVAS) for pesticide residues detection results data, which can help users to inspect the hierarchical structure of pesticide and agricultural products and to explore the associations between pesticides and agricultural products, and associations between different pesticides. The results of user experience test showed that SONHC algorithm overperforms than SA and SR algorithm in ULE and ULE’s variance. AVAS system is effective in helping users to analyze the pesticide residues data. Furthermore, SONHC algorithm can also be adopted to analyze associated hierarchical data in other fields, such as finance, insurance and e-commerce.
Cheng, X, Yuan, J, Tajer, A, Hu, A & Zhou, W 2015, 'Special issue on recent advances in network and information security-security and communication networks journal', Security and Communication Networks, vol. 8, no. 1, pp. 1-1.
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Chuang, C-H, Huang, C-S, Ko, L-W & Lin, C-T 2015, 'An EEG-based perceptual function integration network for application to drowsy driving', KNOWLEDGE-BASED SYSTEMS, vol. 80, pp. 143-152.
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Cignoni, P, Pietroni, N, Malomo, L & Scopigno, R 2015, 'Mesh Joinery: A Method for Building Fabricable Structures', ERCIM NEWS, pp. 44-45.
Delgrande, JP & Peppas, P 2015, 'Belief revision in Horn theories', ARTIFICIAL INTELLIGENCE, vol. 218, pp. 1-22.
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Deng, S, Wang, D, Li, X & Xu, G 2015, 'Exploring user emotion in microblogs for music recommendation', EXPERT SYSTEMS WITH APPLICATIONS, vol. 42, no. 23, pp. 9284-9293.
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Dong, H & Hussain, FK 2015, 'Service-requester-centered service selection and ranking model for digital transportation ecosystems', Computing, vol. 97, no. 1, pp. 79-102.
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Transport services are a fundamental utility that drives human society. A Digital Transportation Ecosystem is a sub-system of the Digital Ecosystem, which uses ICT resources to facilitate transport service transactions. This research focuses on the selection and ranking of online transport service information. The previous research in this area has been unable to achieve satisfactory performance or give sufficient freedom to service requesters to rank services based on their preferences. `User-centered design is a broad term to describe how end-users influence system design. In this research, we propose a Service-Requester-Centered Service Selection and Ranking Model, guided by the philosophy of user-centered design. Three major sub-models are involved in this model: a model for assisting service requesters to search appropriate transport service ontology concepts to denote their service requests, a model for enhancing the accuracy of automatic transport service concept recommendation by observing service requesters click behaviours, and a model for enabling service-requester-preference-based service ranking. Implementations and empirical experiments are conducted to evaluate the three sub-models and the drawn conclusions along with directions for future work are outlined.
Fan, H, Hussain, FK & Hussain, OK 2015, 'Semantic client-side approach for web personalization of SaaS-based cloud services', CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, vol. 27, no. 8, pp. 2144-2169.
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Fan, H, Hussain, FK, Younas, M & Hussain, OK 2015, 'An integrated personalization framework for SaaS-based cloud services', Future Generation Computer Systems, vol. 53, pp. 157-173.
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© 2015 Elsevier B.V. Software as a Service (SaaS) has recently emerged as one of the most popular service delivery models in cloud computing. The number of SaaS services and their users is continuously increasing and new SaaS service providers emerge on a regular basis. As users are exposed to a wide range of SaaS services, they may soon become more demanding when receiving/consuming such services. Similar to the web and/or mobile applications, personalization can play a critical role in modern SaaS-based cloud services. This paper introduces a fully designed, cloud-enabled personalization framework to facilitate the collection of preferences and the delivery of corresponding SaaS services. The approach we adapt in the design and development of the proposed framework is to synthesize various models and techniques in a novel way. The objective is to provide an integrated and structured environment wherein SaaS services can be provisioned with enhanced personalization quality and performance.
Frati, F, Gaspers, S, Gudmundsson, J & Mathieson, L 2015, 'Augmenting Graphs to Minimize the Diameter', Algorithmica, vol. 72, no. 4, pp. 995-1010.
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© 2014, Springer Science+Business Media New York. We study the problem of augmenting a weighted graph by inserting edges of bounded total cost while minimizing the diameter of the augmented graph. Our main result is an FPT $$4$$4-approximation algorithm for the problem.
Gao, F, Musial, K, Cooper, C & Tsoka, S 2015, 'Link prediction methods and their accuracy for different social networks and network metrics', Scientific Programming, vol. 2015.
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Copyright © 2015 Fei Gao et al. Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is the prediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods.We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish "prediction friendly" networks, for which most of the prediction methods give good performance, as well as "prediction unfriendly" networks, for which most of the methods result in high prediction error. Correlation analysis between networkmetrics and prediction accuracy of prediction methodsmay formthe basis of ametalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.
Garcia, JA & Felix Navarro, K 2015, 'StepKinnection: A Fall Prevention Game Mindfully Designed for the Elderly.', Studies in health technology and informatics, vol. 214, pp. 43-49.
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This paper presents the StepKinnection game, a Kinect-driven stepping game for the elderly that delivers stepping exercises to train specific cognitive and physical abilities associated with falls. This system combines a set of suitable age-related features, meaningful exercise routines and an embedded clinical test for fall risk assessment. The combination of these three aspects makes the game potentially useful in practice as the game is appealing to the elderly cohort, trains one of the most important abilities to prevent falls and at the same time allows for a continuous assessment of health outcomes; characteristics not available in the literature nor in current commercial games.
Gill, AQ 2015, 'Agile enterprise architecture modelling: Evaluating the applicability and integration of six modelling standards', INFORMATION AND SOFTWARE TECHNOLOGY, vol. 67, pp. 196-206.
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Gill, AQ 2015, 'Distributed Agile Development: Applying a Coverage Analysis Approach to the Evaluation of a Communication Technology Assessment Tool', International Journal of e-Collaboration, vol. 11, no. 1, pp. 57-76.
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Organizations have shown a significant interest in the adoption of emerging social technologies to support communication and collaboration needs of their Distributed Agile or Adaptive Development Environment (DADE). However, the challenge is how best to assess contemporary social technologies for supporting communication and collaboration in the DADE. Here, a communication technology assessment tool, called CTAT, is developed as a part of the Adaptive Enterprise Service System (AESS) toolkit by using the design research approach. This paper presents the evaluation of the CTAT construct through its use in the assessment of three social technologies within the context of a DADE. The results of this evaluation indicate that CTAT is shown to be useful, for example, when assessing a particular social technology for a specific DADE communication and collaboration context. The CTAT is intended to be used by senior developers for assessing social technologies for their DADE context.
Gill, AQ 2015, 'Social architecture considerations in assessing social media for emergency information management applications', The Australian Journal of Emergency Management, vol. 30, no. 1, pp. 17-21.
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The emergency management industry is showing a significant interest in the adoption of social media for sourcing and disseminating crisis information. The emergency management industry needs to identify social architecture concerns when considering the adoption of a specific social media technology. Social architecture describes the properties and environment of a social system such as the ‘emergency management system’. This paper identifies a set of 21 social architecture concerns based on recent qualitative research. This set of social architecture concerns can be used as a criteria list to assess the effectiveness of social media platforms for emergency information management applications.
Gill, AQ & Qureshi, MA 2015, 'Adaptive Enterprise Architecture Modelling', Journal of Software, vol. 10, no. 5, pp. 628-638.
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Agile or adaptive enterprise architecture driven software development approach requires a modelling standard to describe the existing and to-be developed artifacts both at the high enterprise level and low, detailed level. However, a single modelling standard may not be used off-the-shelf to fully support the modelling needs of an adaptive enterprise architecture driven software development needs. The modelling standards need to be systematically analyzed and integrated for a particular modelling context. This paper reviews two well-known modeling standards ArchiMate and BPMN by using the interoperability research framework. Based on the syntax, semantics and structural analysis of these two modelling standards’ metamodels, it proposes a hybrid adaptive enterprise architecture modelling approach for describing and analysing the artifacts both at the high enterprise level and low, detailed level for a particular context. This paper has both theoretical and practical implications for researchers and practitioners pursuing to integrate various modelling standards.
Gill, AQ, Alam, SL & Eustace, J 2015, 'Social Architecture: An Emergency Management Case Study', Australasian Journal of Information Systems, vol. 19, pp. 23-40.
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Emergency management agencies are progressively using social media for the sourcing and distribution of disaster information. Emergency management agencies are often unsure as to how to best identify and assess social media concerns (e.g. information security, trust) which must be addressed to develop a social media-enabled disaster information management environment. This paper adopts the Social Architecture Viewpoint Assessment (SAVA) framework for identifying and assessing social media concerns from four different viewpoints: IT, Value, Resource and Management. This paper demonstrates the use of the SAVA framework in the context of an in-depth empirical case study of an Australian emergency management agency. The results of this study indicate that the SAVA framework is useful for emergency information management managers in identifying and assessing social media concerns.
Gill, AQ, Bunker, D & Seltsikas, P 2015, 'Moving Forward: Emerging Themes in Financial Services Technologies’ Adoption', Communications of the Association for Information Systems, vol. 36, pp. 206-230.
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Financial services technologies (FST) are core to the continuous transformation of financial services organizations (FSO). To date, however, there has been a lack of empirical research into FST adoption against the backdrop of the recent financial crisis. In this paper, we re-examine how FSO are currently positioned to take advantage of emerging FST. Note that, in this paper, we look forward rather than provide a commentary on the state of the art in technology adoption research. We conducted this research by applying an exploratory qualitative study method: we analyzed interview transcripts from thirty recent interviews of FSO technology executives and CIOs by using a thematic network analysis tool. This analysis uncovered nineteen basic, eight organizing, and two global FST adoption research themes along with their links to FST adoption objectives, challenges, customer centricity, human resources, outsourcing, and overall IT strategy maintenance. This research has both practical and theoretical research implications and serves as a resource base for FSO and researchers to set future research priorities and directions. We intend for the emerging themes that we present in this paper to facilitate research directions by shedding light on the areas of greatest value and potential return in FST adoption.
Goeke, C, Kornpetpanee, S, Koester, M, Fernandez-Revelles, AB, Gramann, K & Koenig, P 2015, 'Cultural background shapes spatial reference frame proclivity', SCIENTIFIC REPORTS, vol. 5.
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Goodswen, SJ, Barratt, JLN, Kennedy, PJ & Ellis, JT 2015, 'Improving the gene structure annotation of the apicomplexan parasite Neospora caninum fulfils a vital requirement towards an in silico-derived vaccine', International Journal for Parasitology, pp. 305-318.
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Neospora caninum is an apicomplexan parasite which can cause abortion in cattle, instigating major economic burden. Vaccination has been proposed as the most cost-effective control measure to alleviate this burden. Consequently the overriding aspiration for N. caninum research is the identification and subsequent evaluation of vaccine candidates in animal models. To save time, cost and effort, it is now feasible to use an in silico approach for vaccine candidate prediction. Precise protein sequences, derived from the correct open reading frame, are paramount and arguably the most important factor determining the success or failure of this approach. The challenge is that publicly available N. caninum sequences are mostly derived from gene predictions. Annotated inaccuracies can lead to erroneously predicted vaccine candidates by bioinformatics programs. This study evaluates the current N. caninum annotation for potential inaccuracies. Comparisons with annotation from a closely related pathogen, Toxoplasma gondii, are also made to distinguish patterns of inconsistency. More importantly, a mRNA sequencing (RNA-Seq) experiment is used to validate the annotation. Potential discrepancies originating from a questionable start codon context and exon boundaries were identified in 1943 protein coding sequences. We conclude, where experimental data were available, that the majority of N. caninum gene sequences were reliably predicted. Nevertheless, almost 28% of genes were identified as questionable. Given the limitations of RNA-Seq, the intention of this study was not to replace the existing annotation but to support or oppose particular aspects of it. Ideally, many studies aimed at improving the annotation are required to build a consensus. We believe this study, in providing a new resource on gene structure and annotation, is a worthy contributor to this endeavour.
Haider, S & Raza, SA 2015, 'Complexity reduction of influence nets using arc removal', Journal of Intelligent & Fuzzy Systems, vol. 28, pp. 1849-1859.
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Han, J, Lu, J, Hu, Y & Zhang, G 2015, 'Tri-level decision-making with multiple followers: Model, algorithm and case study', INFORMATION SCIENCES, vol. 311, pp. 182-204.
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Hayes, G, Khazaei, H, El-Khatib, K, McGregor, C & Eklund, JM 2015, 'Design and Analytical Model of a Platform-as-a-Service Cloud for Healthcare', JOURNAL OF INTERNET TECHNOLOGY, vol. 16, no. 1, pp. 139-149.
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Huang, C-S, Pal, NR, Chuang, C-H & Lin, C-T 2015, 'Identifying changes in EEG information transfer during drowsy driving by transfer entropy', FRONTIERS IN HUMAN NEUROSCIENCE, vol. 9.
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Huang, G, He, J, Zhang, Y, Zhou, W, Liu, H, Zhang, P, Ding, Z, You, Y & Cao, J 2015, 'Mining streams of short text for analysis of world-wide event evolutions', World Wide Web, vol. 18, no. 5, pp. 1201-1217.
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© 2014, Springer Science+Business Media New York. Streams of short text, such as news titles, enable us to effectively and efficiently learn the real world events that occur anywhere and anytime. Short text messages that are companied by timestamps and generally brief events using only a few words differ from other longer text documents, such as web pages, news stories, blogs, technical papers and books. For example, few words repeat in the same news titles, thus frequency of the term (i.e., TF) is not as important in short text corpus as in longer text corpus. Therefore, analysis of short text faces new challenges. Also, detecting and tracking events through short text analysis need to reliably identify events from constant topic clusters; however, existing methods, such as Latent Dirichlet Allocation (LDA), generates different topic results for a corpus at different executions. In this paper, we provide a Finding Topic Clusters using Co-occurring Terms (FTCCT) algorithm to automatically generate topics from a short text corpus, and develop an Event Evolution Mining (EEM) algorithm to discover hot events and their evolutions (i.e., the popularity degrees of events changing over time). In FTCCT, a term (i.e., a single word or a multiple-words phrase) belongs to only one topic in a corpus. Experiments on news titles of 157 countries within 4 months (from July to October, 2013) demonstrate that our FTCCT-based method (combining FTCCT and EEM) achieves far higher quality of the event’s content and description words than LDA-based method (combining LDA and EEM) for analysis of streams of short text. Our method also visualizes the evolutions of the hot events. The discovered world-wide event evolutions have explored some interesting correlations of the world-wide events; for example, successive extreme weather phenomenon occur in different locations - typhoon in Hong Kong and Philippines followed hurricane and storm flood in Mexico in September 2013.
Huang, ML, Lu, L & Zhang, X 2015, 'Using arced axes in parallel coordinates geometry for high dimensional BigData visual analytics in cloud computing', Computing, vol. 97, no. 4, pp. 425-437.
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Huassain, OK, Rahman, ZU, Hussain, FK, Singh, J, Janjua, NK & Chang, E 2015, 'A User-Based Early Warning Service Management Framework in Cloud Computing', Computer Journal, vol. 58, no. 3, pp. 472-796.
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Cloud computing is a very attractive option for service users and service providers for their businesses because of the benefits it provides. A major concern among service users regarding cloud adoption, however, is the unpredictability of performance in relation to the services provided. Even though guarantees in the form of service-level agreements are provided to users by service providers, real-time service-level degradability remains a critical concern; hence, there is a need for an approach that assists users to manage a service before it fails. The approaches proposed in the literature assess and evaluate the performance of the cloud infrastructure of providers, but this does not guarantee that a given service instance will meet the desired quality level because there may be factors other than the provider's infrastructure that will affect the level of quality of the service instance. In this paper, we present an approach that measures the quality of a service instance in real time and provides important analysis for service users as to whether they will achieve their desired objectives. This analysis also constitutes an important input for service users in the assessment and management of a service to avoid the failure to achieve objectives.
Hussain, FK 2015, 'Special issue on intelligent e-services', Concurrency Computation, vol. 27, no. 4, pp. 763-764.
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Ivanyos, G, Karpinski, M, Qiao, Y & Santha, M 2015, 'Generalized Wong sequences and their applications to Edmonds' problems', Journal of Computer and System Sciences, vol. 81, no. 7, pp. 1373-1386.
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© 2015 Elsevier Inc. Given a linear subspace B of the n×n matrices over some field F, we consider the following problems: symbolic matrix rank (SMR) asks to determine the maximum rank among matrices in B, while symbolic determinant identity testing (SDIT) asks to decide whether there exists a nonsingular matrix in B. The constructive versions of these problems ask to find a matrix of maximum rank, respectively a nonsingular matrix, if there exists one. Our first algorithm solves the constructive SMR when B is spanned by unknown rank one matrices, answering an open question of Gurvits. Our second algorithm solves the constructive SDIT when B is spanned by triangularizable matrices. (The triangularization is not given explicitly.) Both algorithms work over fields of size ≥n+1. Our framework is based on generalizing Wong sequences, a classical method to deal with pairs of matrices, to pairs of matrix spaces.
Janjua, N.K., Hussain, O.K, Hussain, FK & Chang, E. 2015, 'Philosophical and Logic-Based Argumentation-Driven Reasoning Approaches and their Realization on the WWW: A Survey', Computer Journal, vol. 57, pp. 1-33.
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Argumentation is the practice of systematic conscious reasoning involving the construction and evaluation of arguments to justify or support a particular conclusion. This article discusses, compares, contrasts and categorizes existing argumentation-based frameworks and applications as either philosophical or logic-based, and provides critical analysis that emphasizes the structure of arguments and the interactions between them. This review compares and contrasts the frameworks and applications of argumentation-based approaches on Web 2.0 and the Semantic Web, and subsequently highlights the importance and challenges of attaining monological argumentation on the Semantic Web.
Jeong, YS, Shyu, ML, Xu, G & Wagner, RR 2015, 'Guest Editorial: Advanced Technologies and Services for Multimedia Big Data Processing', Multimedia Tools and Applications, vol. 74, no. 10, pp. 3413-3418.
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Jiang, H, Wang, J, Dong, Y & Lu, H 2015, 'Comprehensive assessment of wind resources and the low-carbon economy: An empirical study in the Alxa and Xilin Gol Leagues of inner Mongolia, China', Renewable and Sustainable Energy Reviews, vol. 50, pp. 1304-1319.
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© 2015 Elsevier Ltd. All rights reserved. Due to atmospheric pollution from fossil fuels, the reduction of wind turbine costs, and the rise of the low-carbon economy, wind energy conversion systems have become one of the most significant forms of new energy in China. Therefore, to reduce investment risk and maximize profits, it is necessary to assess wind resources before building large wind farms. This paper develops a comprehensive system containing four steps to evaluate the potential of wind resources at two sites in Xilin Gol League and at additional two sites in Alxa League of Inner Mongolia, China: (1) By calculating the total scores of three indexes, including the effective wind power density (EWPD), wind available time (WAT) and population density (PD), an indexes method is applied to assess the theoretical wind energy potential from 2001 to 2010. (2) To judge the fluctuations in the wind speed, the Fisher optimal partition method and the Jonckheere-Terpstra test are used to analyze the changes in the average monthly and yearly wind speeds from 2001 to 2010. (3) Three probability density functions, i.e., Weibull, Gamma and Lognormal, are used to assess the wind speed frequency distribution in 2010. To enhance the evaluation accuracy, three intelligent optimization parameter estimation algorithms, i.e., the particle swarm optimization algorithm (PSO), differential evolution algorithm (DE) and ant colony algorithm (ACO), are used to estimate the parameters of these distributions. (4) It is helpful to analyze the wind characteristics when assessing wind resources and selecting wind turbines. Therefore, the optimal frequency distribution based on the best parameter estimation method can be chosen to calculate the wind power density, the most probable wind speed and the wind speed carrying the maximum energy. The experimental results show that Site 1 and Site 4 are more suitable for large wind farms than Site 2 or Site 3.
Jiang, J, Wen, S, Yu, S, Xiang, Y & Zhou, W 2015, 'K-Center: An Approach on the Multi-Source Identification of Information Diffusion', IEEE Transactions on Information Forensics and Security, vol. 10, no. 12, pp. 2616-2626.
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© 2005-2012 IEEE. The global diffusion of epidemics, computer viruses, and rumors causes great damage to our society. It is critical to identify the diffusion sources and timely quarantine them. However, most methods proposed so far are unsuitable for diffusion with multiple sources because of the high computational cost and the complex spatiotemporal diffusion processes. In this paper, based on the knowledge of infected nodes and their connections, we propose a novel method to identify multiple diffusion sources, which can address three main issues in this area: 1) how many sources are there? 2) where did the diffusion emerge? and 3) when did the diffusion break out? We first derive an optimization formulation for multi-source identification problem. This is based on altering the original network into a new network concerning two key elements: 1) propagation probability and 2) the number of hops between nodes. Experiments demonstrate that the altered network can accurately reflect the complex diffusion processes with multiple sources. Second, we derive a fast method to optimize the formulation. It has been proved that the proposed method is convergent and the computational complexity is O(mn log α) , where α = α (m,n) is the slowly growing inverse-Ackermann function, n is the number of infected nodes, and m is the number of edges connecting them. Finally, we introduce an efficient algorithm to estimate the spreading time and the number of diffusion sources. To evaluate the proposed method, we compare the proposed method with many competing methods in various real-world network topologies. Our method shows significant advantages in the estimation of multiple sources and the prediction of spreading time.
Johnston, A 2015, 'Conversational Interaction in Interactive Dance Works', Leonardo, vol. 48, no. 3, pp. 296-297.
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This paper describes an interactive dance/physical theatre work entitled Encoded, which made use of motion capture techniques and real-time fluid simulations to create systems intended to support, stimulate and augment live performance. Preliminary findings from a qualitative study of performers’ experiences with the system raise a number of issues, including the challenges of creating theatrical meaning with interactive systems, using Contact Improvisation as a metaphor for engaging creative systems, and the impact that large-scale projections can have on performers’ engagement.
Kamaleswaran, R, Wehbe, RR, Edward Pugh, J, Nacke, L, McGregor, C & James, A 2015, 'Collaborative multi-touch clinical handover system for the neonatal intensive care unit', Electronic Journal of Health Informatics, vol. 9, no. 1.
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© by the authors. Background: A critically ill infant admitted to a neonatal intensive care unit requires complex, critical, and coordinated care performed by multidisciplinary healthcare teams. Since the infant's care is not provided by a single, individual physician during the infant's hospital stay, clinical handover is essential to enable the transfer of health information between physicians involved in the infant's care. Objective: Handover at present is largely conducted in an informal and ad hoc way. A study of clinical handover is required to inform the development of automated intelligent systems that facilitate communication and collaboration between critical care health providers. Methods: A qualitative study in a quaternary neonatal intensive care unit, at The Hospital for Sick Children was undertaken to understand clinical handover and derive usability requirements. This is then used to inform a high level design of a multi-touch tabletop application for handover the design was then evaluated against senior neonatologists and neonatal fellows using rapid prototyping methods. Results: The results of the qualitative study showed that an effective handover application should at minimum include: tight integration with workflow and the physical environment, intuitive and simplicity, and minimalistic design following the 'less is more' philosophy. Conclusion: There is a need to optimize handover such that the information transferred is standardized, and the loss of information and/or misinformation is minimized. We argue that natural user interface design employed in the proposed design will result in improved care and less information loss during clinical handover.
Kenning, G 2015, '‘Fiddling with threads’: Craft-‐based textile activities and positive well-‐being', Textiles: Journal of Cloth and Culture, vol. 13, no. 1, pp. 50-65.
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Craft-based textile activities such as knitting, crochet, tatting and lace making have provided challenges, physical and mental stimulation, creative outlets, and social interaction for generations. The role of craft and the relationship between craft and maker vary across cultures, geographic groups and gender. However, a common thread is that craft practitioners are often emotionally invested in these activities and many continue to make through all stages of life and into old age. Given the global ageing of the population, activities that can be carried out by people with reduced mobility and increasing physical or mental limitations as a result of ageing, and which can promote healthy ageing and positive well-being are now becoming increasingly important. Existing research has established a link between creativity and health and well-being. But, it is only recently that multidisciplinary research involving arts and crafts, social science, and medical and health scholars and practitioners, has begun to show the importance of activities, such as craft-textiles, for positive well-being. The relationship between craft activities, creative engagement, mental and physical stimulation, social interaction, self-esteem, and therefore, positive well-being has been insufficiently explored. This article reports findings of a study of lace makers at the Lace Study Centre at the Powerhouse Museum, Sydney, Australia that examined how craft activities contribute to well-being in a variety of ways. This research, which focused on female practitioners, found that craft-based textile activities and associated social practices provide insights into the individual and societal importance of ‘everyday creativity’ for promoting positive well-being and general good health.
Kenning, G 2015, 'Creative craft-based textile activity in the age of digital systems and practices', Leonardo - Oxford the Cambridge MA, vol. 48, no. 5, pp. 450-456.
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Domestic craft-based textile activities, such as knitting, crochet, hand weaving and lace making, are often viewed as being of limited creative potential. The perceived lack of creativity arises, in part, out of the extent to which these activities copy, reproduce and re-create existing pattern forms and use preexisting templates. This paper reports on the findings of an experimental research project that explored the creative potential of crochet lace making using digital media, technologies and practices. It provides critical analysis of how new technologies, practices and theoretical frameworks have implications for ongoing domestic craft-based textile activities.
Kenning, G 2015, 'Fellowships, Residencies and Resonances: A Self-Interview', Media-N: Journal of the New Media Caucus, vol. 11, pp. 1-6.
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My name is Gail Kenning. I am an artist, researcher, educator, and writer. The following article is an interview with myself. I am currently Design United Visiting Research Fellow at Eindhoven University of Technology (TU/e), Netherlands. I will be returning home to Sydney, Australia, where I am Researcher at University of Technology Sydney (UTS), Australia. I wanted to take time to reflect on this particular experience and also to think about what residencies and fellowships means for those in the new media field. The self-interview format allows me to briefly address some of the questions that come to mind when completing a fellowship or residency.
Khazaei, H, McGregor, C, Eklund, JM & El-Khatib, K 2015, 'Real-Time and Retrospective Health-Analytics-as-a-Service: A Novel Framework.', JMIR medical informatics, vol. 3, no. 4, p. e36.
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Analytics-as-a-service (AaaS) is one of the latest provisions emerging from the cloud services family. Utilizing this paradigm of computing in health informatics will benefit patients, care providers, and governments significantly. This work is a novel approach to realize health analytics as services in critical care units in particular.To design, implement, evaluate, and deploy an extendable big-data compatible framework for health-analytics-as-a-service that offers both real-time and retrospective analysis.We present a novel framework that can realize health data analytics-as-a-service. The framework is flexible and configurable for different scenarios by utilizing the latest technologies and best practices for data acquisition, transformation, storage, analytics, knowledge extraction, and visualization. We have instantiated the proposed method, through the Artemis project, that is, a customization of the framework for live monitoring and retrospective research on premature babies and ill term infants in neonatal intensive care units (NICUs).We demonstrated the proposed framework in this paper for monitoring NICUs and refer to it as the Artemis-In-Cloud (Artemis-IC) project. A pilot of Artemis has been deployed in the SickKids hospital NICU. By infusing the output of this pilot set up to an analytical model, we predict important performance measures for the final deployment of Artemis-IC. This process can be carried out for other hospitals following the same steps with minimal effort. SickKids' NICU has 36 beds and can classify the patients generally into 5 different types including surgical and premature babies. The arrival rate is estimated as 4.5 patients per day, and the average length of stay was calculated as 16 days. Mean number of medical monitoring algorithms per patient is 9, which renders 311 live algorithms for the whole NICU running on the framework. The memory and computation power required for Artemis-IC to handle the SickKids NICU will be 32 GB and ...
Khazaei, H, Mench-Bressan, N, McGregor, C & Pugh, JE 2015, 'Health Informatics for Neonatal Intensive Care Units: An Analytical Modeling Perspective', IEEE Journal of Translational Engineering in Health and Medicine, vol. 3.
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� 2013 IEEE. The effective use of data within intensive care units (ICUs) has great potential to create new cloud-based health analytics solutions for disease prevention or earlier condition onset detection. The Artemis project aims to achieve the above goals in the area of neonatal ICUs (NICU). In this paper, we proposed an analytical model for the Artemis cloud project which will be deployed at McMaster Children's Hospital in Hamilton. We collect not only physiological data but also the infusion pumps data that are attached to NICU beds. Using the proposed analytical model, we predict the amount of storage, memory, and computation power required for the system. Capacity planning and tradeoff analysis would be more accurate and systematic by applying the proposed analytical model in this paper. Numerical results are obtained using real inputs acquired from McMaster Children's Hospital and a pilot deployment of the system at The Hospital for Sick Children (SickKids) in Toronto.
Killick, J & Kenning, G 2015, 'Hand i Pockets: Creativity, playfulness and fun', Journal of Dementia Care, vol. 23, no. 5, pp. 25-27.
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A project that engages the creative side of ordinary people is bringing playfulness and fun into the lives of people with mid to late-stage dementia. John Killick and Gail Kenning report - See more at: http://www.careinfo.org/hand-i-pockets-creativity-playfulness-and-fun/#sthash.dLrWe0GA.dpuf
Killick, J & Kenning, G 2015, 'Hand i pockets; playfulness, fun and dementia', Australian Journal of Dementia Care, vol. 4, no. 2, pp. 9-11.
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Kulkarni, R, Qiao, Y & Sun, X 2015, 'Any monotone property of 3-uniform hypergraphs is weakly evasive', Theoretical Computer Science, vol. 588, pp. 16-23.
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© 2014 Elsevier B.V. For a Boolean function f, let D(f) denote its deterministic decision tree complexity, i.e., minimum number of (adaptive) queries required in worst case in order to determine f. In a classic paper, Rivest and Vuillemin [11] show that any non-constant monotone property P:{0,1}(n2)→{0,1} of n-vertex graphs has D(P)=Ω(n2).We extend their result to 3-uniform hypergraphs. In particular, we show that any non-constant monotone property P:{0,1}(n3)→{0,1} of n-vertex 3-uniform hypergraphs has D(P)=Ω(n3).Our proof combines the combinatorial approach of Rivest and Vuillemin with the topological approach of Kahn, Saks, and Sturtevant [6] . Interestingly, our proof makes use of Vinogradov's Theorem (weak Goldbach Conjecture), inspired by its recent use by Babai et al. [1] in the context of the topological approach. Our work leaves the generalization to k-uniform hypergraphs as an intriguing open question.
Lee, T, Ghapanchi, AH, Talaei-Khoei, A & Ray, P 2015, 'Strategic Information System Planning in Healthcare Organizations', Journal of Organizational and End User Computing, vol. 27, no. 2, pp. 1-31.
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Li, J, Lin, X, Rui, X, Rui, Y & Tao, D 2015, 'A Distributed Approach Toward Discriminative Distance Metric Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 9, pp. 2111-2122.
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Distance metric learning (DML) is successful in
discovering intrinsic relations in data. However, most algorithms
are computationally demanding when the problem size becomes
large. In this paper, we propose a discriminative metric learning
algorithm, develop a distributed scheme learning metrics on
moderate-sized subsets of data, and aggregate the results into
a global solution. The technique leverages the power of parallel
computation. The algorithm of the aggregated DML (ADML)
scales well with the data size and can be controlled by the
partition. We theoretically analyze and provide bounds for the
error induced by the distributed treatment. We have conducted
experimental evaluation of the ADML, both on specially designed
tests and on practical image annotation tasks. Those tests have
shown that the ADML achieves the state-of-the-art performance
at only a fraction of the cost incurred by most existing methods.
Li, SY, Chen, SA, Lin, CT, Ko, LW, Yang, CH & Chen, HH 2015, 'Generalized synchronization of nonlinear chaotic systems through natural bioinspired controlling strategy', Abstract and Applied Analysis, vol. 2015.
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© 2015 Shih-Yu Li et al. A novel bioinspired control strategy design is proposed for generalized synchronization of nonlinear chaotic systems, combining the bioinspired stability theory, fuzzy modeling, and a novel, simple-form Lyapunov control function design of derived high efficient, heuristic and bioinspired controllers. Three main contributions are concluded: (1) apply the bioinspired stability theory to further analyze the stability of fuzzy error systems; the high performance of controllers has been shown in previous study by Li and Ge 2009, (2) a new Lyapunov control function based on bioinspired stability theory is designed to achieve synchronization without using traditional LMI method, which is a simple linear homogeneous function of states and the process of designing controller to synchronize two fuzzy chaotic systems becomes much simpler, and (3) three different situations of synchronization are proposed; classical master and slave Lorenz systems, slave Chen's system, and Rossler's system as functional system are illustrated to further show the effectiveness and feasibility of our novel strategy. The simulation results show that our novel control strategy can be applied to different and complicated control situations with high effectiveness.
Li, W, Dai, Y, Hao, H, Lu, H, Albinson, R & Li, Z 2015, 'Oil-saving pathways until 2030 for road freight transportation in China based on a cost-optimization model', Energy, vol. 86, pp. 369-384.
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© 2015 Elsevier Ltd. This paper proposed a COSM (cost-optimization superstructure model) and derived the optimized oil-saving pathways for road freight transportation in China until 2030. The optimization target of the COSM was to minimize the accumulated energy and vehicle costs from 2010 to 2030 by choosing the most cost-effective fuel option for newly registered trucks each year. Based on the COSM, three scenarios were developed to evaluate the oil-saving pathway in terms of imported crude oil price, available alternative fuels and GHG emission reduction. The scenario analysis results indicate that: (1) for scenario A, the accumulated oil-saving potential was approximately about 13%, while the oil-saving potential of improving fuel consumption rate and load running rate was 17% and 16%; (2) for scenario B, the accumulated oil-saving potential increased to 82% in reference oil price and 23% in low oil price; (3) for scenario C, to reduce per ton of GHG emission, the increased cost will increase from 34 USD to 450 USD when the GHG emission target decreased from 15.4 billion tons to the turn point of 13.5 billion tons.
Li, X, Xu, G, Chen, E & Zong, Y 2015, 'Learning recency based comparative choice towards point-of-interest recommendation', Expert Systems with Applications, vol. 42, no. 9, pp. 4274-4283.
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With the prevalence of GPS-enabled smart phones, Location Based Social Network (LBSN) has emerged and become a hot research topic during the past few years. As one of the most important components in LBSN, Points-of-Interests (POIs) has been extensively studied by both academia and industry, yielding POI recommendations to enhance user experience in exploring the city. In conventional methods, rating vectors for both users and POIs are utilized for similarity calculation, which might yield inaccuracy due to the differences of user biases. In our opinion, the rating values themselves do not give exact preferences of users, however the numeric order of ratings given by a user within a certain period provides a hint of preference order of POIs by such user. Firstly, we propose an approach to model users preference by employing utility theory. Secondly, We devise a collection-wise learning method over partial orders through an effective stochastic gradient descent algorithm. We test our model on two real world datasets, i.e., Yelp and TripAdvisor, by comparing with some state-of-the-art approaches including PMF and several user preference modeling methods. In terms of MAP and Recall, we averagely achieve 15% improvement with regard to the baseline methods. The results show the significance of comparative choice in a certain time window and show its superiority to the existing methods.
Liang, J, Vinh Nguyen, Q, Simoff, S & Huang, M 2015, 'Divide and Conquer treemaps: Visualizing large trees with various shapes', Journal of Visual Languages & Computing, vol. 31, no. 2015, pp. 104-127.
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Liao, SH, Hsieh, JG, Chang, JY & Lin, CT 2015, 'Training neural networks via simplified hybrid algorithm mixing Nelder–Mead and particle swarm optimization methods', Soft Computing, vol. 19, no. 3, pp. 679-689.
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In this paper, a new and simplified hybrid algorithm mixing the simplex method of Nelder and Mead (NM) and particle swarm optimization algorithm (PSO), abbreviated as SNM-PSO, is proposed for the training of the parameters of the Artificial Neural Network (ANN). Our method differs from other hybrid PSO methods in that, n+1 particles, where n is the dimension of the search space, are randomly selected (without sorting), at each iteration of the proposed algorithm for use as the initial vertices of the NM algorithm, and each such particle is replaced by the corresponding final vertex after executing the NM algorithm. All the particles are then updated using the standard PSO algorithm. Our proposed method is simpler than other similar hybrid PSO methods and places more emphasis on the exploration of the search space. Some simulation problems will be provided to compare the performances of the proposed method with PSO and other similar hybrid PSO methods in training an ANN. These simulations show that the proposed method outperforms the other compared methods.
Lin, C, Pal, NR, Wu, SL, Liu, YT & Lin, YY 2015, 'An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination (IT2NFS-SIFE)', IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 7, pp. 1442-1455.
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We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.
Lin, C-T, Chiu, T-C & Gramann, K 2015, 'EEG correlates of spatial orientation in the human retrosplenial complex', NEUROIMAGE, vol. 120, pp. 123-132.
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Lin, C-T, Prasad, M & Saxena, A 2015, 'An Improved Polynomial Neural Network Classifier Using Real-Coded Genetic Algorithm', IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, vol. 45, no. 11, pp. 1389-1401.
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Lin, MS & Tomamichel, M 2015, 'Investigating Properties of a Family of Quantum Renyi Divergences', Quantum Information Processing, vol. 14, no. 4, pp. 1501-1512.
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Audenaert and Datta recently introduced a two-parameter family of relative
R\'{e}nyi entropies, known as the $\alpha$-$z$-relative R\'{e}nyi entropies.
The definition of the $\alpha$-$z$-relative R\'{e}nyi entropy unifies all
previously proposed definitions of the quantum R\'{e}nyi divergence of order
$\alpha$ under a common framework. Here we will prove that the
$\alpha$-$z$-relative R\'{e}nyi entropies are a proper generalization of the
quantum relative entropy by computing the limit of the $\alpha$-$z$ divergence
as $\alpha$ approaches one and $z$ is an arbitrary function of $\alpha$. We
also show that certain operationally relevant families of R\'enyi divergences
are differentiable at $\alpha = 1$. Finally, our analysis reveals that the
derivative at $\alpha = 1$ evaluates to half the relative entropy variance, a
quantity that has attained operational significance in second-order quantum
hypothesis testing.
Lu, J, Behbood, V, Hao, P, Zuo, H, Xue, S & Zhang, G 2015, 'Transfer Learning using Computational Intelligence: A Survey', Knowledge-Based Systems, vol. 80, pp. 14-23.
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Abstract Transfer learning aims to provide a framework to utilize previously-acquired
knowledge to solve new but similar problems much more quickly and effectively. In contrast
to classical machine learning methods, transfer learning methods exploit the knowledge
accumulated from data in auxiliary domains to facilitate predictive modeling consisting of
different data patterns in the current domain. To improve the performance of existing transfer
learning methods and handle the knowledge transfer process in real-world systems, ...
Lu, J, Wu, D, Mao, M, Wang, W & Zhang, G 2015, 'Recommender system application developments: A survey', DECISION SUPPORT SYSTEMS, vol. 74, pp. 12-32.
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Lu, J, Zheng, Z, Zhang, G, He, Q & Shi, Z 2015, 'A new solution algorithm for solving rule-sets based bilevel decision problems', Concurrency Computation Practice and Experience, vol. 27, no. 4, pp. 830-854.
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Bilevel decision addresses compromises between two interacting decision entities within a given hierarchical complex system under distributed environments. Bilevel programming typically solves bilevel decision problems. However, formulation of objectives and constraints in mathematical functions is required, which are difficult, and sometimes impossible, in real-world situations because of various uncertainties. Our study develops a rule-set based bilevel decision approach, which models a bilevel decision problem by creating, transforming and reducing related rule sets. This study develops a new rule-sets based solution algorithm to obtain an optimal solution from the bilevel decision problem described by rule sets. A case study and a set of experiments illustrate both functions and the effectiveness of the developed algorithm in solving a bilevel decision problem. © 2012 John Wiley & Sons, Ltd.
Lunghi, T, Kaniewski, J, Bussieres, F, Houlmann, R, Tomamichel, M, Wehner, S & Zbinden, H 2015, 'Practical Relativistic Bit Commitment', PHYSICAL REVIEW LETTERS, vol. 115, no. 3.
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Mai, S, Gramann, K, Herbert, BM, Friederich, H-C, Warschburger, P & Pollatos, O 2015, 'Electrophysiological evidence for an attentional bias in processing body stimuli in bulimia nervosa', BIOLOGICAL PSYCHOLOGY, vol. 108, pp. 105-114.
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Naderpour, M, Lu, J & Zhang, G 2015, 'A human-system interface risk assessment method based on mental models', SAFETY SCIENCE, vol. 79, pp. 286-297.
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Naderpour, M, Lu, J & Zhang, GQ 2015, 'An Abnormal Situation Modeling Method to Assist Operators in Safety-Critical Systems', Reliability Engineering and System Safety, vol. 133, pp. 33-47.
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One of the main causes of accidents in safety-critical systems is human error. In order to reduce human errors in the process of handling abnormal situations that are highly complex and mentally taxing activities, operators need to be supported, from a cognitive perspective, in order to reduce their workload, stress, and the consequent error rate. Of the various cognitive activities, a correct understanding of the situation, i.e. situation awareness (SA), is a crucial factor in improving performance and reducing errors. Despite the importance of SA in decision-making in time- and safety-critical situations, the difficulty of SA modeling and assessment means that very few methods have as yet been developed. This study confronts this challenge, and develops an innovative abnormal situation modeling (ASM) method that exploits the capabilities of risk indicators, Bayesian networks and fuzzy logic systems. The risk indicators are used to identify abnormal situations, Bayesian networks are utilized to model them and a fuzzy logic system is developed to assess them. The ASM method can be used in the development of situation assessment decision support systems that underlie the achievement of SA. The performance of the ASM method is tested through a real case study at a chemical plant.
Najaftorkaman, M, Ghapanchi, AH, Talaei-Khoei, A & Ray, P 2015, 'A Taxonomy of Antecedents to User Adoption of Health Information Systems: A Synthesis of Thirty Years of Research', Journal of the Association for Information Science and Technology, vol. 66, no. 3, pp. 576-598.
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In the past three decades, several studies have extracted antecedents to the user adoption of Health Information Systems (HIS). This study proposes a reflective pause on the HIS adoption literature in order to broaden our understanding of factors contributing to the user adoption of Electronic Medical Record (EMR). This paper aims to provide a comprehensive taxonomy of the factors influencing the user adoption of EMR and to classify these factors into meaningful categories. We searched the selected keywords on several academic databases and found an initial set of 9684 studies. We excluded papers on the basis of their title, abstract and full text (89 remaining papers). The effectiveness of adoption theories has been explored based on the empirical results identified in the EMR research. Furthermore, according to the conceptualization of the factors in the literature, a list of 78 factors affecting EMR adoption was identified. These factors were classified to eight categories: individual factors; psychological factors; behavioural factors; environmental factors; organizational factors; financial factors; legal factors; and technical factors. Finally, the results have implications for researchers and practitioners, including policymakers, marketers, Information Technology (IT) professionals, Health Information Management (HIM) practitioners, health practice managers, and EMR system developers.
Oberst, S & Lai, JCS 2015, 'A statistical approach to estimate the LYAPUNOV spectrum in disc brake squeal', JOURNAL OF SOUND AND VIBRATION, vol. 334, pp. 120-135.
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Oberst, S & Lai, JCS 2015, 'Nonlinear transient and chaotic interactions in disc brake squeal', JOURNAL OF SOUND AND VIBRATION, vol. 342, pp. 272-289.
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Oberst, S & Lai, JCS 2015, 'Pad-mode-induced instantaneous mode instability for simple models of brake systems', MECHANICAL SYSTEMS AND SIGNAL PROCESSING, vol. 62-63, pp. 490-505.
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Oberst, S & Lai, JCS 2015, 'Squeal noise in simple numerical brake models', JOURNAL OF SOUND AND VIBRATION, vol. 352, pp. 129-141.
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Oberst, S, Nava-Baro, E, Lai, JCS & Evans, TA 2015, 'An Innovative Signal Processing Method to Extract Ants' Walking Signals', ACOUSTICS AUSTRALIA, vol. 43, no. 1, pp. 87-96.
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Panetta, J, Zhou, Q, Malomo, L, Pietroni, N, Cignoni, P & Zorin, D 2015, 'Elastic textures for additive fabrication', ACM Transactions on Graphics (TOG), vol. 34, pp. 135-135.
Peppas, P, Williams, M-A, Chopra, S & Foo, N 2015, 'Relevance in belief revision', ARTIFICIAL INTELLIGENCE, vol. 229, pp. 126-138.
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Possible-world semantics are provided for Parikh’s relevance-sensitive axiom for belief
revision, known as axiom (P). Loosely speaking, axiom (P) states that if a belief set K
can be divided into two disjoint compartments, and the new information φ relates only
to the first compartment, then the second compartment should not be effected by the
revision of K by φ. Using the well-known connection between AGM revision functions and
preorders on possible worlds as our starting point, we formulate additional constraints on
such preorders that characterise precisely Parikh’s axiom (P). Interestingly, the additional
constraints essentially generalise a criterion of plausibility between possible worlds that
predates axiom (P). A by-product of our study is the identification of two possible readings
of Parikh’s axiom (P), which we call the strong and the weak versions of the axiom.
Regarding specific operators, we show that Dalal’s belief revision operator satisfies both
weak and strong (P), and it is therefore relevance-sensitive.
Percival, J, McGregor, C, Percival, N & James, A 2015, 'Enabling the integration of clinical event and physiological data for real-time and retrospective analysis', INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT, vol. 13, no. 4, pp. 693-711.
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Prasad, M, Li, DL, Lin, C, Prakash, S, Singh, J & Joshi, S 2015, 'Designing Mamdani-Type Fuzzy Reasoning for Visualizing Prediction Problems Based on Collaborative Fuzzy Clustering', IAENG International Journal of Computer Science, vol. 42, no. 4, pp. 404-411.
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In this paper a collaborative fuzzy c-means (CFCM) is used to generate fuzzy rules for fuzzy inference systems to evaluate the time series model. CFCM helps system to integrate two or more different datasets having similar features which are collected at the different environment with the different time period and it integrates these datasets together in order to visualize some common patterns among the datasets. In order to do any mode of integration between datasets, there is a necessity to define the common features between datasets by using some kind of collaborative process and also need to preserve the privacy and security at higher levels. This collaboration process gives a common structure between datasets which helps to define an appropriate number of rules for structural learning and also improve the accuracy of the system modeling
Prasad, M, Lin, YY, Lin, CT, Er, MJ & Prasad, OK 2015, 'A new data-driven neural fuzzy system with collaborative fuzzy clustering mechanism', NEUROCOMPUTING, vol. 167, pp. 558-568.
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Qiao, M, Bian, W, Xu, RYD & Tao, D 2015, 'Diversified Hidden Markov Models for Sequential Labeling', IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol. 27, no. 11, pp. 2947-2960.
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Raffe, WL, Zambetta, F, Li, X & Stanley, KO 2015, 'Integrated Approach to Personalized Procedural Map Generation Using Evolutionary Algorithms', IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, vol. 7, no. 2, pp. 139-155.
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Ramezani, F, Lu, J, Taheri, J & Hussain, FK 2015, 'Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments', WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, vol. 18, no. 6, pp. 1737-1757.
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Redshaw, S, Ingham, V & Loftus, S 2015, 'Emergency decision making: An exploration of tensions between communities of practice', International Journal of Emergency Management, vol. 11, no. 1, pp. 62-75.
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Copyright © 2015 Inderscience Enterprises Ltd. Through the lens of 'communities of practice' (Wenger, 2000) we examined decision-making boundaries and tensions in multiagency settings. Sixteen semi-structured interviews were conducted with operational decision makers from services across Australia. Data analysis was contextual and narrative, focusing on meanings signifying cultures and practices. Results indicate that protocols and guidelines which provide boundaries for a community of practice, could at times become an obstacle. Participants reported difficulties in sharing a 'common level of understanding' and 'getting the bigger picture'. The strongest theme was the importance placed on building relationships between services prior to emergency events. We found capability of multiagency decision making is enhanced when informal multiagency networks are already in place. These networks contribute to building a shared understanding. We propose that multiagency communities of practice could be enhanced if services increased their level of formal multiagency engagement and promoted the informal multiagency networking of their members and teams.
Rehman, Z-U, Hussain, OK & Hussain, FK 2015, 'User-side cloud service management: State-of-the-art and future directions', JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, vol. 55, pp. 108-122.
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Rehman, ZU, Hussain, OK, Hussain, FK, Chang, E & Dillon, T 2015, 'User-side QoS forecasting and management of cloud services', WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, vol. 18, no. 6, pp. 1677-1716.
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Sasaki, N, Chen, H-T, Sakamoto, D & Igarashi, T 2015, 'Facetons: face primitives for building 3D architectural models in virtual environments', COMPUTER ANIMATION AND VIRTUAL WORLDS, vol. 26, no. 2, pp. 185-194.
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Schoene, D, Valenzuela, T, Toson, B, Delbaere, K, Severino, C, Garcia, J, Davies, TA, Russell, F, Smith, ST & Lord, SR 2015, 'Interactive Cognitive-Motor Step Training Improves Cognitive Risk Factors of Falling in Older Adults - A Randomized Controlled Trial.', PLoS ONE, vol. 10, no. 12, pp. 1-18.
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PURPOSE: Interactive cognitive-motor training (ICMT) requires individuals to perform both gross motor movements and complex information processing. This study investigated the effectiveness of ICMT on cognitive functions associated with falls in older adults. METHODS: A single-blinded randomized controlled trial was conducted in community-dwelling older adults (N = 90, mean age 81.5±7) without major cognitive impairment. Participants in the intervention group (IG) played four stepping games that required them to divide attention, inhibit irrelevant stimuli, switch between tasks, rotate objects and make rapid decisions. The recommended minimum dose was three 20-minute sessions per week over a period of 16 weeks unsupervised at home. Participants in the control group (CG) received an evidence-based brochure on fall prevention. Measures of processing speed, attention/executive function (EF), visuo-spatial ability, concerns about falling and depression were assessed before and after the intervention. RESULTS: Eighty-one participants (90%) attended re-assessment. There were no improvements with respect to the Stroop Stepping Test (primary outcome) in the intervention group. Compared to the CG, the IG improved significantly in measures of processing speed, visuo-spatial ability and concern about falling. Significant interactions were observed for measures of EF and divided attention, indicating group differences varied for different levels of the covariate with larger improvements in IG participants with poorer baseline performance. The interaction for depression showed no change for the IG but an increase in the CG for those with low depressive symptoms at baseline. Additionally, low and high-adherer groups differed in their baseline performance and responded differently to the intervention. Compared to high adherers, low adherers improved more in processing speed and visual scanning while high-adherers improved more in tasks related to EF. CONCLUSIONS: This study sho...
Sharma, N, Shivakumara, P, Pal, U, Blumenstein, M & Tan, CL 2015, 'Piece-wise linearity based method for text frame classification in video', PATTERN RECOGNITION, vol. 48, no. 3, pp. 862-881.
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Song, R, Catchpoole, DR, Kennedy, PJ & Li, J 2015, 'Identification of lung cancer miRNA-miRNA co-regulation networks through a progressive data refining approach', JOURNAL OF THEORETICAL BIOLOGY, vol. 380, pp. 271-279.
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Sundaresan, S, Doss, R & Zhou, W 2015, 'Zero knowledge grouping proof protocol for RFID EPC C1G2 Tags', IEEE Transactions on Computers, vol. 64, no. 10, pp. 2994-3008.
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© 2015 IEEE. In this paper, we propose a novel zero knowledge grouping proof protocol for RFID Systems. Over the years, several protocols have been proposed in this area but they are either found to be vulnerable to certain attacks or do not comply with the EPC Class 1 Gen 2 (C1G2) standard because they use hash functions or other complex encryption schemes. Also, the unique design requirements of grouping proofs have not been fully addressed by many. Our protocol addresses these important security and design gaps in grouping proofs. We present a novel approach based on pseudo random squares and quadratic residuosity to realize a zero knowledge system. Tag operations are limited to functions such as modulo (MOD), exclusive-or (XOR) and 128 bit Pseudo Random Number Generators (PRNG). These can be easily implemented on passive tags and hence achieves compliance with the EPC Global standard while meeting the security requirements.
Sundaresan, S, Doss, R, Piramuthu, S & Zhou, W 2015, 'Secure tag search in RFID systems using mobile readers', IEEE Transactions on Dependable and Secure Computing, vol. 12, no. 2, pp. 230-242.
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© 2015 IEEE. One of the important features of an RFID system is its ability to search for a particular tag among a group of tags. In order to ensure privacy and security of the tags, the search has to be conducted in a secure fashion. To our knowledge not much work has been done in this secure search area of RFID. The minimal work that has been done do not comply with the EPC C1G2 (Class-1 Gen-2) standard since most of them use expensive hash operations or sophisticated encryption schemes that cannot be implemented in the low-cost passive tags that are highly resource constrained. Our work aims to fill this gap by proposing a protocol that is based on simple XOR encryption and 128 bit pseudo random number generators (PRNG), operations that can be easily implemented on low-cost passive tags. Thus, our protocol enables large scale implementations and achieves EPC C1G2 compliance while meeting the security requirements. The protocol provides additional protection by hiding the pseudo-random number during all transmissions using a blind-factor.
Sundaresan, S, Doss, R, Zhou, W & Piramuthu, S 2015, 'Secure ownership transfer for multi-tag multi-owner passive RFID environment with individual-owner-privacy', Computer Communications, vol. 55, pp. 112-124.
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© 2014 Elsevier B.V. All rights reserved. In this paper we propose a secure ownership transfer protocol for a multi-tag multi-owner RFID environment that provides individual-owner-privacy. To our knowledge, the existing schemes do not provide individual-owner-privacy and most of the existing schemes do not comply with the EPC Global Class-1 Gen-2 (C1G2) standard since the protocols use expensive hash operations or sophisticated encryption schemes that cannot be implemented on low-cost passive tags that are highly resource constrained. Our work aims to fill these gaps by proposing a protocol that provides individual-owner-privacy, based on simple XOR and 128-bit pseudo-random number generators (PRNG), operations that are easily implemented on low-cost RFID tags while meeting the necessary security requirements thus making it a viable option for large scale implementations. Our protocol also provides additional protection by hiding the pseudo-random numbers during all transmissions using a blind-factor to prevent tracking attacks.
Tan, VYF & Tomamichel, M 2015, 'The Third-Order Term in the Normal Approximation for the AWGN Channel', IEEE TRANSACTIONS ON INFORMATION THEORY, vol. 61, no. 5, pp. 2430-2438.
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Tomamichel, M & Tan, VYF 2015, 'Second-Order Asymptotics for the Classical Capacity of Image-Additive Quantum Channels', COMMUNICATIONS IN MATHEMATICAL PHYSICS, vol. 338, no. 1, pp. 103-137.
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van den Hoven, E & van Bergen, T 2015, 'Tangible Cooperative Gestures: Improving Control and Initiative in Digital Photo Sharing', Machines, vol. 3, no. 4, pp. 268-295.
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This paper focuses on co-present digital photo sharing on a notebook and investigates how this could be supported. While analyzing the current digital photo sharing situation we noticed that there was a high threshold for visitors to take control of the personal computer of the photo owner, resulting in inequity of participation. It was assumed that visitors would have the opportunity to interact with the notebook more freely if this threshold was lowered by distributing the user interface and creating a more public, instead of personal, interaction space. This, in turn, could make them feel more involved and in control during a session, creating a more enjoyable experience. To test these assumptions a design prototype was created that stimulates participants to use tangible artifacts for cooperative gestures, a promising direction for the future of HCI. The situation with the cooperative gestures was compared with the regular digital photo sharing situation, which makes use of a keyboard. In dyads, visitors felt more involved and in control in the design prototype cooperative gestures condition (especially during storytelling), resulting in a more enjoyable digital photo sharing experience.
Vasauskaite, J & Gill, AQ 2015, 'Rethinking Enterprise Architecture for Sustainable Energy System Development', Journal of Electronic Science and Technology, vol. 13, no. 3, pp. 212-220.
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Vichitvanichphong, S, Talaei-Khoei, A, Kerr, D & Ghapanchi, AH 2015, 'What Does Happen to Our Driving When We Get Older?', TRANSPORT REVIEWS, vol. 35, no. 1, pp. 56-81.
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Wang, W, Zhang, G & Lu, J 2015, 'Collaborative Filtering with Entropy-Driven User Similarity in Recommender Systems', INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, vol. 30, no. 8, pp. 854-870.
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Wang, X, Zhang, Y, Zhang, W, Lin, X & Wang, W 2015, 'AP-Tree: efficiently support location-aware Publish/Subscribe', VLDB Journal, vol. 24, no. 6, pp. 823-848.
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© 2015, Springer-Verlag Berlin Heidelberg. We investigate the problem of efficiently supporting location-aware Publish/Subscribe (Pub/Sub for short), which is essential in many applications such as location-based recommendation and advertising, thanks to the proliferation of geo-equipped devices and the ensuing location-based social media applications. In a location-aware Pub/Sub system (e.g., an e-coupon system), subscribers can register their interest as spatial-keyword subscriptions (e.g., interest in nearby iphone discount); each incoming geo-textual message (e.g., geo-tagged e-coupon) will be delivered to all the relevant subscribers immediately. While there are several prior approaches aiming at providing efficient processing techniques for this problem, their approaches belong to spatial-prioritized indexing method which cannot well exploit the keyword distribution. In addition, their textual filtering techniques are built upon simple variants of traditional inverted indexes, which do not perform well for the textual constraint imposed by the problem. In this paper, we address the above limitations and provide a highly efficient solution based on a novel adaptive index, named AP-Tree. AP-Tree adaptively groups registered subscriptions using keyword and spatial partitions, guided by a cost model. AP-Tree also naturally indexes ordered keyword combinations. Furthermore, we show that our techniques can be extended to process moving spatial-keyword subscriptions, where subscribers can continuously update their locations. We present efficient algorithms to process both stationary and moving subscriptions, which can seamlessly and effectively integrate keyword and spatial partitions. Our extensive experiments demonstrate that AP-Tree and its variant AP + -Tree can achieve up to an order of magnitude improvement on efficiency compared with prior state-of-the-art methods.
Wang, Y-K, Jung, T-P & Lin, C-T 2015, 'EEG-Based Attention Tracking During Distracted Driving', IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, vol. 23, no. 6, pp. 1085-1094.
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Wen, S, Haghighi, MS, Chen, C, Xiang, Y, Zhou, W & Jia, W 2015, 'A sword with two edges: Propagation studies on both positive and negative information in online social networks', IEEE Transactions on Computers, vol. 64, no. 3, pp. 640-653.
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© 1968-2012 IEEE. Online social networks (OSN) have become one of the major platforms for people to exchange information. Both positive information (e.g., ideas, news and opinions) and negative information (e.g., rumors and gossips) spreading in social media can greatly influence our lives. Previously, researchers have proposed models to understand their propagation dynamics. However, those were merely simulations in nature and only focused on the spread of one type of information. Due to the human-related factors involved, simultaneous spread of negative and positive information cannot be thought of the superposition of two independent propagations. In order to fix these deficiencies, we propose an analytical model which is built stochastically from a node level up. It can present the temporal dynamics of spread such as the time people check newly arrived messages or forward them. Moreover, it is capable of capturing people's behavioral differences in preferring what to believe or disbelieve. We studied the social parameters impact on propagation using this model. We found that some factors such as people's preference and the injection time of the opposing information are critical to the propagation but some others such as the hearsay forwarding intention have little impact on it. The extensive simulations conducted on the real topologies confirm the high accuracy of our model.
Wen, S, Wu, D, Li, P, Xiang, Y, Zhou, W & Wei, G 2015, 'Detecting stepping stones by abnormal causality probability', Security and Communication Networks, vol. 8, no. 10, pp. 1831-1844.
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© 2014 John Wiley & Sons, Ltd. Locating the real source of the Internet attacks has long been an important but difficult problem to be addressed. In the real world, attackers can easily hide their identities and evade punishment by relaying their attacks through a series of compromised systems or devices called stepping stones. Currently, researchers mainly use similar features from the network traffic, such as packet timestamps and frequencies, to detect stepping stones. However, these features can be easily destroyed by attackers using evasive techniques. In addition, it is also difficult to implement an appropriate threshold of similarity that can help justify the stepping stones. In order to counter these problems, in this paper, we introduce the consistent causality probability to detect the stepping stones. We formulate the ranges of abnormal causality probabilities according to the different network conditions, and on the basis of it, we further implement to self-adaptive methods to capture stepping stones. To evaluate our proposed detection methods, we adopt theoretic analysis and empirical studies, which demonstrate accuracy of the abnormal causality probability. Moreover, we compare our proposed methods with previous works. The result shows that our methods in this paper significantly outperform previous works in the accuracy of detection malicious stepping stones, even when evasive techniques are adopted by attackers.
Wu, D, Lu, J & Zhang, G 2015, 'A Fuzzy Tree Matching-Based Personalized E-Learning Recommender System', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 23, no. 6, pp. 2412-2426.
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Wu, D, Zhang, G & Lu, J 2015, 'A Fuzzy Preference Tree-Based Recommender System for Personalized Business-to-Business E-Services', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 23, no. 1, pp. 29-43.
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Wu, Z, Shi, J, Lu, C, Chen, E, Xu, G, Li, G, Xie, S & Yu, PS 2015, 'Constructing plausible innocuous pseudo queries to protect user query intention', INFORMATION SCIENCES, vol. 325, pp. 215-226.
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Xu, G, Wu, Z, Li, G & Chen, E 2015, 'Improving contextual advertising matching by using Wikipedia thesaurus knowledge', Knowledge And Information Systems, vol. 43, no. 3, pp. 599-631.
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As a prevalent type of Web advertising, contextual advertising refers to the placement of the most relevant commercial ads within the content of a Web page, to provide a better user experience and as a result increase the user’s ad-click rate. However, due to the intrinsic problems of homonymy and polysemy, the low intersection of keywords, and a lack of sufficient semantics, traditional keyword matching techniques are not able to effectively handle contextual matching and retrieve relevant ads for the user, resulting in an unsatisfactory performance in ad selection. In this paper, we introduce a new contextual advertising approach to overcome these problems, which uses Wikipedia thesaurus knowledge to enrich the semantic expression of a target page (or an ad). First, we map each page into a keyword vector, upon which two additional feature vectors, the Wikipedia concept and category vector derived from the Wikipedia thesaurus structure, are then constructed. Second, to determine the relevant ads for a given page, we propose a linear similarity fusion mechanism, which combines the above three feature vectors in a unified manner. Last, we validate our approach using a set of real ads, real pages along with the external Wikipedia thesaurus. The experimental results show that our approach outperforms the conventional contextual advertising matching approaches and can substantially improve the performance of ad selection.
Xu, G, Wu, Z, Zhang, Y & Cao, J 2015, 'Social networking meets recommender systems: survey', International Journal of Social Network Mining, vol. 2, no. 1, pp. 64-100.
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Today, the emergence of web-based communities and hosted services such as social networking sites, wikis and folksonomies, brings in tremendous freedom of web autonomy and facilitate collaboration and knowledge sharing between users. Along with the interaction between users and computers, social media is rapidly becoming an important part of our digital experience, ranging from digital textual information to diverse multimedia forms. These aspects and characteristics constitute of the core of second generation of web. Social networking (SN) and recommender system (RS) are two hot and popular topics in the current Web 2.0 era, where the former emphasises the generation, dissemination and evolution of user relations, and the latter focuses on the use of collective preferences of users so as to provide the better experience and loyalty of users in various web applications. Leveraging user social connections is able to alleviate the common problems of sparsity and cold-start encountered in RS. This paper aims to summarise the research progresses and findings in these two areas and showcase the empowerment of integrating these two kinds of research strengths.
Xu, G, Zong, Y, Jin, P, Pan, R & Wu, Z 2015, 'KIPTC: a kernel information propagation tag clustering algorithm', Journal of Intelligent Information Systems, vol. 45, no. 1, pp. 95-112.
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In the social annotation systems, users annotate digital data sources by using tags which are freely chosen textual descriptions. Tags are used to index, annotate and retrieve resource as an additional metadata of resource. Poor retrieval performance remains a major challenge of most social annotation systems resulting from several problems of ambiguity, redundancy and less semantic nature of tags. Clustering is a useful tool to handle these problems in social annotation systems. In this paper, we propose a novel tag clustering algorithm based on kernel information propagation. This approach makes use of the kernel density estimation of the kNN neighborhood directed graph as a start to reveal the prestige rank of tags in tagging data. The random walk with restart algorithm is then employed to determine the center points of tag clusters. The main strength of the proposed approach is the capability of partitioning tags from the perspective of tag prestige rank rather than the intuitive similarity calculation itself. Experimental studies on the six real world data sets demonstrate the effectiveness and superiority of the proposed method against other state-of-the-art clustering approaches in terms of various evaluation metrics.
Xuan, J, Lu, J, Zhang, G & Luo, X 2015, 'Topic Model for Graph Mining', IEEE Transactions on Cybernetics, vol. 45, no. 2, pp. 2792-2803.
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Graph mining has been a popular research area
because of its numerous application scenarios. Many unstructured
and structured data can be represented as graphs, such as,
documents, chemical molecular structures, and images. However,
an issue in relation to current research on graphs is that they
cannot adequately discover the topics hidden in graph-structured
data which can be beneficial for both the unsupervised learning
and supervised learning of the graphs. Although topic models
have proved to be very successful in discovering latent topics,
the standard topic models cannot be directly applied to graphstructured
data due to the “bag-of-word” assumption. In this
paper, an innovative graph topic model (GTM) is proposed to
address this issue, which uses Bernoulli distributions to model
the edges between nodes in a graph. It can, therefore, make the
edges in a graph contribute to latent topic discovery and further
improve the accuracy of the supervised and unsupervised learning
of graphs. The experimental results on two different types
of graph datasets show that the proposed GTM outperforms the
latent Dirichlet allocation on classification by using the unveiled
topics of these two models to represent graphs.
Xuan, J, Lu, J, Zhang, G, Xu, RYD & Luo, X 2015, 'Infinite Author Topic Model based on Mixed Gamma-Negative Binomial Process.', CoRR, vol. abs/1503.08535, pp. 1-10.
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Incorporating the side information of text corpus, i.e., authors,
time stamps, and emotional tags, into the traditional
text mining models has gained significant interests in the
area of information retrieval, statistical natural language
processing, and machine learning. One branch of these works
is the so-called Author Topic Model (ATM), which incorporates
the authors’s interests as side information into the
classical topic model. However, the existing ATM needs to
predefine the number of topics, which is difficult and inappropriate
in many real-world settings. In this paper, we propose
an Infinite Author Topic (IAT) model to resolve this
issue. Instead of assigning a discrete probability on fixed
number of topics, we use a stochastic process to determine
the number of topics from the data itself. To be specific, we
extend a gamma-negative binomial process to three levels in
order to capture the author-document-keyword hierarchical
structure. Furthermore, each document is assigned a mixed
gamma process that accounts for the multi-author’s contribution
towards this document. An efficient Gibbs sampling
inference algorithm with each conditional distribution being
closed-form is developed for the IAT model. Experiments
on several real-world datasets show the capabilities of our
IAT model to learn the hidden topics, authors’ interests on
these topics and the number of topics simultaneously.
Yin, H, Cui, B, Chen, L, Hu, Z & Zhang, C 2015, 'Modeling Location-Based User Rating Profiles for Personalized Recommendation', ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, vol. 9, no. 3.
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Yin, H, Cui, B, Chen, L, Hu, Z & Zhou, X 2015, 'Dynamic User Modeling in Social Media Systems', ACM TRANSACTIONS ON INFORMATION SYSTEMS, vol. 33, no. 3.
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Yu, S, Wang, G & Zhou, W 2015, 'Modeling malicious activities in cyber space', IEEE Network, vol. 29, no. 6, pp. 83-87.
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© 1986-2012 IEEE. Cyber attacks are an unfortunate part of society as an increasing amount of critical infrastructure is managed and controlled via the Internet. In order to protect legitimate users, it is critical for us to obtain an accurate and timely understanding of our cyber opponents. However, at the moment we lack effective tools to do this. In this article we summarize the work on modeling malicious activities from various perspectives, discuss the pros and cons of current models, and present promising directions for possible efforts in the near future.
Zeng, X, Bian, W, Liu, W, Shen, J & Tao, D 2015, 'Dictionary Pair Learning on Grassmann Manifolds for Image Denoising', IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 24, no. 11, pp. 4556-4569.
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Zhan, L, Zhang, Y, Zhang, W & Lin, X 2015, 'Finding Top k Most Influential Spatial Facilities over Uncertain Objects', IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 12, pp. 3289-3303.
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—Due to a variety of reasons including data randomness and incompleteness, noise, privacy, etc., uncertainty is inherent in
many important applications, such as location-based services (LBS), sensor network monitoring, and radio-frequency identification
(RFID). Recently, considerable research efforts have been devoted into the field of uncertainty-aware spatial query processing such that
the uncertainty of the data can be effectively and efficiently tackled. In this paper, we study the problem of finding top k most influential
facilities over a set of uncertain objects, which is an important and fundamental spatial query in the above applications. Based on the
maximal utility principle, we propose a new ranking model to identify the top k most influential facilities, which carefully captures influence
of facilities on the uncertain objects. By utilizing two uncertain object indexing techniques, R-tree and U-Quadtree, effective and efficient
algorithms are proposed following the filtering and verification paradigm, which significantly improves the performance of the algorithms
in terms of CPU and I/O costs. To effectively support uncertain objects with a large number of instances, we also develop randomized
algorithms with accuracy guarantee. Then, a hybrid algorithm is devised which effectively combines the randomized and exact
algorithms. Comprehensive experiments on real datasets demonstrate the effectiveness and efficiency of our techniques.
Zhang, G, Lu, J & Gao, Y 2015, 'Bi-level Multi-follower Decision Making', Intelligent Systems Reference Library, vol. 82, pp. 65-104.
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© Springer-Verlag Berlin Heidelberg 2015. A bi-level decision problem may involve multiple decision entities (decision units or decision makers) at the lower level, and these followers may have different reactions for a possible decision made by the leader.
Zhang, G, Lu, J & Gao, Y 2015, 'Bi-level Multi-leader Decision Making', Intelligent Systems Reference Library, vol. 82, pp. 105-120.
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© Springer-Verlag Berlin Heidelberg 2015. In real-world applications, a bi-level decision problem may involve multiple decision entities on the upper level, that is, the bi-level decision problem has multiple leaders. The leaders may have their individual decision variables, objective functions and/or constraint conditions. This kind of bi-level decision problem is called a bi-level multi-leader (BLML) decision problem.
Zhang, G, Lu, J & Gao, Y 2015, 'Bi-level Pricing and Replenishment in Supply Chains', Intelligent Systems Reference Library, vol. 82, pp. 325-336.
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© Springer-Verlag Berlin Heidelberg 2015. Effective pricing and replenishment strategies in supply chain management are the keys to business success. Notably, with rapid technological innovation and strong competition in hi-tech industries such as computer and communication organizations, the upstream component price and the down-stream product cost usually decline significantly with time. As a result, effective pricing and replenishment decision models are very important in supply chain management. This chapter first establishes a bi-level pricing and replenishment strategy optimization model in hi-tech industry. Then, two bi-level pricing models for pricing problems, in which the buyer and the vendor in a supply chain are respectively designated as the leader and the follower, are presented. Experiments illustrate that bi-level decision techniques can solve problems defined by these models and can achieve a profit increase under some situations, compared with the existing methods.
Zhang, G, Lu, J & Gao, Y 2015, 'Bi-level Programming for Competitive Strategic Bidding Optimization in Electricity Markets', Intelligent Systems Reference Library, vol. 82, pp. 315-324.
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© Springer-Verlag Berlin Heidelberg 2015. We focus on the application of bi-level programming in electricity markets (power market) in this chapter. Competitive strategic bidding optimization of electric power plants (companies) is becoming one of the key issues in electricity markets. This chapter presents a strategic bidding optimization technique developed by applying the bi-level programming. By analyzing the strategic bidding behavior of power plants, we understand that this bidding problem includes several power plants and only one market operator respectively known as multiple leaders and single follower.
Zhang, G, Lu, J & Gao, Y 2015, 'Bi-level Programming Models and Algorithms', Intelligent Systems Reference Library, vol. 82, pp. 47-62.
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© Springer-Verlag Berlin Heidelberg 2015. This chapter introduces basic definitions, theorems, models and algorithms for bi-level programming (bi-level decision-making) and also basic models of multi-level programming, which will be used in the remaining chapters of this book.
Zhang, G, Lu, J & Gao, Y 2015, 'Fuzzy Multi-objective Bi-level Goal Programming', Intelligent Systems Reference Library, vol. 82, pp. 229-247.
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© Springer-Verlag Berlin Heidelberg 2015. we presented the definitions, solutions, and algorithms for the fuzzy multi-objective bi-level programming (FMO-BLP) problems. This chapter still addresses the fuzzy multi-objective bi-level problem but applies a goal programming approach. We call it fuzzy multi-objective bi-level goal programming (FMO-BLGP). This chapter will discuss related definitions, solution concepts, and algorithms for the FMO-BLGP problem and will focus on the linear version of the FMO-BLGP problem. First, a fuzzy ranking method is used to give a mathematical definition for a FMO-BLGP problem, and then, based on a fuzzy vectors distance measure definition, a fuzzy bi-level goal programming (FBLGP) model is proposed. An algorithm for solving the FMO-BLGP problem is also developed.
Zhang, G, Lu, J & Gao, Y 2015, 'Optimization Models', Intelligent Systems Reference Library, vol. 82, pp. 25-46.
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© Springer-Verlag Berlin Heidelberg 2015. To model and solve a bi-level or multi-level optimization problem, we have to first understand basic single-level optimization models and related solution methods. This chapter introduces related concepts, models and solution methods of basic single-level optimization including linear programming, non-linear programming, multi-objective programming, goal programming, Stackelberg game theory, and particle swarm optimization. These knowledge will be used in the rest of the book.
Zhang, G, Lu, J & Gao, Y 2015, 'Rule-Set-Based Bi-level Decision Making', Intelligent Systems Reference Library, vol. 82, pp. 251-286.
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© Springer-Verlag Berlin Heidelberg 2015. As discussed in previous chapters, bi-level decision-making problems are normally modeled by bi-level programming.
Zhang, G, Lu, J & Gao, Y 2015, 'Tri-level Multi-follower Decision Making', Intelligent Systems Reference Library, vol. 82, pp. 121-171.
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© Springer-Verlag Berlin Heidelberg 2015. In a tri-level hierarchical decision problem, each decision entity at one level has its objective, constraints and decision variables affected in part by the decision entities at the other two levels. The choice of values for its variables may allow it to influence the decisions made at other levels, and thereby improve its own objective. We called this a tri-level decision problem. When multiple decision entities are involved at the middle and bottom levels, the top-level entity’s decision will be affected not only by these followers’ individual reactions but also by the relationships among the followers. We call this problem a tri-level multi-follower (TLMF) decision.
Zhang, J, Chen, X, Xiang, Y, Zhou, W & Wu, J 2015, 'Robust Network Traffic Classification', IEEE/ACM Transactions on Networking, vol. 23, no. 4, pp. 1257-1270.
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© 2015 IEEE. As a fundamental tool for network management and security, traffic classification has attracted increasing attention in recent years. A significant challenge to the robustness of classification performance comes from zero-day applications previously unknown in traffic classification systems. In this paper, we propose a new scheme of Robust statistical Traffic Classification (RTC) by combining supervised and unsupervised machine learning techniques to meet this challenge. The proposed RTC scheme has the capability of identifying the traffic of zero-day applications as well as accurately discriminating predefined application classes. In addition, we develop a new method for automating the RTC scheme parameters optimization process. The empirical study on real-world traffic data confirms the effectiveness of the proposed scheme. When zero-day applications are present, the classification performance of the new scheme is significantly better than four state-of-the-art methods: random forest, correlation-based classification, semi-supervised clustering, and one-class SVM.
Zhang, J, Huang, ML & Meng, Z-P 2015, 'Visual Analytics for BigData Variety and Its Behaviours', COMPUTER SCIENCE AND INFORMATION SYSTEMS, vol. 12, no. 4, pp. 1171-1191.
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Zhang, W, Li, A, Cheema, MA, Zhang, Y & Chang, L 2015, 'Probabilistic n-of-N skyline computation over uncertain data streams', World Wide Web, vol. 18, no. 5, pp. 1331-1350.
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© 2014, Springer Science+Business Media New York. Skyline operator is a useful tool in multi-criteria decision making in various applications. Uncertainty is inherent in real applications due to various reasons. In this paper, we consider the problem of efficiently computing probabilistic skylines against the most recent N uncertain elements in a data stream seen so far. Specifically, we study the problem in the n-of-N model; that is, computing the probabilistic skyline for the most recent n (∀ n ≤ N) elements, where an element is a probabilistic skyline element if its skyline probability is not below a given probability threshold q. Firstly, an effective pruning technique to minimize the number of uncertain elements to be kept is developed. It can be shown that on average storing only O(log d N) uncertain elements from the most recent N elements is sufficient to support the precise computation of all probabilistic n-of-N skyline queries in a d-dimension space if the data distribution on each dimension is independent. A novel encoding scheme is then proposed together with efficient update techniques so that computing a probabilistic n-of-N skyline query in a d-dimension space is reduced to O(dloglogN + s) if the data distribution is independent, where s is the number of skyline points. A trigger based technique is provided to process continuous n-of-N skyline queries. Extensive experiments demonstrate that the new techniques on uncertain data streams can support on-line probabilistic skyline query computation over rapid data streams.
Zheng, Z, Peng, K, Du, W & Zhang, G 2015, 'Modeling, Control, and Optimization in Aeronautical Engineering', Scientific World Journal, vol. 2015.
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Zhu, T, Li, G, Ren, Y, Zhou, W & Xiong, P 2015, 'Privacy preserving data release for tagging recommender systems', Web Intelligence, vol. 13, no. 4, pp. 229-246.
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© 2015-IOS Press and the authors. All rights reserved. Tagging recommender systems allow Internet users to annotate resources with personalized tags. The connection among users, resources and these annotations, often called a folksonomy, permits users the freedom to explore tags, and to obtain recommendations. Releasing these tagging datasets accelerates both commercial and research work on recommender systems. However, tagging recommender systems has been confronted with serious privacy concerns because adversaries may re-identify a user and her/his sensitive information from the tagging dataset using a little background information. Recently, several private techniques have been proposed to address the problem, but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes an private releasing algorithm to perturb users' profile in a strict privacy notion, differential privacy, with the goal of preserving a user's identity in a tagging dataset. The algorithm includes three privacy-preserving operations: Private Tag Clustering is used to shrink the randomized domain and Private Tag Selection is then applied to find the most suitable replacement tags for the original tags. To hide the numbers of tags, the third operation, Weight Perturbation, finally adds Laplace noise to the weight of tags. We present extensive experimental results on two real world datasets, De.licio.us and Bibsonomy. While the personalization algorithm is successful in both cases, our results further suggest the private releasing algorithm can successfully retain the utility of the datasets while preserving users' identity.
Zhu, T, Xiong, P, Li, G & Zhou, W 2015, 'Correlated differential privacy: Hiding information in Non-IID data set', IEEE Transactions on Information Forensics and Security, vol. 10, no. 2, pp. 229-242.
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© 2005-2012 IEEE. Privacy preserving on data mining and data release has attracted an increasing research interest over a number of decades. Differential privacy is one influential privacy notion that offers a rigorous and provable privacy guarantee for data mining and data release. Existing studies on differential privacy assume that in a data set, records are sampled independently. However, in real-world applications, records in a data set are rarely independent. The relationships among records are referred to as correlated information and the data set is defined as correlated data set. A differential privacy technique performed on a correlated data set will disclose more information than expected, and this is a serious privacy violation. Although recent research was concerned with this new privacy violation, it still calls for a solid solution for the correlated data set. Moreover, how to decrease the large amount of noise incurred via differential privacy in correlated data set is yet to be explored. To fill the gap, this paper proposes an effective correlated differential privacy solution by defining the correlated sensitivity and designing a correlated data releasing mechanism. With consideration of the correlated levels between records, the proposed correlated sensitivity can significantly decrease the noise compared with traditional global sensitivity. The correlated data releasing mechanism correlated iteration mechanism is designed based on an iterative method to answer a large number of queries. Compared with the traditional method, the proposed correlated differential privacy solution enhances the privacy guarantee for a correlated data set with less accuracy cost. Experimental results show that the proposed solution outperforms traditional differential privacy in terms of mean square error on large group of queries. This also suggests the correlated differential privacy can successfully retain the utility while preserving the privacy.
Zong, T, Xiang, Y, Natgunanathan, I, Guo, S, Zhou, W & Beliakov, G 2015, 'Robust histogram shape-based method for image watermarking', IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 5, pp. 717-729.
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© 1991-2012 IEEE. Cropping and random bending are two common attacks in image watermarking. In this paper we propose a novel image-watermarking method to deal with these attacks, as well as other common attacks. In the embedding process, we first preprocess the host image by a Gaussian low-pass filter. Then, a secret key is used to randomly select a number of gray levels and the histogram of the filtered image with respect to these selected gray levels is constructed. After that, a histogram-shape-related index is introduced to choose the pixel groups with the highest number of pixels and a safe band is built between the chosen and nonchosen pixel groups. A watermark-embedding scheme is proposed to insert watermarks into the chosen pixel groups. The usage of the histogram-shape-related index and safe band results in good robustness. Moreover, a novel high-frequency component modification mechanism is also utilized in the embedding scheme to further improve robustness. At the decoding end, based on the available secret key, the watermarked pixel groups are identified and watermarks are extracted from them. The effectiveness of the proposed image-watermarking method is demonstrated by simulation examples.
Abdullaev, S, Mcburney, P & Musial, K 2015, 'Direct exchange mechanisms for option pricing', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 269-284.
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© Springer International Publishing Switzerland 2015. This paper presents the design and simulation of direct exchange mechanisms for pricing European options. It extends McAfee's single-unit double auction to multi-unit format, and then applies it for pricing options through aggregating agent predictions of future asset prices. We will also propose the design of a combinatorial exchange for the simulation of agents using option trading strategies. We present several option trading strategies that are commonly used in real option markets to minimise the risk of future loss, and assume that agents can submit them as a combinatorial bid to the market maker. We provide simulation results for proposed mechanisms, and compare them with existing Black-Scholes model mostly used for option pricing. The simulation also tests the effect of supply and demand changes on option prices. It also takes into account agents with different implied volatility. We also observe how option prices are affected by the agents’ choices of option trading strategies.
Adak, C & Chaudhuri, BB 2015, 'Writer Identification from Offline Isolated Bangia Characters and Numerals', 2015 13TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR), 13th IAPR International Conference on Document Analysis and Recognition (ICDAR), IEEE, Nancy, FRANCE, pp. 486-490.
Adak, C, Maitra, P, Chaudhuri, BB & Blumenstein, M 2015, 'Binarization of old halftone text documents', IEEE Region 10 Annual International Conference, Proceedings/TENCON, IEEE Tencon (IEEE Region 10 Conference), IEEE, Macao, PEOPLES R CHINA, pp. 1-5.
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© 2015 IEEE. A degraded document image should be cleaned before subjecting to Optical Character Recognition (OCR), otherwise the result may be erroneous. Though major studies have been conducted on degraded document image cleaning, halftone documents received less attention. Since halftone documents contain halftone dot patterns, classical binarization techniques do not produce proper output for feeding into the OCR engine. In this paper, old halftone documents are considered for text area cleaning and binarization. At the beginning, the zone of interest (text area) is found using local binary pattern and contour analysis. Reasonably smaller zones are filtered out as noise. Then the foreground pixels are separated using background estimation. After this, an automated spatial smoothing technique is employed on the foreground. At last, a local binarization technique is used to produce the binary image. The proposed method is tested on various old and degraded halftone documents, which has produced fairly good results.
Agrawal, H, Umapathi, U, Kovacs, R, Johannes, F, Chen, HT, Mueller, S & Baudisch, P 2015, 'Protopiper: Physically sketching room-sized objects at actual scale', UIST 2015 - Proceedings of the 28th Annual ACM Symposium on User Interface Software and Technology, ACM Symposium on User Interface Software and Technology, ACM, USA, pp. 427-436.
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Physical sketching of 3D wireframe models using a handheld plastic extruder allows users to explore the design space of 3D models efficiently. Unfortunately, the scale of these devices limits users? design explorations to small-scale objects. We present protopiper, a computer aided, hand-held fabrication device that allows users to sketch room-sized objects at actual scale. The key idea behind protopiper is that it forms adhesive tape into tubes as its main building material, rather than extruded plastic or photopolymer lines. Since the resulting tubes are hollow they offer excellent strength-to-weight ratio, and thus scale well to large structures. Since the tape is pre-coated with adhesive it connects into tubes quickly, unlike extruded plastic that would require heating and cooling in the kilowatt range. We demonstrate protopiper?s use through several demo objects, ranging from more constructive objects, such as furniture, to more decorative objects, such as statues. In our exploratory user study, 16 participants created objects based on their own ideas. They rated the device as being "useful for creative exploration", "its ability to sketch at actual scale helped judge fit", and "fun to use.".
Ahadi, A, Lister, R, Haapala, H & Vihavainen, A 2015, 'Exploring Machine Learning Methods to Automatically Identify Students in Need of Assistance', Proceedings of the Eleventh Annual International Conference on International Computing Education Research, International Computing Education Research Conference, ACM, Omaha, Nebraska, USA, pp. 121-130.
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Ahadi, A, Prior, J, Behbood, V & Lister, R 2015, 'A Quantitative Study of the Relative Difficulty for Novices of Writing Seven Different Types of SQL Queries', Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education, Annual Conference on Innovation and Technology in Computer Science Education, ACM, Lithuania, pp. 201-206.
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This paper presents a quantitative analysis of data collected by an online testing system for SQL "select" queries. The data was collected from almost one thousand students, over eight years. We examine which types of queries our students found harder to write. The seven types of SQL queries studied are: simple queries on one table; grouping, both with and without "having"; natural joins; simple and correlated sub-queries; and self-joins. The order of queries in the preceding sentence reflects the order of student difficulty we see in our data.
Aiello, R, Banterle, F, Pietroni, N, Malomo, L, Cignoni, P & Scopigno, R 2015, 'Compression and Querying of Arbitrary Geodesic Distances', International Conference on Image Analysis and Processing, Springer, Cham, pp. 282-293.
Ali, M & Behbood, V 2015, 'Operation Properties and delta-Equalities of Complex Fuzzy Classes', Proceedings of the 10th International Conference on Intelligent Systems and Knowledge Engineering, International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Taipei, pp. 586-593.
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A complex fuzzy class is a set of fuzzy sets which is characterized by a pure complex fuzzy grade of membership where both the real and imaginary parts are fuzzy functions. The values that a pure complex fuzzy grade of membership may receive all lie within the unite square or unit circle in the complex plane. In this paper, we investigate different operation properties and propose a distance measure for complex fuzzy classes. The distance of two complex fuzzy classes measures the difference between the memberships of the fuzzy sets in the two complex fuzzy classes as well as the difference between the memberships in the related fuzzy sets in the two complex fuzzy classes. d-equalities of two complex fuzzy classes are then defined which mainly base on this distance measure. If the distance between two complex fuzzy classes is less than or equal to d, then they are said to be d-equal. This paper reveals that different operations between complex fuzzy classes can affect given delta-equalities of complex fuzzy classes. Further, an application of utilizing the concept of d-equalities of complex fuzzy classes in stocks and mutual funds in the stock market is presented.
Alkalbani, A, Shenoy, A, Hussain, FK, Hussain, OK & Xiang, Y 2015, 'Design and implementation of the hadoop-based crawler for SaaS service discovery', Proceedings - International Conference on Advanced Information Networking and Applications, AINA, International Conference on Advanced Information Networking and Applications (was ICOIN), IEEE, Gwangju, SOUTH KOREA, pp. 785-790.
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© 2015 IEEE. Software as a Service is the most adopted cloud service (46%) compared with Infrastructure as a Service (IaaS) (35%) and Platform as a Service (PaaS) (34%) [1]. Currently, the capability of discovering a SaaS of interest online across multiple cloud providers and reviews websites is a significant challenge, especially when using general search mechanisms (Google and Yahoo!) and search tools provided by existing reviews and directories. Discovering a SaaS is time-consuming, requiring consumers to browse several websites to select the appropriate service. This paper addresses the issues related to the efficient discovery of SaaS across review websites by developing the SaaS Nutch Hadoop-based Crawler Engine - SaaS Nhbased Crawler. The crawler is capable of crawling cloud reviews to find SaaSs of interest and enable the establishment of a central repository that could be used to discover SaaSs much more efficiently. The results show that the SaaS Nhbased crawler can effectively crawl review websites and provide a list of the latest SaaS being offered.
Alkalbani, AM, Ghamry, AM, Hussain, FK & Hussain, OK 2015, 'Blue Pages: Software as a Service Data Set', Proceedings of the 2015 10th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA), International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA), IEEE, Krakow, Poland, pp. 269-274.
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Blue Pages is a repository that contains data collected on Software as a Service(SaaS). The repository lists SaaS information according to an organization's functional needs. Blue Pages holds thousands of records on SaaS applications' business profiles. The data was extracted between February 2015 and August 2015 from the following web portals: www.cloudreviews.com & www.getApp.com. Every record holds details on the service offerings, including service name, the date the service was founded, service category, free trial (yes/no), mobile app (yes/no), starting price, service description, service type and provider link. The dataset is the first to provide information on SaaS. The dataset presented in this paper can be used in future research in cloud service selection and cloud service discovery. The dataset is available online through the Blue Page website http://www.bluepagesdataset.com/.
Alkhatib, MA, Talaei-Khoei, A & Ghapanchi, AH 2015, 'Analysis of research in healthcare data analytics', ACIS 2015 Proceedings - 26th Australasian Conference on Information Systems.
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© 2015 Alkhatib Mohammad Ahmad, Amir Martin. The main aim of this paper is to provide a deep analysis on the research field of healthcare data analytics., as well as highlighting some of guidelines and gaps in previous studies. This study has focused on searching relevant papers about healthcare analytics by searching in seven popular databases such as google scholar and springer using specific keywords, in order to understand the healthcare topic and conduct our literature review. The paper has listed some data analytics tools and techniques that have been used to improve healthcare performance in many areas such as: Medical operations, reports, decision making, and prediction and prevention system. Moreover, the systematic review has showed an interesting demographic of fields of publication, research approaches, as well as outlined some of the possible reasons and issues associated with healthcare data analytics, based on geographical distribution theme.
Alshehri, MD & Hussain, FK 2015, 'A comparative analysis of scalable and context-aware trust management approaches for internet of things', Neural Information Processing (LNCS), Part iV, International Conference on Neural Information Processing, Springer, Istanbul, TURKEY, pp. 596-605.
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© Springer International Publishing Switzerland 2015. The Internet of Things - IoT - is a new paradigm in technology that allows most physical ‘things’ to contact each other. Trust between IoT devices is a critical factor. Trust in the IoT environment can be modeled using various approaches, such as confidence level and reputation parameters. Furthermore, trust is an important element in engineering reliable and scalable networks. In this paper, we survey scalable and context-aware trust management for IoT from three perspectives. First, we present an overview of the IoT and the importance of trust in relation to it, and then we provide an in-depth trust/reliable management protocol for the IoT and evaluate comparable trust management protocols. We also investigate a scalable solution for trust management in the IoT and provide a comparative evaluation of existing trust solutions. We then pre-sent a context-aware assessment for the IoT and compare the different trust solutions. Lastly, we give a full comparative analysis of trust/reliability management in the IoT. Our results are drawn from this comparative analysis, and directions for future research are outlined.
Anaissi, A & Goyal, ML 2015, 'SVM-based association rules for knowledge discovery and classification', Proceedings of the 2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), Asia-Pacific World Congress on Computer Science and Engineering, IEEE, Nadi, Fiji, pp. 1-5.
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Improving analysis of market basket data requires the development of approaches that lead to recommendation systems that are tailored to specifically benefit grocery chain. The main purpose of that is to find relationships existing among the sales of the products that can help retailer identify new opportunities for cross-selling their products to customers. This paper aims to discover knowledge patterns hidden in large data set that can yield more understanding to the data holders and identify new opportunities for imperative tasks including strategic planning and decision making. This paper delivers a strategy for the implementation of a systematic analysis framework built on the established principles used in data mining and machine learning. The primary goal of that is to form the foundation of what we envisage will be a new recommendation system in the market. Uniquely, our strategy seeks to implement data mining tools that will allow the analyst to interact with the data and address business questions such as promotions advertisement. We employ Apriori algorithm and support vector machine to implement our recommendation systems. Experiments are done using a real market dataset and the 0.632+ bootstrap method is used here in order to evaluate our framework. The obtained results suggest that the proposed framework will be able to generate benefits for grocery chain using a real-world grocery store data.
Angelini, L, Lalanne, D, Van Den Hoven, E, Mazalek, A, Khaled, OA & Mugellini, E 2015, 'Tangible meets gestural: Comparing and blending post-WIMP interaction paradigms', TEI 2015 - Proceedings of the 9th International Conference on Tangible, Embedded, and Embodied Interaction, Association for Computing Machinery, Inc, New York, pp. 473-476.
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More and more objects of our everyday environment are becoming smart and connected, offering us new interaction possibilities. Tangible interaction and gestural interaction are promising communication means with these objects in this post-WIMP interaction era. Although based on different principles, they both exploit our body awareness and our skills to provide a richer and more intuitive interaction. Occasionally, when user gestures involve physical artifacts, tangible interaction and gestural interaction can blend into a new paradigm, i.e., tangible gesture interaction [5]. This workshop fosters the comparison among these different interaction paradigms and offers a unique opportunity to discuss their analogies and differences, as well as the definitions, boundaries, strengths, application domains and perspectives of tangible gesture interaction. Participants from different backgrounds are invited.
Bakker, S, de Waart, S & van den Hoven, E 2015, 'Tactility Trialing: Exploring Materials to Inform Tactile Experience Design', Proceedings of the Design and semantics of Form and Movement, International Conference on Design and Semantics of Form and Movement, Politecnico di Milano, Milan, Italy, pp. 119-128.
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Although materials of tangible interaction designs largely determine
their user experience, material choices are often steered by practical motives. This paper presents ‘tactility trialing’, an approach to explore tactile experiences of materials to inform the design of tangible artifacts. Through experience formulation, material selection, artifact creation and short user studies, designers and design-researchers are enabled to make informed decisions on the materials to be used in order to evoke the intended experience. The approach is illustrated through two case studies of student work. Tactility trialing helped them in getting acquainted with tactile material qualities in practice, and with the applicability of material characteristics such as resilience and hardness in design.
Berry, R, Edmonds, E & Johnston, AJ 2015, 'Unfinished Business: Some Reflections On Adopting A Practice-Based Approach To Technological Research As An Artist', Proceedings of the Annual Conference of the Australasian Computer Music Association, Australasian Computer Music Conference, The Australasian Computer Music Association, Sydney, pp. 13-18.
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This paper reflects upon aspects of my experience as an
artist moving into research and my attempts to reconcile
the two areas of activity and interest. It describes several
tabletop augmented reality music systems in the context
of my experience as an artist working in technology
research environments. It is my intention to show how the
relationship between creative practice and technological
research has changed for me over time and how I have
come to embrace a practice-based approach to research
where creative practice takes a central and crucial role
Beyer, D, Gurevich, S, Mueller, S, Chen, HT & Baudisch, P 2015, 'Platener: Low-fidelity fabrication of 3D objects by substituting 3D print with laser-cut plates', Conference on Human Factors in Computing Systems - Proceedings, International Conference on Human Factors in Computing Systems, ACM, Seoul, Republic of Korea, pp. 1799-1806.
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© Copyright 2015 ACM. This paper presents Platener, a system that allows quickly fabricating intermediate design iterations of 3D models, a process also known as low-fidelity fabrication. Platener achieves its speed-up by extracting straight and curved plates from the 3D model and substituting them with laser cut parts of the same size and thickness. Only the regions that are of relevance to the current design iteration are executed as full-detail 3D prints. Platener connects the parts it has created by automatically inserting joints. To help fast assembly it engraves instructions. Platener allows users to customize substitution results by (1) specifying fidelity-speed tradeoffs, (2) choosing whether or not to convert curved surfaces to plates bent using heat, and (3) specifying the conversion of individual plates and joints interactively. Platener is designed to best preserve the fidelity of func- Tional objects, such as casings and mechanical tools, all of which contain a large percentage of straight/rectilinear elements. Compared to other low-fab systems, such as faBrickator and WirePrint, Platener better preserves the stability and functionality of such objects: The resulting assemblies have fewer parts and the parts have the same size and thickness as in the 3D model. To validate our system, we converted 2,250 3D models downloaded from a 3D model site (Thingiverse). Platener achieves a speed-up of 10x or more for 39.9% of all objects.
Bluff, AJ & Johnston, A 2015, 'Remote Control of Complex Interactive Art Installations', Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition, Conference on Creativity and Cognition, ACM, Glasgow, UK, pp. 197-200.
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Movement based interactive artworks are capable of instantly engaging audiences by reacting to physical motion consistently with real-world physics. Sustaining this engagement, however, requires a constant alteration of both the output and interaction aesthetics. Mobile devices (such as the iPad or iPhone) can be used to control the often-overwhelming plethora of parameters found in many interactive systems. The effect that mobile control of these parameters has on the inception, refinement and live performance of two separate art works is examined. An open-source dynamic remote control system is being developed to further facilitate the creative development and performance of interactive artwork as demonstrated by these case studies.
Bown, O, Loke, L, Ferguson, SJ & Reinhardt, D 2015, 'Distributed Interactive Audio Devices: Creative strategies and audienceresponses to novel musical interaction scenarios', http://isea2015.org/publications/proceedings-of-the-21st-international-symposium-on-electronic-art/, International Symposium on Electronic Art, ISEA, Vancouver, Canada.
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With the rise of ubiquitous computing, comes new possibilities
for experiencing audio, visual and tactile media in
distributed and situated forms, disrupting modes of media
experience that have been relatively stable for decades. We
present the Distributed Interactive Audio Devices (DIADs)
project, a set of experimental interventions to explore future
ubiquitous computing design spaces in which electronic
sound is presented as distributed, interactive and portable.
The DIAD system is intended for creative sound and
music performance and interaction, yet it does not conform
to traditional concepts of musical performance, suggesting
instead a fusion of music performance and other forms of
collaborative digital interaction. We describe the thinking
behind the project, the state of the DIAD system’s technical
development, and our experiences working with userinteraction
in lab-based and public performance scenarios.
Braytee, A, Gill, AQ, Kennedy, PJ & Hussain, FK 2015, 'A Review and comparison of service E-Contract Architecture Metamodels', Neural Information Processing (LNCS), International Conference on Neural Information Processing, Springer, Istanbul, Turkey, pp. 583-595.
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© Springer International Publishing Switzerland 2015. An adaptive service e-contract is an electronic agreement which is required to enable adaptive or agile service sourcing and pro- visioning. There are a number of e-contract metamodels that can be used to create a context specific adaptive service e-contract. The chal- lenge is which one to choose and adopt for adaptive services. This paper presents a review and comparison of well-known e-contract metamod- els using the architecture theory. The architecture theory allows the analysis of the e-contract metamodels using a three-dimension analyt- ical lens: structure, behavior and technology. The results of this paper highlight the metamodels structural, behavioral and technological differ- ences and similarities. This paper will help researchers and practitioners to observe the existing e-contract metamodels are appropriate to the adaptive services or if thwhetherere is a need to merge and integrate the concepts of these metamodels to propose a new unifying adaptive service e-contract metamodel. This paper is limited to the number of compared metamodels.
Braytee, A, Hussain, F, Anaissi, A & Kennedy, PJ 2015, 'ABC-Sampling for balancing imbalanced datasets based on Artificial Bee Colony algorithm', Proceedings 2015 IEEE 14th International Conference on Machine Learning and Applications ICMLA 2015, International Conference on Machine Learning and Applications, IEEE, Miami, Florida, pp. 594-599.
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Class imbalanced data is a common problem for predictive modelling in domains such as bioinformatics. It occurs when the distribution of classes is not uniform among samples and results in a biased prediction of learning towards majority classes. In this study, we propose the ABC-Sampling algorithm based on a swarm optimization method called Artificial Bee Colony, which models the natural foraging behaviour of honeybees. Our algorithm lessens the effects of imbalanced classes by selecting the most informative majority samples using a forward search and storing them in a ranked subset. Then we construct a balanced dataset with a planned undersampling strategy to extract the most frequent majority samples from the top ranked subset and combine them with all minority samples. Our algorithm is superior to a state-of-the-art method on nine benchmark datasets with various levels of imbalance ratios.
Bremner, MJ, Montanaro, A & Shepherd, D 2015, 'Average-case complexity versus approximate simulation of commuting quantum computations', 15th Asian Quantum Information Science Conference, Seoul, Korea.
Cao, ZH, Ko, LW, Lai, KL, Huang, SB, Wang, SJ & Lin, CT 2015, 'Classification of migraine stages based on resting-state EEG power', Proceedings of the International Joint Conference on Neural Networks, IEEE International Joint Conference on Neural Networks, IEEE, Killarney, Ireland.
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© 2015 IEEE. Migraine is a chronic neurological disease characterized by recurrent moderate to severe headaches during a period like one month often in association with symptoms in human brain and autonomic nervous system. Normally, migraine symptoms can be categorized into four different stages: inter-ictal, pre-ictal, ictal, and post-ictal stages. Since migraine patients are difficulty knowing when they will suffer migraine attacks, therefore, early detection becomes an important issue, especially for low-frequency migraine patients who have less than 5 times attacks per month. The main goal of this study is to develop a migraine-stage classification system based on migraineurs' resting-state EEG power. We collect migraineurs' O1 and O2 EEG activities during closing eyes from occipital lobe to identify pre-ictal and non-pre-ictal stages. Self-Constructing Neural Fuzzy Inference Network (SONFIN) is adopted as the classifier in the migraine stages classification which can reach the better classification accuracy (66%) in comparison with other classifiers. The proposed system is helpful for migraineurs to obtain better treatment at the right time.
Chang, L, Lin, X, Zhang, W, Yu, JX, Zhang, Y & Qin, L 2015, 'Optimal Enumeration: Efficient Top-k Tree Matching', Proceedings of the VLDB Endowment, International Conference on Very Large Databases, VLDB Endowment, Kahola Coast, Hawaii, pp. 533-544.
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Driven by many real applications, graph pattern matching has attracted
a great deal of attention recently. Consider that a twigpattern
matching may result in an extremely large number of matches
in a graph; this may not only confuse users by providing too many
results but also lead to high computational costs. In this paper,
we study the problem of top-k tree pattern matching; that is, given
a rooted tree T, compute its top-k matches in a directed graph G
based on the twig-pattern matching semantics. We firstly present
a novel and optimal enumeration paradigm based on the principle
of Lawler’s procedure. We show that our enumeration algorithm
runs in O(nT + log k) time in each round where nT is the number
of nodes in T. Considering that the time complexity to output a
match of T is O(nT ) and nT ≥ log k in practice, our enumeration
technique is optimal. Moreover, the cost of generating top-1 match
of T in our algorithm is O(mR) where mR is the number of edges
in the transitive closure of a data graph G involving all relevant
nodes to T. O(mR) is also optimal in the worst case without preknowledge
of G. Consequently, our algorithm is optimal with the
running time O(mR +k(nT +log k)) in contrast to the time complexity
O(mR log k+knT (log k+dT )) of the existing technique where dT
is the maximal node degree in T. Secondly, a novel priority based
access technique is proposed, which greatly reduces the number of
edges accessed and results in a significant performance improvement.
Finally, we apply our techniques to the general form of top-k
graph pattern matching problem (i.e., query is a graph) to improve
the existing techniques. Comprehensive empirical studies demonstrate
that our techniques may improve the existing techniques by
orders of magnitude.
Che, L, Liang, J, Yuan, X, Shen, J, Xu, J & Li, Y 2015, 'Laplacian-based Dynamic Graph Visualization', Visualization Symposium (PacificVis), 2015 IEEE Pacific, IEEE Pacific Visualization Symposium (was APVIS), IEEE, Hangzhou, China.
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Visualizing dynamic graphs are challenging due to the difficulty to preserving a coherent mental map of the changing graphs. In this paper, we propose a novel layout algorithm which is capable of maintaining the overall structure of a sequence graphs. Through Laplacian constrained distance embedding, our method works online and maintains the aesthetic of individual graphs and the shape similarity between adjacent graphs in the sequence. By preserving the shape of the same graph components across different time steps, our method can effectively help users track and gain insights into the graph changes. Two datasets are tested to demonstrate the effectiveness of our algorithm.
Chen, C, Zhang, J, Chen, X, Xiang, Y & Zhou, W 2015, '6 million spam tweets: A large ground truth for timely Twitter spam detection', IEEE International Conference on Communications, pp. 7065-7070.
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© 2015 IEEE. Twitter has changed the way of communication and getting news for people's daily life in recent years. Meanwhile, due to the popularity of Twitter, it also becomes a main target for spamming activities. In order to stop spammers, Twitter is using Google SafeBrowsing to detect and block spam links. Despite that blacklists can block malicious URLs embedded in tweets, their lagging time hinders the ability to protect users in real-time. Thus, researchers begin to apply different machine learning algorithms to detect Twitter spam. However, there is no comprehensive evaluation on each algorithms' performance for real-time Twitter spam detection due to the lack of large groundtruth. To carry out a thorough evaluation, we collected a large dataset of over 600 million public tweets. We further labelled around 6.5 million spam tweets and extracted 12 light-weight features, which can be used for online detection. In addition, we have conducted a number of experiments on six machine learning algorithms under various conditions to better understand their effectiveness and weakness for timely Twitter spam detection. We will make our labelled dataset for researchers who are interested in validating or extending our work.
Chen, C, Zhang, J, Xiang, Y & Zhou, W 2015, 'Asymmetric self-learning for tackling Twitter Spam Drift', Proceedings - IEEE INFOCOM, pp. 208-213.
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© 2015 IEEE. Spam has become a critical problem on Twitter. In order to stop spammers, security companies apply blacklisting services to filter spam links. However, over 90% victims will visit a new malicious link before it is blocked by blacklists. To eliminate the limitation of blacklists, researchers have proposed a number of statistical features based mechanisms, and applied machine learning techniques to detect Twitter spam. In our labelled large dataset, we observe that the statistical properties of spam tweets vary over time, and thus the performance of existing ML based classifiers are poor. This phenomenon is referred as 'Twitter Spam Drift'. In order to tackle this problem, we carry out deep analysis of 1 million spam tweets and 1 million non-spam tweets, and propose an asymmetric self-learning (ASL) approach. The proposed ASL can discover new information of changed tweeter spam and incorporate it into classifier training process. A number of experiments are performed to evaluate the ASL approach. The results show that the ASL approach can be used to significantly improve the spam detection accuracy of using traditional ML algorithms.
Chen, H, Zhang, G, Lu, J & Zhu, D 2015, 'A Fuzzy Approach for Measuring Development of Topics in Patents Using Latent Dirichlet Allocation', 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015), IEEE International Conference on Fuzzy Systems, IEEE, Istanbul, Turkey, pp. 1-7.
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Technology progress brings the very rapid growth of patent publications, which increases the difficulty of domain experts to measure the development of various topics, handle linguistic terms used in evaluation and understand massive technological content. To overcome the limitations of keyword-ranking type of text mining result in existing research, and at the same time deal with the vagueness of linguistic terms to assist thematic evaluation, this research proposes a fuzzy set-based topic development measurement (FTDM) approach to estimate and evaluate the topics hidden in a large volume of patent claims using Latent Dirichlet Allocation. In this study, latent semantic topics are first discovered from patent corpus and measured by a temporal-weight matrix to reveal the importance of all topics in different years. For each topic, we then calculate a temporal-weight coefficient based on the matrix, which is associated with a set of linguistic terms to describe its development state over time. After choosing a suitable linguistic term set, fuzzy membership functions are created for each term. The temporal-weight coefficients are then transformed to membership vectors related to the linguistic terms, which can be used to measure the development states of all topics directly and effectively. A case study using solar cell related patents is given to show the effectiveness of the proposed FTDM approach and its applicability for estimating hidden topics and measuring their corresponding development states efficiently.
Chen, H, Zhang, YI, Zhang, G, Zhu, D & Lu, J 2015, 'Modeling Technological Topic Changes in Patent Claims', Proceeding of 2015 Portland International Conference on Management of Engineering and Technology (PICMET), Portland International Center for Management of Engineering and Technology Conference, IEEE, Portland, USA, pp. 2049-2059.
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Patent claims usually embody the most essential terms and the core technological scope to define the protection of an invention, which makes them the ideal resource for patent content and topic change analysis. However, manually conducting content analysis on massive technical terms is very time consuming and laborious. Even with the help of traditional text mining techniques, it is still difficult to model topic changes over time, because single keywords alone are usually too general or ambiguous to represent a concept. Moreover, term frequency which used to define a topic cannot separate polysemous words that are actually describing a different theme. To address this issue, this research proposes a topic change identification approach based on Latent Dirichlet Allocation to model and analyze topic changes with minimal human intervention. After textual data cleaning, underlying semantic topics hidden in large archives of patent claims are revealed automatically. Concepts are defined by probability distributions over words instead of term frequency, so that polysemy is allowed. A case study using patents published in the United States Patent and Trademark Office (USPTO) from 2009 to 2013 with Australia as their assignee country is presented to demonstrate the validity of the proposed topic change identification approach. The experimental result shows that the proposed approach can be used as an automatic tool to provide machine-identified topic changes for more efficient and effective R&D management assistance.
Chinchore, A, Jiang, F & Xu, G 2015, 'Intelligent Sybil attack detection on abnormal connectivity behavior in mobile social networks', Knowledge Management in Organizations - Lecture Notes in Business Information Processing, Knowledge Management in Organizations, Springer, Maribor, Slovenia, pp. 602-617.
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© Springer International Publishing Switzerland 2015. There have been a large number of researches on mobile networks in the literature, focusing on a variety of secured applications over the network, including the use of their connections, fake identification and attacks on social group. These applications are created for the intention to collect confidential information, money laundering, blackmailing and to perform other crime activity. The purpose of this research is to identify the behavior of the honest node (network account) and fake node (network account) on mobile social network. In this research, the behavior survey of these nodes is carried out and further analysed with the help of graph-based Sybil detection system. This paper particularly studies Sybil attacks and its defense system for IoT (Internet-of-Things) environment. To be implied, the identification of each forged Sybil node is to be tracked on the basis of nodes connectivity and their timing of connectivity as well as frequency among each other. Sybil node has a forged identity in different locations and also reports its virtual location information to servers.
Chiu, CY, Chen, CY, Lin, YY, Chen, SA & Lin, CT 2014, 'Using a novel LDA-ensemble framework to classification of motor imagery tasks for brain-computer interface applications', Frontiers in Artificial Intelligence and Applications, International Computer Symposium (ICS), IOS Press, Tunghai University, Taichung, Taiwan, pp. 150-156.
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© 2015 The authors and IOS Press. All rights reserved. In this paper, we introduce a novel linear discriminate analysis (LDA) ensemble classifier utilizing the Mindo as a brain-computer interface (BCI) device to deal with the problem of motor imagery classification. With regard to the composition of the proposed system, we combine filter bank, sub-band common spatial pattern (SBCSP), LDA together for extracting features of EEG data and classifying the motor imagery with left or right states. In addition, we also employ a gradient descent (GD) algorithm to find the best weight associated with probability fusion function. This novel architecture not only boosts the accuracy of classification but maintains the computational efficiency of the system. Therefore, the proposed LDA-ensemble framework is able to be satisfied with each subject as demonstrated in Section III.
Chotipant, S, Hussain, FK & Hussain, OK 2015, 'An automated and fuzzy approach for semantically annotating services', IEEE International Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, Istanbul, TURKEY.
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© 2015 IEEE. In the recent past, semantic technologies have played an significant role in service retrieval and service querying. Annotating services semantically enables machines to understand the purpose of services and can further assist in intelligent and precise service retrieval, selection and composition. A key issue in semantically annotating services is the manual nature of service annotation. Manual service annotation requires a large amount of time and updating happens infrequently, hence annotations may get out-of-date due to service description changes. Although some researchers have studied semantic service annotation, they have only focused on web services not business service information. Moreover, their approaches are semi-automated, and still require service providers to select appropriate service annotations. In this paper, we propose a completely automated semantic annotation approach for e-services. The aim of this paper is to semantically annotate a service to relevant service concepts in domain-specific ontologies. Services and service concepts are represented by an extended VSM model, based on fuzzy rules. Then, we link a service to a concept, based on the similarity value of the representing vectors. We found during the experimentation process that the performances of the proposed approach and the VSM-based approach were quite similar and, as a result, developed a system to retrieve services that are annotated to relevant concepts. Experiments using a high service retrieval threshold demonstrated a retrieval approach based on extended VSM annotation performed much better than an approach based on VSM annotation.
Chotipant, S, Hussain, FK, Dong, H & Hussain, OK 2015, 'A neural network based approach for semantic service annotation', Neural Information Processing (LNCS), Part II, International Conference on Neural Information Processing, Springer, Istanbul, Turkey, pp. 292-300.
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© Springer International Publishing Switzerland 2015. Nowadays, a large number of business owners provide advertising for their services on the web. Semantically annotating those services, which assists machines to understand their purpose, is a significant factor for improving the performance of automated service retrieval, selection, and composition. Unfortunately, most of the existing research into semantic service annotation focuses on annotating web services, not on business service information. Moreover, all are semi-automated approaches that require service providers to select proper annotations. As a result, those approaches are unsuitable for annotating very large numbers of services that have accrued or been updated over time. This paper outlines our proposal for a Neural Network (NN)-based approach to annotate business services. Its aim is to link a given service to a relevant service concept. In this case, we treat the task as a service classification problem. We apply a feed forward neural network and a radial basis function network to determine relevance scores between service information and service concepts. A service is then linked to a service concept if its relevance score reaches the threshold. To evaluate the performance of this approach, it is compared with the ECBR algorithm. The experimental results demonstrate that the NN-based approach performs significantly better than the ECBR approach.
Chou, KP, Prasad, M, Lin, YY, Joshi, S, Lin, CT & Chang, JY 2014, 'Takagi-Sugeno-Kang type collaborative fuzzy rule based system', IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings, IEEE Symposium on Computational Intelligence and Data Mining, IEEE, USA, pp. 315-320.
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© 2014 IEEE. In this paper, a Takagi-Sugeno-Kang (TSK) type collaborative fuzzy rule based system is proposed with the help of knowledge learning ability of collaborative fuzzy clustering (CFC). The proposed method split a huge dataset into several small datasets and applying collaborative mechanism to interact each other and this process could be helpful to solve the big data issue. The proposed method applies the collective knowledge of CFC as input variables and the consequent part is a linear combination of the input variables. Through the intensive experimental tests on prediction problem, the performance of the proposed method is as higher as other methods. The proposed method only uses one half information of given dataset for training process and provide an accurate modeling platform while other methods use whole information of given dataset for training.
Cumbo, BJ & Leong, TW 2015, 'Wearable Audio-video Recorders As a Tool for Investigating Child Play Experiences in Nature', Proceedings of the Annual Meeting of the Australian Special Interest Group for Computer Human Interaction, Australian Computer Human Interaction Conference, ACM, Victoria, Australia, pp. 618-622.
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Curiskis, SA, Osborn, TR & Kennedy, PJ 2015, 'Link prediction and topological feature importance in social networks', Proceedings of the 13-th Australasian Data Mining Conference (AusDM 2015), Sydney, Australia, Australian Data Mining Conference, Australian Computer Society, Sydney, pp. 39-50.
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Cuzzocrea, A, Moussa, R, Xu, G & Grasso, GM 2015, 'Cloud-Based OLAP over Big Data: Application Scenarios and Performance Analysis', Proceedings of the 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid), IEEE, Shen Zhen, pp. 921-927.
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Following our previous research results, in this paper we provide two authoritative application scenarios that build on top of OLAP*, a middleware for parallel processing of OLAP queries that truly realizes effective and efficiently OLAP over Big Data. We have provided two authoritative case studies, namely parallel OLAP data cube processing and virtual OLAP data cube design, for which we also propose a comprehensive performance evaluation and analysis. Derived analysis clearly confirms the benefits of our proposed framework.
Cuzzocrea, A, Xu, G & Grasso, GM 2015, 'OLAP-enabled web search of complex objects', Proceedings of the 17th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2015 - Proceedings, International Conference on Information Integration and Web-Based Applications and Services, ACM, Brussels, Belgium.
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© 2015 ACM. Inspired by the actual trend of empowering traditional Web search methodologies by means of novel computational paradigms, in this paper we propose and experimentally assess WebClustCube, a novel system that allows OLAP-enabled Web search of complex objects, thus adding new value to the potentialities of current Web search paradigms. In particular, WebClustCube supports the building and the interactive manipulation of OLAP-enabled Web views over complex objects extracted from distributed databases. The data management, OLAP-like support of WebClustCube is provided by ClustCube, a state-of-the-art framework for coupling OLAP methodologies and clustering algorithms with the goal of analyzing and mining of complex database objects. A case study that clearly shows the potentialities of WebClustCube in the context of next-generation Web search environments is provided. We complement of analytical contribution by means of an experimental assessment and analysis of WebClustCube according to several metric perspectives.
Datta, N, Tomamichel, M & Wilde, MM 2015, 'Second-order coding rates for entanglement-assisted communication', IEEE International Symposium on Information Theory - Proceedings, IEEE International Symposium on Information Theory, IEEE, Hong Kong, China, pp. 2772-2776.
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© 2015 IEEE. The entanglement-assisted capacity of a quantum channel is known to provide the formal quantum generalization of Shannon's classical channel capacity theorem, in the sense that it admits a single-letter characterization in terms of the quantum mutual information and does not increase in the presence of a noiseless quantum feedback channel from receiver to sender. In this work, we investigate second-order asymptotics of the entanglement-assisted communication task. That is, we consider how quickly the rates of entanglement-assisted codes converge to the entanglement-assisted capacity of a channel as a function of the number of channel uses and the error tolerance. We define a quantum generalization of the mutual information variance of a channel in the entanglement-assisted setting. For covariant channels, we show that this quantity is equal to the channel dispersion, and characterizes the convergence towards the entanglement-assisted capacity when the number of channel uses increases. More generally, we prove that the Gaussian approximation for a second-order coding rate is achievable for all quantum channels.
Deen, J & Zhou, W 2015, 'Message from the IUCC 2014 general chairs', Proceedings - 17th IEEE International Conference on Computational Science and Engineering, CSE 2014, Jointly with 13th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2014, 13th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2014 and 8th International Conference on Frontier of Computer Science and Technology, FCST 2014, p. xlvi.
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Di, P, Sui, Y, Ye, D & Xue, J 2015, 'Region-Based May-Happen-in-Parallel Analysis for C Programs', 2015 44TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING (ICPP), 44th Annual International Conference on Parallel Processing Workshops (ICPPW), IEEE, Beijing, PEOPLES R CHINA, pp. 889-898.
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Fan, X, Sui, Y & Xue, J 2015, 'Contention-Aware Scheduling for Asymmetric Multicore Processors', 2015 IEEE 21ST INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS), 21st IEEE International Conference on Parallel and Distributed Systems ICPADS, IEEE, Melbourne, AUSTRALIA, pp. 742-751.
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Fang, XS, Wang, X & Sheng, QZ 2015, 'Ontology Augmentation via Attribute Extraction from Multiple Types of Sources', DATABASES THEORY AND APPLICATIONS, 26th Australasian Database Conference (ADC), SPRINGER-VERLAG BERLIN, Melbourne, AUSTRALIA, pp. 16-27.
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Ferguson, SJ 2015, 'Using audio feature extraction for interactive feature-based sonification of sound', https://smartech.gatech.edu/bitstream/handle/1853/54106/ICAD%20Proceedings%2015-Ferguson.pdf, International Conference on Auditory Display, Georgia Institute of Technology, Graz, Austria, pp. 66-72.
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Fu, B, Xu, G, Cao, L, Wang, Z & Wu, Z 2015, 'Coupling multiple views of relations for recommendation', Advances in Knowledge Discovery and Data Mining - LNCS, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Ho Chi Minh City, Vietnam, pp. 732-743.
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© Springer International Publishing Switzerland 2015. Learning user/item relation is a key issue in recommender system, and existing methods mostly measure the user/item relation from one particular aspect, e.g., historical ratings, etc. However, the relations between users/items could be influenced by multifaceted factors, so any single type of measure could get only a partial view of them. Thus it is more advisable to integrate measures from different aspects to estimate the underlying user/item relation. Furthermore, the estimation of underlying user/item relation should be optimal for current task. To this end, we propose a novel model to couple multiple relations measured on different aspects, and determine the optimal user/item relations via learning the optimal way of integrating these relation measures. Specifically, matrix factorization model is extended in this paper by considering the relations between latent factors of different users/items. Experiments are conducted and our method shows good performance and outperforms other baseline methods.
Gay, V, Leijdekkers, P, Gill, AQ & Felix Navarro, K 2015, 'Le Bon Samaritain: A Community-Based Care Model Supported by Technology', Driving Reform: Digital Health is Everyone's Business - Studies in Health Technology and Informatics, Health Informatics Conference, IOS Press, Brisbane, Australia., pp. 50-55.
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Background: The effective care and well-being of a community is a challenging task especially in an emergency situation. Traditional technology-based silos between health and emergency services are challenged by the changing needs of the community that could benefit from integrated health and safety services. Low-cost smart-home automation solutions, wearable devices and Cloud technology make it feasible for communities to interact with each other, and with health and emergency services in a timely manner.
Objectives: This paper proposes a new community-based care model, supported by technology, that aims at reducing healthcare and emergency services costs while allowing community to become resilient in response to health and emergency situations.
Methods: We looked at models of care in different industries and identified the type of technology that can support the suggested new model of care. Two prototypes were developed to validate the adequacy of the technology.
Results: The result is a new community-based model of care called ‘Le Bon Samaritain’. It relies on a network of people called ‘Bons Samaritains’ willing to help and deal with the basic care and safety aspects of their community. Their role is to make sure that people in their community receive and understand the messages from emergency and health services. The new care model is integrated with existing emergency warning, community and health services.
Conclusion: Le Bon Samaritain model is scalable, community-based and can help people feel safer, less isolated and more integrated in their community. It could be the key to reduce healthcare cost, increase resilience and drive the change for a more integrated emergency and care system.
Gill, AQ 2015, 'Adaptive Enterprise Architecture Driven Agile Development', http://aisel.aisnet.org/isd2014/proceedings2015/, International Conference on Information Systems Development, City University of Hong Kong, Harbin, China..
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Gill, AQ 2015, 'Learning Enterprise Agile Software Engineering: Scaling Agility at the Enterprise Level', Proceedings of the 2015 24th Australasian Software Engineering Conference (ASWEC), Australian Software Engineering Conference, IEEE Computer Society, Adelaide, Australia, pp. 148-154.
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Agile software engineering practices, originated in the context of individual software project development, are getting vast attention from enterprises for handling multiple agile software engineering projects at a large program and portfolio level. Adoption of agility at a large scale is a challenging task. The success of agility adoption at a large scale is dependent on the knowledge and skills of people involved. This suggests that agile software engineering education and training remains one of the important factors for organizations pursuing to scale agile practices for large environments. However, the teaching of agile software engineering practices for a large scale poses many challenges to software engineering educators. These difficulties include how to establish and simulate an appropriate large scale software engineering environment. This paper presents learnings from teaching agile software engineering practices for large scale at the University of Technology - Sydney (UTS), Australia. The learnings from this paper can be used by other educators who are aiming to teach enterprise scale agile software engineering practices.
Gill, AQ, Chew, E, Bird, G & Kricker, D 2015, 'An Agile Service Resilience Architecture Capability: Financial Services Case Study', http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7259951, IEEE Conference on Business Informatics (CBI), IEEE, Lisbon, Portugal, pp. 209-216.
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Service resilience in the face of constant business change is an imperative and complex task for any service organization including those in financial services. Yet, due to its systemic complexity, service resilience as a practice in most organisations is performed in an ad-hoc and inefficient manner resulting in periodic disruptions to day-to-day business operations. Therefore, there is an urgent need for organisations to formulate an agile or adaptive capability for service resilience architecture design and implementation that meets their dynamic business needs. This paper presents one such agile or adaptive service resilience architecture (ASRA) design and implementation capability that has been developed using an adaptive enterprise service system meta-framework (a.k.a. The Gill Framework®). An action-design research method was employed in collaboration with a financial services organisation (FSO) for the establishment of a holistic ASRA design and implementation capability.
Gramann, K 2015, 'MOBILE BRAIN/BODY IMAGING (MOBI) OF NATURAL SPATIAL COGNITION', PSYCHOPHYSIOLOGY, 55th Annual Meeting of the Society-for-Psychophysiological-Research, WILEY-BLACKWELL, Seattle, WA, pp. S10-S10.
Gramann, K 2015, 'Neural correlates of mental rotations for different reference frame proclivities', COGNITIVE PROCESSING, Proceedings of the Sixth International Conference on Spatial Cognition (ICSC), SPRINGER HEIDELBERG, Rome, ITALY, pp. S70-S70.
Grochow, JA & Qiao, Y 2015, 'Polynomial-time isomorphism test of groups that are tame extensions (Extended abstract)', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 578-589.
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© Springer-Verlag Berlin Heidelberg 2015. We give new polynomial-time algorithms for testing isomorphism of a class of groups given by multiplication tables (GpI). Two results (Cannon & Holt, J. Symb. Comput. 2003; Babai, Codenotti & Qiao, ICALP 2012) imply that GpI reduces to the following: given groups G,H with characteristic subgroups of the same type and isomorphic to ℤdp , and given the coset of isomorphisms Iso(G/ℤdp ,H/ℤdp), compute Iso(G,H) in time poly(|G|). Babai&Qiao (STACS 2012) solved this problem when a Sylow p-subgroup of G/ℤdp is trivial. In this paper, we solve the preceding problem in the so-called “tame” case, i. e., when a Sylow p-subgroup of G/ℤdp is cyclic, dihedral, semi-dihedral, or generalized quaternion. These cases correspond exactly to the group algebra (Formula presented.) being of tame type, as in the celebrated tame-wild dichotomy in representation theory. We then solve new cases of GpI in polynomial time. Our result relies crucially on the divide-and-conquer strategy proposed earlier by the authors (CCC 2014), which splits GpI into two problems, one on group actions (representations), and one on group cohomology. Based on this strategy, we combine permutation group and representation algorithms with new mathematical results, including bounds on the number of indecomposable representations of groups in the tame case, and on the size of their cohomology groups. Finally, we note that when a group extension is not tame, the preceding bounds do not hold. This suggests a precise sense in which the tame-wild dichotomy from representation theory may also be a key barrier to cross to put GpI into P.
Guo, M, Yang, K, Musial-Gabrys, K, Min, G, Yin, H, Nguyen, NP, Jiang, Y, Kourtellis, N, Cheng, X, Leng, S, Wang, H & Dokoohaki, N 2015, 'Message from the MSNCom 2015 workshop chairs', Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015, p. lvi.
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Hajinoroozi, M, Jung, TP, Lin, CT & Huang, Y 2015, 'Feature extraction with deep belief networks for driver's cognitive states prediction from EEG data', 2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings, IEEE China Summit and International Conference on Signal and Information Processing, IEEE, Chengdu, China, pp. 812-815.
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© 2015 IEEE. This study considers the prediction of driver's cognitive states from electroencephalographic (EEG) data. Extracting EEG features correlated with driver's cognitive states is key for achieving accurate prediction. However, high dimensionality and temporal-And-spatial correlations of EEG data make extraction of effective features difficult. This study explores the approaches based on deep belief networks (DBN) for feature extraction and dimension reduction. Experimental results of this study showed that DBN applied to channel epochs (DBN-C) produces the most discriminant features and the best classification performance is achieved when DBN-C is applied to the time-frequency and independent-component-Analysis transformed EEG data. The results suggested that DBN-C is a promising new method for extracting complex, discriminant features for EEG-based brain computer interfaces.
Han, J, Hu, Y, Zhang, G & Lu, J 2015, 'A Compromise-Based Particle Swarm Optimization Algorithm for Solving Bi-Level Programming Problems with Fuzzy Parameters', Intelligent Systems and Knowledge Engineering (ISKE), 2015 10th International Conference on, International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Taipei, pp. 214-221.
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Bi-level programming has arisen to handle decentralized decision-making problems that feature interactive decision entities distributed throughout a bi-level hierarchy. Fuzzy parameters often appear in such a problem in applications and this is called a fuzzy bi-level programming problem. Since the existing approaches lack universality in solving such problems, this study aims to develop a particle swarm optimization (PSO) algorithm to solve fuzzy bi-level programming problems in the linear and nonlinear versions. In this paper, we first present a general fuzzy bi-level programming problem and discuss related theoretical properties based on a fuzzy number ranking method commonly used. A PSO algorithm is then developed to solve the fuzzy bi-level programming problem based on different compromised selections by decision entities on the feasible degree for constraint conditions under fuzziness. Lastly, an illustrative numerical example and two benchmark examples are adopted to state the effectiveness of the compromise-based PSO algorithm.
Han, J, Zhang, G, Hu, Y & Lu, J 2015, 'Solving tri-level programming problems using a particle swarm optimization algorithm', 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), IEEE Conference on Industrial Electronics and Applications, IEEE, Auckland, New Zealand, pp. 569-574.
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Tri-level programming, a special case of multilevel programming, arises to deal with decentralized decision-making problems that feature interacting decision entities distributed throughout three hierarchical levels. As tri-level programming problems are strongly NP-hard and the existing solution approaches lack universality in solving such problems, the purpose of this study is to propose an intelligence-based heuristic algorithm to solve tri-level programming problems involving linear and nonlinear versions. In this paper, we first propose a general tri-level programming problem and discuss related theoretical properties. A particle swarm optimization (PSO) algorithm is then developed to solve the tri-level programming problem. Lastly, a numerical example is adopted to illustrate the effectiveness of the proposed PSO algorithm.
Han, Y, Wang, L, Zhang, Y, Zhang, W & Lin, X 2015, 'Spatial keyword range search on trajectories', Database Systems for Advanced Applications (LNCS), Database Systems for Advanced Applications, Springer, Hanoi, Vietnam, pp. 223-240.
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© 2015, Springer International Publishing Switzerland, All rights Reserved. With advances in geo-positioning technologies and ensuing location based service, there are a rapid growing amount of trajectories associated with textual information collected in many emerging applications. For instance, nowadays many people are used to sharing interesting experience through Foursquare or Twitter along their travel routes. In this paper, we investigate the problem of spatial keyword range search on trajectories, which is essential to make sense of large amount of trajectory data. To the best of our knowledge, this is the first work to systematically investigate range search over trajectories where three important aspects, i.e., spatio, temporal and textual, are all taken into consideration. Given a query region, a timespan and a set of keywords, we aim to retrieve trajectories that go through this region during query timespan, and contain all the query keywords. To facilitate the range search, a novel index structure called IOC-Tree is proposed based on the inverted indexing and octree techniques to effectively explore the spatio, temporal and textual pruning techniques. Furthermore, this structure can also support the query with order-sensitive keywords. Comprehensive experiments on several real-life datasets are conducted to demonstrate the efficiency.
Hausmann, V & Williams, SP 2015, 'Social Business Documents', CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS/INTERNATIONAL CONFERENCE ON PROJECT MANAGEMENT/CONFERENCE ON HEALTH AND SOCIAL CARE INFORMATION SYSTEMS AND TECHNOLOGIES, CENTERIS/PROJMAN / HCIST 2015, Conference on ENTERprise Information Systems (CENTERIS) / International Conference on Project MANagement (ProjMAN) / International Conference on Health and Social Care Information Systems and Technologies (HCist), ELSEVIER SCIENCE BV, Vilamoura, PORTUGAL, pp. 360-368.
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Hayashi, M & Tomamichel, M 2015, 'Correlation detection and an operational interpretation of the Rényi mutual information', Proceedings IEEE International Symposium on Information Theory (ISIT 2015), IEEE, Hong Kong, China, pp. 1447-1451.
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He, S & Huang, M 2015, 'Radial Cursor: an Efficient Technique to Explore Radial Hierarchy Visualizations on Touch Screens', Proceedings of the Annual Meeting of the Australian Special Interest Group for Computer Human Interaction, Australian Computer Human Interaction Conference, ACM digital library, Melbourne, pp. 73-77.
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Mouse hover is a common and effective solution in data visualization applications to deal with the "representation problem". Yet it remains un-developed or overlooked with touch screens, which has recently gained popularity in both people's daily life and research studies. In this paper, we proposed an interaction technique named radial cursor, which uses a radial metaphor and functions similar to a mouse cursor. It stimulates 'mouse hover' interaction with radial hierarchical data on touch screens. Together with a level switch, radial cursor provide intuitive and effective controls of 'hover to see more detail' or 'hover to highlight' over every data node. We also applied real dataset such as Flickr taxonomy and Flare package to our design. The result shows radial cursor helps user to quickly get a general sense of an unfamiliar dataset, or review the known dataset. Meanwhile, radial cursor provides a pleasant data exploration experience.
He, S, Huang, M & Zhu, L 2015, 'Natural User Interface Design in DA-TU: An Interactive Clustered Data Visualization System', Proceedings of the 2015 19th International Conference on Information Visualisation, International Conference on Information Visualisation, IEEE, Barcelona, pp. 83-88.
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Clustered Graph Visualization, such as DA-TU
[1], has been successfully applied in the field of large scale
relational data visualization for data analytics. However, it was
very inefficient in interactions by using WIMP based HCI in
DA-TU, in which a Control Panel was required to swap among
eight different modes of operation. In this paper, we attempt
to use a new multi-touch enabled NUI method for supporting
interactions in DA-TU. We aim to increase the efficiency of HCI
process in DA-TU. To achieve this, we first transplanted DA-TU
from WIMP based PC to NUI based iPad. We then designed a
set of new multi-finger gesture vocabularies to support DA-TU
navigations in tablet environment. Adopting such set of rich
HCI vocabularies achieves better efficiency and user experience
in data exploration. We have conducted usability studies that
demonstrated our hypothesis
Hsieh, TY, Lin, YY, Liu, YT, Fang, CN & Lin, CT 2015, 'Developing a novel multi-fusion brain-computer interface (BCI) system with particle swarm optimization for motor imagery task', IEEE International Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, Istanbul, Turkey.
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© 2015 IEEE. In this paper, we develop a novel multi-fusion brain-computer interface (BCI) based on linear discriminant analysis (LDA) to deal with motor imagery (MI) classification problem. We combine filter bank and sub-band common spatial pattern (SBCSP) to extract features from EEG data in the preprocessing phase, and then LDA classifiers are applied to classify brain activities to identify either left or right hand imagery. To further foster the performance of the proposed system, a fuzzy integral (FI) approach is employed to fuse information sources, and particle swarm optimization (PSO) algorithm is exploited to globally update parameters in the fusion structure. Consequently, our experimental results indicate that the proposed system provides superior performance compared to other approaches.
Huang, G, He, J, Chi, CH, Zhou, W & Zhang, Y 2015, 'A Data as a Product Model for Future Consumption of Big Stream Data in Clouds', Proceedings - 2015 IEEE International Conference on Services Computing, SCC 2015, pp. 256-263.
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© 2015 IEEE. Data is becoming the world's new natural resource and big data use grows quickly. The trend of computing technology is that everything is merged into the Internet and 'big data' are integrated to comprise complete information for collective intelligence. With the increasing size of big data, refining big data themselves to reduce data size while keeping critical data (or useful information) is a new approach direction. In this paper, we provide a novel data consumption model, which separates the consumption of data from the raw data, and thus enable cloud computing for big data applications. We define a new Data-as-a-Product (DaaP) concept, a data product is a small sized summary of the original data and can directly answer users' queries. Thus, we separate the mining of big data into two classes of processing modules: the refine modules to change raw big data into small sized data products, and application-oriented mining modules to discover desired knowledge further for applications from well-defined data products. Our practices of mining big stream data, including medical sensor stream data, streams of text data and trajectory data, demonstrated the efficiency and precision of our DaaP model for answering users' queries.
Huang, T, Huang, M & Luo, J 2015, 'Teaching undergraduate algorithms with case studies and quizzes in interactive tutorials', 2015 IEEE INTERNATIONAL CONFERENCE ON TEACHING, ASSESSMENT, AND LEARNING FOR ENGINEERING (TALE), IEEE International Conference on Teaching, Assessment and Learning for Engineering, IEEE, Zhuhai, pp. 272-276.
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The subject of algorithms is a core component of Computer Science for undergraduate students. Many of innovative teaching methods have been proposed in the literature and practiced in classrooms aiming to help students learn the subject effectively. This paper reports on our experience in teaching advanced data structures and classic computer science algorithms for year 2 students. For three consecutive years, we practiced a different teaching approach each year. The details of each approach are provided and discussed. By comparing students' learning performance, we found that teaching undergraduate algorithms with case studies and quizzes in interactive tutorials is relatively more beneficial for effective teaching and learning. We conclude the paper with a short summary and future work.
Hussain, A, Abu Ul Fazal, M & Karim, MS 2015, 'Intra-domain User Model for Content Adaptation', Smart Education and Smart e-Learning, 2nd International KES Conference on Smart Education and Smart e-Learning, SPRINGER-VERLAG BERLIN, Sorrento, ITALY, pp. 285-295.
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Hussain, W, Hussain, FK & Hussain, OK 2015, 'Comparative Analysis of Consumer Profile-based Methods to Predict SLA Violation', 2015 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2015), IEEE International Conference on Fuzzy Systems, IEEE, Istanbul.
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A Service Level Agreement (SLA) is a contract between a service provider and a consumer which specifies in detail the level of service expected from the service provider, obligations, commitment and objectives. In the cloud computing environment, both the cloud provider and the cloud consumer want to know of a likely service violation before the actual violation occurs and to adjust the scaling of the cloud resources appropriately. A consumer's previous resource usage profile is a key element in determining the possibility of service violation in the cloud computing environment, which has not been an area of research focus so far. In this paper, we analyze and compare QoS prediction by considering the consumer's previous resource usage profile in various conditions. From comparative analysis, we observe that by combining a consumer's previous resource usage profile history along with the previous resource usage profile history of its nearest neighbors, we obtain an optimal result.
Hussain, W, Hussain, FK & Hussain, OK 2015, 'Towards soft computing approaches for formulating viable service level agreements in cloud', Neural Information Processing (LNCS), International Conference on Neural Information Processing, Springer, Istanbul, Turkey, pp. 639-646.
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© Springer International Publishing Switzerland 2015. A service level agreement (SLA) is a legal document that binds consumers and providers together for the delivery of specific services for a certain period of time. Providers need a viable SLA to maintain successful relationships with consumers. A viable SLA, based on the previous profile of a consumer, will help a service provider determine whether to accept or reject a consumer’s request and the amount of resources to offer them. In this paper we propose a softcomputing based approach to form a personalized and viable SLA. This process is carried out in the pre-interaction time phase. We build a Fuzzy Inference System (FIS) and consider a consumer’s reliability value and contract duration as the input factors to determine the amount of resources to offer to the consumer. In addition to the Fuzzy Inference System, we tested various Neural Network-based methods for viable SLA formation and compared their prediction accuracy with the output of the FIS.
Hussain, W, Hussain, FK & Hussain, OK 2015, 'Transmitting scalable video streaming over wireless ad hoc networks', Proceedings - International Conference on Advanced Information Networking and Applications, AINA, International Conference on Advanced Information Networking and Applications (was ICOIN), IEEE, Gwangju, SOUTH KOREA, pp. 201-206.
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© 2015 IEEE. Due to the rapid increase in the use of social networking websites and applications, the need to stream video over wireless networks has increased. There are a number of considerations when transmitting streaming video between the nodes connected through wireless networks, such as throughput, the size of the multimedia file, response time, delay, scalability and loss of data. The scalability of ad-hoc networks needs to be analyzed by considering various aspects, such as self-organization, security, routing flexibility, availability of bandwidth, data distribution, Quality of Service, throughput, response time and efficiency. In this paper, we discuss the existing approaches to multimedia routing and transmission over wireless ad-hoc networks by considering scalability. The study draws several conclusions and makes recommendations for future directions.
Hussain, W, Hussain, FK, Hussain, O & Chang, E 2015, 'Profile-based viable Service Level Agreement (SLA) Violation Prediction Model in the Cloud', Proceedings of the 2015 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, IEEE, Krakow, Poland, pp. 268-272.
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The worldwide web (www) provides a platform that enables service providers to transcend barriers and engage with current or potential customers globally resulting in their economic growth and expanded business horizons -- thereby creating the internet economy. It enables customers to receive desired services in a cost effective way, but given the open and ubiquitous nature of the www, particularly in cloud computing, both service providers and service consumers need efficient approach that guarantee their business requirements will be met. Additionally, all stakeholders need an efficient system that predicts any violation before it occurs and recommends how to mitigate those violations to avoid any penalties. In this paper we propose an intelligent, profile-based SLA violation prediction model, from the provider's perspective. The model begins monitoring an SLA in the pre-interaction time phase, before finalizing the SLA. It intelligently predicts the consumer's likely resouce usage, by considering the consumer's reputation from its previous transaction history, and determines the level of required resources based on their reliability. The framework helps service providers: make decisions about whether to form SLAs, maximize profit, and avoid service violations in post-interaction time phase.
Ikram, MA, Alshehri, MD & Hussain, FK 2015, 'Architecture of an IoT-based System for Football Supervision (IoT Football)', Proceedings of the 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), IEEE World Forum on Internet of Things (WF-IoT), IEEE, Milan, Italy, pp. 69-74.
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Football, also called soccer, is one of the most popular sports in the world, if one considers the number of fans as well as the number of players. However, footballers face serious injuries during the match and even during training. Concussion, hypoglycemia, swallowing the tongue and shortness of breath are examples of the health problems footballers face, and in extreme cases, may lead to death. In addition, many sport clubs and sport academies spend millions of dollars contracting new professional footballers or even developing new professional footballers. The Internet of Things (IoT) is a new paradigm that combines various technologies to enhance our lives. Today's technology can protect footballers by diagnosing any health problems, which may occur during the match or training session, which, if detected early, may prevent any adverse effects on their long-term health. This paper proposes an IoT-based architecture for the sport of football, called IoT Football. Our proposal aims to embed sensing devices (e.g. sensors and RFID), telecommunication technologies (e.g. ZigBee) and cloud computing in the sport of football in order monitor the health of footballers and reduce the occurrence of adverse health conditions. The aim is to integrate the IoT environment, in particular the IoT application, into the field of sport in the form of a new application.
Ilsar, A & Johnston, A 2015, 'Choreography in the Mapping of New Instruments', Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition, ACM Creativity and Cognition, ACM, Glasgow, UK, pp. 161-164.
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This paper discusses the use of choreography in mapping sound to movement in the field of new instrument design. Using the analogy of the drum kit player utilising all four limbs in a similar fashion to a dancer, we investigate the notion of mapping movement to prerecorded sound in that order, as opposed to sound mapped to movement. In this way the mapping process becomes a type of "choreography", where a particular piece of music is learnt to be played as the mapping is determined. We outline three main factors which must be balanced within the mapping process. We present findings from the development of a new gestural interface for electronic percussionists and several collaborations that this interface has been used in.
Ivanovo, J, Raffe, WL, Zambetta, F & Li, X 2015, 'Combining Monte Carlo tree search and apprenticeship learning for capture the flag', 2015 IEEE Conference on Computational Intelligence and Game, IEEE Symposium on Computational Intelligence and Games, IEEE, Tainan, Taiwan, pp. 154-161.
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© 2015 IEEE. In this paper we introduce a novel approach to agent control in competitive video games which combines Monte Carlo Tree Search (MCTS) and Apprenticeship Learning (AL). More specifically, an opponent model created through AL is used during the expansion phase of the Upper Confidence Bounds for Trees (UCT) variant of MCTS. We show how this approach can be applied to a game of Capture the Flag (CTF), an environment which is both non-deterministic and partially observable. The performance gain of a controller utilizing an opponent model learned via AL when compared to a controller using just UCT is shown both with win/loss ratios and True Skill rankings. Additionally, we build on previous findings by providing evidence of a bias towards a particular style of play in the AI Sandbox CTF environment. We believe that the approach highlighted here can be extended to a wider range of games other than just CTF.
Jalali, R, El-khatib, K & McGregor, C 2015, 'Smart City Architecture for Community Level Services Through the Internet of Things', 2015 8TH INTERNATIONAL CONFERENCE ON INTELLIGENCE IN NEXT GENERATION NETWORKS, 18th International Conference on Intelligence in Next Generation Networks, IEEE, Paris, FRANCE, pp. 108-113.
Jiang, X, Liu, W, Cao, L & Long, G 2015, 'Coupled Collaborative Filtering for Context-aware Recommendation', AAAI Publications, Twenty-Ninth AAAI Conference on Artificial Intelligence, Student Abstracts, AAAI Conference on Artificial Intelligence, AAAI, Austin Texas, USA, pp. 4172-4173.
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Context-aware features have been widely recognized as important factors in recommender systems. However, as a major technique in recommender systems, traditional Collaborative Filtering (CF) does not provide a straight-forward way of integrating the context-aware information into personal recommendation. We propose a Coupled Collaborative Filtering (CCF) model to measure the contextual information and use it to improve recommendations. In the proposed approach, coupled similarity computation is designed to be calculated by interitem, intra-context and inter-context interactions among item, user and context-ware factors. Experiments based on different types of CF models demonstrate the effectiveness of our design.
Johnston, AJ 2015, 'Conceptualising Interaction in Live Performance: Reflections on 'Encoded'', Proceedings of the 2nd International Workshop on Movement and Computing, Movement and Computing (MOCO), ACM, Vancouver, Canada, pp. 60-67.
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This paper presents a detailed examination of experiences of the creative team responsible for the direction, choreography, interaction design and performance of a dance and physical theatre work, Encoded. Interviews, observations and reflection on personal experience have made visible a range of different perspectives on the design, use and creative exploration of the interactive systems that were created for the work. The work itself, and in particular the use of interactive systems, was overall considered to be successful and coherent, even while participants' approaches and concerns were often markedly different. A trajectory of creative development in which exploratory improvisation and iterative design gradually became 'locked down' in preparation for final performance and touring is described.
Kenning, G 2015, 'Art, affect and ageing: creativity Vs deficit', Proceedings of the 21st International Symposium on Electronic Art, International Symposium Electronic Arts, ISEA, Vancouver.
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As the median age of the global population increases a larger proportion of the population will be what is considered old. The aging process causes physical, cognitive and emotional changes and shifts the individual’s perception of their world. Artists and designers are increasingly working with practices that intervene, interact and bring about transformation. They engage with publics that operate as participants and co-creators and as such are increasingly likely to engage with older people. Western culture has increasingly pathologized aging with a focus on countering its impact and delaying the inevitable end. The individual is addressed as a less able version of him or herself and assessed in terms of deficit, lack and loss. Art and design projects with older people often get caught up in the pathologizing of aging. They are viewed and assessed in terms of art therapies or positioned as assistive technologies within medical, scientific and technological discourses. This critical analysis explores how aging can be addressed in terms of the capacity and potentiality of older people. It investigates how, by employing ethnographic, person-centered approaches focusing on personhood and affect, artists and designers can challenge and transform accepted views of aging.
Khoa, NLD, Zhang, B, Wang, Y, Liu, W, Chen, F, Mustapha, S & Runcie, P 2015, 'On Damage Identification in Civil Structures Using Tensor Analysis', Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference Proceedings, Part 1, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Ho Chi Minh City, Vietnam, pp. 459-471.
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Khoei, TT & Talaei-Khoei, A 2015, 'Peer learning in the class or on facebook? A correlational experiment on learning outcomes', 2015 Americas Conference on Information Systems, AMCIS 2015, Americas Conference on Information Systems (AMCIS), Puerto Rico.
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This paper presents two interventions to improve the peer learning practice in an Information System course; namely (1) class-based peer tutoring in small groups and (2) discussions on Facebook group of the course. The article aims at comparing the correlations between the learning outcomes with class-based peer tutoring as well as with Facebook engagement. The findings show that although the learning outcomes are correlated with the both of these two interventions, the students' engagement on Facebook has a stronger correlation with the learning outcomes. The study also reports the lessons learned in improving students' engagement on the Facebook group of the course. The results have been discussed in the lens of Theory of Peer Learning and the future avenues of research have been suggested. This study also motivates teaching practitioners in Information Systems to improve peer learning practices by the use of social networking sites in their courses.
Ko, LW, Lai, PW, Yang, BJ & Lin, CT 2015, 'Mobile EEG & ECG integration system for monitoring physiological states in performing simulated war game training', 2015 IEEE Conference on Computational Intelligence and Games (CIG), IEEE Symposium on Computational Intelligence and Games, IEEE, Tainan, Taiwan, pp. 542-543.
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© 2015 IEEE. It is interesting to measure the player's physiological states when playing the exciting war game in the simulated platform. Fresh soldiers are usually asked to perform the simulated shooting task in the military training. However, it is lack to understand the soldier's physiological states, especially he is under threaten, high tension, or big challenge situation. This study intends to develop a mobile EEG and ECG integration system to monitor the player's Electroencephalography (EEG) and Electrocardiography (ECG) signals when performing the simulated war game training. Through the real-Time signal processing and implementing the technique on the mobile platform, we can know the player's five major physiological states such as attention, fatigue, stress, emotion and heart rates. Experimental results and demonstration are showed that the proposed integration system is feasible for understanding the military training performance in the future.
Ko, LW, Lai, WK, Liang, WG, Chuang, CH, Lu, SW, Lu, YC, Hsiung, TY, Wu, HH & Lin, CT 2015, 'Single channel wireless EEG device for real-time fatigue level detection', Proceedings of the International Joint Conference on Neural Networks, IEEE International Joint Conference on Neural Networks, IEEE, Killarney, Ireland.
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© 2015 IEEE. Driver fatigue problem is one of the important factors of traffic accidents. Recent years, many research had investigated that using EEG signals can effectively detect driver's drowsiness level. However, real-time monitoring system is required to apply these fatigue level detection techniques in the practical application, especially in the real-road driving. Therefore, it required less channels, portable and wireless, real-time monitoring and processing techniques for developing the real-time monitoring system. In this study, we develop a single channel wireless EEG device which can real-time detect driver's fatigue level on the mobile device such as smart phone or tablet. The developed device is investigated to obtain a better and precise understanding of brain activities of mental fatigue under driving, which is of great benefit for devolvement of detection of driving fatigue system. This system consists of a Bluetooth-enabled one channel EEG, a regression model, and smartphone, which was a platform recording and transforming the raw EEG data to useful driving status. In the experiment, this was a sustained-attention driving task to implement in a virtual-reality (VR) driving simulator. To training model and develop the system, we were performed for 15 subjects to study Electroencephalography (EEG) brain dynamics by using a mobile and wireless EEG device. Based on the outstanding training results, the leave-one-subject-out cross validation test obtained 90% fatigue detection accuracy. These results indicate that the combination of a smartphone and wireless EEG device constitutes an effective and easy wearable solution for detecting and preventing driver fatigue in real driving environments.
Kotamarthi, K, Wang, X, Grossmann, G, Sheng, QZ & Indrakanti, S 2015, 'A Framework towards Model Driven Business Process Compliance and Monitoring', PROCEEDINGS OF THE 2015 IEEE 19TH INTERNATIONAL ENTERPRISE DISTRIBUTED OBJECT COMPUTING CONFERENCE WORKSHOPS AND DEMONSTRATIONS (EDOCW 2015), IEEE 19th International Enterprise Distributed Object Computing (EDOCW), IEEE, Adelaide, AUSTRALIA, pp. 24-32.
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Kulkarni, R, Qiao, Y & Sun, X 2015, 'On the Power of Parity Queries in Boolean Decision Trees', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Conference on Theory and Applications of Models of Computation, Springer, Singapore, pp. 99-109.
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© Springer International Publishing Switzerland 2015. In an influential paper,Kushilevitz and Mansour (1993) introduced a natural extension of Boolean decision trees called parity decision tree (PDT) where one may query the sum modulo 2, i.e., the parity, of an arbitrary subset of variables. Although originally introduced in the context of learning, parity decision trees have recently regained interest in the context of communication complexity (cf. Shi and Zhang 2010) and property testing (cf. Bhrushundi, Chakraborty, and Kulkarni 2013). In this paper, we investigate the power of parity queries. In particular, we show that the parity queries can be replaced by ordinary ones at the cost of the total influence aka average sensitivity per query. Our simulation is tight as demonstrated by the parity function. At the heart of our result lies a qualitative extension of the result of O’Donnell, Saks, Schramme, and Servedio (2005) titled: Every decision tree has an influential variable. Recently Jain and Zhang (2011) obtained an alternate proof of the same. Our main contribution in this paper is a simple but surprising observation that the query elimination method of Jain and Zhang can indeed be adapted to eliminate, seemingly much more powerful, parity queries. Moreover, we extend our result to linear queries for Boolean valued functions over arbitrary finite fields.
Leong, TW & Iversen, OS 2015, 'Values-led Participatory Design As a Pursuit of Meaningful Alternatives', Proceedings of the 27th Australian Computer-Human Interaction Conference (OzCHI 2015), Australian Computer Human Interaction Conference, ACM, University of Melbourne, Melbourne VIC, pp. 314-323.
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Participatory Design (PD) is inherently concerned with inquiring into and supporting human values when designing IT. We argue that a PD approach that is led by a focus upon participants' values can allow participants to discover meaningful alternatives -- alternative uses and alternative conceptualizations for IT that are particularly meaningful to them. However, how PD works with values in the design process has not been made explicit. In this paper, we aim to (i) explicate this values-led PD approach, (ii) illustrate how this approach can lead to outcomes that are meaningful alternatives, and (iii) explain the nature of meaningful alternatives. We use a PD case study to illustrate how we work with participants in a values-led PD approach towards meaningful alternatives.
Li, F, Xu, G & Cao, L 2015, 'Coupled Matrix Factorization within Non-IID Context', Proceedings, Part II, 19th Pacific-Asia Conference, PAKDD 2015, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Ho Chi Minh City, Vietnam, pp. 707-719.
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Recommender systems research has experienced different stages such as from
user preference understanding to content analysis. Typical recommendation
algorithms were built on the following bases: (1) assuming users and items are
IID, namely independent and identically distributed, and (2) focusing on
specific aspects such as user preferences or contents. In reality, complex
recommendation tasks involve and request (1) personalized outcomes to tailor
heterogeneous subjective preferences; and (2) explicit and implicit objective
coupling relationships between users, items, and ratings to be considered as
intrinsic forces driving preferences. This inevitably involves the non-IID
complexity and the need of combining subjective preference with objective
couplings hidden in recommendation applications. In this paper, we propose a
novel generic coupled matrix factorization (CMF) model by incorporating non-IID
coupling relations between users and items. Such couplings integrate the
intra-coupled interactions within an attribute and inter-coupled interactions
among different attributes. Experimental results on two open data sets
demonstrate that the user/item couplings can be effectively applied in RS and
CMF outperforms the benchmark methods.
Li, J, Huang, M, Meng, Z & Zhang, K 2015, 'An Interactive Radial Visualization of Geoscience Observation Data', Proceedings of the 8th International Symposium on Visual Information Communication and Interaction, International Symposium on Visual Information Communication and Interaction, ACM, Tokyo, Japan, pp. 93-102.
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Li, L, Sun, Y, Su, C, Xiong, S & Xu, G 2015, 'Hashtag Biased Ranking for Keyword Extraction from Microblog Posts', Knowledge Science, Engineering and Management, International Conference on Knowledge Science, Engineering and Management, Springer, Chongqing, pp. 348-359.
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Nowadays, a huge amount of text is being generated for social networking purpose on the Web. Keyword extraction from such text benefit many applications such as advertising, search, and content filtering. Recent studies show that graph based ranking is more effective than traditional term or document frequecy based approaches. However, most work in the literature constructs word to word graph within a document or a collection of documents before applying a kind of random walk. Such a graph does not consider the influence of document importance on keyword extraction. Moreover, social text like a microblog post usually has speical social features such as hashtag and so on, which can help us understand its topic. In this paper, we propose hashtag biased ranking for keyword extraction from a collection of microblog posts. We first build a word-post weighted graph by taking into account the posts themselves. Then, a hashtag biased random walk is applied on this graph, which guides our approach to extract keywords according to the hashtag topic. Last, the final ranking of a word is determined by the stationary probability after a number of interations. We evaluate our proposed method on a real Chinese microblog posts. Experiments show that our method is more effective than the traditional word to word graph based ranking in terms of precision.
Li, X, Xu, G, Chen, E & Li, L 2015, 'Learning User Preferences across Multiple Aspects for Merchant Recommendation', Proceedings of the 2015 IEEE International Conference on Data Mining, IEEE International Conference on Data Mining, IEEE, Atlantic City, NJ, pp. 865-870.
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With the pervasive use of mobile devices, Location
Based Social Networks (LBSNs) have emerged in past years.
These LBSNs, allowing their users to share personal experiences
and opinions on visited merchants, have very rich and
useful information which enables a new breed of location-based
services, namely, Merchant Recommendation. Existing techniques
for merchant recommendation simply treat each merchant as an
item and apply conventional recommendation algorithms, e.g.,
Collaborative Filtering, to recommend merchants to a target user.
However, they do not differentiate the user’s real preferences
on various aspects, and thus can only achieve limited success.
In this paper, we aim to address this problem by utilizing and
analyzing user reviews to discover user preferences in different
aspects. Following the intuition that a user rating represents a
personalized rational choice, we propose a novel utility-based
approach by combining collaborative and individual views to
estimate user preference (i.e., rating). An optimization algorithm
based on a Gaussian model is developed to train our merchant
recommendation approach. Lastly we evaluate the proposed
approach in terms of effectiveness, efficiency and cold-start using
two real-world datasets. The experimental results show that our
approach outperforms the state-of-the-art methods. Meanwhile,
a real mobile application is implemented to demonstrate the
practicability of our method.
Li, X, Xu, G, Chen, E & Li, L 2015, 'MARS: A multi-aspect Recommender system for Point-of-Interest', Proceedings of the 2015 IEEE 31st International Conference on Data Engineering (ICDE), IEEE International Conference on Data Engineering, IEEE, Seoul, Korea, pp. 1436-1439.
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With the pervasive use of GPS-enabled smart phones, location-based services, e.g., Location Based Social Networking (LBSN) have emerged . Point-of-Interests (POIs) Recommendation, as a typical component in LBSN, provides additional values to both customers and merchants in terms of user experience and business turnover. Existing POI recommendation systems mainly adopt Collaborative Filtering (CF), which only exploits user given ratings (i.e., user overall evaluation) about a merchant while regardless of the user preference difference across multiple aspects, which exists commonly in real scenarios. Meanwhile, besides ratings, most LBSNs also provide the review function to allow customers to give their opinions when dealing with merchants, which is often overlooked in these recommender systems. In this demo, we present MARS, a novel POI recommender system based on multi-aspect user preference learning from reviews by using utility theory. We first introduce the organization of our system, and then show how the user preferences across multiple aspects are integrated into our system alongside several case studies of mining user preference and POI recommendations.
Light, A, Leong, T & Robertson, T 2015, 'Ageing Well with CSCW', ECSCW 2015: Proceedings of the 14th European Conference on Computer Supported Cooperative Work, 19-23 September 2015, Oslo, Norway, European Conference on Computer Supported Cooperative Work, Springer International Publishing, Oslo, Norway, pp. 295-304.
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This paper rethinks the role of technology in the life of older people by critically considering the discourses around ageing: drawing on insights from literatures on active ageing, findings from two studies conducted with older citizens and prevalent understandings of old age in technology design. It argues for a departure from the deficit model of old age, to an understanding that reveals older people’s agency in the ageing process and the work they do to manage their capacity to age well. This reframing of ageing and the ageing population offers new insights to CSCW and suggests new goals to support when designing technology for older people—goals that are more cognizant of people’s agency and their desires to manage their evolving experiences of the ageing process. We conclude with characteristics of the technologies we might develop.
Lin, C-T, Wang, Y-K, Fang, C-N, Yu, Y-H & King, J-T 2015, 'Extracting patterns of single-trial EEG using an adaptive learning algorithm.', Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Milan, ITALY, pp. 6642-6645.
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The improvement of brain imaging technique brings about an opportunity for developing and investigating brain-computer interface (BCI) which is a way to interact with computer and environment. The measured brain activities usually constitute the signals of interest and noises. Applying the portable device and removing noise are the benefits to real-world BCI. In this study, one portable electroencephalogram (EEG) system non-invasively acquired brain dynamics through wireless transmission while six subjects participated in the rapid serial visual presentation (RSVP) paradigm. The event-related potential (ERP) was traditionally estimated by ensemble averaging (EA) to increase the signal-to-noise ratio. One adaptive filter of data-reusing radial basis function network (DR-RBFN) was also utilized as the estimator. The results showed that this portable EEG system stably acquired brain activities. Furthermore, the task-related potentials could be clearly explored from the limited samples of EEG data through DR-RBFN. According to the artifact-free data from the portable device, this study demonstrated the potential to move the BCI from laboratory research to real-life application in the near future.
Liu, B, Chen, L, Liu, C, Zhang, C & Qiu, W 2015, 'RCP Mining: Towards the Summarization of Spatial Co-location Patterns', Advances in Spatial and Temporal Databases (LNCS), International Symposium on Advances in Spatial and Temporal Databases, Springer, Hong Kong, China, pp. 451-469.
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Co-location pattern mining is an important task in spatial data mining. However, the traditional framework of co-location pattern mining produces an exponential number of patterns because of the downward closure property, which makes it hard for users to understand, or apply. To address this issue, in this paper, we study the problem of mining representative co-location patterns (RCP). We first define a covering relationship between two co-location patterns by finding a new measure to appropriately quantify the distance between patterns in terms of their prevalence, based on which the problem of RCP mining is formally formulated. To solve the problem of RCP mining, we first propose an algorithm called RCPFast, adopting the post-mining framework that is commonly used by existing distance-based pattern summarization techniques. To address the peculiar challenge in spatial data mining, we further propose another algorithm, RCPMS, which employs the mine-and-summarize framework that pushes pattern summarization into the co-location mining process. Optimization strategies are also designed to further improve the performance of RCPMS. Our experimental results on both synthetic and real-world data sets demonstrate that RCP mining effectively summarizes spatial co-location patterns, and RCPMS is more efficient than RCPFast, especially on dense data sets.
Liu, B, Zhou, W, Gao, L, Wen, S & Luan, TH 2015, 'Mobility increases the risk of malware propagations in wireless networks', Proceedings - 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015, pp. 90-95.
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© 2015 IEEE. In this paper, we study the malware propagation issue in wireless networks, considering the features of channel interference, access competition and possible mobility. We propose a basic spread model based on the uniform scanning strategy. Referring to the wireless transmission and network capacity theories, we provide the bound of infection rate in wireless networks with fixed nodes. Furthermore, we evaluate the impact of mobility on malware propagations. Detailed performance analysis shows that mobility will greatly increase the risk of malware attacks in wireless networks. The results in this paper provide insights on the malware propagation characteristics in wireless networks and fundamental guidelines on designing defence schemes.
Liu, S, Li, G, Tran, T & Jiang, Y 2015, 'Preference Relation-based Markov Random Fields forRecommender Systems', ACML 2015 : Proceedings of 7th Asian Conference on Machine Learning, Asian Conference on Machine Learning, The Proceedings of Machine Learning Research, Hong Kong, pp. 1-16.
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A preference relation-based Top-N recommendation approach, PrefMRF, is proposed to capture both the second-order and the higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings fi rst, and then inferring the item rankings, based on the assumption of availability of explicit feed-backs such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed PrefMRF approach drops these assumptions by explicitly exploiting the preference relations, a more practical user feedback. Comparing to related work, the proposed PrefMRF approach has the unique property of modeling both the second-order and the higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference relation-based method. Experiment results on public datasets demonstrate that both types of interactions have been properly captured, and signifi cantly improved Top-N recommendation performance has been achieved.
Liu, YT, Lin, YY, Hsieh, TY, Wu, SL & Lin, CT 2015, 'A global optimized neuro-fuzzy system using artificial bee colony evolutionary algorithm', Frontiers in Artificial Intelligence and Applications, International Computer Symposium, IOS Press, Taiwan, pp. 140-149.
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© 2015 The authors and IOS Press. All rights reserved. This paper proposes a novel Takagi-Sugeno-Kang-type (TSK-type) neuro-fuzzy system (NFS), which utilizes the artificial bee colony (ABC) evolutionary algorithm for parameter optimization. The ABC evolutionary algorithm was developed based on imitating foraging behavior of natural bees for numerical optimization problems, and it has been proved to outperform other metaheuristic approaches on different constrain optimization problems in previous studies. The proposed NFS in this paper adopts an adaptive clustering method to generate fuzzy rules for determining the system architecture, and the TSK-type reasoning is employed for the consequent part of each rule. Subsequently, all free parameters in the NFS designed, including the premise and the consequent parameters, will be optimized by ABC algorithm. This study compares the performance of ABC algorithm with that of Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). The simulation results show that the performance of ABC algorithm is superior to that of the mentioned algorithms for solving dynamic system problems.
Lu, J, Mao, MS, Zhang, GQ & Zhang, JL 2015, 'A Fuzzy Content Matching-based e-Commerce Recommendation Approach', Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE International Conference on Fuzzy Systems, IEEE, Istanbul, Turkey, pp. 1-8.
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E-Commerce products often come with rich and tree-structured content information describing the attributes. To well utilize the content information, this study proposed a fuzzy content matching-based recommendation approach to assist e-Commerce customers to choose their truly interested items. In this paper, users' ratings and preferences are represented using fuzzy numbers to remain uncertainties. Tree-structured content information is transformed to a set of descriptors, and users' preferences on these descriptors are derived from fuzzy ratings by using fuzzy number operations. A kind of preference dependence relations is established between descriptors to explore the relations of different content features, and as a base to sketch the complete profile of users. While the extended preference profile of a user is established, given a new item, the fuzzy match degree of the user preference and the item content information is carried out, and then a fuzzy Topsis ranking method is proposed to able to rank all candidate items according to the fuzzy match degrees, and the highest ranked items are recommended to the target user. We conduct empirical experiments on Yelp and MovieLens datasets. The results indicate that the proposed approach improve recommendation performance in terms of both coverage and accuracy.
Lu, M, Lai, C, Ye, T, Liang, J & Yuan, X 2015, 'Visual Analysis of Route Choice Behaviour based on GPS Trajectories', 21th ACM SIGKDD Workshop on Urban Computing (UrbComp 2015), Sydney, Australia.
Lu, M, Lai, C, Ye, T, Liang, J & Yuan, X 2015, 'Visual analysis of route choice behaviour based on GPS trajectories', Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on, IEEE Symposium on Visual Analytics Science and Technology, IEEE, Chicago, IL, USA.
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There are often multiple routes between regions. Many factors potentially affect driver's route choice, such as expected time cost, length etc. In this work, we present a visual analysis system to explore driver's route choice behaviour based on taxi GPS trajectory data. With interactive trajectory filtering, the system constructs feasible routes between regions of interest. Using a rank-based visualization, the attributes of multiple routes are explored and compared. Based on a statistical model, the system supports to verify trajectory-related factors' impact on route choice behaviour. The effectiveness of the system is demonstrated by applying to real trajectory dataset.
Lu, M, Wang, Z, Liang, J & Yuan, X 2015, 'OD-Wheel: Visual Design to Explore OD Patterns of a Central Region', Visualization Symposium (PacificVis), 2015 IEEE Pacific, IEEE Pacific Visualization Symposium (was APVIS), IEEE, Hangzhou, China, pp. 87-91.
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Luo, L, Liu, W, Koprinska, I & Chen, F 2015, 'Discovering causal structures from time series data via enhanced granger causality', AI 2015: Advances in Artificial Intelligence (LNCS), Australasian Joint Conference on Artificial Intelligence, Springer, Canberra, Australia, pp. 365-378.
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© Springer International Publishing Switzerland 2015. Granger causality has been applied to explore predictive causal relations among multiple time series in various fields. However, the existence of nonstationary distributional changes among the time series variables poses significant challenges. By analyzing a real dataset, we observe that factors such as noise, distribution changes and shifts increase the complexity of the modelling, and large errors often occur when the underlying distribution shifts with time. Motivated by this challenge, we propose a new regression model for causal structure discovery – a Linear Model with Weighted Distribution Shift (linear WDS), which improves the prediction accuracy of the Granger causality model by taking into account the weights of the distribution-shift samples and by optimizing a quadratic-mean based objective function. The linear WDS is integrated in the Granger causality model to improve the inference of the predictive causal structure. The performance of the enhanced Granger causality model is evaluated on synthetic datasets and real traffic datasets, and the proposed model is compared with three different regression-based Granger causality models (standard linear regression, robust regression and quadratic-mean-based regression). The results show that the enhanced Granger causality model outperforms the other models especially when there are distribution shifts in the data.
Luo, L, Liu, W, Koprinska, I & Chen, F 2015, 'Discrimination-aware association rule mining for unbiased data analytics', Big Data Analytics and Knowledge Discovery: 17th International Conference, DaWaK 2015, Valencia, Spain, September 1-4, 2015, Proceedings, International Conference on Big Data Analytics and Knowledge Discovery, Springer International Publishing, Valencia; Spain, pp. 108-120.
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A discriminatory dataset refers to a dataset with undesirable correlation between sensitive attributes and the class label, which often leads to biased decision making in data analytics processes. This paper investigates how to build discrimination-aware models even when the available training set is intrinsically discriminating based on some sensitive attributes, such as race, gender or personal status. We propose a new classification method called Discrimination-Aware Association Rule classifier (DAAR), which integrates a new discrimination-aware measure and an association rule mining algorithm. We evaluate the performance of DAAR on three real datasets from different domains and compare it with two non-discrimination-aware classifiers (a standard association rule classification algorithm and the state-of-the-art association rule algorithm SPARCCC), and also with a recently proposed discrimination-aware decision tree method. The results show that DAAR is able to effectively filter out the discriminatory rules and decrease the discrimination on all datasets with insignificant impact on the predictive accuracy.
McGregor, C, Bonnis, B, Stanfield, B & Stanfield, M 2015, 'A Method for Real-time Stimulation and Response Monitoring using Big Data and its Application to Tactical Training', 2015 IEEE 28TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS), 28th IEEE International Symposium on Computer-Based Medical Systems (CBMS), IEEE COMPUTER SOC, Univ Sao Paulo, Sao Paulo, BRAZIL, pp. 169-170.
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McGregor, C, Heath, J & Choi, Y 2015, 'Streaming Physiological Data: General Public Perceptions of Secondary Use and Application to Research in Neonatal Intensive Care', MEDINFO 2015: EHEALTH-ENABLED HEALTH, 15th World Congress on Health and Biomedical Informatics (MEDINFO), IOS PRESS, Int Med Informat Assoc, Brazilian Hlth Informat Soc, Sao Paulo, BRAZIL, pp. 453-457.
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Medvediev, K, Xu, G, Berkovsky, S & Onikienko, Y 2015, 'An analysis of new visitors' website behaviour before & after TV advertising', Proceedings of the 2015 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC),, International Conference on Behavioral, Economic and Socio-cultural Computing, IEEE, Nanjing, China, pp. 109-115.
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This paper explores and analyses the actions of users on an e-commerce website after they have watched TV-advertising. The analysis considers factors such as month, day and time of the website visit. This article utilises visualization tools for the analysis of the frequency ratios (probabilities) of searches, conversions, bookings made by new visitors on the website.
Mols, I, Broekhuijsen, M, van den Hoven, E, Markopoulos, P & Eggen, B 2015, 'Do We Ruin the Moment? Exploring the Design of Novel Capturing Technologies', Proceedings of the Annual Meeting of the Australian Special Interest Group for Computer Human Interaction, Australian Computer Human Interaction Conference, ACM, Melbourne, VIC, Australia, pp. 653-661.
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By capturing our experiences we often strive to better
remember them in the future. However, the act of media
capturing also influences these same experiences in the
present, an area which is underexplored. This paper
describes a study with the aim to inform the design of
novel media capturing strategies. Adopting an approach
of defamiliarization based on intervention and reflection,
we strive to gain insights in the influences of future
capturing technologies on the experience of a day out. We
conducted an exploratory study in which 28 students went
on a day out and used a variety of capturing strategies.
Individual and group reflections on the experience during
this day identified several important aspects that media
capturing influences: engagement, perception & attention
and social activity. The paper concludes with implications
for design and proposes three potential future directions
for media capturing, that instead of disturbing the
moment enhance the experience.
Moncur, W, Julius, M, van den Hoven, E & Kirk, D 2015, 'Story Shell: The participatory design of a bespoke digital memorial', Proceedings of the 4th Participatory Innovation Conference, Participatory Innovation Conference, The Hague University of Applied Sciences, The Hague, the Netherlands, pp. 470-477.
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this paper describes the participatory process involved in designing a bespoke, tangible, digital memory – Story Shell – with a bereaved parent. We drew on emergent framework for digital memorials in considering who should author and experience the memorial, what content should be included, what form the memorial should take =, and what message it was intended to convey. A key finding was that the participatory design process itself served as a memorial, be presenting opportunities for the participant tp share detailed memories of their loved one. Reflections on the process deliver insights for makers and analysts on how to work in sensitive design spaces, where there is a need to consider not only an object's form bit also its situation within a delicate social context.
Mueller, S, Beyer, D, Mohr, T, Gurevich, S, Teibrich, A, Pfisterer, L, Guenther, K, Frohnhofen, J, Chen, HT, Baudisch, P, Im, S & Guimbretière, F 2015, 'Low-fidelity fabrication: Speeding up design iteration of 3D objects', Conference on Human Factors in Computing Systems - Proceedings, International Conference on Human Factors in Computing Systems, ACM, Seoul, Republic of Korea, pp. 327-330.
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Low-fidelity fabrication systems speed up rapid prototyping by printing intermediate versions of a prototype as fast, low-fidelity previews. Only the final version is fabricated as a full high-fidelity 3D print. This allows designers to iterate more quickly-achieving a better design in less time. Depending on what is currently being tested, low-fidelity fabrication is implemented in different ways: (1) faBrickator allows for a modular approach by substituting sub-volumes of the 3D model with building blocks. (2) WirePrint allows for quickly testing the shape of an object, such as the ergonomic fit, by printing wireframe structures. (3) Platener preserves the technical function by substituting 3D print with laser-cut plates of the same size and thickness. At our CHI'15 interactivity booth, we give a combined live demo of all three low-fidelity fabrication systems- putting special focus on our new low-fidelity fabrication system Platener (paper at CHI'15).
Murray-Leslie, A, Ferguson. S & Johnston, A 2014, 'Colour Tuning', http://www.tuttocongressi.it/website/congresses/congressDetail2.aspx?idc=4173&cont=100, Costume Colloquium IV: Colors in Fashion, Life Beyond Tourism, Florence, Italy.
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Colour Tuning is practice based research into the relationship between colour, dance, fashion and music in the form of an APP (IPad application) developed to be used in conjunction with a performance fashion or live Art context. The APP is used during a performance, encouraging the audience and performers tune into each other, via an IPad APP, to compose acoustic compositions or “Colour Music”.
Colour Tuning enables environments and bodies in space to tune in and out of each other, by using the IPad as a digital viewfinder through the APP. The APP player (eg: Audience member) points the IPad in the direction he or she would like to compose music and create colour feedback to and selects colours (eg: a collection of coloured clothing worn by dancers, models or actors on stage) on the IPad screen, which are then tracked. Each colour denotes a different sound space (each colour being mapped to an acoustic generative algorithim). Once the colours on the screen (colour fields denote the bodies of the actors) start moving, the sounds change according to what colour / actor comes close to another colour / actor and when the colours/actors overlap or make contact, new sounds are generated, like mixing coloured paint, meaning the performative composition of colour and sound is always in flux and never sounds the same.
Colour Tuning addresses multiple themes of the conference, including: “Symbolism of colours in dress and fashion”, by translating colour and fashion into metaphorical sounds and timbres in music, to create a larger synesthesia Live Art experience. Colour Tuning presents a participatory dialogue between audience and designer, through the interactive nature of the Colour Tuning APP’s mode of presentation, by inviting audience members to use the IPad APP to tune into the colours they want to hear whilst watching the actors on stage or on the Street. Colour Tuning presents a critical view on the history of colors in style and fashion; questioning the powerful...
Nassir, S, Leong, TW & Robertson, T 2015, 'Positive Ageing: Elements and Factors for Design', Proceedings of the Annual Meeting of the Australian Special Interest Group for Computer Human Interaction, Australian Computer Human Interaction Conference, ACM, University of Melbourne, pp. 264-268.
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A significant number of models and frameworks have introduced, and been used to support, positive approaches to ageing. They include Successful Ageing, Active Ageing and Ageing in Place, among others. The number of models can create confusion for technology designers who wish to incorporate such models into practice. This paper reviews different models of positive ageing in order to distil a comprehensive list of elements and factors that are important to, and supportive of, positive ageing. This list offers designers a useful source for considering the design of technology to support positive ageing. Finally, we discuss some gaps found in existing models and offer some insights into how designers could use this paper as a resource for design.
Nizami, S, Green, JR & McGregor, C 2015, 'An Artifact Detection Framework for Clinical Decision Support Systems', WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING, 2015, VOLS 1 AND 2, World Congress on Medical Physics and Biomedical Engineering, SPRINGER INTERNATIONAL PUBLISHING AG, Toronto, CANADA, pp. 1393-1396.
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Oberst, S, Griffin, D, Tuttle, S, Lambert, A & Boyce, RR 2015, 'Analysis of thin curved flexible structures for space applications', Acoustics 2015 Hunter Valley, Conference of the Australian Acoustical Society, Hunter Valley, NSW, Australia.
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With the advent of affordable nano-satellite designs (off-the-shelf payloads, standardised launch geometries), increasingly enterprises, governmental agencies and universities have started developing their own space programs to explore the environment of Low Earth Orbits. Thin, flexible and unfolding/deployable structures are common space engineering antenna and solar panel designs owing to their lightweight and ideal packaging characteristics, which are, however, difficult to experimentally validate in a 1-g environment. Further, curvatures or discontinuities to increase functionality without violating prioritised design criteria may lead to system-level trade-offs: stability issues arising from buckling in combination with micro-vibrations which feed back to the satellite's attitude behaviour. It appears that the literature lacks a systematic investigation of these aspects. On-Earth experimental validations (static experiments, model updating) are the starting point for studying the response to static/dynamic loading of thin curved flexible structures such as deployable high frequency antennas. Linear and nonlinear buckling modes owing to varying loadings (aerodynamic drag, solar radiation pressure, residual gravity and magnetic body forces) are found together with a high sensitivity to torsional modes' frequency changes under micro-vibrational forcing.
Pickrell, M, Bongers, A & van den Hoven, E 2015, 'Understanding persuasion and motivation in interactive stroke rehabilitation: A physiotherapists' perspective on patient motivation', Persuasive Technology - LNCS, International Conference on Persuasive Technology, Springer, Chicago, USA, pp. 15-26.
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For the research reported in this paper ethnographic research methodologies were used to explore patient motivation, feedback and the use of interactive technologies in the ward. We have conducted in-depth interviews with physiotherapists, who work closely with stroke patients to help them regain movement and function. From this research, a set of design guidelines have been developed which can be applied in the design of interactive rehabilitation equipment.
Pietroni, N, Tonelli, D, Puppo, E, Froli, M, Scopigno, R & Cignoni, P 2015, 'Statics aware grid shells', Computer Graphics Forum, pp. 627-641.
Prasad, M, Chou, KP, Saxena, A, Kawrtiya, OP, Li, DL & Lin, CT 2014, 'Collaborative fuzzy rule learning for Mamdani type fuzzy inference system with mapping of cluster centers', IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICA 2014: 2014 IEEE Symposium on Computational Intelligence in Control and Automation, Proceedings, IEEE Symposium on Computational Intelligence in Control and Automation, IEEE, USA.
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© 2014 IEEE. This paper demonstrates a novel model for Mamdani type fuzzy inference system by using the knowledge learning ability of collaborative fuzzy clustering and rule learning capability of FCM. The collaboration process finds consistency between different datasets, these datasets can be generated at various places or same place with diverse environment containing common features space and bring together to find common features within them. For any kind of collaboration or integration of datasets, there is a need of keeping privacy and security at some level. By using collaboration process, it helps fuzzy inference system to define the accurate numbers of rules for structure learning and keeps the performance of system at satisfactory level while preserving the privacy and security of given datasets.
Prasad, M, Er, MJ, Lin, C-T, Prasad, OK, Mohanty, M & Singh, J 2015, 'Novel Data Knowledge Representation with TSK-type Preprocessed Collaborative Fuzzy Rule based System', 2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI), IEEE Symposium Series Computational Intelligence, IEEE, Cape Town, SOUTH AFRICA, pp. 14-21.
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Pratama, M, Lu, J & Zhang, GQ 2015, 'A Novel Meta-Cognitive Extreme Learning Machine to Learning from Data Streams', Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE International Conference on Systems, Man and Cybernetics, IEEE, Kowloon, pp. 2792-2797.
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Extreme Learning Machine (ELM) is an answer to an increasing demand for a low-cost learning algorithm to handle big data applications. Nevertheless, existing ELMs leave four uncharted problems: complexity, uncertainty, concept drifts, curse of dimensionality. To correct these issues, a novel incremental meta-cognitive ELM, namely Evolving Type-2 Extreme Learning Machine (eT2ELM), is proposed. Et2Elm is built upon the three pillars of meta-cognitive learning, namely what-to-learn, how-to-learn, when-to-learn, where the notion of ELM is implemented in the how-to-learn component. On the other hand, eT2ELM is driven by a generalized interval type-2 Fuzzy Neural Network (FNN) as the cognitive constituent, where the interval type-2 multivariate Gaussian function is used in the hidden layer, whereas the nonlinear Chebyshev function is embedded in the output layer. The efficacy of eT2ELM is proven with four data streams possessing various concept drifts, comparisons with prominent classifiers, and statistical tests, where eT2ELM demonstrates the most encouraging learning performances in terms of accuracy and complexity.
Pratama, M, Lu, J & Zhang, GQ 2015, 'An Incremental Interval Type-2 Neural Fuzzy Classifier', Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE International Conference on Fuzzy Systems, IEEE, Istanbul, Turkey, pp. 1-8.
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Most real world classification problems involve a high degree of uncertainty, unsolved by a traditional type-1 fuzzy classifier. In this paper, a novel interval type-2 classifier, namely Evolving Type-2 Classifier (eT2Class), is proposed. The eT2Class features a flexible working principle built upon a fully sequential and local working principle. This learning notion allows eT2Class to automatically grow, adapt, prune, recall its knowledge from data streams in the single-pass learning fashion, while employing loosely coupled fuzzy sub-models. In addition, eT2Class introduces a generalized interval type-2 fuzzy neural network architecture, where a multivariate Gaussian function with uncertain non-diagonal covariance matrixes constructs the rule premise, while the rule consequent is crafted by a local non-linear Chebyshev polynomial. The efficacy of eT2Class is numerically validated by numerical studies with four data streams characterizing non-stationary behaviors, where eT2Class demonstrates the most encouraging learning performance in achieving a tradeoff between accuracy and complexity.
Qi, L, Huang, Y, Li, L & Xu, G 2015, 'Learning to rank domain experts in microblogging by combining text and non-text features', Proceedings of Behavioral, Economic and Socio-cultural Computing (BESC), 2015 International Conference on, International Conference on Behavioral, Economic and Socio-cultural Computing, IEEE, Nanjing, China, pp. 28-31.
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Currently microblog search engines have the function to find related users according to input topic keywords. Traditional approaches rank users by their authentication information or their self descriptions (introductions or labels).However, many users may not publish the posts closely related to their certification profile. In this paper, we study the problem of identifying domain-dependent influential users (or topic experts). We propose to fuse of non-text features and text features to analysis the influence of the users. In addition we compare three kinds of sorting methods, i.e., order-based rank aggregation, greedy selection based rank aggregation, SVM Rank method. Our experimental results show that the highest precision is achieved by SVM rank method.
Raffe, WL, Tamassia, M, Zambetta, F, Li, X & Mueller, FF 2015, 'Enhancing theme park experiences through adaptive cyber-physical play', 2015 IEEE Conference on Computational Intelligence and Games (CIG), IEEE Symposium on Computational Intelligence and Games, IEEE, Tainan, Taiwan, pp. 503-510.
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© 2015 IEEE. In this vision paper we explore the potential for enhancing theme parks through the introduction of adaptive cyber-physical attractions. That is, some physical attraction that is controlled by a digital system, which takes participants' actions as input and, in turn, alters the participants' experiences. This paper is thus divided into three main parts; 1) a look at the types of attractions that a typical theme park may offer and, from this, the identification of a gap in an agency versus structure spectrum that recent research and industry developments are starting to fill; 2) a discussion of the advantages that cyber-physical play has in filling this gap and a few examples of envisioned future attractions; and 3) how such cyber-physical play can uniquely allow for adaptive attractions, whereby the physical attraction is personalized to suit the capabilities or preferences of the current attraction participants, as well as some foreseeable design considerations and challenges in doing so. Through the combination of these three parts, we hope to promote further research into augmenting theme parks with adaptive cyber-physical play attractions.
Raffe, WL, Tamassia, M, Zambetta, F, Li, X, Pell, SJ & Mueller, FF 2015, 'Player-computer interaction features for designing digital play experiences across six degrees of water contact', Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play, Annual Symposium on Computer-Human Interaction in Play, ACM, London, United Kingdom, pp. 295-306.
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© 2015 ACM. Physical games involving the use of water or that are played in a water environment can be found in many cultures throughout history. However, these experiences have yet to see much benefit from advancements in digital technology. With advances in interactive technology that is waterproof, we see a great potential for digital water play. This paper provides a guide for commencing projects that aim to design and develop digital water-play experiences. A series of interaction features are provided as a result of reflecting on prior work as well as our own practice in designing playful experiences for water environments. These features are examined in terms of the effect that water has on them in relation to a taxonomy of six degrees of water contact, ranging from the player being in the vicinity of water to them being completely underwater. The intent of this paper is to prompt forward thinking in the prototype design phase of digital water-play experiences, allowing designers to learn and gain inspiration from similar past projects before development begins.
Ramezani, F, Naderpour, M & Lu, J 2015, 'Handling Uncertainty in Cloud Resource Management Using Fuzzy Bayesian Networks', Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE International Conference on Fuzzy Systems, IEEE, Istanbul, Turkey.
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The success of cloud services depends critically on the effective management of virtualized resources. This paper aims to design and implement a decision support method to handle uncertainties in resource management from the cloud provider perspective that enables underlying complexity, automates resource provisioning and controls client-perceived quality of service. The paper includes a probabilistic decision making module that relies upon a fuzzy Bayesian network to determine the current situation status of a cloud infrastructure, including physical and virtual machines, and predicts the near future state, that will help the hypervisor to migrate or expand the VMs to reduce execution time and meet quality of service requirements. First, the framework of resource management is presented. Second, the decision making module is developed. Lastly, a series of experiments to investigate the performance of the proposed module is implemented. Experiments reveal the efficiency of the module prototype.
Ramezani, N & Williams, M 2015, 'Smooth robot motion with an Optimal Redundancy Resolution for PR2 robot based on an analytic inverse kinematic solution', Proceedings of the 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), IEEE-RAS International Conference on Humanoid Robots, IEEE, Seoul, Korea, pp. 338-345.
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Current support for the PR2 humanoid arm motion planning does not provide a redundancy resolution scheme and this can cause excessive movements and discontinuities in the generated trajectories. We provide an innovative solution and implementation in ROS that can be used to deliver smooth motions that people find to be intuitive and easy to predict. This paper, provides a comprehensive analysis of kinematics of the PR2 humanoid robot arm including an analytic Inverse Kinematics solution and an Optimal Redundancy Resolution scheme. First, a closed form IK computation method is introduced for the PR2 arms providing all feasible solutions in global jointspace for a given value of the first joint angle as a redundant parameter. Then, a redundancy optimization technique is customized and formulated based on a desired objective function that finds optimal values for the redundant parameter. The proposed technique computes robot motion plan more effectively so that the robot behaviors are expected to be more reliable. The technique has been implemented successfully on PR2 for a hand writing task.
Ren, Y, Li, G & Zhou, W 2015, 'A survey of recommendation techniques based on offline data processing', Concurrency Computation, pp. 3915-3942.
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© 2015 John Wiley & Sons, Ltd. Recommendations based on offline data processing has attracted increasing attention from both research communities and IT industries. The recommendation techniques could be used to explore huge volumes of data, identify the items that users probably like, translate the research results into real-world applications and so on. This paper surveys the recent progress in the research of recommendations based on offline data processing, with emphasis on new techniques (such as temporal recommendation, graph-based recommendation and trust-based recommendation), new features (such as serendipitous recommendation) and new research issues (such as tag recommendation and group recommendation). We also provide an extensive review of evaluation measurements, benchmark data sets and available open source tools. Finally, we outline some existing challenges for future research.
Shao, J, Yin, J, Liu, W & Cao, L 2015, 'Actionable Combined High Utility Itemset Mining', AAAI'15 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, AAAI Press, Austin, Texas, USA, pp. 4206-4207.
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Shao, J, Yin, J, Liu, W & Cao, L 2015, 'Mining Actionable Combined Patterns of High Utility and Frequency', Proceedings of the IEEE International Conference on Data Science and Advanced Analytics, IEEE International Conference on Data Science and Advanced Analytics, IEEE, Paris, pp. 1-10.
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In recent years, the importance of identifying actionable patterns has become increasingly recognized so that decision-support actions can be inspired by the resultant patterns. A typical shift is on identifying high utility rather than highly frequent patterns. Accordingly, High Utility Itemset (HUI) Mining methods have become quite popular as well as faster and more reliable than before. However, the current research focus has been on improving the efficiency while the coupling relationships between items are ignored. It is important to study item and itemset couplings inbuilt in the data. For example, the utility of one itemset might be lower than user-specified threshold until one additional itemset takes part in; and vice versa, an item's utility might be high until another one joins in. In this way, even though some absolutely high utility itemsets can be discovered, sometimes it is easily to find out that quite a lot of redundant itemsets sharing the same item are mined (e.g., if the utility of a diamond is high enough, all its supersets are proved to be HUIs). Such itemsets are not actionable, and sellers cannot make higher profit if marketing strategies are created on top of such findings. To this end, here we introduce a new framework for mining actionable high utility association rules, called Combined Utility-Association Rules (CUAR), which aims to find high utility and strong association of itemset combinations incorporating item/itemset relations. The algorithm is proved to be efficient per experimental outcomes on both real and synthetic datasets.
Sharma, N, Mandal, R, Sharma, R, Pal, U & Blumenstein, M 2015, 'Bag-of-Visual Words for word-wise video script identification: A study', Proceedings of the International Joint Conference on Neural Networks, International Joint Conference on Neural Networks, IEEE, Killarney, Ireland.
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© 2015 IEEE. Use of multiple scripts for information communication through various media is quite common in a multilingual country. Optical character recognition of such document images or videos assists in indexing them for effective information retrieval. Hence, script identification from multi-lingual documents/images is a necessary step for selecting the appropriate OCR, due the absence of a single OCR system capable of handling multiple scripts. Script identification from printed as well as handwritten documents is a well-researched area, but script identification from video frames has not been explored much. Low resolution, blur, noisy background, to mention a few are the major bottle necks when processing video frames, and makes script identification from video images a challenging task. This paper examines the potential of Bag-of-Visual Words based techniques for word-wise script identification from video frames. Two different approaches namely, Bag-Of-Features (BoF) and Spatial Pyramid Matching (SPM), using patch based SIFT descriptors were considered for the current study. SVM Classifier was used for analysing the three popular south Indian scripts, namely Tamil, Telugu and Kannada in combination with English and Hindi. A comparative study of Bag-of-Visual words with traditional script identification techniques involving gradient based features (e.g. HoG) and texture based features (e.g. LBP) is presented. Experimental results shows that patch-based features along with SPM outperformed the traditional techniques and promising accuracies were achieved on 2534 words from the five scripts. The study reveals that patch-based feature can be used for scripts identification in-order to overcome the inherent problems with video frames.
Sharma, N, Mandal, R, Sharma, R, Pal, U & Blumenstein, M 2015, 'ICDAR2015 Competition on Video Script Identification (CVSI 2015)', Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, pp. 1196-1200.
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© 2015 IEEE. This paper presents the final results of the ICDAR 2015 Competition on Video Script Identification. A description and performance of the participating systems in the competition are reported. The general objective of the competition is to evaluate and benchmark the available methods on word-wise video script identification. It also provides a platform for researchers around the globe to particularly address the video script identification problem and video text recognition in general. The competition was organised around four different tasks involving various combinations of scripts comprising tri-script and multi-script scenarios. The dataset used in the competition comprised ten different scripts. In total, six systems were received from five participants over the tasks offered. This report details the competition dataset specifications, evaluation criteria, summary of the participating systems and their performance across different tasks. The systems submitted by Google Inc. were the winner of the competition for all the tasks, whereas the systems received from Huazhong University of Science and Technology (HUST) and Computer Vision Center (CVC) were very close competitors.
Sharma, N, Mandal, R, Sharma, R, Roy, PP, Pal, U & Blumenstein, M 2015, 'Multi-lingual text recognition from video frames', Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, International Conference on Document Analysis and Recognition (ICDAR), IEEE, Nancy, France, pp. 951-955.
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© 2015 IEEE. Text recognition from video frames is a challenging task due to low resolution, blur, complex and coloured backgrounds, noise, to mention a few. Consequently, the traditional ways of text recognition from scanned documents having simple backgrounds fails when applied to video text. Although there are various techniques available for text recognition from handwritten and printed documents with simple backgrounds, text recognition from video frames has not been comprehensively investigated, especially for multi-lingual videos. In this paper, we present a technique for multi-lingual video text recognition which involves script identification in the first stage, followed by word and character recognition, and finally the results are refined using a post-processing technique. Considering the inherent problems in videos, a Spatial Pyramid Matching (SPM) based technique, using patch-based SIFT descriptors and SVM classifier, is employed for script identification. In the next stage, a Hidden Markov Model (HMM) based approach is used for word and character recognition, which utilizes the context information. Finally, a lexicon-based post-processing technique is applied to verify and refine the word recognition results. The proposed method was tested on a dataset comprising of 4800 words from three different scripts, namely, Roman (English), Hindi and Bengali. The script identification results obtained are encouraging. The word and character recognition results are also encouraging considering the complexity and problems associated with video text processing.
Singh, AK, Wang, Y-K, King, J-T, Lin, C-T & Ko, L-W 2015, 'A simple communication system based on Brain Computer Interface', Proceedings of the Technologies and Applications of Artificial Intelligence (TAAI), Technologies and Applications of Artificial Intelligence, IEEE, Tainan, Taiwan, pp. 363-366.
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The current study presents one Brain Computer
Interface (BCI) based communication system in which the
intentional eye blink were extracted from the single channel EEG
data. This system could be useful for the sufferers of motor
disease, locked in syndrome, and paralysis. In particular, one new
Human Computer Interface (HCI) can also benefit healthy users.
To detect the intentional eye blinking in real-time, the score was
calculated from delta, theta and gamma power band from the
brain dynamics acquired from single channel through NeuroSky
Headset wirelessly. The soft and hard blink represent ' 0 ' and '1'
in this system respectively and formed four continuous bit string
which map to the pre-defined text in this system. The mapped
text was convert into speech and send to the speaker. The
experimental results shows that this system can provide an
accurate and convenient way to communicate through the brain
dynamics.
Song, K, Feng, S, Gao, W, Wang, D, Chen, L & Zhang, C 2015, 'Building emotion lexicon from microblogs by combining effects of seed words and emoticons in a heterogeneous graph', Proceedings of the 26th ACM Conference on Hypertext & Social Media, ACM Conference on HyperText and Social Media, ACM, Guzelyurt, Northern Cyprus, pp. 283-292.
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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 paper, we propose 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
Talaei-Khoei, A, Lewis, L, Talaei Khoei, T, Ghapanchi, AH & Vichitvanichphong, S 2015, 'Seniors' perspective on perceived transfer effects of assistive robots in elderly care: Capability approach analysis', ICIS 2015 Proceedings, International Conference on Information Systems (ICIS 2015), AISEL, Texas, USA.
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There have been widespread critiques towards the lack of contextual approaches in the adoption theories. This paper argues that independent living plays a significant role in adoption of assistive technologies such as robots among seniors. Therefore, the present article introduces perceived transfer effects as a cognitive process in which elderly realize how assistive robots help them in their functional abilities to empower their capabilities required to live independently. The study conducts qualitative interviews to understand the constructs contributing to the perceived transfer effects of assistive robots from seniors’ perspective. The paper demonstrates the potentials of the capability approach in the context of assistive robots for elderly. The paper opens avenues of research in adoption of similar types of assistive technologies among elderly. The article also informs nursing and aged care professionals about the better adoption of robots as an alternative intervention to improve the everyday life of seniors
Tamassia, M, Zambetta, F, Raffe, W & Li, X 2015, 'Learning options for an MDP from demonstrations', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Australasian Conference on Artificial Life and Computational Intelligence, Springer, Newcastle, NSW, Australia, pp. 226-242.
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© Springer International Publishing Switzerland 2015. The options framework provides a foundation to use hierarchical actions in reinforcement learning. An agent using options, along with primitive actions, at any point in time can decide to perform a macro-action made out of many primitive actions rather than a primitive action. Such macro-actions can be hand-crafted or learned. There has been previous work on learning them by exploring the environment. Here we take a different perspective and present an approach to learn options from a set of experts demonstrations. Empirical results are also presented in a similar setting to the one used in other works in this area.
Tan, CT, Leong, TW, Shen, S, Dubravs, C & Si, C 2015, 'Exploring Gameplay Experiences on the Oculus Rift', Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play, Annual Symposium on Computer-Human Interaction in Play, ACM, London, UK, pp. 253-263.
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Compared to previous head-mounted displays, the compact and low-cost Oculus Rift has claimed to offer improved virtual reality experiences. However, how and what kinds of user experiences are encountered by people when using the Rift in actual gameplay has not been examined. We present an exploration of 10 participants' experiences of playing a first-person shooter game using the Rift. Despite cybersickness and a lack of control, participants experienced heightened experiences, a richer engagement with passive game elements, a higher degree of flow and a deeper immersion on the Rift than on a desktop setup. Overly demanding movements, such as the large range of head motion required to navigate the game environment were found to adversely affect gaming experiences. Based on these and other findings, we also present some insights for designing games for the Rift.
Teague, R, Ahadi, A & Lister, R 2015, 'Mired in the Web: Vignettes from Charlotte and Other Novice Programmers', 17th Australasian Computing Education Conference (ACE 2015), Australasian Computing Education Conference, ACS, Sydney, Australia, pp. 165-174.
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Ahadi and Lister (2013) found that many of their
introductory programming students had fallen behind as
early as week 3 of semester, and those students often then
stayed behind. Our later work (Ahadi, Lister and Teague
2014) supported that finding, for students at another
institution. In this paper, we go one step further than those
earlier studies by observing a number of students as they
complete programming tasks while thinking aloud. We
describe the types of inconsistencies students manifest,
which are often not evident on analysis of conventional
written tests. We again interpret our findings using neoPiagetian
theory. We conclude with some thoughts on the
pedagogical implications of our research results.
Tomamichel, M, Wilde, MM & Winter, A 2015, 'Strong converse rates for quantum communication', IEEE International Symposium on Information Theory - Proceedings, IEEE International Symposium on Information Theory, IEEE, Hong Kong, China, pp. 2386-2390.
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© 2015 IEEE. We revisit a fundamental open problem in quantum information theory, namely whether it is possible to transmit quantum information at a rate exceeding the channel capacity if we allow for a non-vanishing probability of decoding error. Here we establish that the Rains information of any quantum channel is a strong converse rate for quantum communication: For any code with a rate exceeding the Rains information of the channel, we show that the fidelity vanishes exponentially fast as the number of channel uses increases. This remains true even if we consider codes that perform classical post-processing on the transmitted quantum data. Our result has several applications. Most importantly, for generalized dephasing channels we show that the Rains information is also achievable, and thereby establish the strong converse property for quantum communication over such channels. This for the first time conclusively settles the strong converse question for a class of quantum channels that have a non-trivial quantum capacity.
Treadaway, C & Kenning, G 2015, 'Designing Sensory E-Textiles for Dementia', Proceedings of The Third International Conference on Design Creativity, The Third International Conference on Design Creativity, The Design Society, Bangalore, India, pp. 235-242.
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This paper describes research investigating the design and development of new kinds of sensory textiles to be used to support the subjective wellbeing (SWB) of persons with dementia (PwD). An international collaborative research project is presented in which participatory and inclusive design methods have been used, informed by the expertise and experience of a variety of stakeholders including carers, occupational therapists, PwD and their families.
Treadaway, C, Kenning, G & Coleman, S 2015, 'Sensor e-Textiles: Designing for persons with late stage dementia', Proceedings of the 3rd European Conference of Design4Health, Design4Health 2015 European Conference, Sheffield Hallam University, Sheffield UK.
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This paper describes an on-going inclusive, participatory design research project investigating ways of supporting the subjective wellbeing (SWB) of people with late stage dementia through the development of sensory e-textiles. This research addresses the World Health Organisation and G8 call for an international response to the global challenge of the ageing population and, in particular, research into care of people with dementia (WHO, 2012). It presents an international collaboration between researchers at the Centre for Applied Research in Inclusive Arts and Design (CARIAD) Cardiff Metropolitan University and the Faculty of Arts and Social Sciences at University of Technology Sydney. The project is partnered by Gwalia Cyf, one of the leading providers of residential social care for the elderly in Wales, providing access to staff expertise (occupational therapists, home managers and carers) and facilitating the involvement of residents and their families in the research. The research team includes textile designers, computer and materials scientists, software and electrical engineers who have brought their multidisciplinary expertise to the co-design process. The authors’ previous research (Treadaway et al, 2014) has identified the current lack of well-designed products for persons with dementia to enhance subjective wellbeing. In late stage dementia people lose the facility to relate to the external world beyond the body and so garments can be used as an important vehicle to stimulate, distract and soothe individuals (Twigg, 2010; Kontos and Martin, 2013). This paper describes the development of new types of personalised textiles that provide sensory stimulation and encourage ‘living in the moment’ through the integration of embedded electronics and smart materials. It reports on the design research process, including case study interviews with family members and care professionals, co-design participatory events and evaluation studies. Early findings from th...
Tsai, WC & van den Hoven, E 2015, 'Retro Jukebox: A Reflection on Designing for Cognitive Stimulation of Postoperative Elderly Patients', Proceedings of the Asia Pacific HCI and UX Design Symposium, The Asia Pacific HCI and UX Design Symposium, ACM, Melbourne, pp. 22-25.
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Retro Jukebox is a tablet-based software application designed for postoperative elderly patients and bedside nurses. The application is designed as a reminiscence aid to support patients' cognitive stimulation. In this paper, we present the lessons learned from a field study that led us to reflect beyond its utility-oriented design. We shed light on some implicit values and benefits that may not be seen as the designer's intentions but are a meaningful appropriation heading toward the same goal.
Umapathi, U, Chen, HT, Mueller, S, Wall, L, Seufert, A & Baudisch, P 2015, 'LaserStacker: Fabricating 3D Objects by Laser Cutting and Welding', UIST 2015 - Proceedings of the 28th Annual ACM Symposium on User Interface Software and Technology, ACM Symposium on User Interface Software and Technology, ACM, USA, pp. 575-582.
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Laser cutters are useful for rapid prototyping because they are fast. However, they only produce planar 2D geometry. One approach to creating non-planar objects is to cut the object in horizontal slices and to stack and glue them. This approach, however, requires manual effort for the assembly and time for the glue to set, defeating the purpose of using a fast fabrication tool. We propose eliminating the assembly step with our system LaserStacker. The key idea is to use the laser cutter to not only cut but also to weld. Users place not one acrylic sheet, but a stack of acrylic sheets into their cutter. In a single process, LaserStacker cuts each individual layer to shape (through all layers above it), welds layers by melting material at their interface, and heals undesired cuts in higher layers. When users take out the object from the laser cutter, it is already assembled. To allow users to model stacked objects efficiently, we built an extension to a commercial 3D editor (SketchUp) that provides tools for defining which parts should be connected and which remain loose. When users hit the export button, LaserStacker converts the 3D model into cutting, welding, and healing instructions for the laser cutter. We show how LaserStacker not only allow making static objects, such as architectural models, but also objects with moving parts and simple mechanisms, such as scissors, a simple pinball machine, and a mechanical toy with gears.
van Gennip, D, van den Hoven, E & Markopoulos, P 2015, 'Things That Make Us Reminisce: Everyday Memory Cues As Opportunities for Interaction Design', Proceedings of the 33rd Annual CHI Conference on Human Factors in Computing Systems, Conference on Human Factors in Computing Systems, ACM, Seoul, Republic of Korea, pp. 3443-3452.
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Interactive devices can support personal remembering to benefit well-being. These designs require insight into what brings the past to mind, and how people relate to such cues. Prior work focused on mementos in the home; instead, this paper presents a diary and interview study of involuntary memory cueing in everyday life. Data was collected from fifteen adult individuals, using sentence completion diaries, combined with debriefing interviews. Qualitative analysis of the data showed that these participants were relying on everyday physical objects like food items for cueing memories during everyday life, locations and (repeated) activities, while digital items and photos were shown to be less frequent stimulants. Meaningful relations to memory cues can be partially explained from a memory cueing perspective. We discuss how design for remembering can benefit from our insights, through careful trade-offs in timing, exposure to cues, and supporting a process of personal attachment with items invoking memories.
Wang, H, Zhang, P, Chen, L & Zhang, C 2015, 'Socialanalysis: A Real-Time query and mining system from social media data streams', Databases Theory and Applications (LNCS), Australasian Database Conference, Springer, Melbourne, Australia, pp. 318-322.
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© Springer International Publishing Switzerland 2015. In this paper, we present our recent progress of designing a real-time system, SocialAnalysis, to discover and summarize emergent social events from social media data streams. In social networks era, people always frequently post messages or comments about their activities and opinions. Hence, there exist temporal correlations between the physical world and virtual social networks, which can help us to monitor and track social events, detecting and positioning anomalous events before their outbreakings, so as to provide early warning. The key technologies in the system include: (1) Data denoising methods based on multi-features, which screens out the query-related event data from massive background data. (2) Abnormal events detection methods based on statistical learning, which can detect anomalies by analyzing and mining a series of observations and statistics on the time axis. (3) Geographical position recognition, which is used to recognize regions where abnormal events may happen.
Wang, H, Zhang, P, Chen, L, Liu, H & Zhang, C 2015, 'Online Diffusion Source Detection in Social Networks', Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), IEEE International Joint Conference on Neural Networks, IEEE, Killarney, Ireland, pp. 1-8.
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In this paper we study a new problem of online diffusion source detection in social networks. Existing work on diffusion source detection focuses on offline learning, which assumes data collected from network detectors are static and a snapshot of network is available before learning. However, an offline learning model does not meet the needs of early warning, real-time awareness, and real-time response of malicious information spreading in social networks. In this paper, we combine online learning and regression-based detection methods for real-time diffusion source detection. Specifically, we propose a new ℓ1 non-convex regression model as the learning function, and an Online Stochastic Sub-gradient algorithm (OSS for short). The proposed model is empirically evaluated on both synthetic and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.
Wang, H, Zhang, P, Tsang, I, Chen, L & Zhang, C 2015, 'Defragging Subgraph Features for Graph Classification', Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, ACM International Conference on Information and Knowledge Management, ACM, Melbourne, VIC, Australia, pp. 1687-1690.
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Graph classification is an important tool for analysing structured and semi-structured data, where subgraphs are commonly used as the feature representation. However, the number and size of subgraph features crucially depend on the threshold parameters of frequent subgraph mining algorithms. Any improper setting of the parameters will generate many trivial short-pattern subgraph fragments which dominate the feature space, distort graph classifiers and bury interesting long-pattern subgraphs. In this paper, we propose a new Subgraph Join Feature Selection (SJFS) algorithm. The SJFS algorithm, by forcing graph classifiers to join short-pattern subgraph fragments, can defrag trivial subgraph features and deliver long-pattern interesting subgraphs. Experimental results on both synthetic and real-world social network graph data demonstrate the performance of the proposed method.
Wang, H-W, Sun, H-J, Chang, T-Y, Lo, H-H, Cheng, W-C, Tseng, GC, Lin, C-T, Chang, S-J, Pal, N & Chung, I-F 2015, 'Discovering monotonic stemness marker genes from time-series stem cell microarray data.', BMC Genomics, Asia-Pacific Bioinformatics Conference, BioMed Central, HsinChu, Taiwan, pp. 1-18.
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BACKGROUND: Identification of genes with ascending or descending monotonic expression patterns over time or stages of stem cells is an important issue in time-series microarray data analysis. We propose a method named Monotonic Feature Selector (MFSelector) based on a concept of total discriminating error (DEtotal) to identify monotonic genes. MFSelector considers various time stages in stage order (i.e., Stage One vs. other stages, Stages One and Two vs. remaining stages and so on) and computes DEtotal of each gene. MFSelector can successfully identify genes with monotonic characteristics. RESULTS: We have demonstrated the effectiveness of MFSelector on two synthetic data sets and two stem cell differentiation data sets: embryonic stem cell neurogenesis (ESCN) and embryonic stem cell vasculogenesis (ESCV) data sets. We have also performed extensive quantitative comparisons of the three monotonic gene selection approaches. Some of the monotonic marker genes such as OCT4, NANOG, BLBP, discovered from the ESCN dataset exhibit consistent behavior with that reported in other studies. The role of monotonic genes found by MFSelector in either stemness or differentiation is validated using information obtained from Gene Ontology analysis and other literature. We justify and demonstrate that descending genes are involved in the proliferation or self-renewal activity of stem cells, while ascending genes are involved in differentiation of stem cells into variant cell lineages. CONCLUSIONS: We have developed a novel system, easy to use even with no pre-existing knowledge, to identify gene sets with monotonic expression patterns in multi-stage as well as in time-series genomics matrices. The case studies on ESCN and ESCV have helped to get a better understanding of stemness and differentiation. The novel monotonic marker genes discovered from a data set are found to exhibit consistent behavior in another independent data set, demonstrating the utility of the proposed method....
Wang, S, Cheema, MA, Zhang, Y & Lin, X 2015, 'Selecting representative objects considering coverage and diversity', GeoRich 2015 - 2nd International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data, in conjunction with SIGMOD 2015, International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data, ACM, Australia, pp. 31-36.
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© 2015 ACM. We say that an object o attracts a user u if o is one of the top-k objects according to the preference function defined by u. Given a set of objects (e.g., restaurants) and a set of users, in this paper, we study the problem of computing a set of representative objects considering two criteria: coverage and diversity. Coverage of a set S of objects is the distinct number of users that are attracted by the objects in S. Although a set of objects with high coverage attracts a large number of users, it is possible that all of these users have quite similar preferences. Consequently, the set of objects may be attractive only for a specific class of users with similar preference functions which may disappoint other users having widely different preferences. The diversity criterion addresses this issue by selecting a set S of objects such that the set of attracted users for each object in S is as different as possible from the sets of users attracted by the other objects in S. The existing work on representative objects considers only one of the coverage and diversity criteria. We are the first to consider both of the criteria where the importance of each criterion can be controlled using a parameter. Our algorithm has two phases. In the first phase, we prune the objects that cannot be among the representative objects and compute the set of attracted users (also called reverse top-k) for each of the remaining objects. In the second phase, the reverse top-k of these objects are used to compute the representative objects maximizing coverage and diversity. Since this problem is NP-hard, the second phase employs a greedy algorithm. For the sake of time and space efficiency, we adopt MinHash and KMV Synopses to assist the set operations. We prove that the proposed greedy algorithm is Q-approximate. Our extensive experimental study on real and synthetic data sets demonstrates the effectiveness of our proposed techniques.
Wang, W, Huang, ML, Zhang, J & Lai, W 2015, 'Detecting Criminal Relationships through SOM Visual Analytics', Proceedings of the 19th International Conference Information Visualization, IEEE Information Visualization Conference, IEEE, Barcelona, pp. 316-321.
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Feature analysis is always beneficial to the detection of anonymous criminals in digital forensics, including people and activities, where vast amount of features extracted from databases are involved. Not all features extracted are continuous or different, some of them are discrete or have the same value with others. We discovered that using visual analytics to select features for forensic investigations is not only improve the analysis time of selection, but can also deeply and obviously display the slight changes of features and criminals and also the relationship between features and criminals in order to find the target with significant difference with others, and also predict the more active features to be used in the future. Experiments show that visual feature analysis can help to catch the desire results quickly and clearly.
Wang, W, Yin, H, Chen, L, Sun, Y, Sadiq, S & Zhou, X 2015, 'GEO-SAGE: A geographical sparse additive generative model for spatial item recommendation', KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM International Conference on Knowledge Discovery and Data Mining, ACM, Sydney, NSW, Australia, pp. 1255-1264.
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Wang, X, Sheng, QZ, Fang, XS, Li, X, Xu, X & Yao, L 2015, 'Approximate truth discovery via problem scale reduction', International Conference on Information and Knowledge Management, Proceedings, pp. 503-512.
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© 2015 ACM. Many real-world applications rely on multiple data sources to provide information on their interested items. Due to the noises and uncertainty in data, given a specific item, the information from different sources may conflict. To make reliable decisions based on these data, it is important to identify the trustworthy information by resolving these conflicts, i.e., the truth discovery problem. Current solutions to this problem detect the veracity of each value jointly with the reliability of each source for every data item. In this way, the efficiency of truth discovery is strictly confined by the problem scale, which in turn limits truth discovery algorithms from being applicable on a large scale. To address this issue, we propose an approximate truth discovery approach, which divides sources and values into groups according to a user-specified approximation criterion. The groups are then used for efficient inter-value influence computation to improve the accuracy. Our approach is applicable to most existing truth discovery algorithms. Experiments on real-world datasets show that our approach improves the efficiency compared to existing algorithms while achieving similar or even better accuracy. The scalability is further demonstrated by experiments on large synthetic datasets.
Wang, X, Sheng, QZ, Fang, XS, Yao, L, Xu, X & Li, X 2015, 'An integrated Bayesian approach for effective multi-truth discovery', International Conference on Information and Knowledge Management, Proceedings, pp. 493-502.
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© 2015 ACM. Truth-finding is the fundamental technique for corroborating reports from multiple sources in both data integration and collective intelligent applications. Traditional truth-finding methods assume a single true value for each data item and therefore cannot deal will multiple true values (i.e., the multi-truth-finding problem). So far, the existing approaches handle the multi-truth-finding problem in the same way as the single-truth-finding problems. Unfortunately, the multi-truth-finding problem has its unique features, such as the involvement of sets of values in claims, different implications of inter-value mutual exclusion, and larger source profiles. Considering these features could provide new opportunities for obtaining more accurate truth-finding results. Based on this insight, we propose an integrated Bayesian approach to the multi-truth-finding problem, by taking these features into account. To improve the truth-finding efficiency, we reformulate the multi-truth-finding problem model based on the mappings between sources and (sets of) values. New mutual exclusive relations are defined to reflect the possible co-existence of multiple true values. A finer-grained copy detection method is also proposed to deal with sources with large profiles. The experimental results on three real-world datasets show the effectiveness of our approach.
Wang, X, Zhang, Y, Zhang, W, Lin, X & Cheema, MA 2015, 'Optimal spatial dominance: An effective search of nearest neighbor candidates', Proceedings of the ACM SIGMOD International Conference on Management of Data, ACM Special Interest Group on Management of Data Conference, ACM, Melbourne, Victoria, Australia, pp. 923-938.
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Copyright © 2015 ACM. In many domains such as computational geometry and database management, an object may be described by multiple instances (points). Then the distance (or similarity) between two objects is captured by the pair-wise distances among their instances. In the past, numerous nearest neighbor (NN) functions have been proposed to define the distance between objects with multiple instances and to identify the NN object. Nevertheless, considering that a user may not have a specific NN function in mind, it is desirable to provide her with a set of NN candidates. Ideally, the set of NN candidates must include every object that is NN for at least one of the NN functions and must exclude every nonpromising object. However, no one has studied the problem of NN candidates computation from this perspective. Although some of the existing works aim at returning a set of candidate objects, they do not focus on the NN functions while computing the candidate objects. As a result, they either fail to include an NN object w.r.t. some NN functions or include a large number of unnecessary objects that have no potential to be the NN regardless of the NN functions. Motivated by this, we classify the existing NN functions for objects with multiple instances into three families by characterizing their key features. Then, we advocate three spatial dominance operators to compute NN candidates where each operator is optimal w.r.t. different coverage of NN functions. Efficient algorithms are proposed for the dominance check and corresponding NN candidates computation. Extensive empirical study on real and synthetic datasets shows that our proposed operators can significantly reduce the number of NN candidates. The comprehensive performance evaluation demonstrates the efficiency of our computation techniques.
Wang, X, Zhang, Y, Zhang, W, Lin, X & Wang, W 2015, 'AP-Tree: Efficiently support continuous spatial-keyword queries over stream', Proceedings - International Conference on Data Engineering, IEEE International Conference on Data Engineering, IEEE, Seoul, Korea, pp. 1107-1118.
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© 2015 IEEE. We investigate the problem of processing a large amount of continuous spatial-keyword queries over streaming data, which is essential in many applications such as location-based recommendation and advertising, thanks to the proliferation of geo-equipped devices and the ensuing location-based social media applications. For example, a location-based e-coupon system may allow potentially millions of users to register their continuous spatial-keyword queries (e.g., interests in nearby sales) by specifying a set of keywords and a spatial region; the system then delivers each incoming spatial-textual object (e.g., a geo-tagged e-coupon) to all the matched queries (i.e., users) whose spatial and textual requirements are satisfied. While there are several prior approaches aiming at providing efficient query processing techniques for the problem, their approaches belong to spatial-first indexing method which cannot well exploit the keyword distribution. In addition, their textual filtering techniques are built upon simple variants of traditional inverted indexes, which do not perform well for the textual constraint imposed by the problem. In this paper, we address the above limitations and provide a highly efficient solution based on a novel adaptive index, named AP-Tree. The AP-Tree adaptively groups registered queries using keyword and spatial partitions, guided by a cost model. The AP-Tree also naturally indexes ordered keyword combinations. We present index construction algorithm that seamlessly and effectively integrates keyword and spatial partitions. Consequently, our method adapts well to the underlying spatial and keyword distributions of the data. Our extensive experiments demonstrate that AP-Tree achieves up to an order of magnitude improvement on efficiency compared with prior state-of-the-art methods.
Wang, Z, Yuan, X, Ye, T, Hao, Y, Chen, S, Liang, J, Li, Q, Wang, H & Wu, Y 2015, 'Visual data quality analysis for taxi GPS data', 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings, pp. 223-224.
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© 2015 IEEE. We present a novel visual analysis method to systematically discover data quality problems in raw taxi GPS data. It combines semi-supervised active learning and interactive visual exploration. It helps analysts interactively discover unknown data quality problems, and automatically extract known problems. We report analysis results on Beijing taxi GPS data.
Wei, C-S, Wang, Y-T, Lin, C-T & Jung, T-P 2015, 'Toward non-hair-bearing brain-computer interfaces for neurocognitive lapse detection.', Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Milan, Italy, pp. 6638-6641.
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Recent advances in mobile electroencephalogram (EEG) acquisition based on dry electrodes have started moving Brain-Computer Interface (BCI) applications from well-controlled laboratory settings to real-world environments. However, the application mechanisms and high impedance of dry electrodes over the hair-covered areas remain challenging for everyday use of BCI. In addition, whole-scalp recordings are not always necessary or applicable due to various practical constrains. Therefore, alternative montages for EEG recordings to meet the everyday needs are in-demand. Inspired by our previous work on measuring non-hair-bearing steady state visual evoked potentials for BCI applications, this study explores the feasibility and efficacy of detecting cognitive lapses of participants based on EEG signals collected from the non-hair-bearing areas. Study results suggest that informative EEG features associated with lapses could be assessed from non-hair-bearing areas with comparable accuracy obtained from the whole-scalp EEG. The design principles, validation processes and promising findings reported in this study may enable and/or facilitate numerous BCI applications in real-world environments.
Williams, JJR, Zhang, Z, Oberst, S & Lai, JCS 2015, 'Model updating of brake components' influence on instability predictions', 22nd International Congress on Sound and Vibration, ICSV 2015, International Congress on Sound and Vibration, Florence, Italy.
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Customers perceive brake squeal as a major annoyance in their automobiles' acoustic performance. Squeal is self-excited, friction induced audible noise above 1 kHz and one of the strongest cost drivers in noise vibration and harshness departments of automotive manufacturers. In order to reduce expensive and time-consuming dynamometer and road vehicle tests, numerical complex eigenvalue analysis has become popular in predicting brake squeal. However, one difficulty in assessing the prediction quality apart from the linearisation of the system is the complexity of the brake system to be modelled. Using structural finite elements the computer model is often insufficiently detailed, insufficiently damped or insufficiently experimentally validated so that instabilities causing brake squeal are over-predicted. Here we present the process of updating components of a brake system's squeal prediction and the improvement in modelling using updated material parameters and a Rayleigh damping model by applying a rigorous mesh refinement study and different friction laws.
Wu, D, Chuang, CH & Lin, CT 2015, 'Online driver's drowsiness estimation using domain adaptation with model fusion', 2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015, International Conference on Affective Computing and Intelligent Interaction, IEEE, Xian, PEOPLES R CHINA, pp. 904-910.
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© 2015 IEEE. Drowsy driving is a pervasive problem among drivers, and is also an important contributor to motor vehicle accidents. It is very important to be able to estimate a driver's drowsiness level online so that preventative actions could be taken to avoid accidents. However, because of large individual differences, it is very challenging to design an estimation algorithm whose parameters fit all subjects. Some subject-specific calibration data must be used to tailor the algorithm for each new subject. This paper proposes a domain adaptation with model fusion (DAMF) online drowsiness estimation approach using EEG signals. By making use of EEG data from other subjects in a transfer learning framework, DAMF requires very little subject-specific calibration data, which significantly increases its utility in practice. We demonstrate using a simulated driving experiment and 15 subjects that DAMF can achieve much better performance than several other approaches.
Xuan, J, Lu, J, Zhang, G, Xu, RYD & Luo, X 2015, 'Infinite author topic model based on mixed gamma-negative binomial process', Proceedings - IEEE International Conference on Data Mining, ICDM, IEEE International Conference on Data Mining, IEEE, Atlantic City, USA, pp. 489-498.
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Incorporating the side information of text corpus, i.e., authors, time stamps, and emotional tags, into the traditionaltext mining models has gained significant interests in the area of information retrieval, statistical natural language processing, andmachine learning. One branch of these works is the so-called Author Topic Model (ATM), which incorporates the authors'sinterests as side information into the classical topic model. However, the existing ATM needs to predefine the number of topics, which is difficult and inappropriate in many real-world settings. In this paper, we propose an Infinite Author Topic (IAT) modelto resolve this issue. Instead of assigning a discrete probability on fixed number of topics, we use a stochastic process to determinethe number of topics from the data itself. To be specific, we extend a gamma-negative binomial process to three levels in orderto capture the author-document-keyword hierarchical structure. Furthermore, each document is assigned a mixed gamma processthat accounts for the multi-author's contribution towards this document. An efficient Gibbs sampling inference algorithm witheach conditional distribution being closed-form is developed for the IAT model. Experiments on several real-world datasets showthe capabilities of our IAT model to learn the hidden topics, authors' interests on these topics and the number of topicssimultaneously.
Xue, S, Lu, J, Zhang, G & Xiong, L 2015, 'Heterogeneous feature space based task selection machine for unsupervised transfer learning', Proceedings - The 2015 10th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2015, International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Taipei Taiwan, pp. 46-51.
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Transfer learning techniques try to transfer knowledge from previous tasks to a new target task with either fewer training data or less training than traditional machine learning techniques. Since transfer learning cares more about relatedness between tasks and their domains, it is useful for handling massive data, which are not labeled, to overcome distribution and feature space gaps, respectively. In this paper, we propose a new task selection algorithm in an unsupervised transfer learning domain, called as Task Selection Machine (TSM). It goes with a key technical problem, i.e., feature mapping for heterogeneous feature spaces. An extended feature method is applied to feature mapping algorithm. Also, TSM training algorithm, which is main contribution for this paper, relies on feature mapping. Meanwhile, the proposed TSM finally meets the unsupervised transfer learning requirements and solves the unsupervised multi-task transfer learning issues conversely.
Xue, S, Lu, J, Zhang, G & Xiong, L 2015, 'SEIR immune strategy for instance weighted Naive bayes classification', Neural Information Processing (LNCS), International Conference on Neural Information Processing, Springer, Istanbul, Turkey, pp. 283-292.
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Naive Bayes (NB) has been popularly applied in many classification tasks. However, in real-world applications, the pronounced advantage of NB is often challenged by insufficient training samples. Specifically, the high variance may occur with respect to the limited number of training samples. The estimated class distribution of a NB classier is inaccurate if the number of training instances is small. To handle this issue, in this paper, we proposed a SEIR (Susceptible, Exposed, Infectious and Recovered) immune-strategy-based instance weighting algorithm for naive Bayes classification, namely SWNB. The immune instance weighting allows the SWNB algorithm adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. Experiments and comparisons on 20 benchmark datasets demonstrated that the proposed SWNB algorithm outperformed existing state-of-the-art instance weighted NB algorithm and other related computational intelligence methods.
Yang, C, Zhang, G & Zhu, D 2015, 'Semantic-Based Technology Trend Analysis', Proceedings of the 10th International Conference on Intelligent Systems and Knowledge Engineering, International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Taipei, Taiwan, pp. 222-228.
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Technology trend analysis offers a flexible instrument to understand both opportunity and competition for emerging technologies. Semantic information is used in Science, Technology & Innovation (ST&I) records which makes the technology trend analysis more challenging. This paper proposes a semantic-based approach for technology trend analysis through emphasizing Subject-Action-Object (SAO) structure, It also applies the trend analysis approach to extract technology information and identify and predict the trend of technology development more effectively. An empirical study on Graphene is completed to demonstrate the proposed trend analysis approach
Yao, L, Sheng, QZ, Qin, Y, Wang, X, Shemshadi, A & He, Q 2015, 'Context-aware Point-of-Interest Recommendation Using Tensor Factorization with Social Regularization', SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, 38th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), ASSOC COMPUTING MACHINERY, Santiago, CHILE, pp. 1007-1010.
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Yao, L, Wang, X, Sheng, QZ, Ruan, W & Zhang, W 2015, 'Service Recommendation for Mashup Composition with Implicit Correlation Regularization', 2015 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS), IEEE International Conference on Web Services (ICWS), IEEE, New York, NY, pp. 217-224.
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Ye, T, Hao, Y, Wang, Z, Lai, C, Chen, S, Li, Z, Liang, J & Yuan, X 2015, 'Behavior analysis through collaborative visual exploration on trajectory data', Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on, IEEE Symposium on Visual Analytics Science and Technology, IEEE, Chicago, IL, USA.
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In VAST Challenge 2015, we proposed a collaborative visual exploration
system for behavior analysis over trajectory records. We
discuss technical details in this report, in order to deliberate how the
system supports multiple users to collaboratively analyze the same
data, assist in sharing their findings, and constructing an overall
picture of their insights.
Yu, YH, Chen, SF, Huang, CS, Ko, LW & Lin, CT 2015, 'Portable and wireless EEG device used in sleep quality tracking', 2015 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015, IEEE International Conference on Consumer Electronics, IEEEE, Taiwan, pp. 86-87.
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© 2015 IEEE. A portable and wireless electroencephalography (EEG) device was proposed in this study. The device had following features: (1) the use of dry and flexible electrodes made the device resilient and repetitively used on forehead without conductive gel; (2) the device was small and low power consumption which provided more comfortable wearing. It provided ordinary wireless (Bluetooth) protocol and memory cards to record EEG data. All electronic components were embedded into a portable mechanism conveniently. The device had past Electro Magnetic Compatibility (EMC), Federal Communications Commission (FCC) certifications. (3) EEG data was stored as EDF format compatible with popular EEG analysis tools. This device was good for sleeping study in general. A particular EEG signal Apps could analyze the EEG raw data and provided the sleep quality report in details.
Zao, JK, Lin, CT, Ko, LW, She, HC, Dung, LR & Chen, BY 2014, 'Natural user interfaces: Cyber-physical challenges and pervasive applications - A panel discussion', Proceedings - 2014 IEEE International Conference on Internet of Things, iThings 2014, 2014 IEEE International Conference on Green Computing and Communications, GreenCom 2014 and 2014 IEEE International Conference on Cyber-Physical-Social Computing, CPS 2014, IEEE International Conference on Internet of Things (iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom), IEEE, Taipei, Taiwan, pp. 467-469.
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© 2014 IEEE. This panel discussion aims at exploring the potential applications of emerging natural user interface (NUI) technologies and the challenges they pose to the design and deployment of cyber-physical systems. Based on their research work, six panelists will take turns to present the outlook, the cyber-physical requirements and the promising applications of implicit NUI. We hope these short presentations will lead to thought-provoking discussions and inspire further innovation.
Zhan, L, Zhang, Y, Zhang, W, Wang, X & Lin, X 2015, 'Range search on uncertain trajectories', Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, ACM International Conference on Information and Knowledge Management, ACM, Melbourne, Australia, pp. 921-930.
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The range search on trajectories is fundamental in a wide spectrum of applications such as environment monitoring and location based services. In practice, a large portion of spatio-temporal data in the above applications is generated with low sampling rate and the uncertainty arises between two subsequent observations of a moving object. To make sense of the uncertain trajectory data, it is critical to properly model the uncertainty of the trajectories and develop efficient range search algorithms on the new model. Assuming uncertain trajectories are modeled by the popular Markov Chains, in this paper we investigate the problem of range search on uncertain trajectories. In particular, we propose a general framework for range search on uncertain trajectories following the filtering-and-refinement paradigm where summaries of uncertain trajectories are constructed to facilitate the filtering process. Moreover, statistics based and partition based filtering techniques are developed to enhance the filtering capabilities. Comprehensive experiments demonstrate the effectiveness and efficiency of our new techniques.
Zhang, J & Huang, M 2015, 'A New Analytics Model for Large Scale Multidimensional Data Visualization', Volume 9106 of the series Lecture Notes in Computer Science (LNCS), The International Conference on Cloud Computing and Big Data, Springer, Huangshan, China, pp. 55-71.
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With the rise of Big Data, the challenge for modern multidimensional data analysis and visualization is how it grows very quickly in size and complexity. In this paper, we first present a classification method called the 5Ws Dimensions which classifies multidimensional data into the 5Ws definitions. The 5Ws Dimensions can be applied to multiple datasets such as text datasets, audio datasets and video datasets. Second, we establish a Pair-Density model to analyze the data patterns to compare the multidimensional data on the 5Ws patterns. Third, we created two additional parallel axes by using pair-density for visualization. The attributes has been shrunk to reduce data over-crowding in pair-density parallel coordinates. This has achieved more than 80 % clutter reduction without the loss of information. The experiment shows that our model can be efficiently used for Big Data analysis and visualization.
Zhang, Q, Zhang, G, Lu, J & Wu, D 2015, 'A framework of hybrid recommender system for personalized clinical prescription', Proceedings of the 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2015), International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Taipei Taiwan, pp. 189-195.
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General practitioners are faced with a great challenge of clinical prescription owing to the increase of new drugs and their complex functions to different diseases. A personalized recommender system can help practitioners deal with mass of medical knowledge hidden in history medical records. To support practitioner’s decision making in prescription, this paper proposes a framework of a hybrid recommender system which integrates artificial neural network and case-based reasoning. Three issues are considered in this system framework: (1) to define a patient’s need by giving his/her symptom, (2) to mine features from free text in medical records and (3) to analyze temporal efficiency of drugs. The proposed recommender system is expected to help general practitioners to improve their efficiency and reduce risks of making errors in daily clinical consultation with patients.
Zhang, Y, Chen, H, Zhang, G, Zhu, D & Lu, J 2015, 'Multiple Science Data-oriented Technology Roadmapping Method', Proceedings of the 2015 Portland International Conference on Management of Engineering and Technology, Portland International Center for Management of Engineering and Technology Conference, IEEE, Portland, USA, pp. 2278-2287.
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Since its first engagement with industry decades ago, Technology Roadmapping (TRM) is taking a more and more important role for technical intelligence in current R&D planning and innovation tracking. Important topics for both science policy and engineering management researchers evolves with the approaches that refer to the real-world problems, explore value-added information from the complex data sets, fuse the analytic results and expert knowledge effectively and reasonable, and demonstrate to the decision makers visually and understandable. Moreover, the growing variety of science data sources in the Big Data Age increases these challenges and opportunities. Addressing these concerns, this paper proposes a TRM composing method with a clustering-based topic identification model, a multiple science data sources integration model, and a semi-automated fuzzy set-based TRM composing model with expert aid. We focus on a case study on computer science related R&D. Empirical data from the United States National Science Foundation Award data (innovative research ideas and proposals) and Derwent Innovation Index data source (patents emphasizing technical products) provide vantage points at two stages of the R&D process. The understanding gained will assist in description of computer science macro-trends for R&D decision makers.
Zhang, Z, Oberst, S & Lai, JCS 2015, 'Instability analysis of coupled friction oscillators with uncertainties in contact conditions', 22nd International Congress on Sound and Vibration, ICSV 2015, International Congress on Sound and Vibration, Florence, Italy.
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Although brake squeal is a significant noise, vibration and harshness (NVH) issue which incurs significant cost in the automotive industry, its prediction is still difficult. This is because brake squeal is essentially a nonlinear phenomenon and traditional complex eigenvalue analysis (CEA) is a linear method. In addition, there are many uncertainties in a brake system such as material properties, operating and contact conditions which cannot be determined accurately with confidence. Here, the influence of uncertainties in contact conditions on the instability of an analytical model consisting of 3x3 coupled oscillators in point contact with a sliding rigid plate is analysed. The uncertainties in contact conditions considered are: percentage of contact, stiffness and friction laws for the contact (Amonton-Coulomb, relative velocity dependent and LuGre law). The instability is analysed in the frequency domain by randomising these three uncertainty parameters. The results will be discussed with a view to applying this approach to the analysis of the squeal propensity for a full brake system.
Zhang, Z, Oberst, S, Williams, JJR & Lai, JCS 2015, 'Improving Brake squeal propensitiy prediction by model updating', Acoustics 2015 Hunter Valley, Conference of the Australian Acoustical Society, Hunter Valley, NSW, Australia.
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Brake squeal as a significant warranty-claim related costs problem to the automotive industry is difficult to model numerically and analyse because of inherent nonlinearities, uncertainties in material properties, contact and boundary conditions, and system complexity. Often, model components are linearised and not experimentally validated. Sophisticated contact or friction models as well as stiffness in joints are often not considered owing to difficulties in experimental validation. In this study, a full brake system is modally updated at the component level and then at the subassembly level (pad assembly alone, pad in bracket). Squeal prediction using the complex eigenvalue analysis on a finite element model of the system is compared to squeal results from a noise dynamometer test. The results are discussed with respect to further refinement of the modelling approach and improvements to brake squeal prediction.
Zhou, H, Lu, J & Zhang, G 2014, 'An extended bilevel programming model and its kth-best algorithm for dynamic decision making in emergency situations', IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - MCDM 2014: 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, Proceedings, IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, IEEE, Orlando, FL, USA, pp. 100-105.
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© 2014 IEEE. Linear bilevel programming has been studied for many years and applied in different domains such as transportation, economics, engineering, environment, and telecommunications. However, there is lack of attention of the impacts on dynamic decision making with abrupt or unusual events caused by unpredictable natural environment or human activities (e.g. Tsunami, earthquake, and malicious or terrorist attacks). In reality these events could happens more often and have more significant impacts on decision making in an increasingly complex and dynamic world. This paper addresses this unique problem by introducing a concept of Virtual Follower (VF). An extended model of bilevel multi-follower programming with a virtual follower (BLMFP-VF) is defined and the kth-best algorithm for solving this problem is proposed. An example is given to illustrate the working of the extended model and approach.
Zuo, H, Zhang, G, Behbood, V & Lu, J 2015, 'Feature Spaces-based Transfer Learning', PROCEEDINGS OF THE 2015 CONFERENCE OF THE INTERNATIONAL FUZZY SYSTEMS ASSOCIATION AND THE EUROPEAN SOCIETY FOR FUZZY LOGIC AND TECHNOLOGY, World Congress of the International-Fuzzy-Systems-Association (IFSA) / Conference of the European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT), Atlantis Press, Gijon, Spain, pp. 1000-1005.
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Transfer learning provides an approach to solve target tasks more quickly and effectively by using previously-acquired knowledge learned from source tasks. Most of transfer learning approaches extract knowledge of source domain in the given feature space. The issue is that single perspective can t mine the relationship of source domain and target domain fully. To deal with this issue, this paper develops a method using Stacked Denoising Autoencoder (SDA) to extract new feature spaces for source domain and target domain, and define two fuzzy sets to analyse the variation of prediction ac-curacy of target task in new feature spaces
Zuo, H, Zhang, G, Behbood, V, Lu, J & Meng, X 2015, 'Transfer Learning in Hierarchical Feature Spaces', Procedings of the 10th International Conference on Intelligent Systems and Knowledge Engineering, International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Taipei, Taiwan, pp. 183-188.
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Transfer learning provides an approach to solve target tasks more quickly and effectively by using previously acquired knowledge learned from source tasks. As one category of transfer learning approaches, feature-based transfer learning approaches aim to find a latent feature space shared between source and target domains. The issue is that the sole feature space can't exploit the relationship of source domain and target domain fully. To deal with this issue, this paper proposes a transfer learning method that uses deep learning to extract hierarchical feature spaces, so knowledge of source domain can be exploited and transferred in multiple feature spaces with different levels of abstraction. In the experiment, the effectiveness of transfer learning in multiple feature spaces is compared and this can help us find the optimal feature space for transfer learning