Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Closing the Loop: Placing Ontology Usage Analysis in the Ontology Development and Deployment Lifecycle' in Studies in Computational Intelligence, pp. 23-47. View/Download from: Publisher's site View description>>
Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Evaluation of U Ontology' in Studies in Computational Intelligence, pp. 243-268. View/Download from: Publisher's site View description>>
Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Investigation Phase: Empirical Analysis of Domain Ontology Usage (EMP-AF)' in Studies in Computational Intelligence, pp. 103-140. View/Download from: Publisher's site View description>>
Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Investigation Phase: Quantitative Analysis of Domain Ontology Usage (QUA-AF)' in Studies in Computational Intelligence, pp. 141-169. View/Download from: Publisher's site View description>>
Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Measuring and Analysing the Use of Ontologies A Semantic Framework for Measuring Ontology Usage' in Studies in Computational Intelligence, pp. 3-26.
Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'New Research Directions' in Studies in Computational Intelligence, pp. 269-271. View/Download from: Publisher's site View description>>
Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Ontology Usage Analysis Framework (OUSAF)' in Studies in Computational Intelligence, pp. 49-62. View/Download from: Publisher's site View description>>
Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Preface', pp. ix-xii.
Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Representation Phase: Ontology Usage Ontology (U Ontology)' in Studies in Computational Intelligence, pp. 171-203. View/Download from: Publisher's site View description>>
Ashraf, J, Hussain, OK, Hussain, FK & Chang, EJ 2018, 'Utilization Phase: Utilization of OUSAF' in Studies in Computational Intelligence, pp. 205-242. View/Download from: Publisher's site View description>>
Dieci, R & He, XT 2018, 'Heterogeneous Agent Models in Finance' in Hommes, C & LeBaron, B (eds), Handbook of Computational Economics, Elsevier, pp. 257-328. View/Download from: Publisher's site
Gil-Lafuente, AM, Merigó, JM, Dass, BK & Verma, R 2018, 'Preface', pp. v-vii.
Song, K, Feng, S, Gao, W, Wang, D, Chen, L & Zhang, C 2018, 'Build emotion lexicon from microblogs by combining effects of seed words and emoticons in a heterogeneous graph' in Social Media Content Analysis: Natural Language Processing and Beyond, World Scientific, Singapore, pp. 171-195. View/Download from: Publisher's site View description>>
Stoianoff, N & Wright, EA 2018, 'Fair Use and Traditional Cultural Expressions' in Corbett, S & Lai, JC (eds), Making Copyright Work for the Asian Pacific: Juxtaposing Harmonisation with Flexibility, ANU Press, pp. 75-94. View/Download from: UTS OPUS or Publisher's site
Abolhasan, M, Abdollahi, M, Ni, W, Jamalipour, A, Shariati, N & Lipman, J 2018, 'A Routing Framework for Offloading Traffic from Cellular Networks to SDN-based Multi-Hop Device-to-Device Networks', IEEE Transactions on Network and Service Management. View/Download from: Publisher's site View description>>
IEEE Device-to-Device (D2D) Communications are set to form an integral part of future 5G wireless networks. D2D communications have a number of benefits such as improving energy efficiency and spectrum utilization. Until now much of the D2D research in LTE and 5G-type network scenarios have focused on direct (one-hop) communications between two adjacent mobile devices. In this paper, we propose a new routing framework called Virtual Ad hoc Routing Protocol (VARP). This framework introduces significant advantages such as better security, lower routing overheads, and higher scalability, when compared to conventional ad hoc routing protocols. It also reduces traffic overhead in LTE networks using multi-hop D2D communications under management of a SDN-controller. Further, it enables the development of various types of routing protocols for different networking scenarios. To this end, a source-routing based protocol was developed on top of VARP, referred to as VARP-S. We present a detailed analytical study of routing overhead in the VARP-S protocol, as compared to overhead analysis of our previous proposed Hybrid SDN Architecture for wireless distributed networks (HSAW)IEEENetworkMehran. Our results show that VARP-S, compared to HSAW, achieves higher network scalability and lower power consumption for mobile nodes.
Alam, M, Jan, MA, 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. View/Download from: UTS OPUS or Publisher's site
Alam, M, Rayes, A, He, X, Atiquzzaman, M, Lloret, J & Tsang, KF 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. View/Download from: UTS OPUS or Publisher's site
Aldebei, KW, 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. View/Download from: UTS OPUS or Publisher's site View description>>
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.
Alfaro-García, VG, Merigó, JM, Gil-Lafuente, AM & Kacprzyk, J 2018, 'Logarithmic aggregation operators and distance measures', International Journal of Intelligent Systems, vol. 33, no. 7, pp. 1488-1506. View/Download from: Publisher's site View description>>
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.
Anderson, C, Hafen, R, Sofrygin, O, Ryan, L & members of the HBGDki Community 2018, 'Comparing predictive abilities of longitudinal child growth models.', Statistics in medicine. View/Download from: Publisher's site View description>>
The Bill and Melinda Gates Foundation's Healthy Birth, Growth and Development knowledge integration project aims to improve the overall health and well-being of children across the world. The project aims to integrate information from multiple child growth studies to allow health professionals and policy makers to make informed decisions about interventions in lower and middle income countries. To achieve this goal, we must first understand the conditions that impact on the growth and development of children, and this requires sensible models for characterising different growth patterns. The contribution of this paper is to provide a quantitative comparison of the predictive abilities of various statistical growth modelling techniques based on a novel leave-one-out validation approach. The majority of existing studies have used raw growth data for modelling, but we show that fitting models to standardised data provide more accurate estimation and prediction. Our work is illustrated with an example from a study into child development in a middle income country in South America.
Andrade-Valbuena, NA & Merigo, JM 2018, 'Outlining new product development research through bibliometrics: Analyzing journals, articles and researchers', Journal of Strategy and Management, vol. 11, no. 3, pp. 328-350. View/Download from: Publisher's site View description>>
Anshar, M & Williams, MA 2018, 'Evolving robot empathy towards humans with motor disabilities through artificial pain generation', AIMS Neuroscience, vol. 5, no. 1, pp. 56-73. View/Download from: Publisher's site View description>>
Avilés-Ochoa, E, León-Castro, E, Perez-Arellano, LA & Merigó, JM 2018, 'Government transparency measurement through prioritized distance operators', Journal of Intelligent and Fuzzy Systems, vol. 34, no. 4, pp. 2783-2794. View/Download from: Publisher's site View description>>
Baier-Fuentes, H, Merigó, JM, Amorós, JE & Gaviria-Marín, M 2018, 'International entrepreneurship: a bibliometric overview', International Entrepreneurship and Management Journal, pp. 1-45. View/Download from: Publisher's site View description>>
Bargi, A, Xu, YD & 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. View/Download from: UTS OPUS or Publisher's site
Beck, D, Thoms, JAI, 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, BJ, Schwarzer, A, Klusmann, J-H, Berdel, WE, Wörmann, B, Büchner, T, Hiddemann, W, Bohlander, SK, To, LB, Scott, HS, Lewis, ID, D'Andrea, RJ, Wong, JWH & Pimanda, JE 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. View/Download from: Publisher's site View description>>
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.
Blanco-Mesa, F, Gil-Lafuente, AM & Merigo, JM 2018, 'Dynamics of stakeholder relations with multi-person aggregation', Kybernetes, vol. 47, no. 9, pp. 1801-1820. View/Download from: Publisher's site View description>>
Blanco-Mesa, F, Gil-Lafuente, AM & Merigó, JM 2018, 'New aggregation operators for decision-making under uncertainty: An applications in selection of entrepreneurial opportunities', Technological and Economic Development of Economy, vol. 24, no. 2, pp. 335-357. View/Download from: Publisher's site View description>>
Blanco-Mesa, F, León-Castro, E & Merigó, JM 2018, 'Bonferroni induced heavy operators in ERM decision-making: A case on large companies in Colombia', Applied Soft Computing Journal, vol. 72, pp. 371-391. View/Download from: Publisher's site View description>>
Blanco-Mesa, F, León-Castro, E, Merigó, JM & Xu, Z 2018, 'Bonferroni means with induced ordered weighted average operators', International Journal of Intelligent Systems. View/Download from: Publisher's site View description>>
Cancino, CA, Merigo, JM, Torres, JP & Diaz, D 2018, 'A bibliometric analysis of venture capital research', Journal of Economics, Finance and Administrative Science, vol. 23, no. 45, pp. 182-195. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
Cao, X, Qiu, B, Li, X, Shi, Z, Xu, G & Xu, J 2018, 'Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data', IEEE Transactions on Neural Networks and Learning Systems. View/Download from: UTS OPUS or Publisher's site View description>>
IEEE The balance of neighborhood space around a central point is an important concept in cluster analysis. It can be used to effectively detect cluster boundary objects. The existing neighborhood analysis methods focus on the distribution of data, i.e., analyzing the characteristic of the neighborhood space from a single perspective, and could not obtain rich data characteristics. In this paper, we analyze the high-dimensional neighborhood space from multiple perspectives. By simulating each dimension of a data point's k nearest neighbors space (kNNs) as a lever, we apply the lever principle to compute the balance fulcrum of each dimension after proving its inevitability and uniqueness. Then, we model the distance between the projected coordinate of the data point and the balance fulcrum on each dimension and construct the DHBlan coefficient to measure the balance of the neighborhood space. Based on this theoretical model, we propose a simple yet effective cluster boundary detection algorithm called Lever. Experiments on both low- and high-dimensional data sets validate the effectiveness and efficiency of our proposed algorithm.
Cheng, H, Zhang, J, Wu, Q & Ping, A 2018, 'A Computational Model for Stereoscopic Visual Saliency Prediction', IEEE Transactions on Multimedia. View/Download from: UTS OPUS or Publisher's site View description>>
IEEE Depth information plays an important role in human vision as it provides additional cues that distinguish objects from their backgrounds. This paper explores depth information for analyzing stereoscopic saliency and presents a computational model that predicts stereoscopic visual saliency based on three aspects of human vision: the pop-out effect, comfort zones, and background effects. Through an analysis of these three phenomena, we find that most of the stereoscopic saliency region can be explained. Our model comprises three modules, each describing one aspect of saliency distribution, and a control function that can be used to adjust the three models independently. The relationship between the three models is not mutually exclusive. One, two, or three phenomena may appear in one image. Therefore, to accurately determine which phenomena the image conforms to, we have devised a selection strategy that chooses the appropriate combination of models based on the content of the image. Our approach is implemented within a framework based on the multi-feature analysis. The framework considers surrounding regions, color/depth contrast, and points of interest. The selection strategy can improve the performance of the framework. A series of experiments on two recent eye-tracking datasets show that our proposed method outperforms several state-of-the-art saliency models.
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. View/Download from: UTS OPUS or Publisher's site View description>>
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.
Chiu, SK, Saw, J, Huang, Y, Sonderegger, SE, Wong, NC, Powell, DR, Beck, D, Pimanda, JE, Tremblay, CS & Curtis, DJ 2018, 'A novel role for Lyl1 in primitive erythropoiesis.', Development (Cambridge, England), vol. 145, no. 19. View/Download from: Publisher's site View description>>
Stem cell leukemia (Scl or Tal1) and lymphoblastic leukemia 1 (Lyl1) encode highly related members of the basic helix-loop-helix family of transcription factors that are co-expressed in the erythroid lineage. Previous studies have suggested that Scl is essential for primitive erythropoiesis. However, analysis of single-cell RNA-seq data of early embryos showed that primitive erythroid cells express both Scl and Lyl1 Therefore, to determine whether Lyl1 can function in primitive erythropoiesis, we crossed conditional Scl knockout mice with mice expressing a Cre recombinase under the control of the Epo receptor, active in erythroid progenitors. Embryos with 20% expression of Scl from E9.5 survived to adulthood. However, mice with reduced expression of Scl and absence of Lyl1 (double knockout; DKO) died at E10.5 because of progressive loss of erythropoiesis. Gene expression profiling of DKO yolk sacs revealed loss of Gata1 and many of the known target genes of the SCL-GATA1 complex. ChIP-seq analyses in a human erythroleukemia cell line showed that LYL1 exclusively bound a small subset of SCL targets including GATA1. Together, these data show for the first time that Lyl1 can maintain primitive erythropoiesis.
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, FK & Hussain, OK 2018, 'SERNOTATE: An automated approach for business service description annotation for efficient service retrieval and composition', Concurrency and Computation: Practice and Experience, vol. 30, no. 1. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
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, XS 2018, 'Hybrid generative–discriminative hash tracking with spatio-temporal contextual cues', Neural Computing and Applications, vol. 29, no. 2, pp. 389-399. View/Download from: UTS OPUS or Publisher's site View description>>
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.
Deng, C, Liu, X, Li, C & Tao, D 2018, 'Active multi-kernel domain adaptation for hyperspectral image classification', Pattern Recognition. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
Dong, X, Gong, Y & Cao, L 2018, 'e-RNSP: An Efficient Method for Mining Repetition Negative Sequential Patterns.', IEEE transactions on cybernetics. View/Download from: Publisher's site View description>>
Negative sequential patterns (NSPs), which capture both frequent occurring and nonoccurring behaviors, become increasingly important and sometimes play a role irreplaceable by analyzing occurring behaviors only. Repetition sequential patterns capture repetitions of patterns in different sequences as well as within a sequence and are very important to understand the repetition relations between behaviors. Though some methods are available for mining NSP and repetition positive sequential patterns (RPSPs), we have not found any methods for mining repetition NSP (RNSP). RNSP can help the analysts to further understand the repetition relationships between items and capture more comprehensive information with repetition properties. However, mining RNSP is much more difficult than mining NSP due to the intrinsic challenges of nonoccurring items. To address the above issues, we first propose a formal definition of repetition negative containment. Then, we propose a method to convert repetition negative containment to repetition positive containment, which fast calculates the repetition supports by only using the corresponding RPSP's information without rescanning databases. Finally, we propose an efficient algorithm, called e-RNSP, to mine RNSP efficiently. To the best of our knowledge, e-RNSP is the first algorithm to efficiently mine RNSP. Intensive experimental results on the first four real and synthetic datasets clearly show that e-RNSP can efficiently discover the repetition negative patterns; results on the fifth dataset prove the effectiveness of RNSP which are captured by the proposed method; and the results on the rest 16 datasets analyze the impacts of data characteristics on mining process.
Dong, X, Gong, Y & Cao, L 2018, 'F-NSP+: A fast negative sequential patterns mining method with self-adaptive data storage', Pattern Recognition, vol. 84, pp. 13-27. View/Download from: Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
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.
Du, B, Wang, S, Xu, C, Wang, N, Zhang, L & Tao, D 2018, 'Multi-Task Learning for Blind Source Separation', IEEE Transactions on Image Processing, vol. 27, no. 9, pp. 4219-4231. View/Download from: Publisher's site View description>>
Durán Santomil, P, Otero González, L, Martorell Cunill, O & Merigó Lindahl, JM 2018, 'Backtesting an equity risk model under Solvency II', Journal of Business Research, vol. 89, pp. 216-222. View/Download from: Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
With the explosive usage of smart mobile devices, sustainable access to wireless networks (e.g., WiFi) has become a pervasive demand. Most mobile users expect seamless network connection with low cost. Indeed,
this can be achieved by using an accurate received signal strength (RSS) map of wireless access points. While existing methods are either costly or unscalable, the recently emerged mobile crowdsensing (MCS)
paradigm is a promising technique for building RSS maps. MCS applications leverage pervasive mobile devices to collaboratively collect data. However, the heterogeneity of devices and the mobility of users
could cause inherent noises and blank spots in collected dataset. In this paper, we study (1) how to tame the sensing noises from heterogenous mobile devices, and (2) how to construct accurate and complete RSS
maps with random mobility of crowdsensing participants. First, we build a mobile crowdsensing system called iMap to collect RSS measurements with heterogeneous mobile devices. Second, through observing
experimental results, we build statistical models of sensing noises and derive different parameters for each kind of mobile device. Third, we present the signal transmission model with measurement error model, and we propose a novel signal recovery scheme to construct accurate and complete RSS maps. The evaluation results show that the proposed method can achieve 90% and 95% recovery rate in geographic coordinate
system and polar coordinate system, respectively.
Feng, X, Wan, W, Xu, RYD, Perry, S, Zhu, S & Liu, Z 2018, 'A new mesh visual quality metric using saliency weighting-based pooling strategy', Graphical Models, vol. 99, pp. 1-12. View/Download from: UTS OPUS or Publisher's site View description>>
Feng, XIANG, Wan, W, Richard Yi Da Xu, Chen, H, Li, P & Sánchez, JA 2018, 'A perceptual quality metric for 3D triangle meshes based on spatial pooling', Frontiers of Computer Science. View/Download from: UTS OPUS
Fu, H, Gong, M, Wang, C & Tao, D 2018, 'MoE-SPNet: A mixture-of-experts scene parsing network', Pattern Recognition, vol. 84, pp. 226-236. View/Download from: Publisher's site View description>>
Fu, Q, Luo, Y, Wen, Y, Tao, D, Li, Y & Duan, LY 2018, 'Toward Intelligent Product Retrieval for TV-to-Online (T2O) Application: A Transfer Metric Learning Approach', IEEE Transactions on Multimedia, vol. 20, no. 8, pp. 2114-2125. View/Download from: Publisher's site View description>>
Gaviria-Marin, M, Merigó, JM & Baier-Fuentes, H 2018, 'Knowledge management: A global examination based on bibliometric analysis', Technological Forecasting and Social Change. View/Download from: Publisher's site View description>>
Gaviria-Marin, M, Merigo, JM & Popa, S 2018, 'Twenty years of the Journal of Knowledge Management: a bibliometric analysis', Journal of Knowledge Management, vol. 22, no. 8, pp. 1655-1687. View/Download from: Publisher's site View description>>
Gheisari, S, Catchpoole, DR, Charlton, A & Kennedy, PJ 2018, 'Convolutional Deep Belief Network with Feature Encoding for Classification of Neuroblastoma Histological Images.', Journal of Pathology Informatics, vol. 9, p. 17. View/Download from: Publisher's site View description>>
Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification.We apply a combination of convolutional deep belief network (CDBN) with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier.We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors.The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods.The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images.
Gheisari, S, Catchpoole, DR, Charlton, A, Melegh, Z, Gradhand, E & Kennedy, PJ 2018, 'Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding.', Diagnostics (Basel, Switzerland), vol. 8, no. 3. View/Download from: Publisher's site View description>>
Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathology study is considered the gold standard for classification of neuroblastoma histological images, computers can help to extract many more features some of which may not be recognizable by human eyes. This paper, proposes a combination of Scale Invariant Feature Transform with feature encoding algorithm to extract highly discriminative features. Then, distinctive image features are classified by Support Vector Machine classifier into five clinically relevant classes. The advantage of our model is extracting features which are more robust to scale variation compared to the Patched Completed Local Binary Pattern and Completed Local Binary Pattern methods. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our approach identified features that outperformed the state-of-the-art on both our neuroblastoma dataset and a benchmark breast cancer dataset. Our method shows promise for classification of neuroblastoma histological images.
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. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
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...
Over the last two decades, various in silico approaches have been developed and refined that attempt to identify protein and/or peptide vaccines candidates from informative signals encoded in protein sequences of a target pathogen. As to date, no signal has been identified that clearly indicates a protein will effectively contribute to a protective immune response in a host. The premise for this study is that proteins under positive selection from the immune system are more likely suitable vaccine candidates than proteins exposed to other selection pressures. Furthermore, our expectation is that protein sequence regions encoding major histocompatibility complexes (MHC) binding peptides will contain consecutive positive selection sites. Using freely available data and bioinformatic tools, we present a high-throughput approach through a pipeline that predicts positive selection sites, protein subcellular locations, and sequence locations of medium to high T-Cell MHC class I binding peptides. Positive selection sites are estimated from a sequence alignment by comparing rates of synonymous (dS) and non-synonymous (dN) substitutions among protein coding sequences of orthologous genes in a phylogeny. The main pipeline output is a list of protein vaccine candidates predicted to be naturally exposed to the immune system and containing sites under positive selection. Candidates are ranked with respect to the number of consecutive sites located on protein sequence regions encoding MHCI-binding peptides. Results are constrained by the reliability of prediction programs and quality of input data. Protein sequences from Toxoplasma gondii ME49 strain (TGME49) were used as a case study. Surface antigen (SAG), dense granules (GRA), microneme (MIC), and rhoptry (ROP) proteins are considered worthy T. gondii candidates. Given 8263 TGME49 protein sequences processed anonymously, the top 10 predicted candidates were all worthy candidates. In particular, the top ten included ROP5 and...
Gu, K, Tao, D, Qiao, JF & 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. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
Guan, N, Liu, T, Zhang, Y, Tao, D & Davis, LS 2018, 'Truncated Cauchy Non-negative Matrix Factorization for Robust Subspace Learning', IEEE Transactions on Pattern Analysis and Machine Intelligence. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
Gui, J, Liu, T, Sun, Z, Tao, D & Tan, T 2018, 'Supervised Discrete Hashing With Relaxation', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 3, pp. 608-617. View/Download from: Publisher's site View description>>
Data-dependent hashing has recently attracted attention due to being able to support efficient retrieval and storage of high-dimensional data, such as documents, images, and videos. In this paper, we propose a novel learning-based hashing method called ''supervised discrete hashing with relaxation'' (SDHR) based on ''supervised discrete hashing'' (SDH). SDH uses ordinary least squares regression and traditional zero-one matrix encoding of class label information as the regression target (code words), thus fixing the regression target. In SDHR, the regression target is instead optimized. The optimized regression target matrix satisfies a large margin constraint for correct classification of each example. Compared with SDH, which uses the traditional zero-one matrix, SDHR utilizes the learned regression target matrix and, therefore, more accurately measures the classification error of the regression model and is more flexible. As expected, SDHR generally outperforms SDH. Experimental results on two large-scale image data sets (CIFAR-10 and MNIST) and a large-scale and challenging face data set (FRGC) demonstrate the effectiveness and efficiency of SDHR.
Guo, F, Wang, W, Shen, J, Shao, L, Yang, J, Tao, D & Tang, YY 2018, 'Video Saliency Detection Using Object Proposals', IEEE Transactions on Cybernetics. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
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, IW, Chen, L, Yu, CP & Fung, SF 2018, 'Progressive Stochastic Learning for Noisy Labels', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp. 5136-5148. View/Download from: Publisher's site View description>>
He, X, He, Z & Zhu, X 2018, 'Editorial Notes', Economic Modelling. View/Download from: Publisher's site
He, XZ, Li, K & Li, Y 2018, 'Asset allocation with time series momentum and reversal', Journal of Economic Dynamics and Control, vol. 91, pp. 441-457. View/Download from: Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
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.
Ho, N, Peng, H, Mayoh, C, Liu, PY, Atmadibrata, B, Marshall, GM, 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. View/Download from: Publisher's site View description>>
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.
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. View/Download from: Publisher's site View description>>
Huang, Y, Xu, J, Wu, Q, Zheng, Z, Zhang, Z & Zhang, J 2018, 'Multi-pseudo Regularized Label for Generated Data in Person Re-Identification', IEEE Transactions on Image Processing. View/Download from: UTS OPUS or Publisher's site View description>>
IEEE Sufficient training data normally is required to train deeply learned models. However, due to the expensive manual process for labelling large number of images (i.e., annotation), the amount of available training data (i.e., real data) is always limited. To produce more data for training a deep network, Generative Adversarial Network (GAN) can be used to generate artificial sample data (i.e., generated data). However, the generated data usually does not have annotation labels. To solve this problem, in this paper, we propose a virtual label called Multi-pseudo Regularized Label (MpRL) and assign it to the generated data. With MpRL, the generated data will be used as the supplementary of real training data to train a deep neural network in a semi-supervised learning fashion. To build the corresponding relationship between the real data and generated data, MpRL assigns each generated data a proper virtual label which reflects the likelihood of the affiliation of the generated data to predefined training classes in the real data domain. Unlike the traditional label which usually is a single integral number, the virtual label proposed in this work is a set of weight-based values each individual of which is a number in (0,1] called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data. A comprehensive evaluation is carried out by adopting two state-of-the-art convolutional neural networks (CNNs) in our experiments to verify the effectiveness of MpRL. Experiments demonstrate that by assigning MpRL to generated data, we can further improve the person re-ID performance on five re-ID datasets, i.e., Market-1501, DukeMTMC-reID, CUHK03, VIPeR, and CUHK01. The proposed method obtains +6.29%, +6.30%, +5.58%, +5.84%, and +3.48% improvements in rank-1 accuracy over a strong CNN baseline on the five datasets respectively, and outperforms...
Huque, MH, 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. View/Download from: Publisher's site View description>>
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.
In cloud computing, service level agreements (SLAs) are legal agreements between a service provider and consumer that contain a list of obligations and commitments which need to be satisfied by both parties during the transaction. From a service provider’s perspective, a violation of such a commitment leads to penalties in terms of money and reputation and thus has to be effectively managed. In the literature, this problem has been studied under the domain of cloud service management. One aspect required to manage cloud services after the formation of SLAs is to predict the future Quality of Service (QoS) of cloud parameters to ascertain if they lead to violations. Various approaches in the literature perform this task using different prediction approaches however none of them study the accuracy of each. However, it is important to do this as the results of each prediction approach vary according to the pattern of the input data and selecting an incorrect choice of a prediction algorithm could lead to service violation and penalties. In this paper, we test and report the accuracy of time series and machine learning-based prediction approaches. In each category, we test many different techniques and rank them according to their order of accuracy in predicting future QoS. Our analysis helps the cloud service provider to choose an appropriate prediction approach (whether time series or machine learning based) and further to utilize the best method depending on input data patterns to obtain an accurate prediction result and better manage their SLAs to avoid violation penalties.
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, MA, Nanda, He, XS & Liu, RP 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. View/Download from: UTS OPUS or Publisher's site View description>>
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, MA, 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. View/Download from: UTS OPUS
Jan, MA, Usman, M, He, X & Rehman, AU 2018, 'SAMS: A Seamless and Authorized Multimedia Streaming framework for WMSN-based IoMT', IEEE Internet of Things Journal. View/Download from: UTS OPUS or Publisher's site View description>>
IEEE An Internet of Multimedia Things (IoMT) architecture aims to provide a support for real-time multimedia applications by using wireless multimedia sensor nodes that are deployed for long-term usage. These nodes are capable of capturing both multimedia and non-multimedia data, and form a network known as Wireless Multimedia Sensor Network (WMSN). In a WMSN, the underlying routing protocols need to provide an acceptable level of Quality-of-Service (QoS) support for the multimedia traffic. In this paper, we propose a seamless and authorized multimedia streaming framework (SAMS) for a cluster-based hierarchical WMSN. SAMS uses authentication at different levels to form secured clusters. The formation of these clusters allows only legitimate nodes to transmit captured data to their cluster heads. Each node senses the environment, stores captured data in its buffer, and waits for its turn to transmit to its cluster head. This waiting may result in an excessive packet loss and end-to-end delay for multimedia traffic. To address these issues, a channel allocation approach is proposed for inter-cluster communication. In case of buffer overflow, a member node in one cluster switches to a neighboring cluster head provided that the latter has an available channel for allocation. The experimental results show that SAMS provides an acceptable level of QoS and enhances the security of the underlying network.
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. View/Download from: UTS OPUS or Publisher's site View description>>
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, vol. 30, no. 9, pp. 1810-1823. View/Download from: Publisher's site View description>>
Jian, S, Pang, G, Cao, L, Lu, K & Gao, H 2018, 'CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning', IEEE Transactions on Knowledge and Data Engineering. View/Download from: Publisher's site View description>>
IEEE The representation of categorical data with hierarchical coupling relationships (i.e., value to value cluster interactions) is very critical yet challenging for capturing data characteristics in learning tasks. This paper proposes a novel and flexible coupled unsupervised categorical data representation (CURE) framework which not only captures the hierarchical couplings but also is flexible to be instantiated for contrastive learning tasks. Based on two complementary value coupling functions, CURE is instantiated into two instances: the coupled data embedding (CDE) for clustering and the coupled outlier scoring of high-dimensional data (COSH) for outlier detection, by customizing the ways of value clustering and coupling learning between value clusters. CDE embeds categorical data into a new space in which features are independent and semantics are rich. COSH represents data with an outlying vector to capture complex outlying behaviors of objects in high-dimensional data. Substantial experiments show that CDE significantly outperforms three popular unsupervised embedding methods and three state-of-the-art similarity-based representation methods, and COSH performs significantly better than five state-of-the-art outlier detection methods on high-dimensional data sets. CDE and COSH are scalable and stable, linear to data size and quadratic to the number of features, and are insensitive to their parameters.
Jiang, X, Pan, S, Long, G, Xiong, F, Jiang, J & Zhang, C 2018, 'Cost-sensitive Parallel Learning Framework for Insurance Intelligence Operation', IEEE Transactions on Industrial Electronics. View/Download from: Publisher's site View description>>
IEEE Recent advancements in artificial intelligence (AI) are providing the insurance industry with new opportunities to create tailored solutions and services based on newfound knowledge of consumers, and the execution of enhanced operations and business functions. However, insurance data is heterogeneous, and imbalanced class distribution with low frequency and high dimensions presents four major challenges to machine learning in real-world business. Traditional machine learning algorithms can typically only be applied to standard data sets, which are normally homogeneous and balanced. In this paper, we focus on an efficient cost-sensitive parallel learning framework (CPLF) to enhance insurance operations with a deep learning approach that does not require pre-processing. Our approach comprises a novel, unified, end-to-end cost-sensitive parallel neural network that learns real-world heterogeneous data. A specifically-designed cost-sensitive matrix then automatically generates a robust model for learning minority classifications, and the parameters of both the cost-sensitive matrix and the hybrid neural network are alternately but jointly optimized during training. We also study the CPLF-based architecture for a real-world insurance intelligence operation system, and demonstrate fraud detection experiments on this system. The results of comparative experiments on real-world insurance data sets reflecting actual business cases demonstrate the effectiveness of our design.
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. View/Download from: Publisher's site View description>>
Kendrick, L, Musial, K & Gabrys, B 2018, 'Change point detection in social networksCritical review with experiments', Computer Science Review, vol. 29, pp. 1-13. View/Download from: Publisher's site View description>>
Kocbek, S, Kocbek, P, Stozer, A, Zupanic, T, Groza, T & Stiglic, G 2018, 'Building interpretable models for polypharmacy prediction in older chronic patients based on drug prescription records', PEERJ, vol. 6. View/Download from: Publisher's site
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. View/Download from: Publisher's site View description>>
(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...
Laengle, S, Merigó, JM, Modak, NM & Yang, JB 2018, 'Bibliometrics in operations research and management science: a university analysis', Annals of Operations Research. View/Download from: Publisher's site View description>>
Laengle, S, Modak, NM, Merigó, JM & De La Sotta, C 2018, 'Thirty years of the International Journal of Computer Integrated Manufacturing: a bibliometric analysis', International Journal of Computer Integrated Manufacturing, vol. 31, no. 12, pp. 1247-1268. View/Download from: Publisher's site View description>>
Laengle, S, Modak, NM, Merigo, JM & Zurita, G 2018, 'Twenty-Five Years of Group Decision and Negotiation: A Bibliometric Overview', Group Decision and Negotiation, vol. 27, no. 4, pp. 505-542. View/Download from: Publisher's site View description>>
León-Castro, E, Avilés-Ochoa, E & Merigó, JM 2018, 'Induced Heavy Moving Averages', International Journal of Intelligent Systems, vol. 33, no. 9, pp. 1823-1839. View/Download from: Publisher's site View description>>
León-Castro, E, Avilés-Ochoa, E, Merigó, JM & Gil-Lafuente, AM 2018, 'Heavy Moving Averages and Their Application in Econometric Forecasting', Cybernetics and Systems, vol. 49, no. 1, pp. 26-43. View/Download from: Publisher's site View description>>
Li, H-S, Fan, P, Xia, H-Y, Song, S & He, X 2018, 'The multi-level and multi-dimensional quantum wavelet packet transforms.', Scientific reports, vol. 8, no. 1, p. 13884. View/Download from: Publisher's site View description>>
The classical wavelet packet transform has been widely applied in the information processing field. It implies that the quantum wavelet packet transform (QWPT) can play an important role in quantum information processing. In this paper, we design quantum circuits of a generalized tensor product (GTP) and a perfect shuffle permutation (PSP). Next, we propose multi-level and multi-dimensional (1D, 2D and 3D) QWPTs, including a Haar QWPT (HQWPT), a D4 QWPT (DQWPT) based on the periodization extension and their inverse transforms for the first time, and prove the correctness based on the GTP and PSP. Furthermore, we analyze the quantum costs and the time complexities of our proposed QWPTs and obtain precise results. The time complexities of HQWPTs is at most 6 on 2n elements, which illustrates high-efficiency of the proposed QWPTs. Simulation experiments demonstrate that the proposed QWPTs are correct and effective.
Li, L, Liu, Z & Zhang, J 2018, 'Unsupervised image co-segmentation via guidance of simple images', Neurocomputing, vol. 275, pp. 1650-1661. View/Download from: UTS OPUS or Publisher's site View description>>
Li, X, Liu, K, Dong, Y & Tao, D 2018, 'Patch Alignment Manifold Matting', IEEE Transactions on Neural Networks and Learning Systems. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
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, MH, 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. View/Download from: Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
Liang, T, Chen, L, Wu, J, Xu, G & Wu, Z 2018, 'SMS: A Framework for Service Discovery by Incorporating Social Media Information', IEEE Transactions on Services Computing, pp. 1-1. View/Download from: UTS OPUS or Publisher's site View description>>
With the explosive growth of services, including Web services, cloud services, APIs and mashups, discovering the appropriate services for consumers is becoming an imperative issue. The traditional service discovery approaches mainly face two challenges: 1) the single source of description documents limits the effectiveness of discovery due to the insufficiency of semantic information; 2) more factors should be considered with the generally increasing functional and nonfunctional requirements of consumers. In this paper, we propose a novel framework, called SMS, for effectively discovering the appropriate services by incorporating social media information. Specifically, we present different methods to measure four social factors (semantic similarity, popularity, activity, decay factor) collected from Twitter. Latent Semantic Indexing (LSI) model is applied to mine semantic information of services from meta-data of Twitter Lists that contains them. In addition, we assume the target query-service matching function as a linear combination of multiple social factors and design a weight learning algorithm to learn an optimal combination of the measured social factors. Comprehensive experiments based on a real-world dataset crawled from Twitter demonstrate the effectiveness of the proposed framework SMS, through some compared approaches.
Liao, H, Xu, Z, Herrera, F & Merigó, JM 2018, 'Editorial Message: Special Issue on Hesitant Fuzzy Linguistic Decision Making: Algorithms, Theory and Applications', International Journal of Fuzzy Systems, vol. 20, no. 7, p. 2083. View/Download from: Publisher's site
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. View/Download from: Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
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, P, Liu, J & Merigó, JM 2018, 'Partitioned Heronian means based on linguistic intuitionistic fuzzy numbers for dealing with multi-attribute group decision making', Applied Soft Computing Journal, vol. 62, pp. 395-422. View/Download from: Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
Liu, Q, Thoms, JAI, Nunez, AC, Huang, Y, Knezevic, K, Packham, D, Poulos, RC, Williams, R, Beck, D, Hawkins, NJ, Ward, RL, Wong, JWH, Hesson, LB, Sloane, MA & Pimanda, JE 2018, 'Disruption of a -35 kb Enhancer Impairs CTCF Binding and MLH1 Expression in Colorectal Cells.', Clinical cancer research : an official journal of the American Association for Cancer Research, vol. 24, no. 18, pp. 4602-4611. View/Download from: Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
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.
Llanos-Herrera, GR & Merigo, JM 2018, 'Overview of brand personality research with bibliometric indicators', Kybernetes. View/Download from: Publisher's site View description>>
Llopis-Albert, C, Merigó, JM, Xu, Y & Liao, H 2018, 'Application of Fuzzy Set/Qualitative Comparative Analysis to Public Participation Projects in Support of the EU Water Framework Directive.', Water environment research : a research publication of the Water Environment Federation, vol. 90, no. 1, pp. 74-83. View/Download from: Publisher's site View description>>
This study analyzes the level of satisfaction of stakeholders in the public participation process (PPP) of water resources management, which is mandatory according to the EU Water Framework Directive (WFD). The methodology uses a fuzzy set/qualitative comparative analysis (fsQCA), which allows the identification of a combination of factors that lead to the outcome that is stakeholders' satisfaction. It allows dealing with uncertain environments due to the heterogeneous nature of stakeholders and factors. The considered causes range from environmental objectives pursued, actual capacity of efficiently carrying out those objectives, socioeconomic development of the region, level of involvement and means of participation of the stakeholders engaged in the PPP, and alternative policies and measures that should be performed. Results support the argument that different causal paths explain the stakeholders' satisfaction. The methodology may help in the implementation of the WFD and conflict resolution since it leads to greater fairness, social equity, and consensus among stakeholders.
Luo, F, Du, B, Zhang, L, Zhang, L & Tao, D 2018, 'Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image', IEEE Transactions on Cybernetics. View/Download from: Publisher's site View description>>
IEEE Hyperspectral image (HSI) contains a large number of spatial-spectral information, which will make the traditional classification methods face an enormous challenge to discriminate the types of land-cover. Feature learning is very effective to improve the classification performances. However, the current feature learning approaches are most based on a simple intrinsic structure. To represent the complex intrinsic spatial-spectral of HSI, a novel feature learning algorithm, termed spatial-spectral hypergraph discriminant analysis (SSHGDA), has been proposed on the basis of spatial-spectral information, discriminant information, and hypergraph learning. SSHGDA constructs a reconstruction between-class scatter matrix, a weighted within-class scatter matrix, an intraclass spatial-spectral hypergraph, and an interclass spatial-spectral hypergraph to represent the intrinsic properties of HSI. Then, in low-dimensional space, a feature learning model is designed to compact the intraclass information and separate the interclass information. With this model, an optimal projection matrix can be obtained to extract the spatial-spectral features of HSI. SSHGDA can effectively reveal the complex spatial-spectral structures of HSI and enhance the discriminating power of features for land-cover classification. Experimental results on the Indian Pines and PaviaU HSI data sets show that SSHGDA can achieve better classification accuracies in comparison with some state-of-the-art methods.
Luo, Y, Wen, Y & Tao, D 2018, 'Heterogeneous Multitask Metric Learning Across Multiple Domains', IEEE Transactions on Neural Networks and Learning Systems. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
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.
Ma, K, Fu, H, Liu, T, Wang, Z & Tao, D 2018, 'Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks', IEEE Transactions on Image Processing, vol. 27, no. 10, pp. 5155-5166. View/Download from: Publisher's site View description>>
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.
Martínez-López, FJ, Merigó, JM, Valenzuela-Fernández, L & Nicolás, C 2018, 'Fifty years of the European Journal of Marketing: a bibliometric analysis', European Journal of Marketing, vol. 52, no. 1-2, pp. 439-468. View/Download from: Publisher's site View description>>
Mei, T, Li, L, Tian, X, Tao, D & Ngo, CW 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. View/Download from: Publisher's site View description>>
Merigó, JM, Cobo, MJ, Laengle, S, Rivas, D & Herrera-Viedma, E 2018, 'Twenty years of Soft Computing: a bibliometric overview', Soft Computing, pp. 1-21. View/Download from: Publisher's site View description>>
Merigó, JM, Gil-Lafuente, AM, Yu, D & Llopis-Albert, C 2018, 'Fuzzy decision making in complex frameworks with generalized aggregation operators', Applied Soft Computing Journal, vol. 68, pp. 314-321. View/Download from: Publisher's site View description>>
Merigó, JM, Pedrycz, W, Weber, R & de la Sotta, C 2018, 'Fifty years of Information Sciences: A bibliometric overview', Information Sciences, vol. 432, pp. 245-268. View/Download from: Publisher's site View description>>
Mirtalaie, MA, Hussain, OK, Chang, E & Hussain, FK 2018, 'Extracting sentiment knowledge from pros/cons product reviews: Discovering features along with the polarity strength of their associated opinions', Expert Systems with Applications, vol. 114, pp. 267-288. View/Download from: Publisher's site View description>>
Mulet-Forteza, C, Martorell-Cunill, O, Merigó, JM, Genovart-Balaguer, J & Mauleon-Mendez, E 2018, 'Twenty five years of the Journal of Travel & Tourism Marketing: a bibliometric ranking', Journal of Travel and Tourism Marketing, vol. 35, no. 9, pp. 1201-1221. View/Download from: Publisher's site View description>>
As we progress in into a digital era where most aspects of our life depend upon a network of computers,it is essential to focus on digital security. Each component of a network, be it a physical network, virtualnetwork or social network requires security when transmitting data. Hence the dynamic wireless meshnetwork must also deploy high levels of security as found in current legacy networks. This paper presentsa secure Geo-Location Oriented Routing (Secure-GLOR) protocol for wireless mesh networks, whichincorporates a hybrid encryption scheme for its multilevel security framework. The hybrid encryptiontechnique improves the network’s overall performance compared to the basic encryption by using acombination of symmetric key as well as asymmetric key encryption. Using the combination of the twoencryption schemes, the performance of the network can be improved by reducing the transmitted datasize, reduced computational overhead and faster encryption–decryption cycles. In this paper discussedmultiple encryption schemes for both symmetric and asymmetric encryption, compare their performancein various experimental scenarios. Proposed security scheme achieves better performance based on theresults obtained with most viable options for our network model.
Nawaz, F, Janjua, NK, Hussain, OK, Hussain, FK, Chang, E & Saberi, M 2018, 'Event-driven approach for predictive and proactive management of SLA violations in the Cloud of Things', Future Generation Computer Systems, vol. 84, pp. 78-97. View/Download from: Publisher's site View description>>
Ojha, S, Williams, MA & Johnston, B 2018, 'The Essence of Ethical Reasoning in Robot-Emotion Processing', International Journal of Social Robotics, vol. 10, no. 2, pp. 211-223. View/Download from: Publisher's site View description>>
Patel, OP, Tiwari, A, Chaudhary, R, Nuthalapati, SV, Bharill, N, Prasad, M, Hussain, FK & Hussain, OK 2018, 'Enhanced quantum-based neural network learning and its application to signature verification', Soft Computing, pp. 1-14. View/Download from: UTS OPUS or Publisher's site View description>>
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), vol. 34, no. 18, pp. 3069-3077. View/Download from: UTS OPUS or Publisher's site View description>>
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.Supplementary data are available at Bioinformatics online.
Qiao, M, Yu, J, Bian, W, Li, Q & Tao, D 2018, 'Adapting Stochastic Block Models to Power-Law Degree Distributions.', IEEE transactions on cybernetics. View/Download from: Publisher's site View description>>
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.
Roselin, AG, Nanda, P, Nepal, S & He, X 2018, 'Testbed Evaluation of Lightweight Authentication Protocol(LAUP) fo r6LoWPAN wireless sensor networks', Concurrency and Computation: Practice and Experience. View/Download from: UTS OPUS or Publisher's site
Saberi, M, Theobald, M, Hussain, OK, Chang, E & Hussain, FK 2018, 'Interactive feature selection for efficient customer recognition in contact centers: Dealing with common names', Expert Systems with Applications, vol. 113, pp. 356-376. View/Download from: Publisher's site View description>>
Schönbach, C, Li, J, Ma, L, Horton, P, Sjaugi, MF & Ranganathan, S 2018, 'A bioinformatics potpourri.', BMC genomics, vol. 19, no. Suppl 1, p. 920. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
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, W, Wu, Y, Yuan, J, Duan, L, Zhang, J & Jia, Y 2018, 'Robust Distracter-Resistive Tracker via Learning a Multi-Component Discriminative Dictionary', IEEE Transactions on Circuits and Systems for Video Technology. View/Download from: UTS OPUS or Publisher's site View description>>
IEEE Discriminative dictionary learning (DDL) provides an appealing paradigm for appearance modeling in visual tracking. However, most existing DDL based trackers cannot handle drastic appearance changes, especially for scenarios with background cluster and/or similar object interference. One reason is that they often suffer from the loss of subtle visual information which is critical to distinguish an object from distracters. In this paper, we explore the use of deep features extracted from the Convolutional Neural Networks (CNNs) to improve the object representation and propose a robust distracter-resistive tracker via learning a multi-component discriminative dictionary. The proposed method exploits both the intra-class and the interclass visual information to learn shared atoms and the classspecific atoms. By imposing several constraints into the objective function, the learned dictionary is reconstructive, compressive and discriminative, thus can better distinguish an object from the background. In addition, our convolutional features (deep features extracted from CNNs) have structural information for object localization and balance the discriminative power and semantic information of the object. Tracking is carried out within a Bayesian inference framework where a joint decision measure is used to construct the observation model. To alleviate the drift problem, the reliable tracking results obtained online are accumulated to update the dictionary. Both the qualitative and quantitative results on the CVPR2013 benchmark, the VOT2015 dataset and the SPOT dataset demonstrate that our tracker achieves better performance over the state-of-the-art approaches.
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. View/Download from: Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
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.
Tur-Porcar, A, Mas-Tur, A, Merigó, JM, Roig-Tierno, N & Watt, J 2018, 'A Bibliometric History of the Journal of Psychology Between 1936 and 2015.', The Journal of psychology, vol. 152, no. 4, pp. 199-225. View/Download from: UTS OPUS or Publisher's site View description>>
The Journal of Psychology: Interdisciplinary and Applied is a leading international journal in psychology dating back to 1935. This study examines its publications since its creation utilizing a bibliometric analysis. The primary objective is to provide a complete overview of the key factors affecting the journal. This analysis includes such key issues as the publication and citation structure of the journal, its most cited articles, and the leading authors, institutions, and countries referenced in the journal. The work uses the Scopus database to classify the bibliographic material. Additionally, the analysis provides a graphical mapping of the bibliographic data by using visualization of similarities viewer software. This software uses several bibliometric techniques including co-citation, bibliographic coupling and co-occurrence of keywords. The Journal of Psychology is strongly connected to most of the current leading journals in psychology, and currently has a 5-year impact factor of 1.77 (Thomson Reuters, 2015 Journal Citation Reports).
Usman, M, He, X, Lam, KKM, Xu, M, Chen, J, Bokhari, SMM & Jan, MA 2018, 'Error Concealment for Cloud-based and Scalable Video Coding of HD Videos', IEEE Transactions on Cloud Computing. View/Download from: UTS OPUS or Publisher's site View description>>
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, MA, He, X & Alam, M 2018, 'Performance evaluation of High Definition video streaming over Mobile Ad Hoc Networks', Signal Processing, vol. 148, pp. 303-313. View/Download from: UTS OPUS or Publisher's site View description>>
Usman, M, Jan, MA, He, X & Chen, J 2018, 'A Mobile Multimedia Data Collection Scheme for Secured Wireless Multimedia Sensor Networks', IEEE Transactions on Network Science and Engineering. View/Download from: UTS OPUS or Publisher's site View description>>
IEEE Wireless Multimedia Sensor Networks (WMSNs) produce an enormous amount of big multimedia data. Due to large size, Multimedia Sensor Nodes (MSNs) cannot store generated multimedia data for a long time. In this scenario, mobile sinks can be utilized for data collection. However, due to vulnerable nature of wireless networks, there is a need for an efficient security scheme to authenticate both MSNs and mobile sinks. In this paper, we propose a scheme to protect an underlying WMSN during mobile multimedia data collection. The proposed scheme is a two-layer scheme. At the first layer, all MSNs are distributed into small clusters, where each cluster is represented by a single Cluster Head (CH). At the second layer, all CHs verify identities of mobile sinks before sharing multimedia data. Authentication at both layers ensures a secure data exchange. We evaluate the performance of proposed scheme through extensive simulation results. The simulation results prove that the proposed scheme performs better as compared to existing state-of-the-art approaches in terms of resilience and handshake duration. The proposed scheme is also analyzed in terms of authentication rate, data freshness, and packet delivery ratio, and has shown a better performance.
Usman, M, Yang, N, Jan, MA, He, X, Xu, M & Lam, KM 2018, 'A Joint Framework for QoS and QoE for Video Transmission over Wireless Multimedia Sensor Networks', IEEE Transactions on Mobile Computing, vol. 17, no. 4. View/Download from: UTS OPUS or Publisher's site View description>>
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.
Valenzuela Fernandez, LM, Nicolas, C, Merigó, JM & Arroyo-Cañada, FJ 2018, 'Industrial marketing research. A bibliometric analysis (1990-2015)', Journal of Business and Industrial Marketing. View/Download from: Publisher's site View description>>
Valenzuela-Fernández, L, Merigó, JM & Nicolas, C 2018, 'The most influential countries in market orientation: A bibliometric analysis between 1990 and 2016', International Journal of Engineering Business Management, vol. 10. View/Download from: Publisher's site View description>>
van Rijmenam, M, Erekhinskaya, T, Schweitzer, J & Williams, MA 2018, 'Avoid being the turkey: How big data analytics changes the game of strategy in times of ambiguity and uncertainty', Long Range Planning. View/Download from: UTS OPUS or Publisher's site View description>>
In order for organisations to remain competitive in times of ambiguity and uncertainty, there is a need to detect and anticipate unknown unknowns, also called ‘black swans’. When these are ignored they may lead to competitive struggles. In this paper, we build on this view and suggest that big data analytics can provide necessary insights to help change strategy making. Research suggests that ambidextrous organisations should focus on developing and maintaining their dynamic capabilities. Following on from this, we take a dynamic capabilities perspective and propose a theoretical framework to explain the intricacies of big data analytics. This framework explains the ability of organisations to detect, anticipate and respond strategically in ambiguous and uncertain business environments. For a meta- synthesis of 101 cases of big data analytics, we employ a multi-method approach that incorporates Natural Language Processing, semantic analysis and case analysis, allowing extraction and analysis of structured information from unstructured data. Overall, we find evidence of big data analytics helping to detect, anticipate and respond to industry disruption. We offer six propositions about the relationships between the levels of data analytics capabilities and strategic dynamic capabilities. We find that descriptive data analytics improves the capability of an organisation to understand the business context (sensing) and that predictive data analytics aids in the realisation of business opportunities (seizing). This study contributes to an understanding of big data analytics as a dynamic organisational capability that supports strategic decision-making in times of ambiguity and uncertainty. We conclude by suggesting areas for further investigation, particularly in regard to the strategic application of prescriptive data analytics.
Wan, Y, Chen, L, Xu, G, Zhao, Z, Tang, J & Wu, J 2018, 'SCSMiner: mining social coding sites for software developer recommendation with relevance propagation', World Wide Web. View/Download from: UTS OPUS or Publisher's site
Wan, Y, Xu, G, Chen, L, Zhao, Z & Wu, J 2018, 'Exploiting cross-source knowledge for warming up community question answering services', Neurocomputing, vol. 320, pp. 25-34. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
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, C, Chi, CH, She, Z, Cao, L & Stantic, B 2018, 'Coupled clustering ensemble by exploring data interdependence', ACM Transactions on Knowledge Discovery from Data, vol. 12, no. 6. View/Download from: Publisher's site View description>>
Wang, H, Wu, J, Zhu, X, Chen, Y & Zhang, C 2018, 'Time-Variant Graph Classification', IEEE Transactions on Systems, Man, and Cybernetics: Systems. View/Download from: Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
Wang, N, Gao, X, Tao, D, Yang, H & Li, X 2018, 'Facial feature point detection: A comprehensive survey', Neurocomputing, vol. 275, pp. 50-65. View/Download from: Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
Wang, R, Liu, T & Tao, D 2018, 'Multiclass Learning With Partially Corrupted Labels', IEEE Transactions on Neural Networks and Learning Systems. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
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, W, Laengle, S, Merigó, JM, Yu, D, Herrera-Viedma, E, Cobo, MJ & Bouchon-Meunier, B 2018, 'A Bibliometric Analysis of the First Twenty-Five Years of the International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems', International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, vol. 26, no. 2, pp. 169-193. View/Download from: Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
Wang, Y, Wang, Z, Jia, W, He, X & Jiang, M 2018, 'Joint Learning of Body and Part Representation for Person Re-Identification', IEEE Access, vol. 6, pp. 44199-44210. View/Download from: UTS OPUS or Publisher's site View description>>
Wang, Y, Xu, C, Xu, C & Tao, D 2018, 'Packing Convolutional Neural Networks in the Frequency Domain', IEEE Transactions on Pattern Analysis and Machine Intelligence. View/Download from: Publisher's site View description>>
IEEE Deep convolutional neural networks (CNNs) are successfully used in a number of applications. However, their storage and computational requirements have largely prevented their widespread use on mobile devices. Here we present a series of approaches for compressing and speeding up CNNs in the frequency domain, which focuses not only on smaller weights but on all the weights and their underlying connections. By treating convolutional filters as images, we decompose their representations in the frequency domain as common parts (i.e., cluster centers) shared by other similar filters and their individual private parts (i.e., individual residuals). A large number of low-energy frequency coefficients in both parts can be discarded to produce high compression without significantly compromising accuracy. Furthermore, we explore a data-driven method for removing redundancies in both spatial and frequency domains, which allows us to discard more useless weights by keeping similar accuracies. After obtaining the optimal sparse CNN in the frequency domain, we relax the computational burden of convolution operations in CNNs by linearly combining the convolution responses of discrete cosine transform (DCT) bases. The compression and speed-up ratios of the proposed algorithm are thoroughly analyzed and evaluated on benchmark image datasets to demonstrate its superiority over state-of-the-art methods.
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. View/Download from: UTS OPUS or Publisher's site View description>>
Wang, Z & Piccardi, M 2018, 'Minimum-risk temporal alignment of videos', Multimedia Tools and Applications. View/Download from: UTS OPUS or Publisher's site
Wright, ST, Ryan, LM & 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. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
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, W, Li, B, Chen, L, Zhang, C & Yu, P 2018, 'Improved Consistent Weighted Sampling Revisited', IEEE Transactions on Knowledge and Data Engineering. View/Download from: Publisher's site View description>>
IEEE Min-Hash is a popular technique for efficiently estimating the Jaccard similarity of binary sets. Consistent Weighted Sampling (CWS) generalizes the Min-Hash scheme to sketch weighted sets and has drawn increasing interest from the community. Due to its constant-time complexity independent of the values of the weights, Improved CWS (ICWS) is considered as the state-of-the-art CWS algorithm. In this paper, we revisit ICWS and analyze its underlying mechanism to show that there actually exists dependence between the two components of the hash-code produced by ICWS, which violates the condition of independence. To remedy the problem, we propose an Improved ICWS (I2CWS) algorithm which not only shares the same theoretical computational complexity as ICWS but also abides by the required conditions of the CWS scheme. The experimental results on a number of synthetic data sets and real-world text data sets demonstrate that our I2CWS algorithm can estimate the Jaccard similarity more accurately, and also competes with or outperforms the compared methods, including ICWS, in classification and top-K retrieval, after relieving the underlying dependence.
Wu, W, Li, B, Chen, L, Zhu, X & Zhang, C 2018, 'K-Ary Tree Hashing for Fast Graph Classification', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 5, pp. 936-949. View/Download from: UTS OPUS or Publisher's site View description>>
IEEE Existing graph classification usually relies on an exhaustive enumeration of substructure patterns, where the number of substructures expands exponentially w.r.t. with the size of the graph set. Recently, the Weisfeiler-Lehman (WL) graph kernel has achieved the best performance in terms of both accuracy and efficiency among state-of-the-art methods. However, it is still time-consuming, especially for large-scale graph classification tasks. In this paper, we present a < formula > < tex > $k$ < /tex > < /formula > -Ary Tree based Hashing (KATH) algorithm, which is able to obtain competitive accuracy with a very fast runtime. The main idea of KATH is to construct a traversal table to quickly approximate the subtree patterns in WL using < formula > < tex > $k$ < /tex > < /formula > -ary trees. Based on the traversal table, KATH employs a recursive indexing process that performs only < formula > < tex > $r$ < /tex > < /formula > times of matrix indexing to generate all < formula > < tex > $(r-1)$ < /tex > < /formula > -depth < formula > < tex > $k$ < /tex > < /formula > -ary trees, where the leaf node labels of a tree can uniquely specify the pattern. After that, the MinHash scheme is used to fingerprint the acquired subtree patterns for a graph. Our experimental results on both real world and synthetic datasets show that KATH runs significantly faster than state-of-the-art methods while achieving competitive or better accuracy.
Wu, Z, Li, G, Liu, Q, Xu, G & Chen, E 2018, 'Covering the Sensitive Subjects to Protect Personal Privacy in Personalized Recommendation', IEEE Transactions on Services Computing, pp. 493-508. View/Download from: UTS OPUS or Publisher's site
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. View/Download from: UTS OPUS or Publisher's site View description>>
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, vol. 126, no. 11, pp. 1157-1179. View/Download from: Publisher's site View description>>
Xiong, F, Wang, X, Pan, S, Yang, H, Wang, H & Zhang, C 2018, 'Social Recommendation With Evolutionary Opinion Dynamics', IEEE Transactions on Systems, Man, and Cybernetics: Systems. View/Download from: Publisher's site View description>>
IEEE When users in online social networks make a decision, they are often affected by their neighbors. Social recommendation models utilize social information to reveal the impact of neighbors on user preferences, and this impact is often described by the linear superposition of neighbor preferences or by global trust propagation. Further exploration needs to be undertaken to determine whether the influence pattern of other users from online interaction behaviors is adequately described. In this paper, we introduce evolutionary opinion dynamics from the field of statistical physics into recommender systems, characterizing the impact of other users. We propose an opinion dynamic model by evolutionary game theory. To describe online user interactions, we define the strategies during an interaction between two users, and present the payoff for each strategy in terms of errors of estimated ratings. Therefore, user behaviors are associated with their preferences and ratings. In addition, we measure user influence according to their topological roles in the social network. We incorporate evolutionary opinion dynamics and user influence into the recommendation framework for the prediction of unknown ratings. Experiment results on two real-world datasets demonstrate that our method outperforms state-of the-art models in terms of accuracy, and it also performs well for cold-start users. Our method reduces the divergence of user preferences, in accordance with online opinion interactions. Furthermore, our method has approximate computational complexity with matrix factorization, and results in less computation than state-of-the-art models. Our method is quite general, and indicates that studies in social physics, statistics, and other research fields may be involved in recommendation to improve the performance.
Xu, G 2018, 'Unsupervised Keyword Extraction from Microblog Posts via Hashtags', Journal of Web Engineering, vol. 17 (1&2). View/Download from: UTS OPUS
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. View/Download from: Publisher's site View description>>
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, vol. 29, no. 11, pp. 5292-5303. View/Download from: Publisher's site View description>>
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.
OAPA With the advancement of wireless networking technologies and communication infrastructures, mobile cloud computing has emerged as a pervasive paradigm to execute computing tasks for capacity-limited mobile devices. More specifically, at the network edge, the resource-rich and trusted cloudlet system can provide in-proximity computing services by executing the workloads for nearby devices. Nevertheless, there are chances for malicious users to generate DDoS (Distributed Denial-of-Service) flooding tasks to overwhelm cloudlet servers and block computing services from legitimate users. Load balancing is one of the most effective methods to solve DDoS attacks in distributed networks. However, existing solutions require overall load information to achieve load balancing in cloudlet networks, making it costly in both communication and computation. To achieve more efficient and low-cost load balancing, we propose CTOM, a novel Collaborative Task Offloading scheMe to avoid DDoS attacks for secure and sustainable mobile cloudlet networks. The proposed solution is based on the balls-and-bins theory and it can balance the task loads with extremely limited information. The CTOM reduces the number of overloaded cloudlets smoothly, thus handling the potential DDoS attacks in mobile cloudlet networks. Extensive simulations and evaluation demonstrate that, the proposed CTOM outperforms the conventional random and proportional allocation schemes in reducing the task gaps between maximum load and minimum load among mobile cloudlets by 65% and 55%, respectively.
Yang, W, Li, J, Zheng, H & Xu, RYD 2018, 'A Nuclear Norm Based Matrix Regression Based Projections Method for Feature Extraction', IEEE Access, vol. 6, pp. 7445-7451. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
Yao, J, Wang, J, Tsang, IW, Zhang, Y, Sun, J, Zhang, C & Zhang, R 2018, 'Deep Learning from Noisy Image Labels with Quality Embedding', IEEE Transactions on Image Processing. View/Download from: Publisher's site View description>>
IEEE There is an emerging trend to leverage noisy image datasets in many visual recognition tasks. However, the label noise among datasets severely degenerates the performance of deep learning approaches. Recently, one mainstream is to introduce the latent label to handle label noise, which has shown promising improvement in the network designs. Nevertheless, the mismatch between latent labels and noisy labels still affects the predictions in such methods. To address this issue, we propose a probabilistic model, which explicitly introduces an extra variable to represent the trustworthiness of noisy labels, termed as the quality variable. Our key idea is to identify the mismatch between the latent and noisy labels by embedding the quality variables into different subspaces, which effectively minimizes the influence of label noise. At the same time, reliable labels are still able to be applied for training. To instantiate the model, we further propose a Contrastive-Additive Noise network (CAN), which consists of two important layers: (1) the contrastive layer that estimates the quality variable in the embedding space to reduce the influence of noisy labels; and (2) the additive layer that aggregates the prior prediction and noisy labels as the posterior to train the classifier. Moreover, to tackle the challenges in optimization, we deduce an SGD algorithm with the reparameterization tricks, which makes our method scalable to big data.We validate the proposed method on a range of noisy image datasets. Comprehensive results have demonstrated that CAN outperforms the state-of-the-art deep learning approaches.
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. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: UTS OPUS
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. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
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.
Yusoff, B, Merigó, JM, Ceballos, D & Peláez, JI 2018, 'Weighted-selective aggregated majority-OWA operator and its application in linguistic group decision making', International Journal of Intelligent Systems, vol. 33, no. 9, pp. 1929-1948. View/Download from: Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
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, B, Li, J & Lü, Q 2018, 'Prediction of 8-state protein secondary structures by a novel deep learning architecture.', BMC bioinformatics, vol. 19, no. 1, p. 293. View/Download from: Publisher's site View description>>
Protein secondary structure can be regarded as an information bridge that links the primary sequence and tertiary structure. Accurate 8-state secondary structure prediction can significantly give more precise and high resolution on structure-based properties analysis.We present a novel deep learning architecture which exploits an integrative synergy of prediction by a convolutional neural network, residual network, and bidirectional recurrent neural network to improve the performance of protein secondary structure prediction. A local block comprised of convolutional filters and original input is designed for capturing local sequence features. The subsequent bidirectional recurrent neural network consisting of gated recurrent units can capture global context features. Furthermore, the residual network can improve the information flow between the hidden layers and the cascaded recurrent neural network. Our proposed deep network achieved 71.4% accuracy on the benchmark CB513 dataset for the 8-state prediction; and the ensemble learning by our model achieved 74% accuracy. Our model generalization capability is also evaluated on other three independent datasets CASP10, CASP11 and CASP12 for both 8- and 3-state prediction. These prediction performances are superior to the state-of-the-art methods.Our experiment demonstrates that it is a valuable method for predicting protein secondary structure, and capturing local and global features concurrently is very useful in deep learning.
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, 'Multilabel Image Classification with Regional Latent Semantic Dependencies', IEEE Transactions on Multimedia, vol. 20, no. 10, pp. 2801-2813. View/Download from: UTS OPUS or Publisher's site View description>>
Zhang, L, Zhang, Q, Du, B, Huang, X, Tang, YY & Tao, D 2018, 'Simultaneous spectral-spatial feature selection and extraction for hyperspectral images', IEEE Transactions on Cybernetics, vol. 48, no. 1, pp. 16-28. View/Download from: Publisher's site View description>>
Zhang, Q, Liu, Y, Blum, RS, 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. View/Download from: Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
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.
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, Y, Wang, X, Bi, X & Tao, D 2018, 'A Light Dual-Task Neural Network for Haze Removal', IEEE Signal Processing Letters, vol. 25, no. 8, pp. 1231-1235. View/Download from: UTS OPUS or Publisher's site View description>>
Zhao, J, Mao, X & Zhang, J 2018, 'Learning deep facial expression features from image and optical flow sequences using 3D CNN', Visual Computer, vol. 34, no. 10, pp. 1461-1475. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: Publisher's site View description>>
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, XY, Zhang, T & Tao, D 2018, 'Multi-view manifold learning with locality alignment', Pattern Recognition, vol. 78, pp. 154-166. View/Download from: Publisher's site View description>>
Zhao, Z, Peng, H, Lan, C, Zheng, Y, Fang, L & Li, J 2018, 'Imbalance learning for the prediction of N6-Methylation sites in mRNAs.', BMC genomics, vol. 19, no. 1, p. 574. View/Download from: UTS OPUS or Publisher's site View description>>
N6-methyladenosine (m6A) is an important epigenetic modification which plays various roles in mRNA metabolism and embryogenesis directly related to human diseases. To identify m6A in a large scale, machine learning methods have been developed to make predictions on m6A sites. However, there are two main drawbacks of these methods. The first is the inadequate learning of the imbalanced m6A samples which are much less than the non-m6A samples, by their balanced learning approaches. Second, the features used by these methods are not outstanding to represent m6A sequence characteristics.We propose to use cost-sensitive learning ideas to resolve the imbalance data issues in the human mRNA m6A prediction problem. This cost-sensitive approach applies to the entire imbalanced dataset, without random equal-size selection of negative samples, for an adequate learning. Along with site location and entropy features, top-ranked positions with the highest single nucleotide polymorphism specificity in the window sequences are taken as new features in our imbalance learning. On an independent dataset, our overall prediction performance is much superior to the existing predictors. Our method shows stronger robustness against the imbalance changes in the tests on 9 datasets whose imbalance ratios range from 1:1 to 9:1. Our method also outperforms the existing predictors on 1226 individual transcripts. It is found that the new types of features are indeed of high significance in the m6A prediction. The case studies on gene c-Jun and CBFB demonstrate the detailed prediction capacity to improve the prediction performance.The proposed cost-sensitive model and the new features are useful in human mRNA m6A prediction. Our method achieves better correctness and robustness than the existing predictors in independent test and case studies. The results suggest that imbalance learning is promising to improve the performa...
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. View/Download from: Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
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. View/Download from: UTS OPUS or Publisher's site View description>>
Alshehri, MD & Hussain, F 2017, 'A Centralized Trust Management Mechanism for the Internet of Things (CTM-IoT)', Advances on Broad-Band Wireless Computing, Communication and Applications, The 12th International Conference on Broad-Band Wireless Computing, Communication and Applications, Springer, Barcelona, Spain, pp. 533-543. View/Download from: UTS OPUS or Publisher's site View description>>
The Internet of Things (IoT) is an extended network that allows all devices to be connected to one another over the Internet. This new network faces numerous challenges, but mainly security issues. One such issue is how the IoT’s nodes can trust each other when they are connected over the Internet. There is a lack of studies that address the issue of trust management in IoT, or that provide a fully trustworthy framework. This paper proposes and delivers a centralized trust management mechanism for IoT by adding trust modules as a feature of the central trust manager, the Super Node (SN). To deliver a comprehensive approach, the SN includes other modules which are integrated with the whole IoT Trust Management framework to provide trustworthy communication between all nodes.
Aravanis, TI, Peppas, P & Williams, MA 2018, 'Iterated belief revision and dalal⇔s operator', ACM International Conference Proceeding Series. View/Download from: Publisher's site View description>>
Biddle, R, Liu, S, Tilocca, P & Xu, G 2018, 'Automated Underwriting in Life Insurance: Predictions and Optimisation', Australasian Database Conference. View/Download from: UTS OPUS
Brownlow, J, Chu, C, Fu, B, Xu, G, Culbert, B & Meng, Q 2018, 'Cost-sensitive churn prediction in fund management services', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 776-788. View/Download from: UTS OPUS or Publisher's site View description>>
Brownlow, J, Chu, C, Fu, B, Xu, G, Culbert, B & Meng, Q 2018, 'Cost-sensitive Churn Prediction in Fund Management Services - Industrial Track', the 23rd International Conference on Database Systems for Advanced Applications, Australia. View/Download from: UTS OPUS
Brownlow, J, Chu, C, Xu, G, Culbert, B, Fu, B & Meng, Q 2018, 'A Multiple Source based Transfer Learning Framework for Marketing Campaigns', the 2018 International Joint Conference on Neural Networks, Rio De Janeiro, Brasil. View/Download from: UTS OPUS
Cobo, MJ, Wang, W, Laengle, S, Merigó, JM, Yu, D & Herrera-Viedma, E 2018, 'Co-words analysis of the last ten years of the international journal of uncertainty, fuzziness and knowledge-based systems', Communications in Computer and Information Science, pp. 667-677. View/Download from: Publisher's site View description>>
Culbert, B, Fu, B, Brownlow, J, Chu, C, Meng, Q & Xu, G 2018, 'Customer Churn Prediction in Superannuation: A Sequential Pattern Mining Approach', Australasian Database Conference. View/Download from: UTS OPUS
Cunill, OM, Gil-Lafuente, AM, Merigó, JM & González, LO 2018, 'Academic contributions in asian tourism research: A bibliometric analysis', Advances in Intelligent Systems and Computing, pp. 326-342. View/Download from: Publisher's site View description>>
Cunill, OM, Gil-Lafuente, AM, Merigó, JM & González, LO 2018, 'Asian academic research in tourism with an international impact: A bibliometric analysis of the main academic contributions', Advances in Intelligent Systems and Computing, pp. 307-325. View/Download from: Publisher's site View description>>
Fan, X, He, X, Puthal, D, Chen, S, Xiang, C, Nanda, P & Rao, X 2018, 'CTOM: Collaborative Task Offloading Mechanism for Mobile Cloudlet Networks', 2018, IEEE International Conference on Communications, ICC 2018, Kansas City, MO, USA. View/Download from: UTS OPUS
Gheisari, S, Catchpoole, DR, Charlton, A & Kennedy, PJ 2018, 'Patched completed local binary pattern is an effective method for neuroblastoma histological image classification', Communications in Computer and Information Science, pp. 57-71. View/Download from: Publisher's site View description>>
Gong, Y, Li, Z, Zhang, J, Liu, W, Zheng, Y & Kirsch, C 2018, 'Network-wide Crowd Flow Prediction of Sydney Trains via customized Online Non-negative Matrix Factorization', The 27th ACM International Conference on Information and Knowledge Management, Turin, Italy. View/Download from: UTS OPUS
Gui, M, Zhang, Z, Yang, Z, Gu, Y & Xu, G 2018, 'An Effective Joint Framework for Document Summarization', Companion of the The Web Conference 2018 on The Web Conference 2018. View/Download from: UTS OPUS
Guo, D, Zhao, W, Cui, Y, Wang, Z, Chen, S & Zhang, J 2018, 'Siamese network based features fusion for adaptive visual tracking', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 759-771. View/Download from: UTS OPUS or Publisher's site View description>>
Herse, S, Vitale, J, Ebrahimian, D, Tonkin, M, Ojha, S, Sidra, S, Johnston, B, Phillips, S, Gudi, SLKC, Clark, J, Judge, W & Williams, MA 2018, 'Bon Appetit! Robot Persuasion for Food Recommendation', ACM/IEEE International Conference on Human-Robot Interaction, pp. 125-126. View/Download from: UTS OPUS or Publisher's site View description>>
Lian, D, Zheng, K, Cao, L, Zheng, VW, Tsang, IW, Ge, Y & Xie, X 2018, 'High-order proximity preserving information network hashing', Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1744-1753. View/Download from: Publisher's site View description>>
Liu, W, Chang, X, Chen, L & Yang, Y 2018, 'Semi-supervised Joint Learning of Representation and Relation for Person Re-identification', Association for the Advancement of Artificial Intelligence, Louisiana, USA.
Malik, N, Nanda, P, Arora, A, He, X & Puthal, D 2018, 'Blockchain Based Secured Identity Authentication and Expeditious Revocation Framework for vehicular Networks', IEEE Computer Society, The 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications (IEEE TrustCom-18), New York. View/Download from: UTS OPUS
Merigó, JM, Herrera-Viedma, E, Cobo, MJ, Laengle, S & Rivas, D 2017, 'A bibliometric analysis of the first twenty years of soft computing', Proceedings of the Conference of the European Society for Fuzzy Logic and Technology, nternational Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, Springer International Publishing, Warsaw, Poland, pp. 517-528. View/Download from: UTS OPUS or Publisher's site View description>>
Nanda, A, Nanda, P, He, X, Puthal, D & Jamdagni, A 2018, 'A Novel Hybrid Authentication Model for Geo Location Oriented Routing in Dynamic Wireless Mesh Networks', Proceedings of the 51st Hawaii International Conference on System Sciences 2018, 51st Hawaii International Conference on System Sciences 2018, Hawaii, USA, pp. 5532-5541. View/Download from: UTS OPUS View description>>
Authentication is an essential part of any network and plays a pivotal role in ensuring the security of a network by preventing unauthorised devices/users access to the network. As dynamic wireless mesh networks are evolving and being accepted in various fields, there is a strong need to improve the security of the network. It’s features like self-organizing and self-healing make it great but get undermined when rigid authentication schemes are used. We propose a hybrid authentication scheme for such dynamic mesh networks under three specified scenarios; full authentication, quick authentication and new node authentication. The proposed schemes are applied on our previous works on dynamic mesh routing protocol, Geo location Oriented Routing Protocol (GLOR Simulation results show our proposed scheme is efficient in terms of resource utilization as well as defending against security threats.
Pang, G, Cao, L, Chen, L, Liu, H & Lian, D 2018, 'Sparse Modeling-based Sequential Ensemble Learning for Effective Outlier Detection in High-dimensional Numeric Data.', Association for the Advancement of Artificial Intelligence.
Pang, G, Chen, L, Cao, L & Liu, H 2018, 'Learning representations of ultrahigh-dimensional data for random distance-based outlier detection', Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2041-2050. View/Download from: UTS OPUS or Publisher's site View description>>
Peng, H, Zheng, Y, Zhao, Z, Liu, T & Li, J 2018, 'Recognition of CRISPR/Cas9 off-target sites through ensemble learning of uneven mismatch distributions', Bioinformatics, pp. i757-i765. View/Download from: Publisher's site View description>>
Peppas, P & Williams, M 2018, 'Parametrised Difference Revision', Principles of Knowledge Representation and Reasoning.
Poostchi, H, Zare Borzeshi, E & Piccardi, M 2018, 'BiLSTM-CRF for Persian Named-Entity RecognitionArmanPersoNERCorpus: the First Entity-Annotated Persian Dataset', 11th Edition of the Language Resources and Evaluation Conference, Miyazaki, Japan. View/Download from: UTS OPUS
Razzak, I, Saris, RA & Xu, G 2018, 'Robust 2D Joint Sparse Principle Component Analysis with F-norm Minimization for Sparse Modelling: 2D-RJSPCA', the 2018 International Joint Conference on Neural Networks, Rio De Janeiro. View/Download from: UTS OPUS
Saeed, Z, Razzak, I, Abbasi, R & Xu, G 2018, 'Text Stream to Temporal Network - A Dynamic Heartbeat Graph to Detect Emerging Events on Twitter', The 22nd Pacific-Asia Conference on Knowledge Discovery and Data, Australia. View/Download from: UTS OPUS or Publisher's site
Seifollahi, S, Piccardi, M & Zare Borzeshi, E 2017, 'A semi-supervised hidden Markov topic model based on prior knowledge', Data Mining, The 15th Australasian Data Mining Conference, Springer, Melbourne, VIC, Australia,, pp. 265-276. View/Download from: UTS OPUS or Publisher's site View description>>
A topic model is an unsupervised model to automatically discover the topics discussed in a collection of documents. Most of the existing topic models only use bag-of-words representations or single-word distributions and do not consider relations between words in the model. As a consequence, these models may generate topics which are not in good agreement with human-judged topic coherence. To mitigate this issue, we present a topic model which employs topically-related knowledge from prior topics and words’ co-occurrence/relations in the collection. To incorporate the prior knowledge, we leverage a two-staged semi-supervised Markov topic model. In the first stage, we estimate a transition matrix and a low-dimensional vocabulary for the final topic model. In the second stage, we produce the final topic model where the topic assignment is performed following a Markov chain process. Experiments on real text documents from a major compensation agency demonstrate improvements of both the PMI score measure and the topic coherence.
Shen, T, Zhou, T, Long, G, Jiang, J, Wang, S & Zhang, C 2018, 'Reinforced self-attention network: A hybrid of hard and soft attention for sequence modeling', IJCAI International Joint Conference on Artificial Intelligence, pp. 4345-4352. View description>>
Shi, H, He, W & Xu, G 2018, 'Workshop Proposal on Knowledge Discovery from Digital Libraries', Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, pp. 429-430. View/Download from: UTS OPUS or Publisher's site View description>>
Tonkin, M, Vitale, J, Herse, S, Williams, MA, Judge, W & Wang, X 2018, 'Design Methodology for the UX of HRI: A Field Study of a Commercial Social Robot at an Airport', ACM/IEEE International Conference on Human-Robot Interaction, pp. 407-415. View/Download from: UTS OPUS or Publisher's site View description>>
Umair, A, Nanda, P, He, X & Choo, K-KR 2018, 'User Relationship Classification of Facebook Messenger Mobile Data using WEKA', Springer, Lecture Notes in Computer Science, 12th International Conference on Network and System Security, Hong Kong. View/Download from: UTS OPUS View description>>
Mobile devices are a wealth of information about its user and
their digital and physical activities (e.g. online browsing and physical
location). Therefore, in any crime investigation artifacts obtained from
a mobile device can be extremely crucial. However, the variety of mobile
platforms, applications (apps) and the significant size of data compound
existing challenges in forensic investigations. In this paper, we explore the
potential of machine learning in mobile forensics, and specifically in the
context of Facebook messenger artifact acquisition and analysis. Using
Quick and Choo (2017)’s Digital Forensic Intelligence Analysis Cycle
(DFIAC) as the guiding framework, we demonstrate how one can acquire
Facebook messenger app artifacts from an Android device and an iOS
device (the latter is , using existing forensic tools. Based on the acquired
evidence, we create 199 data-instances to train WEKA classifiers (i.e.
ZeroR, J48 and Random tree) with the aim of classifying the device
owner’s contacts and determine their mutual relationship strength.
Vitale, J, Tonkin, M, Herse, S, Ojha, S, Clark, J, Williams, M, Wang, X & Judge, W 2018, 'Be More Transparent and Users Will Like You: A Robot Privacy and User Experience Design Experiment', Proceedings of 2018 ACM/IEEE International Conference on Human- Robot Interaction, ACM/IEEE International Conference on Human-Robot Interaction 2018, ACM, Chicago, pp. 379-387. View/Download from: UTS OPUS or Publisher's site
Vo, NNY, Liu, S, Xu, G & He, X 2018, 'Multimodal Mixture Density Boosting Network for Personality Mining', Advances in Knowledge Discovery and Data Mining, The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Melbourne, Australia, pp. 644-655. View/Download from: UTS OPUS or Publisher's site
Vo, NNY, Xu, G, Liu, S, Brownlow, EJ, Culbert, B & Chu, C 2018, 'Client Churn Prediction with Call Log Analysis', Database Systems for Advanced Applications, 23rd International Conference on Database Systems for Advanced Applications (DASFAA 2018), Springer, Gold Coast, Australia, pp. 752-763. View/Download from: UTS OPUS or Publisher's site
Wan, Y, Zhao, Z, Yang, M, Xu, G, Ying, H, Wu, J & Yu, PS 2018, 'Improving Automatic Source Code Summarization via Deep Reinforcement Learning', The 33rd IEEE/ACM International Conference on Automated Software Engineering, Corum, Montpellier, France. View/Download from: UTS OPUS
Wu, W, Li, B, Chen, L & Zhang, C 2018, 'Efficient attributed network embedding via recursive randomized hashing', IJCAI International Joint Conference on Artificial Intelligence, pp. 2861-2867. View description>>
Xu, J & Cao, L 2018, 'Vine copula-based asymmetry and tail dependence modeling', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 285-297. View/Download from: Publisher's site View description>>
Xu, J, Wei, W & Cao, L 2018, 'Copula-based high dimensional cross-market dependence modeling', Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017, pp. 734-743. View/Download from: Publisher's site View description>>
Yao, Y, Zhang, J, Shen, F, Yang, W, Hua, XS & Tang, Z 2018, 'Extracting privileged information from untagged corpora for classifier learning', IJCAI International Joint Conference on Artificial Intelligence, pp. 1085-1091. View/Download from: UTS OPUS View description>>
Yin, J, Zhou, Z, Liu, S, Wu, Z & Xu, G 2018, 'Social Spammer Detection: A Multi-Relational Embedding Approach', The 22nd Pacific-Asia Conference on Knowledge Discovery and Data, Australia. View/Download from: UTS OPUS or Publisher's site
Ying, H, Zhuang, F, Zhang, F, Liu, Y, Xu, G, Xie, X, Xiong, H & Wu, J 2018, 'Sequential Recommender System based on Hierarchical Attention Networks', the 27th International Joint Conference on Artificial Intelligence. View/Download from: UTS OPUS
Yusoff, B, Merigó, JM & Hornero, DC 2018, 'Analysis on extensions of multi-expert decision making model with respect to OWA-based aggregation processes', Advances in Intelligent Systems and Computing, pp. 179-196. View/Download from: Publisher's site View description>>
Yusoff, B, Merigó, JM & Hornero, DC 2018, 'Generalized OWA-TOPSIS model based on the concept of majority opinion for group decision making', Advances in Intelligent Systems and Computing, pp. 124-139. View/Download from: Publisher's site View description>>
Zhang, J, Wu, Q, Shen, C, Zhang, J, Lu, J & van den Hengel, A 2018, 'Goal-Oriented Visual Question Generation via Intermediate Rewards', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 189-204. View/Download from: UTS OPUS or Publisher's site View description>>
Zhang, L, Xu, J, Zhang, J & Gong, Y 2018, 'Information Enhancement for Travelogues via a Hybrid Clustering Model'. View/Download from: UTS OPUS
Zhang, P, Wu, Q, Xu, J & Jian, Z 2018, 'Long-Term Person Re-identification Using True Motion from Videos', 2018 IEEE Winter Conference on Applications of Computer Vision, Lake Tahoe, NV, USA, pp. 494-502. View/Download from: UTS OPUS or Publisher's site
Zhou, Z, Liu, S, Xu, G, Xie, X, Yin, J, Li, Y & Zhang, W 2018, 'Knowledge-based Recommendation with Hierarchical Collaborative Embedding', The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Melbourne, Australia. View/Download from: UTS OPUS