Alam, M., Jan, M.A., Shu, L., He, X. & Chen, Y. 2018, 'Editorial: Current and Future Trends in Wireless Communications Protocols and Technologies', Mobile Networks and Applications, vol. 23, no. 3, pp. 377-381.
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Alam, M., Rayes, A., He, X., Atiquzzaman, M., Lloret, J. & Tsang, K.F. 2018, 'Guest Editorial Introduction to the Special Issue on Dependable Wireless Vehicular Communications for Intelligent Transportation Systems (ITS)', IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 3, pp. 949-952.
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Aldebei, K.W., He, X., Jia, W. & Weichang Yeh 2018, 'SUDMAD: Sequential and unsupervised decomposition of a multi-author document based on a hidden markov model', Journal of the Association for Information Science and Technology, vol. 69, no. 2, pp. 201-214.
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Decomposing a document written by more than one author into sentences based on authorship is of great significance due to the increasing demand for plagiarism detection, forensic analysis, civil law (i.e., disputed copyright issues), and intelligence issues that involve disputed anonymous documents. Among existing studies for document decomposition, some were limited by specific languages, according to topics or restricted to a document of two authors, and their accuracies have big room for improvement. In this paper, we consider the contextual correlation hidden among sentences and propose an algorithm for Sequential and Unsupervised Decomposition of a Multi‐Author Document (SUDMAD) written in any language, disregarding topics, through the construction of a Hidden Markov Model (HMM) reflecting the authors' writing styles. To build and learn such a model, an unsupervised, statistical approach is first proposed to estimate the initial values of HMM parameters of a preliminary model, which does not require the availability of any information of author's or document's context other than how many authors contributed to writing the document. To further boost the performance of this approach, a boosted HMM learning procedure is proposed next, where the initial classification results are used to create labeled training data to learn a more accurate HMM. Moreover, the contextual relationship among sentences is further utilized to refine the classification results. Our proposed approach is empirically evaluated on three benchmark datasets that are widely used for authorship analysis of documents. Comparisons with recent state‐of‐the‐art approaches are also presented to demonstrate the significance of our new ideas and the superior performance of our approach.
Alshehri, M.D., Hussain, F. & Hussain, O. 2018, 'Clustering-Driven Intelligent Trust Management Methodologyfor the Internet of Things (CITM-IoT)', Mobile Networks and Applications, vol. 23, no. 3, pp. 419-431.
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The growth and adoption of the Internet of Things (IoT) is increasing day by day. The large number of IoT devices increases the risk of security threats such as (but not limited to) viruses or cyber-attacks. One possible approach to achieve IoT security is to enable a trustworthy IoT environment in IoT wherein the interactions are based on the trust value of the communicating nodes. Trust management and trust assessment has been extensively studied in distributed networks in general and the IoT in particular, but there are still outstanding pressing issues such as bad-mouthing of trust values which prevent them from being used in practical IoT applications. Furthermore, there is no research in ensuring that the developed IoT trust solutions are scalable across billions of IoT nodes. To address the above-mentioned issues, we propose a methodology for scalable trust management solution in the IoT. The methodology addresses practical and pressing issues related to IoT trust management such as trust-based IoT clustering, intelligent methods for countering bad-mouthing attacks on trust systems, issues of memory-efficient trust computation and trust-based migration of IoT nodes from one cluster to another. Experimental results demonstrate the effectiveness of the proposed approaches.
Anshar, M. & Williams, M.A. 2018, 'Evolving robot empathy towards humans with motor disabilities through artificial pain generation', AIMS Neuroscience, vol. 5, no. 1, pp. 56-73.
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© 2018 the Author(s). In contact assistive robots, a prolonged physical engagement between robots and humans with motor disabilities due to shoulder injuries, for instance, may at times lead humans to experience pain. In this situation, robots will require sophisticated capabilities, such as the ability to recognize human pain in advance and generate counter-responses as follow up emphatic action. Hence, it is important for robots to acquire an appropriate pain concept that allows them to develop these capabilities. This paper conceptualizes empathy generation through the realization of synthetic pain classes integrated into a robot's self-awareness framework, and the implementation of fault detection on the robot body serves as a primary source of pain activation. Projection of human shoulder motion into the robot arm motion acts as a fusion process, which is used as a medium to gather information for analyses then to generate corresponding synthetic pain and emphatic responses. An experiment is designed to mirror a human peer's shoulder motion into an observer robot. The results demonstrate that the fusion takes place accurately whenever unified internal states are achieved, allowing accurate classification of synthetic pain categories and generation of empathy responses in a timely fashion. Future works will consider a pain activation mechanism development.
Bargi, A., Xu, Y.D. & Piccardi, M. 2018, 'AdOn HDP-HMM: An Adaptive Online Model for Segmentation and Classification of Sequential Data', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-16.
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Beck, D., Thoms, J.A.I., Palu, C., Herold, T., Shah, A., Olivier, J., Boelen, L., Huang, Y., Chacon, D., Brown, A., Babic, M., Hahn, C., Perugini, M., Zhou, X., Huntly, B.J., Schwarzer, A., Klusmann, J.-.H., Berdel, W.E., Wörmann, B., Büchner, T., Hiddemann, W., Bohlander, S.K., To, L.B., Scott, H.S., Lewis, I.D., D'Andrea, R.J., Wong, J.W.H. & Pimanda, J.E. 2018, 'A four-gene LincRNA expression signature predicts risk in multiple cohorts of acute myeloid leukemia patients.', Leukemia, vol. 32, no. 2, pp. 263-272.
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Prognostic gene expression signatures have been proposed as clinical tools to clarify therapeutic options in acute myeloid leukemia (AML). However, these signatures rely on measuring large numbers of genes and often perform poorly when applied to independent cohorts or those with older patients. Long intergenic non-coding RNAs (lincRNAs) are emerging as important regulators of cell identity and oncogenesis, but knowledge of their utility as prognostic markers in AML is limited. Here we analyze transcriptomic data from multiple cohorts of clinically annotated AML patients and report that (i) microarrays designed for coding gene expression can be repurposed to yield robust lincRNA expression data, (ii) some lincRNA genes are located in close proximity to hematopoietic coding genes and show strong expression correlations in AML, (iii) lincRNA gene expression patterns distinguish cytogenetic and molecular subtypes of AML, (iv) lincRNA signatures composed of three or four genes are independent predictors of clinical outcome and further dichotomize survival in European Leukemia Net (ELN) risk groups and (v) an analytical tool based on logistic regression analysis of quantitative PCR measurement of four lincRNA genes (LINC4) can be used to determine risk in AML.
Cao, X., Qiu, B. & Xu, G. 2018, 'BorderShift: toward optimal MeanShift vector for cluster boundary detection in high-dimensional data', Pattern Analysis and Applications, pp. 1-13.
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© 2018 Springer-Verlag London Ltd., part of Springer Nature We present a cluster boundary detection scheme that exploits MeanShift and Parzen window in high-dimensional space. To reduce the noises interference in Parzen window density estimation process, the kNN window is introduced to replace the sliding window with fixed size firstly. Then, we take the density of sample as the weight of its drift vector to further improve the stability of MeanShift vector which can be utilized to separate boundary points from core points, noise points, isolated points according to the vector models in multi-density data sets. Under such circumstance, our proposed BorderShift algorithm doesn’t need multi-iteration to get the optimal detection result. Instead, the developed Shift value of each data point helps to obtain it in a liner way. Experimental results on both synthetic and real data sets demonstrate that the F-measure evaluation of BorderShift is higher than that of other algorithms.
Chi, L., Li, B., Zhu, X., Pan, S. & Chen, L. 2018, 'Hashing for Adaptive Real-Time Graph Stream Classification With Concept Drifts.', IEEE Transactions on Cybernetics, vol. 48, no. 5, pp. 1591-1604.
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Many applications involve processing networked streaming data in a timely manner. Graph stream classification aims to learn a classification model from a stream of graphs with only one-pass of data, requiring real-time processing in training and prediction. This is a nontrivial task, as many existing methods require multipass of the graph stream to extract subgraph structures as features for graph classification which does not simultaneously satisfy "one-pass" and "real-time" requirements. In this paper, we propose an adaptive real-time graph stream classification method to address this challenge. We partition the unbounded graph stream data into consecutive graph chunks, each consisting of a fixed number of graphs and delivering a corresponding chunk-level classifier. We employ a random hashing function to compress the original node set of graphs in each chunk for fast feature detection when training chunk-level classifiers. Furthermore, a differential hashing strategy is applied to map unlimited increasing features (i.e., cliques) into a fixed-size feature space which is then used as a feature vector for stochastic learning. Finally, the chunk-level classifiers are weighted in an ensemble learning model for graph classification. The proposed method substantially speeds up the graph feature extraction and avoids unbounded graph feature growth. Moreover, it effectively offsets concept drifts in graph stream classification. Experiments on real-world and synthetic graph streams demonstrate that our method significantly outperforms existing methods in both classification accuracy and learning efficiency.
Chomsiri, T., He, X.S., Nanda, P. & Tan, Z. 2018, 'Hybrid Tree-rule Firewall for High Speed Data Transmission', IEEE Transactions on Cloud Computing.
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Traditional firewalls employ listed rules in both configuration and process phases to regulate network traffic. However, configuring a firewall with listed rules may create rule conflicts, and slows down the firewall. To overcome this problem, we have proposed a Tree-rule firewall in our previous study. Although the Tree-rule firewall guarantees no conflicts within its rule set and operates faster than traditional firewalls, keeping track of the state of network connections using hashing functions incurs extra computational overhead. In order to reduce this overhead, we propose a hybrid Tree-rule firewall in this paper. This hybrid scheme takes advantages of both Tree-rule firewalls and traditional listed-rule firewalls. The GUIs of our Tree-rule firewalls are utilized to provide a means for users to create conflict-free firewall rules, which are organized in a tree structure and called 'tree rules'. These tree rules are later converted into listed rules that share the merit of being conflict-free. Finally, in decision making, the listed rules are used to verify against packet header information. The rules which have matched with most packets are moved up to the top positions by the core firewall. The mechanism applied in this hybrid scheme can significantly improve the functional speed of a firewall.
Chotipant, S., Hussain, F.K. & Hussain, O.K. 2018, 'SERNOTATE: An automated approach for business service description annotation for efficient service retrieval and composition', Concurrency and Computation: Practice and Experience, vol. 30, no. 1.
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Copyright © 2017 John Wiley & Sons, Ltd. Business service advertisements are today published online to convey essential information about services to customers. However, current Web search engines are unable to search and combine online service advertisements. Semantic service annotation is important for its ability to enable machines to understand the meaning of services and support in effective service retrieval and service composition. Existing research in the area of semantic service annotation has focused on the annotation of Web services in a semi-automated approach. It cannot be applied to business service information as it is not in the form of Web Services Description Language but in free text format. Moreover, semi-automated approaches are inappropriate for annotating a large amount of online service information which changes dynamically and they are therefore not suitable for the timely dissemination of service information to customers. To solve these issues, we propose SERNOTATE, which is an automated approach for business service description annotation for efficient service retrieval and composition. We propose new semantic-based linking approaches, namely, Extended Case-based Reasoning, vector-based, and classification-based, that automatically annotate business services to relevant service concepts. Each approach assists in the single-label and multi-label annotation of service terms to concept terms to provide a better representation of services. The experimental results test and validate the applicability of the proposed approaches to the automatic annotation of business service descriptions to service concepts on a real-world dataset.
Cui, L., Wu, J., Pi, D., Zhang, P. & Kennedy, P. 2018, 'Dual Implicit Mining-Based Latent Friend Recommendation', IEEE Transactions on Systems, Man, and Cybernetics: Systems.
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IEEE The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user-user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen-Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics.
Dai, M., Cheng, S. & He, X.S. 2018, 'Hybrid generative–discriminative hash tracking with spatio-temporal contextual cues', Neural Computing and Applications, vol. 29, no. 2, pp. 389-399.
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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.
Dai, M., Cheng, S., He, X. & Wang, D. 2018, 'Object tracking in the presence of shaking motions', Neural Computing and Applications, pp. 1-18.
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© 2018 The Natural Computing Applications Forum Visual tracking can be particularly interpreted as a process of searching for targets and optimizing the searching. In this paper, we present a novel tracker framework for tracking shaking targets. We formulate the underlying geometrical relevance between a search scope and a target displacement. A uniform sampling among the search scopes is implemented by sliding windows. To alleviate any possible redundant matching, we propose a double-template structure comprising of initial and previous tracking results. The element-wise similarities between a template and its candidates are calculated by jointly using kernel functions which provide a better outlier rejection property. The STC algorithm is used to improve the tracking results by maximizing a confidence map incorporating temporal and spatial context cues about the tracked targets. For better adaptation to appearance variations, we employ a linear interpolation to update the context prior probability of the STC method. Both qualitative and quantitative evaluations are performed on all sequences that contain shaking motions and are selected from the OTB-50 challenging benchmark. The proposed approach is compared with and outperforms 12 state-of-the-art tracking methods on the selected sequences while running on MATLAB without code optimization. We have also performed further experiments on the whole OTB-50 and VOT 2015 datasets. Although the most of sequences in these two datasets do not contain motion blur that this paper is focusing on, the results of our method are still favorable compared with all of the state-of-the-art approaches.
Deng, C., Liu, X., Li, C. & Tao, D. 2018, 'Active multi-kernel domain adaptation for hyperspectral image classification', Pattern Recognition.
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© 2017 Elsevier Ltd. Recent years have witnessed the quick progress of the hyperspectral images (HSI) classification. Most of existing studies either heavily rely on the expensive label information using the supervised learning or can hardly exploit the discriminative information borrowed from related domains. To address this issues, in this paper we show a novel framework addressing HSI classification based on the domain adaptation (DA) with active learning (AL). The main idea of our method is to retrain the multi-kernel classifier by utilizing the available labeled samples from source domain, and adding minimum number of the most informative samples with active queries in the target domain. The proposed method adaptively combines multiple kernels, forming a DA classifier that minimizes the bias between the source and target domains. Further equipped with the nested actively updating process, it sequentially expands the training set and gradually converges to a satisfying level of classification performance. We study this active adaptation framework with the Margin Sampling (MS) strategy in the HSI classification task. Our experimental results on two popular HSI datasets demonstrate its effectiveness.
Deng, Z., Chen, J., Zhang, T., Cao, L. & Wang, S. 2018, 'Generalized Hidden-Mapping Minimax Probability Machine for the training and reliability learning of several classical intelligent models', Information Sciences, vol. 436-437, pp. 302-319.
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© 2018 Elsevier Inc. Minimax Probability Machine (MPM) is a binary classifier that optimizes the upper bound of the misclassification probability. This upper bound of the misclassification probability can be used as an explicit indicator to characterize the reliability of the classification model and thus makes the classification model more transparent. However, the existing related work is constrained to linear models or the corresponding nonlinear models by applying the kernel trick. To relax such constraints, we propose the Generalized Hidden-Mapping Minimax Probability Machine (GHM-MPM). GHM-MPM is a generalized MPM. It is capable of training many classical intelligent models, such as feedforward neural networks, fuzzy logic systems, and linear and kernelized linear models for classification tasks, and realizing the reliability learning of these models simultaneously. Since the GHM-MPM, similarly to the classical MPM, was originally developed only for binary classification, it is further extended to multi-class classification by using the obtained reliability indices of the binary classifiers of two arbitrary classes. The experimental results show that GHM-MPM makes the trained models more transparent and reliable than those trained by classical methods.
Ding, C. & Tao, D. 2018, 'Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 1002-1014.
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© 1979-2012 IEEE. Human faces in surveillance videos often suffer from severe image blur, dramatic pose variations, and occlusion. In this paper, we propose a comprehensive framework based on Convolutional Neural Networks (CNN) to overcome challenges in video-based face recognition (VFR). First, to learn blur-robust face representations, we artificially blur training data composed of clear still images to account for a shortfall in real-world video training data. Using training data composed of both still images and artificially blurred data, CNN is encouraged to learn blur-insensitive features automatically. Second, to enhance robustness of CNN features to pose variations and occlusion, we propose a Trunk-Branch Ensemble CNN model (TBE-CNN), which extracts complementary information from holistic face images and patches cropped around facial components. TBE-CNN is an end-to-end model that extracts features efficiently by sharing the low-and middle-level convolutional layers between the trunk and branch networks. Third, to further promote the discriminative power of the representations learnt by TBE-CNN, we propose an improved triplet loss function. Systematic experiments justify the effectiveness of the proposed techniques. Most impressively, TBE-CNN achieves state-of-the-art performance on three popular video face databases: PaSC, COX Face, and YouTube Faces. With the proposed techniques, we also obtain the first place in the BTAS 2016 Video Person Recognition Evaluation.
Dong, X., Dong, J., Zhou, H., Sun, J. & Tao, D. 2018, 'Automatic Chinese Postal Address Block Location Using Proximity Descriptors and Cooperative Profit Random Forests', IEEE Transactions on Industrial Electronics, vol. 65, no. 5, pp. 4401-4412.
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© 1982-2012 IEEE. Locating the destination address block is key to automated sorting of mails. Due to the characteristics of Chinese envelopes used in mainland China, we here exploit proximity cues in order to describe the investigated regions on envelopes. We propose two proximity descriptors encoding spatial distributions of the connected components obtained from the binary envelope images. To locate the destination address block, these descriptors are used together with cooperative profit random forests (CPRFs). Experimental results show that the proposed proximity descriptors are superior to two component descriptors, which only exploit the shape characteristics of the individual components, and the CPRF classifier produces higher recall values than seven state-of-the-art classifiers. These promising results are due to the fact that the proposed descriptors encode the proximity characteristics of the binary envelope images, and the CPRF classifier uses an effective tree node split approach.
Du, B., Tang, X., Wang, Z., Zhang, L. & Tao, D. 2018, 'Robust Graph-Based Semisupervised Learning for Noisy Labeled Data via Maximum Correntropy Criterion', IEEE Transactions on Cybernetics.
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IEEE Semisupervised learning (SSL) methods have been proved to be effective at solving the labeled samples shortage problem by using a large number of unlabeled samples together with a small number of labeled samples. However, many traditional SSL methods may not be robust with too much labeling noisy data. To address this issue, in this paper, we propose a robust graph-based SSL method based on maximum correntropy criterion to learn a robust and strong generalization model. In detail, the graph-based SSL framework is improved by imposing supervised information on the regularizer, which can strengthen the constraint on labels, thus ensuring that the predicted labels of each cluster are close to the true labels. Furthermore, the maximum correntropy criterion is introduced into the graph-based SSL framework to suppress labeling noise. Extensive image classification experiments prove the generalization and robustness of the proposed SSL method.
Fan, M., Zhang, X., Du, L., Chen, L. & Tao, D. 2018, 'Semi-Supervised Learning Through Label Propagation on Geodesics', IEEE Transactions on Cybernetics, vol. 48, no. 5, pp. 1486-1499.
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© 2013 IEEE. Graph-based semi-supervised learning (SSL) has attracted great attention over the past decade. However, there are still several open problems in this paper, including: 1) how to construct an effective graph over data with complex distribution and 2) how to define and effectively use pair-wise similarity for robust label propagation. In this paper, we utilize a simple and effective graph construction method to construct the graph over data lying on multiple data manifolds. The method can guarantee the c onnectiveness between pair-wise data points. Then, the global pair-wise data similarity is naturally characterized by geodesic distance-based joint probability, where the geodesic distance is approximated by the graph distance. The new data similarity is much more effective than previous Euclidean distance-based similarities. To apply data structure for robust label propagation, Kullback-Leibler divergence is utilized to measure the inconsistency between the input pair-wise similarity and the output similarity. In order to further consider intraclass and interclass variances, a novel regularization term on sample-wise margins is introduced to the objective function. This enables the proposed method fully utilizes the input data structure and the label information for classification. An efficient optimization method and the convergence analysis have been proposed for our problem. Besides, out-of-sample extension is discussed and addressed. Comparisons with the state-of-the-art SSL methods on image classification tasks have been presented to show the effectiveness of the proposed method.
Feng, X.I.A.N.G., Wan, W., Richard Yi Da Xu, Chen, H., Li, P. & Sánchez, J.A. 2018, 'A perceptual quality metric for 3D triangle meshes based on spatial pooling', Frontiers of Computer Science.
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Fu, Q., Luo, Y., Wen, Y., Tao, D., Li, Y. & Duan, L. 2018, 'Towards Intelligent Product Retrieval for TV-to-Online (T2O) Application: A Transfer Metric Learning Approach', IEEE Transactions on Multimedia.
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IEEE It is desired (especially for young people) to shop for the same or similar products shown in the multimedia contents (such as online TV programs). This indicates an urgent demand for improving the experience of TV-to-Online (T2O). In this paper, a transfer learning approach as well as a prototype system for effortless T2O experience is developed. In the system, a key component is high precision product search, which is to fulfill exact matching between a query item and the database ones. The matching performance primarily relies on distance estimation, but the data characteristics cannot be well modeled and exploited by a simple Euclidean (EU) distance. This motivates us to introduce distance metric learning (DML) for improving the distance estimation. However, in traditional DML methods, the side information (such as the similar/dissimilar constraints or relevance/irrelevance judgements) in target domain is leveraged. These methods may fail due to limited side information. Fortunately, this issue can be alleviated by utilizing transfer metric learning (TML) to exploit information from other related domains. In this paper, a novel Manifold Regularized Heterogeneous Multi-Task Metric Learning (MRHMTML) framework is proposed, in which each domain is treated equally. The proposed approach allows to simultaneously exploit the information from other domains and the unlabeled information. Furthermore, the ranking-based loss is adopted to make our model more appropriate for search. Experiments on two challenging real-world datasets demonstrate the effectiveness of the proposed method. This transfer metric learning approach is expected to impact the transformation of the emerging T2O trend in both TV and online video domains.
Gong, C., Liu, T., Tang, Y., Yang, J., Yang, J. & Tao, D. 2018, 'A Regularization Approach for Instance-Based Superset Label Learning.', IEEE Transactions on Cybernetics, vol. 48, no. 3, pp. 967-978.
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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., Chang, X. & Yang, J. 2018, 'Ensemble Teaching for Hybrid Label Propagation', IEEE Transactions on Cybernetics.
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IEEE Label propagation aims to iteratively diffuse the label information from labeled examples to unlabeled examples over a similarity graph. Current label propagation algorithms cannot consistently yield satisfactory performance due to two reasons: one is the instability of single propagation method in dealing with various practical data, and the other one is the improper propagation sequence ignoring the labeling difficulties of different examples. To remedy above defects, this paper proposes a novel propagation algorithm called hybrid diffusion under ensemble teaching (HyDEnT). Specifically, HyDEnT integrates multiple propagation methods as base ''learners'' to fully exploit their individual wisdom, which helps HyDEnT to be stable and obtain consistent encouraging results. More importantly, HyDEnT conducts propagation under the guidance of an ensemble of ''teachers''. That is to say, in every propagation round the simplest curriculum examples are wisely designated by a teaching algorithm, so that their labels can be reliably and accurately decided by the learners. To optimally choose these simplest examples, every teacher in the ensemble should comprehensively consider the examples' difficulties from its own viewpoint, as well as the common knowledge shared by all the teachers. This is accomplished by a designed optimization problem, which can be efficiently solved via the block coordinate descent method. Thanks to the efforts of the teachers, all the unlabeled examples are logically propagated from simple to difficult, leading to better propagation quality of HyDEnT than the existing methods. Experiments on six popular datasets reveal that HyDEnT achieves the highest classification accuracy when compared with six state-of-the-art propagation methodologies such as harmonic functions, Fick's law assisted propagation, linear neighborhood propagation, semisupervised ensemble learning, bipartite graph-based consensus maximization, and teaching-to-learn and learnin...
Gu, K., Tao, D., Qiao, J.F. & Lin, W. 2018, 'Learning a No-Reference Quality Assessment Model of Enhanced Images with Big Data', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 4, pp. 1301-1313.
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© 2012 IEEE. 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.
Gu, Y., Gu, M., Long, Y., Xu, G., Yang, Z., Zhou, J. & Qu, W. 2018, 'An enhanced short text categorization model with deep abundant representation', World Wide Web, pp. 1-15.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature Short text categorization is a crucial issue to many applications, e.g., Information Retrieval, Question-Answering System, MRI Database Construction and so forth. Many researches focus on data sparsity and ambiguity issues in short text categorization. To tackle these issues, we propose a novel short text categorization strategy based on abundant representation, which utilizes Bi-directional Recurrent Neural Network(Bi-RNN) with Long Short-Term Memory(LSTM) and topic model to catch more contextual and semantic information. Bi-RNN enriches contextual information, and topic model discovers more latent semantic information for abundant text representation of short text. Experimental results demonstrate that the proposed model is comparable to state-of-the-art neural network models and method proposed is effective.
Guan, N., Liu, T., Zhang, Y., Tao, D. & Davis, L.S. 2018, 'Truncated Cauchy Non-negative Matrix Factorization for Robust Subspace Learning', IEEE Transactions on Pattern Analysis and Machine Intelligence.
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IEEE Non-negative matrix factorization (NMF) minimizes the Euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers. In this paper, we propose a Truncated Cauchy loss that handle outliers by truncating large errors, and develop a Truncated CauchyNMF to robustly learn the subspace on noisy datasets contaminated by outliers. We theoretically analyze the robustness of Truncated CauchyNMF comparing with the competing models and theoretically prove that Truncated CauchyNMF has a generalization bound which converges at a rate of order < formula > < tex > $O(\sqrt{\ln{n}/n})$ < /tex > < /formula > , where < formula > < tex > $n$ < /tex > < /formula > is the sample size. We evaluate Truncated CauchyNMF by image clustering on both simulated and real datasets. The experimental results on the datasets containing gross corruptions validate the effectiveness and robustness of Truncated CauchyNMF for learning robust subspaces.
Gui, J., Liu, T., Sun, Z., Tao, D. & Tan, T. 2018, 'Fast Supervised Discrete Hashing', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 2, pp. 490-496.
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© 2017 IEEE. Learning-based hashing algorithms are 'hot topics' because they can greatly increase the scale at which existing methods operate. In this paper, we propose a new learning-based hashing method called 'fast supervised discrete hashing' (FSDH) based on 'supervised discrete hashing' (SDH). Regressing the training examples (or hash code) to the corresponding class labels is widely used in ordinary least squares regression. Rather than adopting this method, FSDH uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm. To the best of our knowledge, this strategy has not previously been used for hashing. Traditional SDH decomposes the optimization into three sub-problems, with the most critical sub-problem - discrete optimization for binary hash codes - solved using iterative discrete cyclic coordinate descent (DCC), which is time-consuming. However, FSDH has a closed-form solution and only requires a single rather than iterative hash code-solving step, which is highly efficient. Furthermore, FSDH is usually faster than SDH for solving the projection matrix for least squares regression, making FSDH generally faster than SDH. For example, our results show that FSDH is about 12-times faster than SDH when the number of hashing bits is 128 on the CIFAR-10 data base, and FSDH is about 151-times faster than FastHash when the number of hashing bits is 64 on the MNIST data-base. Our experimental results show that FSDH is not only fast, but also outperforms other comparative methods.
Guo, F., Wang, W., Shen, J., Shao, L., Yang, J., Tao, D. & Tang, Y.Y. 2018, 'Video Saliency Detection Using Object Proposals', IEEE Transactions on Cybernetics.
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IEEE In this paper, we introduce a novel approach to identify salient object regions in videos via object proposals. The core idea is to solve the saliency detection problem by ranking and selecting the salient proposals based on object-level saliency cues. Object proposals offer a more complete and high-level representation, which naturally caters to the needs of salient object detection. As well as introducing this novel solution for video salient object detection, we reorganize various discriminative saliency cues and traditional saliency assumptions on object proposals. With object candidates, a proposal ranking and voting scheme, based on various object-level saliency cues, is designed to screen out nonsalient parts, select salient object regions, and to infer an initial saliency estimate. Then a saliency optimization process that considers temporal consistency and appearance differences between salient and nonsalient regions is used to refine the initial saliency estimates. Our experiments on public datasets (SegTrackV2, Freiburg-Berkeley Motion Segmentation Dataset, and Densely Annotated Video Segmentation) validate the effectiveness, and the proposed method produces significant improvements over state-of-the-art algorithms.
Guo, K., Liu, L., Xu, X., Xu, D. & Tao, D. 2018, 'GoDec+: Fast and Robust Low-Rank Matrix Decomposition Based on Maximum Correntropy', IEEE Transactions on Neural Networks and Learning Systems.
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GoDec is an efficient low-rank matrix decomposition algorithm. However, optimal performance depends on sparse errors and Gaussian noise. This paper aims to address the problem that a matrix is composed of a low-rank component and unknown corruptions. We introduce a robust local similarity measure called correntropy to describe the corruptions and, in doing so, obtain a more robust and faster low-rank decomposition algorithm: GoDec+. Based on half-quadratic optimization and greedy bilateral paradigm, we deliver a solution to the maximum correntropy criterion (MCC)-based low-rank decomposition problem. Experimental results show that GoDec+ is efficient and robust to different corruptions including Gaussian noise, Laplacian noise, salt & pepper noise, and occlusion on both synthetic and real vision data. We further apply GoDec+ to more general applications including classification and subspace clustering. For classification, we construct an ensemble subspace from the low-rank GoDec+ matrix and introduce an MCC-based classifier. For subspace clustering, we utilize GoDec+ values low-rank matrix for MCC-based self-expression and combine it with spectral clustering. Face recognition, motion segmentation, and face clustering experiments show that the proposed methods are effective and robust. In particular, we achieve the state-of-the-art performance on the Hopkins 155 data set and the first 10 subjects of extended Yale B for subspace clustering.
Guo, T., Pan, S., Zhu, X. & Zhang, C. 2018, 'CFOND: Consensus Factorization for Co-Clustering Networked Data', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Networked data are common in domains where instances are characterized by both feature values and inter-dependency relationships. Finding cluster structures for networked instances and discovering representative features for each cluster represent a special co-clustering task usefully for many real-world applications, such as automatic categorization of scientific publications and finding representative key-words for each cluster. To date, although co-clustering has been commonly used for finding clusters for both instances and features, all existing methods are focused on instance-feature values, without leveraging valuable topology relationships between instances to help boost co-clustering performance. In this paper, we propose CFOND, a consensus factorization based framework for co-clustering networked data. We argue that feature values and linkages provide useful information from different perspectives, yet they are not always consistent and therefore need to be carefully aligned for best clustering results. In the paper, we advocate a consensus factorization principle, which simultaneously factorizes information from three aspects: network topology structures, instance-feature content relationships, and feature-feature correlations. The consensus factorization ensures that the final cluster structures are consistent across information from the three aspects with minimum errors. CFOND enjoys sound theoretical basis and proved convergence, and its performance is validated on real-world networks.
Han, B., Tsang, I.W., Chen, L., Yu, C.P. & Fung, S.F. 2018, 'Progressive Stochastic Learning for Noisy Labels', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE Large-scale learning problems require a plethora of labels that can be efficiently collected from crowdsourcing services at low cost. However, labels annotated by crowdsourced workers are often noisy, which inevitably degrades the performance of large-scale optimizations including the prevalent stochastic gradient descent (SGD). Specifically, these noisy labels adversely affect updates of the primal variable in conventional SGD. To solve this challenge, we propose a robust SGD mechanism called progressive stochastic learning (POSTAL), which naturally integrates the learning regime of curriculum learning (CL) with the update process of vanilla SGD. Our inspiration comes from the progressive learning process of CL, namely learning from ``easy'' tasks to ``complex'' tasks. Through the robust learning process of CL, POSTAL aims to yield robust updates of the primal variable on an ordered label sequence, namely, from ``reliable'' labels to ``noisy'' labels. To realize POSTAL mechanism, we design a cluster of ``screening losses,'' which sorts all labels from the reliable region to the noisy region. To sum up, POSTAL using screening losses ensures robust updates of the primal variable on reliable labels first, then on noisy labels incrementally until convergence. In theory, we derive the convergence rate of POSTAL realized by screening losses. Meanwhile, we provide the robustness analysis of representative screening losses. Experimental results on UCI simulated and Amazon Mechanical Turk crowdsourcing data sets show that the POSTAL using screening losses is more effective and robust than several existing baselines.
Hao, S., Shi, C., Niu, Z. & Cao, L. 2018, 'Concept coupling learning for improving concept lattice-based document retrieval', Engineering Applications of Artificial Intelligence, vol. 69, pp. 65-75.
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© 2017 Elsevier Ltd The semantic information in any document collection is critical for query understanding in information retrieval. Existing concept lattice-based retrieval systems mainly rely on the partial order relation of formal concepts to index documents. However, the methods used by these systems often ignore the explicit semantic information between the formal concepts extracted from the collection. In this paper, a concept coupling relationship analysis model is proposed to learn and aggregate the intra- and inter-concept coupling relationships. The intra-concept coupling relationship employs the common terms of formal concepts to describe the explicit semantics of formal concepts. The inter-concept coupling relationship adopts the partial order relation of formal concepts to capture the implicit dependency of formal concepts. Based on the concept coupling relationship analysis model, we propose a concept lattice-based retrieval framework. This framework represents user queries and documents in a concept space based on fuzzy formal concept analysis, utilizes a concept lattice as a semantic index to organize documents, and ranks documents with respect to the learned concept coupling relationships. Experiments are performed on the text collections acquired from the SMART information retrieval system. Compared with classic concept lattice-based retrieval methods, our proposed method achieves at least 9%, 8% and 15% improvement in terms of average MAP, IAP@11 and P@10 respectively on all the collections.
Hayes, C.J., Jackson, D., Davidson, P.M., Daly, J. & Power, T. 2018, '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., He, Z. & Zhu, X. 2018, 'Editorial Notes', Economic Modelling.
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He, X.-.Z., Li, K. & Wang, C. 2018, 'Time-varying economic dominance in financial markets: A bistable dynamics approach.', Chaos (Woodbury, N.Y.), vol. 28, no. 5, p. 055903.
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By developing a continuous-time heterogeneous agent financial market model of multi-assets traded by fundamental and momentum investors, we provide a potential mechanism for generating time-varying dominance between fundamental and non-fundamental in financial markets. We show that investment constraints lead to the coexistence of a locally stable fundamental steady state and a locally stable limit cycle around the fundamental, characterized by a Bautin bifurcation. This provides a mechanism for market prices to switch stochastically between the two persistent but very different market states, leading to the coexistence and time-varying dominance of seemingly controversial efficient market and price momentum over different time periods. The model also generates other financial market stylized facts, such as spillover effects in both momentum and volatility, market booms, crashes, and correlation reduction due to cross-sectional momentum trading. Empirical evidence based on the U.S. market supports the main findings. The mechanism developed in this paper can be used to characterize time-varying economic dominance in economics and finance in general.
He, X.Z., Li, K. & Li, Y. 2018, 'Asset allocation with time series momentum and reversal', Journal of Economic Dynamics and Control, vol. 91, pp. 441-457.
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© 2018 Elsevier B.V. To capture the well documented time series momentum and reversal in asset price, we develop a continuous-time asset price model, derive the optimal investment strategy theoretically, and test the strategy empirically. We show that, by combining market fundamentals and timing opportunity with respect to market trend and volatility, the optimal strategy based on time series momentum of moving averages over short-time horizons and reversal significantly outperforms, both in-sample and out-of-sample, the S & P 500 and pure strategies based on either time series momentum or reversal only. The results are robust for different time horizons, short-sale constraints, market states, investor sentiment, and market volatility.
Ho, N., Peng, H., Mayoh, C., Liu, P.Y., Atmadibrata, B., Marshall, G.M., Li, J. & Liu, T. 2018, 'Delineation of the frequency and boundary of chromosomal copy number variations in paediatric neuroblastoma.', Cell cycle (Georgetown, Tex.), vol. 17, no. 6, pp. 749-758.
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Neuroblastoma, the most common solid tumour in early childhood, is characterized by very frequent chromosomal copy number variations (CNVs). While chromosome 2p amplification, 17q gain, 1p and 11q deletion in human neuroblastoma tissues are well-known, the exact frequencies and boundaries of the chromosomal CNVs have not been delineated. We analysed the publicly available single nucleotide polymorphism (SNP) array data which were originally generated by the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative, defined the frequencies and boundaries of chromosomes 2p11.2 - 2p25.3 amplification, 17q11.1-17q25.3 gain, 1p13.3-1p36.33 deletion and 11q13.3-11q25 deletion in neuroblastoma tissues, and identified chromosome 7q14.1 (Chr7:38254795-38346971) and chromosome 14q11.2 (Chr14:21637401-22024617) deletion in blood and bone marrow samples from neuroblastoma patients, but not in tumour tissues. Kaplan Meier analysis showed that double deletion of Chr7q14.1 and Chr14q11.2 correlated with poor prognosis in MYCN gene amplified neuroblastoma patients. In conclusion, the oncogenes amplified or gained and tumour suppressor genes deleted within the boundaries of chromosomal CNVs in tumour tissues should be studied for their roles in tumourigenesis and as therapeutic targets. Focal deletions of Chr7q14.1 and Chr14q11.2 together in blood and bone marrow samples from neuroblastoma patients can be used as a marker for poorer prognosis and more aggressive therapies.
Hua, J., Huang, M. & Wang, G. 2018, 'Graph Layout Performance Comparisons of Force-directed Algorithms', International Journal of Performability Engineering, vol. 14, no. 1, pp. 67-76.
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Due to force-directed algorithms’ capabilities of producing aesthetically pleasing graph layouts, which follow metrics for graph drawing aesthetics, these layouts have become the most common methods in the practical data visualization area. However, evaluating the performance of relevant algorithms remains a challenge, since graph layout quality is largely relying on aspects such as human intuition, personal judgment and methods’ pre-setting parameters. In addition, most aesthetics criteria of graph drawing conflict with each other. This study evaluated the performance measurements of four force-directed algorithms in terms of seven commonly applied aesthetic criteria based on practical raw data collected, and demonstrated the experimental framework. The early outcomes compared twenty final graph layouts and gave empirical evidences; the study may assist with future detailed force-directed algorithms selection based on users’ specific requirements.
Hua, J., Huang, M., Zreika, M. & Wang, G. 2018, 'Applying Data Visualization Techniques for Stock Relationship Analysis', Filomat, vol. 32, no. 4.
Huang, Y., Cao, L., Zhang, J., Pan, L. & Liu, Y. 2018, 'Exploring feature coupling and model coupling for image source identification', IEEE Transactions on Information Forensics and Security, vol. 13, no. 12, pp. 3108-3121.
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© 2005-2012 IEEE. Recently, there has been great interest in feature-based image source identification. Previous statistical learning-based methods usually regarded the identification process as a classification problem. They assumed the dependence of features and the dependence of models. However, the two assumptions are usually problematic because of the genuine coupling of features and models. To address the issues, in this paper, we propose a novel image source identification scheme. For the feature coupling, a coupled feature representation is adopted to analyze the coupled interaction among features. The coupling relations among features and their powers are measured with Pearson's correlations and integrated in a Taylor-like expansion manner. Regarding model coupling, a new coupled probability representation is developed. The model coupling relationships are characterized with conditional probabilities induced by the confusion matrix and then combined with the law of total probability. The experiments carried out on the Dresden image collection confirm the effectiveness of the proposed scheme. Via mining the feature coupling and model coupling, the identification accuracy can be significantly improved.
Huque, M.H., Anderson, C., Walton, R., Woolford, S. & Ryan, L. 2018, 'Smooth individual level covariates adjustment in disease mapping.', Biometrical journal. Biometrische Zeitschrift, vol. 60, no. 3, pp. 597-615.
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Spatial models for disease mapping should ideally account for covariates measured both at individual and area levels. The newly available "indiCAR" model fits the popular conditional autoregresssive (CAR) model by accommodating both individual and group level covariates while adjusting for spatial correlation in the disease rates. This algorithm has been shown to be effective but assumes log-linear associations between individual level covariates and outcome. In many studies, the relationship between individual level covariates and the outcome may be non-log-linear, and methods to track such nonlinearity between individual level covariate and outcome in spatial regression modeling are not well developed. In this paper, we propose a new algorithm, smooth-indiCAR, to fit an extension to the popular conditional autoregresssive model that can accommodate both linear and nonlinear individual level covariate effects while adjusting for group level covariates and spatial correlation in the disease rates. In this formulation, the effect of a continuous individual level covariate is accommodated via penalized splines. We describe a two-step estimation procedure to obtain reliable estimates of individual and group level covariate effects where both individual and group level covariate effects are estimated separately. This distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. We evaluate the performance of smooth-indiCAR through simulation. Our results indicate that the smooth-indiCAR method provides reliable estimates of all regression and random effect parameters. We illustrate our proposed methodology with an analysis of data on neutropenia admissions in New South Wales (NSW), Australia.
Hussain, W., Hussain, F.K., Hussain, O., Bagia, R. & Chang, E. 2018, 'Risk-based framework for SLA violation abatement from the cloud service provider’s perspective', The Computer Journal.
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The constant increase in the growth of the cloud market creates new challenges for cloud service providers. One such challenge is the need to avoid possible service level agreement (SLA) violations and their consequences through good SLA management. Researchers have proposed various frameworks and have made significant advances in managing SLAs from the perspective of both cloud users and providers. However, none of these approaches guides the service provider on the necessary steps to take for SLA violation abatement; that is, the prediction of possible SLA violations, the process to follow when the system identifies the threat of SLA violation, and the recommended action to take to avoid SLA violation. In this paper, we approach this process of SLA violation detection and abatement from a risk management perspective. We propose a Risk Management-based Framework for SLA violation abatement (RMF-SLA) following the formation of an SLA which comprises SLA monitoring, violation prediction and decision recommendation. Through experiments, we validate and demonstrate the suitability of the proposed framework for assisting cloud providers to minimize possible service violations and penalties.
Jan, M.A., Nanda, He, X.S. & Liu, R.P. 2018, '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, vol. 80, pp. 613-626.
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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.
Jan, M.A., Tan, Z., He, X. & Ni, W. 2018, 'Moving towards highly reliable and effective sensor networks', Ad-Hoc and Sensor Wireless Networks, vol. 40, no. 3-4, pp. 163-168.
Ji, K., Chen, Z., Sun, R., Ma, K., Yuan, Z. & Xu, G. 2018, 'GIST: A generative model with individual and subgroup-based topics for group recommendation', Expert Systems with Applications, vol. 94, pp. 81-93.
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© 2017 Elsevier Ltd In this paper, a Topic-based probabilistic model named GIST is proposed to infer group activities, and make group recommendations. Compared with existing individual-based aggregation methods, it not only considers individual members’ interest, but also consider some subgroups’ interest. Intuition might seem that when a group of users want to take part in an activity, not every group member is decisive, instead, more likely the subgroups of members having close relationships lead to the final activity decision. That motivates our study on jointly considering individual members’ choices and subgroups’ choices for group recommendations. Based on this, our model uses two kinds of unshared topics to model individual members’ interest and subgroups’ interest separately, and then make final recommendations according to the choices from the two aspects with a weight-based scheme. Moreover, the link information in the graph topology of the groups can be used to optimize the weights of our model. The experimental results on real-life data show that the recommendation accuracy is significantly improved by GIST comparing with the state-of-the-art methods.
Jian, S., Cao, L., Lu, K. & Gao, H. 2018, 'Unsupervised Coupled Metric Similarity for Non-IID Categorical Data', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Appropriate similarity measures always play a critical role in data analytics, learning and processing. Measuring the intrinsic similarity of categorical data for unsupervised learning has not been substantially addressed, and even less effort has been made for the similarity analysis of categorical data that is not independent and identically distributed (non-IID). In this work, a Coupled Metric Similarity (CMS) is defined for unsupervised learning which flexibly captures the value-to-attribute-to-object heterogeneous coupling relationships. CMS learns the similarities in terms of intrinsic heterogeneous intra- and inter-attribute couplings and attribute-to-object couplings in categorical data. The CMS validity is guaranteed by satisfying metric properties and conditions, and CMS can flexibly adapt to IID to non-IID data. CMS is incorporated into spectral clustering and k-modes clustering and compared with relevant state-of-the-art similarity measures that are not necessarily metrics. The experimental results and theoretical analysis show the CMS effectiveness of capturing independent and coupled data characteristics, which significantly outperforms other similarity measures on most datasets.
Kang, G., Li, J. & Tao, D. 2018, 'Shakeout: A New Approach to Regularized Deep Neural Network Training', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5, pp. 1245-1258.
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© 1979-2012 IEEE. Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper, we present a new regularized training approach: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, Shakeout randomly chooses to enhance or reverse each unit's contribution to the next layer. This minor modification of Dropout has the statistical trait: the regularizer induced by Shakeout adaptively combines L-{0} , L-{1} and L-{2} regularization terms. Our classification experiments with representative deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that Shakeout deals with over-fitting effectively and outperforms Dropout. We empirically demonstrate that Shakeout leads to sparser weights under both unsupervised and supervised settings. Shakeout also leads to the grouping effect of the input units in a layer. Considering the weights in reflecting the importance of connections, Shakeout is superior to Dropout, which is valuable for the deep model compression. Moreover, we demonstrate that Shakeout can effectively reduce the instability of the training process of the deep architecture.
Kusakunniran, W., Wu, Q., Ritthipravat, P. & Zhang, J. 2018, 'Hard exudates segmentation based on learned initial seeds and iterative graph cut.', Computer methods and programs in biomedicine, vol. 158, pp. 173-183.
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(Background and Objective): The occurrence of hard exudates is one of the early signs of diabetic retinopathy which is one of the leading causes of the blindness. Many patients with diabetic retinopathy lose their vision because of the late detection of the disease. Thus, this paper is to propose a novel method of hard exudates segmentation in retinal images in an automatic way. (Methods): The existing methods are based on either supervised or unsupervised learning techniques. In addition, the learned segmentation models may often cause miss-detection and/or fault-detection of hard exudates, due to the lack of rich characteristics, the intra-variations, and the similarity with other components in the retinal image. Thus, in this paper, the supervised learning based on the multilayer perceptron (MLP) is only used to identify initial seeds with high confidences to be hard exudates. Then, the segmentation is finalized by unsupervised learning based on the iterative graph cut (GC) using clusters of initial seeds. Also, in order to reduce color intra-variations of hard exudates in different retinal images, the color transfer (CT) is applied to normalize their color information, in the pre-processing step. (Results): The experiments and comparisons with the other existing methods are based on the two well-known datasets, e_ophtha EX and DIARETDB1. It can be seen that the proposed method outperforms the other existing methods in the literature, with the sensitivity in the pixel-level of 0.891 for the DIARETDB1 dataset and 0.564 for the e_ophtha EX dataset. The cross datasets validation where the training process is performed on one dataset and the testing process is performed on another dataset is also evaluated in this paper, in order to illustrate the robustness of the proposed method. (Conclusions): This newly proposed method integrates the supervised learning and unsupervised learning based techniques. It achieves the improved performance, when compared with the exi...
Lassetter, J.H., Macintosh, C.I., Williams, M., Driessnack, M., Ray, G. & Wisco, J.J. 2018, 'Psychometric testing of the healthy eating and physical activity self-efficacy questionnaire and the healthy eating and physical activity behavior recall questionnaire for children', Journal for Specialists in Pediatric Nursing, vol. 23, no. 2.
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© 2018 Wiley Periodicals, Inc. Purpose: The purpose of this study was to develop and assess the psychometric properties for two related questionnaires: the Healthy Eating and Physical Activity Self-Efficacy Questionnaire for Children (HEPASEQ-C) and the Healthy Eating and Physical Activity Behavior Recall Questionnaire for Children (HEPABRQ-C). Design and Methods: HEPASEQ-C and HEPABRQ-C were administered to 517 participating children with 492 completing. Data were analyzed to evaluate for reliability and validity of the questionnaires. Results: Content validity was established through a 10-person expert panel. For the HEPASEQ-C, item content validity index (CVI) ranged from 0.80 to 1.00. The CVI for the total questionnaire was 1.0. All HEPASEQ-C items loaded on a single factor. Cronbach's alpha was deemed acceptable (.749). For the HEPABRQ-C, item CVI ranged from 0.88 to 1.00. CVI for the total questionnaire was 1.0. Pearson product moment correlation between HEPASEQ-C and HEPABRQ-C scores was significant (r =.501, p =.000). Practice Implications: The HEPASEQ-C and HEPABRQ-C are easily administered and provide helpful insights into children's self-efficacy and behavior recall. They are easy to use and applicable for upper elementary school settings, in clinical settings for individual patients, and in health promotion settings.
Li, L., Liu, Z. & Zhang, J. 2018, 'Unsupervised image co-segmentation via guidance of simple images', Neurocomputing, vol. 275, pp. 1650-1661.
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© 2017 Elsevier B.V. This paper proposes a novel image co-segmentation method, which aims to segment the common objects in a group of images. The proposed method takes advantages of the reliability of simple images and successfully improves the performance. The images are first ranked by the complexities based on their saliency maps. Then, the simple images, in which objects are common and easy to be segmented, are selected and processed to obtain their segmentation results, these segmentation results are taken as the samples of the targeted objects. Finally, the remaining complicated images are segmented with the guidance of the samples. The experiments on the iCoseg dataset demonstrate the outperformance and robustness of the proposed method.
Li, X., Liu, K., Dong, Y. & Tao, D. 2018, 'Patch Alignment Manifold Matting', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE Image matting is generally modeled as a space transform from the color space to the alpha space. By estimating the alpha factor of the model, the foreground of an image can be extracted. However, there is some dimensional information redundancy in the alpha space. It usually leads to the misjudgments of some pixels near the boundary between the foreground and the background. In this paper, a manifold matting framework named Patch Alignment Manifold Matting is proposed for image matting. In particular, we first propose a part modeling of color space in the local image patch. We then perform whole alignment optimization for approximating the alpha results using subspace reconstructing error. Furthermore, we utilize Nesterov's algorithm to solve the optimization problem. Finally, we apply some manifold learning methods in the framework, and obtain several image matting methods, such as named ISOMAP matting and its derived Cascade ISOMAP matting. The experimental results reveal that the manifold matting framework and its two examples are effective when compared with several representative matting methods.
Li, X., Lu, Q., Dong, Y. & Tao, D. 2018, 'SCE: A Manifold Regularized Set-Covering Method for Data Partitioning', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 5, pp. 1760-1773.
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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, X., Zhao, L., Ji, W., Wu, Y., Wu, F., Yang, M.H., Tao, D. & Reid, I. 2018, 'Multi-Task Structure-aware Context Modeling for Robust Keypoint-based Object Tracking', IEEE Transactions on Pattern Analysis and Machine Intelligence.
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IEEE In the fields of computer vision and graphics, keypoint-based object tracking is a fundamental and challenging problem, which is typically formulated in a spatio-temporal context modeling framework. However, many existing keypoint trackers are incapable of effectively modeling and balancing the following three aspects in a simultaneous manner: temporal model coherence across frames, spatial model consistency within frames, and discriminative feature construction. To address this problem, we propose a robust keypoint tracker based on spatio-temporal multi-task structured output optimization driven by discriminative metric learning. Consequently, temporal model coherence is characterized by multi-task structured keypoint model learning over several adjacent frames; spatial model consistency is modeled by solving a geometric verification based structured learning problem; discriminative feature construction is enabled by metric learning to ensure the intra-class compactness and inter-class separability. To achieve the goal of effective object tracking, we jointly optimize the above three modules in a spatio-temporal multi-task learning scheme. Furthermore, we incorporate this joint learning scheme into both single-object and multi-object tracking scenarios, resulting in robust tracking results. Experiments over several challenging datasets have justified the effectiveness of our single-object and multi-object trackers against the state-of-the-art.
Li, Y., Tian, X., Liu, T. & Tao, D. 2018, 'On Better Exploring and Exploiting Task Relationships in Multitask Learning: Joint Model and Feature Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 5, pp. 1975-1985.
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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.
Li, Z., Nie, F., Chang, X., Yang, Y., Zhang, C. & Sebe, N. 2018, 'Dynamic Affinity Graph Construction for Spectral Clustering Using Multiple Features', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE Spectral clustering (SC) has been widely applied to various computer vision tasks, where the key is to construct a robust affinity matrix for data partitioning. With the increase in visual features, conventional SC methods are facing two challenges: 1) how to effectively generate an affinity matrix based on multiple features? and 2) how to deal with high-dimensional visual features which could be redundant? To address these issues mentioned earlier, we present a new approach to: 1) learn a robust affinity matrix using multiple features, allowing us to simultaneously determine optimal weights for each feature; and 2) decide a set of optimal projection matrixes, one for each feature, that decide the lower dimensional space, as well as the optimal affinity weight of each data pair in the lower dimensional space. There are two major advantages of our new approach over the existing clustering techniques. First, our approach assigns affinity weights for data points on a per-data-pair basis. The learning procedure avoids the explicit specification of the size of the neighborhood in the affinity matrix, and the bandwidth parameter required to compute the Gaussian kernel, both of which are sensitive and yet difficult to determine beforehand. Second, the affinity weights are based on the distances in a lower dimensional space, while the low-dimensional space is inferred according to the optimized affinity weights. Both variables are jointly optimized so as to leverage mutual benefits. The experimental results outperform the compared alternatives, which indicate that the proposed method is effective in simultaneously learning the affinity graph and feature fusion, resulting in better clustering results.
Lian, D., Zheng, K., Ge, Y., Cao, L., Chen, E. & Xie, X. 2018, 'GeoMF++: Scalable location recommendation via joint geographical modeling and matrix factorization', ACM Transactions on Information Systems, vol. 36, no. 3.
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© 2018 ACM. Location recommendation is an important means to help people discover attractive locations. However, extreme sparsity of user-location matrices leads to a severe challenge, so it is necessary to take implicit feedback characteristics of user mobility data into account and leverage the location's spatial information. To this end, based on previously developed GeoMF, we propose a scalable and flexible framework, dubbed GeoMF++, for joint geographical modeling and implicit feedback-based matrix factorization. We then develop an efficient optimization algorithm for parameter learning, which scales linearly with data size and the total number of neighbor grids of all locations. GeoMF++ can be well explained from two perspectives. First, it subsumes two-dimensional kernel density estimation so that it captures spatial clustering phenomenon in user mobility data; Second, it is strongly connected with widely used neighbor additive models, graph Laplacian regularized models, and collectivematrix factorization. Finally, we extensively evaluate GeoMF++ on two large-scale LBSN datasets. The experimental results show that GeoMF++ consistently outperforms the state-of-the-art and other competing baselines on both datasets in terms of NDCG and Recall. Besides, the efficiency studies show that GeoMF++ is much more scalable with the increase of data size and the dimension of latent space.
Liu, F., Gong, C., Huang, X., Zhou, T., Yang, J. & Tao, D. 2018, 'Robust Visual Tracking Revisited: From Correlation Filter to Template Matching', IEEE Transactions on Image Processing, vol. 27, no. 6, pp. 2777-2790.
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© 2018 IEEE. In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed mutual buddies similarity is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named memory, which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively understand the target appearance variations, recall some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers.
Liu, M., Xu, C., Luo, Y., Xua, C., Wen, Y. & Tao, D. 2018, 'Cost-Sensitive Feature Selection by Optimizing F-measures', IEEE Transactions on Image Processing, vol. 27, no. 3, pp. 1323-1333.
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IEEE Feature selection is beneficial for improving the performance of general machine learning tasks by extracting an informative subset from the high-dimensional features. Conventional feature selection methods usually ignore the class imbalance problem, thus the selected features will be biased towards the majority class. Considering that F-measure is a more reasonable performance measure than accuracy for imbalanced data, this paper presents an effective feature selection algorithm that explores the class imbalance issue by optimizing F-measures. Since F-measure optimization can be decomposed into a series of cost-sensitive classification problems, we investigate the costsensitive feature selection (CSFS) by generating and assigning different costs to each class with rigorous theory guidance. After solving a series of cost-sensitive feature selection problems, features corresponding to the best F-measure will be selected. In this way, the selected features will fully represent the properties of all classes. Experimental results on popular benchmarks and challenging real-world datasets demonstrate the significance of cost-sensitive feature selection for the imbalanced data setting and validate the effectiveness of the proposed method.
Liu, Q., Ghosh, S., Li, J., Wong, L. & Ramamohanarao, K. 2018, 'Discovering pan-correlation patterns from time course data sets by efficient mining algorithms', Computing, vol. 100, no. 4, pp. 421-437.
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© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive correlation patterns. We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations. Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (1) the algorithm has linear time increment in terms of increasing number of variables; (2) negative correlation patterns are abundant in real-world data sets; and (3) correlation patterns with time lags and gaps are abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely.
Liu, Q., Wu, R., Chen, E., Xu, G., Su, Y., Chen, Z. & Hu, G. 2018, 'Fuzzy Cognitive Diagnosis for Modelling Examinee Performance', ACM Transactions on Intelligent Systems and Technology, vol. 9, no. 4, pp. 1-26.
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Liu, Y., Lan, C., Blumenstein, M. & Li, J. 2018, 'Bi-level error correction for PacBio long reads', IEEE/ACM Transactions on Computational Biology and Bioinformatics.
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IEEE The latest sequencing technologies such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines can generate long reads at the length of thousands of nucleic bases which is much longer than the reads at the length of hundreds generated by Illumina machines. However, these long reads are prone to much higher error rates, for example 15%, making downstream analysis and applications very difficult. Error correction is a process to improve the quality of sequencing data. Hybrid correction strategies have been recently proposed to combine Illumina reads of low error rates to fix sequencing errors in the noisy long reads with good performance. In this paper, we propose a new method named Bicolor, a bi-level framework of hybrid error correction for further improving the quality of PacBio long reads. At the first level, our method uses a de Bruijn graph-based error correction idea to search paths in pairs of solid < formula > < tex > $k$ < /tex > < /formula > -mers iteratively with an increasing length of < formula > < tex > $k$ < /tex > < /formula > -mer. At the second level, we combine the processed results under different parameters from the first level. In particular, a multiple sequence alignment algorithm is used to align those similar long reads, followed by a voting algorithm which determines the final base at each position of the reads. We compare the superior performance of Bicolor with three state-of-the-art methods on three real data sets. Results demonstrate that Bicolor always achieves the highest identity ratio. Bicolor also achieves a higher alignment ratio ( < formula > < tex > $ & #x003E; 1.3\%$ < /tex > < /formula > ) and a higher number of aligned reads than the current methods on two data sets. On the third data set, our method is closely competitive to the current methods in terms of number of aligned reads and genome coverage. The C++ source codes of our algorithm are freely available at https://github.com/yuansliu/Bicolor.
Luo, Y., Wen, Y. & Tao, D. 2018, 'Heterogeneous Multitask Metric Learning Across Multiple Domains', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE Distance metric learning plays a crucial role in diverse machine learning algorithms and applications. When the labeled information in a target domain is limited, transfer metric learning (TML) helps to learn the metric by leveraging the sufficient information from other related domains. Multitask metric learning (MTML), which can be regarded as a special case of TML, performs transfer across all related domains. Current TML tools usually assume that the same feature representation is exploited for different domains. However, in real-world applications, data may be drawn from heterogeneous domains. Heterogeneous transfer learning approaches can be adopted to remedy this drawback by deriving a metric from the learned transformation across different domains. However, they are often limited in that only two domains can be handled. To appropriately handle multiple domains, we develop a novel heterogeneous MTML (HMTML) framework. In HMTML, the metrics of all different domains are learned together. The transformations derived from the metrics are utilized to induce a common subspace, and the high-order covariance among the predictive structures of these domains is maximized in this subspace. There do exist a few heterogeneous transfer learning approaches that deal with multiple domains, but the high-order statistics (correlation information), which can only be exploited by simultaneously examining all domains, is ignored in these approaches. Compared with them, the proposed HMTML can effectively explore such high-order information, thus obtaining more reliable feature transformations and metrics. Effectiveness of our method is validated by the extensive and intensive experiments on text categorization, scene classification, and social image annotation.
Luo, Y., Wen, Y., Liu, T. & Tao, D. 2018, 'Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain', IEEE Transactions on Pattern Analysis and Machine Intelligence.
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IEEE The goal of transfer learning is to improve the performance of target learning task by leveraging information (or transferring knowledge) from other related tasks. In this paper, we examine the problem of transfer distance metric learning (DML), which usually aims to mitigate the label information deficiency issue in the target DML. Most of the current Transfer DML (TDML) methods are not applicable to the scenario where data are drawn from heterogeneous domains. Some existing heterogeneous transfer learning (HTL) approaches can learn target distance metric by usually transforming the samples of source and target domain into a common subspace. However, these approaches lack flexibility in real-world applications, and the learned transformations are often restricted to be linear. This motivates us to develop a general flexible heterogeneous TDML (HTDML) framework. In particular, any (linear/nonlinear) DML algorithms can be employed to learn the source metric beforehand. Then the pre-learned source metric is represented as a set of knowledge fragments to help target metric learning. We show how generalization error in the target domain could be reduced using the proposed transfer strategy, and develop novel algorithm to learn either linear or nonlinear target metric. Extensive experiments on various applications demonstrate the effectiveness of the proposed method.
Maneze, D., Ramjan, L., DiGiacomo, M., Everett, B., Davidson, P.M. & Salamonson, Y. 2018, '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...
Manzoor, M., Hussain, W., Sohaib, O., Hussain, F.K. & Alkhalaf, S. 2018, 'Methodological investigation for enhancing the usability of university websites', Journal of Ambient Intelligence and Humanized Computing, pp. 1-19.
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For university websites to be successful and to increase the chance of converting a prospective student into a current student, it is necessary to increase the visibility and accessibility of all related content so that a student can achieve their desired task in the fastest possible time. The criteria for evaluating university websites are very vague and are usually unknown to most developers, which adversely impacts the user-experience of the students visiting such websites. To solve this problem, we devised a usability metric and examined the leading university websites to analyze whether these websites were able to meet the requirements of students. In this research, we applied qualitative and quantitative approaches by considering 300 students and evaluating 86 university websites (26 from Canada, 30 from the United States, and 30 from Europe) based on a six-attribute metric comprising navigation, organization, ease of use (simplicity), design (layout), communication and content. From the evaluation results, we find that the 88% of the students are satisfied with our proposed usability attributes, but that most universities fail to meet basic standards of usability as desired by the students. The findings also show that the usability evaluation score for each usability feature varies from country to country, such as for (1) multiple language support − 23% of the Canadian websites, 63% of the European websites and none of the USA websites has the feature; for (2) Scholarships/Funding/Financial Aid link − 24% of the Canadian websites, 80% of the European and the USA websites has the feature; for (3) admission link − 88% of the Canadian websites, 20% of the European websites and 90% of the USA websites has the feature. In addition, from the evaluative result we find that our proposed approach will not only increase the usability of academic websites but will also provide an easiest way to covert prospective student inquiries into enrollment opportunities.
McDonagh, J., Martin, L., Ferguson, C., Jha, S., Macdonald, P., Davidson, P. & Newton, P. 2018, 'Frailty assessment instruments in heart failure: a systematic review.', European Journal of Cardiovascular Nursing.
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Mei, T., Li, L., Tian, X., Tao, D. & Ngo, C.W. 2018, 'PageSense: Toward Stylewise Contextual Advertising via Visual Analysis of Web Pages', IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 1, pp. 254-266.
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© 1991-2012 IEEE. The Internet has emerged as the most effective and a highly popular medium for advertising. Current contextual advertising platforms need publishers to manually change the original structure of their Web pages and predefine the position and style of embedded ads. Although publishers spend significant effort optimizing their Web page layout, a large number of Web pages contain noticeable blank regions. We present an innovative stylewise advertising platform for contextual advertising, called PageSense. The 'style' of Web pages refers to the visual appearance of a Web page, such as color and layout. PageSense aims to associate style-consistent ads with Web pages. It provides two advertising options: 1) If publishers predefine ad positions within Web pages, PageSense will analyze the page style and select ads, which are consistent with the Web page layout, and 2) if publishers impose no constraints for ad placement, PageSense will automatically detect blank regions, select the most nonintrusive region for ad insertion, associate color-consistent ads with the Web pages, and deliver them to blank regions without breaking the original Web page style. Our experiments have verified the effectiveness of PageSense as a complement to existing contextual advertising.
Meng, F., Rui, X., Wang, Z., Xing, Y. & Cao, L. 2018, 'Coupled node similarity learning for community detection in attributed networks', Entropy, vol. 20, no. 6.
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© 2018 by the authors. Attributed networks consist of not only a network structure but also node attributes. Most existing community detection algorithms only focus on network structures and ignore node attributes, which are also important. Although some algorithms using both node attributes and network structure information have been proposed in recent years, the complex hierarchical coupling relationships within and between attributes, nodes and network structure have not been considered. Such hierarchical couplings are driving factors in community formation. This paper introduces a novel coupled node similarity (CNS) to involve and learn attribute and structure couplings and compute the similarity within and between nodes with categorical attributes in a network. CNS learns and integrates the frequency-based intra-attribute coupled similarity within an attribute, the co-occurrence-based inter-attribute coupled similarity between attributes, and coupled attribute-to-structure similarity based on the homophily property. CNS is then used to generate the weights of edges and transfer a plain graph to a weighted graph. Clustering algorithms detect community structures that are topologically well-connected and semantically coherent on the weighted graphs. Extensive experiments verify the effectiveness of CNS-based community detection algorithms on several data sets by comparing with the state-of-the-art node similarity measures, whether they involve node attribute information and hierarchical interactions, and on various levels of network structure complexity.
Nawaz, F., Janjua, N.K., Hussain, O.K., Hussain, F.K., Chang, E. & Saberi, M. 2018, 'Event-driven approach for predictive and proactive management of SLA violations in the Cloud of Things', Future Generation Computer Systems, vol. 84, pp. 78-97.
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© 2018 Elsevier B.V. In a dynamic environment such as the cloud-of-things, one of the most critical factors for successful service delivery is the QoS under defined constraints. Even though guarantees in the form of service level agreements (SLAs) are provided to users, many services exhibit dynamic Quality of Service (QoS) variations. This QoS variation as well as changes in the behavior and state of the service is caused by some internal events (such as varying loads) and external events (such as location and weather), which results in frequent SLA violations. Most of the existing violation prediction approaches use historic data to predict future QoS values. They do not consider dynamic changes and the events that cause these changes in QoS attributes. In this paper, we propose an event-driven-based proactive approach for predicting SLA violations by combining logic-based reasoning and probabilistic inferencing. The results show that our proposed approach is efficient and proactively identifies SLA violations under uncertain QoS observations.
Nguyen, Q., Arness, D., Sanderson, C., Simoff, S. & Huang, M. 2018, 'Enabling Effective Tree Exploration Using Visual Cues', Journal of Visual Languages and Computing, vol. 47, pp. 44-61.
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This article presents a new interactive visualization for exploring large hierarchical structures by providing visual cues on a node link tree visualization. Our technique provides topological previews of hidden substructures with three types of visual cues including simple cues, tree cues and treemap cues. We demonstrate the visual cues on Degree-of-Interest Tree (DOITree) due to its familiar mapping, its capability of providing multiple focused nodes, and its dynamic rescaling of substructures to fit the available space. We conducted a usability study with 28 participants that measured completion time and accuracy across five different topology search tasks. The simple cues had the fastest completion time across three of the node identification tasks. The treemap cues had the highest rate of correct answers on four of the five tasks, although only reaching statistical significance for two of these. As predicted, user ratings demonstrated a preference for the easy to understand tree cues followed by the simple cue, despite this not consistently reflected in performance results.
Ojha, S., Williams, M.A. & Johnston, B. 2018, 'The Essence of Ethical Reasoning in Robot-Emotion Processing', International Journal of Social Robotics, vol. 10, no. 2, pp. 211-223.
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© 2017, Springer Science+Business Media B.V., part of Springer Nature. As social robots become more and more intelligent and autonomous in operation, it is extremely important to ensure that such robots act in socially acceptable manner. More specifically, if such an autonomous robot is capable of generating and expressing emotions of its own, it should also have an ability to reason if it is ethical to exhibit a particular emotional state in response to a surrounding event. Most existing computational models of emotion for social robots have focused on achieving a certain level of believability of the emotions expressed. We argue that believability of a robot’s emotions, although crucially necessary, is not a sufficient quality to elicit socially acceptable emotions. Thus, we stress on the need of higher level of cognition in emotion processing mechanism which empowers social robots with an ability to decide if it is socially appropriate to express a particular emotion in a given context or it is better to inhibit such an experience. In this paper, we present the detailed mathematical explanation of the ethical reasoning mechanism in our computational model, EEGS, that helps a social robot to reach to the most socially acceptable emotional state when more than one emotions are elicited by an event. Experimental results show that ethical reasoning in EEGS helps in the generation of believable as well as socially acceptable emotions.
Patel, O.P., Tiwari, A., Chaudhary, R., Nuthalapati, S.V., Bharill, N., Prasad, M., Hussain, F.K. & Hussain, O.K. 2018, 'Enhanced quantum-based neural network learning and its application to signature verification', Soft Computing, pp. 1-14.
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© 2017 Springer-Verlag GmbH Germany, part of Springer Nature In this paper, an enhanced quantum-based neural network learning algorithm (EQNN-S) which constructs a neural network architecture using the quantum computing concept is proposed for signature verification. The quantum computing concept is used to decide the connection weights and threshold of neurons. A boundary threshold parameter is introduced to optimally determine the neuron threshold. This parameter uses min, max function to decide threshold, which assists efficient learning. A manually prepared signature dataset is used to test the performance of the proposed algorithm. To uniquely identify the signature, several novel features are selected such as the number of loops present in the signature, the boundary calculation, the number of vertical and horizontal dense patches, and the angle measurement. A total of 45 features are extracted from each signature. The performance of the proposed algorithm is evaluated by rigorous training and testing with these signatures using partitions of 60–40 and 70–30%, and a tenfold cross-validation. To compare the results derived from the proposed quantum neural network, the same dataset is tested on support vector machine, multilayer perceptron, back propagation neural network, and Naive Bayes. The performance of the proposed algorithm is found better when compared with the above methods, and the results verify the effectiveness of the proposed algorithm.
Peng, H., Zheng, Y., Blumenstein, M., Tao, D. & Li, J. 2018, 'CRISPR/Cas9 cleavage efficiency regression through boosting algorithms and Markov sequence profiling.', Bioinformatics (Oxford, England).
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CRISPR/Cas9 system is a widely used genome editing tool. A prediction problem of great interests for this system is: how to select optimal single guide RNAs (sgRNAs) such that its cleavage efficiency is high meanwhile the off-target effect is low.This work proposed a two-step averaging method (TSAM) for the regression of cleavage efficiencies of a set of sgRNAs by averaging the predicted efficiency scores of a boosting algorithm and those by a support vector machine (SVM).We also proposed to use profiled Markov properties as novel features to capture the global characteristics of sgRNAs. These new features are combined with the outstanding features ranked by the boosting algorithm for the training of the SVM regressor. TSAM improved the mean Spearman correlation coefficiencies comparing with the state-of-the-art performance on benchmark datasets containing thousands of human, mouse and zebrafish sgRNAs. Our method can be also converted to make binary distinctions between efficient and inefficient sgRNAs with superior performance to the existing methods. The analysis reveals that highly efficient sgRNAs have lower melting temperature at the middle of the spacer, cut at 5'-end closer parts of the genome and contain more 'A' but less 'G' comparing with inefficient ones. Comprehensive further analysis also demonstrates that our tool can predict an sgRNA's cutting efficiency with consistently good performance no matter it is expressed from an U6 promoter in cells or from a T7 promoter in vitro.Online tool is available at http://www.aai-bioinfo.com/CRISPR/. Python and Matlab source codes are freely available at https://github.com/penn-hui/TSAM.Jinyan.Li@uts.edu.au.Supplementary data are available at Bioinformatics online.
Pham, P. & Huang, M. 2018, 'Quantitative Approach on Parallel Coordinates andScatter Plots for Multidimensional-Data Visual Analytics', Journal of computers, vol. 13, no. 4, pp. 407-416.
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Parallel coordinates and scatter plots are two well-known visualization techniques for multidimensional data analytics and often employed cooperatively for flexibility increase in exploration of such data. Existing approaches approximately consider qualitative issues and single attribute comparison, which might face statistic challenges in case of quantitative requirement. This paper introduces a new quantitative approach for visual enhancement of parallel coordinates and scatter plots in term of multiple attribute comparison. The method is based on the visual integration of interactive stacked bars and visual queries on parallel axes and scatter charts. The parallel coordinates play the role of a context view while the scatter charts are for focus details. Using the technique, users could not only quantitatively analyze multivariate data, but also flexibly compare multiple target attributes. Moreover, further investigation is enabled for deep understanding of desired information. The characteristics and usefulness of our approach are demonstrated via a case study with two typical use cases.
Prichard, R., Hayward, C., Davidson, P., Newton, P., Goodall, S. & Kershaw, L. 2018, 'Left ventricular device implantation impacts on hospitalisation rates, length of stay and out of hospital time', Heart, Lung and Circulation, vol. 27, no. 6, pp. 708-715.
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Qiao, M., Yu, J., Bian, W., Li, Q. & Tao, D. 2018, 'Adapting Stochastic Block Models to Power-Law Degree Distributions', IEEE Transactions on Cybernetics.
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IEEE Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new variant of SBM, termed power-law degree SBM (PLD-SBM), by introducing degree decay variables to explicitly encode the varying degree distribution over all nodes. With an exponential prior, it is proved that PLD-SBM approximately preserves the scale-free feature in real networks. In addition, from the inference of variational E-Step, PLD-SBM is indeed to correct the bias inherited in SBM with the introduced degree decay factors. Furthermore, experiments conducted on both synthetic networks and two real-world datasets including Adolescent Health Data and the political blogs network verify the effectiveness of the proposed model in terms of cluster prediction accuracies.
Rao, A., Newton, P., DiGiacomo, M., Hickman, L., Hwang, C. & Davidson, P. 2018, 'Optimal gender specific strategies for the secondary prevention of cardiovascular disease in women: a systematic review', Journal of Cardiopulmonary Rehabilitation and Prevention.
Schönbach, C., Li, J., Ma, L., Horton, P., Sjaugi, M.F. & Ranganathan, S. 2018, 'A bioinformatics potpourri.', BMC genomics, vol. 19, no. Suppl 1, p. 920.
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The 16th International Conference on Bioinformatics (InCoB) was held at Tsinghua University, Shenzhen from September 20 to 22, 2017. The annual conference of the Asia-Pacific Bioinformatics Network featured six keynotes, two invited talks, a panel discussion on big data driven bioinformatics and precision medicine, and 66 oral presentations of accepted research articles or posters. Fifty-seven articles comprising a topic assortment of algorithms, biomolecular networks, cancer and disease informatics, drug-target interactions and drug efficacy, gene regulation and expression, imaging, immunoinformatics, metagenomics, next generation sequencing for genomics and transcriptomics, ontologies, post-translational modification, and structural bioinformatics are the subject of this editorial for the InCoB2017 supplement issues in BMC Genomics, BMC Bioinformatics, BMC Systems Biology and BMC Medical Genomics. New Delhi will be the location of InCoB2018, scheduled for September 26-28, 2018.
Shen, J., Liang, Z., Liu, J., Sun, H., Shao, L. & Tao, D. 2018, 'Multiobject Tracking by Submodular Optimization', IEEE Transactions on Cybernetics.
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IEEE In this paper, we propose a new multiobject visual tracking algorithm by submodular optimization. The proposed algorithm is composed of two main stages. At the first stage, a new selecting strategy of tracklets is proposed to cope with occlusion problem. We generate low-level tracklets using overlap criteria and min-cost flow, respectively, and then integrate them into a candidate tracklets set. In the second stage, we formulate the multiobject tracking problem as the submodular maximization problem subject to related constraints. The submodular function selects the correct tracklets from the candidate set of tracklets to form the object trajectory. Then, we design a connecting process which connects the corresponding trajectories to overcome the occlusion problem. Experimental results demonstrate the effectiveness of our tracking algorithm. Our source code is available at https://github.com/shenjianbing/submodulartrack.
Shen, X., Liu, T., Tao, D., Fan, Y., Zhang, J., Li, S., Jiang, J., Zhu, W., Wang, Y., Wang, Y., Brodaty, H., Sachdev, P. & Wen, W. 2018, 'Variation in longitudinal trajectories of cortical sulci in normal elderly.', NeuroImage, vol. 166, pp. 1-9.
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Sulcal morphology has been reported to change with age-related neurological diseases, but the trajectories of sulcal change in normal ageing in the elderly is still unclear. We conducted a study of sulcal morphological changes over seven years in 132 normal elderly participants aged 70-90 years at baseline, and who remained cognitively normal for the next seven years. We examined the fold opening and sulcal depth of sixteen (eight on each hemisphere) prominent sulci based on T1-weighted MRI using automated methods with visual quality control. The trajectory of each individual sulcus with respect to age was examined separately by linear mixed models. Fold opening was best modelled by cubic fits in five sulci, by quadratic models in six sulci and by linear models in five sulci, indicating an accelerated widening of a number of sulci in older age. Sulcal depth showed significant linear decline in three sulci and quadratic trend in one sulcus. Turning points of non-linear trajectories towards accelerated widening of the fold were found to be around the age between 75 and 80, indicating an accelerated atrophy of brain cortex starting in the age of late 70s. Our findings of cortical sulcal changes in normal ageing could provide a reference for studies of neurocognitive disorders, including neurodegenerative diseases, in the elderly.
Shen, X., Tian, X., Liu, T., Xu, F. & Tao, D. 2018, 'Continuous Dropout', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE Dropout has been proven to be an effective algorithm for training robust deep networks because of its ability to prevent overfitting by avoiding the co-adaptation of feature detectors. Current explanations of dropout include bagging, naive Bayes, regularization, and sex in evolution. According to the activation patterns of neurons in the human brain, when faced with different situations, the firing rates of neurons are random and continuous, not binary as current dropout does. Inspired by this phenomenon, we extend the traditional binary dropout to continuous dropout. On the one hand, continuous dropout is considerably closer to the activation characteristics of neurons in the human brain than traditional binary dropout. On the other hand, we demonstrate that continuous dropout has the property of avoiding the co-adaptation of feature detectors, which suggests that we can extract more independent feature detectors for model averaging in the test stage. We introduce the proposed continuous dropout to a feedforward neural network and comprehensively compare it with binary dropout, adaptive dropout, and DropConnect on Modified National Institute of Standards and Technology, Canadian Institute for Advanced Research-10, Street View House Numbers, NORB, and ImageNet large scale visual recognition competition-12. Thorough experiments demonstrate that our method performs better in preventing the co-adaptation of feature detectors and improves test performance.
Smith, T.A., Agar, M., Jenkins, C.R., Ingham, J.M. & Davidson, P.M. 2018, 'Experience of acute noninvasive ventilation-insights from 'Behind the Mask': a qualitative study.', BMJ Supportive and Palliative Care.
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Non-invasive ventilation (NIV) is widely used in the management of acute and acute-on-chronic respiratory failure. Understanding the experiences of patients treated with NIV is critical to person-centred care. We describe the subjective experiences of individuals treated with NIV for acute hypercapnic respiratory failure.Qualitative face-to-face interviews analysed using thematic analysis.Australian tertiary teaching hospital.Individuals with acute hypercapnic respiratory failure treated with NIV outside the intensive care unit. Individuals who did not speak English or were unable or unwilling to consent were excluded.13 participants were interviewed. Thematic saturation was achieved. Participants described NIV providing substantial relief from symptoms and causing discomfort. They described enduring NIV to facilitate another chance at life. Although participants sometimes appeared passive, others expressed a strong conviction that they knew which behaviours and treatments relieved their distress. Most participants described gaps in their recollection of acute hospitalisation and placed a great amount of trust in healthcare providers. All participants indicated that they would accept NIV in the future, if clinically indicated, and often expressed a sense of compulsion to accept NIV. Participants' description of their experience of NIV was intertwined with their experience of chronic disease.Participants described balancing the benefits and burdens of NIV, with the goal of achieving another chance at life. Gaps in recall of their treatment with NIV were frequent, potentially suggesting underlying delirium. The findings of this study inform patient-centred care, have implications for the care of patients requiring NIV and for advance care planning discussions.
Takalkar, M., Xu, M., Wu, Q. & Chaczko, Z. 2018, 'A survey: facial micro-expression recognition', Multimedia Tools and Applications, pp. 1-25.
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© 2017 Springer Science+Business Media, LLC Facial expression recognition plays a crucial role in a wide range of applications of psychotherapy, security systems, marketing, commerce and much more. Detecting a macro-expression, which is a direct representation of an ‘emotion,’ is a relatively straight-forward task. Playing a pivotal role as macro-expressions, micro-expressions are more accurate indicators of a train of thought or even subtle, passive or involuntary thoughts. Compared to macro-expressions, identifying micro-expressions is a much more challenging research question because their time spans are narrowed down to a fraction of a second, and can only be defined using a broader classification scale. This paper is an all-inclusive survey-cum-analysis of the various micro-expression recognition techniques. We analyze the general framework for micro-expression recognition system by decomposing the pipeline into fundamental components, namely face detecting, pre-processing, facial feature detection and extraction, datasets, and classification. We discuss the role of these elements and highlight the models and new trends that are followed in their design. Moreover, we provide an extensive analysis of micro-expression recognition systems by comparing their performance. We also discuss the new deep learning features that can, in the near future, replace the hand-crafted features for facial micro-expression recognition. This survey has been developed, focusing on the methodologies applied, databases used, performance regarding recognition accuracy and comparing these to distil the gaps in the efficiencies, future scope, and research potentials. Through this survey, we intend to look into this problem and develop a comprehensive and efficient recognition scheme. This study allows us to identify open issues and to determine future directions for designing real-world micro-expression recognition systems.
Usman, M., He, X., Lam, K.K.M., Xu, M., Chen, J., Bokhari, S.M.M. & Jan, M.A. 2018, 'Error Concealment for Cloud-based and Scalable Video Coding of HD Videos', IEEE Transactions on Cloud Computing.
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IEEE The encoding of HD videos faces two challenges: requirements for a strong processing power and a large storage space. One time-efficient solution addressing these challenges is to use a cloud platform and to use a scalable video coding technique to generate multiple video streams with varying bit-rates. Packet-loss is very common during the transmission of these video streams over the Internet and becomes another challenge. One solution to address this challenge is to retransmit lost video packets, but this will create end-to-end delay. Therefore, it would be good if the problem of packet-loss can be dealt with at the user's side. In this paper, we present a novel system that encodes and stores the videos using the Amazon cloud computing platform, and recover lost video frames on user side using a new Error Concealment (EC) technique. To efficiently utilize the computation power of a user's mobile device, the EC is performed based on a multiple-thread and parallel process. The simulation results clearly show that, on average, our proposed EC technique outperforms the traditional Block Matching Algorithm (BMA) and the Frame Copy (FC) techniques.
Usman, M., Jan, M.A., He, X. & Alam, M. 2018, 'Performance evaluation of High Definition video streaming over Mobile Ad Hoc Networks', Signal Processing, vol. 148, pp. 303-313.
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© 2018 Video Service Providers (VSPs) can collect and analyze an enormous amount of multimedia data from various cloud storage centers using real-time big data systems for supporting various online customers. The infrastructure-less nature of Mobile Ad Hoc Networks (MANETs) makes the video streaming a challenging task for VSPs. High packet-loss probability in MANETs can create a notable distortion in the received video quality. In this paper, High Definition (HD) videos are streamed over MANETs. First, a transmission model is designed followed by a distortion model to estimate network distortions, such as packet-loss rate and end-to-end delay. Based on the proposed models, a video streaming framework is designed to efficiently utilize the available bandwidth in MANETs, minimize the network distortions, and improve Quality of Service (QoS). Later, an Error Concealment (EC) technique is used to conceal the lost/dropped video frames to improve the Quality of Experience (QoE). Experimental results show that our proposed video streaming framework outperforms the state-of-the-art routing protocols designed for MANETs, such as Destination-Sequenced Distance Vector (DSDV) and Optimized Link Sate Routing (OLSR) protocols. In the end, both subjective and objective evaluations are performed to evaluate the perceptual quality of the concealed video data.
Usman, M., Jan, M.A., He, X. & Nanda, P. 2018, 'QASEC: A secured data communication scheme for mobile Ad-hoc networks', Future Generation Computer Systems.
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© 2018 Elsevier B.V. Mobile Adhoc NETworks (MANETs) are valuable for various applications due to an efficient, flexible, low-cost and dynamic infrastructure. In these networks, proper utilization of network resources is desirable to maintain Quality of Service (QoS). In multi-hop end-to-end communication, intermediate nodes may eavesdrop on data in transit. As a result, a secured and reliable data delivery from source to destination is required. In this paper, we propose a novel scheme, known as QASEC, to achieve better throughput by securing end-to-end communication in MANETs. The QoS is maintained through an optimal link selection from a queue of available transmission links. The end-to-end communication is secured by authentication. A simple secret-key based symmetric encryption is deployed for interacting nodes. Our proposed QASEC scheme prevents the malicious nodes from data exchange with legitimate intermediate nodes on any established path between the source and the destination. Experimental results show that QASEC performs better in terms of packet-loss rate, jitter and end-to-end delay. Furthermore, QASEC is efficient against various attacks and has a much better performance in terms of associated costs, such as key generation, encryption, and storage and communication.
Usman, M., Yang, N., Jan, M.A., He, X., Xu, M. & Lam, K.M. 2018, 'A Joint Framework for QoS and QoE for Video Transmission over Wireless Multimedia Sensor Networks', IEEE Transactions on Mobile Computing.
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With the emergence of Wireless Multimedia Sensor Networks (MWSNs), the distribution of multimedia contents have now become a reality. Without proper management, the transmission of multimedia data over WMSNs affects the performance of networks due to excessive packets dropping. The existing studies on Quality of Service (QoS) mostly deal with simple WSNs and as such do not account for an increasing number of sensor nodes and an increasing volume of data. In this paper, we propose a novel framework to support QoS in WMSNs along with a light weight Error Concealment (EC) scheme. The EC schemes play a vital role to enhance Quality of Experience (QoE) by maintaining an acceptable quality at the receiving ends. The main objectives of the proposed framework are to maximise the network throughput and to cover-up the effects produced by dropped video packets. To control the data rate, Scalable Video Coding (SVC) is applied at multimedia sensor nodes with variable Quantization Parameters (QPs). Multipath routing is exploited to support real-time video transmission. Experimental results show that the proposed framework can efficiently adjust large volumes of video data under certain network distortions and can effectively conceal dropped/corrupted video frames by producing better objective measurements.
Wang, B., Yuan, X., Gao, X., Li, X. & Tao, D. 2018, 'A Hybrid Level Set With Semantic Shape Constraint for Object Segmentation', IEEE Transactions on Cybernetics.
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IEEE This paper presents a hybrid level set method for object segmentation. The method deconstructs segmentation task into two procedures, i.e., shape transformation and curve evolution, which are alternately optimized until convergence. In this framework, only one shape prior encoded by shape context is utilized to estimate a transformation allowing the curve to have the same semantic expression as shape prior, and curve evolution is driven by an energy functional with topology-preserving and kernelized terms. In such a way, the proposed method is featured by the following advantages: 1) hybrid paradigm makes the level set framework possess the ability of incorporating other shape-related techniques about shape descriptor and distance; 2) shape context endows one single prior with semanticity, and hence leads to the competitive performance compared to the ones with multiple shape priors; and 3) additionally, combining topology-preserving and kernelization mechanisms together contributes to realizing a more reasonable segmentation on textured and noisy images. As far as we know, we propose a hybrid level set framework and utilize shape context to guide curve evolution for the first time. Our method is evaluated with synthetic, healthcare, and natural images, as a result, it shows competitive and even better performance compared to the counterparts.
Wang, D., Deng, S. & Xu, G. 2018, 'Sequence-based context-aware music recommendation', Information Retrieval Journal, vol. 21, no. 2-3, pp. 230-252.
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© 2017, Springer Science+Business Media, LLC. Contextual factors greatly affect users’ preferences for music, so they can benefit music recommendation and music retrieval. However, how to acquire and utilize the contextual information is still facing challenges. This paper proposes a novel approach for context-aware music recommendation, which infers users’ preferences for music, and then recommends music pieces that fit their real-time requirements. Specifically, the proposed approach first learns the low dimensional representations of music pieces from users’ music listening sequences using neural network models. Based on the learned representations, it then infers and models users’ general and contextual preferences for music from users’ historical listening records. Finally, music pieces in accordance with user’s preferences are recommended to the target user. Extensive experiments are conducted on real world datasets to compare the proposed method with other state-of-the-art recommendation methods. The results demonstrate that the proposed method significantly outperforms those baselines, especially on sparse data.
Wang, D., Deng, S., Zhang, X. & Xu, G. 2018, 'Learning to embed music and metadata for context-aware music recommendation', World Wide Web, pp. 1-25.
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© 2017 Springer Science+Business Media, LLC, part of Springer Nature Contextual factors greatly influence users’ musical preferences, so they are beneficial remarkably to music recommendation and retrieval tasks. However, it still needs to be studied how to obtain and utilize the contextual information. In this paper, we propose a context-aware music recommendation approach, which can recommend music pieces appropriate for users’ contextual preferences for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require music pieces to be represented by features ahead, but it can learn the representations from users’ historical listening records. Specifically, the proposed approach first learns music pieces’ embeddings (feature vectors in low-dimension continuous space) from music listening records and corresponding metadata. Then it infers and models users’ global and contextual preferences for music from their listening records with the learned embeddings. Finally, it recommends appropriate music pieces according to the target user’s preferences to satisfy her/his real-time requirements. Experimental evaluations on a real-world dataset show that the proposed approach outperforms baseline methods in terms of precision, recall, F1 score, and hitrate. Especially, our approach has better performance on sparse datasets.
Wang, H., Wu, J., Zhu, X., Chen, Y. & Zhang, C. 2018, 'Time-Variant Graph Classification', IEEE Transactions on Systems, Man, and Cybernetics: Systems.
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IEEE Graphs are commonly used to represent objects, such as images and text, for pattern classification. In a dynamic world, an object may continuously evolve over time, and so does the graph extracted from the underlying object. These changes in graph structure with respect to the temporal order present a new representation of the graph, in which an object corresponds to a set of time-variant graphs. In this paper, we formulate a novel time-variant graph classification task and propose a new graph feature, called a graph-shapelet pattern, for learning and classifying time-variant graphs. Graph-shapelet patterns are compact and discriminative graph transformation subsequences. A graph-shapelet pattern can be regarded as a graphical extension of a shapelet--a class of discriminative features designed for vector-based temporal data classification. To discover graph-shapelet patterns, we propose to convert a time-variant graph sequence into time-series data and use the discovered shapelets to find graph transformation subsequences as graph-shapelet patterns. By converting each graph-shapelet pattern into a unique tokenized graph transformation sequence, we can measure the similarity between two graph-shapelet patterns and therefore classify time-variant graphs. Experiments on both synthetic and real-world data demonstrate the superior performance of the proposed algorithms.
Wang, L., Bao, X., Chen, H. & Cao, L. 2018, 'Effective lossless condensed representation and discovery of spatial co-location patterns', Information Sciences, vol. 436-437, pp. 197-213.
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© 2018 Elsevier Inc. A spatial co-location pattern is a set of spatial features frequently co-occuring in nearby geographic spaces. Similar to closed frequent itemset mining, closed co-location pattern (CCP) mining was proposed for losslessly condensing large collections of prevalent co-location patterns. However, the state-of-the-art condensation methods in mining CCP are inspired by closed frequent itemset mining and do not consider the intrinsic characteristics of spatial co-locations, e.g., the participation index and ratio in spatial feature interactions, thus causing serious containment issues in CCP mining. In this paper, we propose a novel lossless condensed representation of prevalent co-location patterns, Super Participation Index-closed (SPI-closed) co-location. An efficient SPI-closed Miner is also proposed to effectively capture the nature of spatial co-location patterns, alongside the development of three additional pruning strategies to make the SPI-closed Miner efficient. This method captures richer feature interactions in spatial co-locations and solves the containment issues in existing CCP methods. A performance evaluation conducted on both synthetic and real-life data sets shows that SPI-closed Miner reduces the number of CCPs by up to 50%, and runs much faster than the baseline CCP mining algorithm described in the literature.
Wang, R. & Tao, D. 2018, 'Training Very Deep CNNs for General Non-Blind Deconvolution', IEEE Transactions on Image Processing, vol. 27, no. 6, pp. 2897-2910.
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© 1992-2012 IEEE. Non-blind image deconvolution is an ill-posed problem. The presence of noise and band-limited blur kernels makes the solution of this problem non-unique. Existing deconvolution techniques produce a residual between the sharp image and the estimation that is highly correlated with the sharp image, the kernel, and the noise. In most cases, different restoration models must be constructed for different blur kernels and different levels of noise, resulting in low computational efficiency or highly redundant model parameters. Here we aim to develop a single model that handles different types of kernels and different levels of noise: general non-blind deconvolution. Specifically, we propose a very deep convolutional neural network that predicts the residual between a pre-deconvolved image and the sharp image rather than the sharp image. The residual learning strategy makes it easier to train a single model for different kernels and different levels of noise, encouraging high effectiveness and efficiency. Quantitative evaluations demonstrate the practical applicability of the proposed model for different blur kernels. The model also shows the state-of-the-art performance on synthesized blurry images.
Wang, R., Liu, T. & Tao, D. 2018, '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, S. & Cao, L. 2018, 'Inferring Implicit Rules by Learning Explicit and Hidden Item Dependency', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE Revealing complex relations between entities (e.g., items within or between transactions) is of great significance for business optimization, prediction, and decision making. Such relations include not only co-occurrence-based explicit relations but also nonco-occurrence-based implicit ones. Explicit relations have been substantially studied by rule mining-based approaches, including association rule mining and causal rule discovery. In contrast, implicit relations have received much less attention but could be more actionable. In this paper, we focus on the implicit relations between items which rarely or never co-occur while each of them co-occurs with other identical items (link items) with a high probability. A framework integrates both explicit and hidden item dependencies and a corresponding efficient algorithm IRRMiner captures such implicit relations with implicit rule inference. Experimental results show that IRRMiner not only infers implicit rules of various sizes consisting of both frequent and infrequent items effectively, it also runs at least four times faster than IARMiner, a typical indirect association rule mining algorithm which can only mine size-2 indirect association rules between frequent items. IRRMiner is applied to make recommendations and shows that the identified implicit rules can increase recommendation reliability.
Wang, Y., Feng, C., Chen, L., Yin, H., Guo, C. & Chu, Y. 2018, 'User identity linkage across social networks via linked heterogeneous network embedding', World Wide Web, pp. 1-22.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature User identity linkage has important implications in many cross-network applications, such as user profile modeling, recommendation and link prediction across social networks. To discover accurate cross-network user correspondences, it is a critical prerequisite to find effective user representations. While structural and content information describe users from different perspectives, there is a correlation between the two aspects of information. For example, a user who follows a celebrity tends to post content about the celebrity as well. Therefore, the projections of structural and content information of a user should be as close to each other as possible, which inspires us to fuse the two aspects of information in a unified space. However, owing to the information heterogeneity, most existing methods extract features from content and structural information respectively, instead of describing them in a unified way. In this paper, we propose a Linked Heterogeneous Network Embedding model (LHNE) to learn the comprehensive representations of users by collectively leveraging structural and content information in a unified framework. We first model the topics of user interests from content information to filter out noise. Next, cross-network structural and content information are embedded into a unified space by jointly capturing the friend-based and interest-based user co-occurrence in intra-network and inter-network, respectively. Meanwhile, LHNE learns user transfer and topic transfer for enhancing information exchange across networks. Empirical results show LHNE outperforms the state-of-the-art methods on both real social network and synthetic datasets and can work well even with little or no structural information.
Wang, Y., Zhang, J., Liu, Z., Wu, Q., Zhang, Z. & Jia, Y. 2018, 'Depth Super-Resolution on RGB-D Video Sequences with Large Displacement 3D Motion', IEEE Transactions on Image Processing, vol. 27, no. 7, pp. 3571-3585.
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© 1992-2012 IEEE. To enhance the resolution and accuracy of depth data, some video-based depth super-resolution methods have been proposed, which utilizes its neighboring depth images in the temporal domain. They often consist of two main stages: motion compensation of temporally neighboring depth images and fusion of compensated depth images. However, large displacement 3D motion often leads to compensation error, and the compensation error is further introduced into the fusion. A video-based depth super-resolution method with novel motion compensation and fusion approaches is proposed in this paper. We claim that 3D nearest neighboring field (NNF) is a better choice than using positions with true motion displacement for depth enhancements. To handle large displacement 3D motion, the compensation stage utilized 3D NNF instead of true motion used in the previous methods. Next, the fusion approach is modeled as a regression problem to predict the super-resolution result efficiently for each depth image by using its compensated depth images. A new deep convolutional neural network architecture is designed for fusion, which is able to employ a large amount of video data for learning the complicated regression function. We comprehensively evaluate our method on various RGB-D video sequences to show its superior performance.
Wang, Z. & Piccardi, M. 2018, 'Minimum-risk temporal alignment of videos', Multimedia Tools and Applications.
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Wong, J.K.W., Ge, J. & He, S.X. 2018, 'Digitisation in facilities management: A literature review and future research directions', Automation in Construction, vol. 92, pp. 312-326.
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© 2018 Elsevier B.V. Research into digital technology (DT) in construction practices has gained widespread attention. While the application of different DTs in facility management (FM) has been growing, to date, there is no holistic review of the various DT developments and research into FM. A total of 120 academic journal papers, conference proceedings and other technical notes published on the subject, mainly between 2004 and 2017, were reviewed in this paper. The applications of various major DTs, including 1) building information modelling (BIM), 2) reality capture technology (including 3D laser scanning, point cloud), 3) the Internet of Things (IoT) (including radio frequency identification (RFID) and sensor network technologies) and 4) geographic information system (GIS), were reviewed and scrutinised. The review identified a number of possibilities for future research into DT in FM, including, enhancing the interoperability of data, improving the accuracy of point cloud data for developing as-built models for existing facilities, and generating effective BIM/GIS asset database integration. It is hoped that this review and the future directions highlighted in this paper will assist researchers in identifying the areas where further research efforts are most required and in identifying which future directions would be most helpful for digital FM research.
Wright, S.T., Ryan, L.M. & Pham, T. 2018, 'A novel case-control subsampling approach for rapid model exploration of large clustered binary data.', Statistics in Medicine, vol. 37, no. 6, pp. 899-913.
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In many settings, an analysis goal is the identification of a factor, or set of factors associated with an event or outcome. Often, these associations are then used for inference and prediction. Unfortunately, in the big data era, the model building and exploration phases of analysis can be time-consuming, especially if constrained by computing power (ie, a typical corporate workstation). To speed up this model development, we propose a novel subsampling scheme to enable rapid model exploration of clustered binary data using flexible yet complex model set-ups (GLMMs with additive smoothing splines). By reframing the binary response prospective cohort study into a case-control-type design, and using our knowledge of sampling fractions, we show one can approximate the model estimates as would be calculated from a full cohort analysis. This idea is extended to derive cluster-specific sampling fractions and thereby incorporate cluster variation into an analysis. Importantly, we demonstrate that previously computationally prohibitive analyses can be conducted in a timely manner on a typical workstation. The approach is applied to analysing risk factors associated with adverse reactions relating to blood donation.
Wu, J., Pan, S., Zhu, X., Zhang, C. & Wu, X. 2018, 'Multi-instance learning with discriminative bag mapping', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 6, pp. 1065-1080.
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© 1989-2012 IEEE. Multi-instance learning (MIL) is a useful tool for tackling labeling ambiguity in learning because it allows a bag of instances to share one label. Bag mapping transforms a bag into a single instance in a new space via instance selection and has drawn significant attention recently. To date, most existing work is based on the original space, using all instances inside each bag for bag mapping, and the selected instances are not directly tied to an MIL objective. As a result, it is difficult to guarantee the distinguishing capacity of the selected instances in the new bag mapping space. In this paper, we propose a discriminative mapping approach for multi-instance learning (MILDM) that aims to identify the best instances to directly distinguish bags in the new mapping space. Accordingly, each instance bag can be mapped using the selected instances to a new feature space, and hence any generic learning algorithm, such as an instance-based learning algorithm, can be used to derive learning models for multi-instance classification. Experiments and comparisons on eight different types of real-world learning tasks (including 14 data sets) demonstrate that MILDM outperforms the state-of-The-Art bag mapping multi-instance learning approaches. Results also confirm that MILDM achieves balanced performance between runtime efficiency and classification effectiveness.
Wu, J., Pan, S., Zhu, X., Zhang, C. & Yu, P.S. 2018, 'Multiple Structure-View Learning for Graph Classification', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE Many applications involve objects containing structure and rich content information, each describing different feature aspects of the object. Graph learning and classification is a common tool for handling such objects. To date, existing graph classification has been limited to the single-graph setting with each object being represented as one graph from a single structure-view. This inherently limits its use to the classification of complicated objects containing complex structures and uncertain labels. In this paper, we advance graph classification to handle multigraph learning for complicated objects from multiple structure views, where each object is represented as a bag containing several graphs and the label is only available for each graph bag but not individual graphs inside the bag. To learn such graph classification models, we propose a multistructure-view bag constrained learning (MSVBL) algorithm, which aims to explore substructure features across multiple structure views for learning. By enabling joint regularization across multiple structure views and enforcing labeling constraints at the bag and graph levels, MSVBL is able to discover the most effective substructure features across all structure views. Experiments and comparisons on real-world data sets validate and demonstrate the superior performance of MSVBL in representing complicated objects as multigraph for classification, e.g., MSVBL outperforms the state-of-the-art multiview graph classification and multiview multi-instance learning approaches.
Wu, Z., Li, G., Liu, Q., Xu, G. & Chen, E. 2018, 'Covering the Sensitive Subjects to Protect Personal Privacy in Personalized Recommendation', IEEE Transactions on Services Computing, pp. 493-508.
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Wu, Z., Xu, G., Lu, C., Chen, E., Jiang, F. & Li, G. 2018, 'An effective approach for the protection of privacy text data in the CloudDB', World Wide Web, pp. 1-24.
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© 2017 Springer Science+Business Media, LLC Due to the advantages of pay-on-demand, expand-on-demand and high availability, cloud databases (CloudDB) have been widely used in information systems. However, since a CloudDB is distributed on an untrusted cloud side, it is an important problem how to effectively protect massive private information in the CloudDB. Although traditional security strategies (such as identity authentication and access control) can prevent illegal users from accessing unauthorized data, they cannot prevent internal users at the cloud side from accessing and exposing personal privacy information. In this paper, we propose a client-based approach to protect personal privacy in a CloudDB. In the approach, privacy data before being stored into the cloud side, would be encrypted using a traditional encryption algorithm, so as to ensure the security of privacy data. To execute various kinds of query operations over the encrypted data efficiently, the encrypted data would be also augmented with additional feature index, so that as much of each query operation as possible can be processed on the cloud side without the need to decrypt the data. To this end, we explore how the feature index of privacy data is constructed, and how a query operation over privacy data is transformed into a new query operation over the index data so that it can be executed on the cloud side correctly. The effectiveness of the approach is demonstrated by theoretical analysis and experimental evaluation. The results show that the approach has good performance in terms of security, usability and efficiency, thus effective to protect personal privacy in the CloudDB.
Xie, Y., Tao, D., Zhang, W., Liu, Y., Zhang, L. & Qu, Y. 2018, 'On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization', International Journal of Computer Vision, pp. 1-23.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature In this paper, we address the multi-view subspace clustering problem. Our method utilizes the circulant algebra for tensor, which is constructed by stacking the subspace representation matrices of different views and then rotating, to capture the low rank tensor subspace so that the refinement of the view-specific subspaces can be achieved, as well as the high order correlations underlying multi-view data can be explored. By introducing a recently proposed tensor factorization, namely tensor-Singular Value Decomposition (t-SVD) (Kilmer et al. in SIAM J Matrix Anal Appl 34(1):148–172, 2013), we can impose a new type of low-rank tensor constraint on the rotated tensor to ensure the consensus among multiple views. Different from traditional unfolding based tensor norm, this low-rank tensor constraint has optimality properties similar to that of matrix rank derived from SVD, so the complementary information can be explored and propagated among all the views more thoroughly and effectively. The established model, called t-SVD based Multi-view Subspace Clustering (t-SVD-MSC), falls into the applicable scope of augmented Lagrangian method, and its minimization problem can be efficiently solved with theoretical convergence guarantee and relatively low computational complexity. Extensive experimental testing on eight challenging image datasets shows that the proposed method has achieved highly competent objective performance compared to several state-of-the-art multi-view clustering methods.
Xu, G. 2018, 'Unsupervised Keyword Extraction from Microblog Posts via Hashtags', Journal of Web Engineering, vol. 17 (1&2).
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Xu, Z., Huang, S., Zhang, Y. & Tao, D. 2018, 'Webly-Supervised Fine-Grained Visual Categorization via Deep Domain Adaptation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5, pp. 1100-1113.
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© 1979-2012 IEEE. Learning visual representations from web data has recently attracted attention for object recognition. Previous studies have mainly focused on overcoming label noise and data bias and have shown promising results by learning directly from web data. However, we argue that it might be better to transfer knowledge from existing human labeling resources to improve performance at nearly no additional cost. In this paper, we propose a new semi-supervised method for learning via web data. Our method has the unique design of exploiting strong supervision, i.e., in addition to standard image-level labels, our method also utilizes detailed annotations including object bounding boxes and part landmarks. By transferring as much knowledge as possible from existing strongly supervised datasets to weakly supervised web images, our method can benefit from sophisticated object recognition algorithms and overcome several typical problems found in webly-supervised learning. We consider the problem of fine-grained visual categorization, in which existing training resources are scarce, as our main research objective. Comprehensive experimentation and extensive analysis demonstrate encouraging performance of the proposed approach, which, at the same time, delivers a new pipeline for fine-grained visual categorization that is likely to be highly effective for real-world applications.
Yang, E., Deng, C., Li, C., Liu, W., Li, J. & Tao, D. 2018, 'Shared Predictive Cross-Modal Deep Quantization', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE With explosive growth of data volume and ever-increasing diversity of data modalities, cross-modal similarity search, which conducts nearest neighbor search across different modalities, has been attracting increasing interest. This paper presents a deep compact code learning solution for efficient cross-modal similarity search. Many recent studies have proven that quantization-based approaches perform generally better than hashing-based approaches on single-modal similarity search. In this paper, we propose a deep quantization approach, which is among the early attempts of leveraging deep neural networks into quantization-based cross-modal similarity search. Our approach, dubbed shared predictive deep quantization (SPDQ), explicitly formulates a shared subspace across different modalities and two private subspaces for individual modalities, and representations in the shared subspace and the private subspaces are learned simultaneously by embedding them to a reproducing kernel Hilbert space, where the mean embedding of different modality distributions can be explicitly compared. In addition, in the shared subspace, a quantizer is learned to produce the semantics preserving compact codes with the help of label alignment. Thanks to this novel network architecture in cooperation with supervised quantization training, SPDQ can preserve intramodal and intermodal similarities as much as possible and greatly reduce quantization error. Experiments on two popular benchmarks corroborate that our approach outperforms state-of-the-art methods.
Yang, X., Wang, M. & Tao, D. 2018, 'Person Re-Identification with Metric Learning Using Privileged Information', IEEE Transactions on Image Processing, vol. 27, no. 2, pp. 791-805.
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© 2017 IEEE. Despite the promising progress made in recent years, person re-identification remains a challenging task due to complex variations in human appearances from different camera views. This paper presents a logistic discriminant metric learning method for this challenging problem. Different with most existing metric learning algorithms, it exploits both original data and auxiliary data during training, which is motivated by the new machine learning paradigm-learning using privileged information. Such privileged information is a kind of auxiliary knowledge, which is only available during training. Our goal is to learn an optimal distance function by constructing a locally adaptive decision rule with the help of privileged information. We jointly learn two distance metrics by minimizing the empirical loss penalizing the difference between the distance in the original space and that in the privileged space. In our setting, the distance in the privileged space functions as a local decision threshold, which guides the decision making in the original space like a teacher. The metric learned from the original space is used to compute the distance between a probe image and a gallery image during testing. In addition, we extend the proposed approach to a multi-view setting which is able to explore the complementatio n of multiple feature representations. In the multi-view setting, multiple metrics corresponding to different original features are jointly learned, guided by the same privileged information. Besides, an effective iterative optimization scheme is introduced to simultaneously optimize the metrics and the assigned metric weights. Experiment results on several widely-used data sets demonstrate that the proposed approach is superior to global decision threshold-based methods and outperforms most state-of-the-art results.
Yao, Y., Shen, F., Zhang, J., Liu, L., Tang, Z. & Shao, L. 2018, 'Discovering and Distinguishing Multiple Visual Senses for Web Learning', IEEE Transactions on Multimedia.
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IEEE Labeled image datasets have played a critical role in high-level image understanding. However, the process of manual labeling is both time-consuming and labor-intensive. To reduce the dependence on manually labeled data, there have been increasing research efforts on learning visual classifiers by directly exploiting web images. One issue that limits their performance is the problem of polysemy. Existing unsupervised approaches attempt to reduce the influence of visual polysemy by filtering out irrelevant images, but do not directly address polysemy. To this end, in this work, we present a multimodal framework that solves the problem of polysemy by allowing sense-specific diversity in search results. Specifically, we first discover a list of possible semantic senses from untagged corpora to retrieve sense-specific images. Then we merge visual similar semantic senses and prune noise by using the retrieved images. Finally, we train one visual classifier for each selected semantic sense and use the learned sense-specific classifiers to distinguish multiple visual senses. Extensive experiments on classifying images into sense-specific categories and re-ranking search results demonstrate the superiority of our proposed approach.
Ying, H., Wu, J., Xu, G., Liu, Y., Liang, T., Zhang, X. & Xiong, H. 2018, 'Time-aware metric embedding with asymmetric projection for successive POI recommendation', World Wide Web, pp. 1-16.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature Successive Point-of-Interest (POI) recommendation aims to recommend next POIs for a given user based on this user’s current location. Indeed, with the rapid growth of Location-based Social Networks (LBSNs), successive POI recommendation has become an important and challenging task, since it can help to meet users’ dynamic interests based on their recent check-in behaviors. While some efforts have been made for this task, most of them do not capture the following properties: 1) The transition between consecutive POIs in user check-in sequences presents asymmetric property, however existing approaches usually assume the forward and backward transition probabilities between a POI pair are symmetric. 2) Users usually prefer different successive POIs at different time, but most existing studies do not consider this dynamic factor. To this end, in this paper, we propose a time-aware metric embedding approach with asymmetric projection (referred to as MEAP-T) for successive POI recommendation, which takes the above two properties into consideration. In addition, we exploit three latent Euclidean spaces to project the POI-POI, POI-user, and POI-time relationships. Finally, the experimental results on two real-world datasets show MEAP-T outperforms the state-of-the-art methods in terms of both precision and recall.
Yu, D., Fu, B., Xu, G. & Qin, A. 2018, 'Constrained nonnegative matrix factorization-based semi-supervised multi-label learning', International Journal of Machine Learning and Cybernetics.
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Yu, J., Hong, C., Rui, Y. & Tao, D. 2018, 'Multitask Autoencoder Model for Recovering Human Poses', IEEE Transactions on Industrial Electronics, vol. 65, no. 6, pp. 5060-5068.
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© 1982-2012 IEEE. Human pose recovery in videos is usually conducted by matching 2-D image features and retrieving relevant 3-D human poses. In the retrieving process, the mapping between images and poses is critical. Traditional methods assume this mapping relationship as local joint detection or global joint localization, which limits recovery performance of these methods since this two tasks are actually unified. In this paper, we propose a novel pose recovery framework by simultaneously learning the tasks of joint localization and joint detection. To obtain this framework, multiple manifold learning is used and the shared parameter is calculated. With them, multiple manifold regularizers are integrated and generalized eigendecomposition is utilized to achieve parameter optimization. In this way, pose recovery is boosted by both global mapping and local refinement. Experimental results on two popular datasets demonstrates that the recovery error has been reduced by 10%-20%, which proves the performance improvement of the proposed method.
Yu, Z., Yu, J., Xiang, C., Fan, J. & Tao, D. 2018, 'Beyond Bilinear: Generalized Multimodal Factorized High-Order Pooling for Visual Question Answering', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE Visual question answering (VQA) is challenging, because it requires a simultaneous understanding of both visual content of images and textual content of questions. To support the VQA task, we need to find good solutions for the following three issues: 1) fine-grained feature representations for both the image and the question; 2) multimodal feature fusion that is able to capture the complex interactions between multimodal features; and 3) automatic answer prediction that is able to consider the complex correlations between multiple diverse answers for the same question. For fine-grained image and question representations, a "coattention" mechanism is developed using a deep neural network (DNN) architecture to jointly learn the attentions for both the image and the question, which can allow us to reduce the irrelevant features effectively and obtain more discriminative features for image and question representations. For multimodal feature fusion, a generalized multimodal factorized high-order pooling approach (MFH) is developed to achieve more effective fusion of multimodal features by exploiting their correlations sufficiently, which can further result in superior VQA performance as compared with the state-of-the-art approaches. For answer prediction, the Kullback-Leibler divergence is used as the loss function to achieve precise characterization of the complex correlations between multiple diverse answers with the same or similar meaning, which can allow us to achieve faster convergence rate and obtain slightly better accuracy on answer prediction. A DNN architecture is designed to integrate all these aforementioned modules into a unified model for achieving superior VQA performance. With an ensemble of our MFH models, we achieve the state-of-the-art performance on the large-scale VQA data sets and win the runner-up in VQA Challenge 2017.
Zhan, K., Chang, X., Guan, J., Chen, L., Ma, Z. & Yang, Y. 2018, 'Adaptive Structure Discovery for Multimedia Analysis Using Multiple Features', IEEE Transactions on Cybernetics.
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IEEE Multifeature learning has been a fundamental research problem in multimedia analysis. Most existing multifeature learning methods exploit graph, which must be computed beforehand, as input to uncover data distribution. These methods have two major problems confronted. First, graph construction requires calculating similarity based on nearby data pairs by a fixed function, e.g., the RBF kernel, but the intrinsic correlation among different data pairs varies constantly. Therefore, feature learning based on such predefined graphs may degrade, especially when there is dramatic correlation variation between nearby data pairs. Second, in most existing algorithms, each single-feature graph is computed independently and then combine them for learning, which ignores the correlation between multiple features. In this paper, a new unsupervised multifeature learning method is proposed to make the best utilization of the correlation among different features by jointly optimizing data correlation from multiple features in an adaptive way. As opposed to computing the affinity weight of data pairs by a fixed function, the weight of affinity graph is learned by a well-designed optimization problem. Additionally, the affinity graph of data pairs from different features is optimized in a global level to better leverage the correlation among different channels. In this way, the adaptive approach correlates the features of all features for a better learning process. Experimental results on real-world datasets demonstrate that our approach outperforms the state-of-the-art algorithms on leveraging multiple features for multimedia analysis.
Zhang, G., Piccardi, M. & Zare Borzeshi, E. 2018, 'Sequential Labeling With Structural SVM Under Nondecomposable Losses', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE Sequential labeling addresses the classification of sequential data, which are widespread in fields as diverse as computer vision, finance, and genomics. The model traditionally used for sequential labeling is the hidden Markov model (HMM), where the sequence of class labels to be predicted is encoded as a Markov chain. In recent years, HMMs have benefited from minimum-loss training approaches, such as the structural support vector machine (SSVM), which, in many cases, has reported higher classification accuracy. However, the loss functions available for training are restricted to decomposable cases, such as the 0-1 loss and the Hamming loss. In many practical cases, other loss functions, such as those based on the F & #x2081; measure, the precision/recall break-even point, and the average precision (AP), can describe desirable performance more effectively. For this reason, in this paper, we propose a training algorithm for SSVM that can minimize any loss based on the classification contingency table, and we present a training algorithm that minimizes an AP loss. Experimental results over a set of diverse and challenging data sets (TUM Kitchen, CMU Multimodal Activity, and Ozone Level Detection) show that the proposed training algorithms achieve significant improvements of the F & #x2081; measure and AP compared with the conventional SSVM, and their performance is in line with or above that of other state-of-the-art sequential labeling approaches.
Zhang, J., Wu, Q., Shen, C., Zhang, J. & Lu, J. 2018, 'Multi-label Image Classification with Regional Latent Semantic Dependencies', IEEE Transactions on Multimedia.
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IEEE Deep convolution neural networks (CNN) have demonstrated advanced performance on single-label image classification, and various progress also has been made to apply CNN methods on multi-label image classification, which requires annotating objects, attributes, scene categories, etc. in a single shot. Recent state-of-the-art approaches to multi-label image classification exploit the label dependencies in an image, at the global level, largely improving the labeling capacity. However, predicting small objects and visual concepts is still challenging due to the limited discrimination of the global visual features. In this paper, we propose a Regional Latent Semantic Dependencies model (RLSD) to address this problem. The utilized model includes a fully convolutional localization architecture to localize the regions that may contain multiple highly-dependent labels. The localized regions are further sent to the recurrent neural networks (RNN) to characterize the latent semantic dependencies at the regional level. Experimental results on several benchmark datasets show that our proposed model achieves the best performance compared to the state-of-the-art models, especially for predicting small objects occurred in the images. Also, we set up an upper bound model (RLSD+ft-RPN) using bounding box coordinates during training, the experimental results also show that our RLSD can approach the upper bound without using the bounding-box annotations, which is more realistic in the real world.
Zhang, J., Yu, J. & Tao, D. 2018, 'Local Deep-Feature Alignment for Unsupervised Dimension Reduction', IEEE Transactions on Image Processing, vol. 27, no. 5, pp. 2420-2432.
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© 1992-2012 IEEE. This paper presents an unsupervised deep-learning framework named local deep-feature alignment (LDFA) for dimension reduction. We construct neighbourhood for each data sample and learn a local stacked contractive auto-encoder (SCAE) from the neighbourhood to extract the local deep features. Next, we exploit an affine transformation to align the local deep features of each neighbourhood with the global features. Moreover, we derive an approach from LDFA to map explicitly a new data sample into the learned low-dimensional subspace. The advantage of the LDFA method is that it learns both local and global characteristics of the data sample set: the local SCAEs capture local characteristics contained in the data set, while the global alignment procedures encode the interdependencies between neighbourhoods into the final low-dimensional feature representations. Experimental results on data visualization, clustering, and classification show that the LDFA method is competitive with several well-known dimension reduction techniques, and exploiting locality in deep learning is a research topic worth further exploring.
Zhang, Q., Liu, Y., Blum, R.S., Han, J. & Tao, D. 2018, 'Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review', Information Fusion, vol. 40, pp. 57-75.
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© 2017 Elsevier B.V. As a result of several successful applications in computer vision and image processing, sparse representation (SR) has attracted significant attention in multi-sensor image fusion. Unlike the traditional multiscale transforms (MSTs) that presume the basis functions, SR learns an over-complete dictionary from a set of training images for image fusion, and it achieves more stable and meaningful representations of the source images. By doing so, the SR-based fusion methods generally outperform the traditional MST image fusion methods in both subjective and objective tests. In addition, they are less susceptible to mis-registration among the source images, thus facilitating the practical applications. This survey paper proposes a systematic review of the SR-based multi-sensor image fusion literature, highlighting the pros and cons of each category of approaches. Specifically, we start by performing a theoretical investigation of the entire system from three key algorithmic aspects, (1) sparse representation models; (2) dictionary learning methods; and (3) activity levels and fusion rules. Subsequently, we show how the existing works address these scientific problems and design the appropriate fusion rules for each application such as multi-focus image fusion and multi-modality (e.g., infrared and visible) image fusion. At last, we carry out some experiments to evaluate the impact of these three algorithmic components on the fusion performance when dealing with different applications. This article is expected to serve as a tutorial and source of reference for researchers preparing to enter the field or who desire to employ the sparse representation theory in other fields.
Zhang, Q., Shi, C., Niu, Z. & Cao, L. 2018, 'HCBC: A Hierarchical Case-Based Classifier Integrated with Conceptual Clustering', IEEE Transactions on Knowledge and Data Engineering.
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IEEE The structured case representation improves case-based reasoning (CBR) by exploring structures in the case base and the relevance of case structures. Recent CBR classifiers have mostly been built upon the attribute-value case representation rather than structured case representation, in which the structural relations embodied in their representation structure are accordingly overlooked in improving the similarity measure. This results in retrieval inefficiency and limitations on the performance of CBR classifiers. This paper proposes a hierarchical case-based classifier, HCBC, which introduces a concept lattice to hierarchically organize cases. By exploiting structural case relations in the concept lattice, a novel dynamic weighting model is proposed to enhance the concept similarity measure. Based on this similarity measure, HCBC retrieves the top-K concepts that are most similar to a new case by using a bottom-up pruning-based recursive retrieval (PRR) algorithm. The concepts extracted in this way are applied to suggest a class label for the case by a weighted majority voting. Experimental results show that HCBC outperforms other classifiers in terms of classification performance and robustness on categorical data, and also works confidently well on numeric datasets. In addition, PRR effectively reduces the search space and greatly improves the retrieval efficiency of HCBC.
Zhang, Q., Wu, J., Zhang, P., Long, G. & Zhang, C. 2018, 'Salient Subsequence Learning for Time Series Clustering', IEEE Transactions on Pattern Analysis and Machine Intelligence.
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IEEE Time series has been a popular research topic over the past decade. Salient subsequences of time series that can benefit the learning task, e.g. classification or clustering, are called shapelets. Shapelet-based time series learning extracts these types of salient subsequences with highly informative features from a time series. Most existing methods for shapelet discovery must scan a large pool of candidate subsequences, which is a time-consuming process. A recent work, Grabocka:KDD14, uses regression learning to discover shapelets in a time series; however, it only considers learning shapelets from labeled time series data. This paper proposes an Unsupervised Salient Subsequence Learning (USSL) model that discovers shapelets without the effort of labeling. We developed this new learning function by integrating the strengths of shapelet learning, shapelet regularization, spectral analysis and pseudo-label to simultaneously and automatically learn shapelets to help clustering unlabeled time series better. The optimization model is iteratively solved via a coordinate descent algorithm. Experiments show that our USSL can learn meaningful shapelets, with promising results on real-world and synthetic data that surpass current state-of-the-art unsupervised time series learning methods.
Zhang, Q., Wu, J., Zhang, Q., Zhang, P., Long, G. & Zhang, C. 2018, 'Dual influence embedded social recommendation', World Wide Web, vol. 21, no. 4, pp. 849-874.
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© 2017 Springer Science+Business Media, LLC Recommender systems are designed to solve the information overload problem and have been widely studied for many years. Conventional recommender systems tend to take ratings of users on products into account. With the development of Web 2.0, Rating Networks in many online communities (e.g. Netflix and Douban) allow users not only to co-comment or co-rate their interests (e.g. movies and books), but also to build explicit social networks. Recent recommendation models use various social data, such as observable links, but these explicit pieces of social information incorporating recommendations normally adopt similarity measures (e.g. cosine similarity) to evaluate the explicit relationships in the network - they do not consider the latent and implicit relationships in the network, such as social influence. A target user’s purchase behavior or interest, for instance, is not always determined by their directly connected relationships and may be significantly influenced by the high reputation of people they do not know in the network, or others who have expertise in specific domains (e.g. famous social communities). In this paper, based on the above observations, we first simulate the social influence diffusion in the network to find the global and local influence nodes and then embed this dual influence data into a traditional recommendation model to improve accuracy. Mathematically, we formulate the global and local influence data as new dual social influence regularization terms and embed them into a matrix factorization-based recommendation model. Experiments on real-world datasets demonstrate the effective performance of the proposed method.
Zhao, J., Mao, X. & Zhang, J. 2018, 'Learning deep facial expression features from image and optical flow sequences using 3D CNN', Visual Computer, pp. 1-15.
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© 2018 Springer-Verlag GmbH Germany, part of Springer Nature Facial expression is highly correlated with the facial motion. According to whether the temporal information of facial motion is used or not, the facial expression features can be classified as static and dynamic features. The former, which mainly includes the geometric features and appearance features, can be extracted by convolution or other learning filters; the latter, which are aimed to model the dynamic properties of facial motion, can be calculated through optical flow or other methods, respectively. When 3D convolutional neural networks (CNNs) are introduced, the extraction of two different types of features mentioned above becomes easy. In this paper, one 3D CNN architecture is presented to learn the static and dynamic features from facial image sequences and extract high-level dynamic features from optical flow sequences. Two types of dense optical flow, which contain the tracking information of facial muscle movement, are calculated according to different image pair construction methods. One is the common optical flow, and the other is an enhanced optical flow which is called accumulative optical flow. Four components of each type of optical flow are used in experiments. Three databases, two acted databases and one nearly realistic database, are selected to conduct the experiments. The experiments on the two acted databases achieve state-of-the-art accuracy, and indicate that the vertical component of optical flow has an advantage over other components in recognizing facial expression. The experimental results on the three selected databases show that more discriminative features can be learned from image sequences than from optical flow or accumulative optical flow sequences, and the accumulative optical flow contains more motion information than optical flow if the frame distance of the image pairs used to calculate them is not too large.
Zhao, L., Wu, S., Jiang, J., Li, W., Luo, J. & Li, J. 2018, 'Novel overlapping subgraph clustering for the detection of antigen epitopes.', Bioinformatics (Oxford, England), vol. 34, no. 12, pp. 2061-2068.
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Antigens that contain overlapping epitopes have been occasionally reported. As current algorithms mainly take a one-antigen-one-epitope approach to the prediction of epitopes, they are not capable of detecting these multiple and overlapping epitopes accurately, or even those multiple and separated epitopes existing in some other antigens.We introduce a novel subgraph clustering algorithm for more accurate detection of epitopes. This algorithm takes graph partitions as seeds, and expands the seeds to merge overlapping subgraphs based on the term frequency-inverse document frequency (TF-IDF) featured similarity. Then, the merged subgraphs are each classified as an epitope or non-epitope. Tests of our algorithm were conducted on three newly collected datasets of antigens. In the first dataset, each antigen contains only a single epitope; in the second, each antigen contains only multiple and separated epitopes; and in the third, each antigen contains overlapping epitopes. The prediction performance of our algorithm is significantly better than the state-of-art methods. The lifts of the averaged f-scores on top of the best existing methods are 60, 75 and 22% for the single epitope detection, the multiple and separated epitopes detection, and the overlapping epitopes detection, respectively.The source code is available at github.com/lzhlab/glep/.Supplementary data are available at Bioinformatics online.
Zhao, Y., You, X., Yu, S., Xu, C., Yuan, W., Jing, X.Y., Zhang, T. & Tao, D. 2018, 'Multi-view manifold learning with locality alignment', Pattern Recognition, vol. 78, pp. 154-166.
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© 2018 Elsevier Ltd Manifold learning aims to discover the low dimensional space where the input high dimensional data are embedded by preserving the geometric structure. Unfortunately, almost all the existing manifold learning methods were proposed under single view scenario, and they cannot be straightforwardly applied to multiple feature sets. Although concatenating multiple views into a single feature provides a plausible solution, it remains a question on how to better explore the independence and interdependence of different views while conducting manifold learning. In this paper, we propose a multi-view manifold learning with locality alignment (MVML-LA) framework to learn a common yet discriminative low-dimensional latent space that contain sufficient information of original inputs. Both supervised algorithm (S-MVML-LA) and unsupervised algorithm (U-MVML-LA) are developed. Experiments on benchmark real-world datasets demonstrate the superiority of our proposed S-MVML-LA and U-MVML-LA over existing state-of-the-art methods.
Zhu, C., Cao, L., Liu, Q., Yin, J. & Kumar, V. 2018, 'Heterogeneous Metric Learning of Categorical Data with Hierarchical Couplings', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 7, pp. 1254-1267.
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© 1989-2012 IEEE. Learning appropriate metric is critical for effectively capturing complex data characteristics. The metric learning of categorical data with hierarchical coupling relationships and local heterogeneous distributions is very challenging yet rarely explored. This paper proposes a Heterogeneous mEtric Learning with hIerarchical Couplings (HELIC for short) for this type of categorical data. HELIC captures both low-level value-to-attribute and high-level attribute-to-class hierarchical couplings, and reveals the intrinsic heterogeneities embedded in each level of couplings. Theoretical analyses of the effectiveness and generalization error bound verify that HELIC effectively represents the above complexities. Extensive experiments on 30 data sets with diverse characteristics demonstrate that HELIC-enabled classification significantly enhances the accuracy (up to 40.93 percent), compared with five state-of-the-art baselines.
Zhu, F., Gao, J., Xu, C., Yang, J. & Tao, D. 2018, 'On Selecting Effective Patterns for Fast Support Vector Regression Training', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE It is time consuming to train support vector regression (SVR) for large-scale problems even with efficient quadratic programming solvers. This issue is particularly serious when tuning the model's parameters. One way to address the issue is to reduce the problem's scale by selecting a subset of the training set. This paper presents a fast pattern selection method by scanning the training data set to reduce a problem's scale. In particular, we find the k-nearest neighbors (kNNs) in a local region around each pattern's target value, and then determine to retain the pattern according to the distribution of its nearest neighbors. There is a high probability that the pattern locates outside the & #x03F5;-tube. Since the kNNs of a pattern are found in a very small region, it is fast to scan the whole training data set. The proposed method deals with the year prediction Million Song Data set, which contains 463,715 patterns, within 10 s on a personal computer with an Intel Core i5-4690 CPU at 3.50 GHz and 8GB DRAM. An additional advantage of the proposed method is that it can predefine the size of the selected subset according to the training set. Comprehensive empirical evaluations demonstrate that the proposed method can significantly eliminate redundant patterns for SVR training with only a slight decrease in performance.
Zhu, M., He, B. & Wu, Q. 2018, 'Single Image Dehazing Based on Dark Channel Prior and Energy Minimization', IEEE Signal Processing Letters, vol. 25, no. 2, pp. 174-178.
Zuo, Y., Wu, Q., Zhang, J. & An, P. 2018, '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.
Zuo, Y., Wu, Q., Zhang, J. & An, P. 2018, 'Minimum Spanning Forest with Embedded Edge Inconsistency Measurement Model for Guided Depth Map Enhancement', IEEE Transactions on Image Processing, vol. 27, no. 8, pp. 4145-4159.
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© 1992-2012 IEEE. Guided depth map enhancement based on Markov random field (MRF) normally assumes edge consistency between the color image and the corresponding depth map. Under this assumption, the low-quality depth edges can be refined according to the guidance from the high-quality color image. However, such consistency is not always true, which leads to texture-copying artifacts and blurring depth edges. In addition, the previous MRF-based models always calculate the guidance affinities in the regularization term via a non-structural scheme, which ignores the local structure on the depth map. In this paper, a novel MRF-based method is proposed. It computes these affinities via the distance between pixels in a space consisting of the minimum spanning trees (forest) to better preserve depth edges. Furthermore, inside each minimum spanning tree, the weights of edges are computed based on the explicit edge inconsistency measurement model, which significantly mitigates texture-copying artifacts. To further tolerate the effects caused by noise and better preserve depth edges, a bandwidth adaption scheme is proposed. Our method is evaluated for depth map super-resolution and depth map completion problems on synthetic and real data sets, including Middlebury, ToF-Mark, and NYU. A comprehensive comparison against 16 state-of-the-art methods is carried out. Both qualitative and quantitative evaluations present the improved performances.