Agar, M., Luckett, T., Luscombe, G., Phillips, J., Beattie, E., Pond, D., Mitchell, G., Davidson, P., Cook, J., Brooks, D., Houltram, J., Goodall, S. & Chenoweth, L. 2017, 'Effects of facilitated family case conferencing for advanced dementia: A cluster randomised clinical trial', PLoS ONE, vol. 12, no. 8, pp. e0181020-e0181020.
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Anderson, C. & Ryan, L.M. 2017, 'A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia.', International Journal of Environmental Research and Public Health, vol. 14, no. 2.
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The field of spatio-temporal modelling has witnessed a recent surge as a result of developments in computational power and increased data collection. These developments allow analysts to model the evolution of health outcomes in both space and time simultaneously. This paper models the trends in ischaemic heart disease (IHD) in New South Wales, Australia over an eight-year period between 2006 and 2013. A number of spatio-temporal models are considered, and we propose a novel method for determining the goodness-of-fit for these models by outlining a spatio-temporal extension of the Moran's I statistic. We identify an overall decrease in the rates of IHD, but note that the extent of this health improvement varies across the state. In particular, we identified a number of remote areas in the north and west of the state where the risk stayed constant or even increased slightly.
Awwad, S. & Piccardi, M. 2017, 'Prototype-based budget maintenance for tracking in depth videos', Multimedia Tools and Applications, pp. 1-16.
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© 2016 Springer Science+Business Media New YorkThe use of conventional video tracking based on color or gray-level videos often raises concerns about the privacy of the tracked targets. To alleviate this issue, this paper presents a novel tracker that operates solely from depth data. The proposed tracker is designed as an extension of the popular Struck algorithm which leverages the effective framework of structural SVM. The main contributions of our paper are: i) a dedicated depth feature based on local depth patterns, ii) a heuristic for handling view occlusions in depth frames, and iii) a technique for keeping the number of the support vectors within a given “budget” so as to limit computational costs. Experimental results over the challenging Princeton Tracking Benchmark (PTB) dataset report a remarkable accuracy compared to the original Struck tracker and other state-of-the-art trackers using depth and RGB data.
Brown, N., Luckett, T., Davidson, P.M. & DiGiacomo, M. 2017, 'Family-focussed interventions to reduce harm from smoking in primary school-aged children: A systematic review of evaluative studies.', Preventive Medicine, vol. 101, pp. 117-125.
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Children living in families where adults smoke are exposed to harmful effects of tobacco smoke and risk a predisposition to smoking initiation. Interventions to support families to reduce risk of harm from smoking have been developed and tested. The purpose of this review is to identify effective family-based interventions used to promote smoke-free home environments in families with primary school age children (aged 5-12years). A systematic search of MEDLINE, Cochrane and CINAHL electronic databases was conducted. Narrative synthesis of included articles was completed. Guidelines for reporting behaviour change interventions were used to summarise and compare intervention timing, content, intensity and delivery. Quality of included studies was critiqued using United States Preventative Services Taskforce (USPST) procedures for internal and external validity. Narrative synthesis was based on methods described by Popay and colleagues. Nineteen articles that evaluated 14 intervention studies focussed on child smoking prevention (n=5), parent smoking cessation (n=4) and environmental tobacco smoke reduction (n=6). Interventions and outcomes were heterogeneous, and were rarely informed by theoretical frameworks relating to family, parenting or child development. Family based interventions may be an important strategy to reduce the effects of smoking for children. There is a need for interventions to be informed by theory relevant to children, parenting and families.
Cajita, M.I., Denhaerynck, K., Dobbels, F., Berben, L., Russell, C.L., Davidson, P.M. & De Geest, S. 2017, 'Health literacy in heart transplantation: Prevalence, correlates and associations with health behaviors—Findings from the international BRIGHT study', Journal of Heart and Lung Transplantation, vol. 36, no. 3, pp. 272-279.
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© 2017 International Society for Heart and Lung Transplantation Background Health literacy (HL) is a major determinant of health outcomes; however, there are few studies exploring the role of HL among heart transplant recipients. The objectives of this study were to: (1) explore and compare the prevalence of inad equate HL among heart transplant recipients internationally; (2) determine the correlates of HL; and (3) assess the relationship between HL and health-related behaviors. Methods A secondary analysis was conducted using data of the 1,365 adult patients from the BRIGHT study, an international multicenter, cross-sectional study that surveyed heart transplant recipients across 11 countries and 4 continents. Using the Subjective Health Literacy Screener, inadequate HL was operationalized as being confident in filling out medical forms none/a little/some of the time (HL score of 0 to 2). Correlates of HL were determined using backward stepwise logistic regression. The relationship between HL and the health-related behaviors were examined using hierarchical logistic regression. Results Overall, 33.1% of the heart transplant recipients had inadequate HL. Lower education level (adjusted odds ratio [AOR] 0.24, p < 0.001), unemployment (AOR 0.69, p = 0.012) and country (residing in Brazil, AOR 0.25, p < 0.001) were shown to be associated with inadequate HL. Heart transplant recipients with adequate HL had higher odds of engaging in sufficient physical activity (AOR 1.6, p = 0.016). HL was not significantly associated with the other health behaviors. Conclusions Clinicians should recognize that almost one third of heart transplant participants have inadequate health literacy. Furthermore, they should adopt communication strategies that could mitigate the potential negative impact of inadequate HL.
Cao, L. 2017, 'Data science: A comprehensive overview', ACM Computing Surveys, vol. 50, no. 3.
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The 21st century has ushered in the age of big data and data economy, in which data DNA, which carries important knowledge, insights, and potential, has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics. Although it is widely debated whether big data is only hype and buzz, and data science is still in a very early phase, significant challenges and opportunities are emerging or have been inspired by the research, innovation, business, profession, and education of data science. This article provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of data science, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of data science. This article is the first in the field to draw a comprehensive big picture, in addition to offering rich observations, lessons, and thinking about data science and analytics.2017 Copyright is held by the owner/author(s).
Cao, L. 2017, 'Data science: Challenges and directions', Communications of the ACM, vol. 60, no. 8, pp. 59-68.
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© ACM 2017. WHILE DATA SCIENCE has emerged as an ambitious new scientific field, related debates and discussions have sought to address why science in general needs data science and what even makes data science a science. However, few such discussions concern the intrinsic complexities and intelligence in data science.
Chai, R., Ling, S.H., San, P.P., Naik, G., Nguyen, T.N., Tran, Y., Craig, A. & Nguyen, H.T. 2017, 'Improving EEG-based Driver Fatigue Classification using Sparse-Deep Belief Networks', Frontiers in Neuroscience, vol. 11, no. 103, pp. 1-14.
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This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN) and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6% and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3% and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8%, 9.5% and 2.5% over ANN, BNN and DBN classifiers respectively.
Chai, R., Naik, G., Nguyen, T.N., Ling, S., Tran, Y., Craig, A. & Nguyen, H. 2017, 'Driver Fatigue Classification with Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-based System.', IEEE journal of biomedical and health informatics, vol. 21, no. 3, pp. 715-724.
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This paper presents a two-class electroencephalography (EEG)-based classification for classifying of driver fatigue (fatigue state vs. alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8% and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor) and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC=0.93) against other methods such as power spectral density (PSD) as feature extractor (AUC-ROC=0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.
Chai, R., Naik, G.R., Ling, S.H. & Nguyen, H.T. 2017, 'Hybrid brain–computer interface for biomedical cyber-physical system application using wireless embedded EEG systems', BioMedical Engineering OnLine, vol. 16, no. 5, pp. 1-23.
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Chen, Q., Wang, Y., Chen, B., Zhang, C., Wang, L. & Li, J. 2017, 'Using propensity scores to predict the kinases of unannotated phosphopeptides', Knowledge-Based Systems.
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© 2017. Protein phosphorylation is the process of binding a protein kinase to a specific site in a protein substrate for post-translational modification. Thousands of distinct phosphorylation sites have been identified, but most of them are not annotated with any kinase information. This work proposes a novel kinase-subgrouping propensity method (kiSP) to predict the binding kinases for phosphopeptides. Existing methods do not distinguish the residue conservation properties of the kinase family subgroups for annotation. Our method exploits maximum entropy variance to prune non-conserved sites from the subset of phosphopeptides that bind to the same kinase family. We also use maximal mutual information to estimate an appropriate upstream-downstream window size for this subset. A propensity score for every kinase family is calculated from its positive and negative data, which indicates its effectiveness as a site for each test phosphopeptide. Experimental results demonstrate that our method outperforms current algorithms in specificity and sensitivity under cross-validation. kiSP is also demonstrated to correctly predict kinase families for phosphopeptides with unknown kinase information.
Chen, Y., Yue, X., Xu, R.Y.D. & Fujita, H. 2017, 'Region scalable active contour model with global constraint', Knowledge-Based Systems, vol. 120, pp. 57-73.
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© 2016Existing Active Contour methods suffer from the deficiencies of initialization sensitivity, slow convergence, and being insufficient in the presence of image noise and inhomogeneity. To address these problems, this paper proposes a region scalable active contour model with global constraint (RSGC). The energy function is formulated by incorporating local and global constraints. The local constraint is a region scalable fitting term that draws upon local region information under controllable scales. The global constraint is constructed through estimating the global intensity distribution of image content. Specifically, the global intensity distribution is approximated with a Gaussian Mixture Model (GMM) and estimated by Expectation Maximization (EM) algorithm as a prior. The segmentation process is implemented through optimizing the improved energy function. Comparing with two other representative models, i.e. region-scalable fitting model (RSF) and active contour model without edges (CV), the proposed RSGC model achieves more efficient, stable and precise results on most testing images under the joint actions of local and global constraints.
Chen, Z., You, X., Zhong, B., Li, J. & Tao, D. 2017, 'Dynamically Modulated Mask Sparse Tracking', IEEE Transactions on Cybernetics.
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Visual tracking is a critical task in many computer vision applications such as surveillance and robotics. However, although the robustness to local corruptions has been improved, prevailing trackers are still sensitive to large scale corruptions, such as occlusions and illumination variations. In this paper, we propose a novel robust object tracking technique depends on subspace learning-based appearance model. Our contributions are twofold. First, mask templates produced by frame difference are introduced into our template dictionary. Since the mask templates contain abundant structure information of corruptions, the model could encode information about the corruptions on the object more efficiently. Meanwhile, the robustness of the tracker is further enhanced by adopting system dynamic, which considers the moving tendency of the object. Second, we provide the theoretic guarantee that by adapting the modulated template dictionary system, our new sparse model can be solved by the accelerated proximal gradient algorithm as efficient as in traditional sparse tracking methods. Extensive experimental evaluations demonstrate that our method significantly outperforms 21 other cutting-edge algorithms in both speed and tracking accuracy, especially when there are challenges such as pose variation, occlusion, and illumination changes.
Cheng, H., Zhang, J., Wu, Q., An, P. & Liu, Z. 2017, 'Stereoscopic visual saliency prediction based on stereo contrast and stereo focus', EURASIP Journal on Image and Video Processing.
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In this paper, we exploit two characteristics of stereoscopic vision: the pop-out effect and the comfort zone. We propose a visual saliency prediction model for stereoscopic images based on stereo contrast and stereo focus models. The stereo contrast model measures stereo saliency based on the color/depth contrast and the pop-out effect. The stereo focus model describes the degree of focus based on monocular focus and the comfort zone. After obtaining the values of the stereo contrast and stereo focus models in parallel, an enhancement based on clustering is performed on both values. We then apply a multi-scale fusion to form the respective maps of the two models. Last, we use a Bayesian integration scheme to integrate the two maps (the stereo contrast and stereo focus maps) into the stereo saliency map. Experimental results on two eye-tracking databases show that our proposed method outperforms the state-of-the-art saliency models.
Cheng, J., Tao, D., Wong, D.W.K. & Liu, J. 2017, 'Quadratic divergence regularized SVM for optic disc segmentation', Biomedical Optics Express, vol. 8, no. 5, pp. 2687-2696.
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© 2017 Optical Society of America. Machine learning has been used in many retinal image processing applications such as optic disc segmentation. It assumes that the training and testing data sets have the same feature distribution. However, retinal images are often collected under different conditions and may have different feature distributions. Therefore, the models trained from one data set may not work well for another data set. However, it is often too expensive and time consuming to label the needed training data and rebuild the models for all different data sets. In this paper, we propose a novel quadratic divergence regularized support vector machine (QDSVM) to transfer the knowledge from domains with sufficient training data to domains with limited or even no training data. The proposed method simultaneously minimizes the distribution difference between the source domain and target domain while training the classifier. Experimental results show that the proposed transfer learning based method reduces the classification error in superpixel level from 14.2% without transfer learning to 2.4% with transfer learning. The proposed method is effective to transfer the label knowledge from source to target domain, which enables it to be used for optic disc segmentation in data sets with different feature distributions.
Cheng, J., Zhang, Z., Tao, D., Wong, D.W.K., Liu, J., Baskaran, M., Aung, T. & Wong, T.Y. 2017, 'Similarity regularized sparse group lasso for cup to disc ratio computation', Biomedical Optics Express, vol. 8, no. 8, pp. 3763-3777.
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© 2017 Optical Society of America. Automatic cup to disc ratio (CDR) computation from color fundus images has shown to be promising for glaucoma detection. Over the past decade, many algorithms have been proposed. In this paper, we first review the recent work in the area and then present a novel similarity-regularized sparse group lasso method for automated CDR estimation. The proposed method reconstructs the testing disc image based on a set of reference disc images by integrating the similarity between testing and the reference disc images with the sparse group lasso constraints. The reconstruction coefficients are then used to estimate the CDR of the testing image. The proposed method has been validated using 650 images with manually annotated CDRs. Experimental results show an average CDR error of 0.0616 and a correlation coefficient of 0.7, outperforming other methods. The areas under curve in the diagnostic test reach 0.843 and 0.837 when manual and automatically segmented discs are used respectively, better than other methods as well.
Chi, L., Li, B., Zhu, X., Pan, S. & Chen, L. 2017, 'Hashing for Adaptive Real-Time Graph Stream Classification With Concept Drifts', IEEE Transactions on Cybernetics.
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IEEE Many applications involve processing networked streaming data in a timely manner. Graph stream classification aims to learn a classification model from a stream of graphs with only one-pass of data, requiring real-time processing in training and prediction. This is a nontrivial task, as many existing methods require multipass of the graph stream to extract subgraph structures as features for graph classification which does not simultaneously satisfy ''one-pass'' and ''real-time'' requirements. In this paper, we propose an adaptive real-time graph stream classification method to address this challenge. We partition the unbounded graph stream data into consecutive graph chunks, each consisting of a fixed number of graphs and delivering a corresponding chunk-level classifier. We employ a random hashing function to compress the original node set of graphs in each chunk for fast feature detection when training chunk-level classifiers. Furthermore, a differential hashing strategy is applied to map unlimited increasing features (i.e., cliques) into a fixed-size feature space which is then used as a feature vector for stochastic learning. Finally, the chunk-level classifiers are weighted in an ensemble learning model for graph classification. The proposed method substantially speeds up the graph feature extraction and avoids unbounded graph feature growth. Moreover, it effectively offsets concept drifts in graph stream classification. Experiments on real-world and synthetic graph streams demonstrate that our method significantly outperforms existing methods in both classification accuracy and learning efficiency.
Chiarella, C., He, X.Z., Shi, L. & Wei, L. 2017, 'A behavioural model of investor sentiment in limit order markets', Quantitative Finance, vol. 17, no. 1, pp. 71-86.
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© 2016 Informa UK Limited, trading as Taylor & Francis GroupBy incorporating behavioural sentiment in a model of a limit order market, we show that behavioural sentiment not only helps to replicate most of the stylized facts in limit order markets simultaneously, but it also plays a unique role in explaining those stylized facts that cannot be explained by noise trading, such as fat tails in the return distribution, long memory in the trading volume, an increasing and non-linear relationship between trade imbalance and mid-price returns, as well as the diagonal effect, or event clustering, in order submission types. The results show that behavioural sentiment is an important driving force behind many of the well-documented stylized facts in limit order markets.
Chomsiri, T., He, X.S., Nanda, P. & Tan, Z. 2017, 'Hybrid Tree-rule Firewall for High Speed Data Transmission', IEEE Transactions on Cloud Computing.
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Chotipant, S., Hussain, F.K. & Hussain, O.K. 2017, 'SERNOTATE: An automated approach for business service description annotation for efficient service retrieval and composition', Concurrency Computation.
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© 2017 John Wiley & Sons, Ltd. Business service advertisements are today published online to convey essential information about services to customers. However, current Web search engines are unable to search and combine online service advertisements. Semantic service annotation is important for its ability to enable machines to understand the meaning of services and support in effective service retrieval and service composition. Existing research in the area of semantic service annotation has focused on the annotation of Web services in a semi-automated approach. It cannot be applied to business service information as it is not in the form of Web Services Description Language but in free text format. Moreover, semi-automated approaches are inappropriate for annotating a large amount of online service information which changes dynamically and they are therefore not suitable for the timely dissemination of service information to customers. To solve these issues, we propose SERNOTATE, which is an automated approach for business service description annotation for efficient service retrieval and composition. We propose new semantic-based linking approaches, namely, Extended Case-based Reasoning, vector-based, and classification-based, that automatically annotate business services to relevant service concepts. Each approach assists in the single-label and multi-label annotation of service terms to concept terms to provide a better representation of services. The experimental results test and validate the applicability of the proposed approaches to the automatic annotation of business service descriptions to service concepts on a real-world dataset.
Dai, M., Cheng, S. & He, X.S. 2017, 'Hybrid generative–discriminative hash tracking with spatio-temporal contextual cues', Neural Computing and Applications.
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Visual object tracking is of a great application value in video monitoring systems. Recent work on video tracking has taken into account spatial relationship between the targeted object and its background. In this paper, the spatial relationship is combined with the temporal relationship between features on different video frames so that a real-time tracker is designed based on a hash algorithm with spatio-temporal cues. Different from most of the existing work on video tracking, which is regarded as a mechanism for image matching or image classification alone, we propose a hierarchical framework and conduct both matching and classification tasks to generate a coarse-to-fine tracking system. We develop a generative model under a modified particle filter with hash fingerprints for the coarse matching by the maximum a posteriori and a discriminative model for the fine classification by maximizing a confidence map based on a context model. The confidence map reveals the spatio-temporal dynamics of the target. Because hash fingerprint is merely a binary vector and the modified particle filter uses only a small number of particles, our tracker has a low computation cost. By conducting experiments on eight challenging video sequences from a public benchmark, we demonstrate that our tracker outperforms eight state-of-the-art trackers in terms of both accuracy and speed.
Deek, H., Chang, S., Newton, P.J., Noureddine, S., Inglis, S.C., Arab, G.A., Kabbani, S., Chalak, W., Timani, N., Macdonald, P.S. & Davidson, P.M. 2017, 'An evaluation of involving family caregivers in the self-care of heart failure patients on hospital readmission: Randomised controlled trial (the FAMILY study).', International Journal of Nursing Studies, vol. 75, pp. 101-111.
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BACKGROUND: The prevalence of heart failure is increasing in Lebanon but to date there is no systematic evaluation of a disease management intervention. OBJECTIVE: The aim of this study was to evaluate the effect of involving family caregivers in the self-care of patients with heart failure on the risk of hospital readmission. DESIGN: A multi-site, block randomised controlled trial. SETTINGS: The study was conducted over a 13-month period in three tertiary medical centres in Beirut and Mount Lebanon, Lebanon. PARTICIPANTS: Adult patients presenting for an exacerbation of heart failure to one of the study centres were included. Patients with limited life expectancy or physical functionality, planned cardiac bypass or valve replacement surgery, living alone or in nursing homes, or aged less than 18 years were excluded. METHODS: Patients allocated to the intervention group and their family caregivers were provided with a comprehensive, culturally appropriate, educational session on self-care maintenance and symptom management along with self-care resources. The usual care group received the self-care resources only. Follow-up phone calls were conducted 30days following discharge by a research assistant blinded to treatment assignment. The primary outcome was hospital readmission and the secondary outcomes were self-care, quality of life, major vascular events and healthcare utilization. RESULTS: The final sample included 256 patients hospitalized for heart failure randomised into control (130 patients) and intervention (126 patients) groups. The mean age was 67 (SD=8)years, and the majority (55%) were male. Readmission at 30days was significantly lower in the intervention group compared to the control group (n=10, 9% vs. n=20, 19% respectively, OR=0.40, 95% CI=0.02, 0.10, p=0.02). Self-care scores improved in both groups at 30days, with a significantly larger improvement in the intervention group than the control group in the maintenance and confidence sub-scales, b...
Deng, S., Huang, L., Xu, G., Wu, X. & Wu, Z. 2017, 'On Deep Learning for Trust-Aware Recommendations in Social Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 5, pp. 1164-1177.
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With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user's trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users' interests and their trusted friends' interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.
Ding, C. & Tao, D. 2017, 'Pose-invariant face recognition with homography-based normalization', Pattern Recognition, vol. 66, pp. 144-152.
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© 2016 Elsevier LtdPose-invariant face recognition (PIFR) refers to the ability that recognizes face images with arbitrary pose variations. Among existing PIFR algorithms, pose normalization has been proved to be an effective approach which preserves texture fidelity, but usually depends on precise 3D face models or at high computational cost. In this paper, we propose an highly efficient PIFR algorithm that effectively handles the main challenges caused by pose variation. First, a dense grid of 3D facial landmarks are projected to each 2D face image, which enables feature extraction in an pose adaptive manner. Second, for the local patch around each landmark, an optimal warp is estimated based on homography to correct texture deformation caused by pose variations. The reconstructed frontal-view patches are then utilized for face recognition with traditional face descriptors. The homography-based normalization is highly efficient and the synthesized frontal face images are of high quality. Finally, we propose an effective approach for occlusion detection, which enables face recognition with visible patches only. Therefore, the proposed algorithm effectively handles the main challenges in PIFR. Experimental results on four popular face databases demonstrate that the propose approach performs well on both constrained and unconstrained environments.
Dong, Y., Du, B., Zhang, L., Zhang, L. & Tao, D. 2017, 'LAM3L: Locally adaptive maximum margin metric learning for visual data classification', Neurocomputing, vol. 235, pp. 1-9.
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© 2016.Visual data classification, which is aimed at determining a unique label for each class, is an increasingly important issue in the machine learning community. In recent years, increasing attention has been paid to the application of metric learning for classification, which has been proven to be a good way to obtain a promising performance. However, as a result of the limited training samples and data with complex distributions, the vast majority of these algorithms usually fail to perform well. This has motivated us to develop a novel locally adaptive maximum margin metric learning (LAM3L) algorithm in order to maximally separate similar and dissimilar classes, based on the changes between the distances before and after the maximum margin metric learning. The experimental results on two widely used UCI datasets and a real hyperspectral dataset demonstrate that the proposed method outperforms the state-of-the-art metric learning methods.
Du, B., Wang, Z., Zhang, L., Zhang, L. & Tao, D. 2017, 'Robust and Discriminative Labeling for Multi-Label Active Learning Based on Maximum Correntropy Criterion', IEEE Transactions on Image Processing, vol. 26, no. 4, pp. 1694-1707.
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© 1992-2012 IEEE. Multi-label learning draws great interests in many real world applications. It is a highly costly task to assign many labels by the oracle for one instance. Meanwhile, it is also hard to build a good model without diagnosing discriminative labels. Can we reduce the label costs and improve the ability to train a good model for m ulti-label learning simultaneously? Active learning addresses the less training samples problem by querying the most valuable samples to achieve a better performance with little costs. In multi-label active learning, some researches have been done for querying the relevant labels with less training samples or querying all labels without diagnosing the discriminative information. They all cannot effectively handle the outlier labels for the measurement of uncertainty. Since maximum correntropy criterion (MCC) provides a robust analysis for outliers in many machine learning and data mining algorithms, in this paper, we derive a robust multi-label active learning algorithm based on an MCC by merging uncertainty and representativeness, and propose an efficient alternating optimization method to solve it. With MCC, our method can eliminate the influence of outlier labels that are not discriminative to measure the uncertainty. To make further improvement on the ability of information measurement, we merge uncertainty and representativeness with the prediction labels of unknown data. It cannot only enhance the uncertainty but also improve the similarity measurement of multi-label data with labels information. Experiments on benchmark multi-label data sets have shown a superior performance than the state-of-the-art methods.
Du, B., Wang, Z., Zhang, L., Zhang, L., Liu, W., Shen, J. & Tao, D. 2017, 'Exploring Representativeness and Informativeness for Active Learning', IEEE Transactions on Cybernetics, vol. 47, no. 1, pp. 14-26.
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How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling, etc. Although combining representativeness and informativeness of samples has been proven promising for active sampling, state-of-the-art methods perform well under certain data structures. Then can we find a way to fuse the two active sampling criteria without any assumption on data? This paper proposes a general active learning framework that effectively fuses the two criteria. Inspired by a two-sample discrepancy problem, triple measures are elaborately designed to guarantee that the query samples not only possess the representativeness of the unlabeled data but also reveal the diversity of the labeled data. Any appropriate similarity measure can be employed to construct the triple measures. Meanwhile, an uncertain measure is leveraged to generate the informativeness criterion, which can be carried out in different ways. Rooted in this framework, a practical active learning algorithm is proposed, which exploits a radial basis function together with the estimated probabilities to construct the triple measures and a modified best-versus-second-best strategy to construct the uncertain measure, respectively. Experimental results on benchmark datasets demonstrate that our algorithm consistently achieves superior performance over the state-of-the-art active learning algorithms.
Du, B., Xiong, W., Wu, J., Zhang, L., Zhang, L. & Tao, D. 2017, 'Stacked Convolutional Denoising Auto-Encoders for Feature Representation', IEEE Transactions on Cybernetics, vol. 47, no. 4, pp. 1017-1027.
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Deep networks have achieved excellent performance in learning representation from visual data. However, the supervised deep models like convolutional neural network require large quantities of labeled data, which are very expensive to obtain. To solve this problem, this paper proposes an unsupervised deep network, called the stacked convolutional denoising auto-encoders, which can map images to hierarchical representations without any label information. The network, optimized by layer-wise training, is constructed by stacking layers of denoising auto-encoders in a convolutional way. In each layer, high dimensional feature maps are generated by convolving features of the lower layer with kernels learned by a denoising auto-encoder. The auto-encoder is trained on patches extracted from feature maps in the lower layer to learn robust feature detectors. To better train the large network, a layer-wise whitening technique is introduced into the model. Before each convolutional layer, a whitening layer is embedded to sphere the input data. By layers of mapping, raw images are transformed into high-level feature representations which would boost the performance of the subsequent support vector machine classifier. The proposed algorithm is evaluated by extensive experimentations and demonstrates superior classification performance to state-of-the-art unsupervised networks.
Du, B., Zhang, M., Zhang, L., Hu, R. & Tao, D. 2017, 'PLTD: Patch-Based Low-Rank Tensor Decomposition for Hyperspectral Images', IEEE Transactions on Multimedia, vol. 19, no. 1, pp. 67-79.
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© 1999-2012 IEEE. Recent years has witnessed growing interest in hyperspectral image (HSI) processing. In practice, however, HSIs always suffer from huge data size and mass of redundant information, which hinder their application in many cases. HSI compression is a straightforward way of relieving these problems. However, most of the conventional image encoding algorithms mainly focus on the spatial dimensions, and they need not consider the redundancy in the spectral dimension. In this paper, we propose a novel HSI compression and reconstruction algorithm via patch-based low-rank tensor decomposition (PLTD). Instead of processing the HSI separately by spectral channel or by pixel, we represent each local patch of the HSI as a third-order tensor. Then, the similar tensor patches are grouped by clustering to form a fourth-order tensor per cluster. Since the grouped tensor is assumed to be redundant, each cluster can be approximately decomposed to a coefficient tensor and three dictionary matrices, which leads to a low-rank tensor representation of both the spatial and spectral modes. The reconstructed HSI can then be simply obtained by the product of the coefficient tensor and dictionary matrices per cluster. In this way, the proposed PLTD algorithm simultaneously removes the redundancy in both the spatial and spectral domains in a unified framework. The extensive experimental results on various public HSI datasets demonstrate that the proposed method outperforms the traditional image compression approaches and other tensor-based methods.
Edwards, D., Cheng, M., Wong, A., Zhang, J. & Wu, Q. 2017, 'Ambassadors of Knowledge Sharing: Co-produced Travel Information Through Tourist-Local Social Media Exchange', International Journal of Contemporary Hospitality Management, vol. 29, no. 2, pp. 690-708.
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Purpose: The aim of this study is to understand the knowledge sharing structure and co-production of trip-related knowledge through online travel forums.
Design/methodology/approach: The travel forum threads were collected from TripAdvisor Sydney travel forum for the period from 2010 to 2014, which contains 115,847 threads from 8,346 conversations. The data analytical technique was based on a novel methodological approach - visual analytics including semantic pattern generation and network analysis.
Findings: Findings indicate that the knowledge structure is created by community residents who camouflage as local experts, serve as ambassadors of a destination. The knowledge structure presents collective intelligence co-produced by community residents and tourists. Further findings reveal how these community residents associate with each other and form a knowledge repertoire with information covering various travel domain areas.
Practical implications: The study offers valuable insights to help destination management organizations and tour operators identify existing and emerging tourism issues to achieve a competitive destination advantage.
Originality/value: This study highlights the process of social media mediated travel knowledge co-production. It also discovers how community residents engage in reaching out to tourists by camouflaging as ordinary users.
Fan, X., Xu, R.Y.D., Cao, L. & Song, Y. 2017, 'Learning Nonparametric Relational Models by Conjugately Incorporating Node Information in a Network', IEEE Transactions on Cybernetics, vol. 47, no. 3, pp. 589-599.
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Relational model learning is useful for numerous practical applications. Many algorithms have been proposed in recent years to tackle this important yet challenging problem. Existing algorithms utilize only binary directional link data to recover hidden network structures. However, there exists far richer and more meaningful information in other parts of a network which one can (and should) exploit. The attributes associated with each node, for instance, contain crucial information to help practitioners understand the underlying relationships in a network. For this reason, in this paper, we propose two models and their solutions, namely the node-information involved mixed-membership model and the node-information involved latent-feature model, in an effort to systematically incorporate additional node information. To effectively achieve this aim, node information is used to generate individual sticks of a stick-breaking process. In this way, not only can we avoid the need to prespecify the number of communities beforehand, the algorithm also encourages that nodes exhibiting similar information have a higher chance of assigning the same community membership. Substantial efforts have been made toward achieving the appropriateness and efficiency of these models, including the use of conjugate priors. We evaluate our framework and its inference algorithms using real-world data sets, which show the generality and effectiveness of our models in capturing implicit network structures.
Fang, M., Yin, J., Hall, L.O. & Tao, D. 2017, 'Active Multitask Learning With Trace Norm Regularization Based on Excess Risk', IEEE Transactions on Cybernetics.
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This paper addresses the problem of active learning on multiple tasks, where labeled data are expensive to obtain for each individual task but the learning problems share some commonalities across multiple related tasks. To leverage the benefits of jointly learning from multiple related tasks and making active queries, we propose a novel active multitask learning approach based on trace norm regularized least squares. The basic idea is to induce an optimal classifier which has the lowest risk and at the same time which is closest to the true hypothesis. Toward this aim, we devise a new active selection criterion that takes into account not only the risk but also the excess risk, which measures the distance to the true hypothesis. Based on this criterion, our proposed algorithm actively selects the instance to query for its label based on the combination of the two risks. Experiments on both synthetic and real-world datasets show that our proposed algorithm provides superior performance as compared to other state-of-the-art active learning methods.
Feng, X.I.A.N.G., Wan, W., Richard Yi Da Xu, Chen, H., Li, P. & Sánchez, J.A. 2017, 'A perceptual quality metric for 3D triangle meshes based on spatial pooling', Frontiers of Computer Science.
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Ferguson, C., Inglis, S.C., Newton, P.J., Middleton, S., Macdonald, P.S. & Davidson, P.M. 2017, 'Barriers and enablers to adherence to anticoagulation in heart failure with atrial fibrillation: patient and provider perspectives', Journal of Clinical Nursing.
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Aims & Objectives
The purpose of this study was to elucidate the barriers and enablers to adherence to anticoagulation in individuals with chronic heart failure (CHF) with concomitant atrial fibrillation (AF) from the perspective of patients and providers.
Background
CHF and AF commonly coexist and are associated with increased stroke risk and mortality. Oral anticoagulation significantly reduces stroke risk and improves outcomes. Yet, in approximately 30% of cases anticoagulation is not commenced for a variety of reasons.
Design
Qualitative study using narrative inquiry.
Methods
Data from face to face individual interviews with patients and information retrieved from healthcare file note review documented the clinician perspective. This study is a synthesis of the two data sources, obtained during patient clinical assessments as part of the Atrial Fibrillation And Stroke Thromboprophylaxis in hEart failuRe (AFASTER) Study.
Results
Patient choice and preference were important factors in anticoagulation decisions, including treatment burden, unfavourable or intolerable side effects and patient refusal. Financial barriers included cost of travel, medication cost and reimbursement. Psychological factors included psychiatric illness, cognitive impairment and depression. Social barriers included homelessness and the absence of a caregiver or lack of caregiver assistance. Clinician reticence included fear of falls, frailty, age, fear of bleeding and the challenges of multi-morbidity. Facilitators to successful prescription and adherence were caregiver support, reminders and routine, self-testing and the use of technology.
Conclusions
Many barriers remain to high risk individuals being prescribed anticoagulation for stroke prevention. There are a number of enabling factors that facilitate prescription and optimize treatment adherence. Nurses should challenge these treatment barriers and seek enabling factors to optimise therapy.
Relevance to clinical practice
Nurs...
Ferguson, C., Inglis, S.C., Newton, P.J., Middleton, S., Macdonald, P.S. & Davidson, P.M. 2017, 'Multi-morbidity, frailty and self-care: important considerations in treatment with anticoagulation drugs. Outcomes of the AFASTER study.', European Journal of Cardiovascular Nursing, vol. 16, no. 2, pp. 113-124.
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Chronic heart failure (CHF) and atrial fibrillation (AF) are complex cardiogeriatric syndromes mediated by physical, psychological and social factors. Thromboprophylaxis is an important part of avoiding adverse events in these syndromes, particularly stroke.This study sought to describe the clinical characteristics of a cohort of patients admitted to hospital with CHF and concomitant AF and to document the rate and type of thromboprophylaxis. We examined the practice patterns of the prescription of treatment and determined the predictors of adverse events.Prospective consecutive participants with CHF and concomitant AF were enrolled during the period April to October 2013. Outcomes were assessed at 12 months, including all-cause readmission to hospital and mortality, stroke or transient ischaemic attack, and bleeding.All-cause readmission to hospital was frequent (68%) and the 12-month all-cause mortality was high (29%). The prescription of anticoagulant drugs at discharge was statistically significantly associated with a lower mortality at 12 months (23 vs. 40%; p=0.037; hazards ratio 0.506; 95% confidence interval 0.267-0.956), but was not associated with lower rates of readmission to hospital among patients with CHF and AF. Sixty-six per cent of participants were prescribed anticoagulant drugs on discharge from hospital. Self-reported self-care behaviour and 'not for cardiopulmonary resuscitation' were associated with not receiving anticoagulant drugs at discharge. Although statistical significance was not achieved, those patients who were assessed as frail or having greater comorbidity were less likely to receive anticoagulant drugs at discharge.This study highlights multi-morbidity, frailty and self-care to be important considerations in thromboprophylaxis. Shared decision-making with patients and caregivers offers the potential to improve treatment knowledge, adherence and outcomes in this group of patients with complex care needs.
Forber, J., Carter, B., DiGiacomo, M., Davidson, P.M. & Jackson, D. 2017, 'Future proofing undergraduate nurse clinical education: continuing the dialogue', Journal of Nursing Management.
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Gholizadeh, L., Ali Khan, S., Vahedi, F. & Davidson, P.M. 2017, 'Sensitivity and specificity of Urdu version of the PHQ-9 to screen depression in patients with coronary artery disease.', Contemporary Nurse, vol. 53, no. 1, pp. 75-81.
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BACKGROUND: The Patient Health Questionnaire (PHQ-9) possesses many characteristics of a good screening tool and has the capacity to be used for screening depression in patients with coronary artery disease (CAD). AIM: To examine the psychometric properties and criterion validity of the PHQ-9 to screen and detect depression in patients with CAD in Pakistan. DESIGN: In this validation study, 150 patients with CAD completed the Urdu version of the PHQ-9. The major depressive episode module of the Mini International Neuropsychiatric Interview (MINI) was used as the gold standard. RESULTS: The Urdu version of the PHQ-9 revealed a good internal consistency with Cronbach's alpha of 0.83. Optimal sensitivity (76%) and specificity (76%) were achieved using the cut-off score of PHQ-9 ≥6, with area under the ROC curve of 0.86. CONCLUSION: The Urdu version of the PHQ-9 has acceptable psychometric properties to screen and detect major depression in patients with CAD.
Ghosh, S., Li, J., Cao, L. & Ramamohanarao, K. 2017, 'Septic shock prediction for ICU patients via coupled HMM walking on sequential contrast patterns.', J Biomed Inform, vol. 66, pp. 19-31.
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BACKGROUND AND OBJECTIVE: Critical care patient events like sepsis or septic shock in intensive care units (ICUs) are dangerous complications which can cause multiple organ failures and eventual death. Preventive prediction of such events will allow clinicians to stage effective interventions for averting these critical complications. METHODS: It is widely understood that physiological conditions of patients on variables such as blood pressure and heart rate are suggestive to gradual changes over a certain period of time, prior to the occurrence of a septic shock. This work investigates the performance of a novel machine learning approach for the early prediction of septic shock. The approach combines highly informative sequential patterns extracted from multiple physiological variables and captures the interactions among these patterns via coupled hidden Markov models (CHMM). In particular, the patterns are extracted from three non-invasive waveform measurements: the mean arterial pressure levels, the heart rates and respiratory rates of septic shock patients from a large clinical ICU dataset called MIMIC-II. EVALUATION AND RESULTS: For baseline estimations, SVM and HMM models on the continuous time series data for the given patients, using MAP (mean arterial pressure), HR (heart rate), and RR (respiratory rate) are employed. Single channel patterns based HMM (SCP-HMM) and multi-channel patterns based coupled HMM (MCP-HMM) are compared against baseline models using 5-fold cross validation accuracies over multiple rounds. Particularly, the results of MCP-HMM are statistically significant having a p-value of 0.0014, in comparison to baseline models. Our experiments demonstrate a strong competitive accuracy in the prediction of septic shock, especially when the interactions between the multiple variables are coupled by the learning model. CONCLUSIONS: It can be concluded that the novelty of the approach, stems from the integration of sequence-based physiological pa...
Gong, C., Liu, T., Tang, Y., Yang, J., Yang, J. & Tao, D. 2017, 'A Regularization Approach for Instance-Based Superset Label Learning.', IEEE Transactions on Cybernetics.
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Different from the traditional supervised learning in which each training example has only one explicit label, superset label learning (SLL) refers to the problem that a training example can be associated with a set of candidate labels, and only one of them is correct. Existing SLL methods are either regularization-based or instance-based, and the latter of which has achieved state-of-the-art performance. This is because the latest instance-based methods contain an explicit disambiguation operation that accurately picks up the groundtruth label of each training example from its ambiguous candidate labels. However, such disambiguation operation does not fully consider the mutually exclusive relationship among different candidate labels, so the disambiguated labels are usually generated in a nondiscriminative way, which is unfavorable for the instance-based methods to obtain satisfactory performance. To address this defect, we develop a novel regularization approach for instance-based superset label (RegISL) learning so that our instance-based method also inherits the good discriminative ability possessed by the regularization scheme. Specifically, we employ a graph to represent the training set, and require the examples that are adjacent on the graph to obtain similar labels. More importantly, a discrimination term is proposed to enlarge the gap of values between possible labels and unlikely labels for every training example. As a result, the intrinsic constraints among different candidate labels are deployed, and the disambiguated labels generated by RegISL are more discriminative and accurate than those output by existing instance-based algorithms. The experimental results on various tasks convincingly demonstrate the superiority of our RegISL to other typical SLL methods in terms of both training accuracy and test accuracy.
Gong, C., Tao, D., Liu, W., Liu, L. & Yang, J. 2017, 'Label Propagation via Teaching-to-Learn and Learning-to-Teach.', IEEE transactions on neural networks and learning systems.
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How to propagate label information from labeled examples to unlabeled examples over a graph has been intensively studied for a long time. Existing graph-based propagation algorithms usually treat unlabeled examples equally, and transmit seed labels to the unlabeled examples that are connected to the labeled examples in a neighborhood graph. However, such a popular propagation scheme is very likely to yield inaccurate propagation, because it falls short of tackling ambiguous but critical data points (e.g., outliers). To this end, this paper treats the unlabeled examples in different levels of difficulties by assessing their reliability and discriminability, and explicitly optimizes the propagation quality by manipulating the propagation sequence to move from simple to difficult examples. In particular, we propose a novel iterative label propagation algorithm in which each propagation alternates between two paradigms, teaching-to-learn and learning-to-teach (TLLT). In the teaching-to-learn step, the learner conducts the propagation on the simplest unlabeled examples designated by the teacher. In the learning-to-teach step, the teacher incorporates the learner's feedback to adjust the choice of the subsequent simplest examples. The proposed TLLT strategy critically improves the accuracy of label propagation, making our algorithm substantially robust to the values of tuning parameters, such as the Gaussian kernel width used in graph construction. The merits of our algorithm are theoretically justified and empirically demonstrated through experiments performed on both synthetic and real-world data sets.
Gong, D., Li, Z., Huang, W., Li, X. & Tao, D. 2017, 'Heterogeneous Face Recognition: A Common Encoding Feature Discriminant Approach', IEEE Transactions on Image Processing, vol. 26, no. 5, pp. 2079-2089.
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© 1992-2012 IEEE. Heterogeneous face recognition is an important, yet challenging problem in face recognition community. It refers to matching a probe face image to a gallery of face images taken from alternate imaging modality. The major challenge of heterogeneous face recognition lies in the great discrepancies between different image modalities. Conventional face feature descriptors, e.g., local binary patterns, histogram of oriented gradients, and scale-invariant feature transform, are mostly designed in a handcrafted way and thus generally fail to extract the common discriminant information from the heterogeneous face images. In this paper, we propose a new feature descriptor called common encoding model for heterogeneous face recognition, which is able to capture common discriminant information, such that the large modality gap can be significantly reduced at the feature extraction stage. Specifically, we turn a face image into an encoded one with the encoding model learned from the training data, where the difference of the encoded heterogeneous face images of the same person can be minimized. Based on the encoded face images, we further develop a discriminant matching method to infer the hidden identity information of the cross-modality face images for enhanced recognition performance. The effectiveness of the proposed approach is demonstrated (on several public-domain face datasets) in two typical heterogeneous face recognition scenarios: matching NIR faces to VIS faces and matching sketches to photographs.
Green, A., Luckett, T., DiGiacomo, M., Abbott, P., Delaney, P., Delaney, J. & Davidson, P.M. 2017, 'An asset-informed approach to service development', Nurse Researcher.
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Gu, K., Tao, D., Qiao, J.F. & Lin, W. 2017, 'Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data', IEEE Transactions on Neural Networks and Learning Systems.
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In this paper, we investigate into the problem of image quality assessment (IQA) and enhancement via machine learning. This issue has long attracted a wide range of attention in computational intelligence and image processing communities, since, for many practical applications, e.g., object detection and recognition, raw images are usually needed to be appropriately enhanced to raise the visual quality (e.g., visibility and contrast). In fact, proper enhancement can noticeably improve the quality of input images, even better than originally captured images, which are generally thought to be of the best quality. In this paper, we present two most important contributions. The first contribution is to develop a new no-reference (NR) IQA model. Given an image, our quality measure first extracts 17 features through analysis of contrast, sharpness, brightness and more, and then yields a measure of visual quality using a regression module, which is learned with big-data training samples that are much bigger than the size of relevant image data sets. The results of experiments on nine data sets validate the superiority and efficiency of our blind metric compared with typical state-of-the-art full-reference, reduced-reference and NA IQA methods. The second contribution is that a robust image enhancement framework is established based on quality optimization. For an input image, by the guidance of the proposed NR-IQA measure, we conduct histogram modification to successively rectify image brightness and contrast to a proper level. Thorough tests demonstrate that our framework can well enhance natural images, low-contrast images, low-light images, and dehazed images. The source code will be released at https://sites.google.com/site/guke198701/publications.
Guo, D., Xu, J., Zhang, J., Xu, M., Cui, Y. & He, X. 2017, 'User relationship strength modeling for friend recommendation on Instagram', Neurocomputing, vol. 239, pp. 9-18.
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© 2017 Elsevier B.V.Social strength modeling in the social media community has attracted increasing research interest. Different from Flickr, which has been explored by many researchers, Instagram is more popular for mobile users and is conducive to likes and comments but seldom investigated. On Instagram, a user can post photos/videos, follow other users, comment and like other users' posts. These actions generate diverse forms of data that result in multiple user relationship views. In this paper, we propose a new framework to discover the underlying social relationship strength. User relationship learning under multiple views and the relationship strength modeling are coupled into one process framework. In addition, given the learned relationship strength, a coarse-to-fine method is proposed for friend recommendation. Experiments on friend recommendations for Instagram are presented to show the effectiveness and efficiency of the proposed framework. As exhibited by our experimental results, it can obtain better performance over other related methods. Although our method has been proposed for Instagram, it can be easily extended to any other social media communities.
Hasan, M.A.M., Li, J., Ahmad, S. & Molla, M.K.I. 2017, 'predCar-site: Carbonylation sites prediction in proteins using support vector machine with resolving data imbalanced issue.', Anal Biochem, vol. 525, pp. 107-113.
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The carbonylation is found as an irreversible post-translational modification and considered a biomarker of oxidative stress. It plays major role not only in orchestrating various biological processes but also associated with some diseases such as Alzheimer's disease, diabetes, and Parkinson's disease. However, since the experimental technologies are costly and time-consuming to detect the carbonylation sites in proteins, an accurate computational method for predicting carbonylation sites is an urgent issue which can be useful for drug development. In this study, a novel computational tool termed predCar-Site has been developed to predict protein carbonylation sites by (1) incorporating the sequence-coupled information into the general pseudo amino acid composition, (2) balancing the effect of skewed training dataset by Different Error Costs method, and (3) constructing a predictor using support vector machine as classifier. This predCar-Site predictor achieves an average AUC (area under curve) score of 0.9959, 0.9999, 1, and 0.9997 in predicting the carbonylation sites of K, P, R, and T, respectively. All of the experimental results along with AUC are found from the average of 5 complete runs of the 10-fold cross-validation and those results indicate significantly better performance than existing predictors. A user-friendly web server of predCar-Site is available at http://research.ru.ac.bd/predCar-Site/.
Hayes, C., Jackson, D., Davidson, P.M., Daly, J. & Power, T. 2017, 'Calm to chaos: Engaging undergraduate nursing students with the complex nature of interruptions during medication administration.', Journal of Clinical Nursing.
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AIMS AND OBJECTIVES: To describe undergraduate student nurse responses to a simulated role-play experience focussing on managing interruptions during medication administration. BACKGROUND: Improving patient safety requires that we find creative and innovative methods of teaching medication administration to undergraduate nurses in real-world conditions. Nurses are responsible for the majority of medication administrations in health care. Incidents and errors associated with medications are a significant patient safety issue and often occur as a result of interruptions. Undergraduate nursing students are generally taught medication administration skills in a calm and uninterrupted simulated environment. However, in the clinical environment medication administration is challenged by multiple interruptions. DESIGN/METHODS: A qualitative study using convenience sampling was used to examine student perceptions of a simulated role-play experience. Data were collected from 451 of a possible 528 student written reflective responses and subject to thematic analysis. RESULTS: Students reported an increased understanding of the impacts of interruptions while administering medications and an improved awareness of how to manage disruptions. This study reports on one of three emergent themes: "Calm to chaos: engaging with the complex nature of clinical practice." CONCLUSIONS: Interrupting medication administration in realistic and safe settings facilitates awareness, allows for students to begin to develop management strategies in relation to interruption and increases their confidence. Students were given the opportunity to consolidate and integrate prior and new knowledge and skills through this role-play simulation.
Hayes, C.J., Jackson, D., Davidson, P.M., Daly, J. & Power, T. 2017, 'Pondering practice: Enhancing the art of reflection', Journal of Clinical Nursing.
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Aims and objectives
The aim of this study was to describe the effect that immersive simulation experiences and guided reflection can have on the undergraduate nurses' understanding of how stressful environments impact their emotions, performance and ability to implement safe administration of medications.
Background
Patient safety can be jeopardised if nurses are unsure of how to appropriately manage and respond to interruptions. Medication administration errors are a major patient safety issue and often occur as a consequence of ineffective interruption management. The skills associated with medication administration are most often taught to, and performed by, undergraduate nurses in a controlled environment. However, the clinical environment in which nurses are expected to administer medications is often highly stressed and nurses are frequently interrupted.
Design/Methods
This study used role-play simulation and written reflections to facilitate deeper levels of student self-awareness. A qualitative approach was taken to explore students' understanding of the effects of interruptions on their ability to undertake safe medication administration. Convenience sampling of second-year undergraduate nursing students enrolled in a medical–surgical subject was used in this study. Data were obtained from 451:528 (85.42%) of those students and analysed using thematic analysis.
Results
Students reported increasing consciousness and the importance of reflection for evaluating performance and gaining self-awareness. They described self-awareness, effective communication, compassion and empathy as significant factors in facilitating self-efficacy and improved patient care outcomes.
Conclusions
Following a role-play simulation experience, student nurses reported new knowledge and skill acquisition related to patient safety, and new awareness of the need for empathetic and compassionate care during medication administration. Practicing medication administration in realis...
He, X. & Shi, L. 2017, 'Index Portfolio and Welfare Analysis Under Heterogeneous Beliefs', Journal of Banking and Finance, vol. 75, pp. 64-79.
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He, X. & Treich, N. 2017, 'Prediction market prices under risk aversion and heterogeneous beliefs', Journal of Mathematical Economics, vol. 70, pp. 105-114.
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He, X.Z., Lütkebohmert, E. & Xiao, Y. 2017, 'Rollover Risk and Credit Risk under Time-varying Margin', Quantitative Finance, vol. 17, no. 3, pp. 455-469.
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For a firm financed by a mixture of collateralized (short-term) debt and uncollateralized (long-term) debt, we show that fluctuations in margin requirements, reflecting funding liquidity shocks, lead to increasing the firm’s default risk and credit spreads. The severity with which a firm is hit by increasing margin requirements highly depends on both its financing structure and debt maturity structure. Our results imply that an additional premium should be added when evaluating debt in order to account for rollover risks, especially for short-matured bonds. In terms of policy implications, our results strongly indicate that regulators should intervene fast to curtail margins in crisis periods and maintain a reasonably low margin level in order to effectively prevent creditors’ run on debt.
Ho-Le, T.P., Center, J.R., Eisman, J.A., Nguyen, H.T. & Nguyen, T.V. 2017, 'Prediction of Bone Mineral Density and Fragility Fracture by Genetic Profiling.', Journal of Bone and Mineral Research, vol. 32, no. 2, pp. 285-293.
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Although the susceptibility to fracture is partly determined by genetic factors, the contribution of newly discovered genetic variants to fracture prediction is still unclear. This study sought to define the predictive value of a genetic profiling for fracture prediction.Sixty-two bone mineral density (BMD)-associated single-nucleotide polymorphism (SNP) were genotyped in 557 men and 902 women who had participated in the Dubbo Osteoporosis Epidemiology Study. The incidence of fragility fracture was ascertained from X-ray reports between 1990 and 2015. Femoral neck BMD was measured by dual-energy X-ray absorptiometry. A weighted polygenic risk score (GRS) was created as a function of the number of risk alleles and their BMD-associated regression coefficients for each SNP. The association between GRS and fracture risk was assessed by the Cox's proportional hazards model.Individuals with greater GRS had lower femoral neck BMD (P < 0.01), but the variation in GRS accounted for less than 2% of total variance in BMD. Each unit increase in GRS was associated with a hazard ratio of 1.20 (95%CI, 1.04-1.38) for fracture, and this association was independent of age, prior fracture, fall, and in a subset of 33 SNPs, independent of femoral neck BMD. The significant association between GRS and fracture was observed for the vertebral and wrist fractures, but not for hip fracture. The area under the receiver operating characteristic (ROC) curve for the model with GRS and clinical risk factors was 0.71 (95% CI, 0.68-0.74). With GRS, the correct reclassification of fracture vs non-fracture ranged from 12% for hip fracture to 23% for wrist fracture.A genetic profiling of BMD-associated genetic variants could improve the accuracy of fracture prediction over and above that of clinical risk factors alone, and help stratify individuals by fracture status. This article is protected by copyright. All rights reserved.
Hosie, A., Agar, M., Lobb, E., Davidson, P.M. & Phillips, J. 2017, 'Improving delirium recognition and assessment for people receiving inpatient palliative care: a mixed methods meta-synthesis.', Int J Nurs Stud, vol. 75, pp. 123-129.
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BACKGROUND: Delirium is a serious acute neurocognitive condition frequently occurring for hospitalized patients, including those receiving care in specialist palliative care units. There are many delirium evidence-practice gaps in palliative care, including that the condition is under-recognized and challenging to assess. OBJECTIVES: To report the meta-synthesis of a research project investigating delirium epidemiology, systems and nursing practice in palliative care units. METHODS: The Delirium in Palliative Care (DePAC) project was a two-phase sequential transformative mixed methods design with knowledge translation as the theoretical framework. The project answered five different research questions about delirium epidemiology, systems of care and nursing practice in palliative care units. Data integration and metasynthesis occurred at project conclusion. RESULTS: There was a moderate to high rate of delirium occurrence in palliative care unit populations; and palliative care nurses had unmet delirium knowledge needs and worked within systems and team processes that were inadequate for delirium recognition and assessment. The meta-inference of the DePAC project was that a widely-held but paradoxical view that palliative care and dying patients are different from the wider hospital population has separated them from the overall generation of delirium evidence, and contributed to the extent of practice deficiencies in palliative care units. CONCLUSION: Improving palliative care nurses' capabilities to recognize and assess delirium will require action at the patient and family, nurse, team and system levels. A broader, hospital-wide perspective would accelerate implementation of evidence-based delirium care for people receiving palliative care, both in specialist units, and the wider hospital setting.
Hou, S., Chen, L., Tao, D., Zhou, S., Liu, W. & Zheng, Y. 2017, 'Multi-layer multi-view topic model for classifying advertising video', Pattern Recognition, vol. 68, pp. 66-81.
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© 2017 Elsevier Ltd The recent proliferation of advertising (ad) videos has driven the research in multiple applications, ranging from video analysis to video indexing and retrieval. Among them, classifying ad video is a key task because it allows automatic organization of videos according to categories or genres, and this further enables ad video indexing and retrieval. However, classifying ad video is challenging compared to other types of video classification because of its unconstrained content. While many studies focus on embedding ads relevant to videos, to our knowledge, few focus on ad video classification. In order to classify ad video, this paper proposes a novel ad video representation that aims to sufficiently capture the latent semantics of video content from multiple views in an unsupervised manner. In particular, we represent ad videos from four views, including bag-of-feature (BOF), vector of locally aggregated descriptors (VLAD), fisher vector (FV) and object bank (OB). We then devise a multi-layer multi-view topic model, mlmv_LDA, which models the topics of videos from different views. A topical representation for video, supporting category-related task, is finally achieved by the proposed method. Our empirical classification results on 10,111 real-world ad videos demonstrate that the proposed approach effectively differentiate ad videos.
Hu, S.S., Chen, P., Wang, B. & Li, J. 2017, 'Protein binding hot spots prediction from sequence only by a new ensemble learning method', Amino Acids, pp. 1-13.
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© 2017 Springer-Verlag GmbH Austria Hot spots are interfacial core areas of binding proteins, which have been applied as targets in drug design. Experimental methods are costly in both time and expense to locate hot spot areas. Recently, in-silicon computational methods have been widely used for hot spot prediction through sequence or structure characterization. As the structural information of proteins is not always solved, and thus hot spot identification from amino acid sequences only is more useful for real-life applications. This work proposes a new sequence-based model that combines physicochemical features with the relative accessible surface area of amino acid sequences for hot spot prediction. The model consists of 83 classifiers involving the IBk (Instance-based k means) algorithm, where instances are encoded by important properties extracted from a total of 544 properties in the AAindex1 (Amino Acid Index) database. Then top-performance classifiers are selected to form an ensemble by a majority voting technique. The ensemble classifier outperforms the state-of-the-art computational methods, yielding an F1 score of 0.80 on the benchmark binding interface database (BID) test set.Availability: http://www2.ahu.edu.cn/pchen/web/HotspotEC.htm.
Hu, W., Yan, L., Wang, H., Du, B. & Tao, D. 2017, 'Real-time traffic jams prediction inspired by Biham, Middleton and Levine (BML) model', Information Sciences, vol. 381, pp. 209-228.
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© 2016 Elsevier Inc. Urban traffic jams severely affect many cities around the world. Therefore, there is an urgent need for accurate prediction of traffic jams in real time. With many existing approaches, it is difficult to obtain favorable results for dynamic and uncertain traffic flows in urban networks. This paper proposes a real-time traffic jam prediction method based on a two-dimension cellular automaton model inspired by the well-known Biham, Middleton and Levine (BML) model. The proposed model combines the simplicity and high efficiency of the BML model with real urban traffic networks. Our work can be divided into two parts: (1) the proposal of a practical approach for mapping the urban traffic topological structure into a modified BML (M-BML) model, and (2) the development of a novel strategy to deal with different cells of the M-BML model, especially conflict points and fuzzy points, which facilitates the recognition of real traffic jams at intersections. To the best of our knowledge, this is the first time that real urban traffic road networks are directly mapped into a descriptive model for jam prediction. Extensive experiments confirm the accuracy and efficiency of the proposed M-BML model in predicting traffic jams in dynamic and uncertain real urban traffic networks.
Huang, S., Zhang, J., Schonfeld, D., Wang, L. & Hua, X.-.S. 2017, 'Two-Stage Friend Recommendation Based on Network Alignment and Series Expansion of Probabilistic Topic Model', IEEE Transactions on Multimedia, vol. 19, no. 6, pp. 1314-1326.
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Huang, T., Huang, M.L., Nguyen, Q., Zhao, L., Huang, W. & Chen, J. 2017, 'A Space-Filling Multidimensional Visualization (SFMDVis) for Exploratory Data Analysis', Information Sciences, vol. 390, pp. 32-53.
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The space-filling visualization model was first invented by Ben Shneiderman [28] for maximizing the utilization of display space in relational data (or graph) visualization, especially for tree visualization. It uses the concept of Enclosure which dismisses the “edges” in the graphic representation that are all too frequently used in traditional node-link based graph visualizations. Therefore, the major issue in graph visualization which is the edge crossing can be naturally solved through the adoption of a space filling approach.
However in the past, the space-filling concept has not attracted much attention from researchers in the field of multidimensional visualization. Although the problem of ‘edge crossing’ has also occurred among polylines which are used as the basic visual elements in the parallel coordinates visualization, it is problematic if those ‘edge crossings’ among polylines are not evenly distributed on the display plate as visual clutter will occur. This problem could significantly reduce the human readability in terms of reviewing a particular region of the visualization. In this study, we propose a new Space-Filling Multidimensional Data Visualization (SFMDVis) that for the first-time introduces a space-filling approach into multidimensional data visualization. The main contributions are: (1) achieving the maximization of space utilization in multidimensional visualization (i.e. 100% of the display area is fully used), (2) eliminating visual clutter in SFMDVis through the use of the non-classic geometric primitive and (3) improving the quality of visualization for the visual perception of linear correlations among different variables as well as recognizing data patterns.
To evaluate the quality of SFMDVis, we have conducted a usability study to measure the performance of SFMDVis in comparison with parallel coordinates and a scatterplot matrix for finding linear correlations and data patterns. The evaluation results have suggested that the acc...
Huang, X., Zhang, J., Fan, L., Wu, Q. & Yuan, C. 2017, 'A systematic approach for cross-source point cloud registration by preserving macro and micro structures', IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3261-3276.
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© 2016 IEEE. We propose a systematic approach for registering cross-source point clouds that come from different kinds of sensors. This task is especially challenging due to the presence of significant missing data, large variations in point density, scale difference, large proportion of noise, and outliers. The robustness of the method is attributed to the extraction of macro and micro structures. Macro structure is the overall structure that maintains similar geometric layout in cross-source point clouds. Micro structure is the element (e.g., local segment) being used to build the macro structure. We use graph to organize these structures and convert the registration into graph matching. With a novel proposed descriptor, we conduct the graph matching in a discriminative feature space. The graph matching problem is solved by an improved graph matching solution, which considers global geometrical constraints. Robust cross source registration results are obtained by incorporating graph matching outcome with RANSAC and ICP refinements. Compared with eight state-of-the-art registration algorithms, the proposed method invariably outperforms on Pisa Cathedral and other challenging cases. In order to compare quantitatively, we propose two challenging cross-source data sets and conduct comparative experiments on more than 27 cases, and the results show we obtain much better performance than other methods. The proposed method also shows high accuracy in same-source data sets.
Huang, X., Zhang, J., Wu, Q., Fan, L. & Yuan, C. 2017, 'A coarse-to-fine algorithm for matching and registration in 3D cross-source point clouds', IEEE Transactions on Circuits and Systems for Video Technology.
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IEEE We propose an efficient method to deal with the matching and registration problem found in cross-source point clouds captured by different types of sensors. This task is especially challenging due to the presence of density variation, scale difference, a large proportion of noise and outliers, missing data and viewpoint variation. The proposed method has two stages: in the coarse matching stage, we use the ESF descriptor to select potential K regions from the candidate point clouds for the target. In the fine stage, we propose a scale embedded generative GMM registration method to refine the results from the coarse matching stage. Following the fine stage, both the best region and accurate camera pose relationships between the candidates and target are found. We conduct experiments in which we apply the method to two applications: one is 3D object detection and localization in street-view ourdoor (LiDAR/VSFM) cross-source point clouds, and the other is 3D scene matching and registration in indoor (KinectFusion/VSFM) cross-source point clouds. The experiment results show that the proposed method performs well when compared with the existing methods. It also shows that the proposed method is robust under various sensing techniques such as LiDAR, Kinect and RGB camera.
Hunt, L., Frost, S.A., Newton, P.J., Salamonson, Y. & Davidson, P.M. 2017, 'A survey of critical care nurses’ knowledge of intra-abdominal hypertension and absdominal compartment syndrome', Australian Critical Care, vol. 30, no. 1, pp. 21-27.
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Background
Intra-abdominal hypertension and abdominal compartment syndrome are potentially life threatening conditions. Critical care nurses need to understand the factors that predispose patients to intra-abdominal hypertension (IAH) and abdominal compartment syndrome (ACS). Predicting and managing IAH and ACS are important to improve health outcomes.
Aim
The aim of this paper was to (1) assess the knowledge of Australian critical care nurses about current IAH and ACS practice guidelines, measurement techniques, predictors for the development of IAH and ACS and (2) identify barriers in recognizing IAH, ACS and measuring IAP.
Methods
Between October 2014 and April 2015 86 registered nurses employed in the area of critical care were recruited via the form to participate in an on-line, 19-item questionnaire. The survey was distributed to critical care nurses via the Australian College of Critical Care Nurses (ACCCN) mailing list and directly to intensive care units via The majority of participants were women (n = 62) all participants were registered nurses employed in critical care the response rate was 3.2%. The study design was used to establish demographic data, employment data, and individuals’ knowledge related to IAH and ACS. Participants had the option to write hand written responses in addition to selecting a closed question response.
Results
The results showed that most survey participants were able to identify some obvious causes of IAH. However, less than 20% were able to recognize less apparent indices of risk. A lack of education related to IAP monitoring was identified by nearly half (44.2%) of respondents as the primary barrier to monitoring IAP.
Hussain, W., Hussain, F.H. & Hussain, O.H. 2017, 'Formulating and managing viable SLAs in Cloud Computing from a small to medium service provider's viewpoint: A state-of-the-art review', Information Systems.
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In today's competitive world, service providers need to be customer-focused and proactive in their marketing strategies to create consumer awareness of their services. Cloud computing provides an open and ubiquitous computing feature in which a large random number of consumers can interact with providers and request services. In such an environment, there is a need for intelligent and efficient methods that increase confidence in the successful achievement of business requirements. One such method is the Service Level Agreement (SLA), which is comprised of service objectives, business terms, service relations, obligations and the possible action to be taken in the case of SLA violation. Most of the emphasis in the literature has, until now, been on the formation of meaningful SLAs by service consumers, through which their requirements will be met. However, in an increasingly competitive market based on the cloud environment, service providers too need a framework that will form a viable SLA, predict possible SLA violations before they occur, and generate early warning alarms that flag a potential lack of resources. This is because when a provider and a consumer commit to an SLA, the service provider is bound to reserve the agreed amount of resources for the entire period of that agreement – whether the consumer uses them or not. It is therefore very important for cloud providers to accurately predict the likely resource usage for a particular consumer and to formulate an appropriate SLA before finalizing an agreement. This problem is more important for a small to medium cloud service provider which has limited resources that must be utilized in the best possible way to generate maximum revenue. A viable SLA in cloud computing is one that intelligently helps the service provider to determine the amount of resources to offer to a requesting consumer, and there are number of studies on SLA management in the literature. The aim of this paper is two-fold. First, it pr...
Hussain, W., Hussain, O.K., Hussain, F.K. & Khan MQ 2017, 'Usability Evaluation of English, Local and Plain Languages to Enhance On-Screen Text Readability: A Use Case of Pakistan', Global Journal of Flexible Systems Management.
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Hussein, F. & Piccardi, M. 2017, 'V-JAUNE: A framework for joint action recognition and video summarization', ACM Transactions on Multimedia Computing, Communications and Applications, vol. 13, no. 2.
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© 2017 ACM. Video summarization and action recognition are two important areas of multimedia video analysis. While these two areas have been tackled separately to date, in this article, we present a latent structural SVM framework to recognize the action and derive the summary of a video in a joint, simultaneous fashion. Efficient inference is provided by a submodular score function that accounts for the action and summary jointly. In this article, we also define a novel measure to evaluate the quality of a predicted video summary against the annotations of multiple annotators. Quantitative and qualitative results over two challenging action datasets-the ACE and MSR DailyActivity3D datasets-show that the proposed joint approach leads to higher action recognition accuracy and equivalent or better summary quality than comparable approaches that perform these tasks separately.
Jackson, D., Durrant, L., Bishop, E., Walthall, H., Betteridge, R., Gardner, S., Coulton, W., Hutchinson, M., Neville, S., Davidson, P.M. & Usher, K. 2017, 'Health service provision and the use of pressure-redistributing devices: mixed methods study of community dwelling individuals with pressure injuries', Contemporary Nurse, pp. 1-12.
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© 2017 Informa UK Limited, trading as Taylor & Francis Group Background: Health care within the home setting is a vital and growing component of pressure injury (PI) prevention and management. Objectives: To describe the use of health services and pressure-redistributing devices in community dwelling patients with PI’s. Design: Mixed-methods collective case study of a defined, diverse geographic postcode area in the United Kingdom. Methods: Quantitative retrospective analysis of electronic and paper medical records of adult PI patients from 2015 district nursing reports. Qualitative semi-structured interviews of community dwelling adult patients receiving, or received, treatment for PI in 2016. Results: Mandatory reports (n = 103) revealed that 90 patients were supplied with a variety of pressure-redistributing devices but only one-third of patients used the equipment as recommended. Qualitative interviews (n = 12), reported to COREQ guidelines, revealed that patients felt reliant on community health services, and were concerned about the consistency of their care. Conclusions: Authentic patient involvement is required to provide care and interventions that are acceptable to PI patients and can be incorporated into self-care strategies and effectively monitored.
Jan, M., Nanda, P., Usman, M. & He, X. 2017, 'PAWN: A Payload-based mutual Authentication scheme for Wireless Sensor Networks', Concurrency and Computation: Practice and Experience.
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Jan, M.A., Nanda, He, X.S. & Liu, R.P. 2017, 'A Sybil Attack Detection Scheme for a Forest Wildfire Monitoring Application', Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications.
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Wireless Sensor Networks (WSNs) have experienced phenomenal growth over the past decade. They are typically deployed in human-inaccessible terrains to monitor and collect time-critical and delay-sensitive events. There have been several studies on the use of WSN in different applications. All such studies have mainly focused on Quality of Service (QoS) parameters such as delay, loss, jitter, etc. of the sensed data. Security provisioning is also an important and challenging task lacking in all previous studies. In this paper, we propose a Sybil attack detection scheme for a cluster-based hierarchical network mainly deployed to monitor forest wildfire. We propose a two-tier detection scheme. Initially, Sybil nodes and their forged identities are detected by high-energy nodes. However, if one or more identities of a Sybil node sneak through the detection process, they are ultimately detected by the two base stations. After Sybil attack detection, an optimal percentage of cluster heads are elected and each one is informed using nomination packets. Each nomination packet contains the identity of an elected cluster head and an end user’s specific query for data collection within a cluster. These queries are user-centric, on-demand and adaptive to an end user requirement. The undetected identities of Sybil nodes reside in one or more clusters. Their goal is to transmit high false-negative alerts to an end user for diverting attention to those geographical regions which are less vulnerable to a wildfire. Our proposed approach has better network lifetime due to efficient sleep–awake scheduling, higher detection rate and low false-negative rate.
Jiang, Y., Tsai, P., Yeh, W.C. & Cao, L. 2017, 'A honey-bee-mating based algorithm for multilevel image segmentation using Bayesian theorem', Applied Soft Computing Journal, vol. 52, pp. 1181-1190.
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© 2016 Elsevier B.V. The image thresholding techniques are considered as a must for objects segmentation, compression and target recognition, and they have been widely studied for the last few decades; for example, the multi-level thresholding methods, and as such (they) render more great challenges for image segmentation techniques that remain computationally more expensive, when their choices of threshold numbers were increased. Therefore, our aim was to propose an algorithm based on Bayesian theorem and the so-called honey-bee-mating algorithm (HBMA), called a Bayesian honey bee mating algorithm BHBMA. It can not only reduce the computational time and curse of dimensionality, but also can run more reliably and more stably. This enhanced capability was technically accomplished by embedding a new population initialization strategy based on the characteristics of multi-level thresholding technique in pixel-based intensity images arranged from lower grey levels to higher ones. Extensive experiments have shown that our proposed method outperformed other state-of-the-art algorithms empirically in terms of their effectiveness and efficiency, when applying to complex image processing scenario such as automatic target recognition.
Li, J., Deng, C., Xu, R.Y.D., Tao, D. & Zhao, B. 2017, 'Robust Object Tracking with Discrete Graph-Based Multiple Experts', IEEE Transactions on Image Processing, vol. 26, no. 6, pp. 2736-2750.
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© 1992-2012 IEEE. Variations of target appearances due to illumination changes, heavy occlusions, and target deformations are the major factors for tracking drift. In this paper, we show that the tracking drift can be effectively corrected by exploiting the relationship between the current tracker and its historical tracker snapshots. Here, a multi-expert framework is established by the current tracker and its historical trained tracker snapshots. The proposed scheme is formulated into a unified discrete graph optimization framework, whose nodes are modeled by the hypotheses of the multiple experts. Furthermore, an exact solution of the discrete graph exists giving the object state estimation at each time step. With the unary and binary compatibility graph scores defined properly, the proposed framework corrects the tracker drift via selecting the best expert hypothesis, which implicitly analyzes the recent performance of the multi-expert by only evaluating graph scores at the current frame. Three base trackers are integrated into the proposed framework to validate its effectiveness. We first integrate the online SVM on a budget algorithm into the framework with significant improvement. Then, the regression correlation filters with hand-crafted features and deep convolutional neural network features are introduced, respectively, to further boost the tracking performance. The proposed three trackers are extensively evaluated on three data sets: TB-50, TB-100, and VOT2015. The experimental results demonstrate the excellent performance of the proposed approaches against the state-of-the-art methods.
Li, J., Mei, X., Prokhorov, D. & Tao, D. 2017, 'Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene', IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 3, pp. 690-703.
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© 2016 IEEE. Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful analysis. This paper extends the framework of deep neural networks by accounting for the structural cues in the visual signals. In particular, two kinds of neural networks have been proposed. First, we develop a multitask deep convolutional network, which simultaneously detects the presence of the target and the geometric attributes (location and orientation) of the target with respect to the region of interest. Second, a recurrent neuron layer is adopted for structured visual detection. The recurrent neurons can deal with the spatial distribution of visible cues belonging to an object whose shape or structure is difficult to explicitly define. Both the networks are demonstrated by the practical task of detecting lane boundaries in traffic scenes. The multitask convolutional neural network provides auxiliary geometric information to help the subsequent modeling of the given lane structures. The recurrent neural network automatically detects lane boundaries, including those areas containing no marks, without any explicit prior knowledge or secondary modeling.
Li, J., Meng, Z., Huang, M. & Zhang, K. 2017, 'An interactive visualization approach to the overview of geoscience data', Journal of Visualization, vol. 20, no. 3, pp. 433-451.
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Geoscience observation data refer to the datasets consisting of time series of multiple parameters generated from the sensors at fixed locations. Although a few works have attempted to visualize such data, none of them views these data as a specific type and attempts to show the overview in all the space, time and attribute aspects. It is important for domain experts to select the subsets of interest from huge amounts of observation data according to the high level patterns shown in the overview. We present a novel approach to visualize geoscience observation data in a compact radial view. Our solution consists of three visual elements. A map showing the spatial aspect is in the center of the visualization, while temporal and attribute aspects are seamlessly combined with the spatial information. Our approach is equipped with interactive mechanisms for highlighting the selected features, adjusting the display range, as well as interactively generating a fisheye view. We demonstrate the usability of our approach with three case studies of different domains. Eye tracking records and user feedbacks obtained in a small experiment also prove the effectiveness of our approach.
Li, X., Lu, Q., Dong, Y. & Tao, D. 2017, 'SCE: A Manifold Regularized Set-Covering Method for Data Partitioning', IEEE Transactions on Neural Networks and Learning Systems.
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Cluster analysis plays a very important role in data analysis. In these years, cluster ensemble, as a cluster analysis tool, has drawn much attention for its robustness, stability, and accuracy. Many efforts have been done to combine different initial clustering results into a single clustering solution with better performance. However, they neglect the structure information of the raw data in performing the cluster ensemble. In this paper, we propose a Structural Cluster Ensemble (SCE) algorithm for data partitioning formulated as a set-covering problem. In particular, we construct a Laplacian regularized objective function to capture the structure information among clusters. Moreover, considering the importance of the discriminative information underlying in the initial clustering results, we add a discriminative constraint into our proposed objective function. Finally, we verify the performance of the SCE algorithm on both synthetic and real data sets. The experimental results show the effectiveness of our proposed method SCE algorithm.
Li, Y., Tian, X., Liu, T. & Tao, D. 2017, 'On Better Exploring and Exploiting Task Relationships in Multitask Learning: Joint Model and Feature Learning', IEEE Transactions on Neural Networks and Learning Systems.
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Multitask learning (MTL) aims to learn multiple tasks simultaneously through the interdependence between different tasks. The way to measure the relatedness between tasks is always a popular issue. There are mainly two ways to measure relatedness between tasks: common parameters sharing and common features sharing across different tasks. However, these two types of relatedness are mainly learned independently, leading to a loss of information. In this paper, we propose a new strategy to measure the relatedness that jointly learns shared parameters and shared feature representations. The objective of our proposed method is to transform the features of different tasks into a common feature space in which the tasks are closely related and the shared parameters can be better optimized. We give a detailed introduction to our proposed MTL method. Additionally, an alternating algorithm is introduced to optimize the nonconvex objection. A theoretical bound is given to demonstrate that the relatedness between tasks can be better measured by our proposed MTL algorithm. We conduct various experiments to verify the superiority of the proposed joint model and feature MTL method.
Ling, S.H., San, P.P., Lam, H.K. & Nguyen, H.T. 2017, 'Hypoglycemia detection: multiple regression-based combinational neural logic approach', Soft Computing, vol. 21, no. 2, pp. 543-553.
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© 2015 Springer-Verlag Berlin Heidelberg Hypoglycemia is a common and serious side effect of type 1 diabetes. We measure physiological parameters continuously to provide detection of hypoglycemic episodes in type 1 diabetes mellitus patients using a multiple regression-based combinational neural logic approach. In this work, a neural logic network with multiple regression is applied to the development of non-invasive hypoglycemia monitoring system. It is an alarm system which measures the physiological parameters of electrocardiogram signal (heart rate and corrected QT interval) and determine the onset of hypoglycemia by the use of proposed hybrid neural logic approach. In this clinical application, a combinational neural logic network with multiple regression is systematically designed to hypoglycemia detection based on the characteristic of this application. To optimize the parameter of the hybrid combinational neural logic system, hybrid particle swarm optimization with wavelet mutation is applied to tuned the parameters of the system. To illustrate the effectiveness of the proposed method, hypoglycemia monitoring system which will be practically analyzed using real data sets collected from 15 children ((Formula presented.) years) with type 1 diabetes at the Department of Health, Government of Western Australia. With the use of proposed method, the best testing sensitivity of 79.07 % and specificity of 53.64 % were obtained.
Liu, B., Xiao, Y. & Cao, L. 2017, 'SVM-based multi-state-mapping approach for multi-class classification', Knowledge-Based Systems.
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© 2017.Traditional SVM-based multi-class classification algorithms mainly adopt the strategy of mapping the data set with all classes into a single feature space via a kernel function, in which SVM is constructed for each decomposed binary classification problem. However, it is not always possible to find an appropriate kernel function to render all the classes distinguishable in a single feature space, since each class is always derived from different data distributions. Consequently, the performance is not always as good as expected. To improve the performance of multi-class classification, this paper proposes an improved approach, called multi-state-mapping (MSM) with SVM based on hierarchical architecture, which maps the data set with all classes into different feature spaces at the different states of the decomposition of a multi-class classification problem in terms of a binary tree architecture. We prove that the computational complexity of MSM at its worst lies between that of the one-against-all scheme and one-against-one scheme. Substantial experiments have been conducted on sixteen UCI data sets to show the performance of our method. The statistical results show that MSM outperforms state-of-the-art methods in terms of accuracy and standard deviation.
Liu, F., Gong, C., Zhou, T., Fu, K., He, X.S. & Yang, J. 2017, 'Visual Tracking via Nonnegative Multiple Coding', IEEE Transactions on Multimedia.
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It has been extensively observed that an accurate appearance
model is critical to achieving satisfactory performance
for robust object tracking. Most existing top-ranked methods
rely on linear representation over a single dictionary, which
brings about improper understanding on the target appearance.
To address this problem, in this paper, we propose a novel
appearance model named as “Nonnegative Multiple Coding”
(NMC) to accurately represent a target. First, a series of local
dictionaries are created with different pre-defined numbers of
nearest neighbors, and then the contributions of these dictionaries
are automatically learned. As a result, this ensemble of
dictionaries can comprehensively exploit the appearance information
carried by all the constituted dictionaries. Second, the
existing methods explicitly impose the nonnegative constraint
to coefficient vectors, but in the proposed model, we directly
deploy an efficient `2 norm regularization to achieve the similar
nonnegative purpose with theoretical guarantees. Moreover, an
efficient occlusion detection scheme is designed to alleviate
tracking drifts, and it investigates whether negative templates are
selected to represent the severely occluded target. Experimental
results on two benchmarks demonstrate that our NMC tracker is
able to achieve superior performance to state-of-the-art methods.
Liu, H., Wu, J., Liu, T., Tao, D. & Fu, Y. 2017, 'Spectral Ensemble Clustering via Weighted K-Means: Theoretical and Practical Evidence', IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 5, pp. 1129-1143.
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© 2017 IEEE. As a promising way for heterogeneous data analytics, consensus clustering has attracted increasing attention in recent decades. Among various excellent solutions, the co-association matrix based methods form a landmark, which redefines consensus clustering as a graph partition problem. Nevertheless, the relatively high time and space complexities preclude it from wide real-life applications. We, therefore, propose Spectral Ensemble Clustering (SEC) to leverage the advantages of co-association matrix in information integration but run more e fficiently. We disclose the theoretical equivalence between SEC and weighted K-means clustering, which dramatically reduces the algorithmic complexity. We also derive the latent consensus function of SEC, which to our best knowledge is the first to bridge co-association matrix based methods to the methods with explicit global objective functions. Further, we prove in theory that SEC holds the robustness, generalizability, and convergence properties. We finally extend SEC to meet the challenge arising from incomplete basic partitions, based on which a row-segmentation scheme for big data clustering is proposed. Experiments on various real-world data sets in both ensemble and multi-view clustering scenarios demonstrate the superiority of SEC to some state-of-the-art methods. In particular, SEC seems to be a promising candidate for big data clustering.
Liu, Q., Deng, J., Yang, J., Liu, G. & Tao, D. 2017, 'Adaptive cascade regression model for robust face alignment', IEEE Transactions on Image Processing, vol. 26, no. 2, pp. 797-807.
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Liu, Q., Sun, Y., Wang, C., Liu, T. & Tao, D. 2017, 'Elastic net hypergraph learning for image clustering and semi-supervised classification', IEEE Transactions on Image Processing, vol. 26, no. 1, pp. 452-463.
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© 1992-2012 IEEE. Graph model is emerging as a very effective tool for learning the complex structures and relationships hidden in data. In general, the critical purpose of graph-oriented learning algorithms is to construct an informative graph for image clustering and classification tasks. In addition to the classical K -nearest-neighbor and r-neighborhood methods for graph construction, l 1 -graph and its variants are emerging methods for finding the neighboring samples of a center datum, where the corresponding ingoing edge weights are simultaneously derived by the sparse reconstruction coefficients of the remaining samples. However, the pairwise links of l 1 -graph are not capable of capturing the high-order relationships between the center datum and its prominent data in sparse reconstruction. Meanwhile, from the perspective of variable selection, the l 1 norm sparse constraint, regarded as a LASSO model, tends to select only one datum from a group of data that are highly correlated and ignore the others. To simultaneously cope with these drawbacks, we propose a new elastic net hypergraph learning model, which consists of two steps. In the first step, the robust matrix elastic net model is constructed to find the canonically related samples in a somewhat greedy way, achieving the grouping effect by adding the l 2 penalty to the l 1 constraint. In the second step, hypergraph is used to represent the high order relationships between each datum and its prominent samples by regarding them as a hyperedge. Subsequently, hypergraph Laplacian matrix is constructed for further analysis. New hypergraph learning algorithms, including unsupervised clustering and multi-class semi-supervised classification, are then derived. Extensive experiments on face and handwriting databases demonstrate the effectiveness of the proposed method.
Liu, T., Gong, M. & Tao, D. 2017, 'Large-Cone Nonnegative Matrix Factorization', IEEE Transactions on Neural Networks and Learning Systems.
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Nonnegative matrix factorization (NMF) has been greatly popularized by its parts-based interpretation and the effective multiplicative updating rule for searching local solutions. In this paper, we study the problem of how to obtain an attractive local solution for NMF, which not only fits the given training data well but also generalizes well on the unseen test data. Based on the geometric interpretation of NMF, we introduce two large-cone penalties for NMF and propose large-cone NMF (LCNMF) algorithms. Compared with NMF, LCNMF will obtain bases comprising a larger simplicial cone, and therefore has three advantages. 1) the empirical reconstruction error of LCNMF could mostly be smaller; (2) the generalization ability of the proposed algorithm is much more powerful; and (3) the obtained bases of LCNMF have a low-overlapping property, which enables the bases to be sparse and makes the proposed algorithms very robust. Experiments on synthetic and real-world data sets confirm the efficiency of LCNMF.
Liu, T., Tao, D., Song, M. & Maybank, S. 2017, 'Algorithm-Dependent Generalization Bounds for Multi-Task Learning.', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 2, pp. 227-241.
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Often, tasks are collected for multi-task learning (MTL) because they share similar feature structures. Based on this observation, in this paper, we present novel algorithm-dependent generalization bounds for MTL by exploiting the notion of algorithmic stability. We focus on the performance of one particular task and the average performance over multiple tasks by analyzing the generalization ability of a common parameter that is shared in MTL. When focusing on one particular task, with the help of a mild assumption on the feature structures, we interpret the function of the other tasks as a regularizer that produces a specific inductive bias. The algorithm for learning the common parameter, as well as the predictor, is thereby uniformly stable with respect to the domain of the particular task and has a generalization bound with a fast convergence rate of order O(1=n), where n is the sample size of the particular task. When focusing on the average performance over multiple tasks, we prove that a similar inductive bias exists under certain conditions on the feature structures. Thus, the corresponding algorithm for learning the common parameter is also uniformly stable with respect to the domains of the multiple tasks, and its generalization bound is of the order O(1=T ), where T is the number of tasks. These theoretical analyses naturally show that the similarity of feature structures in MTL will lead to specific regularizations for predicting, which enables the learning algorithms to generalize fast and correctly from a few examples.
Liu, W. & Huang, J. 2017, 'Adaptive leader-following consensus for a class of higher-order nonlinear multi-agent systems with directed switching networks', Automatica, vol. 79, pp. 84-92.
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Liu, W., Chen, X., Yang, J. & Wu, Q. 2017, 'Robust Color Guided Depth Map Restoration', IEEE Transactions on Image Processing, vol. 26, no. 1, pp. 315-327.
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One of the most challenging issues in color guided depth map restoration is the inconsistency between color edges in guidance color images and depth discontinuities on depth maps. This makes the restored depth map suffer from texture copy artifacts and blurring depth discontinuities. To handle this problem, most state-of-the-art methods design complex guidance weight based on guidance color images and heuristically make use of the bicubic interpolation of the input depth map. In this paper, we show that using bicubic interpolated depth map can blur depth discontinuities when the upsampling factor is large and the input depth map contains large holes and heavy noise. In contrast, we propose a robust optimization framework for color guided depth map restoration. By adopting a robust penalty function to model the smoothness term of our model, we show that the proposed method is robust against the inconsistency between color edges and depth discontinuities even when we use simple guidance weight. To the best of our knowledge, we are the first to solve this problem with a principled mathematical formulation rather than previous heuristic weighting schemes. The proposed robust method performs well in suppressing texture copy artifacts. Moreover, it can better preserve sharp depth discontinuities than previous heuristic weighting schemes. Through comprehensive experiments on both simulated data and real data, we show promising performance of the proposed method
Liu, W., Chen, X., Yang, J. & Wu, Q. 2017, 'Variable Bandwidth Weighting for Texture Copy Artifacts Suppression in Guided Depth Upsampling', IEEE Transactions on Circuits and Systems for Video Technology.
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Liu, Y., Peng, H., Wong, L. & Li, J. 2017, 'High-speed and high-ratio referential genome compression.', Bioinformatics.
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Motivation: The rapidly increasing number of genomes generated by high-throughput sequencing platforms and assembly algorithms is accompanied by problems in data storage, compression and communication. Traditional compression algorithms are unable to meet the demand of high compression ratio due to the intrinsic challenging features of DNA sequences such as small alphabet size, frequent repeats and palindromes. Reference-based lossless compression, by which only the differences between two similar genomes are stored, is a promising approach with high compression ratio. Results: We present a high-performance referential genome compression algorithm named HiRGC. It is based on a 2-bit encoding scheme and an advanced greedy-matching search on a hash table. We compare the performance of HiRGC with four state-of-the-art compression methods on a benchmark data set of eight human genomes. HiRGC takes less than 30 minutes to compress about 21 gigabytes of each set of the seven target genomes into 96 to 260 megabytes, achieving compression ratios of 217 to 82 times. This performance is at least 1.9 times better than the best competing algorithm on its best case. Our compression speed is also at least 2.9 times faster. HiRGC is stable and robust to deal with different reference genomes. In contrast, the competing methods' performance varies widely on different reference genomes. More experiments on 100 human genomes from the 1000 Genome Project and on genomes of several other species again demonstrate that HiRGC's performance is consistently excellent. Availability and Implementation: The C++ and Java source codes of our algorithm are freely available for academic and non-commercial use. They can be downloaded from https://github.com/yuansliu/HiRGC . Contact: Jinyan.Li@uts.edu.au ; yyuanshengliu@gmail.com. Supplementary information: Supplementary data are available at Bioinformatics online.
Lu, J., Xuan, J., Zhang, G., Xu, Y.D. & Luo, X. 2017, 'Bayesian Nonparametric Relational Topic Model through Dependent Gamma Processes', IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 7, pp. 1357-1369.
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Traditional relational topic models provide a successful way to discover the hidden topics from a document network. Many
theoretical and practical tasks, such as dimensional reduction, document clustering, and link prediction, could benefit from this revealed
knowledge. However, existing relational topic models are based on an assumption that the number of hidden topics is known a priori,
which is impractical in many real-world applications. Therefore, in order to relax this assumption, we propose a nonparametric relational
topic model using stochastic processes instead of fixed-dimensional probability distributions in this paper. Specifically, each document
is assigned a Gamma process, which represents the topic interest of this document. Although this method provides an elegant solution,
it brings additional challenges when mathematically modeling the inherent network structure of typical document network, i.e., two
spatially closer documents tend to have more similar topics. Furthermore, we require that the topics are shared by all the documents. In
order to resolve these challenges, we use a subsampling strategy to assign each document a different Gamma process from the global
Gamma process, and the subsampling probabilities of documents are assigned with a Markov Random Field constraint that inherits the
document network structure. Through the designed posterior inference algorithm, we can discover the hidden topics and its number
simultaneously. Experimental results on both synthetic and real-world network datasets demonstrate the capabilities of learning the
hidden topics and, more importantly, the number of topics.
Luckett, T., Chenoweth, L., Phillips, J., Brooks, D., Cook, J., Mitchell, G., Pond, D., Davidson, P., Beattie, E., Luscombe, G., Goodall, S., Fischer, T. & Agar, M. 2017, 'A facilitated approach to family case conferencing for people with advanced dementia living in nursing homes: Perceptions of Palliative Care Planning Coordinators and other health professionals in the IDEAL Study', International Psychogeriatrics.
Lv, L., Fan, S., Huang, M., Huang, W. & Yang, G. 2017, 'Golden Rectangle Treemap', Journal of Physics: Conference Series, vol. 787, no. 1, pp. 1-6.
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Treemaps, a visualization method of representing hierarchical data sets, are becoming more and more popular for its efficient and compact displays. Several algorithms have been proposed to create more useful display by controlling the aspect ratios of the rectangles that make up a treemap. In this paper, we introduce a new treemap algorithm, generating layout in which the rectangles are easier to select and hierarchy information is easier to obtain. This algorithm generates rectangles which approximate golden rectangles. To prove the effectiveness of our algorithm, at the end of this paper several analyses on golden rectangle treemap have been done on disk file system.
Maneze, D., Ramjan, L., DiGiacomo, M., Everett, B., Davidson, P.M. & Salamonson, Y. 2017, 'Negotiating health and chronic illness in Filipino-Australians: A qualitative study with implications for health promotion', Ethnicity and Health, pp. 1-18.
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Objective: In spite of the healthy immigrant effect, the prevalence of lifestyle-related chronic diseases among migrants is reported to approximate that of the host country with longer duration of stay. For example, higher rates of chronic diseases such as Type 2 diabetes and hypertension have been observed among Filipino migrants and these have been linked to acculturation. The aim of this study was to explore the experiences of Filipino-Australian migrants in managing their chronic health conditions in a Western host country.
Design: This paper reports on qualitative findings of a mixed methods study that used an explanatory sequential design. Nine focus group discussions were undertaken with 58 Filipino-Australian migrants with chronic disease. Thematic analysis was undertaken using a five-stage general purpose thematic framework ensuring that themes closely identified key participants’ experiences.
Results: Findings revealed that health benefits provided by the health system in Australia were considered advantageous. However, a lack of social and instrumental support compounded isolation and disempowerment, limiting self-management strategies for chronic illnesses. Cultural beliefs and practices influenced their knowledge, attitude to and management of chronic disease, which health service providers overlooked because of perceived acculturation and English language skills. Overall this study has clearly identified recognition of cultural beliefs, language needs and support as three core needs of Filipino-Australian migrants with the elderly the most vulnerable.
Conclusion: This paper highlights that self-management of chronic disease among elderly Filipino immigrants may be adversely affected by host language difficulties, a lack of social support and cultural issues, impacting on access to services, health-seeking behaviours and participation in health promotion initiatives. Language, culture-specific health interventions and resources and enhancing socia...
McDonagh, J., Martin, L., Ferguson, C., Jha, S., Macdonald, P., Davidson, P. & Newton, P. 2017, 'Frailty assessment instruments in heart failure: a systematic review.', European Journal of Cardiovascular Nursing.
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Meng, X., Cao, L., Zhang, X. & Shao, J. 2017, 'Top-k coupled keyword recommendation for relational keyword queries', Knowledge and Information Systems, vol. 50, no. 3, pp. 883-916.
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© 2016 Springer-Verlag LondonProviding top-k typical relevant keyword queries would benefit the users who cannot formulate appropriate queries to express their imprecise query intentions. By extracting the semantic relationships both between keywords and keyword queries, this paper proposes a new keyword query suggestion approach which can provide typical and semantically related queries to the given query. Firstly, a keyword coupling relationship measure, which considers both intra- and inter-couplings between each pair of keywords, is proposed. Then, the semantic similarity of different keyword queries can be measured by using a semantic matrix, in which the coupling relationships between keywords in queries are reserved. Based on the query semantic similarities, we next propose an approximation algorithm to find the most typical queries from query history by using the probability density estimation method. Lastly, a threshold-based top-k query selection method is proposed to expeditiously evaluate the top-k typical relevant queries. We demonstrate that our keyword coupling relationship and query semantic similarity measures can capture the coupling relationships between keywords and semantic similarities between keyword queries accurately. The efficiency of query typicality analysis and top-k query selection algorithm is also demonstrated.
Mirtalaie, M.A., Hussain, O.K., Chang, E. & Hussain, F.K. 2017, 'A decision support framework for identifying novel ideas in new product development from cross-domain analysis', Information Systems, vol. 69, pp. 59-80.
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© 2017 Elsevier Ltd In current competitive times, product manufacturers need not only to retain their existing customer base, but also to increase their market share. One way they can achieve this is by generating new ideas and developing novel products with new features. As highlighted in the literature, in generating new ideas to develop novel and innovative products, it is important that product designers satisfy the needs of both current customers and new customers. However, despite the large number of existing studies that identify novel features in the ideation phase, product designers do not have a systematic framework that utilises additional information relating to products from either far-field or related domains to generate such new ideas in the ideation phase. This paper presents our proposed framework FEATURE which provides just such a systemic framework for product designers in the ideation phase of new product development. FEATURE has three phases. The first phase identifies and recommends to the product designers novel features that can be added to the next version of a reference product. In order to incorporate the customer's voice into the ideation phase, the second phase ascertains the popularity of the proposed features by using social media. The third phase ranks the proposed features based on the designer's decision criteria to select those that should be considered further in the next phases of new product development. We explain the importance of each phase of FEATURE and show the working of its first module in detail.
Naik, G.R., Al-Ani, A., Gobbo, M. & Nguyen, H.T. 2017, 'Does Heel Height Cause Imbalance during Sit-to-Stand Task: Surface EMG Perspective', Frontiers in Physiology, vol. 8, pp. 1-20.
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The purpose of this study was to determine whether electromyography (EMG) muscle activities around the knee differ during sit-to-stand (STS) and returning task for females wearing shoes with different heel heights. Sixteen healthy young women (age = 25.2 ± 3.9 years, body mass index = 20.8 ± 2.7 kg/m2) participated in this study. Electromyography signals were recorded fromthe twomuscles, vastusmedialis (VM) and vastus lateralis (VL) that involve in the extension of knee. The participants wore shoes with five different heights, including 4, 6, 8, 10, and 12 cm. Surface electromyography (sEMG) data were acquired during STS and stand-to-sit-returning (STSR) tasks. The data was filtered using a fourth order Butterworth (band pass) filter of 20–450 Hz frequency range. For each heel height, we extractedmedian frequency (MDF) and root mean square (RMS) features to measure sEMG activities between VM and VL muscles. The experimental results (based on MDF and RMS-values) indicated that there is imbalance between vasti muscles for more elevated heels. The results are also quantified with statistical measures.The study findings suggest that there would be an increased likelihood of knee imbalance
and fatigue with regular usage of high heel shoes (HHS) in women.
Nguyen, H., Liu, W. & Chen, F. 2017, 'Discovering Congestion Propagation Patterns in Spatio-Temporal Traffic Data', IEEE Transactions on Big Data, vol. 3, no. 2, pp. 169-180.
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Traffic congestion is a condition of a segment in the road network where the traffic demand is greater than the available
road capacity. The detection of unusual traffic patterns including congestions is a significant research problem in the data mining and
knowledge discovery community. However, to the best of our knowledge, the discovery of propagations, or causal interactions among
detected traffic congestions has not been appropriately investigated before. In this research, we introduce algorithms which construct
causality trees from congestions and estimate their propagation probabilities based on temporal and spatial information of the
congestions. Frequent sub-structures of these causality trees reveal not only recurring interactions among spatio-temporal
congestions, but potential bottlenecks or flaws in the designs of existing traffic networks. Our algorithms have been validated by
experiments on a travel time data set recorded from an urban road network.
Pan, S., Wu, J., Zhu, X., Long, G. & Zhang, C. 2017, 'Boosting for graph classification with universum', Knowledge and Information Systems, vol. 50, no. 1, pp. 53-77.
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© 2016 Springer-Verlag London Recent years have witnessed extensive studies of graph classification due to the rapid increase in applications involving structural data and complex relationships. To support graph classification, all existing methods require that training graphs should be relevant (or belong) to the target class, but cannot integrate graphs irrelevant to the class of interest into the learning process. In this paper, we study a new universum graph classification framework which leverages additional “non-example” graphs to help improve the graph classification accuracy. We argue that although universum graphs do not belong to the target class, they may contain meaningful structure patterns to help enrich the feature space for graph representation and classification. To support universum graph classification, we propose a mathematical programming algorithm, ugBoost, which integrates discriminative subgraph selection and margin maximization into a unified framework to fully exploit the universum. Because informative subgraph exploration in a universum setting requires the search of a large space, we derive an upper bound discriminative score for each subgraph and employ a branch-and-bound scheme to prune the search space. By using the explored subgraphs, our graph classification model intends to maximize the margin between positive and negative graphs and minimize the loss on the universum graph examples simultaneously. The subgraph exploration and the learning are integrated and performed iteratively so that each can be beneficial to the other. Experimental results and comparisons on real-world dataset demonstrate the performance of our algorithm.
Pan, S., Wu, J., Zhu, X., Long, G. & Zhang, C. 2017, 'Task Sensitive Feature Exploration and Learning for Multitask Graph Classification', IEEE Transactions on Cybernetics, vol. 47, no. 3, pp. 744-758.
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Multitask learning (MTL) is commonly used for jointly optimizing multiple learning tasks. To date, all existing MTL methods have been designed for tasks with feature-vector represented instances, but cannot be applied to structure data, such as graphs. More importantly, when carrying out MTL, existing methods mainly focus on exploring overall commonality or disparity between tasks for learning, but cannot explicitly capture task relationships in the feature space, so they are unable to answer important questions, such as what exactly is shared between tasks and what is the uniqueness of one task differing from others? In this paper, we formulate a new multitask graph learning problem, and propose a task sensitive feature exploration and learning algorithm for multitask graph classification. Because graphs do not have features available, we advocate a task sensitive feature exploration and learning paradigm to jointly discover discriminative subgraph features across different tasks. In addition, a feature learning process is carried out to categorize each subgraph feature into one of three categories: 1) common feature; 2) task auxiliary feature; and 3) task specific feature, indicating whether the feature is shared by all tasks, by a subset of tasks, or by only one specific task, respectively. The feature learning and the multiple task learning are iteratively optimized to form a multitask graph classification model with a global optimization goal. Experiments on real-world functional brain analysis and chemical compound categorization demonstrate the algorithm's performance. Results confirm that our method can be used to explicitly capture task correlations and uniqueness in the feature space, and explicitly answer what are shared between tasks and what is the uniqueness of a specific task.
Peng, H., Lan, C., Zheng, Y., Hutvagner, G., Tao, D. & Li, J. 2017, 'Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite.', BMC Bioinformatics, vol. 18, no. 1, p. 193.
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BACKGROUND: MicroRNAs always function cooperatively in their regulation of gene expression. Dysfunctions of these co-functional microRNAs can play significant roles in disease development. We are interested in those multi-disease associated co-functional microRNAs that regulate their common dysfunctional target genes cooperatively in the development of multiple diseases. The research is potentially useful for human disease studies at the transcriptional level and for the study of multi-purpose microRNA therapeutics. METHODS AND RESULTS: We designed a computational method to detect multi-disease associated co-functional microRNA pairs and conducted cross disease analysis on a reconstructed disease-gene-microRNA (DGR) tripartite network. The construction of the DGR tripartite network is by the integration of newly predicted disease-microRNA associations with those relationships of diseases, microRNAs and genes maintained by existing databases. The prediction method uses a set of reliable negative samples of disease-microRNA association and a pre-computed kernel matrix instead of kernel functions. From this reconstructed DGR tripartite network, multi-disease associated co-functional microRNA pairs are detected together with their common dysfunctional target genes and ranked by a novel scoring method. We also conducted proof-of-concept case studies on cancer-related co-functional microRNA pairs as well as on non-cancer disease-related microRNA pairs. CONCLUSIONS: With the prioritization of the co-functional microRNAs that relate to a series of diseases, we found that the co-function phenomenon is not unusual. We also confirmed that the regulation of the microRNAs for the development of cancers is more complex and have more unique properties than those of non-cancer diseases.
Prichard, R., Hayward, C., Davidson, P., Newton, P., Goodall, S. & Kershaw, L. 2017, 'Left ventricular device implantation impacts on hospitalisation rates, length of stay and out of hospital time', Heart, Lung and Circulation.
Qiao, M., Liu, L., Yu, J., Xu, C. & Tao, D. 2017, 'Diversified dictionaries for multi-instance learning', Pattern Recognition, vol. 64, pp. 407-416.
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© 2016 Elsevier LtdMultiple-instance learning (MIL) has been a popular topic in the study of pattern recognition for years due to its usefulness for such tasks as drug activity prediction and image/text classification. In a typical MIL setting, a bag contains a bag-level label and more than one instance/pattern. How to bridge instance-level representations to bag-level labels is a key step to achieve satisfactory classification accuracy results. In this paper, we present a supervised learning method, diversified dictionaries MIL, to address this problem. Our approach, on the one hand, exploits bag-level label information for training class-specific dictionaries. On the other hand, it introduces a diversity regularizer into the class-specific dictionaries to avoid ambiguity between them. To the best of our knowledge, this is the first time that the diversity prior is introduced to solve the MIL problems. Experiments conducted on several benchmark (drug activity and image/text annotation) datasets show that the proposed method compares favorably to state-of-the-art methods.
Rao, A., Newton, P., DiGiacomo, M., Hickman, L., Hwang, C. & Davidson, P. 2017, 'Optimal gender specific strategies for the secondary prevention of cardiovascular disease in women: a systematic review', Journal of Cardiopulmonary Rehabilitation and Prevention.
Ren, J., Song, J., Ellis, J. & Li, J. 2017, 'Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences.', BMC Genomics, vol. 18, no. Suppl 2, p. 113.
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BACKGROUND: The broad heterogeneity of antigen-antibody interactions brings tremendous challenges to the design of a widely applicable learning algorithm to identify conformational B-cell epitopes. Besides the intrinsic heterogeneity introduced by diverse species, extra heterogeneity can also be introduced by various data sources, adding another layer of complexity and further confounding the research. RESULTS: This work proposed a staged heterogeneity learning method, which learns both characteristics and heterogeneity of data in a phased manner. The method was applied to identify antigenic residues of heterogenous conformational B-cell epitopes based on antigen sequences. In the first stage, the model learns the general epitope patterns of each kind of propensity from a large data set containing computationally defined epitopes. In the second stage, the model learns the heterogenous complementarity of these propensities from a relatively small guided data set containing experimentally determined epitopes. Moreover, we designed an algorithm to cluster the predicted individual antigenic residues into conformational B-cell epitopes so as to provide strong potential for real-world applications, such as vaccine development. With heterogeneity well learnt, the transferability of the prediction model was remarkably improved to handle new data with a high level of heterogeneity. The model has been tested on two data sets with experimentally determined epitopes, and on a data set with computationally defined epitopes. This proposed sequence-based method achieved outstanding performance - about twice that of existing methods, including the sequence-based predictor CBTOPE and three other structure-based predictors. CONCLUSIONS: The proposed method uses only antigen sequence information, and thus has much broader applications.
Rihari-Thomas, J., DiGiacomo, M., Phillips, J., Newton, P. & Davidson, P.M. 2017, 'Clinician Perspectives of Barriers to Effective Implementation of a Rapid Response System in an Academic Health Centre: A Focus Group Study', International Journal of Health Policy and Management, vol. 6, no. 8, pp. 447-456.
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Background: Systemic and structural issues of rapid response system (RRS) models can hinder implementation. This study sought to understand the ways in which acute care clinicians (physicians and nurses) experience and negotiate care for deteriorating patients within the RRS.
Methods: Physicians and nurses working within an Australian academic health centre within a jurisdictional-based model of clinical governance participated in focus group interviews. Verbatim transcripts were analysed using thematic content analysis.
Results: Thirty-four participants (21 physicians and 13 registered nurses [RNs]) participated in six focus groups over five weeks in 2014. Implementing the RRS in daily practice was a process of informal communication and negotiation in spite of standardised protocols. Themes highlighted several systems or organisational-level barriers to an effective RRS, including (1) responsibility is inversely proportional to clinical experience; (2) actions around system flexibility contribute to deviation from protocol; (3) misdistribution of resources leads to perceptions of inadequate staffing levels inhibiting full optimisation of the RRS; and (4) poor communication and documentation of RRS increases clinician workloads.
Conclusion: Implementing a RRS is complex and multifactorial, influenced by various inter- and intra-professional factors, staffing models and organisational culture. The RRS is not a static model; it is both reflexive and iterative, perpetually transforming to meet healthcare consumer and provider demands and local unit contexts and needs. Requiring more than just a strong initial implementation phase, new models of care such as a RRS demand good governance processes, ongoing support and regular evaluation and refinement. Cultural, organizational and professional factors, as well as systems-based processes, require consideration if RRSs are to achieve their intended outcomes in dynamic healthcare settings.
Shehab, S., Allida, S.M., Davidson, P.M., Newton, P.J., Robson, D., Jansz, P.C. & Hayward, C.S. 2017, 'Right Ventricular Failure Post LVAD Implantation Corrected with Biventricular Support: An In Vitro Model.', ASAIO Journal, vol. 63, no. 1, pp. 41-47.
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Right ventricular failure after left ventricular assist device (LVAD) implantation is associated with high mortality. Management remains limited to pharmacologic therapy and temporary mechanical support. Delayed right ventricular assist device (RVAD) support after LVAD implantation is associated with poorer outcomes. With the advent of miniaturized, durable, continuous flow ventricular assist device systems, chronic RVAD and biventricular assist device (BiVAD) support has been used with some success. The purpose of this study was to assess combined BiVAD and LVAD with delayed RVAD support within a four-elemental mock circulatory loop (MCL) simulating the human cardiovascular system. Our hypothesis was that delayed continuous flow RVAD (RVAD) would produce similar hemodynamic and flow parameters to those of initial BiVAD support. Using the MCL, baseline biventricular heart failure with elevated right and left filling pressures with low cardiac output was simulated. The addition of LVAD within a biventricular configuration improved cardiac output somewhat, but was associated with persistent right heart failure with elevated right-sided filling pressures. The addition of an RVAD significantly improved LVAD outputs and returned filling pressures to normal throughout the circulation. In conclusion, RVAD support successfully restored hemodynamics and flow parameters of biventricular failure supported with isolated LVAD with persistent elevated right atrial pressure.
Shishehgar, S., Gholizadeh, L., DiGiacomo, M., Green, A. & Davidson, P.M. 2017, 'Health and socio-cultural experiences of refugee women: An integrative review', The Journal of Immigrant and Minority Health, vol. 19, no. 4, pp. 959-973.
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Smith, T.A., Disler, R.T., Jenkins, C.R., Ingham, J.M. & Davidson, P.M. 2017, 'Perspectives on advance care planning among patients recently requiring non-invasive ventilation for acute respiratory failure: A qualitative study using thematic analysis.', Palliative Medicine, vol. 31, no. 6, pp. 566-574.
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BACKGROUND: Patients requiring non-invasive ventilation for acute-on-chronic respiratory failure due to chronic obstructive pulmonary disease or heart failure exacerbations may have a poor prognosis underscoring the importance of advance care planning. AIM: We aimed to describe attitudes to, and experiences of, discussing the future among patients recently treated with non-invasive ventilation. DESIGN: Qualitative research using thematic analysis. SETTINGS AND PARTICIPANTS: Tertiary teaching hospital. Patients with acute hypercapnic respiratory failure requiring non-invasive ventilation. RESULTS: Individuals recently treated with non-invasive ventilation describe feeling the future is beyond their control and instead controlled by their illness. Participants often recognised their poor prognosis but avoided discussing some difficult topics. The majority preferred not to undergo cardiopulmonary resuscitation but most had not discussed this with healthcare professionals. When participants voiced concerns about their future health to family members, they were met with polarised responses. Some encountered willingness for further discussion, while others met deflection, deterring further conversation. An overarching narrative of 'Looking through my illness to an uncertain but concerning future' unites these themes. CONCLUSION: This study suggests opportunities and barriers for advance care planning in individuals with chronic disease. Patients' understanding of their prognosis and their attitudes to cardiopulmonary resuscitation suggests an opportunity for advance care planning. Structuring discussions around patients' preferences for care during future exacerbations may foster a sense of control over the future despite illness. The diversity of familial responses to patients' concerns about their future health has implications for advance care planning. These findings have the potential to improve care for patients with respiratory failure and suggest an important o...
Stoianoff, N.P. & Walpole, M. 2017, 'Tax and the environment: an evaluation framework for tax policy reform - group Delphi study', Australian Tax Forum: a journal of taxation policy, law and reform, vol. 31, pp. 693-716.
Tao, D., Tao, D., Li, X. & Gao, X. 2017, 'Large sparse cone non-negative matrix factorization for image annotation', ACM Transactions on Intelligent Systems and Technology, vol. 8, no. 3.
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© 2017 ACM. Image annotation assigns relevant tags to query images based on their semantic contents. Since Non-negative Matrix Factorization (NMF) has the strong ability to learn parts-based representations, recently, a number of algorithms based on NMF have been proposed for image annotation and have achieved good performance. However, most of the efforts have focused on the representations of images and annotations. The properties of the semantic parts have not been well studied. In this article, we revisit the sparseness-constrained NMF (sNMF) proposed by Hoyer [2004]. By endowing the sparseness constraint with a geometric interpretation and sNMF with theoretical analyses of the generalization ability, we show that NMF with such a sparseness constraint has three advantages for image annotation tasks: (i) The sparseness constraint is more ℓ0-norm oriented than the ℓ1-norm-based sparseness, which significantly enhances the ability of NMF to robustly learn semantic parts. (ii) The sparseness constraint has a large cone interpretation and thus allows the reconstruction error of NMF to be smaller, which means that the learned semantic parts are more powerful to represent images for tagging. (iii) The learned semantic parts are less correlated, wh ich increases the discriminative ability for annotating images. Moreover, we present a new efficient large sparse cone NMF (LsCNMF) algorithm to optimize the sNMF problem by employing the Nesterov's optimal gradient method. We conducted experiments on the PASCAL VOC07 dataset and demonstrated the effectiveness of LsCNMF for image annotation.
Tian, D. & Tao, D. 2017, 'Global Hashing System for Fast Image Search', IEEE Transactions on Image Processing, vol. 26, no. 1, pp. 49-89.
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© 1992-2012 IEEE. Hashing methods have been widely investigated for fast approximate nearest neighbor searching in large data sets. Most existing methods use binary vectors in lower dimensional spaces to represent data points that are usually real vectors of higher dimensionality. We divide the hashing process into two steps. Data points are first embedded in a low-dimensional space, and the global positioning system method is subsequently introduced but modified for binary embedding. We devise dataindependent and data-dependent methods to distribute the satellites at appropriate locations. Our methods are based on finding the tradeoff between the information losses in these two steps. Experiments show that our data-dependent method outperforms other methods in different-sized data sets from 100k to 10M. By incorporating the orthogonality of the code matrix, both our data-independent and data-dependent methods are particularly impressive in experiments on longer bits.
Usman, M., Jan, M.A. & He, X.S. 2017, 'Cryptography-Based Secure Data Storage and Sharing Using HEVC and Public Clouds', Information Sciences, vol. 387, pp. 90-102.
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Mobile devices are widely used for uploading/downloading media files such as audio, video and images to/from the remote servers. These devices have limited resources and are required to offload resource-consuming media processing tasks to the clouds for further processing. Migration of these tasks means that the media services provided by the clouds need to be authentic and trusted by the mobile users. The existing schemes for secure exchange of media files between the mobile devices and the clouds have limitations in terms of memory support, processing load, battery power, and data size. These schemes lack the support for large-sized video files and are not suitable for resource-constrained mobile devices. This paper proposes a secure, lightweight, robust and efficient scheme for data exchange between the mobile users and the media clouds. The proposed scheme considers High Efficiency Video Coding (HEVC) Intra-encoded video streams in unsliced mode as a source for data hiding. Our proposed scheme aims to support real-time processing with power-saving constraint in mind. Advanced Encryption Standard (AES) is used as a base encryption technique by our proposed scheme. The simulation results clearly show that the proposed scheme outperforms AES-256 by decreasing the processing time up to 4.76% and increasing the data size up to 0.72% approximately. The proposed scheme can readily be applied to real-time cloud media streaming.
Virdun, C., Luckett, T., Lorenz, K., Davidson, P.M. & Phillips, J. 2017, 'Dying in the hospital setting: A meta-synthesis identifying the elements of end-of-life care that patients and their families describe as being important.', Palliat Med, vol. 31, no. 7, pp. 587-601.
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BACKGROUND: Despite most expected deaths occurring in hospital, optimal end-of-life care is not available for all in this setting. AIM: To gain a richer and deeper understanding of elements of end-of-life care that consumers consider most important within the hospital setting. DESIGN: A meta-synthesis. DATA SOURCES: A systematic search of Academic Search Complete, AMED, CINAHL, MEDLINE, EMBASE, PsycINFO, PubMed, Google, Google Scholar and CareSearch for qualitative studies published between 1990 and April 2015 reporting statements by consumers regarding important elements of end-of-life hospital care. Study quality was appraised by two independent researchers using an established checklist. A three-stage synthesis approach focusing on consumer quotes, rather than primary author themes, was adopted for this review. RESULTS: Of 1922 articles, 16 met the inclusion criteria providing patient and family data for analysis. Synthesis yielded 7 patient and 10 family themes including 6 common themes: (1) expert care, (2) effective communication and shared decision-making, (3) respectful and compassionate care, (4) adequate environment for care, (5) family involvement and (6) financial affairs. Maintenance of sense of self was the additional patient theme, while the four additional family themes were as follows: (1) maintenance of patient safety, (2) preparation for death, (3) care extending to the family after patient death and (4) enabling patient choice at the end of life. CONCLUSION: Consumer narratives help to provide a clearer direction as to what is important for hospital end-of-life care. Systems are needed to enable optimal end-of-life care, in accordance with consumer priorities, and embedded into routine hospital care.
Walczak, A., Butow, P.N., Tattersall, M.H.N., Davidson, P.M., Young, J., Epstein, R.M., Costa, D.S.J. & Clayton, J.M. 2017, 'Encouraging early discussion of life expectancy and end-of-life care: A randomised controlled trial of a nurse-led communication support program for patients and caregivers', International Journal of Nursing Studies, vol. 67, pp. 31-40.
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© 2016 Elsevier Ltd Background Patients are often not given the information needed to understand their prognosis and make informed treatment choices, with many consequently experiencing less than optimal care and quality-of-life at end-of-life. Objectives To evaluate the efficacy of a nurse-facilitated communication support program for patients with advanced, incurable cancer to assist them in discussing prognosis and end-of-life care. Design A parallel-group randomised controlled trial design was used. Settings This trial was conducted at six cancer treatment centres affiliated with major hospitals in Sydney, Australia. Participants 110 patients with advanced, incurable cancer participated. Methods The communication support program included guided exploration of a question prompt list, communication challenges, patient values and concerns and the value of discussing end-of-life care early, with oncologists cued to endorse question-asking and question prompt list use. Patients were randomised after baseline measure completion, a regular oncology consultation was audio-recorded and a follow-up questionnaire was completed one month later. Communication, health-related quality-of-life and satisfaction measures and a manualised consultation-coding scheme were used. Descriptive, Mixed Modelling and Generalised Linear Mixed Modelling analyses were conducted using SPSS version 22. Results Communication support program recipients gave significantly more cues for discussion of prognosis, end-of-life care, future care options and general issues not targeted by the intervention during recorded consultations, but did not ask more questions about these issues or overall. Oncologists’ question prompt list and question asking endorsement was inconsistent. Communication support program recipients’ self-efficacy in knowing what questions to ask their doctor significantly improved at follow-up while control arm patients’ self-efficacy declined. The communication support program d...
Wang, H., Wu, J., Pan, S., Zhang, P. & Chen, L. 2017, 'Towards large-scale social networks with online diffusion provenance detection', Computer Networks, vol. 114, pp. 154-166.
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© 2016 Elsevier B.V.In this paper we study a new problem of online discovering diffusion provenances in large networks. Existing work on network diffusion provenance identification focuses on offline learning where data collected from network detectors are static and a snapshot of the network is available before learning. However, an offline learning model does not meet the need for early warning, real-time awareness, or a real-time response to malicious information spreading in networks. To this end, we propose an online regression model for real-time diffusion provenance identification. Specifically, we first use offline collected network cascades to infer the edge transmission weights, and then use an online l 1 non-convex regression model as the identification model. The proposed methods are empirically evaluated on both synthetic and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.
Wang, H., Zhang, P., Zhu, X., Tsang, I.W.H., Chen, L., Zhang, C. & Wu, X. 2017, 'Incremental Subgraph Feature Selection for Graph Classification', IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 1, pp. 128-142.
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Wang, J.J.J., Bartlett, M. & Ryan, L. 2017, 'Non-ignorable missingness in logistic regression.', Stat Med, vol. 36, no. 19, pp. 3005-3021.
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Nonresponses and missing data are common in observational studies. Ignoring or inadequately handling missing data may lead to biased parameter estimation, incorrect standard errors and, as a consequence, incorrect statistical inference and conclusions. We present a strategy for modelling non-ignorable missingness where the probability of nonresponse depends on the outcome. Using a simple case of logistic regression, we quantify the bias in regression estimates and show the observed likelihood is non-identifiable under non-ignorable missing data mechanism. We then adopt a selection model factorisation of the joint distribution as the basis for a sensitivity analysis to study changes in estimated parameters and the robustness of study conclusions against different assumptions. A Bayesian framework for model estimation is used as it provides a flexible approach for incorporating different missing data assumptions and conducting sensitivity analysis. Using simulated data, we explore the performance of the Bayesian selection model in correcting for bias in a logistic regression. We then implement our strategy using survey data from the 45 and Up Study to investigate factors associated with worsening health from the baseline to follow-up survey. Our findings have practical implications for the use of the 45 and Up Study data to answer important research questions relating to health and quality-of-life. Copyright © 2017 John Wiley & Sons, Ltd.
Wang, J.J.J., Bartlett, M. & Ryan, L. 2017, 'On the impact of nonresponse in logistic regression: application to the 45 and Up study.', BMC Med Res Methodol, vol. 17, no. 1, p. 80.
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BACKGROUND: In longitudinal studies, nonresponse to follow-up surveys poses a major threat to validity, interpretability and generalisation of results. The problem of nonresponse is further complicated by the possibility that nonresponse may depend on the outcome of interest. We identified sociodemographic, general health and wellbeing characteristics associated with nonresponse to the follow-up questionnaire and assessed the extent and effect of nonresponse on statistical inference in a large-scale population cohort study. METHODS: We obtained the data from the baseline and first wave of the follow-up survey of the 45 and Up Study. Of those who were invited to participate in the follow-up survey, 65.2% responded. Logistic regression model was used to identify baseline characteristics associated with follow-up response. A Bayesian selection model approach with sensitivity analysis was implemented to model nonignorable nonresponse. RESULTS: Characteristics associated with a higher likelihood of responding to the follow-up survey include female gender, age categories 55-74, high educational qualification, married/de facto, worked part or partially or fully retired and higher household income. Parameter estimates and conclusions are generally consistent across different assumptions on the missing data mechanism. However, we observed some sensitivity for variables that are strong predictors for both the outcome and nonresponse. CONCLUSIONS: Results indicated in the context of the binary outcome under study, nonresponse did not result in substantial bias and did not alter the interpretation of results in general. Conclusions were still largely robust under nonignorable missing data mechanism. Use of a Bayesian selection model is recommended as a useful strategy for assessing potential sensitivity of results to missing data.
Wang, R., Liu, T. & Tao, D. 2017, 'Multiclass Learning With Partially Corrupted Labels', IEEE Transactions on Neural Networks and Learning Systems.
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Traditional classification systems rely heavily on sufficient training data with accurate labels. However, the quality of the collected data depends on the labelers, among which inexperienced labelers may exist and produce unexpected labels that may degrade the performance of a learning system. In this paper, we investigate the multiclass classification problem where a certain amount of training examples are randomly labeled. Specifically, we show that this issue can be formulated as a label noise problem. To perform multiclass classification, we employ the widely used importance reweighting strategy to enable the learning on noisy data to more closely reflect the results on noise-free data. We illustrate the applicability of this strategy to any surrogate loss functions and to different classification settings. The proportion of randomly labeled examples is proved to be upper bounded and can be estimated under a mild condition. The convergence analysis ensures the consistency of the learned classifier to the optimal classifier with respect to clean data. Two instantiations of the proposed strategy are also introduced. Experiments on synthetic and real data verify that our approach yields improvements over the traditional classifiers as well as the robust classifiers. Moreover, we empirically demonstrate that the proposed strategy is effective even on asymmetrically noisy data.
Wang, W., Yin, H., Chen, L., Sun, Y., Sadiq, S. & Zhou, X. 2017, 'ST-SAGE: A spatial-Temporal sparse additive generative model for spatial item recommendation', ACM Transactions on Intelligent Systems and Technology, vol. 8, no. 3.
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©c 2017 ACM 2157-6904/2017/04-ART48 15.00. With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important mobile application, especially when users travel away from home. However, this type of recommendation is very challenging compared to traditional recommender systems. A user may visit only a limited number of spatial items, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place, as most of the items visited by a user are usually located within a short distance from the user's home. Moreover, user interests and behavior patterns may vary dramatically across different time and geographical regions. In light of this, we propose ST-SAGE, a spatial-Temporal sparse additive generative model for spatial item recommendation in this article. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns and content of spatial items. To further alleviate the data-sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd's preferences over a well-designed spatial index structure called the spatial pyramid. To speed up the training process of ST-SAGE, we implement a parallel version of themodel inference algorithm on the GraphLab framework.We conduct extensive experiments; the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-The-Art recommender systems in terms of recommendation effectiveness, model training efficiency, and online recommendation efficiency.
Wei, L., Xiong, X., Zhang, W., He, X.T. & Zhang, Y. 2017, 'The effect of genetic algorithm learning with a classifier system in limit order markets', Engineering Applications of Artificial Intelligence.
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Wu, J., Pan, S., Zhu, X., Zhang, C. & Wu, X. 2017, 'Positive and Unlabeled Multi-Graph Learning', IEEE Transactions on Cybernetics, vol. 47, no. 4, pp. 818-829.
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In this paper, we advance graph classification to handle multi-graph learning for complicated objects, where each object is represented as a bag of graphs and the label is only available to each bag but not individual graphs. In addition, when training classifiers, users are only given a handful of positive bags and many unlabeled bags, and the learning objective is to train models to classify previously unseen graph bags with maximum accuracy. To achieve the goal, we propose a positive and unlabeled multi-graph learning (puMGL) framework to first select informative subgraphs to convert graphs into a feature space. To utilize unlabeled bags for learning, puMGL assigns a confidence weight to each bag and dynamically adjusts its weight value to select “reliable negative bags.” A number of representative graphs, selected from positive bags and identified reliable negative graph bags, form a “margin graph pool” which serves as the base for deriving subgraph patterns, training graph classifiers, and further updating the bag weight values. A closed-loop iterative process helps discover optimal subgraphs from positive and unlabeled graph bags for learning. Experimental comparisons demonstrate the performance of puMGL for classifying real-world complicated objects.
Wu, Z., Lei, L., Li, G., Huang, H., Zheng, C., Chen, E. & Xu, G. 2017, 'A topic modeling based approach to novel document automatic summarization', Expert Systems with Applications, vol. 84, pp. 12-23.
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Wu, Z., Zhu, H., Li, G., Cui, Z., Huang, H., Li, J., Chen, E. & Xu, G. 2017, 'An efficient Wikipedia semantic matching approach to text document classification', Information Sciences, vol. 393, pp. 15-28.
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© 2017 Elsevier Inc. A traditional classification approach based on keyword matching represents each text document as a set of keywords, without considering the semantic information, ther eby, reducing the accuracy of classification. To solve this problem, a new classification approach based on Wikipedia matching was proposed, which represents each document as a concept vector in the Wikipedia semantic space so as to understand the text semantics, and has been demonstrated to improve the accuracy of classification. However, the immense Wikipedia semantic space greatly reduces the generation efficiency of a concept vector, resulting in a negative impact on the availability of the approach in an online environment. In this paper, we propose an efficient Wikipedia semantic matching approach to document classification. First, we define several heuristic selection rules to quickly pick out related concepts for a document from the Wikipedia semantic space, making it no longer necessary to match all the concepts in the semantic space, thus greatly improving the generation efficiency of the concept vector. Second, based on the semantic representation of each text document, we compute the similarity between documents so as to accurately classify the documents. Finally, evaluation experiments demonstrate the effectiveness of our approach, i.e., which can improve the classification efficiency of the Wikipedia matching under the precondition of not compromising the classification accuracy.
Xiong, W., Zhang, L., Du, B. & Tao, D. 2017, 'Combining local and global: Rich and robust feature pooling for visual recognition', Pattern Recognition, vol. 62, pp. 225-235.
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© 2016 Elsevier Ltd The human visual system proves expert in discovering patterns in both global and local feature space. Can we design a similar way for unsupervised feature learning? In this paper, we propose a novel spatial pooling method within an unsupervised feature learning framework, named Rich and Robust Feature Pooling (R 2 FP), to better extract rich and robust representation from sparse feature maps learned from the raw data. Both local and global pooling strategies are further considered to instantiate such a method. The former selects the most representative features in the sub-region and summarizes the joint distribution of the selected features, while the latter is utilized to extract multiple resolutions of features and fuse the features with a feature balance kernel for rich representation. Extensive experiments on several image recognition tasks demonstrate the superiority of the proposed method.
Xu, G., Hu, L., Cao, L. & Cao, J. 2017, 'Improving the Quality of Recommendations for Users and Items in the Tail of Distribution', ACM Transactions on Information Systems, vol. 35, no. 3.
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Xu, Z., Tao, D., Huang, S. & Zhang, Y. 2017, 'Friend or Foe: Fine-Grained Categorization with Weak Supervision', IEEE Transactions on Image Processing, vol. 26, no. 1, pp. 135-146.
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© 2016 IEEE. Multi-instance learning (MIL) is widely acknowledged as a fundamental method to solve weakly supervised problems. While MIL is usually effective in standard weakly supervised object recognition tasks, in this paper, we investigate the applicability of MIL on an extreme case of weakly supervised learning on the task of fine-grained visual categorization, in which intra-class variance could be larger than inter-class due to the subtle differences between su bordinate categories. For this challenging task, we propose a new method that generalizes the standard multi-instance learning framework, for which a novel multi-task co-localization algorithm is proposed to take advantage of the relationship among fine-grained categories and meanwhile performs as an effective initialization strategy for the non-convex multi-instance objective. The localization results also enable object-level domain-specific fine-tuning of deep neural networks, which significantly boosts the performance. Experimental results on three fine-grained datasets reveal the effectiveness of the proposed method, especially the importance of exploiting inter-class relationships between object categories in weakly supervised fine-grained recognition.
Yao, Y., Zhang, J., Shen, F., Hua, X., Xu, J. & Tang, Z. 2017, 'A new web-supervised method for image dataset constructions', Neurocomputing, vol. 236, pp. 23-31.
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© 2017.The goal of this work is to automatically collect a large number of highly relevant natural images from Internet for given queries. A novel automatic image dataset construction framework is proposed by employing multiple query expansions. In specific, the given queries are first expanded by searching in the Google Books Ngrams Corpora to obtain a richer semantic descriptions, from which the visually non-salient and less relevant expansions are then filtered. After retrieving images from the Internet with filtered expansions, we further filter noisy images by clustering and progressively Convolutional Neural Networks (CNN) based methods. To evaluate the performance of our proposed method for image dataset construction, we build an image dataset with 10 categories. We then run object detections on our image dataset with three other image datasets which were constructed by weak supervised, web supervised and full supervised learning, the experimental results indicated the effectiveness of our method is superior to weak supervised and web supervised state-of-the-art methods. In addition, we do a cross-dataset classification to evaluate the performance of our dataset with two publically available manual labelled dataset STL-10 and CIFAR-10.
Yao, Y., Zhang, J., Shen, F., Hua, X., Xu, J. & Tang, Z. 2017, 'Exploiting Web Images for Dataset Construction: A Domain Robust Approach', IEEE Transactions on Multimedia, vol. 19, no. 8, pp. 1771-1784.
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Yu, J., Yang, X., Gao, F. & Tao, D. 2017, 'Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking', IEEE Transactions on Cybernetics.
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How do we retrieve images accurately? Also, how do we rank a group of images precisely and efficiently for specific queries? These problems are critical for researchers and engineers to generate a novel image searching engine. First, it is important to obtain an appropriate description that effectively represent the images. In this paper, multimodal features are considered for describing images. The images unique properties are reflected by visual features, which are correlated to each other. However, semantic gaps always exist between images visual features and semantics. Therefore, we utilize click feature to reduce the semantic gap. The second key issue is learning an appropriate distance metric to combine these multimodal features. This paper develops a novel deep multimodal distance metric learning (Deep-MDML) method. A structured ranking model is adopted to utilize both visual and click features in distance metric learning (DML). Specifically, images and their related ranking results are first collected to form the training set. Multimodal features, including click and visual features, are collected with these images. Next, a group of autoencoders is applied to obtain initially a distance metric in different visual spaces, and an MDML method is used to assign optimal weights for different modalities. Next, we conduct alternating optimization to train the ranking model, which is used for the ranking of new queries with click features. Compared with existing image ranking methods, the proposed method adopts a new ranking model to use multimodal features, including click features and visual features in DML. We operated experiments to analyze the proposed Deep-MDML in two benchmark data sets, and the results validate the effects of the method.
Zeng, K., Yu, J., Wang, R., Li, C. & Tao, D. 2017, 'Coupled Deep Autoencoder for Single Image Super-Resolution', IEEE Transactions on Cybernetics, vol. 47, no. 1, pp. 27-37.
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Sparse coding has been widely applied to learning-based single image super-resolution (SR) and has obtained promising performance by jointly learning effective representations for low-resolution (LR) and high-resolution (HR) image patch pairs. However, the resulting HR images often suffer from ringing, jaggy, and blurring artifacts due to the strong yet ad hoc assumptions that the LR image patch representation is equal to, is linear with, lies on a manifold similar to, or has the same support set as the corresponding HR image patch representation. Motivated by the success of deep learning, we develop a data-driven model coupled deep autoencoder (CDA) for single image SR. CDA is based on a new deep architecture and has high representational capability. CDA simultaneously learns the intrinsic representations of LR and HR image patches and a big-data-driven function that precisely maps these LR representations to their corresponding HR representations. Extensive experimentation demonstrates the superior effectiveness and efficiency of CDA for single image SR compared to other state-of-the-art methods on Set5 and Set14 datasets.
Zhang, C., Du, L. & Tao, D. 2017, 'LSV-based tail inequalities for sums of random matrices', Neural Computation, vol. 29, no. 1, pp. 247-262.
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© 2016 Massachusetts Institute of Technology. The techniques of random matrices have played an important role in many machine learning models. In this letter, we present a new method to study the tail inequalities for sums of random matrices. Different from other work (Ahlswede & Winter, 2002; Tropp, 2012; Hsu, Kakade, & Zhang, 2012), our tail results are based on the largest singular value (LSV) and independent of the matrix dimension. Since the LSV operation and the expectation are noncommutative, we introduce a diagonalization method to convert the LSV operation into the trace operation of an infinitely dimensional diagonal matrix. In thisway,we obtain another version of Laplace-transform bounds and then achieve the LSV-based tail inequalities for sums of random matrices.
Zhang, K., Tao, D., Gao, X., Li, X. & Li, J. 2017, 'Coarse-to-Fine Learning for Single-Image Super-Resolution', IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 5, pp. 1109-1122.
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This paper develops a coarse-to-fine framework for single-image super-resolution (SR) reconstruction. The coarse-to-fine approach achieves high-quality SR recovery based on the complementary properties of both example learning- and reconstruction-based algorithms: example learning-based SR approaches are useful for generating plausible details from external exemplars but poor at suppressing aliasing artifacts, while reconstruction-based SR methods are propitious for preserving sharp edges yet fail to generate fine details. In the coarse stage of the method, we use a set of simple yet effective mapping functions, learned via correlative neighbor regression of grouped low-resolution (LR) to high-resolution (HR) dictionary atoms, to synthesize an initial SR estimate with particularly low computational cost. In the fine stage, we devise an effective regularization term that seamlessly integrates the properties of local structural regularity, nonlocal self-similarity, and collaborative representation over relevant atoms in a learned HR dictionary, to further improve the visual quality of the initial SR estimation obtained in the coarse stage. The experimental results indicate that our method outperforms other state-of-the-art methods for producing high-quality images despite that both the initial SR estimation and the followed enhancement are cheap to implement.
Zhang, S., Lan, X., Yao, H., Zhou, H., Tao, D. & Li, X. 2017, 'A Biologically Inspired Appearance Model for Robust Visual Tracking', IEEE Transactions on Neural Networks and Learning Systems.
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In this paper, we propose a biologically inspired appearance model for robust visual tracking. Motivated in part by the success of the hierarchical organization of the primary visual cortex (area V1), we establish an architecture consisting of five layers: whitening, rectification, normalization, coding, and pooling. The first three layers stem from the models developed for object recognition. In this paper, our attention focuses on the coding and pooling layers. In particular, we use a discriminative sparse coding method in the coding layer along with spatial pyramid representation in the pooling layer, which makes it easier to distinguish the target to be tracked from its background in the presence of appearance variations. An extensive experimental study shows that the proposed method has higher tracking accuracy than several state-of-the-art trackers.
Zhang, T., Jia, W., He, X.S. & Yang, J. 2017, 'Discriminative Dictionary Learning with Motion Weber Local Descriptor for Violence Detection', IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no. 3, pp. 696-709.
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Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in developing an algorithm that can detect violence in surveillance videos with high performance. In this paper, following our recently proposed idea of motion Weber local descriptor (WLD), we make two major improvements and propose a more effective and efficient algorithm for detecting violence from motion images. First, we propose an improved WLD (IWLD) to better depict low-level image appearance information, and then extend the spatial descriptor IWLD by adding a temporal component to capture local motion information and hence form the motion IWLD. Second, we propose a modified sparse-representation-based classification model to both control the reconstruction error of coding coefficients and minimize the classification error. Based on the proposed sparse model, a class-specific dictionary containing dictionary atoms corresponding to the class labels is learned using class labels of training samples. With this learned dictionary, not only the representation residual but also the representation coefficients become discriminative. A classification scheme integrating the modified sparse model is developed to exploit such discriminative information. The experimental results on three benchmark data sets have demonstrated the superior performance of the proposed approach over the state of the arts.
Zhang, T., Jia, W., Yang, B., Yang, J., He, X. & Zheng, Z. 2017, 'MoWLD: a robust motion image descriptor for violence detection', Multimedia Tools and Applications, vol. 76, no. 1, pp. 1419-1438.
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© 2015 Springer Science+Business Media New York Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in designing an algorithm that can detect violence in surveillance videos with high performance. Existing methods typically apply the Bag-of-Words (BoW) model on local spatiotemporal descriptors. However, traditional spatiotemporal features are not discriminative enough, and also the BoW model roughly assigns each feature vector to only one visual word and therefore ignores the spatial relationships among the features. To tackle these problems, in this paper we propose a novel Motion Weber Local Descriptor (MoWLD) in the spirit of the well-known WLD and make it a powerful and robust descriptor for motion images. We extend the WLD spatial descriptions by adding a temporal component to the appearance descriptor, which implicitly captures local motion information as well as low-level image appear information. To eliminate redundant and irrelevant features, the non-parametric Kernel Density Estimation (KDE) is employed on the MoWLD descriptor. In order to obtain more discriminative features, we adopt the sparse coding and max pooling scheme to further process the selected MoWLDs. Experimental results on three benchmark datasets have demonstrated the superiority of the proposed approach over the state-of-the-arts.
Zhang, Y., Su, S., Savkin, A., Celler, B. & Nguyen, H. 2017, 'Multiloop Integral Controllability Analysis for Nonlinear Multiple-Input Single-Output Processes', Industrial and Engineering Chemistry Research, vol. 56, no. 28, pp. 8054-8065.
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© 2017 American Chemical Society. The decentralized integral controllability (DIC) for linear/nonlinear square processes implies the existence of stable decentralized controllers with integral actions capable of achieving offset-free control and detuning any subset of the control loops independently. However, the current version of the DIC cannot be directly applied to nonsquare processes specifically for multiple-input single-output (MISO) processes. This paper presents the new definition and theorem of multiloop integral controllability (MIC) to nonlinear MISO processes, and proposes the sufficient MIC conditions in order for such processes to guarantee decentralized unconditional stability under contro l loop failure as well as to achieve offset-free tracking performance. Two examples, the quadruple-tank system (model based) and the temperature control system, are modified as two-input single-output (TISO) plants and given to quantitatively interpret the effectiveness of the proposed MIC analysis where the desirable performance of both applications can be obtained.
Zhao, L., Chen, Q., Li, W., Jiang, P., Wong, L. & Li, J. 2017, 'MapReduce for accurate error correction of next-generation sequencing data.', Bioinformatics.
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Motivation: Next-generation sequencing platforms have produced huge amounts of sequence data. This is revolutionizing every aspect of genetic and genomic research. However, these sequence datasets contain quite a number of machine-induced errors-e.g. errors due to substitution can be as high as 2.5%. Existing error-correction methods are still far from perfect. In fact, more errors are sometimes introduced than correct corrections, especially by the prevalent k-mer based methods. The existing methods have also made limited exploitation of on-demand cloud computing. Results: We introduce an error-correction method named MEC, which uses a two-layered MapReduce technique to achieve high correction performance. In the first layer, all the input sequences are mapped to groups to identify candidate erroneous bases in parallel. In the second layer, the erroneous bases at the same position are linked together from all the groups for making statistically reliable corrections. Experiments on real and simulated datasets show that our method outperforms existing methods remarkably. Its per-position error rate is consistently the lowest, and the correction gain is always the highest. Availability and Implementation: The source code is available at bioinformatics.gxu.edu.cn/ngs/mec. Contacts: : wongls@comp.nus.edu.sg or jinyan.li@uts.edu.au. Supplementary information: Supplementary data are available at Bioinformatics online.
Zhao, Y., Di, H., Zhang, J., Lu, Y., Lv, F. & Li, Y. 2017, 'Region-based Mixture Models for human action recognition in low-resolution videos', Neurocomputing.
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© 2017.State-of-the-art performance in human action recognition is achieved by the use of dense trajectories which are extracted by optical flow algorithms. However, optical flow algorithms are far from perfect in low-resolution (LR) videos. In addition, the spatial and temporal layout of features is a powerful cue for action discrimination. While, most existing methods encode the layout by previously segmenting body parts which is not feasible in LR videos. Addressing the problems, we adopt the Layered Elastic Motion Tracking (LEMT) method to extract a set of long-term motion trajectories and a long-term common shape from each video sequence, where the extracted trajectories are much denser than those of sparse interest points (SIPs); then we present a hybrid feature representation to integrate both of the shape and motion features; and finally we propose a Region-based Mixture Model (RMM) to be utilized for action classification. The RMM encodes the spatial layout of features without any needs of body parts segmentation. Experimental results show that the approach is effective and, more importantly, the approach is more general for LR recognition tasks.
Zuo, Y., Wu, Q., Zhang, J. & An, P. 2017, 'Explicit Edge Inconsistency Evaluation Model for Color-guided Depth Map Enhancement', IEEE Transactions on Circuits and Systems for Video Technology.
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Color-guided depth enhancement is to refine depth maps according to the assumption that the depth edges and the color edges at the corresponding locations are consistent. In the methods on such low-level vision task, Markov Random Fields (MRF) including its variants is one of major approaches, which has dominated this area for several years. However, the assumption above is not always true. To tackle the problem, the state-of-the-art solutions are to adjust the weighting coefficient inside the smoothness term of MRF model. These methods are lack of explicit evaluation model to quantitatively measure the inconsistency between the depth edge map and the color edge map, so it cannot adaptively control the efforts of the guidance from the color image for depth enhancement leading to various defects such as texture-copy artifacts and blurring depth edges. In this paper, we propose a quantitative measurement on such inconsistency and explicitly embed it into the smoothness term. The proposed method demonstrates the promising experimental results when compared with benchmark and the state-of-the-art methods on Middlebury datasets, ToF-Mark datasets and NYU datasets.
Zwar, N., Hermiz, O., Halcomb, E., Davidson, P. & Bodenheimer, T. 2017, 'Improving blood pressure control in general practice: A pilot study of the ImPress intervention', Australian Family Physician, vol. 46, no. 5, pp. 306-311.
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© The Royal Australian College of General Practitioners 2017. Background and objectives Patients with hypertension and at high absolute cardiovascular disease risk are a priority group for improved blood pressure control. This study examined the impact of an intervention, primarily delivered by the general practice nurse, to identify, recall and manage patients with uncontrolled hypertension who are at high risk of cardiovascular disease. Methods A before-and-after pilot study with a six-month follow-up period was conducted in eight general practices in Sydney, Australia. Results From 507 patients identified, 82 (16.2%) attended an assessment visit, were eligible and provided baseline data. Of these, 55 (67.1%) completed the six-month follow-up. The mean decrease in blood pressure was 14.5 mmHg systolic and 7 mmHg diastolic. Significant decreases were also found in mean weight (1.3 kg), body mass index (0.5 kg/m 2 ) and waist circumference (1.9 cm). Adherence to blood pressure treatment, as measured by the Hill-Bone scale, significantly improved (P = 0.01) Discussion The results of this study justify further investigation in a randomised trial. If effective, the approach could alter the way hypertension care is organised and delivered in Australian general practice.
Agrawal, S. & Williams, M.A. 2017, 'Robot authority and human obedience: A study of human behaviour using a robot security guard', ACM/IEEE International Conference on Human-Robot Interaction, pp. 57-58.
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© 2017 Authors. There has been much debate, sci-fi movie scenes, and several scientific studies exploring the concept of robot authority. Some of the key research questions include: when should humans follow/question robot instructions; how can a robot increase its ability to convince humans to follow their instructions or to change their behaviour. In this paper, we describe a recent experiment designed to explore the notions of robot authority and human obedience. We set up a robot in a publicly accessible building to act as a security guard that issued instructions to specific humans. We identified and analysed the factors that affected a human's decisions to follow the robot's instruction. The four key factors were: perceived aggression, responsiveness, anthropomorphism, level of safety and intelligence in the robot's behaviour. We implemented various social cues to exhibit and convey authority and aggressiveness in the robot's behaviour. The results suggest that the degree of aggression that different people perceived in the robot's behaviour did not have a significant impact in their decision to follow the robot's instruction. Although, the people who disobeyed the robot, perceived the robot's behaviour to be more unsafe and less human-like than the people who followed the robot's instructions and also found the robot to be more responsive.
aldebei, K., Farhood, H., Jia, W., Nanda, P. & He, X. 2017, 'Sequential and Unsupervised Document Authorial Clustering Based on Hidden Markov Model', Trustcom/BigDataSE/ICESS.2017, IEEE TrustCom2017, Sydney.
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Document clustering groups documents of certain
similar characteristics in one cluster. Document clustering has
shown advantages on organization, retrieval, navigation and
summarization of a huge amount of text documents on Internet.
This paper presents a novel, unsupervised approach for clustering
single-author documents into groups based on authorship. The
key novelty is that we propose to extract contextual correlations
to depict the writing style hidden among sentences of each
document for clustering the documents. For this purpose, we
build an Hidden Markov Model (HMM) for representing the
relations of sequential sentences, and a two-level, unsupervised
framework is constructed. Our proposed approach is evaluated
on four benchmark datasets, widely used for document authorship
analysis. A scientific paper is also used to demonstrate the
performance of the approach on clustering short segments of
a text into authorial components. Experimental results show
that the proposed approach outperforms the state-of-the-art
approaches.
Alkalbani, A.M., Gadhvi, L., Patel, B., Hussain, F.K., Ghamry, A.M. & Hussain, O.K. 2017, 'Analysing cloud services reviews using opining mining', Proceedings - International Conference on Advanced Information Networking and Applications, AINA, pp. 1124-1129.
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© 2017 IEEE. There is increasing interest in sharing the experience of products and services on the web platform, and social media has opened a way for product and service providers to understand their consumers needs and expectations. This paper explores reviews by cloud consumers that reflect consumers experiences with cloud services. The reviews of around 6,000 cloud service users were analysed using sentiment analysis to identify the attitude of each review, and to determine whether the opinion expressed was positive, negative, or neutral. The analysis used two data mining tools, KNIME and RapidMiner, and the results were compared. We developed four prediction models in this study to predict the sentiment of users reviews. The proposed model is based on four supervised machine learning algorithms: K-Nearest Neighbour (k-NN), Nave Bayes, Random Tree, and Random Forest. The results show that the Random Forest predictions achieve 97.06% accuracy, which makes this model a better prediction model than the other three.
Candra, H., Yuwono, M., Chai, R., Nguyen, H.T. & Su, S. 2017, 'EEG Emotion Recognition using Reduced Channel Wavelet Entropy and Average Wavelet Coefficient Features with Normal Mutual Information Method', Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), IEEE, Jeju Island, Korea, pp. 463-466.
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Recognizing emotion from EEG signals is a complicated
task that requires complex features and a substantial
number of EEG channels. Simple algorithms to analyse the
feature and reduce the EEG channel number will give an
indispensable advantages. Therefore, this study explores a
combination of wavelet entropy and average wavelet coefficient
(WEAVE) as a potential EEG-emotion feature to classify
valence and arousal emotions with the advantage of the ability
to identify the occurrence of a pattern while at the same time
identify the shape of a pattern in EEG emotion signal. The
complexity of the feature was reduced using the Normalized
Mutual Information (NMI) method to obtain a reduced number
of channels. Classification with the WEAVE feature achieved
76.8% accuracy for valence and 74.3% for arousal emotion,
respectively. The analysis with NMI shows that the WEAVE
feature has linear characteristics and offers possibilities to
reduce the EEG channels to a certain number. Further analysis
also reveals that detection of valence emotion with reduced
EEG channels has a different combination of EEG channels
compared to arousal emotion.
Chai, R., Naik, G.R., Ling, S.H., Tran, Y., Craig, A. & Nguyen, H.T. 2017, 'Channels Selection using Independent Component Analysis and Scalp Map Projection for EEG-based Driver Fatigue Classification', Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), IEEE, Jeju Island, Korea, pp. 1808-1811.
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This paper presents a classification of driver
fatigue with electroencephalography (EEG) channels selection
analysis. The system employs independent component analysis
(ICA) with scalp map back projection to select the dominant of
EEG channels. After channel selection, the features of the
selected EEG channels were extracted based on power spectral
density (PSD), and then classified using a Bayesian neural
network. The results of the ICA decomposition with the backprojected
scalp map and a threshold showed that the EEG
channels can be reduced from 32 channels into 16 dominants
channels involved in fatigue assessment as chosen channels,
which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8,
CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert
classification of the selected 16 channels yielded a sensitivity of
76.8%, specificity of 74.3% and an accuracy of 75.5%. Also,
the classification results of the selected 16 channels are
comparable to those using the original 32 channels. So, the
selected 16 channels is preferable for ergonomics improvement
of EEG-based fatigue classification system.
Chen, W., Liu, G., Su, S., Jiang, Q. & Nguyen, H.T. 2017, 'A CHF Detection Method based on Deep Learning with RR Intervals', Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), IEEE, Jeju Island, Korea, pp. 3369-3472.
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There are extensive studies investigating
congestive heart failure (CHF) detection based on heart rate
variability. Although a high level of accuracy has been achieved,
its robustness under different conditions is not guaranteed. To
improve the robustness, we applied sparse auto-encoder-based
deep learning algorithm in CHF detection with RR intervals. A
total data size of 30,592 (5-min RR interval) was obtained from
72 healthy persons and 44 CHF patients. The deep learning
algorithm first extracts unsupervised features using a sparse
auto-encoder from raw RR intervals, then constructs a deep
neural network model with various hidden nodes combinations.
Results showed that the model achieved 72.41% accuracy. This
demonstrated that RR intervals have potential in CHF detection
but cannot fully reflect dynamic change in 24-h.
Guo, K., Candra, H., Yu, H., Li, H., Nguyen, H.T. & Su, S. 2017, 'EEG-based Emotion Classification using Innovative Features and Combined SVM and HMM Classifier', Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), IEEE, Jeju Island, Korea, pp. 489-492.
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Emotion classification is one of the state-of-theart
topics in biomedical signal research, and yet a significant
portion remains unknown. This paper offers a novel approach
with a combined classifier to recognise human emotion states
based on electroencephalogram (EEG) signal. The objective is
to achieve high accuracy using the combined classifier designed,
which categorises the extracted features calculated from time
domain features and Discrete Wavelet Transform (DWT). Two
innovative designs are involved in this project: a novel variable
is established as a new feature and a combined SVM and
HMM classifier is developed. The result shows that the joined
features raise the accuracy by 5% on valence axis and 1.5% on
arousal axis. The combined classifier can improve the accuracy
by 3% comparing with SVM classifier. One of the important
applications for high accuracy emotion classification system is
offering a powerful tool for psychologists to diagnose emotion
related mental diseases and the system developed in this project
has the potential to serve such purpose.
Ho-Le, T.P., Center, J.R., Eisman, J.A., Nguyen, T.V. & Nguyen, H.T. 2017, 'Prediction of Hip Fracture in Post-menopausal Women using Artificial Neural Network Approach', Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), IEEE, Jeju Island, Korea, pp. 4207-4210.
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Hip fracture is one of the most serious health
problems among post-menopausal women with osteoporosis. It
is very difficult to predict hip fracture, because it is affected by
multiple risk factors. Existing statistical models for predicting
hip fracture risk yield area under the receiver operating
characteristic curve (AUC) ~0.7-0.85. In this study, we trained
an artificial neural network (ANN) to predict hip fracture in
one cohort, and validated its predictive performance in another
cohort. The data for training and validation included age, bone
mineral density (BMD), clinical factors, and lifestyle factors
which had been obtained from a longitudinal study that
involved 1167 women aged 60 years and above. The women had
been followed up for up to 10 years, and during the period, the
incidence of new hip fractures was ascertained. We applied
feed-forward neural networks to learn from the data, and then
used the learning for predicting hip fracture. Results of
prediction showed that the accuracy of model I (which included
only lumbar spine and femoral neck BMD) and model II
(which included non-BMD factors) was 82% and 84%,
respectively. When both BMD and non-BMD factors were
combined (Model III), the accuracy increased to 87%. The
AUC for model III was 0.94. These findings indicate that ANNs
are able to predict hip fracture more accurately than any
existing statistical models, and that ANNs can help stratify
individuals for clinical management.
Hussain, W., Farookh Hussain & Omar Hussain 2016, 'Risk Management Framework to Avoid SLA Violation in Cloud from a Provider’s Perspective', Advances on P2P, Parallel, Grid, Cloud and Internet Computing, Advances on P2P, Parallel, Grid, Cloud and Internet Computing, Springer International Publishing, Asan, Korea, pp. 233-241.
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Managing risk is an important issue for a service provider to avoid SLA violation in any business. The elastic nature of cloud allows consumers to use a number of resources depending on their business needs. Therefore, it is crucial for service providers; particularly SMEs to first form viable SLAs and then manage them. When a provider and a consumer execute an agreed SLA, the next step is monitoring and, if a violation is predicted, appropriate action should be taken to manage that risk. In this paper we propose a Risk Management Framework to avoid SLA violation (RMF-SLA) that assists cloud service providers to manage the risk of service violation. Our framework uses a Fuzzy Inference System (FIS) and considers inputs such as the reliability of a consumer; the attitude towards risk of the provider; and the predicted trajectory of consumer behavior to calculate the amount of risk and the appropriate action to manage it. The framework will help small-to-medium sized service providers manage the risk of service violation in an optimal way.
Jiahui, G., Bin, Y., Xu, G., Zhenglu, Y. & Jin-Mao, W. 2017, 'An Enhanced Convolutional Neural Network Model for Answer Selection', 26th International World Wide Web Conference, Perth, Western Australia.
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Answer selection is an important task in question answering (QA) from the Web. To address the intrinsic difficulty in encoding sentences with semantic meanings, we introduce a general framework, i.e., Lexical Semantic Feature based Skip Convolution Neural Network (LSF-SCNN), with several op- timization strategies. The intuitive idea is that the granular representations with more semantic features of sentences are deliberately designed and estimated to capture the similar- ity between question-answer pairwise sentences. The experi- mental results demonstrate the effectiveness of the proposed strategies and our model outperforms the state-of-the-art ones by up to 3.5% on the metrics of MAP and MRR.
Kim, J., Shim, K., Cao, L., Lee, J.G., Lin, X. & Moon, Y.S. 2017, 'Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia Conference, PAKDD 2017 Jeju, South Korea, May 23–26, 2017 Proceedings, Part I', Lecture Notes in Computer Science, Springer Verlag (Germany).
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Kim, J., Shim, K., Cao, L., Lee, J.G., Lin, X. & Moon, Y.S. 2017, 'Advances in knowledge discovery and data mining: 21st Pacific-Asia conference, PAKDD 2017 Jeju, South Korea, may 23–26, 2017 proceedings, part II', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
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Li, Q., Tan, Z., Jamdagni, A., Nanda, P., He, X. & Han, W. 2017, 'An Intrusion Detection System Based on Polynomial Feature Correlation Technique', 2017 IEEE Trustcom/BigDataSE/ICESS, 2017 IEEE Trustcom/BigDataSE/ICESS, IEEE Computer Society, Sydney, pp. 978-983.
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Li, Y. & Tao, D. 2017, 'Online Semi-Supervised Multi-Task Distance Metric Learning', IEEE International Conference on Data Mining Workshops, ICDMW, pp. 474-479.
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© 2016 IEEE. Given several related tasks, multi-Task learning can improve the performance of each task through sharing parameters or feature representations. In this paper, we apply multi-Task learning to a particular case of distance metric learning, in which we have a small amount of labeled data. Consider the effectiveness of semi-supervised learning handling few labeled machine learning problems, we integrate semi-supervised learning with multi-Task learning and distance metric learning. One of the defect of multi-Task learning is its low training efficiency, as we need all the training examples from all tasks to train a model. We propose an online learning algorithm to overcome this drawback of multi-Task learning. Experiments are conducted on one landmark multi-Task learning dataset to demonstrate the efficiency and effectiveness of our online semi-supervised multi-Task learning algorithm.
Li, Y., Tian, X. & Tao, D. 2017, 'Regularized large margin distance metric learning', Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 1015-1022.
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© 2016 IEEE. Distance metric learning plays an important role in many applications, such as classification and clustering. In this paper, we propose a novel distance metric learning using two hinge losses in the objective function. One is the constraint of the pairs which makes the similar pairs (the same label) closer and the dissimilar (different labels) pairs separated as far as possible. The other one is the constraint of the triplets which makes the largest distance between pairs intra the class larger than the smallest distance between pairs inter the classes. Previous works only consider one of the two kinds of constraints. Additionally, different from the triplets used in previous works, we just need a small amount of such special triplets. This improves the efficiency of our proposed method. Consider the situation in which we might not have enough labeled samples, we extend the proposed distance metric learning into a semi-supervised learning framework. Experiments are conducted on several landmark datasets and the results demonstrate the effectiveness of our proposed method.
Liu, L. & Tao, D. 2017, 'Sublinear dual coordinate ascent for regularized loss minimization', Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 1065-1070.
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© 2016 IEEE. We present a sublinear version of the dual coordinate ascent method for solving a group of regularized loss minimization problems in machine learning. The proposed method seamlessly integrates sampling techniques, the dual coordinate ascent method, and a multiplicative update algorithm. The sampling techniques choose the "expected" examples, and estimate the corresponding inner products. The dual coordinate ascent method generates an updated iterative step, which outperforms the time-learning step used in the previous sublinear perceptron algorithm. The multiplicative update algorithm updates the example weighting. The proposed method is implemented with an iterative step of order O(log(n)), where n is the size of examples, and achieves a better result than other methods, with high probability. We present a theoretical analysis of the sublinear iterative in order to justify its benefits. We then apply the proposed optimization method to support vector machine and conduct experiments on three large-scale datasets. Our experimental results validate our theoretical findings.
Liu, S., Pang, N., Xu, G. & Liu, H. 2017, 'Collaborative Filtering via Different Preference Structures', The 10th International Conference on Knowledge Science, Engineering and Management, Springer, Melbourne, Australia.
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Ly, Q.T., Handojoseno, A.M.A., Gilat, M., Chai, R., Marten, K.A.E., Georgiades, M., Naik, G.R., Tran, Y., Lewis, S.J.G. & Nguyen, H.T. 2017, 'Detection of Gait Initiation Failure in Parkinson’s Disease based on Wavelet Transform and Support Vector Machine', Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), IEEE, Jeju Island, Korea, pp. 3048-3051.
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Gait initiation Failure (GIF) is the situation in
which patients with Parkinson’s disease (PD) feel as if their feet
get “stuck” to the floor when initiating their first steps. GIF is a
subtype of Freezing of Gait (FOG) and often leads to falls and
related injuries. Understanding of neurobiological mechanisms
underlying GIF has been limited by difficulties in eliciting and
objectively characterizing such gait phenomena in the clinical
setting. Studies investigating the effects of GIF on brain activity
using EEG offer the potential to study such behavior. In this
preliminary study, we present a novel methodology where
wavelet transform was used for feature extraction and Support
Vector Machine for classifying GIF events in five patients with
PD and FOG. To deal with the large amount of EEG data, a
Principal Component Analysis (PCA) was applied to reduce the
data dimension from 15 EEG channels into 6 principal
components (PCs), retaining 93% of the information.
Independent Component Analysis using Entropy Bound
Minimization (ICA-EBM) was applied to 6 PCs for source
separation with the aim of improving detection ability of GIF
events as compared to the normal initiation of gait (Good
Starts). The results of this analysis demonstrated the correct
identification of GIF episodes with an 83.1% sensitivity, 89.5%
specificity and 86.3% accuracy. These results suggest that our
proposed methodology is a promising non-invasive approach to
improve GIF detection in PD and FOG.
Ly, Q.T., Handojoseno, A.M.A., Gilat, M., Chai, R., Martens, K.A.E., Georgiades, M., Naik, G.R., Tran, Y., Lewis, S.J.G. & Nguyen, H.T. 2017, 'Detection of Turning Freeze in Parkinson’s Disease based on S-Transform Decomposition of EEG Signals', Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), IEEE, Jeju Island, Korea, pp. 3044-3047.
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Freezing of Gait (FOG) is a highly debilitating
and poorly understood symptom of Parkinson’s disease (PD),
causing severe immobility and decreased quality of life.
Turning Freezing (TF) is known as the most common sub-type
of FOG, also causing the highest rate of falls in PD patients.
During a TF, the feet of PD patients appear to become stuck
whilst making a turn. This paper presents an
electroencephalography (EEG) based classification method for
detecting turning freezing episodes in six PD patients during
Timed Up and Go Task experiments. Since EEG signals have a
time-variant nature, time-frequency Stockwell Transform (STransform)
techniques were used for feature extraction. The
EEG sources were separated by means of independent
component analysis using entropy bound minimization (ICAEBM).
The distinctive frequency-based features of selected
independent components of EEG were extracted and classified
using Bayesian Neural Networks. The classification
demonstrated a high sensitivity of 84.2%, a specificity of 88.0%
and an accuracy of 86.2% for detecting TF. These promising
results pave the way for the development of a real-time device
for detecting different sub-types of FOG during ambulation.
Naik, G.R., Pratihast, M., Chai, R., Al-Ani, A., Acharyya, A. & Nguyen, H.T. 2017, 'Differences in Lower Limb Muscle Activation Patterns during Sit to Stand Task for Different Heel Heights', Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), IEEE, Jeju Island, Korea, pp. 2486-2489.
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The purpose of this study was to investigate
differences in lower limb muscle activation patterns for females
wearing shoes with different heel heights during Sit to Stand
Task (STS). Ten female participants with no prior history of
neurological disorders participated in this study. Surface
electromyography (sEMG) characteristics were recorded for
four different heel heights (ranging from 4cm to 10cm) while
performing the STS task. Signal processing analysis suggests
that muscle activities increases on elevated heel heights, which
may induce muscle imbalance for frequent STS tasks. In
addition, results of muscle utilisation (percentage) for different
heel heights suggest that lower limb muscles tend to compensate
in order to maintain postural balance.
Nanda, A., Nanda, P., He, X., Jamdagni, A. & Puthal, D. 2017, 'Secure-GLOR: An Adaptive Secure Routing Protocol for Dynamic Wireless Mesh Networks', 2017 IEEE Trustcom/BigDataSE/ICESS, 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-17), IEEE Computer Society, Sydney, pp. 269-276.
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Novianto, R. & Williams, M.A. 2017, 'Emotion in robot decision making', Advances in Intelligent Systems and Computing, pp. 221-232.
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© Springer international publishing switzerland 2017. Social robots are expected to behave in a socially acceptablemanner. They have to accommodate emotions in their decision-makings when dealing with people in social environments. In this paper, we present a novel emotion mechanism that influences decision making and behaviors through attention. We describe its implementation in a cognitive architecture and demonstrate its capability in a robot companion experiment. Results show that the robot can successfully bias its behaviors in order to make users happy. Our proposed emotion mechanism can be used in social robots to predict emotions and bias behaviors in order to improve their performances.
Ojha, S. & Williams, M.-.A. 2017, 'Emotional Appraisal : A Computational Perspective', Fifth Annual Conference on Advances in Cognitive Systems, Troy, USA.
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Research on computational modelling of emotions has received significant attention in the last few decades. As such, several computational models of emotions have been proposed which have provided an unprecedented insight into the implications of the emotion theories emerging from cognitive psychology studies. Yet the existing computational models of emotion have distinct limitations namely:(i) low replicability - difficult to implement the given computational model by reading the description of the model, (ii) domain dependence - model only applicable in one or more predefined scenarios or domains, (iii) low scalability and integrability - difficult to use the system in larger or different domains and difficult to integrate the model in wide range of other intelligent systems. In this paper, we propose a completely domain-independent mathematical representation for computational modelling of emotion that provides better replicability and integrability. The implementation of our model is inspired by appraisal theory - an emotion theory which assumes that emotions result from the cognitive evaluation of a situation.
Ojha, S., Vitale, J. & Williams, M.-.A. 2017, 'A Domain-Independent Approach of Cognitive Appraisal Augmented by Higher Cognitive Layer of Ethical Reasoning', Annual Meeting of the Cognitive Science Society, London, pp. 2833-2838.
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According to cognitive appraisal theory, emotion in an individual is the result of how a situation/event is evaluated by the individual. This evaluation has different outcomes among people and it is often suggested to be operationalised by a set of rules or beliefs acquired by the subject throughout development. Unfortunately, this view is particularly detrimental for computational applications of emotion appraisal. In fact, it requires providing a knowledge base that is particularly difficult to establish and manage, especially in systems designed for highly complex scenarios, such as social robots. In addition,
according to appraisal theory, an individual might elicit more than one emotion at a time in reaction to an event. Hence, determining which emotional state should be attributed in relationship to a specific event is another critical issue not yet fully addressed by the available literature. In this work, we show that: (i) the cognitive appraisal process can be realised without a complex set of rules; instead, we propose that this process can be operationalised by knowing only the positive or negative
perceived effect the event has on the subject, thus facilitating extensibility and integrability of the emotional system; (ii) the final emotional state to attribute in relation to a specific situation is better explained by ethical reasoning mechanisms. These hypotheses are supported by our experimental results. Therefore, this contribution is particularly significant to provide a more simple and generalisable explanation of cognitive appraisal theory and to promote the integration between theories of emotion and ethics studies, currently often neglected by the available literature.
Pham, P.G., Huang, M. & Nguyen, Q. 2017, 'Interactive Data Exploration through Multiple Visual Contexts with Different Data Models and Dimensions', Proccdings of 21st International Conference Information Visualisation, 2017 21st International Conference Information Visualisation, IEEE CPS, London, UK, pp. 84-89.
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Visual analytics plays a key role in bringing insights
to audiences who are interested and dedicated in data
exploration. In the area of relational data, many
advanced visualization tools and frameworks are
proposed in order to dealing with such data features.
However, the majority of those have not greatly
considered the whole process from data-model mining to
query utilizing on dimensions and data values, which
might cause interruption to exploration activities. This
paper presents a new interactive exploration framework
for relational data analysis through automatic
interconnection of data models, data dimensions and
data values. The basic idea is to construct a relative and
switchable chain of those context representations by
integrating our previous techniques on node-link,
parallel coordinate and scatterplot graphics. This
approach enables users to flexibly make relative queries
on desired contexts at any stage of exploration for deep
data understanding. The result from a typical case study
for the framework demonstration indicates that our
approach is able to handle the addressed challenge.
Roxby, D.N., Ting, S. & Nguyen, H.T. 2017, 'Polypyrrole RVC Biofuel Cells for Powering Medical Implants', 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Jeju Island, Korea, pp. 779-782.
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Batteries for implanted medical devices such as pacemakers typically require surgical replacement every 5 to 10 years causing stress to the patient and their families. A Biofuel cell uses two electrodes with enzymes embedded to convert sugar into electricity. To evaluate the power producing capabilities of biofuel cells to replace battery technology, polypyrrole electrodes were fabricated by compression with Glucose oxidase and Laccase. Vitreous carbon was added to increase the conductivity, whilst glutaraldehyde acted as a crosslinking molecule. A maximum open circuit potential of 558.7 mV, short circuit current of 1.09 mA and maximum power of 0.127 mW was obtained from the fuel cells. This was able to turn on a medical thermometer through a TI BQ25504 energy harvesting circuit, hence showing the powering potential for biomedical devices.
Run-ze, W., Xu, G., Enhong, C., Qi, L. & Ng, W. 2017, 'Knowledge or Gaming? Cognitive Modelling Based on Multiple-Attempt Response', 26th International World Wide Web Conference, 2017, Perth, Western Australia, pp. 321-329.
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Recent decades have witnessed the rapid growth of intelli- gent tutoring systems (ITS), in which personalized adaptive techniques are successfully employed to improve the learning of each individual student. However, the problem of using cognitive analysis to distill the knowledge and gaming factor from students learning history is still underexplored. To this end, we propose a Knowledge Plus Gaming Response Model (KPGRM) based on multiple-attempt responses. Specifi- cally, we first measure the explicit gaming factor in each multiple-attempt response. Next, we utilize collaborative filtering methods to infer the implicit gaming factor of one- attempt responses. Then we model student learning cog- nitively by considering both gaming and knowledge factors simultaneously based on a signal detection model. Extensive experiments on two real-world datasets prove that KPGRM can model student learning more effectively as well as obtain a more reasonable analysis.
Tonkin, M., Vitale, J., Ojha, S., Williams, M.-.A., Fuller, P., Judge, W. & Wang, X. 2017, 'Would You Like to Sample? Robot Engagement in a Shopping Centre', International Symposium on Robot and Human Interactive Communication, Lisbon, Portugal.
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Nowadays, robots are gradually appearing in public spaces such as libraries, train stations, airports and shopping centres. Only a limited percentage of research literature explores robot applications in public spaces. Studying robot applications in the wild is particularly important for designing commercially viable applications able to meet a specific goal. Therefore, in this paper we conduct an experiment to test a robot application in a shopping centre, aiming to provide results relevant for today’s technological capability and market. We compared the performance of a robot and a human in promoting food samples in a shopping centre, a well known commercial application, and then analysed the effects of the type of engagement used to achieve this goal. Our results show that the robot is able to engage customers similarly to a human as expected. However unexpectedly, while an actively engaging human was able to perform better than a passively engaging human, we found the opposite effect for the robot. In this paper we investigate this phenomenon, with possible explanation ready to be explored and tested in subsequent research.
Umair, A., Nanda, P. & He, X. 2017, 'Online Social Network Information Forensics', Trustcom/BigDataSE/ICESS.2017, IEEE TructCom2017, IEEE Compuer Society, Sydney.
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Online Social Networks (OSN) such as Facebook,
Twitter, LinkedIn, and Instagram are heavily used to socialize,
entertain or gain insights on people behavior and their activities.
Everyday terabytes of data is generated over these networks,
which is then used by the businesses to generate revenue or
misused by the wrongdoers to exploit vulnerabilities of these
social network platforms. Specifically social network information
helps in extracting various important features such as; user
association, access pattern, location information etc. Recent
research shows, many such features could be used to develop
novel attack models and investigate further into defending the
users from exposing their information to outsiders. This paper
analyzes some of the available tools to extract OSN information
and discusses research work on similar type of unstructured
data. Recent research works, which focus on gathering bits and
pieces of information to extract meaningful results for digital
forensics, has been discussed. An online survey is conducted to
gauge the cautiousness of users in social media usage in terms of
personal information dissemination.
Van Kha Ly Ha, Chai, R. & Nguyen, H.T. 2017, 'Real-Time WebRTC-Based Design for a Telepresence Wheelchair', Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), The 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), IEEE, Jeju Island, Korea, pp. 2676-2679.
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This paper presents a novel approach to the
telepresence wheelchair system which is capable of real-time
video communication and remote interaction. The investigation
of this emerging technology aims at providing a low-cost and
efficient way for assisted-living of people with disabilities. The
proposed system has been designed and developed by deploying
the JavaScript with Hyper Text Markup Language 5 (HTML5)
and Web Real-time Communication (WebRTC) in which the
adaptive rate control algorithm for video transmission is
invoked. We conducted experiments in real-world
environments, and the wheelchair was controlled from a
distance using the Internet browser to compare with existing
methods. The results show that the adaptively encoded video
streaming rate matches the available bandwidth. The video
streaming is high-quality with approximately 30 frames per
second (fps) and round trip time less than 20 milliseconds (ms).
These performance results confirm that the WebRTC approach
is a potential method for developing a telepresence wheelchair
system.
van Rijmenam, M., Erekhinskaya, T., Schweitzer, J. & Williams, M.A. 2017, 'How Big Data Analytics Changes the Practice of Strategy When Navigating Times of Disruptive Innovation', Transforming Entrepreneurial - Thinking into Dynamic Capabilities, Strategic Management Society (SMS) Special Conference, Banff, Alberta, Canada.
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Wang, D., Xu, G.U.A.N.D.O.N.G. & Deng, S. 2017, 'Music recommendation via heterogeneous information graph embedding', International Joint Conference on Neural Networks (IJCNN 2017), Anchorage, Alaska, USA.
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Traditional music recommendation techniques suffer from limited performance due to the sparsity of user-music interaction data, which is addressed by incorporating auxiliary information. In this paper, we study the problem of personalized music recommendation that takes different kinds of auxiliary information into consideration. To achieve this goal, a Heterogeneous Information Graph (HIG) is first constructed to encode different kinds of heterogeneous information, including the interactions between users and music pieces, music playing sequences, and the metadata of music pieces. Based on HIG, a Heterogeneous Information Graph Embedding method (HIGE) is proposed to learn the latent low-dimensional representations of music pieces. Then, we further develop a context-aware music recommendation method. Extensive experiments have been conducted on real-world datasets to compare the proposed method with other state-of-the-art recommendation methods. The results demonstrate that the proposed method significantly outperforms those baselines, especially on sparse datasets.
Xiong, W., Du, B., Zhang, L., Hu, R. & Tao, D. 2017, 'Regularizing deep convolutional neural networks with a structured decorrelation constraint', Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 519-528.
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© 2016 IEEE. Deep convolutional networks have achieved successful performance in data mining field. However, training large networks still remains a challenge, as the training data may be insufficient and the model can easily get overfitted. Hence the training process is usually combined with a model regularization. Typical regularizers include weight decay, Dropout, etc. In this paper, we propose a novel regularizer, named Structured Decorrelation Constraint (SDC), which is applied to the activations of the hidden layers to prevent overfitting and achieve better generalization. SDC impels the network to learn structured representations by grouping the hidden units and encouraging the units within the same group to have strong connections during the training procedure. Meanwhile, it forces the units in different groups to learn non-redundant representations by minimizing the cross-covariance between them. Compared with Dropout, SDC reduces the co-Adaptions between the hidden units in an explicit way. Besides, we propose a novel approach called Reg-Conv that can help SDC to regularize the complex convolutional layers. Experiments on exten sive datasets show that SDC significantly reduces overfitting and yields very meaningful improvements on classification performance (on CIFAR-10 6.22% accuracy promotion and on CIFAR-100 9.63% promotion).
Xu, G., Yueqing, S., Lin, L., Zhongwei, X., Qing, X. & Xin, L. 2017, 'DASFAA', International Conference on Database Systems for Advanced Applications, Suzhou, China.
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Ye, L., Argha, A., Celler, B.G., Zhang, Y., Nguyen, H.T. & Su, S. 2017, 'Non-parametric Modelling of VO2 Response to Excercise', Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), IEEE, Jeju Island, Korea, pp. 1525-1528.
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This paper investigates the modelling of oxygen
consumption (VO2) response to jogging exercise on treadmill.
Unlike most of the previous methods, which often use simple
parametric models, e.g., first order linear time invariant model,
this study applied a nonparametric kernel based regularised
method to estimate VO2 to address the ill-conditioned modelling
problem and achieve accurate estimation. In particular, it is
worthy to be noted that the selection of kernels will affect
the results for different modelling scenarios. Therefore, in this
research, both radial basis kernel and stable spline kernel were
selected for testing. In order to select the favourable kernel
for this system, a simulation related to VO2-jogging speed was
carried out. The results of simulation indicated that spline
kernel can achieve higher accuracy comparing to radial basis
function kernel. Experimentally, the kernel based estimation
method and spline kernel were tested using six participants.
From the results, an average impulse response is obtained. It
showed the VO2 estimation, based on the average finite impulse
response, is fitted well to the six observations collected from the
participants.
Yu, H., Guo, K., Luo, J., Cao, K., Nguyen, H.T. & Su, S. 2017, 'The Classification for Equilibrium Triad Sensory Loss based on sEMG Signals of Calf Muscles', Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), IEEE, Jeju Island, Korea, pp. 2143-2145.
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Surface Electromyography (sEMG) has been commonly
applied for analysing the electrical activities of skeletal
muscles. The sensory system of maintaining posture balance
includes vision, proprioception and vestibular senses. In this
work, an attempt is made to classify whether the body is
missing one of the sense during balance control by using
sEMG signals. A trial of combination with different features
and muscles is also developed. The results demonstrate that
the classification accuracy between vision loss and the normal
condition is higher than the one between vestibular sense
loss and normal condition. When using different features and
muscles, the impact on classification results is also different.
The outcomes of this study could aid the development of sEMG
based classification for the function of sensory systems during
human balance movement.
Yueqing, S., Lin, L., Zhongwei, X., Qing, X., Xin, L. & Xu, G. 2017, 'Co-training an Improved Recurrent Neural Network with Probability Statistic Models for Named Entity Recognition', The 22nd International Conference on Database Systems for Advanced Applications, Suzhou, China.
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Named Entity Recognition (NER) is a subtask of information extraction in Natural Language Processing (NLP) field and thus being wildly studied. Currently Recurrent Neural Network (RNN) has become a popular way to do NER task, but it needs a lot of train data. The lack of labeled train data is one of the hard problems and traditional co-training strategy is a way to alleviate it. In this paper, we consider this situation and focus on doing NER with co-training using RNN and two probability statistic models i.e. Hidden Markov Model (HMM) and Conditional Random Field (CRF). We proposed a modified RNN model by redefining its activation function. Compared to traditional sigmoid function, our new function avoids saturation to some degree and makes its output scope very close to [0, 1], thus improving recognition accuracy. Our experiments are conducted ATIS benchmark. First, supervised learning using those models are compared when using different train data size. The experimental results show that it is not necessary to use whole data, even small part of train data can also get good performance. Then, we compare the results of our modified RNN with original RNN. 0.5% improvement is obtained. Last, we compare the co-training results. HMM and CRF get higher improvement than RNN after co-training. Moreover, using our modified RNN in co-training, their performances are improved further.
Yuqiang, H., Yao, W., Liang, C., Xu, G. & Jian, W. 2017, 'Exploiting Geographical Location for Team Formation in Social Coding Sites', The 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, Jeju Island, Korea.
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Social coding sites (SCSs) such as GitHub and BitBucket are collaborative platforms where developers from different background (e.g., culture, language, location, skills) form a team to contribute to a shared project collaboratively. One essential task of such collaborative development is how to form a optimal team where each member makes his/her greatest contribution, which may have a great effect on the effi- ciency of collaboration. To the best of knowledge, all existing related works model the team formation problem as minimizing the commu- nication cost among developers or taking the workload of individuals into account, ignoring the impact of geographical location of each devel- oper. In this paper, we aims to exploit the geographical proximity factor to improve the performance of team formation in social coding sites. Specifically, we incorporate the communication cost and geographical proximity into a unified objective function and propose a genetic algo- rithm to optimize it. Comprehensive experiments on a real-world dataset (e.g., GitHub) demonstrate the performance of the proposed model with the comparison of some state-of-the-art ones.
Zhao, Y., You, X., Wei, Y., Yin, S., Tao, D. & Cheung, Y.M. 2017, 'Multi-view latent space learning based on local discriminant embedding', Proceedings - 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016, pp. 225-230.
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© 2016 IEEE. In many computer vision systems, one object can be described by multi-view data. Compared with individual view, multi-view data can contain complete and complementary information of the problem. But when views capture information which is uniquely but not complete enough to give an uniform learning performance, multi-view data may degrade the learning performance and it is therefore not an ideal solution to simply concatenate multiple views into single view. In this paper, we proposed an multi-view latent space learning algorithm which assume that multi-view data is extracted from the same latent space via distinct transformation. Under this assumption, our algorithm can have a good performance even though views are not complete and the space obtained can contain the valuable information of each view as well as get the underlying connections between multi-view data. Due to the local discriminant embedding of the input space, this multi-view latent space is more suitable for classification or recognition problems. The proposed algorithm is evaluated on two tasks: indoor scene classification and abnormal objects classification on MIT scene 67, Abnormal Objects database respectively. Extensive experiments show that the algorithm we proposed achieves comparable improvements when compared with many other outstanding methods.
Zhou, Z., Xu, G.U.A.N.G.D.O.N.G., Zhu, W., Li, J. & Zhang, W. 2017, 'Structure embedding for knowledge base completion and analytics.', International Joint Conference on Neural Networks (IJCNN 2017), Anchorage, Alaska, USA.
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To explore the latent information of Human Knowledge, the analysis for Knowledge Bases (KBs) (e.g. WordNet, Freebase) is essential. Some previous KB element embedding frameworks are used for KBs structure analysis and completion. These embedding frameworks use low-dimensional vector space representation for large scale of entities and relations in KB. Based on that, the vector space representation of entities and relations which are not contained by KB can be measured. The embedding idea is reasonable, while the current embedding methods have some issues to get proper embeddings for KB elements. The embedding methods use entity-relation-entity triplet, contained by most of current KB, as training data to output the embedding representation of entities and relations. To measure the truth of one triplet (The knowledge represented by triplet is true or false), some current embedding methods such as Structured Embedding (SE) project entity vectors into subspace, the meaning of such subspace is not clear for knowledge reasoning. Some other embedding methods such as TransE use simple linear vector transform to represent relation (such as vector add or minus), which can't deal with the multiple relations match or multiple entities match problem. For example, there are multiple relations between two entities, or there are multiple entities have same relation with one entity. Insipred by previous KB element structured embedding methods, we propose a new method, Bipartite Graph Network Structured Embedding (BGNSE). BGNSE combines the current KB embedding methods with bipartite graph network model, which is widely used in many fields including image data compression, collaborative filtering. BGNSE embeds each entity-relation-entity KB triplet into a bipartite graph network structure model, represents each entity by one bipartite graph layer, represents relation by link weights matrix of bipartite graph network. Based on bipartite graph model, our proposed method has followi...
Zhu, X. & Xu, G. 2017, 'Applying Visual Analytics on Traditional Data Mining Process: Quick Prototype, Simple Expertise Transformation, and Better Interpretation', Proceedings - 4th International Conference on Enterprise Systems: Advances in Enterprise Systems, ES 2016, pp. 208-213.
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© 2016 IEEE. Due to a lack of experience, business might not be confident about the completeness of their proposed data mining (DM) project objectives at early stage. Besides, business domain expertise usually shrinks when delivered to data analysts. This expertise ought to contribute more throughout whole project. In addition, the outcome from DM project might fail to transform into actionable advice as the interpretation for the outcome is hard to understand and, as a result, unconvincing to apply in real. To fill the above three gaps, Visual Analytics (VA) tools are applied in different stages to optimize traditional data analytics process. In my practice, VA tools have offered both an easy access to generate quick insights for evaluating project objective's viability, and a bidirectional channel between data analysts and stakeholders to break the background barrier. Consequently, more applicable outcomes and better client satisfaction are gained.