Publications

Stoianoff, NP, Chilton, F & Monotti, AL 2019, Commercialisation of Intellectual Property, First, Lexis Nexis Butterworths, Chatswood, Sydney.
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This important new book takes an integrated approach to the legal, business and technical aspects of the commercialisation of intellectual property in Australia. The expert commentary examines much of the law and some of the business and economics of commercialising and licensing intellectual property rights in an interdisciplinary and comparative context. Topics addressed include the international trade context of intellectual property rights and their role in driving innovation, funding and development of intellectual property in the private and university sectors, identifying appropriate legal structures for the business of commercialising intellectual property, licensing intellectual property including the special case study of multimedia, intellectual property as security, insolvency, and the taxation of the intellectual property life cycle.

Alshehri, M & Hussain, F 2019, 'A Distributed Trust Management Model for the Internet of Things (DTM-IoT)' in Recent Trends and Advances in Wireless and IoT-enabled Networks, Springer.
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The Internet of Things (IoT) is a paradigm that facilitates autonomous communications among various IoT devices with minimal human intervention. This raises many security challenges, which are critical and must be addressed to allow for the wide deployment of IoT. Trust must be provisioned among the various heterogeneous IoT devices. In this paper, a distributed trust management model is proposed. The model offers solutions to trust management and negotiations in the IoT. It is inspired by clustering techniques that are adopted in WSNs. After introducing the structural design and the main components of the trust model, we demonstrate how our new approach supports trust negotiations and management in the IoT.

Dasgupta, A, Gill, A & Hussain, F 2019, 'Privacy of IoT-Enabled Smart Home Systems' in IoT and Smart Home Automation, IntechOpen.
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Liu, W, Yin, Z & Chawla, S 2019, 'Adversarial Attack, Defense, and Applications with Deep Learning Frameworks' in Alazab, M & Tang, M (eds), Deep Learning Applications for Cyber Security, Springer.
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This book addresses questions of how deep learning methods can be used to advance cyber security objectives, including detection, modeling, monitoring and analysis of as well as defense against various threats to sensitive data and security ...

Abidi, S, Piccardi, M, Tsang, WH & Williams, M-A 2019, 'Well-M³N: A Maximum-Margin Approach to Unsupervised Structured Prediction', IEEE Transactions on Emerging Topics in Computational Intelligence.
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Unsupervised structured prediction is of fundamental importance for the clustering and classification of unannotated structured data. To date, its most common approach still relies on the use of structural probabilistic models and the expectation-maximization (EM) algorithm. Conversely, structural maximum-margin approaches, despite their extensive success in supervised and semi-supervised classification, have not raised equivalent attention in the unsupervised case. For this reason, in this paper we propose a novel approach that extends the maximum-margin Markov networks (M3N) to an unsupervised training framework. The main contributions of our extension are new formulations for the feature map and loss function of M3N that decouple the labels from the measurements and support multiple ground-truth training. Experiments on two challenging segmentation datasets have achieved competitive accuracy and generalization compared to other unsupervised algorithms such as k-means, EM and unsupervised structural SVM, and comparable performance to a contemporary deep learning-based approach.

Alshehri, MD & Hussain, FK 2019, 'A fuzzy security protocol for trust management in the internet of things (Fuzzy-IoT)', Computing.
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© 2018, Springer-Verlag GmbH Austria, ein Teil von Springer Nature. Recently, the Internet of things (IoT) has received a lot of attention from both industry and academia. A reliable and secure IoT connection and communication is essential for the proper working of the IoT network as a whole. One of the ways to achieve robust security in an IoT network is to enable and build trusted communication among the things (nodes). In this area, the existing IoT literature faces many critical issues, such as the lack of intelligent cluster-based trust approaches for IoT networks and the detection of attacks on the IoT trust system from malicious nodes, such as bad service providers. The existing literature either does not address these issues or only addresses them partially. Our proposed solution can firstly detect on-off attacks using the proposed fuzzy-logic based approach, and it can detect contradictory behaviour attacks and other malicious nodes. Secondly, we develop a fuzzy logic-based approach to detect malicious nodes involved in bad service provisioning. Finally, to maintain the security of the IoT network, we develop a secure messaging system that enables secure communication between nodes. This messaging system uses hexadecimal values with a structure similar to serial communication. We carried out extensive experimentation under varying network sizes to validate the working of our proposed solution and also to test the efficiency of the proposed methods in relation to various types of malicious behavior. The experiment results demonstrate the effectiveness of our approach under various conditions.

Altaee, A, Braytee, A, Millar, GJ & Naji, O 2019, 'Energy efficiency of hollow fibre membrane module in the forward osmosis seawater desalination process', Journal of Membrane Science, vol. 587.
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© 2019 This study provided new insights regarding the energy efficiency of hollow fibre forward osmosis modules for seawater desalination; and as a consequence an approach was developed to improve the process performance. Previous analysis overlooked the relationship between the energy efficiency and operating modes of the hollow fibre forward osmosis membrane when the process was scaled-up. In this study, the module length and operating parameters were incorporated in the design of an energy-efficient forward osmosis system. The minimum specific power consumption for seawater desalination was calculated at the thermodynamic limits. Two FO operating modes: (1) draw solution in the lumen and (2) feed solution in the lumen, were evaluated in terms of the desalination energy requirements at a minimum draw solution flow rate. The results revealed that the operating mode of the forward osmosis membrane was important in terms of reducing the desalination energy. In addition, the length of the forward osmosis module was also a significant factor and surprisingly increasing the length of the forward osmosis module was not always advantageous in improving the performance. The study outcomes also showed that seawater desalination by the forward osmosis process was less energy efficient at low and high osmotic draw solution concentration and performed better at 1.2–1.4 M sodium chloride draw solution concentrations. The findings of this study provided a platform to the manufacturers and operators of hollow fibre forward osmosis membrane to improve the energy efficiency of the desalination process.

Amirbagheri, K, Núñez-Carballosa, A, Guitart-Tarrés, L & Merigó, JM 2019, 'Research on green supply chain: a bibliometric analysis', Clean Technologies and Environmental Policy, vol. 21, no. 1, pp. 3-22.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Abstract: Recently, the emergent concept of green supply chain has received increasing attention. Although popular among scholars, many literature reviews have only examined GSC from a general point of view or focused on a specific issue related to GSC. This study presents a comprehensive analysis of the influence and productivity of research on GSC from 1995 to 2017 by reporting trends among authors, countries and institutions based on a bibliometric approach. To this end, the study analyzes around 1900 papers on GSC. This study uses the Web of Science Core Collection database to analyze the bibliometric data and the visualization of similarities viewer method to graphically map those data. The graphical analysis uses bibliographic coupling, co-citation, co-authorship and co-occurrence of keywords. Graphical abstract: [Figure not available: see fulltext.].

Amiri, M, Tofigh, F, Shariati, N, Lipman, J & Abolhasan, M 2019, 'Miniature tri-wideband Sierpinski-Minkowski fractals metamaterial perfect absorber', IET MICROWAVES ANTENNAS & PROPAGATION, vol. 13, no. 7, pp. 991-996.
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Anderson, C, Hafen, R, Sofrygin, O, Ryan, L & members of the HBGDki Community 2019, 'Comparing predictive abilities of longitudinal child growth models.', Statistics in Medicine, vol. 38, pp. 3555-3570.
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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.

Aravanis, T, Peppas, P & Williams, MA 2019, 'An investigation of parametrized difference revision operators', Annals of Mathematics and Artificial Intelligence.
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© 2019, Springer Nature Switzerland AG. In this article, we provide the epistemic-entrenchment and partial-meet characterizations of a new, important class of concrete revision operators (all of which satisfy the AGM postulates for revision), called Parametrized Difference revision operators (PD operators, for short). PD operators are natural generalizations of Dalal’s revision operator, with a much greater range of applicability, hence, the epistemic-entrenchment and partial-meet characterizations of the latter are also provided, as a by-product. Lastly, we prove that PD operators satisfy the strong version of Parikh’s relevance-sensitive axiom for belief revision, showing that they are fully compatible with the notion of relevance.

Awwad, S, Tarvade, S, Piccardi, M & Gattas, DJ 2019, 'The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene', International Journal for Quality in Health Care, vol. 31, no. 1, pp. 36-42.
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(i) To demonstrate the feasibility of automated, direct observation and collection of hand hygiene data, (ii) to develop computer visual methods capable of reporting compliance with moment 1 (the performance of hand hygiene before touching a patient) and (iii) to report the diagnostic accuracy of automated, direct observation of moment 1.Observation of simulated hand hygiene encounters between a healthcare worker and a patient.Computer laboratory in a university.Healthy volunteers.Sensitivity and specificity of automatic detection of the first moment of hand hygiene.We captured video and depth images using a Kinect camera and developed computer visual methods to automatically detect the use of alcohol-based hand rub (ABHR), rubbing together of hands and subsequent contact of the patient by the healthcare worker using depth imagery.We acquired images from 18 different simulated hand hygiene encounters where the healthcare worker complied with the first moment of hand hygiene, and 8 encounters where they did not. The diagnostic accuracy of determining that ABHR was dispensed and that the patient was touched was excellent (sensitivity 100%, specificity 100%). The diagnostic accuracy of determining that the hands were rubbed together after dispensing ABHR was good (sensitivity 83%, specificity 88%).We have demonstrated that it is possible to automate the direct observation of hand hygiene performance in a simulated clinical setting. We used cheap, widely available consumer technology and depth imagery which potentially increases clinical application and decreases privacy concerns.

Baier-Fuentes, H, Merigó, JM, Amorós, JE & Gaviria-Marín, M 2019, 'International entrepreneurship: a bibliometric overview', International Entrepreneurship and Management Journal, pp. 1-45.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature The aim of this paper is to provide an overview of the academic research on International Entrepreneurship (IE). To accomplish this, an exhaustive bibliometric analysis was carried out, involving a bibliometric performance analysis and a graphic mapping of the references in this field. Our analysis focuses on journals, papers, authors, institutions and countries. To perform the performance analysis, the work uses a series of bibliometric indicators such as h-index, productivity and citations. Furthermore, the VOS viewer to graphically map the bibliographic material is used. The graphical analysis uses co-citation, bibliographic coupling and co-occurrence of keywords. The results of both analyzes are consistent among them, and show that the USA is the most influential country in IE research as it houses the main authors and institutions in this research field. Moreover, is observed and expected the continued growth of the field globally. Our research plays an informative and complementary role as it presents most of the key aspects in International Entrepreneurship research.

Ben, X, Gong, C, Zhang, P, Jia, X, Wu, Q & Meng, W 2019, 'Coupled Patch Alignment for Matching Cross-View Gaits', IEEE Transactions on Image Processing, vol. 28, no. 6, pp. 3142-3157.
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© 1992-2012 IEEE. Gait recognition has attracted growing attention in recent years, as the gait of humans has a strong discriminative ability even under low resolution at a distance. Unfortunately, the performance of gait recognition can be largely affected by view change. To address this problem, we propose a coupled patch alignment (CPA) algorithm that effectively matches a pair of gaits across different views. To realize CPA, we first build a certain amount of patches, and each of them is made up of a sample as well as its intra-class and inter-class nearest neighbors. Then, we design an objective function for each patch to balance the cross-view intra-class compactness and the cross-view inter-class separability. Finally, all the local-independent patches are combined to render a unified objective function. Theoretically, we show that the proposed CPA has a close relationship with canonical correlation analysis. Algorithmically, we extend CPA to 'multi-dimensional patch alignment' that can handle an arbitrary number of views. Comprehensive experiments on CASIA(B), USF, and OU-ISIR gait databases firmly demonstrate the effectiveness of our methods over other existing popular methods in terms of cross-view gait recognition.

Ben, X, Gong, C, Zhang, P, Yan, R, Wu, Q & Meng, W 2019, 'Coupled Bilinear Discriminant Projection for Cross-view Gait Recognition', IEEE Transactions on Circuits and Systems for Video Technology.
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IEEE A problem that hinders good performance of general gait recognition systems is that the appearance features of gaits are more affected-prone by views than identities, especially when the walking direction of the probe gait is different from the register gait. This problem cannot be solved by traditional projection learning methods, because these methods can learn only one projection matrix, thus for the same subject, it cannot transfer cross-view gait features into similar ones. This paper presents an innovative method to overcome this problem by aligning gait energy images (GEIs) across views with the Coupled bilinear discriminant projection (CBDP). Specifically, CBDP generates the aligned gait matrix features for two views with two sets of bilinear transformation matrices, so that the original GEIs’ spatial structure information can be preserved. By iteratively maximizing the ratio of inter-class distance metric to intra-class distance metric, CBDP can learn the optimal matrix subspace where GEIs across views are aligned in both horizontal and vertical coordinates. Therefore, CBDP is also able to avoid the under-sample problem. We also theoretically prove that the upper and lower bounds of the objective function sequence of CBDP are both monotonically increasing, so the convergence of CBDP is demonstrated. In terms of accuracy, the comparative experiments on the CASIA(B) and OU-ISIR gait databases show that our method is superior to the state-of-the-art cross-view gait recognition methods. More impressively, the encouraging performance is obtained by our method even in matching a lateral-view gait with a frontal-view gait.

Blanco-Mesa, F, León-Castro, E, Merigó, JM & Xu, Z 2019, 'Bonferroni means with induced ordered weighted average operators', International Journal of Intelligent Systems, vol. 34, no. 1, pp. 3-23.
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© 2018 Wiley Periodicals, Inc. The induced ordered weighted average is an averaging aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum. This paper presents some new generalizations by using Bonferroni means (BM) forming induced BM. The main advantage of this approach is the possibility of reordering the results according to complex ranking processes based on order-inducing variables. The work also presents some additional extensions by using the weighted ordered weighted average, immediate weights, and hybrid averages. Some further generalizations with generalized and quasi-arithmetic means are also developed to consider a wide range of particular cases including quadratic and geometric aggregations. The article also considers the applicability of the new approach in-group decision-making developing an application in sales forecasting.

CAO, X, Qiu, B & Xu, G 2019, 'BorderShift: toward optimal MeanShift vector for cluster boundary detection in high-dimensional data', Pattern Analysis and Applications, vol. 22, no. 3, pp. 1015-1027.
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© 2018 Springer-Verlag London Ltd., part of Springer Nature We present a cluster boundary detection scheme that exploits MeanShift and Parzen window in high-dimensional space. To reduce the noises interference in Parzen window density estimation process, the kNN window is introduced to replace the sliding window with fixed size firstly. Then, we take the density of sample as the weight of its drift vector to further improve the stability of MeanShift vector which can be utilized to separate boundary points from core points, noise points, isolated points according to the vector models in multi-density data sets. Under such circumstance, our proposed BorderShift algorithm doesn’t need multi-iteration to get the optimal detection result. Instead, the developed Shift value of each data point helps to obtain it in a liner way. Experimental results on both synthetic and real data sets demonstrate that the F-measure evaluation of BorderShift is higher than that of other algorithms.

Cao, X, Qiu, B, Li, X, Shi, Z, Xu, G & Xu, J 2019, 'Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data', IEEE Transactions on Neural Networks and Learning Systems.
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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.

Cao, Z, Ding, W, Wang, YK, Hussain, FK, Al-Jumaily, A & Lin, CT 2019, 'Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy', Neurocomputing.
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© 2019 Elsevier B.V. Multiscale inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the habituation of brain systems. Entropy dynamics are generally believed to reflect the ability of the brain to adapt to a visual stimulus environment. In this study, we explored repetitive steady-state visual evoked potential (SSVEP)-based EEG complexity by assessing multiscale inherent fuzzy entropy with relative measurements. We used a wearable EEG device with Oz and Fpz electrodes to collect EEG signals from 40 participants under the following three conditions: a resting state (closed-eyes (CE) and open-eyes (OE) stimulation with five 15-Hz CE SSVEPs and stimulation with five 20-Hz OE SSVEPs. We noted monotonic enhancement of occipital EEG relative complexity with increasing stimulus times in CE and OE conditions. The occipital EEG relative complexity was significantly higher for the fifth SSVEP than for the first SSEVP (FDR-adjusted p < 0.05). Similarly, the prefrontal EEG relative complexity tended to be significantly higher in the OE condition compared to that in the CE condition (FDR-adjusted p < 0.05). The results also indicate that multiscale inherent fuzzy entropy is superior to other competing multiscale-based entropy methods. In conclusion, EEG relative complexity increases with stimulus times, a finding that reflects the strong habituation of brain systems. These results suggest that multiscale inherent fuzzy entropy is an EEG pattern with which brain complexity can be assessed using repetitive SSVEP stimuli.

Cheng, H, Zhang, J, Wu, Q & An, P 2019, 'A computational model for stereoscopic visual saliency prediction', IEEE Transactions on Multimedia, vol. 21, no. 3, pp. 678-689.
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© 2018 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: 1) the pop-out effect; 2) comfort zones; and 3) 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 multifeature 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 shows that our proposed method outperforms several state-of-the-art saliency models.

Chiu, SK, Orive, SL, Moon, MJ, Saw, J, Ellis, S, Kile, BT, Huang, Y, Chacon, D, Pimanda, JE, Beck, D, Hamilton, JR, Tremblay, CS & Curtis, DJ 2019, 'Shared roles for Scl and Lyl1 in murine platelet production and function.', Blood, vol. 134, no. 10, pp. 826-835.
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The stem cell leukemia (Scl or Tal1) protein forms part of a multimeric transcription factor complex required for normal megakaryopoiesis. However, unlike other members of this complex such as Gata1, Fli1, and Runx1, mutations of Scl have not been observed as a cause of inherited thrombocytopenia. We postulated that functional redundancy with its closely related family member, lymphoblastic leukemia 1 (Lyl1) might explain this observation. To determine whether Lyl1 can substitute for Scl in megakaryopoiesis, we examined the platelet phenotype of mice lacking 1 or both factors in megakaryocytes. Conditional Scl knockout (KO) mice crossed with transgenic mice expressing Cre recombinase under the control of the mouse platelet factor 4 (Pf4) promoter generated megakaryocytes with markedly reduced but not absent Scl These Pf4Sclc-KO mice had mild thrombocytopenia and subtle defects in platelet aggregation. However, Pf4Sclc-KO mice generated on an Lyl1-null background (double knockout [DKO] mice) had severe macrothrombocytopenia, abnormal megakaryocyte morphology, defective pro-platelet formation, and markedly impaired platelet aggregation. DKO megakaryocytes, but not single-knockout megakaryocytes, had reduced expression of Gata1, Fli1, Nfe2, and many other genes that cause inherited thrombocytopenia. These gene expression changes were significantly associated with shared Scl and Lyl1 E-box binding sites that were also enriched for Gata1, Ets, and Runx1 motifs. Thus, Scl and Lyl1 share functional roles in platelet production by regulating expression of partner proteins including Gata1. We propose that this functional redundancy provides one explanation for the absence of Scl and Lyl1 mutations in inherited thrombocytopenia.

Chomsiri, T, He, XS, Nanda, P & Tan, Z 2019, 'Hybrid Tree-rule Firewall for High Speed Data Transmission', IEEE Transactions on Cloud Computing.
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Traditional firewalls employ listed rules in both configuration and process phases to regulate network traffic. However, configuring a firewall with listed rules may create rule conflicts, and slows down the firewall. To overcome this problem, we have proposed a Tree-rule firewall in our previous study. Although the Tree-rule firewall guarantees no conflicts within its rule set and operates faster than traditional firewalls, keeping track of the state of network connections using hashing functions incurs extra computational overhead. In order to reduce this overhead, we propose a hybrid Tree-rule firewall in this paper. This hybrid scheme takes advantages of both Tree-rule firewalls and traditional listed-rule firewalls. The GUIs of our Tree-rule firewalls are utilized to provide a means for users to create conflict-free firewall rules, which are organized in a tree structure and called 'tree rules'. These tree rules are later converted into listed rules that share the merit of being conflict-free. Finally, in decision making, the listed rules are used to verify against packet header information. The rules which have matched with most packets are moved up to the top positions by the core firewall. The mechanism applied in this hybrid scheme can significantly improve the functional speed of a firewall.

Cui, L, Wu, J, Pi, D, Zhang, P & Kennedy, P 2019, 'Dual Implicit Mining-Based Latent Friend Recommendation', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user-user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen-Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics.

Curiskis, SA, Drake, B, Osborn, TR & Kennedy, PJ 2019, 'An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit', Information Processing and Management.
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© 2019 Methods for document clustering and topic modelling in online social networks (OSNs) offer a means of categorising, annotating and making sense of large volumes of user generated content. Many techniques have been developed over the years, ranging from text mining and clustering methods to latent topic models and neural embedding approaches. However, many of these methods deliver poor results when applied to OSN data as such text is notoriously short and noisy, and often results are not comparable across studies. In this study we evaluate several techniques for document clustering and topic modelling on three datasets from Twitter and Reddit. We benchmark four different feature representations derived from term-frequency inverse-document-frequency (tf-idf) matrices and word embedding models combined with four clustering methods, and we include a Latent Dirichlet Allocation topic model for comparison. Several different evaluation measures are used in the literature, so we provide a discussion and recommendation for the most appropriate extrinsic measures for this task. We also demonstrate the performance of the methods over data sets with different document lengths. Our results show that clustering techniques applied to neural embedding feature representations delivered the best performance over all data sets using appropriate extrinsic evaluation measures. We also demonstrate a method for interpreting the clusters with a top-words based approach using tf-idf weights combined with embedding distance measures.

Cutler, RL, Torres-Robles, A, Wiecek, E, Drake, B, Van der Linden, N, Benrimoj, SIC & Garcia-Cardenas, V 2019, 'Pharmacist-led medication non-adherence intervention: reducing the economic burden placed on the Australian health care system', PATIENT PREFERENCE AND ADHERENCE, vol. 13, pp. 853-862.
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Dai, M, Cheng, S, He, X & Wang, D 2019, 'Object tracking in the presence of shaking motions', Neural Computing and Applications, pp. 1-18.
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© 2018 The Natural Computing Applications Forum Visual tracking can be particularly interpreted as a process of searching for targets and optimizing the searching. In this paper, we present a novel tracker framework for tracking shaking targets. We formulate the underlying geometrical relevance between a search scope and a target displacement. A uniform sampling among the search scopes is implemented by sliding windows. To alleviate any possible redundant matching, we propose a double-template structure comprising of initial and previous tracking results. The element-wise similarities between a template and its candidates are calculated by jointly using kernel functions which provide a better outlier rejection property. The STC algorithm is used to improve the tracking results by maximizing a confidence map incorporating temporal and spatial context cues about the tracked targets. For better adaptation to appearance variations, we employ a linear interpolation to update the context prior probability of the STC method. Both qualitative and quantitative evaluations are performed on all sequences that contain shaking motions and are selected from the OTB-50 challenging benchmark. The proposed approach is compared with and outperforms 12 state-of-the-art tracking methods on the selected sequences while running on MATLAB without code optimization. We have also performed further experiments on the whole OTB-50 and VOT 2015 datasets. Although the most of sequences in these two datasets do not contain motion blur that this paper is focusing on, the results of our method are still favorable compared with all of the state-of-the-art approaches.

Dong, X, Gong, Y & Cao, L 2019, 'e-RNSP: An Efficient Method for Mining Repetition Negative Sequential Patterns.', IEEE transactions on cybernetics.
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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, Qiu, P, Lu, J, Cao, L & Xu, T 2019, 'Mining Top-k Useful Negative Sequential Patterns via Learning', IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, vol. 30, no. 9, pp. 2764-2778.
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Du, B, Xinyao, T, Wang, Z, Zhang, L & Tao, D 2019, 'Robust Graph-Based Semisupervised Learning for Noisy Labeled Data via Maximum Correntropy Criterion.', IEEE Transactions on Cybernetics, vol. 49, no. 4, pp. 1440-1453.
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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, X, Yin, H, Chen, L, Wang, Y, Yang, Y & Zhou, X 2019, 'Personalized Video Recommendation Using Rich Contents from Videos', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods.

Esmaili, N, Piccardi, M, Kruger, B & Girosi, F 2019, 'Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models (vol 13, e0206274, 2018)', PLOS ONE, vol. 14, no. 4.
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Feng, X, Hormuth, DA & Yankeelov, TE 2019, 'An adjoint-based method for a linear mechanically-coupled tumor model: Application to estimate the spatial variation of murine glioma growth based on diffusion weighted magnetic resonance imaging.', Comput Mech, vol. 63, no. 2, pp. 159-180.
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We present an efficient numerical method to quantify the spatial variation of glioma growth based on subject-specific medical images using a mechanically-coupled tumor model. The method is illustrated in a murine model of glioma in which we consider the tumor as a growing elastic mass that continuously deforms the surrounding healthy-appearing brain tissue. As an inverse parameter identification problem, we quantify the volumetric growth of glioma and the growth component of deformation by fitting the model predicted cell density to the cell density estimated using the diffusion-weighted magnetic resonance imaging (DW-MRI) data. Numerically, we developed an adjoint-based approach to solve the optimization problem. Results on a set of experimentally measured, in vivo rat glioma data indicate good agreement between the fitted and measured tumor area and suggest a wide variation of in-plane glioma growth with the growth-induced Jacobian ranging from 1.0 to 6.0.

Gaviria-Marin, M, Merigó, JM & Baier-Fuentes, H 2019, 'Knowledge management: A global examination based on bibliometric analysis', Technological Forecasting and Social Change, vol. 140, pp. 194-220.
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© 2018 Knowledge management (KM) is a field of research that has gained wide acceptance in the scientific community and management literature. This article presents a bibliometric overview of the academic research on KM in the business and management areas. Various bibliometric methods are used to perform this overview, including performance analysis and science mapping of the KM field. The performance analysis uses a series of bibliometric indicators, such as the h-index, productivity and citations. In addition, the VOSviewer software is used to map the bibliographic material. Science mapping uses co-citations and the concurrency of keywords. References were obtained from the Web of Science database. We identified and classified the most relevant research in the field according to journals, articles, authors, institutions and countries. The results show that research in this field has increased significantly in the last ten years and that the USA is the most influential country in all aspects in this field. It is important to consider, however, that science continues to advance in this and in all fields and that data rapidly change over time. Therefore, this paper fulfills an informational role that shows that most of the fundamental research of KM is in business and management areas.

Gong, C, Tao, D, Chang, X & Yang, J 2019, 'Ensemble Teaching for Hybrid Label Propagation', IEEE Transactions on Cybernetics, vol. 49, no. 2, pp. 388-402.
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IEEE Label propagation aims to iteratively diffuse the label information from labeled examples to unlabeled examples over a similarity graph. Current label propagation algorithms cannot consistently yield satisfactory performance due to two reasons: one is the instability of single propagation method in dealing with various practical data, and the other one is the improper propagation sequence ignoring the labeling difficulties of different examples. To remedy above defects, this paper proposes a novel propagation algorithm called hybrid diffusion under ensemble teaching (HyDEnT). Specifically, HyDEnT integrates multiple propagation methods as base ''learners'' to fully exploit their individual wisdom, which helps HyDEnT to be stable and obtain consistent encouraging results. More importantly, HyDEnT conducts propagation under the guidance of an ensemble of ''teachers''. That is to say, in every propagation round the simplest curriculum examples are wisely designated by a teaching algorithm, so that their labels can be reliably and accurately decided by the learners. To optimally choose these simplest examples, every teacher in the ensemble should comprehensively consider the examples' difficulties from its own viewpoint, as well as the common knowledge shared by all the teachers. This is accomplished by a designed optimization problem, which can be efficiently solved via the block coordinate descent method. Thanks to the efforts of the teachers, all the unlabeled examples are logically propagated from simple to difficult, leading to better propagation quality of HyDEnT than the existing methods. Experiments on six popular datasets reveal that HyDEnT achieves the highest classification accuracy when compared with six state-of-the-art propagation methodologies such as harmonic functions, Fick's law assisted propagation, linear neighborhood propagation, semisupervised ensemble learning, bipartite graph-based consensus maximization, and teaching-to-learn and learnin...

Guan, N, Liu, T, Zhang, Y, Tao, D & Davis, LS 2019, 'Truncated Cauchy Non-negative Matrix Factorization for Robust Subspace Learning', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 1, pp. 1-14.
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IEEE Non-negative matrix factorization (NMF) minimizes the Euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers. In this paper, we propose a Truncated Cauchy loss that handle outliers by truncating large errors, and develop a Truncated CauchyNMF to robustly learn the subspace on noisy datasets contaminated by outliers. We theoretically analyze the robustness of Truncated CauchyNMF comparing with the competing models and theoretically prove that Truncated CauchyNMF has a generalization bound which converges at a rate of order < formula > < tex > $O(\sqrt{\ln{n}/n})$ < /tex > < /formula > , where < formula > < tex > $n$ < /tex > < /formula > is the sample size. We evaluate Truncated CauchyNMF by image clustering on both simulated and real datasets. The experimental results on the datasets containing gross corruptions validate the effectiveness and robustness of Truncated CauchyNMF for learning robust subspaces.

Guo, D, Lui, GYL, Lai, SL, Wilmott, JS, Tikoo, S, Jackett, LA, Quek, C, Brown, DL, Sharp, DM, Kwan, RYQ, Chacon, D, Wong, JH, Beck, D, van Geldermalsen, M, Holst, J, Thompson, JF, Mann, GJ, Scolyer, RA, Stow, JL, Weninger, W, Haass, NK & Beaumont, KA 2019, 'RAB27A promotes melanoma cell invasion and metastasis via regulation of pro-invasive exosomes.', International journal of cancer.
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Despite recent advances in targeted and immune-based therapies, advanced stage melanoma remains a clinical challenge with a poor prognosis. Understanding the genes and cellular processes that drive progression and metastasis is critical for identifying new therapeutic strategies. Here, we found that the GTPase RAB27A was overexpressed in a subset of melanomas, which correlated with poor patient survival. Loss of RAB27A expression in melanoma cell lines inhibited 3D spheroid invasion and cell motility in vitro, and spontaneous metastasis in vivo. The reduced invasion phenotype was rescued by RAB27A-replete exosomes, but not RAB27A-knockdown exosomes, indicating that RAB27A is responsible for the generation of pro-invasive exosomes. Furthermore, while RAB27A loss did not alter the number of exosomes secreted, it did change exosome size and altered the composition and abundance of exosomal proteins, some of which are known to regulate cancer cell movement. Our data suggest that RAB27A promotes the biogenesis of a distinct pro-invasive exosome population. These findings support RAB27A as a key cancer regulator, as well as a potential prognostic marker and therapeutic target in melanoma.

Guo, T, Pan, S, Zhu, X & Zhang, C 2019, 'CFOND: Consensus Factorization for Co-Clustering Networked Data', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Networked data are common in domains where instances are characterized by both feature values and inter-dependency relationships. Finding cluster structures for networked instances and discovering representative features for each cluster represent a special co-clustering task usefully for many real-world applications, such as automatic categorization of scientific publications and finding representative key-words for each cluster. To date, although co-clustering has been commonly used for finding clusters for both instances and features, all existing methods are focused on instance-feature values, without leveraging valuable topology relationships between instances to help boost co-clustering performance. In this paper, we propose CFOND, a consensus factorization based framework for co-clustering networked data. We argue that feature values and linkages provide useful information from different perspectives, yet they are not always consistent and therefore need to be carefully aligned for best clustering results. In the paper, we advocate a consensus factorization principle, which simultaneously factorizes information from three aspects: network topology structures, instance-feature content relationships, and feature-feature correlations. The consensus factorization ensures that the final cluster structures are consistent across information from the three aspects with minimum errors. CFOND enjoys sound theoretical basis and proved convergence, and its performance is validated on real-world networks.

Han, B, Tsang, IW, Chen, L, Zhou, JT & Yu, CP 2019, 'Beyond Majority Voting: A Coarse-to-Fine Label Filtration for Heavily Noisy Labels.', IEEE transactions on neural networks and learning systems.
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Crowdsourcing has become the most appealing way to provide a plethora of labels at a low cost. Nevertheless, labels from amateur workers are often noisy, which inevitably degenerates the robustness of subsequent learning models. To improve the label quality for subsequent use, majority voting (MV) is widely leveraged to aggregate crowdsourced labels due to its simplicity and scalability. However, when crowdsourced labels are ``heavily'' noisy (e.g., 40% of noisy labels), MV may not work well because of the fact ``garbage (heavily noisy labels) in, garbage (full aggregated labels) out.'' This issue inspires us to think: if the ultimate target is to learn a robust model using noisy labels, why not provide partial aggregated labels and ensure that these labels are reliable enough for learning models? To solve this challenge by improving MV, we propose a coarse-to-fine label filtration model called double filter machine (DFM), which consists of a (majority) voting filter and a sparse filter serially. Specifically, the DFM refines crowdsourced labels from coarse filtering to fine filtering. In the stage of coarse filtering, the DFM aggregates crowdsourced labels by voting filter, which yields (quality-acceptable) full aggregated labels. In the stage of fine filtering, DFM further digs out a set of high-quality labels from full aggregated labels by sparse filter, since this filter can identify high-quality labels by the methodology of support selection. Based on the insight of compressed sensing, DFM recovers a ground-truth signal from heavily noisy data under a restricted isometry property. To sum up, the primary benefits of DFM are to keep the scalability by voting filter, while improve the robustness by sparse filter. We also derive theoretical guarantees for the convergence and recovery of DFM and reveal its complexity. We conduct comprehensive experiments on both the UCI simulated and the AMT crowdsourced datasets. Empirical results show that partial aggregated la...

Hao, S, Shi, C, Niu, Z & Cao, L 2019, 'Modeling positive and negative feedback for improving document retrieval', Expert Systems with Applications, vol. 120, pp. 253-261.
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© 2018 Elsevier Ltd Pseudo-relevance feedback (PRF) has evident potential for enriching the representation of short queries. Traditional PRF methods treat top-ranked documents as feedback, since they are assumed to be relevant to the query. However, some of these feedback documents may actually distract from the query topic for a range of reasons and accordingly downgrade PRF system performance. Such documents constitute negative examples (negative feedback) but could also be valuable in retrieval. In this paper, a novel framework of query language model construction is proposed in order to improve retrieval performance by integrating both positive and negative feedback. First, an improvement-based method is proposed to automatically identify the types of feedback documents (i.e. positive or negative) according to whether the document enhances the retrieval's effectiveness. Subsequently, based on the learned positive and negative examples, the positive feedback models and the negative feedback models are estimated using an Expectation-Maximization algorithm with the assumptions: the positive term distribution is affected by the context term distribution and the negative term distribution is affected by both the positive term distribution and the context term distribution (such that the positive feedback model upgrades the rankings of relevant documents and the negative feedback model prunes the irrelevant documents from a query). Finally, a content-based representativeness criterion is proposed in order to obtain the representative negative feedback documents. Experiments conducted on the TREC collections demonstrate that our proposed approach results in better retrieval accuracy and robustness than baseline methods.

He, X, Li, Y & Zheng, M 2019, 'Heterogeneous Agent Models in Financial Markets: A Nonlinear Dynamics Approach', International Review of Financial Analysis, vol. 62, pp. 135-149.
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Hesamian, MH, Jia, W, He, X & Kennedy, P 2019, 'Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges.', Journal of Digital Imaging, vol. 32, no. 4, pp. 582-596.
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Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.

Huang, L, Zhe, T, Wu, J, Wu, Q, Pei, C & Chen, D 2019, 'Robust Inter-Vehicle Distance Estimation Method Based on Monocular Vision', IEEE Access, vol. 7, pp. 46059-46070.
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© 2013 IEEE. Advanced driver assistance systems (ADAS) based on monocular vision are rapidly becoming a popular research subject. In ADAS, inter-vehicle distance estimation from an in-car camera based on monocular vision is critical. At present, related methods based on a monocular vision for measuring the absolute distance of vehicles ahead experience accuracy problems in terms of the ranging result, which is low, and the deviation of the ranging result between different types of vehicles, which is large and easily affected by a change in the attitude angle. To improve the robustness of a distance estimation system, an improved method for estimating the distance of a monocular vision vehicle based on the detection and segmentation of the target vehicle is proposed in this paper to address the vehicle attitude angle problem. The angle regression model (ARN) is used to obtain the attitude angle information of the target vehicle. The dimension estimation network determines the actual dimensions of the target vehicle. Then, a 2D base vector geometric model is designed in accordance with the image analytic geometric principle to accurately recover the back area of the target vehicle. Lastly, area-distance modeling based on the principle of camera projection is performed to estimate distance. The experimental results on the real-world computer vision benchmark, KITTI, indicate that our approach achieves superior performance compared with other existing published methods for different types of vehicles (including front and sideway vehicles).

Huang, X, An, P, Cao, F, Liu, D & Wu, Q 2019, 'Light-field compression using a pair of steps and depth estimation', Optics Express, vol. 27, no. 3, pp. 3557-3573.
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Huang, Y, Xu, J, Wu, Q, Zheng, Z, Zhang, Z & Zhang, J 2019, 'Multi-pseudo Regularized Label for Generated Data in Person Re-Identification.', IEEE Transactions on Image Processing, vol. 28, no. 3, pp. 1391-1403.
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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 state-of-the-art methods.

Huang, Y, Zhong, Y, Wu, Q, Dutkiewicz, E & Jiang, T 2019, 'Cost-Effective Foliage Penetration Human Detection under Severe Weather Conditions based on Auto-Encoder/Decoder Neural Network', IEEE Internet of Things Journal.
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IEEE Military surveillance events and rescue activities are vital missions for the Internet-of-things. To this end, foliage penetration for human detection plays an important role. However, although the feasibility of that mission has been validated, we observe that it still cannot performs promisingly under severe weather conditions such as rainy, foggy, and snowy days. Therefore, in this paper, experiments are conducted under severe weather conditions based on a proposed deep learning approach. We present an Auto-Encoder/Decoder (Auto-ED) deep neural network that can learn the deep representation and conduct classification task concurrently. Since the property of cost-effective, the device-free sensing (DFS) techniques are used to address human detection in our case. As we pursue the signal-based mission, two components are involved in the proposed Auto-ED approach. First, an encoder is utilized that encode signal-based inputs into higher dimensional tensors by fractionally-strided convolution operations. Then, a decoder is leveraged with convolution operations to extract deep representations and learn the classifier simultaneously. To verify the effectiveness of the proposed approach, we compare it with several machine learning approaches under different weather conditions. Also, a simulation experiment is conducted by adding Additive White Gaussian Noise (AWGN) to the original target signals with different Signal to Noise Ratios (SNRs). Experimental results demonstrate that the proposed approach can best tackle the challenge of human detection under severe weather conditions in the high-clutter foliage environment, which indicates its potential application values in the near future.

Hwang, H & Ryan, L 2019, 'Statistical strategies for the analysis of massive data sets.', Biometrical journal. Biometrische Zeitschrift.
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The advent of the big data age has changed the landscape for statisticians. Public and private organizations alike these days are interested in capturing and analyzing complex customer data in order to improve their service and drive efficiency gains. However, the large volume of data involved often means that standard statistical methods fail and new ways of thinking are needed. Although great gains can be obtained through the use of more advanced computing environments or through developing sophisticated new statistical algorithms that handle data in a more efficient way, there are also many simpler things that can be done to handle large data sets in an efficient and intuitive manner. These include the use of distributed analysis methodologies, clever subsampling, data coarsening, and clever data reductions that exploit concepts such as sufficiency. These kinds of strategies represent exciting opportunities for statisticians to remain front and center in the data science world.

Jan, MA, Usman, M, He, X & Rehman, AU 2019, 'SAMS: A Seamless and Authorized Multimedia Streaming framework for WMSN-based IoMT', IEEE Internet of Things Journal.
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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.

Jian, S, Pang, G, Cao, L, Lu, K & Gao, H 2019, 'CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning', IEEE Transactions on Knowledge and Data Engineering, vol. 14, no. 8, pp. 1-14.
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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, S, Li, K & Xu, YDR 2019, 'Relative Pairwise Relationship Constrained Non-negative Matrix Factorisation', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Non-negative Matrix Factorisation (NMF) has been extensively used in machine learning and data analytics applications. Most existing variations of NMF only consider how each row/column vector of factorised matrices should be shaped, and ignore the relationship among pairwise rows or columns. In many cases, such pairwise relationship enables better factorisation, for example, image clustering and recommender systems. In this paper, we propose an algorithm named, Relative Pairwise Relationship constrained Non-negative Matrix Factorisation (RPR-NMF), which places constraints over relative pairwise distances amongst features by imposing penalties in a triplet form. Two distance measures, squared Euclidean distance and Symmetric divergence, are used, and exponential and hinge loss penalties are adopted for the two measures respectively. It is well known that the so-called "multiplicative update rules" result in a much faster convergence than gradient descend for matrix factorisation. However, applying such update rules to RPR-NMF and also proving its convergence is not straightforward. Thus, we use reasonable approximations to relax the complexity brought by the penalties, which are practically verified. Experiments on both synthetic datasets and real datasets demonstrate that our algorithms have advantages on gaining close approximation, satisfying a high proportion of expected constraints, and achieving superior performance compared with other algorithms.

Jung, JY, Kang, P-W, Kim, E, Chacon, D, Beck, D & McNevin, D 2019, 'Ancestry informative markers (AIMs) for Korean and other East Asian and South East Asian populations.', International journal of legal medicine.
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Inference of ancestry from biological evidence can provide investigative information, especially for unknown DNA donors. Although tools for predicting ancestry have been developing, ancestry research focusing on populations relevant for South Korea is not common and markers are seldom chosen specifically to differentiate Koreans from other East Asian and South East Asian populations. Here, we report ancestry informative markers (AIMs) for distinguishing six East/South East Asian regional populations: China, Japan, Indonesia, Philippines, South Korea and Thailand. Individual genotypes from these six populations were available in PanSNPdb: The HUGO Pan-Asian SNP Database. To select AIMs, we calculated four population divergence metrics for each SNP: Nei's FST, Rosenberg's Informativeness (In), the average absolute allele frequency difference between populations (δFmean) and the maximum allele frequency difference between populations (δFmax). Based on these values, we selected 100 single nucleotide polymorphisms (SNPs) for distinguishing the six populations, 13 of which exhibited large allele frequency differences between Koreans and non-Koreans. To assess the performance of the AIMs, we performed principal coordinates analysis (PCoA) on the individuals from all six populations and inferred ancestral population clusters using the STRUCTURE program. In conclusion, we found that the selected AIMs can be applied to distinguish the six East/South East Asian groups and we suggest the markers in this study will be helpful to establish ancestry panels for Korea and neighbouring populations.

Kacprzyk, J, Yager, RR & Merigo, JM 2019, 'Towards human-centric aggregation via ordered weighted aggregation operators and linguistic data summaries: A new perspective on zadeh's inspirations', IEEE Computational Intelligence Magazine, vol. 14, no. 1, pp. 16-30.
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© 2005-2012 IEEE. This work presents a new perspective on how Zadeh's ideas related to fuzzy logic and computing with words have influenced the crucial issue of information aggregation and have led to what may be called a human-centric aggregation. We indicate a need to develop tools and techniques to reflect some fine shades of meaning regarding what can be considered the very purpose of human-centric aggregation, notably stated by various modalities in natural language specifications, in particular the usuality. We advocate the use of the ordered weighted average (OWA) operator, which is a formidable tool that can easily be tailored to a user?s intention as to the purpose and method of aggregation, generalizing many simple and natural aggregation types, such as the arithmetic mean, maximum and minimum, and probability. We show some of the most representative extensions and generalizations, including the induced OWA, the generalized OWA, the probabilistic OWA, and the OWA distance. We show their use in the basic case of the aggregation of numerical values and in social choice (voting) results. Then, we claim that linguistic data summaries in Yager?s sense can be considered an »ultimately human consistent» form of human-centric aggregation and show how the OWA operators can be used therein.

Khan, AA, Abolhasan, M, Ni, W, Lipman, J & Jamalipour, A 2019, 'A Hybrid-Fuzzy Logic Guided Genetic Algorithm (H-FLGA) Approach for Resource Optimization in 5G VANETs', IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, vol. 68, no. 7, pp. 6964-6974.
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Kumar, P, Beck, D, Galeev, R, Thoms, JAI, Talkhoncheh, MS, de Jong, I, Unnikrishnan, A, Baudet, A, Subramaniam, A, Pimanda, JE & Larsson, J 2019, 'HMGA2 promotes long-term engraftment and myeloerythroid differentiation of human hematopoietic stem and progenitor cells', Blood advances, vol. 3, no. 4, pp. 681-691.
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© 2019 by The American Society of Hematology. Identification of determinants of fate choices in hematopoietic stem cells (HSCs) is essential to improve the clinical use of HSCs and to enhance our understanding of the biology of normal and malignant hematopoiesis. Here, we show that high-mobility group AT hook 2 (HMGA2), a nonhistone chromosomal-binding protein, is highly and preferentially expressed in HSCs and in the most immature progenitor cell subset of fetal, neonatal, and adult human hematopoiesis. Knockdown of HMGA2 by short hairpin RNA impaired the long-term hematopoietic reconstitution of cord blood (CB)-derived CB CD34+ cells. Conversely, overexpression of HMGA2 in CB CD34+ cells led to overall enhanced reconstitution in serial transplantation assays accompanied by a skewing toward the myeloerythroid lineages. RNA-sequencing analysis showed that enforced HMGA2 expression in CD34+ cells induced gene-expression signatures associated with differentiation toward megakaryocyte-erythroid and myeloid lineages, as well as signatures associated with growth and survival, which at the protein level were coupled with strong activation of AKT. Taken together, our findings demonstrate a key role of HMGA2 in regulation of both proliferation and differentiation of human HSPCs.

La Paz, A, Merigo, JM, Powell, P, Ramaprasad, A & Syn, T 2019, 'Twenty-five years of the Information Systems Journal: A bibliometric and ontological overview', INFORMATION SYSTEMS JOURNAL.
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Laengle, S, Merigó, JM, Modak, NM & Yang, JB 2019, 'Bibliometrics in operations research and management science: a university analysis', Annals of Operations Research.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Many universities around the World have made important contributions in the field of operations research and management science. This article presents the most productive and influential universities between 1991 and 2015. For doing so, we use the Web of Science database in order to search for the information which is usually regarded as the most relevant for scientific research. The results show the country of origin of the leading universities being mainly from North America and Asia and especially from USA and China. The Centre National de la Recherche Scientifique (CNRS) of France is the most productive university while the Massachusetts Institute of Technology (MIT) of USA is the most influential one. The temporal evolution shows that USA is trailing its dominancy while China progressing quickly. The evaluation also reveals that Asian universities outperform North American universities during the last 5 years.

Li, X, Zhao, L, Ji, W, Wu, Y, Wu, F, Yang, M-H, Tao, D & Reid, I 2019, 'Multi-Task Structure-Aware Context Modeling for Robust Keypoint-Based Object Tracking.', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 4, pp. 915-927.
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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.

Liang, H, Wang, H, Li, Q, Wang, J, Xu, G, Chen, J, Wei, JM & Yang, Z 2019, 'A general framework for learning prosodic-enhanced representation of rap lyrics', World Wide Web.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Learning and analyzing rap lyrics is a significant basis for many Web applications, such as music recommendation, automatic music categorization, and music information retrieval, due to the abundant source of digital music in the World Wide Web. Although numerous studies have explored the topic, knowledge in this field is far from satisfactory, because critical issues, such as prosodic information and its effective representation, as well as appropriate integration of various features, are usually ignored. In this paper, we propose a hierarchical attention variational a utoe ncoder framework (HAVAE), which simultaneously considers semantic and prosodic features for rap lyrics representation learning. Specifically, the representation of the prosodic features is encoded by phonetic transcriptions with a novel and effective strategy (i.e., rhyme2vec). Moreover, a feature aggregation strategy is proposed to appropriately integrate various features and generate prosodic-enhanced representation. A comprehensive empirical evaluation demonstrates that the proposed framework outperforms the state-of-the-art approaches under various metrics in different rap lyrics learning tasks.

Liang, T, Chen, L, Wu, J, Xu, G & Wu, Z 2019, 'SMS: A Framework for Service Discovery by Incorporating Social Media Information', IEEE Transactions on Services Computing, pp. 1-1.
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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.

Lin, L, Xu, L, Huang, Y, Xiang, Y & He, X 2019, 'On exploiting priority relation graph for reliable multi-path communication in mobile social networks', Information Sciences, vol. 477, pp. 490-507.
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© 2018 Elsevier Inc. A mobile social network (MSN) consists of certain amount of mobile users with social characteristics, and it provides data delivery concerning social relationships between mobile users. In MSN, ordinary people contact each other more frequently if they have more social features in common. In this paper, we apply a new topology structure–priority relation graph (PRG) to evaluate the data delivery routing in MSN on the system-level. By using the natural order of nodes’ representation, the diameter, the regular degree and the multi-path technology, we determine the priority relation graph-based social feature routing (PRG-SFR) algorithm to find disjointed multi-paths in MSN. Here, the multi-path technology can be exploited for ensuring that, between each pair of sender and receiver, the important information can be delivered through a highly reliable path. Then we calculate the tolerant ability of ‘faults’ and estimate the availability of MSN on the theoretical level. Finally, we analyze the efficiency of PRG-SFR algorithm from the numerical standpoint in terms of fault tolerance, forwarding number, transmission time and delivery rate. Moreover, we make comparisons between PRG-SFR algorithm and certain state-of-the-art technologies.

Linares-Mustarós, S, Ferrer-Comalat, JC, Corominas-Coll, D & Merigó, JM 2019, 'The ordered weighted average in the theory of expertons', International Journal of Intelligent Systems, vol. 34, no. 3, pp. 345-365.
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© 2018 Wiley Periodicals, Inc. This work presents a data-fusion mathematical object that incorporates the optimism level of a decision-making agent. The new fusion object is constructed by extending the ordered weighted averaging (OWA) operator in the process of creating an experton. The main advantage of this approach is that it can represent the attitudinal character of the decision maker in the construction of the experton. Therefore, this approach represents a new method for addressing multiperson problems by using optimistic and pessimistic perspectives. The work presents different practical examples based on the absolute hierarchical relationships of the “minimum of the bottom end of the intervals,” “minimum of the top end of the intervals,” and “minimum size of the interval.” The work also considers a wide range of particular cases of the OWA-experton, including the minimum experton, the maximum experton, the average experton, and the olympic experton. In addition, the study presents software for the calculation of OWA-expertons. Finally, the paper ends with an application in business decision-making regarding the calculation of expected benefits.

Liu, B, Chen, L, Zhu, X & Qiu, W 2019, 'Encrypted data indexing for the secure outsourcing of spectral clustering', Knowledge and Information Systems.
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© 2018, Springer-Verlag London Ltd., part of Springer Nature. Spectral clustering is one of the most popular clustering methods and is particularly useful for pattern recognition and image analysis. When using spectral clustering for analysis, users are either required to implement their own platforms, which requires strong data analytics and machine learning skills, or allow a third party to access and analyze their data, which may compromise their data privacy or security. Traditionally, this problem is solved by privacy-preserving data mining using randomization perturbation or secure multi-party computation. However, the existing methods suffer from the problems of inaccurate results or high computational requirements on the data owner’s side. To address these problems, in this paper, we propose a new secure outsourcing data mining (SODM) paradigm, which allows data owners to encrypt their data to ensure maximum data security. After the encryption, data owners can outsource their encrypted data to data analytics service providers (i.e., data analytics agent) for knowledge discovery, with a guarantee that neither the data analytics agent nor the other parties can compromise data privacy. To allow data mining to be efficiently carried out on encrypted data, we design a secure KD-tree to index all the encrypted data. Based on the SODM framework, a secure spectral clustering algorithm is proposed. The experiments on real-world datasets demonstrate the effectiveness and the efficiency of the system for the secure outsourcing of data mining.

Liu, C, Zhu, L, He, X & Chen, J 2019, 'Enabling Privacy-Preserving Shortest Distance Queries on Encrypted Graph Data', IEEE Transactions on Dependable and Secure Computing.
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IEEE When coming to perform shortest distance queries on encrypted graph data outsourced in external storage infrastructure such as cloud, a significant challenge is how to compute the shortest distance in an accurate, efficient and secure way. This issue is addressed by a recent work, which makes use of somewhat homomorphic encryption (SWHE) to encrypt distance values output by a 2-hop cover labeling (2HCL) scheme. However, it may import large errors and even yield negative results. Besides, SWHE would be too time-consuming for normal clients. In this paper, we propose GENOA, a novel Graph ENcryption scheme for shOrtest distAnce queries. GENOA employs only efficient symmetric-key primitives while significantly enhances the accuracy compared to the prior work. As a reasonable trade-off, it additionally reveals the order information among queried distance values in the 2HCL index. We theoretically prove the accuracy and security of GENOA under rigorous cryptographic model. Detailed experiments on eight real-world graphs demonstrate that GENOA is efficient and can produce almost exact results.

Liu, L, Amirgholipour, S, Jiang, J, Jia, W, Zeibots, M & He, X 2019, 'Performance-enhancing network pruning for crowd counting', Neurocomputing, vol. 360, pp. 246-253.
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© 2019 The Counting Convolutional Neural Network (CCNN) has been widely used for crowd counting. However, they typically end up with a complicated network model resulting in a challenge for real-time processing. Existing solutions aim to reduce the size of the network model, but unavoidably sacrifice the network accuracy. Different from existing pruning solutions, in this paper, a new pruning strategy is proposed by considering the contributions of various filters to the final result. The filters in the original CCNN model are grouped into positive, negative and irrelevant types. We prune the irrelevant filters of which feature maps contain little information, and the negative filters determined by a mask learned from the training dataset. Our solution improves the results of the counting model without fine-tuning or retraining the pruned model. We demonstrate the advantages of our proposed approach on the problem of crowd counting. Our experimental results on benchmark datasets show that the network model pruned using our approach not only reduces the network size but also improves the counting accuracies by 4% to 17% less MAE than the state of the arts.

Liu, M, Xue, Z, He, X & Chen, J 2019, 'Cyberthreat-Intelligence Information Sharing', IEEE CONSUMER ELECTRONICS MAGAZINE, vol. 8, no. 3, pp. 17-22.
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Liu, Y, Lan, C, Blumenstein, M & Li, J 2019, 'Bi-level error correction for PacBio long reads', IEEE/ACM Transactions on Computational Biology and Bioinformatics.
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IEEE The latest sequencing technologies such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines can generate long reads at the length of thousands of nucleic bases which is much longer than the reads at the length of hundreds generated by Illumina machines. However, these long reads are prone to much higher error rates, for example 15%, making downstream analysis and applications very difficult. Error correction is a process to improve the quality of sequencing data. Hybrid correction strategies have been recently proposed to combine Illumina reads of low error rates to fix sequencing errors in the noisy long reads with good performance. In this paper, we propose a new method named Bicolor, a bi-level framework of hybrid error correction for further improving the quality of PacBio long reads. At the first level, our method uses a de Bruijn graph-based error correction idea to search paths in pairs of solid < formula > < tex > $k$ < /tex > < /formula > -mers iteratively with an increasing length of < formula > < tex > $k$ < /tex > < /formula > -mer. At the second level, we combine the processed results under different parameters from the first level. In particular, a multiple sequence alignment algorithm is used to align those similar long reads, followed by a voting algorithm which determines the final base at each position of the reads. We compare the superior performance of Bicolor with three state-of-the-art methods on three real data sets. Results demonstrate that Bicolor always achieves the highest identity ratio. Bicolor also achieves a higher alignment ratio ( < formula > < tex > $ & #x003E; 1.3\%$ < /tex > < /formula > ) and a higher number of aligned reads than the current methods on two data sets. On the third data set, our method is closely competitive to the current methods in terms of number of aligned reads and genome coverage. The C++ source codes of our algorithm are freely available at https://github.com/yuansliu/Bicolor.

Liu, Y, Yu Zhang, L & Li, J 2019, 'Fast detection of maximal exact matches via fixed sampling of query k-mers and Bloom filtering of index k-mers.', Bioinformatics (Oxford, England).
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MOTIVATION:Detection of maximal exact matches (MEMs) between two long sequences is a fundamental problem in pairwise reference-query genome comparisons. To efficiently compare larger and larger genomes, reducing the number of indexed k-mers as well as the number of query k-mers has been adopted as a mainstream approach which saves the computational resources by avoiding a significant number of unnecessary matches. RESULTS:Under this framework, we proposed a new method to detect all MEMs from a pair of genomes. The method first performs a fixed sampling of k-mers on the query sequence, and add these selected k-mers to a Bloom filter. Then all the k-mers of the reference sequence are tested by the Bloom filter. If a k-mer passes the test, it is inserted into a hash table for indexing. Compared with the existing methods, much less number of query k-mers are generated and much less k-mers are inserted into the index to avoid unnecessary matches, leading to an efficient matching process and memory usage savings. Experiments on large genomes demonstrate that our method is at least 1.8 times faster than the best of the existing algorithms. This performance is mainly attributed to the key novelty of our method that the fixed k-mer sampling must be conducted on the query sequence and the index k-mers are filtered from the reference sequence via a Bloom filter. AVAILABILITY AND IMPLEMENTATION:https://github.com/yuansliu/bfMEM. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Liu, Y, Yu, Y, Dinger, ME & Li, J 2019, 'Index suffix-prefix overlaps by (w, k)-minimizer to generate long contigs for reads compression.', Bioinformatics.
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Motivation:Advanced high-throughput sequencing technologies have produced massive amount of reads data, and algorithms have been specially designed to contract the size of these data sets for efficient storage and transmission. Reordering reads with regard to their positions in de novo assembled contigs or in explicit reference sequences has been proven to be one of the most effective reads compression approach. As there is usually no good prior knowledge about the reference sequence, current focus is on the novel construction of de novo assembled contigs. Results:We introduce a new de novo compression algorithm named minicom. This algorithm uses large k-minimizers to index the reads and subgroup those that have the same minimizer. Within each subgroup, a contig is constructed. Then some pairs of the contigs derived from the subgroups are merged into longer contigs according to a (w; k)-minimizer indexed suffix-prefix overlap similarity between two contigs. This merging process is repeated after the longer contigs are formed until no pair of contigs can be merged. We compare the performance of minicom with two reference-based methods and four de novo methods on 18 data sets (13 RNA-seq data sets and 5 whole genome sequencing data sets). In the compression of single-end reads, minicom obtained the smallest file size for 22 of 34 cases with significant improvement. In the compression of paired-end reads, minicom achieved 20-80% compression gain over the best state-of-the-art algorithm. Our method also achieved a 10% size reduction of compressed files in comparison with the best algorithm under the reads-order preserving mode. These excellent performances are mainly attributed to the exploit of the redundancy of the repetitive substrings in the long contigs. Availability and Implementation:https://github.com/yuansliu/minicom. Supplementary Information:Supplementary data are available at Bioinformatics online.

Llanos-Herrera, GR & Merigo, JM 2019, 'Overview of brand personality research with bibliometric indicators', Kybernetes.
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© 2018, Emerald Publishing Limited. Purpose: The purpose of this paper is to present a global view of the research that has been conducted regarding brand personality by using the Core Collection of the Web of Science (WoS) as a reference. The main bibliometric indicators considered are number of articles, number of citations, main authors, principal journals, institutions, countries and keywords. Design/methodology/approach: Through a bibliometric investigation, this paper performs an analysis of investigations of brand personality that have been conducted to date. In particular, the analysis focuses on the papers that have generated the greatest impact in the scientific community, the journals that have given the most attention to this concept and the authors who have most strongly influenced the academic world in this field. The analysis reveals a series of relationships between the bases of knowledge considered for different authors and journals and the structure of those relationships based on the keywords considered in each contribution. Findings: This analysis allows to obtain a general and impartial view of brand personality research, and it reveals the most relevant contributions to the academic world in terms of authors, journals, institutions, countries and keywords. The analysis shows that the concept under study seems to still be in an early stage of development and there may well be an important amount of development ahead. Although there have been important contributions to this field, work is still required to consolidate this knowledge. Research limitations/implications: The information provided pertains to a relatively specific subject but is still general when considered within the context of this topic and thus leaves aside elements that could greatly enrich the analysis. However, this work presents some important guidelines for conducting in-depth academic research and publication. Practical implications: This work identifies the most product...

Luo, F, Du, B, Zhang, L, Zhang, L & Tao, D 2019, 'Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image', IEEE Transactions on Cybernetics.
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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, M, Yan, C, Zheng, Q, Chang, X, Chen, L & Nie, F 2019, 'Discrete Multi-Graph Clustering', IEEE Transactions on Image Processing, vol. 28, no. 9, pp. 4701-4712.
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© 1992-2012 IEEE. Spectral clustering plays a significant role in applications that rely on multi-view data due to its well-defined mathematical framework and excellent performance on arbitrarily-shaped clusters. Unfortunately, directly optimizing the spectral clustering inevitably results in an NP-hard problem due to the discrete constraints on the clustering labels. Hence, conventional approaches intuitively include a relax-and-discretize strategy to approximate the original solution. However, there are no principles in this strategy that prevent the possibility of information loss between each stage of the process. This uncertainty is aggravated when a procedure of heterogeneous features fusion has to be included in multi-view spectral clustering. In this paper, we avoid an NP-hard optimization problem and develop a general framework for multi-view discrete graph clustering by directly learning a consensus partition across multiple views, instead of using the relax-and-discretize strategy. An effective re-weighting optimization algorithm is exploited to solve the proposed challenging problem. Further, we provide a theoretical analysis of the model's convergence properties and computational complexity for the proposed algorithm. Extensive experiments on several benchmark datasets verify the effectiveness and superiority of the proposed algorithm on clustering and image segmentation tasks.

Luo, Y, Wen, Y, Liu, T & Tao, D 2019, 'Transferring Knowledge Fragments for Learning Distance Metric from a Heterogeneous Domain.', IEEE transactions on pattern analysis and machine intelligence, vol. 41, no. 4, pp. 1013-1026.
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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.

Makhdoom, I, Abolhasan, M, Lipman, J, Liu, RP & Ni, W 2019, 'Anatomy of Threats to The Internet of Things', Communications Surveys and Tutorials, IEEE Communications Society, vol. 21, no. 2, pp. 1636-1675.
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The world is resorting to the Internet of Things (IoT) for ease of control and monitoring of smart devices. The ubiquitous use of IoT ranges from industrial control systems (ICS) to e-Health, e-Commerce, smart cities, supply chain management, smart cars, cyber physical systems (CPS), and a lot more. Such reliance on IoT is resulting in a significant amount of data to be generated, collected, processed, and analyzed. The big data analytics is no doubt beneficial for business development. However, at the same time, numerous threats to the availability and privacy of the user data, message, and device integrity, the vulnerability of IoT devices to malware attacks and the risk of physical compromise of devices pose a significant danger to the sustenance of IoT. This paper thus endeavors to highlight most of the known threats at various layers of the IoT architecture with a focus on the anatomy of malware attacks. We present a detailed attack methodology adopted by some of the most successful malware attacks on IoT, including ICS and CPS. We also deduce an attack strategy of a distributed denial of service attack through IoT botnet followed by requisite security measures. In the end, we propose a composite guideline for the development of an IoT security framework based on industry best practices and also highlight lessons learned, pitfalls and some open research challenges.

Manzoor, M, Hussain, W, Sohaib, O, Hussain, FK & Alkhalaf, S 2019, 'Methodological investigation for enhancing the usability of university websites', Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 2, pp. 531-549.
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For university websites to be successful and to increase the chance of converting a prospective student into a current student, it is necessary to increase the visibility and accessibility of all related content so that a student can achieve their desired task in the fastest possible time. The criteria for evaluating university websites are very vague and are usually unknown to most developers, which adversely impacts the user-experience of the students visiting such websites. To solve this problem, we devised a usability metric and examined the leading university websites to analyze whether these websites were able to meet the requirements of students. In this research, we applied qualitative and quantitative approaches by considering 300 students and evaluating 86 university websites (26 from Canada, 30 from the United States, and 30 from Europe) based on a six-attribute metric comprising navigation, organization, ease of use (simplicity), design (layout), communication and content. From the evaluation results, we find that the 88% of the students are satisfied with our proposed usability attributes, but that most universities fail to meet basic standards of usability as desired by the students. The findings also show that the usability evaluation score for each usability feature varies from country to country, such as for (1) multiple language support − 23% of the Canadian websites, 63% of the European websites and none of the USA websites has the feature; for (2) Scholarships/Funding/Financial Aid link − 24% of the Canadian websites, 80% of the European and the USA websites has the feature; for (3) admission link − 88% of the Canadian websites, 20% of the European websites and 90% of the USA websites has the feature. In addition, from the evaluative result we find that our proposed approach will not only increase the usability of academic websites but will also provide an easiest way to covert prospective student inquiries into enrollment opportunities.

Martin, F, Cahill, A, Wright, E & Stoianoff, N 2019, 'An international approach to establishing a Competent Authority to manage and protect traditional knowledge', Alternative Law Journal, vol. 44, no. 1, pp. 48-55.
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This article discusses the establishment of a Competent Authority in accordance with the Nagoya Protocol to ensure that traditional knowledge of Indigenous communities is accessed subject to free, prior and informed consent and the fair and equitable sharing of benefits arising out of such use. It builds on research expressing the view that the design and development of a Competent Authority should take a grass roots approach. It analyses the authorities established in the Cook Islands and Vanuatu that include significant Indigenous voice and concludes with comments on the attributes of each system and its limitations

Mas-Tur, A, Modak, NM, Merigó, JM, Roig-Tierno, N, Geraci, M & Capecchi, V 2019, 'Half a century of Quality & Quantity: a bibliometric review', Quality and Quantity, vol. 53, no. 2, pp. 981-1020.
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© 2018, Springer Nature B.V. The Quality & Quantity was established in 1967 and in 2017 it completed its half century. The journal is interdisciplinary in nature and it mainly discusses methodological application of mathematics and statistics in the social sciences, particularly sociology, economics, and social psychology. It was created with the idea of advancing methodology of the various social studies. This study looks back journey of the journal from 1967 to 2017 aims to develop a bibliometric analysis of all the publications of the journal. Web of Science Core Collection database is used to collect data. The present study discovered the significant contributions of the journal in terms of impact, topics, authors, universities and countries. Utrecht University of Netherlands is the most productive university. Asian Universities are emerging and growing quickly in the recent years. Although USA leads among the countries but Europe leads among the six supranational regions. Finally, the visualization of similarities viewer software is used to present network visualization of the bibliographic coupling, co-citation, citation, co-authorship and co-occurrence of keywords.

Merigó, JM, Cobo, MJ, Laengle, S, Rivas, D & Herrera-Viedma, E 2019, 'Twenty years of Soft Computing: a bibliometric overview', Soft Computing, vol. 23, no. 5, pp. 1477-1497.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The journal Soft Computing was launched in 1997, and it is dedicated to promote advancements in soft computing theories, which includes fuzzy sets theory, neural networks, evolutionary computation, probabilistic reasoning and hybrid theories. 2017 marks the 20th anniversary of the journal. Motivated by this anniversary, this study presents a bibliometric analysis of the current publications in the journal in order to identify the leading trends ruling the journal. The paper also develops a mapping analysis of the bibliographic material by using the visualization of similarities viewer software. The results show that researchers from all over the world publish regularly in the journal. Soft Computing is growing significantly during the last years, becoming one of the leading journals in the field.

Modak, NM, Merigó, JM, Weber, R, Manzor, F & Ortúzar, JDD 2019, 'Fifty years of Transportation Research journals: A bibliometric overview', Transportation Research Part A: Policy and Practice, vol. 120, pp. 188-223.
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© 2018 Elsevier Ltd Transportation Research (TR) was established in 1967 with the vision of promoting multi-disciplinary (economics, engineering, sociology, psychology) research on transport systems. The journal has continuously expanded its wings becoming a world-leading journal, now publishing research work through six parts, A to F, respectively addressing Policy and Practice, Methodological, Emerging Technologies, Transport and Environment, Logistics and Transportation Review, and Traffic Psychology and Behaviour. This study aims to celebrate the first half century of the journal through a bibliometric study of the publications on all six parts between 1967 and 2016. It uses the most reliable database for academic research, the Web of Science Core Collection, to identify the leading trends in all TR journals in terms of impact, topics, authors, universities, and countries. Moreover, it uses the Visualization of Similarities (VOS) viewer software to analyse bibliographic coupling, co-citation, citation, co-authorship, and co-occurrence of keywords.

Mulet-Forteza, C, Genovart-Balaguer, J, Mauleon-Mendez, E & Merigó, JM 2019, 'A bibliometric research in the tourism, leisure and hospitality fields', Journal of Business Research, vol. 101, pp. 819-827.
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© 2018 Elsevier Inc. This paper presents a study of the most cited papers, the most productive and influential institutions and countries, and the most influential authors in the tourism, leisure, and hospitality fields. The number of publications in journals focused on these areas has increased exponentially over the past 40 years. This paper examines the fundamental contributions in these areas using a bibliometric approach. This paper also uses the visualization of similarities to graphically map the main topics and keywords. No study has examined all journals indexed in the Web of Science in these fields over a period as wide as the one considered in this study. This study is valuable for several reasons. It can help scholars and researchers to identify the countries and institutions with the most potential to develop and share research, as well as where it would be interesting to carry out their doctoral studies and develop their careers.

Musial, K, Brodka, P & De Meo, P 2019, 'Analysis and Applications of Complex Social Networks 2018', COMPLEXITY.
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Musial-Gabrys, K, Wahid-Ul-Ashraf, A & Budka, M 2019, 'Simulation and Augmentation of Social Networks for Building Deep Learning Models'.
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Nanda, A, Nanda, P, He, X, Jamdagni, A & Puthal, D 2019, 'A hybrid encryption technique for Secure-GLOR: The adaptive secure routing protocol for dynamic wireless mesh networks', Future Generation Computer Systems.
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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.

Naseer, A, Rani, M, Naz, S, Razzak, MI, Imran, M & Xu, G 2019, 'Refining Parkinson’s neurological disorder identification through deep transfer learning', Neural Computing and Applications.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Parkinson’s disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life. However, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson’s disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that help as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning-based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that the proposed approach provides better detection of Parkinson’s disease as compared to state-of-the-art work.

Nawaz, F, Hussain, O, Hussain, FK, Janjua, NK, Saberi, M & Chang, E 2019, 'Proactive management of SLA violations by capturing relevant external events in a Cloud of Things environment', Future Generation Computer Systems, vol. 95, pp. 26-44.
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© 2018 Elsevier B.V. The cloud of things (CoT) is an emerging paradigm that has merged and combined cloud computing and the Internet of Things (IoT). Such a paradigm has enabled service providers to provide on-demand computing resources from devices spread across different locations for service users to be dynamically connected to them. While this benefits the CoT service providers and users in many ways, it also brings a key challenge of ensuring that the service is delivered according to the promised quality. Failure to ensure this will result in the service provider experiencing penalties of different types and the service user experiencing disruptions. The literature addresses this problem by proactively managing for SLA violations. However, given the geographically dispersed region of a formed CoT service, in this paper we argue that for proactive SLA violation identification, we need specialized techniques that also consider events that are outside the usual control of service providers and users, but will impact the CoT environment and the quality of service. We propose a framework that identifies such external events of interest and ascertains their impact on achieving the service according to the promised quality. We explain the working of our proposed framework in detail and demonstrate its superiority in proactively determining SLA violations as compared to existing approaches.

Pan, S, Hu, R, Fung, S-F, Long, G, Jiang, J & Zhang, C 2019, 'Learning Graph Embedding with Adversarial Training Methods', arXiv preprint arXiv:1901.01250.

Patel, OP, Tiwari, A, Chaudhary, R, Nuthalapati, SV, Bharill, N, Prasad, M, Hussain, FK & Hussain, OK 2019, 'Enhanced quantum-based neural network learning and its application to signature verification', Soft Computing, pp. 1-14.
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© 2017 Springer-Verlag GmbH Germany, part of Springer Nature In this paper, an enhanced quantum-based neural network learning algorithm (EQNN-S) which constructs a neural network architecture using the quantum computing concept is proposed for signature verification. The quantum computing concept is used to decide the connection weights and threshold of neurons. A boundary threshold parameter is introduced to optimally determine the neuron threshold. This parameter uses min, max function to decide threshold, which assists efficient learning. A manually prepared signature dataset is used to test the performance of the proposed algorithm. To uniquely identify the signature, several novel features are selected such as the number of loops present in the signature, the boundary calculation, the number of vertical and horizontal dense patches, and the angle measurement. A total of 45 features are extracted from each signature. The performance of the proposed algorithm is evaluated by rigorous training and testing with these signatures using partitions of 60–40 and 70–30%, and a tenfold cross-validation. To compare the results derived from the proposed quantum neural network, the same dataset is tested on support vector machine, multilayer perceptron, back propagation neural network, and Naive Bayes. The performance of the proposed algorithm is found better when compared with the above methods, and the results verify the effectiveness of the proposed algorithm.

Qiao, C, Lu, L, Yang, L & Kennedy, PJ 2019, 'Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology', APPLIED SCIENCES-BASEL, vol. 9, no. 10.
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Qiao, M, Yu, J, Bian, W, Li, Q & Tao, D 2019, 'Adapting Stochastic Block Models to Power-Law Degree Distributions.', IEEE Transactions on Cybernetics, vol. 49, no. 2, pp. 626-637.
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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.

Raza, M, Hussain, FK, Hussain, OK, Zhao, M & Rehman, ZU 2019, 'A comparative analysis of machine learning models for quality pillar assessment of SaaS services by multi-class text classification of users’ reviews', Future Generation Computer Systems, vol. 101, pp. 341-371.
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© 2019 Elsevier B.V. Software as a Service (SaaS) has emerged as the most widely used of all the current software delivery models. With the growth of edge computing, as SaaS services increasingly become distributed, selecting the best SaaS provider from those available is challenging but it is of critical importance. In the recent past, well-known cloud service providers such as Amazon Web Services and Microsoft have developed frameworks and service quality pillars for cloud applications. However, there are currently no mechanisms for product users to know if and to what extent a service satisfies the defined service pillar. Having such information would enable users to form trustworthy associations in edge computing. In this paper, we address this drawback by adopting a systematic approach of analysing customer reviews related to SaaS products and ascertain to which service quality pillar they refer. We use eleven traditional machine learning classification approaches and a weighted voting ensemble of these classifiers to achieve this task and test the performance of each of them. Since the dataset is unbalanced in terms of sample distribution per class, we use 10-fold cross-validation on the training dataset to determine the best parameters for each machine learning algorithm to achieve optimal performance. Friedman test and Nemenyi's post hoc test is applied to identify the significant difference among the classifiers performance during cross-validation. Based on the experimental results, a comparative analysis is conducted to identify the best performing machine learning classification model on the SaaS reviews. The results show that the performance of the logistic regression model has a higher performance among the individual classifiers and the weighted voting ensemble shows minimal improvement in overall performance.

Razzak, I, Imran, M & Xu, G 2019, 'Efficient Brain Tumor Segmentation with Multiscale Two-Pathway-Group Conventional Neural Networks', IEEE Journal of Biomedical and Health Informatics.
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IEEE Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the automatic brain tumor segmentation methods use hand designed features. Similarly, traditional methods of Deep learning such as Convolutional Neural Networks require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. Here we describe a new model Two-Pathway-Group CNN architecture for brain tumor segmentation, which exploits local features and global contextual features simultaneously. This model enforces equivariance in the Two-Pathway CNN model to reduce instabilities and overfitting parameter sharing. Finally, we embed the cascade architecture into Two-Pathway-Group CNN in which the output of a basic CNN is treated as an additional source and concatenated at the last layer. Validation of the model on BRATS2013 and BRATS2015 datasets revealed that embedding of a group CNN into a two pathway architecture improved the overall performance over the currently published state-of-the-art while computational complexity remains attractive.

Razzak, MI, Blumenstein, M & Xu, G 2019, 'Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification.', IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, vol. 27, no. 6, pp. 1117-1127.
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Accurate classification of Electroencephalogram (EEG) signals plays an important role in diagnoses of different type of mental activities. One of the most important challenges, associated with classification of EEG signals is how to design an efficient classifier consisting of strong generalization capability. Aiming to improve the classification performance, in this paper, we propose a novel multiclass support matrix machine (M-SMM) from the perspective of maximizing the inter-class margins. The objective function is a combination of binary hinge loss that works on C matrices and spectral elastic net penalty as regularization term. This regularization term is a combination of Frobenius and nuclear norm, which promotes structural sparsity and shares similar sparsity patterns across multiple predictors. It also maximizes the inter-class margin that helps to deal with complex high dimensional noisy data. The extensive experiment results supported by theoretical analysis and statistical tests show the effectiveness of the M-SMM for solving the problem of classifying EEG signals associated with motor imagery in brain-computer interface applications.

Razzak, MI, Imran, M & Xu, G 2019, 'Big data analytics for preventive medicine', Neural Computing and Applications.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.

Roselin, AG, Nanda, P, Nepal, S & He, X 2019, 'Testbed Evaluation of Lightweight Authentication Protocol(LAUP) fo r6LoWPAN wireless sensor networks', Concurrency and Computation: Practice and Experience.
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Ryan, L 2019, 'Four papers on child growth modelling', STATISTICS IN MEDICINE, vol. 38, no. 19, pp. 3505-3506.
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Saeed, Z, Abbasi, RA, Maqbool, O, Sadaf, A, Razzak, I, Daud, A, Aljohani, NR & Xu, G 2019, 'What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter', Journal of Grid Computing, vol. 17, no. 2, pp. 279-312.
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© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter.

Saeed, Z, Abbasi, RA, Razzak, I, Maqbool, O, Sadaf, A & Xu, G 2019, 'Enhanced Heartbeat Graph for emerging event detection on Twitter using time series networks', Expert Systems with Applications, vol. 136, pp. 115-132.
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© 2019 Elsevier Ltd With increasing popularity of social media, Twitter has become one of the leading platforms to report events in real-time. Detecting events from Twitter stream requires complex techniques. Event-related trending topics consist of a group of words which successfully detect and identify events. Event detection techniques must be scalable and robust, so that they can deal with the huge volume and noise associated with social media. Existing event detection methods mostly rely on burstiness, mainly the frequency of words and their co-occurrences. However, burstiness sometimes dominates other relevant details in the data which could be equally significant. Besides, the topological and temporal relationships in the data are often ignored. In this work, we propose a novel graph-based approach, called the Enhanced Heartbeat Graph (EHG), which detects events efficiently. EHG suppresses dominating topics in the subsequent data stream, after their first detection. Experimental results on three real-world datasets (i.e., Football Association Challenge Cup Final, Super Tuesday, and the US Election 2012) show superior performance of the proposed approach in comparison to the state-of-the-art techniques.

Saeed, Z, Ayaz Abbasi, R, Razzak, MI & Xu, G 2019, 'Event Detection in Twitter Stream Using Weighted Dynamic Heartbeat Graph Approach [Application Notes]', IEEE Computational Intelligence Magazine, vol. 14, no. 3, pp. 29-38.
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© 2019 IEEE. Once an event is detected, WDHG approach suppresses the bursty keywords at subsequent time intervals. This characteristic enables other related information to be more visible and helps in capturing new and emerging events.

Salamai, A, Hussain, OK, Saberi, M, Chang, E & Hussain, FK 2019, 'Highlighting the Importance of Considering the Impacts of Both External and Internal Risk Factors on Operational Parameters to Improve Supply Chain Risk Management', IEEE Access, vol. 7, pp. 49297-49315.
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© 2013 IEEE. Operational risk management in supply chain activities is important for the successful achievement of the desired outcomes. Although it is an active area of research with an aim of improving a firm's success in its operations, a drawback of existing approaches is that they analyze it from only the perspective of events local to the supply chain. In this paper, we argue that it is also important for firms in a supply chain to consider external events as they will directly influence the internal ones and use various real-world examples of the risks in different processes of a supply chain as justification to prove our point. We then consider supply chain risk management not only as an operational research process, as do all the relevant survey papers, but a data science problem to gain deeper real-time insights for information risk management. Then, we suggest directions for future research that will assist supply chain risk managers to undertake better supply chain risk management processes.

Salvador, MM, Budka, M & Gabrys, B 2019, 'Automatic Composition and Optimization of Multicomponent Predictive Systems With an Extended Auto-WEKA', IEEE Transactions on Automation Science and Engineering, vol. 16, no. 2, pp. 1-14.
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IEEE Composition and parameterization of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps are a challenging task. Auto-WEKA is a tool to automate the combined algorithm selection and hyperparameter (CASH) optimization problem. In this paper, we extend the CASH problem and Auto-WEKA to support the MCPS, including preprocessing steps for both classification and regression tasks. We define the optimization problem in which the search space consists of suitably parameterized Petri nets forming the sought MCPS solutions. In the experimental analysis, we focus on examining the impact of considerably extending the search space (from approximately 22,000 to 812 billion possible combinations of methods and categorical hyperparameters). In a range of extensive experiments, three different optimization strategies are used to automatically compose MCPSs for 21 publicly available data sets. The diversity of the composed MCPSs found is an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. We also present the results on seven data sets from real chemical production processes. Our findings can have a major impact on the development of high-quality predictive models as well as their maintenance and scalability aspects needed in modern applications and deployment scenarios.

Seifollahi, S, Bagirov, A, Zare Borzeshi, E & Piccardi, M 2019, 'A simulated annealing‐based maximum‐margin clustering algorithm', Computational Intelligence, vol. 35, pp. 23-41.
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Maximum‐margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing‐based algorithm that is able to mitigate the issue of local minima in the maximum‐margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k‐means++ and SVM at each step of the annealing process. More precisely, k‐means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms.

Shen, J, Liang, Z, Liu, J, Sun, H, Shao, L & Tao, D 2019, 'Multiobject Tracking by Submodular Optimization', IEEE Transactions on Cybernetics.
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IEEE In this paper, we propose a new multiobject visual tracking algorithm by submodular optimization. The proposed algorithm is composed of two main stages. At the first stage, a new selecting strategy of tracklets is proposed to cope with occlusion problem. We generate low-level tracklets using overlap criteria and min-cost flow, respectively, and then integrate them into a candidate tracklets set. In the second stage, we formulate the multiobject tracking problem as the submodular maximization problem subject to related constraints. The submodular function selects the correct tracklets from the candidate set of tracklets to form the object trajectory. Then, we design a connecting process which connects the corresponding trajectories to overcome the occlusion problem. Experimental results demonstrate the effectiveness of our tracking algorithm. Our source code is available at https://github.com/shenjianbing/submodulartrack.

Shen, W, Wu, Y, Yuan, J, Duan, L, Zhang, J & Jia, Y 2019, 'Robust Distracter-Resistive Tracker via Learning a Multi-Component Discriminative Dictionary', IEEE Transactions on Circuits and Systems for Video Technology.
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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.

Sun, L, Dong, H, Hussain, OK, Hussain, FK & Liu, AX 2019, 'A framework of cloud service selection with criteria interactions', Future Generation Computer Systems, vol. 94, pp. 749-764.
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© 2018 Elsevier B.V. Existing cloud service selection techniques assume that service evaluation criteria are independent. In reality, there are different types of interactions between criteria. These interactions influence the performance of a service selection system in different ways. In addition, a lack of measurement indices to validate the performance of service selection methods has hindered the development of decision making techniques in the service selection area. This paper addresses these critical issues of modeling the interactions between cloud service selection criteria, and designing indices to validate service selection methods. In this paper, we propose a Cloud Service Selection with Criteria Interactions framework (CSSCI) that applies a fuzzy measure and Choquet integral to measure and aggregate non-linear relations between criteria. We employ a non-linear constraint optimization model to estimate the Shapley importance and criteria interaction indices. In addition, we design a priority-based CSSCI (PCSSCI) to solve service selection problems in the situation where there is a lack of historical information to determine criteria relations and weights. Furthermore, we discuss an approximate solution for CSSCI to reduce its computing complexity. Finally, we design three indices to validate the cloud service selection methods. The experimental results preliminarily prove the technical advantage of the proposed models in contrast to several existing models.

Thoms, JAI, Beck, D & Pimanda, JE 2019, 'Transcriptional networks in acute myeloid leukemia', GENES CHROMOSOMES & CANCER.
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Tofigh, F, Amiri, M, Shariati, N, Lipman, J & Abolhasan, M 2019, 'Low-Frequency Metamaterial Absorber Using Space-Filling Curve', Journal of Electronic Materials.
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Torres Robles, A, Wiecek, E, Cutler, R, Drake, B, Benrimoj, SI, Fernandez-Llimos, F & Garcia Cardenas, M 2019, 'Using dispensing data to evaluate adherence implementation rates in community pharmacy', Frontiers in Pharmacology.
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Usman, M, He, X, Lam, KKM, Xu, M, Chen, J, Bokhari, SMM & Jan, MA 2019, 'Error Concealment for Cloud-based and Scalable Video Coding of HD Videos', IEEE Transactions on Cloud Computing.
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IEEE The encoding of HD videos faces two challenges: requirements for a strong processing power and a large storage space. One time-efficient solution addressing these challenges is to use a cloud platform and to use a scalable video coding technique to generate multiple video streams with varying bit-rates. Packet-loss is very common during the transmission of these video streams over the Internet and becomes another challenge. One solution to address this challenge is to retransmit lost video packets, but this will create end-to-end delay. Therefore, it would be good if the problem of packet-loss can be dealt with at the user's side. In this paper, we present a novel system that encodes and stores the videos using the Amazon cloud computing platform, and recover lost video frames on user side using a new Error Concealment (EC) technique. To efficiently utilize the computation power of a user's mobile device, the EC is performed based on a multiple-thread and parallel process. The simulation results clearly show that, on average, our proposed EC technique outperforms the traditional Block Matching Algorithm (BMA) and the Frame Copy (FC) techniques.

Usman, M, Jan, MA, He, X & Chen, J 2019, 'A Mobile Multimedia Data Collection Scheme for Secured Wireless Multimedia Sensor Networks', IEEE Transactions on Network Science and Engineering.
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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, Jan, MA, He, X & Chen, J 2019, 'P2DCA: A Privacy-Preserving-Based Data Collection and Analysis Framework for IoMT Applications', IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol. 37, no. 6, pp. 1222-1230.
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Usman, M, Jan, MA, He, X & Nanda, P 2019, 'QASEC: A secured data communication scheme for mobile Ad-hoc networks', Future Generation Computer Systems.
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© 2018 Elsevier B.V. Mobile Adhoc NETworks (MANETs) are valuable for various applications due to an efficient, flexible, low-cost and dynamic infrastructure. In these networks, proper utilization of network resources is desirable to maintain Quality of Service (QoS). In multi-hop end-to-end communication, intermediate nodes may eavesdrop on data in transit. As a result, a secured and reliable data delivery from source to destination is required. In this paper, we propose a novel scheme, known as QASEC, to achieve better throughput by securing end-to-end communication in MANETs. The QoS is maintained through an optimal link selection from a queue of available transmission links. The end-to-end communication is secured by authentication. A simple secret-key based symmetric encryption is deployed for interacting nodes. Our proposed QASEC scheme prevents the malicious nodes from data exchange with legitimate intermediate nodes on any established path between the source and the destination. Experimental results show that QASEC performs better in terms of packet-loss rate, jitter and end-to-end delay. Furthermore, QASEC is efficient against various attacks and has a much better performance in terms of associated costs, such as key generation, encryption, and storage and communication.

Valenzuela Fernandez, LM, Nicolas, C, Merigó, JM & Arroyo-Cañada, FJ 2019, 'Industrial marketing research. A bibliometric analysis (1990-2015)', Journal of Business and Industrial Marketing.
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© 2018, Emerald Publishing Limited. Purpose: The purpose of this paper is to determine the most influential countries and universities that have contributed to science in the field of industrial marketing research during the period from 1990 to2015. Design/methodology/approach: Bibliometric methodology is adopted, focusing on the most productive and influential countries and universities within this discipline, for the scientific community analyzing journals listed in the Web of Science (WoS) database from 1990 to 2015 and is supplemented by using VOS viewer to graph the existing bibliometric networks for each and every variable. Findings: Evidence that the USA and UK remain leaders in the investigation of industrial marketing research. Finland stands at the third place, leaving Australia and Germany behind. In reference to the universities, Michigan State University ranks as the leader. Research limitations/implications: The process of data classification originates from WoS. Moreover, to provide a comprehensive analytical scenario, other factors could have potentially been considered such as the editor’s commitment to leading journals, to partnerships and conferences, as well as other databases. Originality/value: This paper takes into account alternative variables that have not been previously considered in previous studies, such as universities and countries in which the transcendental contributions to this field have taken place, providing a closer look, which gives rise to further discussions and studies with more detail to the history of this science in the future.

van Rijmenam, M, Erekhinskaya, T, Schweitzer, J & Williams, MA 2019, 'Avoid being the turkey: How big data analytics changes the game of strategy in times of ambiguity and uncertainty', Long Range Planning, vol. 52, no. 5.
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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.

Verma, R & Merigó, JM 2019, 'Variance measures with ordered weighted aggregation operators', International Journal of Intelligent Systems, vol. 34, no. 6, pp. 1184-1205.
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© 2019 Wiley Periodicals, Inc. The variance is a statistical measure widely used in many real-life application areas. This article makes an extensive investigation on variance measure in the case when the uncertainty is not of a probabilistic nature. It generalizes the notion of variance as well as the theory of ordered weighted aggregation operators. First, we extend the idea of representative value/expected value of a decision maker and develop some new deviation measures based on ordered weighted geometric (OWG) average and ordered weighted harmonic average (OWHA) operators. These measures are developed with the consideration that decision maker can represent his/her attitudinal expected value by using any one of the ordered weighted aggregation (OWA) operators. Further, this study proposes some deviation measures by using the generalized-OWA (GOWA) and Quasi-OWA as an expected value of decision maker and discusses their particular cases. Second, a number of generalized deviation measures are introduced by taking the generalized arithmetic mean and quasi-arithmetic means for aggregation of the individual dispersion. This approach provides an ability to the user for considering the deviation under different realistic-scenario. These measures lead to many interesting particular and limiting cases and enrich the use of ordered weighted aggregation operators in the variance.

Vo, NNY, He, X, Liu, S & Xu, G 2019, 'Deep learning for decision making and the optimization of socially responsible investments and portfolio', Decision Support Systems, vol. 124.
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© 2019 Elsevier B.V. A socially responsible investment portfolio takes into consideration the environmental, social and governance aspects of companies. It has become an emerging topic for both financial investors and researchers recently. Traditional investment and portfolio theories, which are used for the optimization of financial investment portfolios, are inadequate for decision-making and the construction of an optimized socially responsible investment portfolio. In response to this problem, we introduced a Deep Responsible Investment Portfolio (DRIP) model that contains a Multivariate Bidirectional Long Short-Term Memory neural network, to predict stock returns for the construction of a socially responsible investment portfolio. The deep reinforcement learning technique was adapted to retrain neural networks and rebalance the portfolio periodically. Our empirical data revealed that the DRIP framework could achieve competitive financial performance and better social impact compared to traditional portfolio models, sustainable indexes and funds.

Wahid-Ul-Ashraf, A, Budka, M & Musial, K 2019, 'How to predict social relationships — Physics-inspired approach to link prediction', Physica A: Statistical Mechanics and its Applications, vol. 523, pp. 1110-1129.
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© 2019 Elsevier B.V. Link prediction in social networks has a long history in complex network research area. The formation of links in networks has been approached by scientists from different backgrounds, ranging from physics to computer science. To predict the formation of new links, we consider measures which originate from network science and use them in the place of mass and distance within the formalism of Newton's Gravitational Law. The attraction force calculated in this way is treated as a proxy for the likelihood of link formation. In particular, we use three different measures of vertex centrality as mass, and 13 dissimilarity measures including shortest path and inverse Katz score in place of distance, leading to over 50 combinations that we evaluate empirically. Combining these through gravitational law allows us to couple popularity with similarity, two important characteristics for link prediction in social networks. Performance of our predictors is evaluated using Area Under the Precision–Recall Curve (AUC)for seven different real-world network datasets. The experiments demonstrate that this approach tends to outperform the setting in which vertex similarity measures like Katz are used on their own. Our approach also gives us the opportunity to combine network's global and local properties for predicting future or missing links. Our study shows that the use of the physical law which combines node importance with measures quantifying how distant the nodes are, is a promising research direction in social link prediction.

Wan, KR, Maszczyk, T, See, AAQ, Dauwels, J & King, NKK 2019, 'A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease.', Clinical Neurophysiology.
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Wang, B, Yuan, X, Gao, X, Li, X & Tao, D 2019, 'A Hybrid Level Set With Semantic Shape Constraint for Object Segmentation.', IEEE Transactions on Cybernetics, vol. 49, no. 5, pp. 1558-1569.
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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, H, Wu, J, Zhu, X, Chen, Y & Zhang, C 2019, 'Time-Variant Graph Classification', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE Graphs are commonly used to represent objects, such as images and text, for pattern classification. In a dynamic world, an object may continuously evolve over time, and so does the graph extracted from the underlying object. These changes in graph structure with respect to the temporal order present a new representation of the graph, in which an object corresponds to a set of time-variant graphs. In this paper, we formulate a novel time-variant graph classification task and propose a new graph feature, called a graph-shapelet pattern, for learning and classifying time-variant graphs. Graph-shapelet patterns are compact and discriminative graph transformation subsequences. A graph-shapelet pattern can be regarded as a graphical extension of a shapelet--a class of discriminative features designed for vector-based temporal data classification. To discover graph-shapelet patterns, we propose to convert a time-variant graph sequence into time-series data and use the discovered shapelets to find graph transformation subsequences as graph-shapelet patterns. By converting each graph-shapelet pattern into a unique tokenized graph transformation sequence, we can measure the similarity between two graph-shapelet patterns and therefore classify time-variant graphs. Experiments on both synthetic and real-world data demonstrate the superior performance of the proposed algorithms.

Wang, S & Cao, L 2019, 'Inferring Implicit Rules by Learning Explicit and Hidden Item Dependency', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE Revealing complex relations between entities (e.g., items within or between transactions) is of great significance for business optimization, prediction, and decision making. Such relations include not only co-occurrence-based explicit relations but also nonco-occurrence-based implicit ones. Explicit relations have been substantially studied by rule mining-based approaches, including association rule mining and causal rule discovery. In contrast, implicit relations have received much less attention but could be more actionable. In this paper, we focus on the implicit relations between items which rarely or never co-occur while each of them co-occurs with other identical items (link items) with a high probability. A framework integrates both explicit and hidden item dependencies and a corresponding efficient algorithm IRRMiner captures such implicit relations with implicit rule inference. Experimental results show that IRRMiner not only infers implicit rules of various sizes consisting of both frequent and infrequent items effectively, it also runs at least four times faster than IARMiner, a typical indirect association rule mining algorithm which can only mine size-2 indirect association rules between frequent items. IRRMiner is applied to make recommendations and shows that the identified implicit rules can increase recommendation reliability.

Wang, Y, Shuai, Y, Zhu, Y, Zhang, J & An, P 2019, 'Jointly learning perceptually heterogeneous features for blind 3D video quality assessment', Neurocomputing, vol. 332, pp. 298-304.
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© 2018 Elsevier B.V. 3D videos quality assessment (3D-VQA) is essential to various 3D video processing applications. However, it has not been well investigated on how to make use of perceptual multi-channel video information to improve 3D-VQA under different distortion categories and degrees, especially under asymmetrical distortions. In the paper, we propose a new blind 3D-VQA metric by jointly learning perceptually heterogeneous features. Firstly, a binocular spatio-temporal internal generative mechanism (BST-IGM) is proposed to decompose the views of 3D video into multi-channel videos. Then, we extract perceptually heterogeneous features by proposed multi-channel natural video statistics (MNVS) model, which are characterized 3D video information. Furthermore, a robust AdaBoosting Radial Basis Function (RBF) neural network is utilized to map the features to the overall quality of 3D video. On two benchmark databases, the extensive evaluations demonstrate that the proposed algorithm significantly outperforms several state-of-the-art quality metrics in term of prediction accuracy and robustness.

Wang, Y, Xu, C, Xu, C & Tao, D 2019, 'Packing Convolutional Neural Networks in the Frequency Domain', IEEE Transactions on Pattern Analysis and Machine Intelligence.
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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.

Wen, D, Qin, L, Zhang, Y, Chang, L & Chen, L 2019, 'Enumerating k-vertex connected components in large graphs', Proceedings - International Conference on Data Engineering, vol. 2019-April, pp. 52-63.
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© 2019 IEEE. In social network analysis, structural cohesion (or vertex connectivity) is a fundamental metric in measuring the cohesion of social groups. Given an undirected graph, a k-vertex connected component (k-VCC) is a maximal connected subgraph whose structural cohesion is at least k. A k-VCC has many outstanding structural properties, such as high cohesiveness, high robustness, and subgraph overlapping. In this paper, given a graph G and an integer k, we study the problem of computing all k-VCCs in G. The general idea for this problem is to recursively partition the graph into overlapped subgraphs. We prove the upper bound of the number of partitions, which implies the polynomial running time algorithm for the k-VCC enumeration. However, the basic solution is costly in computing the vertex cut. To improve the algorithmic efficiency, we observe that the key is reducing the number of local connectivity testings. We propose two effective optimization strategies, namely neighbor sweep and group sweep, to significantly reduce the number of local connectivity testings. We conduct extensive performance studies using ten large real datasets to demonstrate the efficiency of our proposed algorithms. The experimental results demonstrate that our approach can achieve a speedup of up to two orders of magnitude compared to the state-of-the-art algorithm.

Wu, W, Li, B, Chen, L, Zhang, C & Yu, P 2019, 'Improved Consistent Weighted Sampling Revisited', IEEE Transactions on Knowledge and Data Engineering.
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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.

Xiong, F, Wang, X, Pan, S, Yang, H, Wang, H & Zhang, C 2019, 'Social Recommendation With Evolutionary Opinion Dynamics', IEEE Transactions on Systems Man and Cybernetics: Systems.
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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.

Yan, Y, Tan, M, Tsang, I, Yang, Y, Shi, Q & Zhang, C 2019, 'Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis and Case Study', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Matrix factorization has been widely applied to various applications. With the fast development of storage and internet technologies, we have been witnessing a rapid increase of data. In this paper, we propose new algorithms for matrix factorization with the emphasis on efficiency. In addition, most existing methods of matrix factorization only consider a general smooth least square loss. Differently, many real-world applications have distinctive characteristics. As a result, different losses should be used accordingly. Therefore, it is beneficial to design new matrix factorization algorithms that are able to deal with both smooth and non-smooth losses. To this end, one needs to analyze the characteristics of target data and use the most appropriate loss based on the analysis. We particularly study two representative cases of low-rank matrix recovery, i.e., collaborative filtering for recommendation and high dynamic range imaging. To solve these two problems, we respectively propose a stage-wise matrix factorization algorithm by exploiting manifold optimization techniques. From our theoretical analysis, they are both are provably guaranteed to converge to a stationary point. Extensive experiments on recommender systems and high dynamic range imaging demonstrate the satisfactory performance and efficiency of our proposed method on large-scale real data.

Yan, Z, Liu, J & Liu, S 2019, 'DPWeVote: differentially private weighted voting protocol for cloud-based decision-making', Enterprise Information Systems, vol. 13, no. 2, pp. 236-256.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. With the advent of Industry 4.0, cloud computing techniques have been increasingly adopted by industry practitioners to achieve better workflows. One important application is cloud-based decision-making, in which multiple enterprise partners need to arrive an agreed decision. Such cooperative decision-making problem is sometimes formed as a weighted voting game, in which enterprise partners express ‘YES/NO’ opinions. Nevertheless, existing cryptographic approaches to Cloud-Based Weighted Voting Game have restricted collusion tolerance and heavily rely on trusted servers, which are not always available. In this work, we consider the more realistic scenarios of having semi-honest cloud server/partners and assuming maximal collusion tolerance. To resolve the privacy issues in such scenarios, the DPWeVote protocol is proposed which incorporates Randomized Response technique and consists the following three phases: the Randomized Weights Collection phase, the Randomized Opinions Collection phase, and the Voting Results Release phase. Experiments on synthetic data have demonstrated that the proposed DPWeVote protocol managed to retain an acceptable utility for decision-making while preserving privacy in semi-honest environment.

Yan, Z, Wu, Q, Ren, M, Liu, J, Liu, S & Qiu, S 2019, 'Locally private Jaccard similarity estimation', Concurrency Computation.
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© 2018 John Wiley & Sons, Ltd. Jaccard Similarity has been widely used to measure the distance between two sets (or preference profiles) owned by two different users. Yet, in the private data collection scenario, it requires the untrusted curator could only estimate an approximately accurate Jaccard similarity of the involved users but without being allowed to access their preference profiles. This paper aims to address the above requirements by considering the local differential privacy model. To achieve this, we initially focused on a particular hash technique, MinHash, which was originally invented to estimate the Jaccard similarity efficiently. We designed the PrivMin algorithm to achieve the perturbation of MinHash signature by adopting Exponential mechanism and build the Locally Differentially Private Jaccard Similarity Estimation (LDP-JSE) protocol for allowing the untrusted curator to approximately estimate Jaccard similarity. Theoretical and empirical results demonstrate that the proposed protocol can retain a highly acceptable utility of the estimated similarity as well as preserving privacy.

Yang, P, Ormerod, JT, Liu, W, Ma, C, Zomaya, AY & Yang, JYH 2019, 'AdaSampling for Positive-Unlabeled and Label Noise Learning With Bioinformatics Applications.', IEEE Transactions on Cybernetics, vol. 49, no. 5, pp. 1932-1943.
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Class labels are required for supervised learning but may be corrupted or missing in various applications. In binary classification, for example, when only a subset of positive instances is labeled whereas the remaining are unlabeled, positive-unlabeled (PU) learning is required to model from both positive and unlabeled data. Similarly, when class labels are corrupted by mislabeled instances, methods are needed for learning in the presence of class label noise (LN). Here we propose adaptive sampling (AdaSampling), a framework for both PU learning and learning with class LN. By iteratively estimating the class mislabeling probability with an adaptive sampling procedure, the proposed method progressively reduces the risk of selecting mislabeled instances for model training and subsequently constructs highly generalizable models even when a large proportion of mislabeled instances is present in the data. We demonstrate the utilities of proposed methods using simulation and benchmark data, and compare them to alternative approaches that are commonly used for PU learning and/or learning with LN. We then introduce two novel bioinformatics applications where AdaSampling is used to: 1) identify kinase-substrates from mass spectrometry-based phosphoproteomics data and 2) predict transcription factor target genes by integrating various next-generation sequencing data.

Yao, J, Wang, J, Tsang, IW, Zhang, Y, Sun, J, Zhang, C & Zhang, R 2019, 'Deep Learning from Noisy Image Labels with Quality Embedding.', IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.
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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, L, Kusakunniran, W, Wu, Q, Zhang, J, Tang, Z & Yang, W 2019, 'Robust gait recognition using hybrid descriptors based on Skeleton Gait Energy Image', Pattern Recognition Letters.
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© 2019 Gait features have been widely applied in human identification. The commonly-used representations for gait recognition can be roughly classified into two categories: model-free features and model-based features. However, due to the view variances and clothes changes, model-free features are sensitive to the appearance changes. For model-based features, there is great difficulty in extracting the underlying models from gait sequences. Based on the confidence maps and the part affinity fields produced by a two-branch multi-stage CNN network, a new model-based representation, Skeleton Gait Energy Image (SGEI), has been proposed in this paper. Another contribution is that a hybrid representation has been produced, which uses SGEI to remedy the deficiency of model-free features, Gait Energy Image (GEI) for instance. The experimental performances indicate that our proposed methods are more robust to the cloth changes, and contribute to increasing the robustness of gait recognition in the unconstrained environments with view variances and clothes changes.

Yao, X, Wu, Q, Zhang, P & Bao, F 2019, 'Adaptive rational fractal interpolation function for image super-resolution via local fractal analysis', Image and Vision Computing, vol. 82, pp. 39-49.
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© 2019 Elsevier B.V. Image super-resolution aims to generate high-resolution image based on the given low-resolution image and to recover the details of the image. The common approaches include reconstruction-based methods and interpolation-based methods. However, these existing methods show difficulty in processing the regions of an image with complicated texture. To tackle such problems, fractal geometry is applied on image super-resolution, which demonstrates its advantages when describing the complicated details in an image. The common fractal-based method regards the whole image as a single fractal set. That is, it does not distinguish the complexity difference of texture across all regions of an image regardless of smooth regions or texture rich regions. Due to such strong presumption, it causes artificial errors while recovering smooth area and texture blurring at the regions with rich texture. In this paper, the proposed method produces rational fractal interpolation model with various setting at different regions to adapt to the local texture complexity. In order to facilitate such mechanism, the proposed method is able to segment the image region according to its complexity which is determined by its local fractal dimension. Thus, the image super-resolution process is cast to an optimization problem where local fractal dimension in each region is further optimized until the optimization convergence is reached. During the optimization (i.e. super-resolution), the overall image complexity (determined by local fractal dimension) is maintained. Compared with state-of-the-art method, the proposed method shows promising performance according to qualitative evaluation and quantitative evaluation.

Yao, Y, Shen, F, Zhang, J, Liu, L, Tang, Z & Shao, L 2019, 'Extracting Multiple Visual Senses for Web Learning', IEEE Transactions on Multimedia, vol. 21, no. 1, pp. 184-196.
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© 1999-2012 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 paper, 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 reranking search results demonstrate the superiority of our proposed approach.

Yao, Y, Shen, F, Zhang, J, Liu, L, Tang, Z & Shao, L 2019, 'Extracting Privileged Information for Enhancing Classifier Learning', IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 436-450.
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© 1992-2012 IEEE. The accuracy of data-driven learning approaches is often unsatisfactory when the training data is inadequate either in quantity or quality. Manually labeled privileged information (PI), e.g., attributes, tags or properties, is usually incorporated to improve classifier learning. However, the process of manually labeling is time-consuming and labor-intensive. Moreover, due to the limitations of personal knowledge, manually labeled PI may not be rich enough. To address these issues, we propose to enhance classifier learning by exploring PI from untagged corpora, which can effectively eliminate the dependency on manually labeled data and obtain much richer PI. In detail, we treat each selected PI as a subcategory and learn one classifier for each subcategory independently. The classifiers for all subcategories are integrated together to form a more powerful category classifier. Particularly, we propose a novel instance-level multi-instance learning model to simultaneously select a subset of training images from each subcategory and learn the optimal SVM classifiers based on the selected images. Extensive experiments on four benchmark data sets demonstrate the superiority of our proposed approach.

Ying, H, Wu, J, Xu, G, Liu, Y, Liang, T, Zhang, X & Xiong, H 2019, 'Time-aware metric embedding with asymmetric projection for successive POI recommendation', World Wide Web, pp. 1-16.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature Successive Point-of-Interest (POI) recommendation aims to recommend next POIs for a given user based on this user’s current location. Indeed, with the rapid growth of Location-based Social Networks (LBSNs), successive POI recommendation has become an important and challenging task, since it can help to meet users’ dynamic interests based on their recent check-in behaviors. While some efforts have been made for this task, most of them do not capture the following properties: 1) The transition between consecutive POIs in user check-in sequences presents asymmetric property, however existing approaches usually assume the forward and backward transition probabilities between a POI pair are symmetric. 2) Users usually prefer different successive POIs at different time, but most existing studies do not consider this dynamic factor. To this end, in this paper, we propose a time-aware metric embedding approach with asymmetric projection (referred to as MEAP-T) for successive POI recommendation, which takes the above two properties into consideration. In addition, we exploit three latent Euclidean spaces to project the POI-POI, POI-user, and POI-time relationships. Finally, the experimental results on two real-world datasets show MEAP-T outperforms the state-of-the-art methods in terms of both precision and recall.

Yu, D, Fu, B, Xu, G & Qin, A 2019, 'Constrained nonnegative matrix factorization-based semi-supervised multi-label learning', International Journal of Machine Learning and Cybernetics.
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Zhan, K, Chang, X, Guan, J, Chen, L, Ma, Z & Yang, Y 2019, 'Adaptive Structure Discovery for Multimedia Analysis Using Multiple Features.', IEEE transactions on cybernetics.
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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, D, Yin, J, Zhu, X & Zhang, C 2019, 'Attributed network embedding via subspace discovery', Data Mining and Knowledge Discovery.
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© 2019, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature. Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on network embedding focus primarily on preserving network topology structure to learn node representations, recently proposed attributed network embedding algorithms attempt to integrate rich node content information with network topological structure for enhancing the quality of network embedding. In reality, networks often have sparse content, incomplete node attributes, as well as the discrepancy between node attribute feature space and network structure space, which severely deteriorates the performance of existing methods. In this paper, we propose a unified framework for attributed network embedding–attri2vec—that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space. The resultant latent subspace can respect network structure in a more consistent way towards learning high-quality node representations. We formulate an optimization problem which is solved by an efficient stochastic gradient descent algorithm, with linear time complexity to the number of nodes. We investigate a series of linear and non-linear transformations performed on node attributes and empirically validate their effectiveness on various types of networks. Another advantage of attri2vec is its ability to solve out-of-sample problems, where embeddings of new coming nodes can be inferred from their node attributes through the learned mapping function. Experiments on various types of networks confirm that attri2vec is superior to state-of-the-art baselines for node classification, node clustering, as well as out-of-sample link prediction tasks. The source code of this paper is available at https://github.com/...

Zhang, J, Wu, Q, Zhang, J, Shen, C, Lu, J & Wu, Q 2019, 'Heritage image annotation via collective knowledge', Pattern Recognition, vol. 93, pp. 204-214.
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© 2019 Elsevier Ltd The automatic image annotation can provide semantic illustrations to understand image contents, and builds a foundation to develop algorithms that can search images within a large database. However, most current methods focus on solving the annotation problem by modeling the image visual content and tag semantic information, which overlooks the additional information, such as scene descriptions and locations. Moreover, the majority of current annotation datasets are visually consistent and only annotated by common visual objects and attributes, which makes the classic methods vulnerable to handle the more diverse image annotation. To address above issues, we propose to annotate images via collective knowledge, that is, we uncover relationships between the image and its neighbors by measuring similarities among metadata and conduct the metric learning to obtain the representations of image contents, we also generate semantic representations for images given collective semantic information from their neighbors. Two representations from different paradigms are embedded together to train an annotation model. We ground our model on the heritage image collection we collected from the library online open data. Annotations on the heritage image collection are not limited to common visual objects, and are highly relevant to historical events, and the diversity of the heritage image content is much larger than the current datasets, which makes it more suitable for this task. Comprehensive experimental results on the benchmark dataset indicate that the proposed model achieves the best performance compared to baselines and state-of-the-art methods.

Zhang, Q, Shi, C, Niu, Z & Cao, L 2019, 'HCBC: A Hierarchical Case-Based Classifier Integrated with Conceptual Clustering', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 1, pp. 152-165.
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© 1989-2012 IEEE. The structured case representation improves case-based reasoning (CBR) by exploring structures in the case base and the relevance of case structures. Recent CBR classifiers have mostly been built upon the attribute-value case representation rather than structured case representation, in which the structural relations embodied in their representation structure are accordingly overlooked in improving the similarity measure. This results in retrieval inefficiency and limitations on the performance of CBR classifiers. This paper proposes a hierarchical case-based classifier, HCBC, which introduces a concept lattice to hierarchically organize cases. By exploiting structural case relations in the concept lattice, a novel dynamic weighting model is proposed to enhance the concept similarity measure. Based on this similarity measure, HCBC retrieves the top-K concepts that are most similar to a new case by using a bottom-up pruning-based recursive retrieval (PRR) algorithm. The concepts extracted in this way are applied to suggest a class label for the case by a weighted majority voting. Experimental results show that HCBC outperforms other classifiers in terms of classification performance and robustness on categorical data, and also works confidently well on numeric datasets. In addition, PRR effectively reduces the search space and greatly improves the retrieval efficiency of HCBC.

Zhang, Q, Wu, J, Zhang, P, Long, G & Zhang, C 2019, 'Salient Subsequence Learning for Time Series Clustering', IEEE Transactions on Pattern Analysis and Machine Intelligence.
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IEEE Time series has been a popular research topic over the past decade. Salient subsequences of time series that can benefit the learning task, e.g. classification or clustering, are called shapelets. Shapelet-based time series learning extracts these types of salient subsequences with highly informative features from a time series. Most existing methods for shapelet discovery must scan a large pool of candidate subsequences, which is a time-consuming process. A recent work, Grabocka:KDD14, uses regression learning to discover shapelets in a time series; however, it only considers learning shapelets from labeled time series data. This paper proposes an Unsupervised Salient Subsequence Learning (USSL) model that discovers shapelets without the effort of labeling. We developed this new learning function by integrating the strengths of shapelet learning, shapelet regularization, spectral analysis and pseudo-label to simultaneously and automatically learn shapelets to help clustering unlabeled time series better. The optimization model is iteratively solved via a coordinate descent algorithm. Experiments show that our USSL can learn meaningful shapelets, with promising results on real-world and synthetic data that surpass current state-of-the-art unsupervised time series learning methods.

Zhang, X, Zhao, Z, Zheng, Y & Li, J 2019, 'Prediction of Taxi Destinations Using a Novel Data Embedding Method and Ensemble Learning', IEEE Transactions on Intelligent Transportation Systems, pp. 1-11.
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Zheng, Y, Peng, H, Ghosh, S, Lan, C & Li, J 2019, 'Inverse similarity and reliable negative samples for drug side-effect prediction', BMC Bioinformatics, vol. 19.
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© 2019 The Author(s). Background: In silico prediction of potential drug side-effects is of crucial importance for drug development, since wet experimental identification of drug side-effects is expensive and time-consuming. Existing computational methods mainly focus on leveraging validated drug side-effect relations for the prediction. The performance is severely impeded by the lack of reliable negative training data. Thus, a method to select reliable negative samples becomes vital in the performance improvement. Methods: Most of the existing computational prediction methods are essentially based on the assumption that similar drugs are inclined to share the same side-effects, which has given rise to remarkable performance. It is also rational to assume an inverse proposition that dissimilar drugs are less likely to share the same side-effects. Based on this inverse similarity hypothesis, we proposed a novel method to select highly-reliable negative samples for side-effect prediction. The first step of our method is to build a drug similarity integration framework to measure the similarity between drugs from different perspectives. This step integrates drug chemical structures, drug target proteins, drug substituents, and drug therapeutic information as features into a unified framework. Then, a similarity score between each candidate negative drug and validated positive drugs is calculated using the similarity integration framework. Those candidate negative drugs with lower similarity scores are preferentially selected as negative samples. Finally, both the validated positive drugs and the selected highly-reliable negative samples are used for predictions. Results: The performance of the proposed method was evaluated on simulative side-effect prediction of 917 DrugBank drugs, comparing with four machine-learning algorithms. Extensive experiments show that the drug similarity integration framework has superior capability in capturing drug features, achieving mu...

Zuo, Y, Wu, Q, Fang, Y, An, P, Huang, L & Chen, Z 2019, 'Multi-scale Frequency Reconstruction for Guided Depth Map Super-resolution via Deep Residual Network', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Abdollahi, M, Abolhasan, M, Shariati, N, Lipman, J, Jamalipour, A & Ni, W 2019, 'A Routing Protocol for SDN-based Multi-hop D2D Communications', 2019 16th IEEE Annual Consumer Communications and Networking Conference, CCNC 2019, EEE Annual Consumer Communications & Networking Conference, IEEE, USA, pp. 895-898.
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© 2019 IEEE. This paper presents a new Multi-hop Device-to-Device (MD2D) routing protocol, referred to as SMDRP (SDN-based Multi-hop D2D Routing Protocol), for SDN-based wireless networks. Our proposed protocol can be considered as a semi-distributed routing protocol, where an SDN controller manages and controls part of the overall MD2D routing functionality to increase scalability while enabling network operators to control and maintain the out-of-band packet forwarding network. This paper also extends prior work on the Hybrid SDN Architecture for Wireless Distributed Networks (HSAW) [1] and is adapted to the framework presented in this paper. In HSAW, since all link state information is flooded by the controller to the nodes, the network will experience scalability problem. In our approach, this problem is overcome by only passing the next hop for each active route to the mobile nodes. To investigate this, we performed a theoretical and simulation studies comparing HSAW with SMDRP. From our result, it can be seen that for larger density populated networks, SMDRP shows better scalability than HSAW. In addition, mobile nodes need less memory and energy for their communications.

Akbar, MS & Gabrys, B 2018, 'Data analytics enhanced data visualization and interrogation with parallel coordinates plots', 26th International Conference on Systems Engineering, ICSEng 2018 - Proceedings, International Conference on Systems Engineering, IEEE, Sydney, Australia, Australia.
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© 2018 IEEE. Parallel coordinates plots (PCPs) suffer from curse of dimensionality when used with larger multidimensional datasets. Curse of dimentionality results in clutter which hides important visual data trends among coordinates. A number of solutions to address this problem have been proposed including filtering, aggregation, and dimension reordering. These solutions, however, have their own limitations with regard to exploring relationships and trends among the coordinates in PCPs. Correlation based coordinates reordering techniques are among the most popular and have been widely used in PCPs to reduce clutter, though based on the conducted experiments, this research has identified some of their limitations. To achieve better visualization with reduced clutter, we have proposed and evaluated dimensions reordering approach based on minimization of the number of crossing pairs. In the last step, k-means clustering is combined with reordered coordinates to highlight key trends and patterns. The conducted comparative analysis have shown that minimum crossings pairs approach performed much better than other applied techniques for coordinates reordering, and when combined with k-means clustering, resulted in better visualization with significantly reduced clutter.

Ashtari, S, Tofigh, F, Abolhasan, M, Lipman, J & Ni, W 2019, 'Efficient Cellular Base Stations Sleep Mode Control Using Image Matching', 2019 IEEE 89TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2019-SPRING), 89th IEEE Vehicular Technology Conference (VTC Spring), IEEE, Kuala Lumpur, MALAYSIA.

Butler, A, Xu, G & Musial-Gabrys, K 2018, 'Research Performance Reporting is Fallacious', International Conference on Behavioral, Economic, and Socio-Cultural Computing, IEEE, Taiwan.
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Chemalamarri, VD, Braun, R, Lipman, J & Abolhasan, M 2018, 'A Multi-agent Controller to enable Cognition in Software Defined Networks', 2018 28th International Telecommunication Networks and Applications Conference (ITNAC), International Telecommunication Networks and Applications Conference, IEEE, Sydney, Australia, pp. 52-56.
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Current SDN controllers are not cognitive. We propose a new architecture for an SDN controller to enable intelligence. The proposed new architecture is based on Multi-agent systems. As a prototype, we have built a MAS-SDN controller using the GOAL agent programming language. We highlight the motivation behind the new architecture, describe the architecture and provide some initial results

Dasgupta, A, Gill, A & Hussain, F 2019, 'A Conceptual Framework for Data Governance in IoT-enabled Digital IS Ecosystems', Proceedings of the 8th International Conference on Data Science, Technology and Applications: DATA, 8th International Conference on Data Science, Technology and Applications, SCITEPRESS – Science and Technology Publications, Prague, Czech Republic, pp. 209-216.
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Dasgupta, A, Gill, A & Hussain, F 2019, 'A Review of General Data Protection Regulation for Supply Chain Ecosystem', https://link.springer.com/chapter/10.1007/978-3-030-22263-5_44, International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Springer, Cham, Sydney, Australia.
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Gardenfors, P, Williams, M-A, Johnston, B, Billingsley, R, Vitale, J, Peppas, P & Clark, J 2018, 'Event boards as tools for holistic AI', International Workshop on Artificial Intelligence and Cognition 2018, Palermo, Italy.
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Gudi, SLKC, Ojha, S, Sidra, Johnston, B & Williams, MA 2017, 'A proactive robot tutor based on emotional intelligence', Advances in Intelligent Systems and Computing, International Conference on Robot Intelligence Technology and Applications, Springer, Korea, pp. 113-120.
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© Springer International Publishing AG, part of Springer Nature 2019. In recent years, social robots are playing a vital role in various aspects of acting as a companion, assisting in regular tasks, health, interaction, teaching, etc. Coming to the case of robot tutor, the actions of the robot are limited. It may not fully understand the emotions of the student. It may continue to give lecture even though the user is bored or left away from the robot. This situation makes a user feel that robot cannot supersede a human being because it is not in a position to understand emotions. To overcome this issue, in this paper, we present an Emotional Classification System (ECS) where the robot adapts to the mood of the user and behaves accordingly by becoming proactive. It works based on the emotion tracked by the robot using its emotional intelligence. A robot as a sign language tutor scenario is considered to assist speech and hearing impairment people for validating our model. Real-time implementations and analysis are further discussed by considering Pepper robot as a platform.

Hesamian, MH, Jia, W, He, X & Kennedy, PJ 2019, 'Atrous Convolution for Binary Semantic Segmentation of Lung Nodule', ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 1015-1019.
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© 2019 IEEE. Accurately estimating the size of tumours and reproducing their boundaries from lung CT images provides crucial information for early diagnosis, staging and evaluating patients response to cancer therapy. This paper presents an advanced solution to segment lung nodules from CT images by employing a deep residual network structure with Atrous convolution. The Atrous convolution increases the field of view of the filters and helps to improve classification accuracy. Moreover, in order to address the significant class imbalance issue between the nodule pixels and background non-nodule pixels, a weighted loss function is proposed. We evaluate our proposed solution on the widely adopted benchmark dataset LIDC. A promising result of an average DCS of 81.24% is achieved, outperforming the state of the arts. This demonstrates the effectiveness and importance of applying the Atrous convolution and weighted loss for such problems.

Huang, H, Xu, J, Zhang, J, Wu, Q & Kirsch, C 2018, 'Railway Infrastructure Defects Recognition using Fine-grained Deep Convolutional Neural Networks', 2018 Digital Image Computing: Techniques and Applications (DICTA), Digital Image Computing: Techniques and Applications, IEEE, Canberra, Australia.
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Li, Q, Wu, Q, Zhu, C, Zhang, J & Zhao, W 2019, 'Unsupervised User Behavior Representation for Fraud Review Detection with Cold-Start Problem', Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, China, pp. 222-236.
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Liu, L, Zhou, T, Long, G, Yao, L, Jiang, J & Zhang, C 2019, 'Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph', The 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China.
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Madhisetty, S & Williams, MA 2019, 'Managing privacy through key performance indicators when photos and videos are shared via social media', Advances in Intelligent Systems and Computing, pp. 1103-1117.
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© Springer Nature Switzerland AG 2019. There are many definitions of privacy. What is considered sensitive varies from individual to individual. When a document is shared it may reveal certain information, the exchange of information is grounded with a specific context. This contextual grounding may not be afforded when photos and videos are shared, because they may contain rich semantic and syntactic information coded as tacit knowledge. Identifying sensitive information in a photo or a video is a major problem; therefore, rather than making assumptions about what is sensitive in a photo or a video, this research asked a group of study participants why they share content and what their concerns are (if any)? This enabled inferences to be made about categories of sensitivity in accordance with the participants’ responses. Interview data was gathered and Grounded Theory was applied. The following themes emerged from the data: a major theme, in which no privacy concerns were developed, three sub-themes in which varying levels of privacy concerns were developed and key performance indicators which manage levels of privacy were determined. This paper focuses on the main themes’ key performance indicators and how they can manage privacy when photos and videos are shared over social media.

Madhisetty, S & Williams, MA 2019, 'The role of trust and control in managing privacy when photos and videos are stored or shared', Advances in Intelligent Systems and Computing, pp. 127-140.
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© Springer Nature Switzerland AG 2019. A photo or a video could contain sensitive information coded as tacit information, which makes it difficult gauge, the loss of privacy, if such photo or a video were shared. Social media applications like Facebook, Twitter, WhatsApp and many more such applications are becoming popular. The instant sharing of information via photos and videos is making the management of issues which rise out of loss of privacy more difficult. Many users of social media trust that their content will not be misused other than purposes that were originally intended. This paper discusses not only about how much of that trust is real and how much of it was forced, but demonstrates the reasoning behind forced trust. These interferences were made after data collection via interviews and data analysis using Grounded Theory.

Madhisetty, S, Williams, MA, Massy-Greene, J, Franco, L & El Khoury, M 2019, 'How to manage privacy in photos after publication', ICEIS 2019 - Proceedings of the 21st International Conference on Enterprise Information Systems, pp. 162-168.
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Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. Photos and videos once published may stay available for people to view it unless they are deleted by the publisher of the photograph. If the content is downloaded and uploaded by others then they lose all the privacy settings once afforded by the publisher of the photograph or video via social media settings. This means that they could be modified or in some cases misused by others. Photos also contain tacit information, which cannot be completely interpreted at the time of their publication. Sensitive information may be revealed to others as the information is coded as tacit information. Tacit information allows different interpretations and creates difficulty in understanding loss of privacy. Free flow and availability of tacit information embedded in a photograph could have serious privacy problems. Our solution discussed in this paper illuminates the difficulty of managing privacy due the tacit information embedded in a photo. It also provides an offline solution for the photograph such that it cannot be modified or altered and gets automatically deleted over a period of time. By extending the Exif data of a photograph by incorporating an in-built feature of automatic deletion, and the access to an image by scrambling the image via adding a hash value. Only a customized application can unscramble the image therefore making it available. This intends to provide a novel offline solution to manage the availability of the image post publication.

Mahdavi, F, Hayati, H, Kennedy, P & Eager, D 2019, 'Ageing and resulting injuries – effects on racing greyhounds', European Society of Biomechanics, Vienna, Austria.
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Mahdavi, F, Hayati, H, Kennedy, P & Eager, D 2019, 'Effects of the number of starts on greyhound racing dynamics', International Society of Biomechanics Conference, Calgary, Canada.
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Mirtalaie, MA, Hussain, OK, Chang, E & Hussain, FK 2018, 'A fine-grained ontology-based sentiment aggregation approach', Advances in Intelligent Systems and Computing, International Conference on Complex, Intelligent, and Software Intensive Systems, Springer, Japan, pp. 252-262.
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© 2019, Springer International Publishing AG, part of Springer Nature. Sentiment analysis techniques are widely used to capture the voice of customers about different products/services. Aspect or feature-based sentiment detection tools as one of the sentiment analyses’ types are developed to find the customers’ opinions about various features of a product. However, as a product may contain many features, presenting the final obtained results to the users is a challenge. Even though this issue is addressed in the literature by developing different sentiment aggregation methods, their results are mostly presented at the basic-level features of a product. This may cause in losing customers’ opinion about at minor sub-features. However, as the performance of a basic feature is dependent on those of its different sub-features, we propose an approach which aggregates the extracted results at a fine-grained level features using a product ontology tree. We interpret the polarity of each feature as a satisfaction score which can help managers in investigating the weaknesses of their products even at minor levels in a more informed way.

Mohammed, F, He, X, Chen, J & Lin, Y 2019, 'A Novel Model for Classification of Parkinson’s Disease: AccuratelyIdentifying Patients for Surgical Therapy', HICSS, Hawaii International Conference on System Sciences, USA, pp. 3741-3750.
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Parkinson’s disease (PD) is a neurodegenerative disorder and a global health problem that has no curative therapies. Surgery is a well-established therapy for controlling symptoms of advanced PD patients. This paper proposes a streamlined model to classify PD and to identify appropriate patients for surgical therapy. The data was gathered from the Parkinson’s Progressive Markers Initiative consisting of 1080 subjects. Multilayer Perceptron (MLP), Decision trees, Support Vector Machine and Na¨ıve Bayes are used as classifiers. MLP achieves the highest accuracy as compared to other three classifiers. The dataset used in our experiments is from the Parkinson Progressive Markers Initiative. With feature selection, it is observed that the same classification accuracy is achieved with 60% of the attributes as that using all attributes. It is demonstrated that our classification model for PD patients produces the most accurate results and achieves the highest accuracy of 98.13%

Musial-Gabrys, K, Wang, Y, Jin, D & Dang, J 2019, 'Community Detection in Social Networks Considering Topic Correlations', Proceedings of the AAAI Conference on Artificial Intelligence, Thirty Third AAAI Conference on Artificial Intelligence, Hawaii, USA.
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Naji, M, Al-Ani, A, Braytee, A, Anaissi, A & Kennedy, P 2019, 'Queue Formation Augmented with Particle Swarm Optimisation to Improve Waiting Time in Airport Security Screening', Advances in Intelligent Systems and Computing, pp. 923-935.
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© 2019, Springer Nature Switzerland AG. Airport security screening processes are essential to ensure the safety of both passengers and the aviation industry. Security at airports has improved noticeably in recent years through the utilisation of state-of-the-art technologies and highly trained security officers. However, maintaining a high level of security can be costly to operate and implement. It may also lead to delays for passengers and airlines. This paper proposes a novel queue formation method based on a queueing theory model augmented with a particle swarm optimisation method known as QQT-PSO to improve the average waiting time in airport security areas. Extensive experiments were conducted using real-world datasets collected from Sydney airport. Compared to the existing system, our method significantly reduces the average waiting time and operating cost by 11.89% compared to the one-queue formation.

Ojha, S, Gudi, SLKC, Vitale, J, Williams, MA & Johnston, B 2019, 'I remember what you did: A behavioural guide-robot', Advances in Intelligent Systems and Computing, pp. 273-282.
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© Springer International Publishing AG, part of Springer Nature 2019. Robots are coming closer to human society following the birth of emerging field called Social Robotics. Social Robotics is a branch of robotics that specifically pertains to the design and development of robots that can be employed in human society for the welfare of mankind. The applications of social robots may range from household domains such as elderly and child care to educational domains like personal psychological training and tutoring. It is crucial to note that if such robots are intended to work closely with young children, it is extremely important to make sure that these robots teach not only the facts but also important social aspects like knowing what is right and what is wrong. It is because we do not want to produce a generation of kids that knows only the facts but not morality. In this paper, we present a mechanism used in our computational model (i.e EEGS) for social robots, in which emotions and behavioural response of the robot depends on how one has previously treated a robot. For example, if one has previously treated a robot in a good manner, it will respond accordingly while if one has previously mistreated the robot, it will make the person realise the issue. A robot with such a quality can be very useful in teaching good manners to the future generation of kids.

Saki, M, Abolhasan, M & Lipman, J 2019, 'A big sensor data offloading scheme in rail networks', IEEE Vehicular Technology Conference, IEEE Vehicular Technology Conference, IEEE, Kuala Lumpur, Malaysia, Malaysia.
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© 2019 IEEE. In this paper, we propose an offloading scheme to transfer massive stored sensor data from rolling stock to railway data centers. We apply a delayed offloading strategy for non-critical stored data assuming that the critical data has been already separated through an appropriate edge processing task and has been sent via a real-time communication such as cellular networks. We propose train stations as potential and feasible spots for data offloading via available wireless local area networks (WLAN) such as existing WiFi network at stations. Thus, stations will not only be the places of passenger exchange but also data exchange. We develop an analytical model customized for the proposed offloading strategy in rail applications. Then we validate the performance of our model through simulation in various scenarios in Omnet. The simulation results shows an accuracy of %98.67 for the proposed analytical model with reference to the simulation results in Omnetpp. Additionally, by using our proposed scheme, we can theoretically offload up to 5.43 GB per each stopping station.

Seifollahi, S, Piccardi, M, Borzeshi, EZ & Kruger, B 2019, 'Taxonomy-augmented features for document clustering', Communications in Computer and Information Science, pp. 241-252.
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© Springer Nature Singapore Pte Ltd. 2019. In document clustering, individual documents are typically represented by feature vectors based on term-frequency or bag-of-word models. However, such feature vectors intrinsically dismiss the order of the words in the document and suffer from very high dimensionality. For these reasons, in this paper we present novel taxonomy-augmented features that enjoy two promising characteristics: (1) they leverage semantic word embeddings to take the word order into account, and (2) they reduce the feature dimensionality to a very manageable size. Our feature extraction approach consists of three main steps: first, we apply a word embedding technique to represent the words in a word embedding space. Second, we partition the word vocabulary into a hierarchy of clusters by using k-means hierarchically. Lastly, the individual documents are projected to the hierarchy and a compact feature vector is extracted. We propose two methods for generating the features: the first uses all the clusters in the hierarchy and results in a feature vector whose dimensionality is equal to the number of the clusters. The second uses a small set of user-defined words and results in an even smaller feature vector whose dimensionality is equal to the size of the set. Numerical experiments on document clustering show that the proposed approach is capable of achieving comparable or even higher accuracy than conventional feature vectors with a much more compact representation.

Shen, J, Wang, Y & Zhang, J 2018, 'Memory optimized Deep Dense Network for Image Super-resolution', Digital Image Computing: Techniques and Applications, IEEE, Canberra, Australia.
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CNN methods for image super-resolution consume a large number of training-time memory, due to the feature size will not decrease as the network goes deeper. To reduce the memory consumption during training, we propose a memory optimized deep dense network for image super-resolution. We first reduce redundant features learning, by rationally designing the skip connection and dense connection in the network. Then we adopt share memory allocations to store concatenated features and Batch Normalization intermediate feature maps. The memory optimized network consumes less memory than normal dense network. We also evaluate our proposed architecture on highly competitive super-resolution benchmark datasets. Our deep dense network outperforms some existing methods, and requires relatively less computation.

Shen, J, Zhang, J & Wang, Y 2018, 'Deep Bi-Dense Networks for Image Super-Resolution', Digital Image Computing: Techniques and Applications, Canberra.
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Shen, T, Zhou, T, Long, G, Jiang, J & Zhang, C 2019, 'Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together', (NAACL) Annual Conference of the North American Chapter of the Association for Computational Linguisticss, Minneapolis, USA.
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Shi, Z, Zhang, JA, Xu, Y & Fang, G 2018, 'Human Activity Recognition Using Deep Learning Networks with Enhanced Channel State information', IEEE Globecom Workshops, IEEE, Abu Dhabi, United Arab Emirates.
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Skillicorn, D, Alsadhan, N, Billingsley, R & Williams, MA 2019, 'Measuring Human Emotion in Short Documents to Improve Social Robot and Agent Interactions', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 29-41.
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© 2019, Springer Nature Switzerland AG. Social robots and agents can interact with people better if they can infer their affective state (emotions). While they cannot yet recognise affective state from tone and body language, they can use the fragments of speech that they (over)hear. We show that emotions – as conventionally framed – are difficult to detect. We suggest, from empirical results, that this is because emotions are the wrong granularity; and that emotions contain subemotions that are much more clearly separated from one another, and so are both easier to detect and to exploit.

Tofigh, F, Mao, G, Lipman, J & Abolhasan, M 2018, 'Crowd Density Mapping Based on Wi-Fi Measurements on Train Platforms', 2018 12TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATION SYSTEMS (ICSPCS), International Conference on Signal Processing and Communication Systems, IEEE, Cairns, Australia.
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Crowd distribution is a challenging issue in the management and design levels. This paper provides a passive method to derive the crowd density distribution using Wi-Fi measurements on a real scenario. Six WiFi access points (AP) are deployed in the platform 2/3 of Redfern station, Sydney to monitor the platform for a week. Based on the probability maps that are built using RSSI measurements and prior knowledge, the crowd distribution is calculated on the platform and its results are compared with distributions acquired from CCTV images. Final density heat maps are in good agreement with the acquired results from CCTV cameras.

Vishwa, A & Hussain, FK 2018, 'A Blockchain based approach for multimedia privacy protection and provenance', Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, IEEE Symposium Series on Computational Intelligence, IEEE, Bangalore, India, India, pp. 1941-1945.
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© 2018 IEEE. There has been a vast increase in incidents related to multimedia copyright and security breaches in the past few years, compromising users' privacy. One such breach involved the seventh season of the TV series 'Game of Thrones', where episodes were illegally downloaded before the official release date etc. Such security breaches raise questions about the approaches and models that currently apply to data privacy and security, where the user saves and distributes his data personally or depends on a third party or stakeholder to manage the distribution rights of sensitive data. When it comes to multimedia, many companies or multimedia owners rely on third parties, distributors and sales persons to monitor their publicity, maintain their popularity and sell their multimedia content. Blockchain technology, which was originally devised for the digital currency (cryptocurrency), has distinct features such as distributed networking, data privacy, trust less computing etc. This technology attracts great interest from the research community due to its innovative properties which can be applied to many business applications, one being access control over data. In this paper, we present a decentralized data management framework that ensures user data privacy and control. We propose a protocol that uses blockchain technology to take control of the user's data. This protocol enables the user to have full control over his multimedia files and he doesn't need to trust a third party. The framework allows the user to not only store data but also to query and share data as well as auditing. Finally, we discuss possible future extensions of blockchain technology as a medium to ensure privacy, data control, auditing and trust management in different areas.

Wang, C, Pan, S, Hu, R, Long, G, Jiang, J & Zhang, C 2019, 'Attributed Graph Clustering: A Deep Attentional Embedding Approach', The 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China.

Wu, Z, Pan, S, Long, G, Jiang, J & Zhang, C 2019, 'Graph WaveNet for Deep Spatial-Temporal Graph Modeling', The 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China.

Yao, L, Kusakunniran, W, Wu, Q, Zhang, J & Tang, Z 2018, 'Robust CNN-based Gait Verification and Identification using Skeleton Gait Energy Image', 2018 Digital Image Computing: Techniques and Applications (DICTA), Digital Image Computing: Techniques and Applications, IEEE, Canberra, Australia.
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Ye, P, Wang, Y, Xia, Y, An, P & Zhang, J 2019, 'Enhanced saliency prediction via free energy principle', Communications in Computer and Information Science, pp. 31-44.
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© Springer Nature Singapore Pte Ltd 2019. Saliency prediction can be treated as the activity of human brain. Most saliency prediction methods employ features to determine the contrast of an image area relative to its surroundings. However, only few studies have investigated how human brain activities affect saliency prediction. In this paper, we propose an enhanced saliency prediction model via free energy principle. A new AR-RTV model, which combines the relative total variation (RTV) structure extractor with autoregressive (AR) operator, is firstly utilized to decompose an original image into the predictable component and the surprise component. Then, we adopt the local entropy of ‘surprise’ map and the gradient magnitude (GM) map to estimate the component saliency maps-sub-saliency respectively. Finally, inspired by visual error sensitivity, a saliency augment operator is designed to enhance the final saliency combined two sub-saliency maps. Experimental results on two benchmark databases demonstrate the superior performance of the proposed method compared to eleven state-of-the-art algorithms.

Zhang, L, Xu, J, Zhang, J & Gong, Y 2018, 'Information Enhancement for Travelogues via a Hybrid Clustering Model', Digital Image Computing: Techniques and Applications, IEEE, Canberra, ACT, Australia, pp. 1-8.
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Zhang, Z, Wu, Q, Wang, Y & Chen, F 2018, 'Size-Invariant Attention Accuracy Metric for Image Captioning with High-Resolution Residual Attention', 2018 Digital Image Computing: Techniques and Applications (DICTA), Digital Image Computing: Techniques and Applications, IEEE, Canberra, Australia.
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