Publications

Alshehri, MD, Hussain, F, Elkhodr, M & Alsinglawi, BS 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 International Publishing, Switzerland, pp. 1-9.
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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, Qumer Gill, A & Hussain, F 2019, 'Privacy of IoT-Enabled Smart Home Systems' in IoT and Smart Home Automation [Working Title], IntechOpen, USA.
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Abstract Digital ecosystems are going through a period of change due to the advancement in technologies such as Internet of Things (IoT) as well as proliferation of less expensive hardware sensors. Through this chapter, we present current emerging trends in IoT in different industry sectors as well as discuss the key privacy challenges impeding the growth of IoT to reach its potential in the smart home context. The majority of the existing literature on IoT smart home platforms focuses on functionalities provided by smarter connected devices; however, it does not address the concerns from a consumer’s viewpoint. Thus, the key questions are: What are the privacy concerns related to IoT, particularly from a “smart home device” consumer viewpoint? What are the existing remedial approaches for privacy management? This chapter proposes a framework to assist smart home user and IoT device manufacturer to make informed privacy management decisions. The findings of this research intend to help practitioners and researchers interested in the privacy of IoT-enabled smart systems

Kacprzyk, J, Kondratenko, YP, Merigó, JM, Hormazabal, JH, Sirbiladze, G & Gil-Lafuente, AM 2019, 'A Status Quo Biased Multistage Decision Model for Regional Agricultural Socioeconomic Planning Under Fuzzy Information' in Studies in Systems, Decision and Control, Springer International Publishing, Germany, pp. 201-226.
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© 2019, Springer Nature Switzerland AG. We proposed a novel fuzzy multistage control model of sustainable regional agricultural development which better reflects specific features of human stakeholders. First, we use fuzzy logic for the modeling of imprecision in human judgments, intentions, preferences, evaluations, etc. Second, we propose to reflect in the model the so called status quo bias of the humans which basically stands for a common propensity of the humans to stay with known and already employed procedures and courses of action, avoiding larger changes. We develop therefore a human centric model. We also indicate that the inclusion of the status quo bias can be viewed as a way to mitigate risk which is crucial, notably in the case of agriculture. We present some simple example of how a best (optimal) investment policy can be obtained under different development scenarios, and indicate what change the inclusion of the status quo bias brings.

León-Castro, E, Blanco-Mesa, F & Merigó, JM 2019, 'Weighted Averages in the Ordered Weighted Average Inflation' in Advances in Intelligent Systems and Computing, Springer International Publishing, Switzerland, pp. 87-95.
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© Springer Nature Switzerland AG 2019. This paper presents the ordered weighted average weighted average inflation (OWAWAI). The OWAWAI operator is a new formulation for calculating inflation that provides different criteria for the association between the arguments and weights. OWAWAI presents the possibility to generate new approaches that under- or overestimate the results according to the knowledge and expertise of the decision maker. The works present an approach in Chile inflation.

Peris-Ortiz, M, Ferreira, JJ & Merigó Lindahl, JM 2019, 'Knowledge, Innovation, and Sustainable Development in Organizations: A Dynamic Capability Perspective: An Overview' in Innovation, Technology, and Knowledge Management, Springer International Publishing, pp. 1-10.
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Yin, Z, Liu, W & 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 International Publishing, Germany, pp. 1-25.
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© Springer Nature Switzerland AG 2019. In recent years, deep learning frameworks have been applied in many domains and achieved promising performance. However, recent work have demonstrated that deep learning frameworks are vulnerable to adversarial attacks. A trained neural network can be manipulated by small perturbations added to legitimate samples. In computer vision domain, these small perturbations could be imperceptible to human. As deep learning techniques have become the core part for many security-critical applications including identity recognition camera, malware detection software, self-driving cars, adversarial attacks have become one crucial security threat to many deep learning applications in real world. In this chapter, we first review some state-of-the-art adversarial attack techniques for deep learning frameworks in both white-box and black-box settings. We then discuss recent methods to defend against adversarial attacks on deep learning frameworks. Finally, we explore recent work applying adversarial attack techniques to some popular commercial deep learning applications, such as image classification, speech recognition and malware detection. These projects demonstrate that many commercial deep learning frameworks are vulnerable to malicious cyber security attacks.

A. Cancino, C, Nuñez, A & M. Merigó, J 2019, 'Influence of a seed capital program for supporting high growth firms in Chile', Contaduría y Administración, vol. 64, no. 1, pp. 65-65.
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<p>The main economic development agency in Chile, CORFO, implemented in 2001 a Seed Capital Program (SCP) to promote the development of high-growth firms. The SCP not only provides financial aid to entrepreneurs but also technical and administrative assistance through the support of incubators. Incubators may be universities incubators (UI) or private firms (NUI). The aim of this paper is to know the performance of beneficiaries according to the assistance of UI or NUI. A total of 238 new firms beneficiaries with the CORFO program were surveyed (84 supported by UI and 154 supported by NUI). Two logistic regression models were used, a first model to assess the probability that a new firm achieves positive sales, and a second model to assess the probability that the new firm reaches a high growth during the first five years from its inception. Overall, mixed results were found. SCP’s beneficiaries supported by either UI and NUI have the same probability of having positive sales when starting their operations. However, five years after started their operations, businesses supported by UI have higher probabilities of achieving high growth than businesses supported by NUI. The results highlight a positive interaction between private entrepreneurs, public agencies and university incubators.<strong></strong></p>

Abidi, S, Piccardi, M, Tsang, IW & Williams, M-A 2019, 'Well-M$^3$N: A Maximum-Margin Approach to Unsupervised Structured Prediction', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 3, no. 6, pp. 427-439.
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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.

Alfaro-García, VG, Merigó, JM, Plata-Pérez, L, Alfaro-Calderón, GG & Gil-Lafuente, AM 2019, 'INDUCED AND LOGARITHMIC DISTANCES WITH MULTI-REGION AGGREGATION OPERATORS', Technological and Economic Development of Economy, vol. 0, no. 0, pp. 1-29.
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This paper introduces the induced ordered weighted logarithmic averaging IOWLAD and multiregion induced ordered weighted logarithmic averaging MR-IOWLAD operators. The distinctive characteristic of these operators lies in the notion of distance measures combined with the complex reordering mechanism of inducing variables and the properties of the logarithmic averaging operators. The main advantage of MR-IOWLAD operators is their design, which is specifically thought to aid in decision-making when a set of diverse regions with different properties must be considered. Moreover, the induced weighting vector and the distance measure mechanisms of the operator allow for the wider modeling of problems, including heterogeneous information and the complex attitudinal character of experts, when aiming for an ideal scenario. Along with analyzing the main properties of the IOWLAD operators, their families and specific cases, we also introduce some extensions, such as the induced generalized ordered weighted averaging IGOWLAD operator and Choquet integrals. We present the induced Choquet logarithmic distance averaging ICLD operator and the generalized induced Choquet logarithmic distance averaging IGCLD operator. Finally, an illustrative example is proposed, including real-world information retrieved from the United Nations World Statistics for global regions.

Alshehri, MD & Hussain, FK 2019, 'A fuzzy security protocol for trust management in the internet of things (Fuzzy-IoT)', Computing, vol. 101, no. 7, pp. 791-818.
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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, pp. 117165-117165.
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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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© The Institution of Engineering and Technology 2019. With rapidly growing adoption of wireless technologies, requirements for the design of a miniature wideband multiresonators are increasing. In this study, a compact fractal-based metamaterial structure with lumped resistors is described. The structure of the authors proposed absorber is a combination of Sierpinski curve and Minkowski fractal. The new combination provides larger capacitance and inductance in the system enabling perfect absorption at lower frequencies. The final structure with dimensions of 20 × 20 × 1.6 mm3 and an air gap of 12.5 mm provides three main resonances at frequencies of 2.1, 5.1, and 12.8 GHz with bandwidth (absorption ratio over 90%) of 840 MHz, 1.05 GHz, and 910 MHz, respectively.

Anderson, C, Hafen, R, Sofrygin, O & Ryan, L 2019, 'Comparing predictive abilities of longitudinal child growth models', Statistics in Medicine, vol. 38, no. 19, 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.

Andrade-Valbuena, NA, Merigó-Lindahl, JM, Fernández, LV & Nicolas, C 2019, 'Mapping leading universities in strategy research: Three decades of collaborative networks', Cogent Business & Management, vol. 6, no. 1, pp. 1632569-1632569.
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© 2019, © 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. This paper presents a longitudinal classification of the impact that universities have on strategy research from three decades of publications, between 1987 and 2016, by using bibliometric techniques and distance-based analysis of networks applied at the level of universities. Using the WoS database, this study proposes a general overview of three decades of strategic management research. Using these techniques we (i) categorize the last 30 years of academic production of research institutions in terms of strategy, evaluating their impact; (ii) analyze which universities are publishing the most in journals whose scope of publication covers strategic management; and (iii) map the network of collaboration structures among research organizations, determining its relationship and analyzing its evolution in those three decades. We found that the University of Pennsylvania was the most prominent institution throughout the years, showing the broadest network of citations according to our network analysis. There was also a remarkable presence of international universities from the UK, Canada, France and the Netherlands, however, the citation pattern among them is still low. We also observed evidence of inner knowledge flowing among different fields based on the deliberate multidisciplinary nature of research in strategy, as the strong coincidence with the ranking of the main journals in the marketing field when comparing the bibliometric studies of both fields. This analysis contributes to strategy research, first by delivering insights based on the impact of academic production and secondly through the evolution of collaborative network linkages in terms of strategy investigations undertaken to build collective knowledge.

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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© 2018 The Author(s). Objectives: (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. Design: Observation of simulated hand hygiene encounters between a healthcare worker and a patient. Setting: Computer laboratory in a university. Participants: Healthy volunteers. Main outcome measures: Sensitivity and specificity of automatic detection of the first moment of hand hygiene. Methods: 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. Results: 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%). Conclusions: 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, vol. 15, no. 2, pp. 385-429.
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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.

Beydoun, G, Abedin, B, Merigó, JM & Vera, M 2019, 'Twenty Years of Information Systems Frontiers.', Inf. Syst. Frontiers, vol. 21, no. 2, pp. 485-494.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Information Systems Frontiers is a leading international journal that publishes research at the interface between information systems and information technology. The journal was launched in 1999. In 2019, the journal celebrates the 20th anniversary. Motivated by this event, this paper aims to review this first twenty years of publication record to uncover trends most influential on ISF. The analysis considers various metics including citation structure of the journal, most-cited papers, the most influential authors, institutions and countries, and citing articles. Importantly, the paper presents a thematic analysis of the publications that appeared in ISF in the past 20 years. The thematic analysis is evidenced by two sources of data: First, a bibliometric analysis highlighting core topics within the past 20 years is presented. Second, a semantic analysis of keywords introduced by the authors themselves is applied.

Blanco-Mesa, F, León-Castro, E & Merigó, JM 2019, 'A bibliometric analysis of aggregation operators', Applied Soft Computing, vol. 81, pp. 105488-105488.
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© 2019 Elsevier B.V. Aggregation operators consist of mathematical functions that enable the combining and processing of different types of information. The aim of this work is to present the main contributions in this field by a bibliometric review approach. The paper employs an extensive range of bibliometric indicators using the Web of Science (WoS) Core Collection and Scopus datasets. The work considers leading journals, articles, authors, institutions countries and patterns. This paper highlights that Xu is the most productive author and Yager is the most influential author in the field. Likewise, China is leading the field with many new researchers who have entered the field in recent years. This discipline has been strengthening to create a unique theory and will continue to expand with many new theoretical developments and applications.

Blanco‐Mesa, F, León‐Castro, E, Merigó, JM & Herrera‐Viedma, E 2019, 'Variances with Bonferroni means and ordered weighted averages', International Journal of Intelligent Systems, vol. 34, no. 11, pp. 3020-3045.
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© 2019 Wiley Periodicals, Inc. The variance is a statistical measure frequently used for analysis of dispersion in the data. This paper presents new types of variances that use Bonferroni means and ordered weighted averages in the aggregation process of the variance. The main advantage of this approach is that we can underestimate or overestimate the variance according to the attitudinal character of the decision-maker. The work considers several particular cases including the minimum and the maximum variance and presents some numerical examples. The article also develops some extensions and generalizations by using induced aggregation operators and generalized and quasi-arithmetic means. These approaches provide a more general framework that can consider a lot of other particular cases and a complex attitudinal character that could be affected by a wide range of variables. The study ends with an application of the new approach in a business decision-making problem regarding strategic analysis in enterprise risk management.

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.

Braytee, A, Liu, W, Anaissi, A & Kennedy, PJ 2019, 'Correlated Multi-label Classification with Incomplete Label Space and Class Imbalance', ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 5, pp. 1-26.
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Multi-label classification is defined as the problem of identifying the multiple labels or categories of new observations based on labeled training data. Multi-labeled data has several challenges, including class imbalance, label correlation, incomplete multi-label matrices, and noisy and irrelevant features. In this article, we propose an integrated multi-label classification approach with incomplete label space and class imbalance (ML-CIB) for simultaneously training the multi-label classification model and addressing the aforementioned challenges. The model learns a new label matrix and captures new label correlations, because it is difficult to find a complete label vector for each instance in real-world data. We also propose a label regularization to handle the imbalanced multi-labeled issue in the new label, and l 1 regularization norm is incorporated in the objective function to select the relevant sparse features. A multi-label feature selection (ML-CIB-FS) method is presented as a variant of the proposed ML-CIB to show the efficacy of the proposed method in selecting the relevant features. ML-CIB is formulated as a constrained objective function. We use the accelerated proximal gradient method to solve the proposed optimisation problem. Last, extensive experiments are conducted on 19 regular-scale and large-scale imbalanced multi-labeled datasets. The promising results show that our method significantly outperforms the state-of-the-art.

Bródka, P, Musial, K & Jankowski, J 2019, 'Interacting spreading processes in multilayer networks', IEEE Access, volume 8, 2020, vol. 8, pp. 10316-10341.
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The world of network science is fascinating and filled with complex phenomenathat we aspire to understand. One of them is the dynamics of spreadingprocesses over complex networked structures. Building the knowledge-base in thefield where we can face more than one spreading process propagating over anetwork that has more than one layer is a challenging task, as the complexitycomes both from the environment in which the spread happens and fromcharacteristics and interplay of spreads' propagation. As thiscross-disciplinary field bringing together computer science, network science,biology and physics has rapidly grown over the last decade, there is a need tocomprehensively review the current state-of-the-art and offer to the researchcommunity a roadmap that helps to organise the future research in this area.Thus, this survey is a first attempt to present the current landscape of themulti-processes spread over multilayer networks and to suggest the potentialways forward.

Cancino, CA, Amirbagheri, K, Merigó, JM & Dessouky, Y 2019, 'A bibliometric analysis of supply chain analytical techniques published in Computers & Industrial Engineering', Computers & Industrial Engineering, vol. 137, pp. 106015-106015.
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© 2019 Elsevier Ltd Computers & Industrial Engineering (CAIE) is a leading international journal that publishes manuscripts in the field of supply chain. Due to the recent advances of different analytical techniques applied in order to address supply chain related problems, the aim of this work is to study CAIE publications with a focus on the supply chain using a bibliometric approach that can identify the leading trends in this area by analysing the most significant papers, keywords, authors, institutions and countries. The work also develops a graphical mapping of the bibliographic material by using the visualization of similarities (VOS) viewer software. With this software, the study analyses bibliographic coupling, co-occurrence of author keywords and how the journal is connected with other journals through co-citation analysis. The results indicate that Computers and Industrial Engineering has the fourth highest publications in this area among leading journals that publish in Supply Chain, and China and Iran are the leading publishing countries while Taiwan and Singapore have the highest publications per capita. Finally, supply chain optimization modelling received the highest number of publications in the study.

Cao, L 2019, 'Data Science: Profession and Education', IEEE Intelligent Systems, vol. 34, no. 5, pp. 35-44.
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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, vol. 30, no. 6, pp. 1867-1880.
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© 2018 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 (k NNs) 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.

Chacon, D, Braytee, A, Huang, Y, Thoms, J, Subramanian, S, Sauerland, MC, Bohlander, SK, Braess, J, Wörmann, BJ, Berdel, WE, Hiddemann, W, Gabrys, B, Metzeler, KH, Herold, T, Pimanda, J & Beck, D 2019, 'Prospective Identification of Acute Myeloid Leukemia Patients Who Benefit from Gene-Expression Based Risk Stratification', Blood, vol. 134, no. Supplement_1, pp. 1397-1397.
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Background: Acute myeloid leukemia (AML) is a highly heterogeneous malignancy and risk stratification based on genetic and clinical variables is standard practice. However, current models incorporating these factors accurately predict clinical outcomes for only 64-80% of patients and fail to provide clear treatment guidelines for patients with intermediate genetic risk. A plethora of prognostic gene expression signatures (PGES) have been proposed to improve outcome predictions but none of these have entered routine clinical practice and their role remains uncertain. Methods: To clarify clinical utility, we performed a systematic evaluation of eight highly-cited PGES i.e. Marcucci-7, Ng-17, Li-24, Herold-29, Eppert-LSCR-48, Metzeler-86, Eppert-HSCR-105, and Bullinger-133. We investigated their constituent genes, methodological frameworks and prognostic performance in four cohorts of non-FAB M3 AML patients (n= 1175). All patients received intensive anthracycline and cytarabine based chemotherapy and were part of studies conducted in the United States of America (TCGA), the Netherlands (HOVON) and Germany (AMLCG). Results: There was a minimal overlap of individual genes and component pathways between different PGES and their performance was inconsistent when applied across different patient cohorts. Concerningly, different PGES often assigned the same patient into opposing adverse- or favorable- risk groups (Figure 1A: Rand index analysis; RI=1 if all patients were assigned to equal risk groups and RI =0 if all patients were assigned to different risk groups). Differences in the underlying methodological framework of different PGES and the molecular heterogeneity between AMLs contributed to these low-fidelity risk assignments. However, all PGES consistently assigned a significant subset of patients into the same adverse- or favorable-risk groups (40%-70%; Figure 1B: Principal componen...

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.

Cutler, RL, Torres-Robles, A, Wiecek, E, Drake, B, Van der Linden, N, Benrimoj, SIC & Garcia-Cardenas, V 2019, '<p>Pharmacist-led medication non-adherence intervention: reducing the economic burden placed on the Australian health care system</p>', Patient Preference and Adherence, vol. Volume 13, pp. 853-862.
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© 2019 Cutler et al. Background: Scarcity of prospective medication non-adherence cost measurements for the Australian population with no directly measured estimates makes determining the burden medication non-adherence places on the Australian health care system difficult. This study aims to indirectly estimate the national cost of medication non-adherence in Australia comparing the cost prior to and following a community pharmacy-led intervention. Methods: Retrospective observational study. A de-identified database of dispensing data from 20,335 patients (n=11,257 on rosuvastatin, n=6,797 on irbesartan and n=2,281 on desvenlafaxine) was analyzed and average adherence rate determined through calculation of PDC. Included patients received a pharmacist-led medication adherence intervention and had twelve months dispensing records; six months before and six months after the intervention. The national cost estimate of medication non-adherence in hypertension, dyslipidemia and depression pre-and post-intervention was determined through utilization of disease prevalence and comorbidity, non-adherence rates and per patient disease-specific adherence-related costs. Results: The total national cost of medication non-adherence across three prevalent conditions, hypertension, dyslipidemia and depression was $10.4 billion equating to $517 per adult. Following enrollment in the pharmacist-led intervention medication non-adherence costs per adult decreased $95 saving the Australian health care system and patients $1.9 billion annually. Conclusion: In the absence of a directly measured national cost of medication non-adherence, this estimate demonstrates that pharmacists are ideally placed to improve patient adherence and reduce financial burden placed on the health care system due to non-adherence. Funding of medication adherence programs should be considered by policy and decision makers to ease the current burden and improve patient health outcomes moving forward.

Ding, G, Zhang, S, Khan, S, Tang, Z, Zhang, J & Porikli, F 2019, 'Feature Affinity-Based Pseudo Labeling for Semi-Supervised Person Re-Identification', IEEE Transactions on Multimedia, vol. 21, no. 11, pp. 2891-2902.
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© 1999-2012 IEEE. Vision-based person re-identification aims to match a person's identity across multiple images, which is a fundamental task in multimedia content analysis and retrieval. Deep neural networks have recently manifested great potential in this task. However, a major bottleneck of existing supervised deep networks is their reliance on a large amount of annotated training data. Manual labeling for person identities in large-scale surveillance camera systems is quite challenging and incurs significant costs. Some recent studies adopt generative model outputs as training data augmentation. To more effectively use these synthetic data for an improved feature learning and re-identification performance, this paper proposes a novel feature affinity-based pseudo labeling method with two possible label encodings. To the best of our knowledge, this is the first study that employs pseudo-labeling by measuring the affinity of unlabeled samples with the underlying clusters of labeled data samples using the intermediate feature representations from deep networks. We propose training the network with the joint supervision of cross-entropy loss together with a center regularization term, which not only ensures discriminative feature representation learning but also simultaneously predicts pseudo-labels for unlabeled data. We show that both label encodings can be learned in a unified manner and help improve the overall performance. Our extensive experiments on three person re-identification datasets: Market-1501, DukeMTMC-reID, and CUHK03, demonstrate significant performance boost over the state-of-the-art person re-identification approaches.

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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As an important tool for behavior informatics, negative sequential patterns (NSPs) (such as missing a medical treatment) are sometimes much more informative than positive sequential patterns (PSPs) (e.g., attending a medical treatment) in many applications. However, NSP mining is at an early stage and faces many challenging problems, including 1) how to mine an expected number of NSPs; 2) how to select useful NSPs; and 3) how to reduce high time consumption. To solve the first problem, we propose an algorithm Topk-NSP to mine the k most frequent negative patterns. In Topk-NSP, we first mine the top- k PSPs using the existing methods, and then we use an idea which is similar to top- k PSPs mining to mine the top- k NSPs from these PSPs. To solve the remaining two problems, we propose three optimization strategies for Topk-NSP. The first optimization strategy is that, in order to consider the influence of PSPs when selecting useful top- k NSPs, we introduce two weights, wP and wN , to express the user preference degree for NSPs and PSPs, respectively, and select useful NSPs by a weighted support wsup. The second optimization strategy is to merge wsup and an interestingness metric to select more useful NSPs. The third optimization strategy is to introduce a pruning strategy to reduce the high computational costs of Topk-NSP. Finally, we propose an optimization algorithm Topk-NSP+. To the best of our knowledge, Topk-NSP+ is the first algorithm that can mine the top- k useful NSPs. The experimental results on four synthetic and two real-life data sets show that the Topk-NSP+ is very efficient in mining the top- k NSPs in the sense of computational cost and scalability.

Esmaili, N, Piccardi, M, Kruger, B & Girosi, F 2019, 'Correction: Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models', PLOS ONE, vol. 14, no. 4, pp. e0214973-e0214973.
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© 2019 Esmaili et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. After publication of this article [1], concerns were raised that the references to the software packages used for this analysis had been omitted. The authors utilized Stata Statistical Software: Release 15. The reference is StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC. The authors also utilized the seqHMM package package in R. The reference is: Helske S, Helske J. Mixture hidden markov models for sequence data: the seqhmm package in R. arXiv preprint arXiv:1704.00543. 2017 Apr 3.

Etchebarne, MS, Cancino, CA & Merigó, JM 2019, 'Evolution of the business and management research in Chile', International Journal of Technology, Policy and Management, vol. 19, no. 2, pp. 108-108.
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Copyright © 2019 Inderscience Enterprises Ltd. Different aspects have enhanced the development of scientific research in business and management in Chile. The aim of this paper is to analyse the characterisation of this scientific evolution. The method used is a Bibliometric analysis. Our sample examines any paper published between 1991 and 2015 in the Web of Science (WoS) database in the area of business and management. The main results show that the publications have had a significant increase. Scientific productivity increase may be related, among other factors: to the efforts of the Chilean universities that reward and incentivise publications in WoS; the participation of academics in competitive grants (Fondecyt); and international accreditations that demand more productive universities in terms of research. The results of the study could be interesting for universities from developing countries wishing to generate policies to increase the productivity in the areas of business and management.

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.

Guo, B, Ouyang, Y, Guo, T, Cao, L & Yu, Z 2019, 'Enhancing Mobile App User Understanding and Marketing With Heterogeneous Crowdsourced Data: A Review', IEEE Access, vol. 7, pp. 68557-68571.
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© 2013 IEEE. The mobile app market has been surging in recent years. It has some key differentiating characteristics which make it different from traditional markets. To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback mining, and so on. This paper reviews CrowdApp, a research field that leverages heterogeneous crowdsourced data for mobile app user understanding and marketing. We first characterize the opportunities of the CrowdApp, and then present the key research challenges and state-of-the-art techniques to deal with these challenges. We further discuss the open issues and future trends of the CrowdApp. Finally, an evolvable app ecosystem architecture based on heterogeneous crowdsourced data is presented.

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, vol. 144, no. 12, pp. 3070-3085.
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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, vol. 31, no. 4, pp. 706-719.
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© 1989-2012 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, but 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. Experiments on real-life networks validate the performance of our algorithm.

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, vol. 30, no. 12, pp. 3774-3787.
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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 labels...

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, W, Sun, C, Wunsch, DC & Xu, RYD 2019, 'Guest Editorial Special Issue on Intelligent Control Through Neural Learning and Optimization for Human–Machine Hybrid Systems', IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 12, pp. 3530-3533.
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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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© 2019, The Author(s). 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.

Hu, L, Chen, Q, Cao, L, Jian, S, Zhao, H & Cao, J 2019, 'Evolving Coauthorship Modeling and Prediction via Time-Aware Paired Choice Analysis', IEEE Access, vol. 7, pp. 98639-98651.
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Coauthorship prediction is challenging yet important for academic collaboration and novel research topics discovery. The challenges lie in the dynamics of social or organizational relationships, changing preferences of suitable collaborators, and the evolution of research interests or topics. However, most current approaches and systems developed so far are mainly based on past coauthorships from a static viewpoint and do not capture the above evolving characteristics in coauthoring. Accordingly, this paper proposes a time-aware approach to capture the evolving coauthorships from online academic databases in terms of capturing the dynamics of social relationships and research interests. In particular, in order to understand the underlying factors influencing researchers to make choices of coauthors, we incorporate choice modeling based on utility theory. More specifically, our model conducts a series of pairwise choices over a poset induced by a utility function so as to learn the preference over all candidate coauthors. To complete the model inference, a gradient-based algorithm is devised to efficiently learn the model parameters for large-scale data. Finally, extensive experiments conducted on a real-world dataset show that our approach consistently outperforms other state-of-the-art methods.

Huang, L, Zhang, J, Zuo, Y & Wu, Q 2019, 'Pyramid-Structured Depth MAP Super-Resolution Based on Deep Dense-Residual Network', IEEE Signal Processing Letters, vol. 26, no. 12, pp. 1723-1727.
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© 1994-2012 IEEE. Although deep convolutional neural networks (DCNN) show significant improvement for single depth map (SD) super-resolution (SR) over the traditional counterparts, most SDSR DCNNs do not reuse the hierarchical features for depth map SR resulting in blurred high-resolution (HR) depth maps. They always stack convolutional layers to make network deeper and wider. In addition, most SDSR networks generate HR depth maps at a single level, which is not suitable for large up-sampling factors. To solve these problems, we present pyramid-structured depth map super-resolution based on deep dense-residual network. Specially, our networks are made up of dense residual blocks that use densely connected layers and residual learning to model the mapping between high-frequency residuals and low-resolution (LR) depth map. Furthermore, based on the pyramid structure, our network can progressively generate depth maps of various levels by taking advantages of features from different levels. The proposed network adopts a deep supervision scheme to reduce the difficulty of model training and further improve the performance. The proposed method is evaluated on Middlebury datasets which shows improved performance compared with 6 state-of-the-art methods.

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. 3557-3557.
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© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement. Advanced handheld plenoptic cameras are being rapidly developed to capture information about light fields (LFs) from the 3D world. Rich LF data can be used to develop dense sub-aperture images (SAIs) that can provide a more immersive experience for users. Unlike conventional 2D images, 4D SAIs contain both the positional and directional information of light rays; the practical applications of handheld plenoptic cameras are limited by the huge volume of data required to capture this information. Therefore, an efficient LF compression method is vital for further application of the cameras. To this end, the pair of steps and depth estimation (PoS&DE) method is proposed in this paper, and the multiview video and depth (MVD) coding structure is used to relieve the LF coding burden. More specifically, a precise depth-estimation approach is presented for SAIs based on the cost function, and an SAI-guided depth optimization algorithm is designed to refine the initial depth map based on pixel variation tendency. Meanwhile, to reduce running time, intermediate SAI synthesis quality and coding bitrates, including the key SAIs selected and cost-computation steps, are set via extensive statistical experiments. In this way, only a limited number of optimally selected SAIs and their corresponding depth maps must be encoded. The experimental results demonstrate that our proposed LF compression solution using PoS&DE can obtain a satisfied coding performance.

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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© 1992-2012 IEEE. Sufficient training data normally is required to train deeply learned models. However, due to the expensive manual process for a labeling 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 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 pre-defined training classes in the real data domain. Unlike the traditional label which usually is a single integral number, the virtual label proposed in this paper 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, vol. 6, no. 4, pp. 6190-6200.
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© 2014 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 perform 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 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 to the original target signals with different signal to noise ratios. 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.

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. 31, no. 5, pp. 853-866.
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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 & Da Xu, RY 2019, 'Relative Pairwise Relationship Constrained Non-Negative Matrix Factorisation', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 8, pp. 1595-1609.
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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.

Jiang, X, Pan, S, Long, G, Xiong, F, Jiang, J & Zhang, C 2019, 'Cost-Sensitive Parallel Learning Framework for Insurance Intelligence Operation', IEEE Transactions on Industrial Electronics, vol. 66, no. 12, pp. 9713-9723.
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IEEE Recent advancements in artificial intelligence (AI) are providing the insurance industry with new opportunities to create tailored solutions and services based on newfound knowledge of consumers, and the execution of enhanced operations and business functions. However, insurance data is heterogeneous, and imbalanced class distribution with low frequency and high dimensions presents four major challenges to machine learning in real-world business. Traditional machine learning algorithms can typically only be applied to standard data sets, which are normally homogeneous and balanced. In this paper, we focus on an efficient cost-sensitive parallel learning framework (CPLF) to enhance insurance operations with a deep learning approach that does not require pre-processing. Our approach comprises a novel, unified, end-to-end cost-sensitive parallel neural network that learns real-world heterogeneous data. A specifically-designed cost-sensitive matrix then automatically generates a robust model for learning minority classifications, and the parameters of both the cost-sensitive matrix and the hybrid neural network are alternately but jointly optimized during training. We also study the CPLF-based architecture for a real-world insurance intelligence operation system, and demonstrate fraud detection experiments on this system. The results of comparative experiments on real-world insurance data sets reflecting actual business cases demonstrate the effectiveness of our design.

Jin, Y, Wu, H, Merigó, JM & Peng, B 2019, 'Generalized Hamacher Aggregation Operators for Intuitionistic Uncertain Linguistic Sets: Multiple Attribute Group Decision Making Methods', Information, vol. 10, no. 6, pp. 206-206.
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In this paper, we consider multiple attribute group decision making (MAGDM) problems in which the attribute values take the form of intuitionistic uncertain linguistic variables. Based on Hamacher operations, we developed several Hamacher aggregation operators, which generalize the arithmetic aggregation operators and geometric aggregation operators, and extend the algebraic aggregation operators and Einstein aggregation operators. A number of special cases for the two operators with respect to the parameters are discussed in detail. Also, we developed an intuitionistic uncertain linguistic generalized Hamacher hybrid weighted average operator to reflect the importance degrees of both the given intuitionistic uncertain linguistic variables and their ordered positions. Based on the generalized Hamacher aggregation operator, we propose a method for MAGDM for intuitionistic uncertain linguistic sets. Finally, a numerical example and comparative analysis with related decision making methods are provided to illustrate the practicality and feasibility of the proposed method.

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, vol. 133, no. 6, pp. 1711-1719.
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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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© 2019 IEEE. To support diversified quality of service demands and dynamic resource requirements of users in 5G driven VANETs, network resources need flexible and scalable resource allocation strategies. Current heterogeneous vehicular networks are designed and deployed with a connection-centric mindset with fixed resource allocation to a cell regardless of traffic conditions, static coverage, and capacity. In this paper, we propose a hybrid-fuzzy logic guided genetic algorithm (H-FLGA) approach for the software defined networking controller, to solve a multi-objective resource optimization problem for 5G driven VANETs. Realizing the service oriented view, the proposed approach formulates five different scenarios of network resource optimization in 5G VANETs. Furthermore, the proposed fuzzy inference system is used to optimize weights of multi-objectives, depending on the type of service requirements of customers. The proposed approach shows the minimized value of multi-objective cost function when compared with the GA. The simulation results show the minimized value of end-to-end delay as compared to other schemes. The proposed approach will help the network service providers to implement a customer-centric network infrastructure, depending on dynamic customer needs of users.

Khuat, TT & Gabrys, B 2019, 'A comparative study of general fuzzy min-max neural networks for pattern classification problems', Neurocomputing, 2019, vol. 386, pp. 110-125.
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General fuzzy min-max (GFMM) neural network is a generalization of fuzzyneural networks formed by hyperbox fuzzy sets for classification and clusteringproblems. Two principle algorithms are deployed to train this type of neuralnetwork, i.e., incremental learning and agglomerative learning. This paperpresents a comprehensive empirical study of performance influencing factors,advantages, and drawbacks of the general fuzzy min-max neural network onpattern classification problems. The subjects of this study include (1) theimpact of maximum hyperbox size, (2) the influence of the similarity thresholdand measures on the agglomerative learning algorithm, (3) the effect of datapresentation order, (4) comparative performance evaluation of the GFMM withother types of fuzzy min-max neural networks and prevalent machine learningalgorithms. The experimental results on benchmark datasets widely used inmachine learning showed overall strong and weak points of the GFMM classifier.These outcomes also informed potential research directions for this class ofmachine learning algorithms in the future.

Khuat, TT & Le, MH 2019, 'Binary teaching–learning-based optimization algorithm with a new update mechanism for sample subset optimization in software defect prediction', Soft Computing, vol. 23, no. 20, pp. 9919-9935.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Software defect prediction has gained considerable attention in recent years. A broad range of computational methods has been developed for accurate prediction of faulty modules based on code and design metrics. One of the challenges in training classifiers is the highly imbalanced class distribution in available datasets, leading to an undesirable bias in the prediction performance for the minority class. Data sampling is a widespread technique to tackle this problem. However, traditional sampling methods, which depend mainly on random resampling from a given dataset, do not take advantage of useful information available in training sets, such as sample quality and representative instances. To cope with this limitation, evolutionary undersampling methods are usually used for identifying an optimal sample subset for the training dataset. This paper proposes a binary teaching–learning- based optimization algorithm employing a distribution-based solution update rule, namely BTLBOd, to generate a balanced subset of highly valuable examples. This subset is then applied to train a classifier for reliable prediction of potentially defective modules in a software system. Each individual in BTLBOd includes two vectors: a real-valued vector generated by the distribution-based update mechanism, and a binary vector produced from the corresponding real vector by a proposed mapping function. Empirical results showed that the optimal sample subset produced by BTLBOd might ameliorate the classification accuracy of the predictor on highly imbalanced software defect data. Obtained results also demonstrated the superior performance of the proposed sampling method compared to other popular sampling techniques.

Khuat, TT & Le, MH 2019, 'Ensemble learning for software fault prediction problem with imbalanced data', International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 4, pp. 3241-3241.
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Fault prediction problem has a crucial role in the software development process because it contributes to reducing defects and assisting the testing process towards fault-free software components. <span lang='EN-US'>Therefore, there are a lot of efforts aiming to address this type of issues, in which static code characteristics are usually adopted to construct fault classification models. </span> One of the challenging problems influencing the performance of predictive classifiers is the high imbalance among patterns belonging to different classes. This paper aims to integrate the sampling techniques and common classification techniques to form a useful ensemble model for the software defect prediction problem. The empirical results conducted on the benchmark datasets of software projects have shown the promising performance of our proposal in comparison with individual classifiers.

Khuat, TT, Chen, F & Gabrys, B 2019, 'An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network', IEEE Transactions on Fuzzy Systems, pp. 1-1, 2019, vol. 29, no. 2, pp. 427-441.
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Motivated by the practical demands for simplification of data towards beingconsistent with human thinking and problem solving as well as tolerance ofuncertainty, information granules are becoming important entities in dataprocessing at different levels of data abstraction. This paper proposes amethod to construct classifiers from multi-resolution hierarchical granularrepresentations (MRHGRC) using hyperbox fuzzy sets. The proposed approach formsa series of granular inferences hierarchically through many levels ofabstraction. An attractive characteristic of our classifier is that it canmaintain relatively high accuracy at a low degree of granularity based onreusing the knowledge learned from lower levels of abstraction. In addition,our approach can reduce the data size significantly as well as handling theuncertainty and incompleteness associated with data in real-world applications.The construction process of the classifier consists of two phases. The firstphase is to formulate the model at the greatest level of granularity, while thelater stage aims to reduce the complexity of the constructed model and deduceit from data at higher abstraction levels. Experimental outcomes conductedcomprehensively on both synthetic and real datasets indicated the efficiency ofour method in terms of training time and predictive performance in comparisonto other types of fuzzy min-max neural networks and common machine learningalgorithms.

Khuat, TT, Ruta, D & Gabrys, B 2019, 'Hyperbox based machine learning algorithms: A comprehensive survey', Soft Computing, vol. 25, no. 2, pp. 1325-1363.
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With the rapid development of digital information, the data volume generatedby humans and machines is growing exponentially. Along with this trend, machinelearning algorithms have been formed and evolved continuously to discover newinformation and knowledge from different data sources. Learning algorithmsusing hyperboxes as fundamental representational and building blocks are abranch of machine learning methods. These algorithms have enormous potentialfor high scalability and online adaptation of predictors built using hyperboxdata representations to the dynamically changing environments and streamingdata. This paper aims to give a comprehensive survey of literature onhyperbox-based machine learning models. In general, according to thearchitecture and characteristic features of the resulting models, the existinghyperbox-based learning algorithms may be grouped into three major categories:fuzzy min-max neural networks, hyperbox-based hybrid models, and otheralgorithms based on hyperbox representations. Within each of these groups, thispaper shows a brief description of the structure of models, associated learningalgorithms, and an analysis of their advantages and drawbacks. Mainapplications of these hyperbox-based models to the real-world problems are alsodescribed in this paper. Finally, we discuss some open problems and identifypotential future research directions in this field.

Krivtsov, AV, Evans, K, Gadrey, JY, Eschle, BK, Hatton, C, Uckelmann, HJ, Ross, KN, Perner, F, Olsen, SN, Pritchard, T, McDermott, L, Jones, CD, Jing, D, Braytee, A, Chacon, D, Earley, E, McKeever, BM, Claremon, D, Gifford, AJ, Lee, HJ, Teicher, BA, Pimanda, JE, Beck, D, Perry, JA, Smith, MA, McGeehan, GM, Lock, RB & Armstrong, SA 2019, 'A Menin-MLL Inhibitor Induces Specific Chromatin Changes and Eradicates Disease in Models of MLL-Rearranged Leukemia', Cancer Cell, vol. 36, no. 6, pp. 660-673.e11.
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© 2019 Elsevier Inc. Inhibition of the Menin (MEN1) and MLL (MLL1, KMT2A) interaction is a potential therapeutic strategy for MLL-rearranged (MLL-r) leukemia. Structure-based design yielded the potent, highly selective, and orally bioavailable small-molecule inhibitor VTP50469. Cell lines carrying MLL rearrangements were selectively responsive to VTP50469. VTP50469 displaced Menin from protein complexes and inhibited chromatin occupancy of MLL at select genes. Loss of MLL binding led to changes in gene expression, differentiation, and apoptosis. Patient-derived xenograft (PDX) models derived from patients with either MLL-r acute myeloid leukemia or MLL-r acute lymphoblastic leukemia (ALL) showed dramatic reductions of leukemia burden when treated with VTP50469. Multiple mice engrafted with MLL-r ALL remained disease free for more than 1 year after treatment. These data support rapid translation of this approach to clinical trials.

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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Abstract 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.

Lan, C, Peng, H, Hutvagner, G & Li, J 2019, 'Construction of competing endogenous RNA networks from paired RNA-seq data sets by pointwise mutual information', BMC Genomics, vol. 20, no. S9, p. 943.
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Abstract Background A long noncoding RNA (lncRNA) can act as a competing endogenous RNA (ceRNA) to compete with an mRNA for binding to the same miRNA. Such an interplay between the lncRNA, miRNA, and mRNA is called a ceRNA crosstalk. As an miRNA may have multiple lncRNA targets and multiple mRNA targets, connecting all the ceRNA crosstalks mediated by the same miRNA forms a ceRNA network. Methods have been developed to construct ceRNA networks in the literature. However, these methods have limits because they have not explored the expression characteristics of total RNAs. Results We proposed a novel method for constructing ceRNA networks and applied it to a paired RNA-seq data set. The first step of the method takes a competition regulation mechanism to derive candidate ceRNA crosstalks. Second, the method combines a competition rule and pointwise mutual information to compute a competition score for each candidate ceRNA crosstalk. Then, ceRNA crosstalks which have significant competition scores are selected to construct the ceRNA network. The key idea, pointwise mutual information, is ideally suitable for measuring the complex point-to-point relationships embedded in the ceRNA networks. Conclusion Computational experiments and results demonstrate that the ceRNA networks can capture important regulatory mechanism of breast cancer, and have also revealed new insights into the treatment of breast cancer. The proposed method can be directly applied to other RNA-seq data sets for deeper disease understanding.

León-Castro, E, Espinoza-Audelo, LF, Aviles-Ochoa, E, Merigó, JM & Kacprzyk, J 2019, 'A NEW MEASURE OF VOLATILITY USING INDUCED HEAVY MOVING AVERAGES', Technological and Economic Development of Economy, vol. 25, no. 4, pp. 576-599.
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The volatility is a dispersion technique widely used in statistics and economics. This paper presents a new way to calculate volatility by using different extensions of the ordered weighted average (OWA) operator. This approach is called the induced heavy ordered weighted moving average (IHOWMA) volatility. The main advantage of this operator is that the classical volatility formula only takes into account the standard deviation and the average, while with this formulation it is possible to aggregate information according to the decision maker knowledge, expectations and attitude about the future. Some particular cases are also presented when the aggregation information process is applied only on the standard deviation or on the average. An example in three different exchange rates for 2016 are presented, these are for: USD/MXN, EUR/MXN and EUR/USD

León-Castro, E, Merigó, JM, Avilés-Ochoa, E, Gil-Lafuent, AM & Herrera-Viedma, E 2019, 'MODELLING AND SIMULATION IN BUSINESS, ECONOMICS AND MANAGEMENT', Technological and Economic Development of Economy, vol. 25, no. 4, pp. 571-575.
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Modelling and Simulation in Business, Economics and Management. Technological and Economic Development of Economy, 25(4), pp. 571-575.

Li, C, Xie, H-B, Fan, X, Xu, RYD, Van Huffel, S, Sisson, SA & Mengersen, K 2019, 'Image Denoising Based on Nonlocal Bayesian Singular Value Thresholding and Stein’s Unbiased Risk Estimator', IEEE Transactions on Image Processing, vol. 28, no. 10, pp. 4899-4911.
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© 1992-2012 IEEE. Singular value thresholding (SVT)- or nuclear norm minimization (NNM)-based nonlocal image denoising methods often rely on the precise estimation of the noise variance. However, most existing methods either assume that the noise variance is known or require an extra step to estimate it. Under the iterative regularization framework, the error in the noise variance estimate propagates and accumulates with each iteration, ultimately degrading the overall denoising performance. In addition, the essence of these methods is still least squares estimation, which can cause a very high mean-squared error (MSE) and is inadequate for handling missing data or outliers. In order to address these deficiencies, we present a hybrid denoising model based on variational Bayesian inference and Stein's unbiased risk estimator (SURE), which consists of two complementary steps. In the first step, the variational Bayesian SVT performs a low-rank approximation of the nonlocal image patch matrix to simultaneously remove the noise and estimate the noise variance. In the second step, we modify the conventional SURE full-rank SVT and its divergence formulas for rank-reduced eigen-triplets to remove the residual artifacts. The proposed hybrid BSSVT method achieves better performance in recovering the true image compared with state-of-the-art methods.

Liang, H, Wang, H, Li, Q, Wang, J, Xu, G, Chen, J, Wei, J-M & Yang, Z 2019, 'A general framework for learning prosodic-enhanced representation of rap lyrics', World Wide Web, vol. 22, no. 6, pp. 2267-2289.
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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, vol. 12, no. 3, pp. 384-397.
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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.

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, vol. 60, no. 3, pp. 1307-1328.
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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, PY, Tee, AE, Milazzo, G, Hannan, KM, Maag, J, Mondal, S, Atmadibrata, B, Bartonicek, N, Peng, H, Ho, N, Mayoh, C, Ciaccio, R, Sun, Y, Henderson, MJ, Gao, J, Everaert, C, Hulme, AJ, Wong, M, Lan, Q, Cheung, BB, Shi, L, Wang, JY, Simon, T, Fischer, M, Zhang, XD, Marshall, GM, Norris, MD, Haber, M, Vandesompele, J, Li, J, Mestdagh, P, Hannan, RD, Dinger, ME, Perini, G & Liu, T 2019, 'The long noncoding RNA lncNB1 promotes tumorigenesis by interacting with ribosomal protein RPL35', Nature Communications, vol. 10, no. 1.
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AbstractThe majority of patients with neuroblastoma due to MYCN oncogene amplification and consequent N-Myc oncoprotein over-expression die of the disease. Here our analyses of RNA sequencing data identify the long noncoding RNA lncNB1 as one of the transcripts most over-expressed in MYCN-amplified, compared with MYCN-non-amplified, human neuroblastoma cells and also the most over-expressed in neuroblastoma compared with all other cancers. lncNB1 binds to the ribosomal protein RPL35 to enhance E2F1 protein synthesis, leading to DEPDC1B gene transcription. The GTPase-activating protein DEPDC1B induces ERK protein phosphorylation and N-Myc protein stabilization. Importantly, lncNB1 knockdown abolishes neuroblastoma cell clonogenic capacity in vitro and leads to neuroblastoma tumor regression in mice, while high levels of lncNB1 and RPL35 in human neuroblastoma tissues predict poor patient prognosis. This study therefore identifies lncNB1 and its binding protein RPL35 as key factors for promoting E2F1 protein synthesis, N-Myc protein stability and N-Myc-driven oncogenesis, and as therapeutic targets.

Liu, Y, Yu, Z, Dinger, ME & Li, J 2019, 'Index suffix–prefix overlaps by (w, k)-minimizer to generate long contigs for reads compression', Bioinformatics, vol. 35, no. 12, pp. 2066-2074.
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Abstract 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 datasets 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 datasets (13 RNA-seq datasets and 5 whole genome sequencing datasets). 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 redund...

Liu, Y, Zhang, LY & Li, J 2019, 'Fast detection of maximal exact matches via fixed sampling of queryK-mers and Bloom filtering of indexK-mers', Bioinformatics, vol. 35, no. 22, pp. 4560-4567.
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AbstractMotivationDetection 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.ResultsUnder 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 adds 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 implementationhttps://github.com/yuansliu/bfMEMSupplementary informationSupplementary data are available at Bioinformatics online.

Llanos-Herrera, GR & Merigo, JM 2019, 'Overview of brand personality research with bibliometric indicators', Kybernetes, vol. 48, no. 3, pp. 546-569.
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PurposeThe 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/approachThrough 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.FindingsThis 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/implicationsThe information provided pertains to a re...

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.

Makhdoom, I, Abolhasan, M, Lipman, J, Liu, RP & Ni, W 2019, 'Anatomy of Threats to the Internet of Things', IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1636-1675.
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© 1998-2012 IEEE. 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.', J. Ambient Intell. Humaniz. Comput., vol. 10, no. 2, pp. 531-549.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. 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 ...

MARICRUZ, O-L, ERNESTO, L-C, LUIS FERNANDO, E-A, JOSE MARIA, M & ANNA MARÍA, GL 2019, 'Forgotten Effects and Heavy Moving Averages in Exchange Rate Forecasting', ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, vol. 53, no. 4/2019, pp. 79-96.
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Martin Salvador, M, 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. 946-959.
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© 2004-2012 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 22000 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. Note to Practitioners - The extension of Auto-WEKA to compose and optimize multicomponent predictive systems (MCPSs) developed as part of this paper is freely available on GitHub under GPL license, and we encourage practitioners to use it on a broad variety of classification and regression problems. The software can either be used as a blackbox - where search space is made of all possible WEKA filters, predictors, and metapredictors (e.g., ensembles) - or as an optimization tool on a subset of preselected machine ...

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 & 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 & Yager, RR 2019, 'Aggregation operators with moving averages', Soft Computing, vol. 23, no. 21, pp. 10601-10615.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. A moving average is an average that aggregates a subset of variables from the set and moves across the sample. It is widely used in time-series forecasting. This paper studies the use of moving averages in some representative aggregation operators. The ordered weighted averaging weighted moving averaging (OWAWMA) operator is introduced. It is a new approach based on the use of the moving average in a unified model between the weighted average and the ordered weighted average. Its main advantage is that it provides a parameterized family of moving aggregation operators between the moving minimum and the moving maximum. Moreover, it also includes the weighted moving average and the ordered weighted moving average as particular cases. This approach is further extended by using generalized aggregation operators, obtaining the generalized OWAWMA operator. The construction of interval and fuzzy numbers with these operators obtaining the concept of moving interval number and moving fuzzy number is also studied. The paper ends analyzing the applicability of this new approach in some key statistical concepts such as the variance and the covariance and with a numerical example regarding sales forecasting.

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.

Merigó, JM, Etchebarne, MS & Cancino, CA 2019, 'Evolution of the business and management research in Chile', International Journal of Technology, Policy and Management, vol. 19, no. 2, pp. 108-108.
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Merigó, JM, Miranda, J, Modak, NM, Boustras, G & de la Sotta, C 2019, 'Forty years of Safety Science: A bibliometric overview', Safety Science, vol. 115, pp. 66-88.
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© 2019 Elsevier Ltd Safety Science was established in 1976 as the Journal of Occupational Accidents. Safety Science was established with the vision of promoting multidisciplinary research in the science and technology of human and industrial safety and serving as a guide for the safety of people at work and in other spheres, such as transportation, energy or infrastructure, as well as in every other field of hazardous human activities. To celebrate 40 years of publishing outstanding research, this study intends to develop a bibliometric analysis of the publications of the journal between 1976 and 2016. The purpose is to identify the leading trends of the journal in terms of impact, topics, authors, universities and countries. This study uses the most reliable database, the Web of Science Core Collection. Moreover, the work analyses the mapping of bibliographic couplings, co-citations, citations, co-authorships and co-occurrences of keywords.

Merigó, JM, Mulet-Forteza, C, Valencia, C & Lew, AA 2019, 'Twenty years of Tourism Geographies: a bibliometric overview', Tourism Geographies, vol. 21, no. 5, pp. 881-910.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Tourism Geographies is a prominently ranked journal that emerged from activities of the Tourism Commission of the International Geographical Union. It is indexed in the ‘Tourism, Leisure and Hospitality Management’ and ‘Geography, Planning and Development’ fields in the Scopus database and published its 20th volume in 2018. A bibliometric assessment of the articles and authors who have contributed to Tourism Geographies over its first two decades highlights major trends and dominant issues covered by the journal’s content. Key indicators include the most published and most cited authors and articles, the institutions and countries that those authors are affiliated with, other academic journals that are closely linked to the journal through citations, and the most used keywords in the journal. The Scopus database provides access to these basic bibliometric data, while the VOSviewer software enables graphical analyses and displays of co-citations, co-occurrences of keywords, and bibliographic couplings (shared references) across papers and authors. Overall, Tourism Geographies is closely linked to other leading journals indexed by Scopus in the ‘Tourism’ and ‘Geography’ fields and publishes papers from around the world. Research topics that have been most prominent in the journal include tourism development, tourist destinations, tourist attractions, heritage tourism, tourism perceptions, sustainable tourism, and travel behavior. Among the most viewed individual papers have been those addressing issues related to sustainability, poverty issues (related to tourism in poor areas, volunteering, sustainable tourism, and the environment), and community planning (sustainable tourism planning, tourist routes and movement, and new locations for tourism development).

Merigó, JM, Muller, C, Modak, NM & Laengle, S 2019, 'Research in Production and Operations Management: A University-Based Bibliometric Analysis', Global Journal of Flexible Systems Management, vol. 20, no. 1, pp. 1-29.
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© 2018, Global Institute of Flexible Systems Management. Universities across the world are contributing greatly to production and operations management (POM) research and playing significant roles in social and economic development. This article analyzes the performance of universities in POM research and development between 1990 and 2014. The Web of Science core collection database is used to collect all the necessary data. The results show a wide diversity among the countries of origin of the top universities, with some of them being in Asia, Europe, and North America. These results are quite different from many other management areas where English-speaking countries, especially the USA, tend to be dominant. Hong Kong Polytechnic University is the most productive university, while Michigan State University is the most influential one. Time-based evolution reveals that the USA previously had a more dominant position, while now there is more distribution of top universities around the world. The analysis of selected journals indicates that many journals tend to be more influenced by their respective countries of origin. However, other journals show a more general profile by publishing papers from most of the countries around the world.

Milfont, TL, Amirbagheri, K, Hermanns, E & Merigó, JM 2019, 'Celebrating Half a Century of Environment and Behavior: A Bibliometric Review', Environment and Behavior, vol. 51, no. 5, pp. 469-501.
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Environment and Behavior is a leading international journal that publishes research examining the relationships between human behavior and the built and natural environments since 1969. Motivated by its half-century anniversary, the present article uses the Web of Science Core Collection database to provide a bibliometric overview of the leading trends that have occurred in the journal during the 1969-2018 period. The impact of the journal has increased over the years, Gary W. Evans is the author with most published papers, articles by Paul C. Stern and Thomas Dietz have made a notable scientific impact, the University of Michigan is the institution with the highest number of publications, and there is a growing trend in the number of women and international contributors to the journal. This bibliographic review provides strong evidence of the scientific impact of the journal, and the wider Environment-and-Behavior community should be proud of its story of success.

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.

Mulet-Forteza, C, Genovart-Balaguer, J, Merigó, JM & Mauleon-Mendez, E 2019, 'Bibliometric structure of IJCHM in its 30 years', International Journal of Contemporary Hospitality Management, vol. 31, no. 12, pp. 4574-4604.
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PurposeThe International Journal of Contemporary Hospitality Management is a leading international journal in the field of hospitality and tourism management. It was started in 1989, and it turns 30 years old this year. To celebrate this anniversary, this paper presents a bibliometric overview of the publication and citation structure of the journal over the past 30 years. The purpose of this paper is to identify the relevant issues in terms of keywords and topics and who is achieving better results in terms of authors, universities and countries.Design/methodology/approachThe Scopus database is used to collect the bibliographical material. A graphical mapping of the bibliographic data is developed by using VOSviewer software. It produces graphical maps with several bibliometric techniques, including co-citation, bibliographic coupling and co-occurrence of keywords.FindingsThe results indicate that English-speaking countries are producing the highest number of articles in the journal, followed by Asian institutions, with the Hong Kong Polytechnic University as the most productive institution.Originality/valueTo the best of the authors’ knowledge, there are no papers that present a general overview of the publication and citation structure of this journal. Its 30th anniversary is a good moment to develop this study.

Musial, K, Bródka, P & De Meo, P 2019, 'Analysis and Applications of Complex Social Networks 2018', Complexity, vol. 2019, pp. 1-2.
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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.

Nicolas, C, Valenzuela-Fernandez, L & Merigó, JM 2019, 'Mapping retailing research with bibliometric indicators', Journal of Promotion Management, vol. 25, no. 5, pp. 664-680.
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© 2019, © 2019 Taylor & Francis Group, LLC. Our study aims to give a global perspective regarding scientific research on retailing for the 1990–2014 period. The research shows a knowledge-domain-map that identifies the collaboration networks between authors and the links between journals. This was conducted through a bibliometric study that can be viewed with Visualization of similarities (VOS) viewer software. The results show that the Journal of Retailing and Management Science is the current leader in the field. In addition, Morgan and Hunt’s (1994) article in the Journal of Marketing is the most cited source to date.

Odriozola-Fernández, I, Berbegal-Mirabent, J & Merigó-Lindahl, JM 2019, 'Open innovation in small and medium enterprises: a bibliometric analysis', Journal of Organizational Change Management, vol. 32, no. 5, pp. 533-557.
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PurposeThe open innovation (OI) paradigm suggests that firms should use inflows and outflows of knowledge in order to accelerate innovation and leverage markets. Literature examining how firms are adopting OI practices is rich; notwithstanding, little research has addressed this topic from the perspective of small- and medium-sized enterprises (SMEs). Given the relevance of SMEs in worldwide economies, the purpose of this paper is to provide a comprehensive overview of research on OI in SMEs.Design/methodology/approachIn total, 112 academic articles were selected from the Web of Science database. Following a bibliometric analysis, the most relevant authors, journals, institutions and countries are presented. Additionally, the main areas these articles cover are summarized.FindingsResults are consistent in that the most prolific authors are affiliated with the universities leading the ranking of institutions. However, it is remarkable that top authors in this field do not possess a large number of publications on OI in SMEs, but combine this research topic with other related ones. At the country level, European countries are on the top together with South Korea.Research limitations/implicationsDespite following a rigorous method, other relevant documents not included in the selected databases might have been ignored.Practical implicationsThis paper outlines the main topics of interest within this area: impact of OI on firm performance and on organizations’ structure, OI as a mechanism to haste...

Oltra-Badenes, R, Gil-Gomez, H, Merigo, JM & Palacios-Marques, D 2019, 'Methodology and model-based DSS to managing the reallocation of inventory to orders in LHP situations. Application to the ceramics sector', PLOS ONE, vol. 14, no. 7, pp. e0219433-e0219433.
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© 2019 Oltra-Badenes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Lack of homogeneity in the product (LHP) is a problem when customers require homogeneous units of a single product. In such cases, the optimal allocation of inventory to orders becomes much more complex. Furthermore, in an MTS environment, an optimal initial allocation may become less than ideal over time, due to different circumstances. This problem occurs in the ceramics sector, where the final product varies in tone and calibre. This paper proposes a methodology for the reallocation of inventory to orders in LHP situation (MERIO-LHP) and a model-based decision-support system (DSS) to support the methodology, which enables an optimal reallocation of inventory to order lines to be carried out in real businesses environments in which LHP is inherent. The proposed methodology and modelbased DSS were validated by applying it to a real case at a ceramics company. The analysis of the results indicates that considerable improvements can be obtained with regard to the quantity of orders fulfilled and sales turnover.

Pan, S, Hu, R, Fung, S-F, Long, G, Jiang, J & Zhang, C 2019, 'Learning Graph Embedding with Adversarial Training Methods', IEEE Transactions on Cybernetics, vol. 50, no. 6, pp. 2475-2487.
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Graph embedding aims to transfer a graph into vectors to facilitatesubsequent graph analytics tasks like link prediction and graph clustering.Most approaches on graph embedding focus on preserving the graph structure orminimizing the reconstruction errors for graph data. They have mostlyoverlooked the embedding distribution of the latent codes, which unfortunatelymay lead to inferior representation in many cases. In this paper, we present anovel adversarially regularized framework for graph embedding. By employing thegraph convolutional network as an encoder, our framework embeds the topologicalinformation and node content into a vector representation, from which a graphdecoder is further built to reconstruct the input graph. The adversarialtraining principle is applied to enforce our latent codes to match a priorGaussian or Uniform distribution. Based on this framework, we derive twovariants of adversarial models, the adversarially regularized graph autoencoder(ARGA) and its variational version, adversarially regularized variational graphautoencoder (ARVGA), to learn the graph embedding effectively. We also exploitother potential variations of ARGA and ARVGA to get a deeper understanding onour designs. Experimental results compared among twelve algorithms for linkprediction and twenty algorithms for graph clustering validate our solutions.

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, vol. 23, no. 9, pp. 3067-3080.
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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.

Pérez-Arellano, LA, León-Castro, E, Avilés-Ochoa, E & Merigó, JM 2019, 'Prioritized induced probabilistic operator and its application in group decision making', International Journal of Machine Learning and Cybernetics, vol. 10, no. 3, pp. 451-462.
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© 2017, Springer-Verlag GmbH Germany. A new extension of the ordered weighted average (OWA) operator is presented. This new operator includes the characteristics of three other operators: the prioritized, induced and probabilistic. The name is the prioritized induced probabilistic ordered weighted average (PIPOWA) operator. This operator can be used in a group decision-making process for selection of an alternative, taking into account three aspects: (1) not all of the decision-makers are equally important, (2) the probability of success of each alternative, and (3) an induced weighted vector. In the paper, some families of this operator are presented such as the prioritized probabilistic weighted average (PPOWA) operator and the prioritized induced ordered weighted average (PIOWA) operator. Additionally, some of the parameterized family of the aggregation operators, such as the minimum, maximum and total operator, are presented as special cases. The article also generalizes the PIPOWA operator by using quasi-arithmetic means. Finally, an example for selecting an alternative dispute resolution method in a commercial dispute is presented.

Qiao, C, Lu, L, Yang, L & Kennedy, PJ 2019, 'Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology', Applied Sciences, vol. 9, no. 10, pp. 2148-2148.
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Many medical imaging data, especially the magnetic resonance imaging (MRI) data, usually have a small sample size, but a large number of features. How to reduce effectively the data dimension and locate accurately the biomarkers from such kinds of data are quite crucial for diagnosis and further precision medicine. In this paper, we propose a hybrid feature selection method based on machine learning and traditional statistical approaches and explore the brain abnormalities of schizophrenia by using the functional and structural MRI data. The results show that the abnormal brain regions are mainly distributed in the supramarginal gyrus, cingulate gyrus, frontal gyrus, precuneus and caudate, and the abnormal functional connections are related to the caudate nucleus, insula and rolandic operculum. In addition, some complex network analyses based on graph theory are utilized on the functional connection data, and the results demonstrate that the located abnormal functional connections in brain can distinguish schizophrenia patients from healthy controls. The identified abnormalities in brain with schizophrenia by the proposed hybrid feature selection method show that there do exist some abnormal brain regions and abnormal disruption of the network segregation and network integration for schizophrenia, and these changes may lead to inaccurate and inefficient information processing and synthesis in the brain, which provide further evidence for the cognitive dysmetria of schizophrenia.

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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Razzak, I, A. Hameed, I & Xu, G 2019, 'Robust Sparse Representation and Multiclass Support Matrix Machines for the Classification of Motor Imagery EEG Signals', IEEE Journal of Translational Engineering in Health and Medicine, vol. 7, pp. 1-8.
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© 2013 IEEE. Background: EEG signals are extremely complex in comparison to other biomedical signals, thus require an efficient feature selection as well as classification approach. Traditional feature extraction and classification methods require to reshape the data into vectors that results in losing the structural information exist in the original featured matrix. Aim: The aim of this work is to design an efficient approach for robust feature extraction and classification for the classification of EEG signals. Method: In order to extract robust feature matrix and reduce the dimensionality of from original epileptic EEG data, in this paper, we have applied robust joint sparse PCA (RJSPCA), Outliers Robust PCA (ORPCA) and compare their performance with different matrix base feature extraction methods, followed by classification through support matrix machine. The combination of joint sparse PCA with robust support matrix machine showed good generalization performance for classification of EEG data due to their convex optimization. Results: A comprehensive experimental study on the publicly available EEG datasets is carried out to validate the robustness of the proposed approach against outliers. Conclusion: The experiment results, supported by the theoretical analysis and statistical test, show the effectiveness of the proposed framework for solving classification of EEG signals.

Razzak, I, 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, vol. 27, no. 6, pp. 1117-1127.
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© 2001-2011 IEEE. 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, 'Efficient Brain Tumor Segmentation With Multiscale Two-Pathway-Group Conventional Neural Networks', IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 5, pp. 1911-1919.
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© 2013 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 data sets 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.

Rialp, A, Merigó, JM, Cancino, CA & Urbano, D 2019, 'Twenty-five years (1992–2016) of the International Business Review: A bibliometric overview', International Business Review, vol. 28, no. 6, pp. 101587-101587.
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© 2019 Elsevier Ltd The International Business Review (IBR) is a leading international academic journal in the field of International Business (IB). Such leadership is reflected in the large number of publications that grow year after year and particularly in the large number of citations received from other journals of high academic prestige. The aim of this study is to conduct a bibliometric overview of the leading trends regarding the journal's publications and citations since its creation in 1992 until 2016. The work identifies the authors, universities, and countries that publish the most in IBR by mainly using the Scopus database though eventually complemented with Web of Science (WoS) Core Collection. It also analyzes the most cited papers and articles of the journal. Besides, the study graphically maps the bibliographic material by using the visualization of similarities (VOS) viewer software. In order to do so, the work uses co-citation analysis, bibliographic coupling, and co-occurrence of author keywords. The results show the prominent European profile of the journal where contributors from European universities and countries are the most productive ones in the journal. Particularly, British and Scandinavian universities obtain the most remarkable results. However, mostly scholars from North America, but also from Oceania and East Asia are increasingly and regularly publishing in the journal. In addition, IBR is very well connected to other leading journals in the field, such as the Journal of International Business Studies (JIBS) and the Journal of World Business (JWB), as well as with other top management journals, thus demonstrating its core position in IB research conducted worldwide.

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.

Seifollahi, S, Bagirov, A, Zare Borzeshi, E & Piccardi, M 2019, 'A simulated annealing‐based maximum‐margin clustering algorithm', Computational Intelligence, vol. 35, no. 1, pp. 23-41.
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AbstractMaximum‐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, 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, vol. 29, no. 7, pp. 2012-2028.
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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.

Stoianoff, NP 2019, 'Indigenous Knowledge Governance: Developments from the Garuwanga Project', Intellectual Property Forum, no. 117, pp. 9-23.
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The protection of Indigenous knowledge and cultural expressions has become a major topic in Australian law reform in recent years. This has occurred in two streams, one which is predicated on intellectual property rights and the other from the perspective of environment and heritage regulation. The latter is grounded in Australia’s obligations under the Convention on Biological Diversity (“CBD”). While the former has its impetus from Australia’s engagement with the World Intellectual Property Organization (“WIPO”) Intergovernmental Committee on Intellectual Property and Genetic Resources, Traditional Knowledge and Folklore (“IGC”), the IGC was established in 2000 in response to the WIPO and United Nations Environment Programme (responsible for the introduction of the CBD) jointly commissioned “study on the role of intellectual property rights in the sharing of benefits arising from the use of biological resources and associated traditional knowledge”. IP Australia has led the developments on the intellectual property front while the Australian states and territories have led developments on the environment and heritage front.This article reports on the outcomes of the Garuwanga Project commencing with an outline of the study undertaken to compare nearly 70 nations with access and benefit sharing regimes. The article explains the development of key governance principles for the evaluation of governance structures and provides a summary of the Discussion Paper that formed the basis of the “on Country” community consultations. An overview of the outcomes of those consultations is provided with a summary of project conclusions.

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.

Tang, T, Liu, Y, Zhang, B, Su, B & Li, J 2019, 'Sketch distance-based clustering of chromosomes for large genome database compression', BMC Genomics, vol. 20, no. S10, pp. 978-978.
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AbstractBackgroundThe rapid development of Next-Generation Sequencing technologies enables sequencing genomes with low cost. The dramatically increasing amount of sequencing data raised crucial needs for efficient compression algorithms. Reference-based compression algorithms have exhibited outstanding performance on compressing single genomes. However, for the more challenging and more useful problem of compressing a large collection ofngenomes, straightforward application of these reference-based algorithms suffers a series of issues such as difficult reference selection and remarkable performance variation.ResultsWe propose an efficient clustering-based reference selection algorithm for reference-based compression within separate clusters of thengenomes. This method clusters the genomes into subsets of highly similar genomes using MinHash sketch distance, and uses the centroid sequence of each cluster as the reference genome for an outstanding reference-based compression of the remaining genomes in each cluster. A final reference is then selected from these reference genomes for the compression of the remaining reference genomes. Our method significantly improved the performance of the-state-of-art compression algorithms on large-scale human and rice genome databases containing thousands of genome sequences. The compression ratio gain can reach up to 20-30% in most cases for the datasets from NCBI, the 1000 Human Genomes Project and the 3000 Rice Genomes Project. The best improvement boosts the performance from 351.74 compression folds to 443.51 folds.ConclusionsThe compression ratio of reference-based compression on large scale genome datasets can be improved via reference selection by applying appropriate...

Thoms, JAI, Beck, D & Pimanda, JE 2019, 'Transcriptional networks in acute myeloid leukemia', Genes, Chromosomes and Cancer, vol. 58, no. 12, pp. 859-874.
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AbstractAcute myeloid leukemia (AML) is a complex disease characterized by a diverse range of recurrent molecular aberrations that occur in many different combinations. Components of transcriptional networks are a common target of these aberrations, leading to network‐wide changes and deployment of novel or developmentally inappropriate transcriptional programs. Genome‐wide techniques are beginning to reveal the full complexity of normal hematopoietic stem cell transcriptional networks and the extent to which they are deregulated in AML, and new understandings of the mechanisms by which AML cells maintain self‐renewal and block differentiation are starting to emerge. The hope is that increased understanding of the network architecture in AML will lead to identification of key oncogenic dependencies that are downstream of multiple network aberrations, and that this knowledge will be translated into new therapies that target these dependencies. Here, we review the current state of knowledge of network perturbation in AML with a focus on major mechanisms of transcription factor dysregulation, including mutation, translocation, and transcriptional dysregulation, and discuss how these perturbations propagate across transcriptional networks. We will also review emerging mechanisms of network disruption, and briefly discuss how increased knowledge of network disruption is already being used to develop new therapies.

Tofigh, F, Amiri, M, Shariati, N, Lipman, J & Abolhasan, M 2019, 'Low-Frequency Metamaterial Absorber Using Space-Filling Curve', Journal of Electronic Materials, vol. 48, no. 10, pp. 6451-6459.
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© 2019, The Minerals, Metals & Materials Society. The extensive use of metamaterials and metamaterial absorbers increases the demand for compact structures in various frequencies. Designing electrically small absorbers for lower frequencies, especially sub-gigahertz applications, is one of the open issues in this field. In this paper, a space filling curve is used to design an absorber operating on low frequencies. The unit cell design is based on a Sierpinski curve with the size of 25×25×1.6mm3 and air-gap of 10 mm. The structure shows 99.9% absorption at 900 MHz on the third step. The system also shows multiple resonances due to its structure. The proposed structure is fabricated and tested and shows a good agreement with simulation results.

Torres-Robles, A, Wiecek, E, Cutler, R, Drake, B, Benrimoj, SI, Fernandez-Llimos, F & Garcia-Cardenas, V 2019, 'Using Dispensing Data to Evaluate Adherence Implementation Rates in Community Pharmacy', Frontiers in Pharmacology, vol. 10, no. FEB, p. 130.
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Copyright © 2019 Torres-Robles, Wiecek, Cutler, Drake, Benrimoj, Fernandez-Llimos and Garcia-Cardenas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Background: Medication non-adherence remains a significant problem for the health care system with clinical, humanistic and economic impact. Dispensing data is a valuable and commonly utilized measure due accessibility in electronic health data. The purpose of this study was to analyze the changes on adherence implementation rates before and after a community pharmacist intervention integrated in usual real life practice, incorporating big data analysis techniques to evaluate Proportion of Days Covered (PDC) from pharmacy dispensing data. Methods: Retrospective observational study. A de-identified database of dispensing data from 20,335 patients (n = 11,257 on rosuvastatin, n = 6,797 on irbesartan, and n = 2,281 on desvenlafaxine) was analyzed. Included patients received a pharmacist-led medication adherence intervention and had dispensing records before and after the intervention. As a measure of adherence implementation, PDC was utilized. Analysis of the database was performed using SQL and Python. Results: Three months after the pharmacist intervention there was an increase on average PDC from 50.2% (SD: 30.1) to 66.9% (SD: 29.9) for rosuvastatin, from 50.8% (SD: 30.3) to 68% (SD: 29.3) for irbesartan and from 47.3% (SD: 28.4) to 66.3% (SD: 27.3) for desvenlafaxine. These rates declined over 12 months to 62.1% (SD: 32.0) for rosuvastatin, to 62.4% (SD: 32.5) for irbesartan and to 58.1% (SD: 31.1) for desvenla...

Valenzuela Fernandez, LM, Nicolas, C, Merigó, JM & Arroyo-Cañada, F-J 2019, 'Industrial marketing research: a bibliometric analysis (1990-2015)', Journal of Business & Industrial Marketing, vol. 34, no. 3, pp. 550-560.
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PurposeThe 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 to 2015.Design/methodology/approachBibliometric 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.FindingsEvidence 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/implicationsThe 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/valueThis 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 histor...

Valenzuela-Fernandez, L, Merigó, JM, Lichtenthal, JD & Nicolas, C 2019, 'A Bibliometric Analysis of the First 25 Years of the Journal of Business-to-Business Marketing', Journal of Business-to-Business Marketing, vol. 26, no. 1, pp. 75-94.
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© 2019, © 2019 Taylor & Francis Group, LLC. Purpose: As part of the recognition of the 25th anniversary of the Journal of Business-to-Business Marketing (JBBM), this paper presents an overview of the JBBM through a bibliometric analysis (BA) of its content from 1992 to 2016. The analysis focuses on the most cited articles and authors, h-index, publications per year, among others that typically are conducted for BAs. Design/Methodology/Approach: This paper begins with an introduction to the JBBM, showing its characteristics, its history as well as the editorial development and subsequent journal positioning. This information is followed by an analysis based on bibliometric methodology (BM) which considers the h-index, total citations (TCs), total papers (TPs), TC/TP ratio and other similar measures. To display this information, investigation was done to determine the most cited journals, articles, authors, universities and countries, ergo with the greatest incidence within JBBM. Analyzed are 329 articles, reviews and notes taken from the Scopus database for the periods between 1992 and 2016 for the JBBM. Findings: At the time of this work, the completion of the 25th anniversary of this journal, there is a rising trend in the number of JBBM publications per year. The researchers from the United States were most frequent contributors to the journal, while researchers from Germany, Australia, Norway and the United Kingdom were well represented. Multiple coauthors were more frequent while topics across the general model of business-to-business (B-to-B) marketing were typically found. Special issues on all three university-level education, technology in the classroom as well as Internet in effect B-to-B tactical marketing. Practical Implications: After observing the different perspectives of the journal’s production, we gain another objective view on the evolution of the JBBM in prior 25 years. This approach is useful for the readers of this journal in order to obtai...

Valenzuela-Fernández, LM, Merigó, JM, Nicolas, C & Kleinaltenkamp, M 2019, 'Leaders in industrial marketing research: 25 years of analysis', Journal of Business & Industrial Marketing, vol. 35, no. 3, pp. 586-601.
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PurposeThis paper aims to present a bibliometric overview of the leading trends of the journals in industrial marketing during for 25 years. Thus, the purpose is to carry out an analysis about contributions that industrial marketing or business to business (B2B) marketing discipline has done for scientific investigation, presenting a ranking of the 30 most influential journals and their global evolution by five-year periods from 1992 to 2016. Moreover, this study presents the amount of citations, who quotes who from the top 15 ranking and self-citations.Design/methodology/approachThis study analyzes 3,587 documents classified as articles, letters, notes and reviews from Clarivate Analytics’ Web of Science for the period 1992- 2016, by bibliometric indicators such as H-index, total citations (TC), total papers (TP), TC/TP. Furthermore, this paper develops a graphical visualization of the bibliographic material by using the visualization of similarities viewer software for constructing and visualizing bibliometric networks in leading journals, publications and keywords with bibliographic coupling and co-citation analysis.FindingsIndustrial Marketing Management is the leader of the ranking, representing 34 per cent of the total manuscripts considered in this study. The most influential journals were classified by periods of five years and the top five for the period 2012-2016 were in ascending order: Industrial Marketing Management, Journal of Business & Industrial Marketing, Journal of Business-to-Business Marketing, Journal of Business Research

Vallaster, C, Kraus, S, Merigó Lindahl, JM & Nielsen, A 2019, 'Ethics and entrepreneurship: A bibliometric study and literature review', Journal of Business Research, vol. 99, pp. 226-237.
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© 2019 The entrepreneurship literature pays increasing attention to the ethical aspects of the field. However, only a fragmented understanding is known about how the context influences the ethical judgment of entrepreneurs. We argue that individual socio-cultural background, organizational and societal context shape entrepreneurial ethical judgment. In our article, we contribute to contemporary literature by carving out the intersections between Ethics and Entrepreneurship. We do this by employing a two-step research approach: 1) We use bibliometric techniques to analyze 719 contributions in Business and Economics research and present a comprehensive contextual picture of ethics in entrepreneurship research by a analyzing the 30 most relevant foundation articles. 2) A subsequent content analysis of the 50 most relevant academic contributions was carried with an enlarged database out to augment these findings, detailing ethics and entrepreneurship research on an individual, organizational and societal level of analyses. By comparing the two analyses, this paper concludes by outlining possible avenues for future research.

Verma, R & Merigó, JM 2019, 'On generalized similarity measures for Pythagorean fuzzy sets and their applications to multiple attribute decision‐making', International Journal of Intelligent Systems, vol. 34, no. 10, pp. 2556-2583.
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© 2019 Wiley Periodicals, Inc. In this paper, we develop a new and flexible method for Pythagorean fuzzy decision-making using some trigonometric similarity measures. We first introduce two new generalized similarity measures between Pythagorean fuzzy sets based on cosine and cotangent functions and prove their validity. These similarity measures include some well-known Pythagorean fuzzy similarity measures as their particular and limiting cases. The measures are demonstrated to satisfy some very elegant properties which prepare the ground for applications in different areas. Further, the work defines a generalized hybrid trigonometric Pythagorean fuzzy similarity measure and discuss its properties with particular cases. Then, based on the generalized hybrid trigonometric Pythagorean fuzzy similarity measure, a method for dealing with multiple attribute decision-making problems under Pythagorean fuzzy environment is developed. Finally, a numerical example is given to demonstrate the flexibility and effectiveness of the developed approach in solving real-life problems.

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.

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 Mohd, WR, Abdullah, L, Yusoff, B, Taib, CMIC & Merigo, JM 2019, 'An Integrated MCDM Model based on Pythagorean Fuzzy Sets for Green Supplier Development Program', Malaysian Journal of Mathematical Sciences, vol. 13, pp. 23-37.
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Green supplier development is becoming vital for many industrial firms for effective green supply chain management. Most of the suppliers are willing to invest in many green supplier programs that developed in their firms’ performance. The evaluation and selection of an adequate green supplier development program is too complex and challenging as it has multiple criteria and alternatives to be chosen. These criteria involve both qualitative and quantitative information. To select the best alternative of the green supplier development program, it is necessary to settle these problems using multi-criteria decision-making (MCDM) method. This paper proposes the integration of Pythagorean fuzzy AHP and Pythagorean fuzzy VIKOR approach to resolve the green supplier development program selection. The main goal of this study is to present a useful and reliable method to identify the most important criteria and alternatives using Pythagorean fuzzy AHP and Pythagorean fuzzy VIKOR. The first innovation is finding the weight for each criteria using Pythagorean fuzzy AHP. In order to do so, the crisp value evaluated by the decision makers (DMs) are presented in the pair-wise comparison matrix and converted to Pythagorean fuzzy number. The VIKOR is used to rank the alternatives of the green supplier development programs and suggest which program is the best program. Then, the obtained results are compared with the existing VIKOR method in the same case study. The results found the supplier training is the best alternative to select in the green supplier development programs. It is noted that the integration of Pythagorean fuzzy AHP and Pythagorean fuzzy VIKOR is a holistic approach to the MCDM problem.

Wang, Y, Feng, C, Chen, L, Yin, H, Guo, C & Chu, Y 2019, 'User identity linkage across social networks via linked heterogeneous network embedding', World Wide Web, vol. 22, no. 6, pp. 2611-2632.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature User identity linkage has important implications in many cross-network applications, such as user profile modeling, recommendation and link prediction across social networks. To discover accurate cross-network user correspondences, it is a critical prerequisite to find effective user representations. While structural and content information describe users from different perspectives, there is a correlation between the two aspects of information. For example, a user who follows a celebrity tends to post content about the celebrity as well. Therefore, the projections of structural and content information of a user should be as close to each other as possible, which inspires us to fuse the two aspects of information in a unified space. However, owing to the information heterogeneity, most existing methods extract features from content and structural information respectively, instead of describing them in a unified way. In this paper, we propose a Linked Heterogeneous Network Embedding model (LHNE) to learn the comprehensive representations of users by collectively leveraging structural and content information in a unified framework. We first model the topics of user interests from content information to filter out noise. Next, cross-network structural and content information are embedded into a unified space by jointly capturing the friend-based and interest-based user co-occurrence in intra-network and inter-network, respectively. Meanwhile, LHNE learns user transfer and topic transfer for enhancing information exchange across networks. Empirical results show LHNE outperforms the state-of-the-art methods on both real social network and synthetic datasets and can work well even with little or no structural information.

Wang, Y, 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.

Wu, P, Li, H, Merigo, JM & Zhou, L 2019, 'Integer Programming Modeling on Group Decision Making With Incomplete Hesitant Fuzzy Linguistic Preference Relations', IEEE Access, vol. 7, pp. 136867-136881.
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Wu, W, Li, B, Chen, L, Zhang, C & Yu, PS 2019, 'Improved Consistent Weighted Sampling Revisited', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 12, pp. 2332-2345.
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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.

Wu, Z, Pan, S, Chen, F, Long, G, Zhang, C & Yu, PS 2019, 'A Comprehensive Survey on Graph Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 4-24.
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Deep learning has revolutionized many machine learning tasks in recent years,ranging from image classification and video processing to speech recognitionand natural language understanding. The data in these tasks are typicallyrepresented in the Euclidean space. However, there is an increasing number ofapplications where data are generated from non-Euclidean domains and arerepresented as graphs with complex relationships and interdependency betweenobjects. The complexity of graph data has imposed significant challenges onexisting machine learning algorithms. Recently, many studies on extending deeplearning approaches for graph data have emerged. In this survey, we provide acomprehensive overview of graph neural networks (GNNs) in data mining andmachine learning fields. We propose a new taxonomy to divide thestate-of-the-art graph neural networks into four categories, namely recurrentgraph neural networks, convolutional graph neural networks, graph autoencoders,and spatial-temporal graph neural networks. We further discuss the applicationsof graph neural networks across various domains and summarize the open sourcecodes, benchmark data sets, and model evaluation of graph neural networks.Finally, we propose potential research directions in this rapidly growingfield.

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, vol. 28, no. 4, pp. 1909-1922.
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© 1992-2012 IEEE. There is an emerging trend to leverage noisy image datasets in many visual recognition tasks. However, the label noise among datasets severely degenerates the performance of deep learning approaches. Recently, one mainstream is to introduce the latent label to handle label noise, which has shown promising improvement in the network designs. Nevertheless, the mismatch between latent labels and noisy labels still affects the predictions in such methods. To address this issue, we propose a probabilistic model, which explicitly introduces an extra variable to represent the trustworthiness of noisy labels, termed as the quality variable. Our key idea is to identify the mismatch between the latent and noisy labels by embedding the quality variables into different subspaces, which effectively minimizes the influence of label noise. At the same time, reliable labels are still able to be applied for training. To instantiate the model, we further propose a contrastive-additive noise network (CAN), which consists of two important layers: 1) the contrastive layer that estimates the quality variable in the embedding space to reduce the influence of noisy labels and 2) the additive layer that aggregates the prior prediction and noisy labels as the posterior to train the classifier. Moreover, to tackle the challenges in optimization, we deduce an SGD algorithm with the reparameterization tricks, which makes our method scalable to big data. We validate the proposed method on a range of noisy image datasets. Comprehensive results have demonstrated that CAN outperforms the state-of-the-art deep learning approaches.

Yao, 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, vol. 22, no. 5, pp. 2209-2224.
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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, L, Zeng, S, Merigó, JM & Zhang, C 2019, 'A new distance measure based on the weighted induced method and its application to Pythagorean fuzzy multiple attribute group decision making', International Journal of Intelligent Systems, vol. 34, no. 7, pp. 1440-1454.
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© 2019 Wiley Periodicals, Inc. This paper investigates a novel induced ordered weighted averaging (IOWA) distance operator and its application in Pythagorean fuzzy (PF) multiattribute group decision making (MAGDM). First, a new induced aggregated distance operator named the weighted IOWA distance (WIOWAD) operator is developed, which differs from the existing methods in that it considers the dual roles of the order-inducing variables at the same time. In other words, in addition to inducing the order of the arguments, the order-inducing variables of the WIOWAD operator also plays an important role in moderating the associated weight vector. Some useful properties and different families of the WIOWAD are also discussed. Then, an extension of the WIOWAD within the PF situation is presented, thus obtaining the PFWIOWAD operator. Furthermore, a MAGDM method based on the PFWIOWAD is introduced. Finally, the practicality and effectiveness of proposed approach are illustrated in a research and development project selection problem.

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, vol. 49, no. 5, pp. 1826-1834.
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© 2018 IEEE. Multifeature learning has been a fundamental research problem in multimedia analysis. Most existing multifeature learning methods exploit graph, which must be computed beforehand, as input to uncover data distribution. These methods have two major problems confronted. First, graph construction requires calculating similarity based on nearby data pairs by a fixed function, e.g., the RBF kernel, but the intrinsic correlation among different data pairs varies constantly. Therefore, feature learning based on such predefined graphs may degrade, especially when there is dramatic correlation variation between nearby data pairs. Second, in most existing algorithms, each single-feature graph is computed independently and then combine them for learning, which ignores the correlation between multiple features. In this paper, a new unsupervised multifeature learning method is proposed to make the best utilization of the correlation among different features by jointly optimizing data correlation from multiple features in an adaptive way. As opposed to computing the affinity weight of data pairs by a fixed function, the weight of affinity graph is learned by a well-designed optimization problem. Additionally, the affinity graph of data pairs from different features is optimized in a global level to better leverage the correlation among different channels. In this way, the adaptive approach correlates the features of all features for a better learning process. Experimental results on real-world datasets demonstrate that our approach outperforms the state-of-the-art algorithms on leveraging multiple features for multimedia analysis.

Zhang, 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, vol. 41, no. 9, pp. 2193-2207.
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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, Z, Wu, Q, Wang, Y & Chen, F 2019, 'High-Quality Image Captioning With Fine-Grained and Semantic-Guided Visual Attention', IEEE Transactions on Multimedia, vol. 21, no. 7, pp. 1681-1693.
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© 1999-2012 IEEE. The soft-attention mechanism is regarded as one of the representative methods for image captioning. Based on the end-to-end convolutional neural network (CNN)-long short term memory (LSTM) framework, the soft-attention mechanism attempts to link the semantic representation in text (i.e., captioning) with relevant visual information in the image for the first time. Motivated by this approach, several state-of-the-art attention methods are proposed. However, due to the constraints of CNN architecture, the given image is only segmented to the fixed-resolution grid at a coarse level. The visual feature extracted from each grid indiscriminately fuses all inside objects and/or their portions. There is no semantic link between grid cells. In addition, the large area 'stuff' (e.g., the sky or a beach) cannot be represented using the current methods. To address these problems, this paper proposes a new model based on the fully convolutional network (FCN)-LSTM framework, which can generate an attention map at a fine-grained grid-wise resolution. Moreover, the visual feature of each grid cell is contributed only by the principal object. By adopting the grid-wise labels (i.e., semantic segmentation), the visual representations of different grid cells are correlated to each other. With the ability to attend to large area 'stuff,' our method can further summarize an additional semantic context from semantic labels. This method can provide comprehensive context information to the language LSTM decoder. In this way, a mechanism of fine-grained and semantic-guided visual attention is created, which can accurately link the relevant visual information with each semantic meaning inside the text. Demonstrated by three experiments including both qualitative and quantitative analyses, our model can generate captions of high quality, specifically high levels of accuracy, completeness, and diversity. Moreover, our model significantly outperforms all other meth...

Zhao, Z, Peng, H, Zhang, X, Zheng, Y, Chen, F, Fang, L & Li, J 2019, 'Identification of lung cancer gene markers through kernel maximum mean discrepancy and information entropy', BMC Medical Genomics, vol. 12, no. S8.
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AbstractBackgroundThe early diagnosis of lung cancer has been a critical problem in clinical practice for a long time and identifying differentially expressed gene as disease marker is a promising solution. However, the most existing gene differential expression analysis (DEA) methods have two main drawbacks: First, these methods are based on fixed statistical hypotheses and not always effective; Second, these methods can not identify a certain expression level boundary when there is no obvious expression level gap between control and experiment groups.MethodsThis paper proposed a novel approach to identify marker genes and gene expression level boundary for lung cancer. By calculating a kernel maximum mean discrepancy, our method can evaluate the expression differences between normal, normal adjacent to tumor (NAT) and tumor samples. For the potential marker genes, the expression level boundaries among different groups are defined with the information entropy method.ResultsCompared with two conventional methods t-test and fold change, the top average ranked genes selected by our method can achieve better performance under all metrics in the 10-fold cross-validation. Then GO and KEGG enrichment analysis are conducted to explore the biological function of the top 100 ranked genes. At last, we choose the top 10 average ranked genes as lung cancer markers and their expression boundaries are calculated and reported.ConclusionThe proposed approach is effective to identify gene markers for lung cancer diagnosis. It is not only more accurate than conventional DEA methods but also provides a reliable method to identify the gene expression level boundaries.

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, no. S13, pp. 554-554.
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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, ac...

Zheng, Y, Peng, H, Zhang, X, Zhao, Z, Gao, X & Li, J 2019, 'DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions', BMC Bioinformatics, vol. 20, no. S19.
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AbstractBackgroundDrug-drug interactions (DDIs) are a major concern in patients’ medication. It’s unfeasible to identify all potential DDIs using experimental methods which are time-consuming and expensive. Computational methods provide an effective strategy, however, facing challenges due to the lack of experimentally verified negative samples.ResultsTo address this problem, we propose a novel positive-unlabeled learning method named DDI-PULearn for large-scale drug-drug-interaction predictions. DDI-PULearn first generates seeds of reliable negatives via OCSVM (one-class support vector machine) under a high-recall constraint and via the cosine-similarity based KNN (k-nearest neighbors) as well. Then trained with all the labeled positives (i.e., the validated DDIs) and the generated seed negatives, DDI-PULearn employs an iterative SVM to identify a set of entire reliable negatives from the unlabeled samples (i.e., the unobserved DDIs). Following that, DDI-PULearn represents all the labeled positives and the identified negatives as vectors of abundant drug properties by a similarity-based method. Finally, DDI-PULearn transforms these vectors into a lower-dimensional space via PCA (principal component analysis) and utilizes the compressed vectors as input for binary classifications. The performance of DDI-PULearn is evaluated on simulative prediction for 149,878 possible interactions between 548 drugs, comparing with two baseline methods and five state-of-the-art methods. Related experiment results show that the proposed method for the representation of DDIs characterizes them accurately. DDI-PULearn achieves superior performance owing to the identified reliable negatives, outperforming all other methods significantly. In addition, the predicted novel DDIs suggest that DDI-PULearn is capable to identify novel DDIs.

Zheng, Y, Peng, H, Zhang, X, Zhao, Z, Gao, X & Li, J 2019, 'Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces', BMC Bioinformatics, vol. 20, no. S23, p. 605.
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AbstractBackgroundDetection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interactions (i.e., positive samples) for the predictions. Their performance is severely impeded by the lack of reliable negative samples.ResultsWe propose a method to construct highly-reliable negative samples for drug target prediction by a pairwise drug-target similarity measurement and OCSVM with a high-recall constraint. On one hand, we measure the pairwise similarity between every two drug-target interactions by combining the chemical similarity between their drugs and the Gene Ontology-based similarity between their targets. Then we calculate the accumulative similarity with all known drug-target interactions for each unobserved drug-target interaction. On the other hand, we obtain the signed distance from OCSVM learned from the known interactions with high recall (≥0.95) for each unobserved drug-target interaction. After normalizing all accumulative similarities and signed distances to the range [0,1], we compute the score for each unobserved drug-target interaction via averaging its accumulative similarity and signed distance. Unobserved interactions with lower scores are preferentially served as reliable negative samples for the classification algorithms. The performance of the proposed method is evaluated on the interaction data between 1094 drugs and 1556 target proteins. Extensive comparison experiments using four classical classifiers and one domain predictive method demonstrate the superior performance of the proposed method. A better decision boundary has been learned from the constructed reliable negative samples.Conc...

Zhu, C, Mesiar, R, Yager, RR, Merigo, J, Qin, J, Feng, X & Jin, L 2019, 'Two-layer preference models with methodologies using induced aggregation in management administration and decision making', Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 1213-1221.
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In this work, we propose some two-layer preference models that can be appropriately applied in management problems such as the group decision making about predicting the future market share of certain product. By introducing the convex IOWA operator paradigm and some related properties and definitions, we list some detailed preference and inducing preference models to demonstrate and exemplify the proposed conceptual frame of two-layer preference model. The convex IOWA operator paradigm facilitates the modeling process and, from mathematical view, makes it stricter. When relevant inducing information and aggregation selection change, the proposed models can be easily adapted to accommodate more different applications in decision making and evaluation.

Abdo, P, Huynh, BP, Braytee, A & Taghipour, R 1970, 'Effect of Phase Change Material on Temperature in a Room Fitted With a Windcatcher', Volume 7: Fluids Engineering, ASME 2019 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers.
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Abstract Global warming and climate change have been considered as major challenges over the past few decades. Sustainable and renewable energy sources are nowadays needed to overcome the undesirable consequences of rapid development in the world. Phase change materials (PCM) are substances with high latent heat storage capacity which absorb or release the heat from or to the surrounding environment. They change from solid to liquid and vice versa. PCMs could be used as a passive cooling method which enhances energy efficiency in buildings. Integrating PCM with natural ventilation is investigated in this study by exploring the effect of phase change material on the temperature in a room fitted with a windcatcher. A chamber made of acrylic sheets fitted with a windcatcher is used to monitor the temperature variations. The dimensions of the chamber are 1250 × 1000 × 750 mm3. Phase change material is integrated respectively at the walls of the room, its floor and ceiling and within the windcatchers inlet channel. Temperature is measured at different locations inside the chamber. Wind is blown through the room using a fan with heating elements.

Abdollahi, M, Abolhasan, M, Shariati, N, Lipman, J, Jamalipour, A & Ni, W 1970, 'A Routing Protocol for SDN-based Multi-hop D2D Communications', 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), 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.

Abdollahi, M, Gao, X, Mei, Y, Ghosh, S & Li, J 1970, 'An Ontology-based Two-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation', 2019 IEEE Congress on Evolutionary Computation (CEC), 2019 IEEE Congress on Evolutionary Computation (CEC), IEEE, New Zealand, pp. 119-126.
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© 2019 IEEE. Document classification (DC) is the task of assigning pre-defined labels to unseen documents by utilizing a model trained on the available labeled documents. DC has attracted much attention in medical fields recently because many issues can be formulated as a classification problem. It can assist doctors in decision making and correct decisions can reduce the medical expenses. Medical documents have special attributes that distinguish them from other texts and make them difficult to analyze. For example, many acronyms and abbreviations, and short expressions make it more challenging to extract information. The classification accuracy of the current medical DC methods is not satisfactory. The goal of this work is to enhance the input feature sets of the DC method to improve the accuracy. To approach this goal, a novel two-stage approach is proposed. In the first stage, a domain-specific dictionary, namely the Unified Medical Language System (UMLS), is employed to extract the key features belonging to the most relevant concepts such as diseases or symptoms. In the second stage, PSO is applied to select more related features from the extracted features in the first stage. The performance of the proposed approach is evaluated on the 2010 Informatics for Integrating Biology and the Bedside (i2b2) data set which is a widely used medical text dataset. The experimental results show substantial improvement by the proposed method on the accuracy of classification.

Abdollahi, M, Gao, X, Mei, Y, Ghosh, S & Li, J 1970, 'Stratifying Risk of Coronary Artery Disease Using Discriminative Knowledge-Guided Medical Concept Pairings from Clinical Notes', PRICAI 2019: Trends in Artificial Intelligence, Pacific Rim International Conference on Artificial Intelligence, Springer International Publishing, Cuvu, Yanuca Island, Fiji, pp. 457-473.
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© 2019, Springer Nature Switzerland AG. Document classification (DC) is one of the broadly investigated natural language processing tasks. Medical document classification can support doctors in making decision and improve medical services. Since the data in document classification often appear in raw form such as medical discharge notes, extracting meaningful information to use as features is a challenging task. There are many specialized words and expressions in medical documents which make them more challenging to analyze. The classification accuracy of available methods in medical field is not good enough. This work aims to improve the quality of the input feature sets to increase the accuracy. A new three-stage approach is proposed. In the first stage, the Unified Medical Language System (UMLS) which is a medical-specific dictionary is used to extract the meaningful phrases by considering disease or symptom concepts. In the second stage, all the possible pairs of the extracted concepts are created as new features. In the third stage, Particle Swarm Optimisation (PSO) is employed to select features from the extracted and constructed features in the previous stages. The experimental results show that the proposed three-stage method achieved substantial improvement over the existing medical DC approaches.

Ali, AR, Budka, M & Gabrys, B 1970, 'A Meta-Reinforcement Learning Approach to Optimize Parameters and Hyper-parameters Simultaneously', PRICAI 2019: Trends in Artificial Intelligence, Pacific Rim International Conference on Artificial Intelligence, Springer International Publishing, Yanuca Island, Fiji,, pp. 93-106.
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© 2019, Springer Nature Switzerland AG. In the last few years, we have witnessed a resurgence of interest in neural networks. The state-of-the-art deep neural network architectures are however challenging to design from scratch and requiring computationally costly empirical evaluations. Hence, there has been a lot of research effort dedicated to effective utilisation and adaptation of previously proposed architectures either by using transfer learning or by modifying the original architecture. The ultimate goal of designing a network architecture is to achieve the best possible accuracy for a given task or group of related tasks. Although there have been some efforts to automate network architecture design process, most of the existing solutions are still very computationally intensive. This work presents a framework to automatically find a good set of hyper-parameters resulting in reasonably good accuracy, which at the same time is less computationally expensive than the existing approaches. The idea presented here is to frame the hyper-parameter selection and tuning within the reinforcement learning regime. Thus, the parameters of a meta-learner, RNN, and hyper-parameters of the target network are tuned simultaneously. Our meta-learner is being updated using policy network and simultaneously generates a tuple of hyper-parameters which are utilized by another network. The network is trained on a given task for a number of steps and produces validation accuracy whose delta is used as reward. The reward along with the state of the network, comprising statistics of network’s final layer outcome and training loss, are fed back to the meta-learner which in turn generates a tuned tuple of hyper-parameters for the next time-step. Therefore, the effectiveness of a recommended tuple can be tested very quickly rather than waiting for the network to converge. This approach produces accuracy close to the state-of-the-art approach and is found to be comparatively l...

Ali, AR, Budka, M & Gabrys, B 1970, 'Towards Meta-learning of Deep Architectures for Efficient Domain Adaptation', PRICAI 2019: Trends in Artificial Intelligence, Pacific Rim International Conference on Artificial Intelligence, Springer International Publishing, Yanuca Island, Fiji., pp. 66-79.
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© 2019, Springer Nature Switzerland AG. This paper proposes an efficient domain adaption approach using deep learning along with transfer and meta-level learning. The objective is to identify how many blocks (i.e. groups of consecutive layers) of a pre-trained image classification network need to be fine-tuned based on the characteristics of the new task. In order to investigate it, a number of experiments have been conducted using different pre-trained networks and image datasets. The networks were fine-tuned, starting from the blocks containing the output layers and progressively moving towards the input layer, on various tasks with characteristics different from the original task. The amount of fine-tuning of a pre-trained network (i.e. the number of top layers requiring adaptation) is usually dependent on the complexity, size, and domain similarity of the original and new tasks. Considering these characteristics, a question arises of how many blocks of the network need to be fine-tuned to get maximum possible accuracy? Which of a number of available pre-trained networks require fine-tuning of the minimum number of blocks to achieve this accuracy? The experiments, that involve three network architectures each divided into 10 blocks on average and five datasets, empirically confirm the intuition that there exists a relationship between the similarity of the original and new tasks and the depth of network needed to fine-tune in order to achieve accuracy comparable with that of a model trained from scratch. Further analysis shows that the fine-tuning of the final top blocks of the network, which represent the high-level features, is sufficient in most of the cases. Moreover, we have empirically verified that less similar tasks require fine-tuning of deeper portions of the network, which however is still better than training a network from scratch.

Ashtari, S, Tofigh, F, Abolhasan, M, Lipman, J & Ni, W 1970, 'Efficient Cellular Base Stations Sleep Mode Control Using Image Matching', 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), IEEE, Kuala Lumpur, MALAYSIA.
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© 2019 IEEE. Green cellular network helps to decrease environmental pollution. In contrast, massive connectivity and demand for higher data rate promise the presence of new generation of cellular system (5G) and small cell networks. Hence, expectation on increasing the number of base stations (BSs), which leads to increase in energy usage. One way to improve energy consumption is by shutting down the redundant BSs while sustaining the Quality-of-Service (QoS) for each user. In this paper, we propose a dynamic structural algorithm based on transportation problem, to switch on/off the BSs in cellular networks without compromising its coverage, and maintain the networks load by neighboring cells. We use weighted graphs to translate our problem as a transportation problem and then use linear programming to solve it. The cost of transport, turning a BS into sleep mode, is illustrated as a function of energy usage,coverage area and load on the BSs. Running the propose method consecutively provides the maximum number of BSs whom are at sleep mode. The methodology explained in this paper reduces energy consumption to almost 40%, whereas maintaining all the existing loads in the network.

Awan, Z, Kahlke, T, Ralph, P & Kennedy, P 1970, 'Chemical Named Entity Recognition with Deep Contextualized Neural Embeddings', Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 11th International Conference on Knowledge Discovery and Information Retrieval, SCITEPRESS - Science and Technology Publications, Austria, pp. 135-144.
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Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved Chemical named entity recognition (ChemNER) is a preliminary step in chemical information extraction pipelines. ChemNER has been approached using rule-based, dictionary-based, and feature-engineered based machine learning, and more recently also deep learning based methods. Traditional word-embeddings, like word2vec and Glove, are inherently problematic because they ignore the context in which an entity appears. Contextualized embeddings called embedded language models (ELMo) have been recently introduced to represent contextual information of a word in its embedding space. In this work, we quantify the impact of contextualized embeddings for ChemNER by using Bi-LSTM-CRF (bidirectional long short term memory networks - conditional random fields) networks. We benchmarked our approach using four well-known corpora for chemical named entity recognition. Our results show that incorporation of ELMo results in statistically significant improvements in F1 score in all of the tested datasets.

Blanco-Mesa, F, Leon-Castro, E & Merigo, JM 1970, 'The IOWAWA operator with bonferroni means', 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE.
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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 a new operator that takes into the same formulation the Iowa operator and the Bonferroni means. This new operator is called Bonferroni Induced Ordered Weighted Averaging-Weighted Average (BON-IowaWA) operator. The main advantage of this approach is the possibility of reordering the results according to complex ranking processes based on order inducing variables.

Brunker, A, Catchpoole, D, Kennedy, P, Simoff, S & Nguyen, QV 1970, 'Two-Dimensional Immersive Cohort Analysis Supporting Personalised Medical Treatment', 2019 23rd International Conference in Information Visualization – Part II, 2019 23rd International Conference in Information Visualization – Part II, IEEE, Adelaide, Australia, pp. 34-41.
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© 2019 IEEE. Genomic data are large and complex which are challenges to visualize them effectively on ordinary screens due to the limited display spaces. Large and high resolution displays could enable the capability to show more information at once for better comprehension from the visualization. This paper presents a two-dimensional interactive visualization system and supporting algorithm for multi-dimensional large genomic data analysis that can be used in both ordinary displays or immersive environments. We provide both view of the entire patient cohort in the similarity space and the genomic details currently for comparison among the patients. Through the similarity space and on the selected genes of interest, we are able to perceive the genetic similarity throughout the cohort. From the linked heat map visualisation of the selected genes, we apply hierarchical clustering on both the horizontal and vertical axes to group together the genetically similar patients. We demonstrate the effectiveness of the visualization with two case studies on pediatric cancer patients suffering from Acute Lymphoblastic Leukemia (ALL) and from Rhabdomyosarcoma (RMS)

Cancino, CA, Amirbagheri, K, Merigó, JM & Dessouky, Y 1970, 'Evolution of the academic research on supply chain and global warming', Proceedings of International Conference on Computers and Industrial Engineering, CIE.
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The aim of this work is to study supply chain publications with a focus on global warming effects using a bibliometric approach. The study uses the Web of Science Core Collection database to analyze the bibliometric data from 1994 to 2018. The main objective is to identify the leading trends in this area by analyzing the most significant journals, papers, institutions and supra-regions. This work also develops a graphical mapping of the bibliographic material by using visualization of similarities (VOS) viewer software. With this software, the study analyses co-citations of journals and co-occurrence of author keywords. The results show the growth of the development of supply chain models that consider global warming factors between 2014-2018, which is consistent with the general public awareness of climate change. The researchers from Imperial College London and Hong Kong Polytechnic University have the greatest number of publications in this area. In terms of supra-regions, more than 25% of the publications come from Asian universities, followed by American and British universities with 20%. Given the growing global concern about the effects of supply chains on global warming, it is expected that the number of publications from different parts of the world and the greater number of citations will strongly increase.

Cetindamar, D, Kocaoglu, D, Lammers, T & Merigo, JM 1970, 'A Bibliometric Analysis of Technology Management Research at PICMET for 2009–2018', 2019 Portland International Conference on Management of Engineering and Technology (PICMET), 2019 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE, Portland, Oregon, pp. 1-5.
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© 2019 PICMET. The Portland International Centre for Management of Engineering and Technology (PICMET) was established in 1989. It has since become one of the leading organizations in the field of management of engineering and technology in the world. PICMET provides a strong platform for academicians, industry professionals and government representatives to exchange new knowledge derived from both research and implementation of technology management. To celebrate its 30-year journey, and to show the trends in technology management research and implementation over the past ten years (2009-2018), this paper presents a bibliometric analysis of the more than 3000 papers accepted for inclusion in PICMET conferences. The study highlights the topics, authors, journals and countries where significant research on technology management is conducted.

Chen, Y, An, P, Huang, X, Meng, C & Wu, Q 1970, 'Modified Baseline for Light Field Stitching', 2019 IEEE Visual Communications and Image Processing (VCIP), 2019 IEEE Visual Communications and Image Processing (VCIP), IEEE, Australia, pp. 1-4.
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© 2019 IEEE. In traditional 2D image stitching, the baseline method usually means global homography via Direct Linear Transformation (DLT) on inliers. In this paper, a modified baseline method for light field (LF) stitching is proposed to stitch two LFs. The depth map and the center sub-Aperture image (SAI) are used to filter the feature points of the entire LF. The global 4D homography is then calculated by DLT to align all SAIs corresponding to the same angular domain coordinates of two LFs. Finally, the improved Markov Random Field (MRF) energy considering the global LF is used to find the seam of 2D SAIs instead of computational 4D graph cut. Experimental results show that the proposed method can effectively stitch the 4D LFs, and preserve the consistency of the angular and spatial domains of the stitched LF compared with implementing 2D image stitching to the corresponding SAIs. Moreover, the method proposed in this paper can easily extend all advanced 2D image stitching methods to 4D LF, so that the acquired LF can have larger field of view and wider applications.

Dasgupta, A, Gill, A & Hussain, F 1970, 'A Conceptual Framework for Data Governance in IoT-enabled Digital IS Ecosystems', Proceedings of the 8th International Conference on Data Science, Technology and Applications, 8th International Conference on Data Science, Technology and Applications, SCITEPRESS - Science and Technology Publications, Prague, Czech Republic, pp. 209-216.
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Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved There is a growing interest in the use of Internet of Things (IoT) in information systems (IS). Data or information governance is a critical component of IoT enabled digital IS ecosystem. There is insufficient guidance available on how to effectively establish data governance for IoT enabled digital IS ecosystem. The introduction of new regulations related to privacy such as General Data Protection Regulation (GDPR) as well as existing regulations such as Health Insurance Portability and Accountability Act (HIPPA) has added complexity to this issue of data governance. This could possibly hinder the effective IoT adoption in healthcare digital IS ecosystem. This paper enhances the 4I framework, which is iteratively developed and updated using the design science research (DSR) method to address this pressing need for organizations to have a robust governance model to provide the coverage across the entire data lifecycle in IoT-enabled digital IS ecosystem. The 4I framework has four major phases: Identify, Insulate, Inspect and Improve. The application of this framework is demonstrated with the help of a Healthcare case study. It is anticipated that the proposed framework can help the practitioners to identify, insulate, inspect and improve governance of data in IoT enabled digital IS ecosystem.

Dasgupta, A, Gill, AQ & Hussain, FK 1970, 'A Review of General Data Protection Regulation for Supply Chain Ecosystem.', IMIS, International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Springer, Sydney, Australia, pp. 456-465.
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© 2020, Springer Nature Switzerland AG. The data-intensive digital supply chain management (SCM) ecosystems seem to be impacted by the recent changes in the regulations and advancement in technologies such as Artificial Intelligence, Big Data, Analytics, Networking, IoT including proliferation of less expensive hardware devices. There is limited guidance available on how to govern the logistics sector, particularly from a regulatory compliance perspective. Through this paper, we investigate the impact of General Data Protection Regulation (GDPR) on digitized SCM. The key questions are: What are the GPDR specific legal obligations? What is the best approach to manage data access, quality, privacy, security and ownership effectively in SCM? This research paper aims to assist researchers and practitioners to understand the impact of GDPR on SCM, provide the 4I (Identify, Insulate, Inspect, Improve) Framework and its applicability to streamline the GDPR compliance activities.

Du, A, Huang, X, Zhang, J, Yao, L & Wu, Q 1970, 'Kpsnet: Keypoint Detection and Feature Extraction for Point Cloud Registration', 2019 IEEE International Conference on Image Processing (ICIP), 2019 IEEE International Conference on Image Processing (ICIP), IEEE, Taipei, Taiwan, pp. 2576-2580.
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© 2019 IEEE. This paper presents the KPSNet, a KeyPoint Siamese Network to simultaneously learn task-desirable keypoint detector and feature extractor. The keypoint detector is optimized to predict a score vector, which signifies the probability of each candidate being a keypoint. The feature extractor is optimized to learn robust features of keypoints by exploiting the correspondence between the keypoints generated from two inputs, respectively. For training, the KPSNet does not require to manually annotate keypoints and local patches pairwise. Instead, we design an alignment module to establish the correspondence between the two inputs and generate positive and negative samples on-the-fly. Therefore, our method can be easily extended to new scenes. We test the proposed method on the open-source benchmark and experiments show the validity of our method.

Fan, X, Li, B, Sisson, SA, Li, C & Chen, L 1970, 'Scalable deep generative relational models with high-order node dependence', Advances in Neural Information Processing Systems, Vancouver, Canada.
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We propose a probabilistic framework for modelling and exploring the latent structure of relational data. Given feature information for the nodes in a network, the scalable deep generative relational model (SDREM) builds a deep network architecture that can approximate potential nonlinear mappings between nodes' feature information and the nodes' latent representations. Our contribution is two-fold: (1) We incorporate high-order neighbourhood structure information to generate the latent representations at each node, which vary smoothly over the network. (2) Due to the Dirichlet random variable structure of the latent representations, we introduce a novel data augmentation trick which permits efficient Gibbs sampling. The SDREM can be used for large sparse networks as its computational cost scales with the number of positive links. We demonstrate its competitive performance through improved link prediction performance on a range of real-world datasets.

Gamal, M, Abolhasan, M, jafarizadeh, S, Lipman, J & Ni, W 1970, 'Mapping and Scheduling of Virtual Network Functions using Multi Objective Optimization Algorithm', 2019 19th International Symposium on Communications and Information Technologies (ISCIT), 2019 19th International Symposium on Communications and Information Technologies (ISCIT), IEEE, Ho Chi Minh City, Vietnam, pp. 328-333.
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© 2019 IEEE. Within the context of Software-Defined Networking (SDN), the problem of resource allocation for a set of incoming Virtual Network Functions (VNF) service requests has been the focus of many studies. In this paper, a new optimization model has been developed to find the near to optimal mapping and scheduling for the incoming VNF service requests. This model while considering delay, aims to achieve three objectives functions, namely, minimizing the transmission delays occurring in every link, minimizing the processing capacity for every Virtual Machine (VM) and minimizing the processing delay at every VM. The resultant problem is formulated as a multi-objective optimization problem and the developed solution is based on a multi-objective evolutionary algorithm utilizing the decomposition algorithm. Simulation results illustrate that the resulting algorithm is scalable while considering delay and it outperforms the genetic bandwidth link allocation (GA-BA) and genetic non-bandwidth link allocation (GA-NBA) algorithms.

Gamal, M, Jafarizadeh, S, Abolhasan, M, Lipman, J & Ni, W 1970, 'Mapping and Scheduling for Non-Uniform Arrival of Virtual Network Function (VNF) Requests', 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), IEEE, Honolulu, HI, USA.
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© 2019 IEEE. As a new research concept for both academia and industry, there are several challenges faced by the Network Function Virtualization (NFV). One such challenge is to find the optimal mapping and scheduling for the incoming service requests which is the focus of this study. This optimization has been done by maximizing the number of accepted service requests, minimizing the number of bottleneck links and the overall processing time. The resultant problem is formulated as a multi- objective optimization problem, and two novel algorithms based on genetic algorithm have been developed. Through simulations, it has been shown that the developed algorithms can converge to the near to optimal solutions and they are scalable to large networks.

Hu, L, Jian, S, Cao, L, Gu, Z, Chen, Q & Amirbekyan, A 1970, 'HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation', Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Honolulu, Hawaii USA, pp. 3830-3837.
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Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item interaction is often influenced by other users and items, which form the user’s/item’s influential contexts. This induces important yet challenging issues, including modeling heterogeneous relations, interactions, and the strength of the influence from users/items in the influential contexts. To this end, we design Influential-Context Aggregation Units (ICAU) to aggregate the user-user/item-item relations within a given context as the influential context embeddings. Accordingly, we propose a Heterogeneous relations-Embedded Recommender System (HERS) based on ICAUs to model and interpret the underlying motivation of user-item interactions by considering user-user and item-item influences. The experiments on two real-world datasets show the highly improved recommendation quality made by HERS and its superiority in handling the cold-start problem. In addition, we demonstrate the interpretability of modeling influential contexts in explaining the recommendation results.

Huang, H, Zhang, J, Zhang, J, Wu, Q & Xu, J 1970, 'Compare More Nuanced: Pairwise Alignment Bilinear Network for Few-Shot Fine-Grained Learning', 2019 IEEE International Conference on Multimedia and Expo (ICME), 2019 IEEE International Conference on Multimedia and Expo (ICME), IEEE, Shanghai, China, pp. 91-96.
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© 2019 IEEE. The recognition ability of human beings is developed in a progressive way. Usually, children learn to discriminate various objects from coarse to fine-grained with limited supervision. Inspired by this learning process, we propose a simple yet effective model for the Few-Shot Fine-Grained (FSFG) recognition, which tries to tackle the challenging fine-grained recognition task using meta-learning. The proposed method, named Pairwise Alignment Bilinear Network (PABN), is an end-to-end deep neural network. Unlike traditional deep bilinear networks for fine-grained classification, which adopt the self-bilinear pooling to capture the subtle features of images, the proposed model uses a novel pairwise bilinear pooling to compare the nuanced differences between base images and query images for learning a deep distance metric. In order to match base image features with query image features, we design feature alignment losses before the proposed pairwise bilinear pooling. Experiment results on four fine-grained classification datasets and one generic few-shot dataset demonstrate that the proposed model outperforms both the state-of-the-art few-shot fine-grained and general few-shot methods.

Huang, X, Fan, L, Wu, Q, Zhang, J & Yuan, C 1970, 'Fast Registration for cross-source point clouds by using weak regional affinity and pixel-wise refinement', Proceedings - IEEE International Conference on Multimedia and Expo, IEEE International Conference on Multimedia and Expo, IEEE, Shanghai, China, pp. 1552-1557.
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Many types of 3D acquisition sensors have emerged in recent years and pointcloud has been widely used in many areas. Accurate and fast registration ofcross-source 3D point clouds from different sensors is an emerged researchproblem in computer vision. This problem is extremely challenging becausecross-source point clouds contain a mixture of various variances, such asdensity, partial overlap, large noise and outliers, viewpoint changing. In thispaper, an algorithm is proposed to align cross-source point clouds with bothhigh accuracy and high efficiency. There are two main contributions: firstly,two components, the weak region affinity and pixel-wise refinement, areproposed to maintain the global and local information of 3D point clouds. Then,these two components are integrated into an iterative tensor-based registrationalgorithm to solve the cross-source point cloud registration problem. Weconduct experiments on synthetic cross-source benchmark dataset and realcross-source datasets. Comparison with six state-of-the-art methods, theproposed method obtains both higher efficiency and accuracy.

Huang, Y, Wu, Q, Xu, J & Zhong, Y 1970, 'Celebrities-ReID: A Benchmark for Clothes Variation in Long-Term Person Re-Identification', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary.
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© 2019 IEEE. This paper considers person re-identification (re-ID) in the case of long-time gap (i.e., long-term re-ID) that concentrates on the challenge of clothes variation of each person. We introduce a new dataset, named Celebrities-reID to handle that challenge. Compared with current datasets, the proposed Celebrities-reID dataset is featured in two aspects. First, it contains 590 persons with 10,842 images, and each person does not wear the same clothing twice, making it the largest clothes variation person re-ID dataset to date. Second, a comprehensive evaluation using state of the arts is carried out to verify the feasibility and new challenge exposed by this dataset. In addition, we propose a benchmark approach to the dataset where a two-step fine-tuning strategy on human body parts is introduced to tackle the challenge of clothes variation. In experiments, we evaluate the feasibility and quality of the proposed Celebrities-reID dataset. The experimental results demonstrate that the proposed benchmark approach is not only able to best tackle clothes variation shown in our dataset but also achieves competitive performance on a widely used person re-ID dataset Market1501, which further proves the reliability of the proposed benchmark approach.

Huang, Y, Wu, Q, Xu, J & Zhong, Y 1970, 'SBSGAN: Suppression of Inter-Domain Background Shift for Person Re-Identification', 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Seoul, South Korea, pp. 9526-9535.
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© 2019 IEEE. Cross-domain person re-identification (re-ID) is challenging due to the bias between training and testing domains. We observe that if backgrounds in the training and testing datasets are very different, it dramatically introduces difficulties to extract robust pedestrian features, and thus compromises the cross-domain person re-ID performance. In this paper, we formulate such problems as a background shift problem. A Suppression of Background Shift Generative Adversarial Network (SBSGAN) is proposed to generate images with suppressed backgrounds. Unlike simply removing backgrounds using binary masks, SBSGAN allows the generator to decide whether pixels should be preserved or suppressed to reduce segmentation errors caused by noisy foreground masks. Additionally, we take ID-related cues, such as vehicles and companions into consideration. With high-quality generated images, a Densely Associated 2-Stream (DA-2S) network is introduced with Inter Stream Densely Connection (ISDC) modules to strengthen the complementarity of the generated data and ID-related cues. The experiments show that the proposed method achieves competitive performance on three re-ID datasets, i.e., Market-1501, DukeMTMC-reID, and CUHK03, under the cross-domain person re-ID scenario.

Jauregi Unanue, I, Zare Borzeshi, E, Esmaili, N & Piccardi, M 1970, 'ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems', Proceedings of the 2019 Conference of the North, Proceedings of the 2019 Conference of the North, Association for Computational Linguistics, Minneapolis, pp. 430-436.
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Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuous value). Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.91 and 2.54 BLEU points, and also a marked improvement over a state-of-the-art system.

Jian, S, Hu, L, Cao, L, Lu, K & Gao, H 1970, 'Evolutionarily Learning Multi-Aspect Interactions and Influences from Network Structure and Node Content', Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Honolulu, Hawaii USA, pp. 598-605.
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The formation of a complex network is highly driven by multi-aspect node influences and interactions, reflected on network structures and the content embodied in network nodes. Limited work has jointly modeled all these aspects, which typically focuses on topological structures but overlooks the heterogeneous interactions behind node linkage and contributions of node content to the interactive heterogeneities. Here, we propose a multi-aspect interaction and influence-unified evolutionary coupled system (MAI-ECS) for network representation by involving node content and linkage-based network structure. MAI-ECS jointly and iteratively learns two systems: a multi-aspect interaction learning system to capture heterogeneous hidden interactions between nodes and an influence propagation system to capture multiaspect node influences and their propagation between nodes. MAI-ECS couples, unifies and optimizes the two systems toward an effective representation of explicit node content and network structure, and implicit node interactions and influences. MAI-ECS shows superior performance in node classification and link prediction in comparison with the stateof-the-art methods on two real-world datasets. Further, we demonstrate the semantic interpretability of the results generated by MAI-ECS.

Khuat, TT & Gabrys, B 1970, 'Accelerated Training Algorithms of General Fuzzy Min-Max Neural Network Using GPU for Very High Dimensional Data', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Neural Information Processing, Springer International Publishing, Sydney, Australia, pp. 583-595.
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© Springer Nature Switzerland AG 2019. One of the issues of training a general fuzzy min-max neural network (GFMM) on very high dimensional data is a long training time even if the number of samples is relatively low. This is a quite common problem shared by many prototype-based methods requiring frequently repeated distance or similarity calculations. This paper proposes the method of accelerating the learning algorithms of the GFMM by, first, reformulating and representing them in a format allowing for their parallel execution and subsequently leveraging the computational power of the graphics processing unit (GPU). The original implementation of GFMM is modified by matrix computations to be executed on the GPU for the very high-dimensional datasets. The empirical results on two very high-dimensional datasets indicated that the training and testing processes performed on Nvidia Quadro P5000 GPU were from 10 to 35 times faster compared to those running serially on the Xeon CPU while retaining the same classification accuracy.

Kocbek, S & Gabrys, B 1970, 'Automated Machine Learning Techniques in Prognostics of Railway Track Defects', 2019 International Conference on Data Mining Workshops (ICDMW), 2019 International Conference on Data Mining Workshops (ICDMW), IEEE, China, pp. 777-784.
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© 2019 IEEE. The readiness and usefulness of Automated Machine Learning (AutoML) methods in classification of railway track defects is explored. Safety of railway networks is the top priority in the railroad industry, and track defects are a common cause of train accidents and service disruptions around the world. Effective classification and prediction of these defects based on historical inspection data can help in planning maintenance activities before critical defects occur. This increases safety of the network and lowers costs of the maintenance. The experimental analysis carried out on data from an international predictive modelling competition has shown that the proposed AutoML approaches resulted in an improved performance in comparison to the competition winning solutions and have an excellent potential for building robust predictive models in railway industry.

Kocbek, S, Kocbek, P, Zupanic, T, Stiglic, G & Gabrys, B 1970, 'Using (Automated) Machine Learning and Drug Prescription Records to Predict Mortality and Polypharmacy in Older Type 2 Diabetes Mellitus Patients', Communications in Computer and Information Science, International Conference on Neural Information Processing, Springer International Publishing, Sydney, NSW, Australia, pp. 624-632.
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We analyse a large drug prescription dataset and test the hypothesis that drug prescription data can be used to predict further complications in older patients newly diagnosed with type 2 diabetes mellitus. More specifically, we focus on mortality and polypharmacy prediction. We also examine the balance between interpretability and predictive performance for both prediction tasks, and compare performance of interpretable models with models generated with automated methods. Our results show good predictive performance in the polypharmacy prediction task with AUC of 0.859 (95% CI: 0.857–0.861). On the other hand, we were only able to achieve the average predictive performance for mortality prediction task with AUC of 0.754 (0.747–0.761). It was also shown that adding additional drug related features increased the performance only in the polypharmacy prediction task, while additional information on prescribed drugs did not influence the performance in the mortality prediction. Despite the limited success in mortality prediction, this study demonstrates the added value of the systematic collection and use of Electronic Health Record (EHR) data in solving the problem of polypharmacy related complications in older age.

Li, L, Liu, Z, Zhang, J & Zhou, X 1970, 'Learn Image Object Co-segmentation with Multi-scale Feature Fusion', 2019 IEEE Visual Communications and Image Processing (VCIP), 2019 IEEE Visual Communications and Image Processing (VCIP), IEEE, Sydney, Australia.
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© 2019 IEEE. Image object co-segmentation aims to segment common objects in a group of images. This paper proposes a novel neural network, which extracts multi-scale convolutional features at multiple layers via a modified VGG network and fuses them both within and across images as the intra-image and the inter-image features. Then these two kinds of features are further fused at each scale as the multi-scale co-features of common objects, and finally the multi-scale co-features are summed up and upsampled to obtain the co-segmentation results. To simplify the network and reduce the rapidly rising resource cost along with the inputs, the reduced input size, less downsampling and dilation convolution are adopted in the proposed model. Experimental results on the public dataset demonstrate that the proposed model achieves a comparable performance to the state-of-The-Art co-segmentation methods while the computation cost has been effectively reduced.

Li, Q, Wu, Q & Liu, X 1970, 'Multi-scale and Hierarchical Embedding for Polarity Shift Sensitive Sentiment Classification', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Artificial Intelligence and Security, Springer International Publishing, New York, NY, USA, pp. 227-238.
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© 2019, Springer Nature Switzerland AG. Appropriate paragraph embedding is critical for sentiment classification. However, the embedding for paragraph with polarity shift is very challenging and insufficiently explored. In this paper, a MUlti-Scale and Hierarchical embedding method, MUSH, is proposed to learn a more accurate paragraph embedding for polarity shift sensitive sentiment classification. MUSH adopts CNN with multi-size filters to reveal multi-scale sentiment atoms and utilizes hierarchical multi-line CNN-RNN structures to simultaneously capture polarity shift in both sentence level and paragraph level. Extensive experiments on four large real-world data sets demonstrate that the MUSH-enabled sentiment classification significantly enhances the accuracy compared with three state-of-the-art and four baseline competitors.

Li, Q, Wu, Q, Zhu, C & Zhang, J 1970, 'Bi-Level Masked Multi-scale CNN-RNN Networks for Short Text Representation', 2019 International Conference on Document Analysis and Recognition (ICDAR), 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, Sydney, Australia.
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Representing short text is becoming extremely important for a variety of valuable applications. However, representing short text is critical yet challenging because it involves lots of informal words and typos (i.e. the noise problem) but only a few vocabularies in each text (i.e. the sparsity problem). Most of the existing work on representing short text relies on noise recognition and sparsity expansion. However, the noises in short text are with various forms and changing fast, but, most of the current methods may fail to adaptively recognize the noise. Also, it is hard to explicitly expand a sparse text to a high-quality dense text. In this paper, we tackle the noise and sparsity problems in short text representation by learning multi-grain noise-tolerant patterns and then embedding the most significant patterns in a text as its representation. To achieve this goal, we propose a bi-level multi-scale masked CNN-RNN network to embed the most significant multi-grain noise-tolerant relations among words and characters in a text into a dense vector space. Comprehensive experiments on five large real-world data sets demonstrate our method significantly outperforms the state-of-the-art competitors.

Li, Q, Wu, Q, Zhu, C, Zhang, J & Zhao, W 1970, 'Unsupervised User Behavior Representation for Fraud Review Detection with Cold-Start Problem', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, China, pp. 222-236.
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© Springer Nature Switzerland AG 2019. Detecting fraud review is becoming extremely important in order to provide reliable information in cyberspace, in which, however, handling cold-start problem is a critical and urgent challenge since the case of cold-start fraud review rarely provides sufficient information for further assessing its authenticity. Existing work on detecting cold-start cases relies on the limited contents of the review posted by the user and a traditional classifier to make the decision. However, simply modeling review is not reliable since reviews can be easily manipulated. Also, it is hard to obtain high-quality labeled data for training the classifier. In this paper, we tackle cold-start problems by (1) using a user’s behavior representation rather than review contents to measure authenticity, which further (2) consider user social relations with other existing users when posting reviews. The method is completely (3) unsupervised. Comprehensive experiments on Yelp data sets demonstrate our method significantly outperforms the state-of-the-art methods.

Li, Z, Liu, W, Chang, X, Yao, L, Prakash, M & Zhang, H 1970, 'Domain-Aware Unsupervised Cross-dataset Person Re-identification', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 15th International Conference on Advanced Data Mining and Applications, Springer International Publishing, Dalian, China, pp. 406-420.
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© 2019, Springer Nature Switzerland AG. We focus on the person re-identification (re-id) problem of matching people across non-overlapping camera views. While most existing works rely on the abundance of labeled exemplars, we consider a more difficult unsupervised scenario, where no labeled exemplar is provided. One solution for unsupervised re-id that attracts much attention in the recent researches is cross-dataset transfer learning. It utilizes knowledge from multiple source datasets from different domains to enhance the unsupervised learning performance on the target domain. In previous works, much effect is taken on extraction of the generic and robust common appearances representations across domains. However, we observe that there also particular appearances in different domains. Simply ignoring these domain-unique appearances will misleading the matching schema in re-id application. Few unsupervised cross-dataset algorithms are proposed to learn the common appearances across multiple domains, even less of them consider the domain-unique representations. In this paper, we propose a novel domain-aware representation learning algorithm for unsupervised cross-dataset person re-id problem. The proposed algorithm not only learns a common appearances across-datasets but also captures the domain-unique appearances on the target dataset via minimization of the overlapped signal supports across different domains. Extensive experimental studies on benchmark datasets show superior performances of our algorithm over state-of-the-art algorithms. Sample analysis on selected samples also verifies the ability of diversity learning of our algorithm.

Li, Z, Zhang, J, Wu, Q, Gong, Y, Yi, J & Kirsch, C 1970, 'Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points', Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, Anchorage AK USA, pp. 2848-2856.
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© 2019 Association for Computing Machinery. Railway points are among the key components of railway infrastructure. As a part of signal equipment, points control the routes of trains at railway junctions, having a significant impact on the reliability, capacity, and punctuality of rail transport. Meanwhile, they are also one of the most fragile parts in railway systems. Points failures cause a large portion of railway incidents. Traditionally, maintenance of points is based on a fixed time interval or raised after the equipment failures. Instead, it would be of great value if we could forecast points' failures and take action beforehand, min-imising any negative effect. To date, most of the existing prediction methods are either lab-based or relying on specially installed sensors which makes them infeasible for large-scale implementation. Besides, they often use data from only one source. We, therefore, explore a new way that integrates multi-source data which are ready to hand to fulfil this task. We conducted our case study based on Sydney Trains rail network which is an extensive network of passenger and freight railways. Unfortunately, the real-world data are usually incomplete due to various reasons, e.g., faults in the database, operational errors or transmission faults. Besides, railway points differ in their locations, types and some other properties, which means it is hard to use a unified model to predict their failures. Aiming at this challenging task, we firstly constructed a dataset from multiple sources and selected key features with the help of domain experts. In this paper, we formulate our prediction task as a multiple kernel learning problem with missing kernels. We present a robust multiple kernel learning algorithm for predicting points failures. Our model takes into account the missing pattern of data as well as the inherent variance on different sets of railway points. Extensive experiments demonstrate the superiority of our algorit...

Li, Z, Zou, Y, Wang, G & Zhang, J 1970, 'Scale-Informed Density Estimation for Dense Crowd Counting', 2019 IEEE Visual Communications and Image Processing (VCIP), 2019 IEEE Visual Communications and Image Processing (VCIP), IEEE, Sydney, Australia.
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© 2019 IEEE. Dense crowd counting (DCC) remains challenging due to the scale variation and occlusion. Several deep learning based DCC methods have achieved the state-of-Arts on public datasets. However, experimental results show that the scale variation is still the main factor to hinder the DCC performance. In this work, we propose a scale-informed dense crowd counting method focusing on handling the negative effect caused by scale variation. More specifically, we propose a method to obtain the scale information of the patch from its GT density maps via estimating the mean value of the Gaussian kernel width and then a scale-classifier is deigned and trained accordingly. Moreover, with the estimated scale information, two sub-nets are dedicatedly deigned to learn the density maps for large-scale head patch and small-scale patch separately. Experimental results validate the performance of our proposed method which achieves the best performance on three dense crowd datasets.

Liu, L, Zhou, T, Long, G, Jiang, J & Zhang, C 1970, 'Learning to Propagate for Graph Meta-Learning', Advances in Neural Information Processing Systems 32 (NIPS 2019), Conference on Neural Information Processing Systems, NIPS, Canada, pp. 1-12.
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Meta-learning extracts common knowledge from learning different tasks anduses it for unseen tasks. It can significantly improve tasks that suffer frominsufficient training data, e.g., few shot learning. In most meta-learningmethods, tasks are implicitly related by sharing parameters or optimizer. Inthis paper, we show that a meta-learner that explicitly relates tasks on agraph describing the relations of their output dimensions (e.g., classes) cansignificantly improve few shot learning. The graph's structure is usually freeor cheap to obtain but has rarely been explored in previous works. We develop anovel meta-learner of this type for prototype-based classification, in which aprototype is generated for each class, such that the nearest neighbor searchamong the prototypes produces an accurate classification. The meta-learner,called 'Gated Propagation Network (GPN)', learns to propagate messages betweenprototypes of different classes on the graph, so that learning the prototype ofeach class benefits from the data of other related classes. In GPN, anattention mechanism aggregates messages from neighboring classes of each class,with a gate choosing between the aggregated message and the message from theclass itself. We train GPN on a sequence of tasks from many-shot to few shotgenerated by subgraph sampling. During training, it is able to reuse and updatepreviously achieved prototypes from the memory in a life-long learning cycle.In experiments, under different training-test discrepancy and test taskgeneration settings, GPN outperforms recent meta-learning methods on twobenchmark datasets. The code of GPN and dataset generation is available athttps://github.com/liulu112601/Gated-Propagation-Net.

Liu, Y, Yan, Y, Chen, L, Han, Y & Yang, Y 1970, 'Adaptive sparse confidence-weighted learning for online feature selection', 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, 33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence, ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE, Honolulu, HI, pp. 4408-4415.
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In this paper, we propose a new online feature selection algorithm for streaming data. We aim to focus on the following two problems which remain unaddressed in literature. First, most existing online feature selection algorithms merely utilize the first-order information of the data streams, regardless of the fact that second-order information explores the correlations between features and significantly improves the performance. Second, most online feature selection algorithms are based on the balanced data presumption, which is not true in many real-world applications. For example, in fraud detection, the number of positive examples are much less than negative examples because most cases are not fraud. The balanced assumption will make the selected features biased towards the majority class and fail to detect the fraud cases. We propose an Adaptive Sparse Confidence-Weighted (ASCW) algorithm to solve the aforementioned two problems. We first introduce an `0-norm constraint into the second-order confidence-weighted (CW) learning for feature selection. Then the original loss is substituted with a cost-sensitive loss function to address the imbalanced data issue. Furthermore, our algorithm maintains multiple sparse CW learner with the corresponding cost vector to dynamically select an optimal cost. We theoretically enhance the theory of sparse CW learning and analyze the performance behavior in F-measure. Empirical studies show the superior performance over the state-of-the-art online learning methods in the online-batch setting.

Mahdavi, F, Hayati, H, Kennedy, P & Eager, D 1970, 'Ageing and resulting injuries – effects on racing greyhounds', European Society of Biomechanics, European Society of Biomechanics, Vienna, Austria.

Mahdavi, F, Hayati, H, Kennedy, P & Eager, D 1970, 'Effects of the number of starts on greyhound racing dynamics', International Society of Biomechanics Conference, International Society of Biomechanics Conference, Calgary, Canada.

Mirtalaie, MA, Hussain, OK, Chang, E & Hussain, FK 1970, 'A Fine-Grained Ontology-Based Sentiment Aggregation Approach', Advances in Intelligent Systems and Computing, International Conference on Complex, Intelligent, and Software Intensive Systems, Springer International Publishing, 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.

Naji, M, Al-Ani, A, Braytee, A, Anaissi, A & Kennedy, P 1970, 'Queue Formation Augmented with Particle Swarm Optimisation to Improve Waiting Time in Airport Security Screening', Advances in Intelligent Systems and Computing, Workshops of the 33rd International Conference on Advanced Information Networking and Applications, Springer International Publishing, Japan, 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.

Naseem, U & Musial, K 1970, 'DICE: Deep Intelligent Contextual Embedding for Twitter Sentiment Analysis', 2019 International Conference on Document Analysis and Recognition (ICDAR), 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, Sydney, Australia, pp. 953-958.
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© 2019 IEEE. The sentiment analysis of the social media-based short text (e.g., Twitter messages) is very valuable for many good reasons, explored increasingly in different communities such as text analysis, social media analysis, and recommendation. However, it is challenging as tweet-like social media text is often short, informal and noisy, and involves language ambiguity such as polysemy. The existing sentiment analysis approaches are mainly for document and clean textual data. Accordingly, we propose a Deep Intelligent Contextual Embedding (DICE), which enhances the tweet quality by handling noises within contexts, and then integrates four embeddings to involve polysemy in context, semantics, syntax, and sentiment knowledge of words in a tweet. DICE is then fed to a Bi-directional Long Short Term Memory (BiLSTM) network with attention to determine the sentiment of a tweet. The experimental results show that our model outperforms several baselines of both classic classifiers and combinations of various word embedding models in the sentiment analysis of airline-related tweets.

Poostchi, H, Borzeshi, EZ & Piccardi, M 1970, 'BILSTM-CRF for Persian named-entity recognition armanpersonercorpus: The first entity-annotated Persian dataset', LREC 2018 - 11th International Conference on Language Resources and Evaluation, Language Resources and Evaluation Conference, European Language Resources Association (ELRA, Miyazaki, Japan, pp. 4427-4431.
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Named-entity recognition (NER) can still be regarded as work in progress for a number of Asian languages due to the scarcity of annotated corpora. For this reason, with this paper we publicly release an entity-annotated Persian dataset and we present a performing approach for Persian NER based on a deep learning architecture. In addition to the entity-annotated dataset, we release a number of word embeddings (including GloVe, skip-gram, CBOW and Hellinger PCA) trained on a sizable collation of Persian text. The combination of the deep learning architecture (a BiLSTM-CRF) and the pre-trained word embeddings has allowed us to achieve a 77.45% CoNLL F1 score, a result that is more than 12 percentage points higher than the best previous result and interesting in absolute terms.

Poostchimohammadabadi, H & Piccardi, M 1970, 'A multi-constraint structured hinge loss for named-entity recognition', Proceedings of The 17th Annual Workshop of the Australasian Language Technology Association, Annual Workshop of the Australasian Language Technology Association, ACLWEB, Sydney, NSW, Australia, pp. 41-46.
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The negative log-likelihood or cross entropy is the usual training objective of NLP models owing to its versatility and empirical performance. However, training objectives which directly target the performance measure usedto evaluate the task have the potential to lead to higher empirical accuracy. For this reason, in this short paper we propose using a multi-constraint structured hinge loss as the training objective of a contemporary name identity recognition (NER) model. Experimental results over the challenging OntoNotes 5.0 dataset have shown that the proposed objective has been able to achieve an improvement of 0.62 CoNLL score points at a complete parity of testing set-up.

Saki, M, Abolhasan, M & Lipman, J 1970, 'A Big Sensor Data Offloading Scheme in Rail Networks', 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), 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 1970, 'Taxonomy-Augmented Features for Document Clustering', Communications in Computer and Information Science, Australasian Conference on Data Mining, Springer Singapore, Bathurst, NSW, Australia, 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, T, Geng, X, Qin, T, Long, G, Jiang, J & Jiang, D 1970, 'Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering', Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}, International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan, pp. 2227-2233.
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Many algorithms for Knowledge-Based Question Answering (KBQA) depend onsemantic parsing, which translates a question to its logical form. When onlyweak supervision is provided, it is usually necessary to search valid logicalforms for model training. However, a complex question typically involves a hugesearch space, which creates two main problems: 1) the solutions limited bycomputation time and memory usually reduce the success rate of the search, and2) spurious logical forms in the search results degrade the quality of trainingdata. These two problems lead to a poorly-trained semantic parsing model. Inthis work, we propose an effective search method for weakly supervised KBQAbased on operator prediction for questions. With search space constrained bypredicted operators, sufficient search paths can be explored, more validlogical forms can be derived, and operators possibly causing spurious logicalforms can be avoided. As a result, a larger proportion of questions in a weaklysupervised training set are equipped with logical forms, and fewer spuriouslogical forms are generated. Such high-quality training data directlycontributes to a better semantic parsing model. Experimental results on one ofthe largest KBQA datasets (i.e., CSQA) verify the effectiveness of ourapproach: improving the precision from 67% to 72% and the recall from 67% to72% in terms of the overall score.

Shi, L, Li, S, Cao, L, Yang, L & Pan, G 1970, 'TBQ(σ): Improving efficiency of trace utilization for off-policy reinforcement learning', Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, International Joint Conference on Autonomous Agents and Multiagent Systems, IFAAMAS, Montreal, Canada, pp. 1025-1032.
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OfF-policy reinforcement learning with eligibility traces faces is challenging because of the discrepancy between target policy and behavior policy One common approach is to measure the difference between two policies in a probabilistic way, such as importance sampling and tree-backup However, existing off-policy learning methods based on probabilistic policy measurement are inefficient when utilizing traces under a greedy target policy, which is ineffective for control problems The traces are cut immediately when a non-greedy action is taken, which may lose the advantage of eligibility traccs and slow down the learning process Alternatively, some non-probabdistic measurement methods such as General Q(A) and Naive Q(A) never cut traces, but face convergence problems in practice To address the above issues, this paper introduces a new method named TBQ(a) which effectively unifies the tree-backup algorithm and Naive Q(A) By introducing a new parameter a to illustrate the degree of utilizing traces, TBQ(

Shi, Z, Zhang, JA, Xu, R & Cheng, Q 1970, 'Deep Learning Networks for Human Activity Recognition with CSI Correlation Feature Extraction', ICC 2019 - 2019 IEEE International Conference on Communications (ICC), ICC 2019 - 2019 IEEE International Conference on Communications (ICC), IEEE, Shanghai, China.
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© 2019 IEEE. Device free WiFi Sensing using channel state information (CSI) has been shown great potentials for human activity recognition (HAR). However, extracting reliable and concise feature signals remains as a challenging problem, especially in a dynamic and complex environment. In this paper, we propose a novel scheme for CSI-based HAR using deep learning network (CH-DLN), with an innovative CSI correlation feature extraction (CCFE) method. The CCFE method pre-processes the signals input to the DLN in two steps. Firstly, it uses a recursive algorithm to reduce non-activity-related information from the signal and hence enhance the activity-dependent signals. Secondly, it computes the correlation over both the time and frequency domain to disclose better signal structure and compress the signal. From such enhanced and compressed signals, we utilize the recurrent neural networking (RNN) to automatically extract deeper features, and then apply the softmax regression algorithm for classifying activities. Through extensive experimental results, our proposed scheme is shown to outperform state-of-the-art methods in recognition accuracy, with much less training time.

Shu, Y & Xu, G 1970, 'Emotion Recognition from Music Enhanced by Domain Knowledge', PRICAI 2019: Trends in Artificial Intelligence, PACIFIC RIM INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, Springer International Publishing, Yanuca Island, Cuvu, Fiji, pp. 121-134.
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Music elements have been widely used to influence the audiences’ emotional experience by its music grammar. However, these domain knowledge, has not been thoroughly explored as music grammar for music emotion analyses in previous work. In this paper, we propose a novel method to analyze music emotion via utilizing the domain knowledge of music elements. Specifically, we first summarize the domain knowledge of music elements and infer probabilistic dependencies between different main musical elements and emotions from the summarized music theory. Then, we transfer the domain knowledge to constraints, and formulate affective music analysis as a constrained optimization problem. Experimental results on the Music in 2015 database and the AMG1608 database demonstrate that the proposed music content analyses method outperforms the state-of-the-art performance prediction methods.

Sreevallabh Chivukula, A, Yang, X & Liu, W 1970, 'Adversarial Deep Learning with Stackelberg Games', Communications in Computer and Information Science, Springer International Publishing, pp. 3-12.
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© Springer Nature Switzerland AG 2019. Deep networks are vulnerable to adversarial attacks from malicious adversaries. Currently, many adversarial learning algorithms are designed to exploit such vulnerabilities in deep networks. These methods focus on attacking and retraining deep networks with adversarial examples to do either feature manipulation or label manipulation or both. In this paper, we propose a new adversarial learning algorithm for finding adversarial manipulations to deep networks. We formulate adversaries who optimize game-theoretic payoff functions on deep networks doing multi-label classifications. We model the interactions between a classifier and an adversary from a game-theoretic perspective and formulate their strategies into a Stackelberg game associated with a two-player problem. Then we design algorithms to solve for the Nash equilibrium, which is a pair of strategies from which there is no incentive for either the classifier or the adversary to deviate. In designing attack scenarios, the adversary’s objective is to deliberately make small changes to test data such that attacked samples are undetected. Our results illustrate that game-theoretic modelling is significantly effective in securing deep learning models against performance vulnerabilities attached by intelligent adversaries.

Sun, K, Qian, T, Yin, H, Chen, T, Chen, Y & Chen, L 1970, 'What Can History Tell Us?', Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM '19: The 28th ACM International Conference on Information and Knowledge Management, ACM, pp. 1593-1602.
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© 2019 Association for Computing Machinery. Recommendation systems have been widely applied to many E-commerce and online social media platforms. Recently, sequential item recommendation, especially session-based recommendation, has aroused wide research interests. However, existing sequential recommendation approaches either ignore the historical sessions or consider all historical sessions without any distinction that whether the historical sessions are relevant or not to the current session, which motivates us to distinguish the effect of each historical session and identify relevant historical sessions for recommendation. In light of this, we propose a novel deep learning based sequential recommender framework for session-based recommendation, which takes Nonlocal Neural Network and Recurrent Neural Network as the main building blocks. Specifically, we design a two-layer nonlocal architecture to identify historical sessions that are relevant to the current session and learn the long-term user preferences mostly from these relevant sessions. Besides, we also design a gated recurrent unit (GRU) enhanced by the nonlocal structure to learn the short-term user preferences from the current session. Finally, we propose a novel approach to integrate both long-term and short-term user preferences in a unified way to facilitate training the whole recommender model in an end-to-end manner. We conduct extensive experiments on two widely used real-world datasets, and the experimental results show that our model achieves significant improvements over the state-of-the-art methods.

Tao, Q, Luo, X, Wang, H & Xu, R 1970, 'Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts', 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, USA, pp. 1574-1580.
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© 2019 IEEE. State-of-the-art methods for relation extraction consider the sentential context by modeling the entire sentence. However, syntactic indicators, certain phrases or words like prepositions that are more informative than other words and may be beneficial for identifying semantic relations. Other approaches using fixed text triggers capture such information but ignore the lexical diversity. To leverage both syntactic indicators and sentential contexts, we propose an indicator-aware approach for relation extraction. Firstly, we extract syntactic indicators under the guidance of syntactic knowledge. Then we construct a neural network to incorporate both syntactic indicators and the entire sentences into better relation representations. By this way, the proposed model alleviates the impact of noisy information from entire sentences and breaks the limit of text triggers. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model significantly outperforms the state-of-the-art methods.

Unanue, IJ, Arratibel, LG, Borzeshi, EZ & Piccardi, M 1970, 'English-Basque statistical and neural machine translation', LREC 2018 - 11th International Conference on Language Resources and Evaluation, Language Resources and Evaluation Conference, European Language Resource Association, Miyazaki, Japan, pp. 880-885.
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Neural Machine Translation (NMT) has attracted increasing attention in the recent years. However, it tends to require very large training corpora which could prove problematic for languages with low resources. For this reason, Statistical Machine Translation (SMT) continues to be a popular approach for low-resource language pairs. In this work, we address English-Basque translation and compare the performance of three contemporary statistical and neural machine translation systems: OpenNMT, Moses SMT and Google Translate. For evaluation, we employ an open-domain and an IT-domain corpora from the WMT16 resources for machine translation. In addition, we release a small dataset (Berriak) of 500 highly-accurate English-Basque translations of complex sentences useful for a thorough testing of the translation systems.

Unanue, IJ, Borzeshi, EZ, Esmaili, N & Piccardi, M 1970, 'ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems', NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, pp. 430-436.
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Regularization of neural machine translation is still a significant problem,especially in low-resource settings. To mollify this problem, we proposeregressing word embeddings (ReWE) as a new regularization technique in a systemthat is jointly trained to predict the next word in the translation(categorical value) and its word embedding (continuous value). Such a jointtraining allows the proposed system to learn the distributional propertiesrepresented by the word embeddings, empirically improving the generalization tounseen sentences. Experiments over three translation datasets have showed aconsistent improvement over a strong baseline, ranging between 0.91 and 2.54BLEU points, and also a marked improvement over a state-of-the-art system.

Verma, R & Merigo, JM 1970, 'On Generalized Intuitionistic Fuzzy Interaction Partitioned Bonferroni Mean Operators', 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE.
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Wan, Y, Shu, J, Sui, Y, Xu, G, Zhao, Z, Wu, J & Yu, P 1970, 'Multi-modal Attention Network Learning for Semantic Source Code Retrieval', 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), IEEE, San Diego, CA, USA, pp. 13-25.
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© 2019 IEEE. Code retrieval techniques and tools have been playing a key role in facilitating software developers to retrieve existing code fragments from available open-source repositories given a user query (e.g., a short natural language text describing the functionality for retrieving a particular code snippet). Despite the existing efforts in improving the effectiveness of code retrieval, there are still two main issues hindering them from being used to accurately retrieve satisfiable code fragments from large-scale repositories when answering complicated queries. First, the existing approaches only consider shallow features of source code such as method names and code tokens, but ignoring structured features such as abstract syntax trees (ASTs) and control-flow graphs (CFGs) of source code, which contains rich and well-defined semantics of source code. Second, although the deep learning-based approach performs well on the representation of source code, it lacks the explainability, making it hard to interpret the retrieval results and almost impossible to understand which features of source code contribute more to the final results. To tackle the two aforementioned issues, this paper proposes MMAN, a novel Multi-Modal Attention Network for semantic source code retrieval. A comprehensive multi-modal representation is developed for representing unstructured and structured features of source code, with one LSTM for the sequential tokens of code, a Tree-LSTM for the AST of code and a GGNN (Gated Graph Neural Network) for the CFG of code. Furthermore, a multi-modal attention fusion layer is applied to assign weights to different parts of each modality of source code and then integrate them into a single hybrid representation. Comprehensive experiments and analysis on a large-scale real-world dataset show that our proposed model can accurately retrieve code snippets and outperforms the state-of-the-art methods.

Wang, C, Pan, S, Hu, R, Long, G, Jiang, J & Zhang, C 1970, 'Attributed Graph Clustering: A Deep Attentional Embedding Approach', Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}, International Joint Conferences on Artificial Intelligence Organization, Macao, China, pp. 3670-3676.
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Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compact graph embedding, upon which classic clustering methods like k-means or spectral clustering algorithms are applied. These two-step frameworks are difficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i.e., designed for the specific clustering task. In this paper, we propose a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for short). Our method focuses on attributed graphs to sufficiently explore the two sides of information in graphs. By employing an attention network to capture the importance of the neighboring nodes to a target node, our DAEGC algorithm encodes the topological structure and node content in a graph to a compact representation, on which an inner product decoder is trained to reconstruct the graph structure. Furthermore, soft labels from the graph embedding itself are generated to supervise a self-training graph clustering process, which iteratively refines the clustering results. The self-training process is jointly learned and optimized with the graph embedding in a unified framework, to mutually benefit both components. Experimental results compared with state-of-the-art algorithms demonstrate the superiority of our method.

Wang, S, Hu, L, Wang, Y, Cao, L, Sheng, QZ & Orgun, M 1970, 'Sequential Recommender Systems: Challenges, Progress and Prospects', Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}, International Joint Conferences on Artificial Intelligence Organization, Macao, pp. 6332-6338.
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The emerging topic of sequential recommender systems (SRSs) has attracted increasing attention in recent years. Different from the conventional recommender systems (RSs) including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users’ preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations. In this paper, we provide a systematic review on SRSs. We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic. Finally, we discuss the important research directions in this vibrant area.

Wang, S, Hu, L, Wang, Y, Sheng, QZ, Orgun, M & Cao, L 1970, 'Modeling Multi-Purpose Sessions for Next-Item Recommendations via Mixture-Channel Purpose Routing Networks', Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}, International Joint Conferences on Artificial Intelligence Organization, Macao, pp. 3771-3777.
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A session-based recommender system (SBRS) suggests the next item by modeling the dependencies between items in a session. Most of existing SBRSs assume the items inside a session are associated with one (implicit) purpose. However, this may not always be true in reality, and a session may often consist of multiple subsets of items for different purposes (e.g., breakfast and decoration). Specifically, items (e.g., bread and milk) in a subsethave strong purpose-specific dependencies whereas items (e.g., bread and vase) from different subsets have much weaker or even no dependencies due to the difference of purposes. Therefore, we propose a mixture-channel model to accommodate the multi-purpose item subsets for more precisely representing a session. Filling gaps in existing SBRSs, this model recommends more diverse items to satisfy different purposes. Accordingly, we design effective mixture-channel purpose routing networks (MCPRN) with a purpose routing network to detect the purposes of each item and assign it into the corresponding channels. Moreover, a purpose specific recurrent network is devised to model the dependencies between items within each channel for a specific purpose. The experimental results show the superiority of MCPRN over the state-of-the-art methods in terms of both recommendation accuracy and diversity.  

Wang, X, Jin, D, Liu, M, He, D, Musial, K & Dang, J 1970, 'Emotional Contagion-Based Social Sentiment Mining in Social Networks by Introducing Network Communities', Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM '19: The 28th ACM International Conference on Information and Knowledge Management, ACM, Beijing, China, pp. 1763-1772.
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© 2019 Association for Computing Machinery. The rapid development of social media services has facilitated the communication of opinions through online news, blogs, microblogs, instant-messages, and so on. This article concentrates on the mining of readers' social sentiments evoked by social media materials. Existing methods are only applicable to a minority of social media like news portals with emotional voting information, while ignore the emotional contagion between writers and readers. However, incorporating such factors is challenging since the learned hidden variables would be very fuzzy (because of the short and noisy text in social networks). In this paper, we try to solve this problem by introducing a high-order network structure, i.e. communities. We first propose a new generative model called Community-Enhanced Social Sentiment Mining (CESSM), which 1) considers the emotional contagion between writers and readers to capture precise social sentiment, and 2) incorporates network communities to capture coherent topics. We then derive an inference algorithm based on Gibbs sampling. Empirical results show that, CESSM achieves significantly superior performance against the state-of-the-art techniques for text sentiment classification and interestingness in social sentiment mining.

Wang, Y, Jin, D, Musial, K & Dang, J 1970, 'Community Detection in Social Networks Considering Topic Correlations', Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Hawaii, USA, pp. 321-328.
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Network contents including node contents and edge contents can be utilized for community detection in social networks. Thus, the topic of each community can be extracted as its semantic information. A plethora of models integrating topic model and network topologies have been proposed. However, a key problem has not been resolved that is the semantic division of a community. Since the definition of community is based on topology, a community might involve several topics. To ach

Wang, Z, Li, Q, Li, G & Xu, G 1970, 'Polynomial Representation for Persistence Diagram', 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Long Beach, CA, USA, pp. 6116-6125.
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© 2019 IEEE. Persistence diagram (PD) has been considered as a compact descriptor for topological data analysis (TDA). Unfortunately, PD cannot be directly used in machine learning methods since it is a multiset of points. Recent efforts have been devoted to transforming PDs into vectors to accommodate machine learning methods. However, they share one common shortcoming: the mapping of PDs to a feature representation depends on a pre-defined polynomial. To address this limitation, this paper proposes an algebraic representation for PDs, i.e., polynomial representation. In this work, we discover a set of general polynomials that vanish on vectorized PDs and extract the task-adapted feature representation from these polynomials. We also prove two attractive properties of the proposed polynomial representation, i.e., stability and linear separability. Experiments also show that our method compares favorably with state-of-the-art TDA methods.

Wu, J, Yao, L, Huang, Y, Xu, J, Wu, Q & Huang, L 1970, 'Improving Person Re-Identification Performance Using Body Mask Via Cross-Learning Strategy', 2019 IEEE Visual Communications and Image Processing (VCIP), 2019 IEEE Visual Communications and Image Processing (VCIP), IEEE, Sydney, Australia.
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© 2019 IEEE. The task of person re-identification (re-id) is to find the same pedestrian across non-overlapping cameras. Normally, the performance of person re-id can be affected by background clutters. However, existing segmentation algorithms are hard to obtain perfect foreground person images. To effectively leverage the body (foreground) cue, and in the meantime pay attention to discriminative information in the background (e.g., companion or vehicle), we propose to use a cross-learning strategy to take both foreground and other discriminative information into account. In addition, since currently existing foreground segmentation result always involves noise, we use Label Smoothing Regularization (LSR) to strengthen the generalization capability during our learning process. In experiments, we pick up two state-of-The-Art person re-id methods to verify the effectiveness of our proposed cross-learning strategy. Our experiments are carried out on two publicly available person re-id datasets. Obvious performance improvements can be observed on both datasets.

Wu, Z, Pan, S, Long, G, Jiang, J & Zhang, C 1970, 'Graph WaveNet for Deep Spatial-Temporal Graph Modeling', IJCAI International Joint Conference on Artificial Intelligence, The 28th International Joint Conference on Artificial Intelligence (IJCAI), International Joint Conferences on Artificial Intelligence Organization, Macao, China, pp. 1907-1913.
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Spatial-temporal graph modeling is an important task to analyze the spatialrelations and temporal trends of components in a system. Existing approachesmostly capture the spatial dependency on a fixed graph structure, assuming thatthe underlying relation between entities is pre-determined. However, theexplicit graph structure (relation) does not necessarily reflect the truedependency and genuine relation may be missing due to the incompleteconnections in the data. Furthermore, existing methods are ineffective tocapture the temporal trends as the RNNs or CNNs employed in these methodscannot capture long-range temporal sequences. To overcome these limitations, wepropose in this paper a novel graph neural network architecture, Graph WaveNet,for spatial-temporal graph modeling. By developing a novel adaptive dependencymatrix and learn it through node embedding, our model can precisely capture thehidden spatial dependency in the data. With a stacked dilated 1D convolutioncomponent whose receptive field grows exponentially as the number of layersincreases, Graph WaveNet is able to handle very long sequences. These twocomponents are integrated seamlessly in a unified framework and the wholeframework is learned in an end-to-end manner. Experimental results on twopublic traffic network datasets, METR-LA and PEMS-BAY, demonstrate the superiorperformance of our algorithm.

Xie, H-B, Li, C, Xu, RYD & Mengersen, K 1970, 'Robust Kernelized Bayesian Matrix Factorization for Video Background/Foreground Separation', Machine Learning, Optimization, and Data Science (LNCS), International Conference on Machine Learning, Optimization, and Data Science, Springer International Publishing, Siena, Italy, pp. 484-495.
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© Springer Nature Switzerland AG 2019. Development of effective and efficient techniques for video analysis is an important research area in machine learning and computer vision. Matrix factorization (MF) is a powerful tool to perform such tasks. In this contribution, we present a hierarchical robust kernelized Bayesian matrix factorization (RKBMF) model to decompose a data set into low rank and sparse components. The RKBMF model automatically infers the parameters and latent variables including the reduced rank using variational Bayesian inference. Moreover, the model integrates the side information of similarity between frames to improve information extraction from the video. We employ RKBMF to extract background and foreground information from a traffic video. Experimental results demonstrate that RKBMF outperforms state-of-the-art approaches for background/foreground separation, particularly where the video is contaminated.

Xu, P, Deng, Z, Choi, K-S, Cao, L & Wang, S 1970, 'Multi-View Information-Theoretic Co-Clustering for Co-Occurrence Data', Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Honolulu,Hawaii, USA, pp. 379-386.
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Multi-view clustering has received much attention recently. Most of the existing multi-view clustering methods only focus on one-sided clustering. As the co-occurring data elements involve the counts of sample-feature co-occurrences, it is more efficient to conduct two-sided clustering along the samples and features simultaneously. To take advantage of two-sided clustering for the co-occurrences in the scene of multi-view clustering, a two-sided multi-view clustering method is proposed, i.e., multi-view information-theoretic co-clustering (MV-ITCC). The proposed method realizes two-sided clustering for co-occurring multi-view data under the formulation of information theory. More specifically, it exploits the agreement and disagreement among views by sharing a common clustering results along the sample dimension and keeping the clustering results of each view specific along the feature dimension. In addition, the mechanism of maximum entropy is also adopted to control the importance of different views, which can give a right balance in leveraging the agreement and disagreement. Extensive experiments are conducted on text and image multiview datasets. The results clearly demonstrate the superiority of the proposed method.

Yang, H, Pan, S, Chen, L, Zhou, C & Zhang, P 1970, 'Low-Bit Quantization for Attributed Network Representation Learning', Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}, International Joint Conferences on Artificial Intelligence Organization, Macao, pp. 4047-4053.
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Attributed network embedding plays an important role in transferring network data into compact vectors for effective network analysis. Existing attributed network embedding models are designed either in continuous Euclidean spaces which introduce data redundancy or in binary coding spaces which incur significant loss of representation accuracy. To this end, we present a new Low-Bit Quantization for Attributed Network Representation Learning model (LQANR for short) that can learn compact node representations with low bitwidth values while preserving high representation accuracy. Specifically, we formulate a new representation learning function based on matrix factorization that can jointly learn the low-bit node representations and the layer aggregation weights under the low-bit quantization constraint. Because the new learning function falls into the category of mixed integer optimization, we propose an efficient mixed-integer based alternating direction method of multipliers (ADMM) algorithm as the solution. Experiments on real-world node classification and link prediction tasks validate the promising results of the proposed LQANR model.

Ye, P, Wang, Y, Xia, Y, An, P & Zhang, J 1970, 'Enhanced Saliency Prediction via Free Energy Principle', Digital TV and Multimedia Communication, International Forum on Digital TV and Wireless Multimedia Communications, Springer Singapore, Shanghai, China, 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, J, Wu, Q, Zhang, J, Shen, C & Lu, J 1970, 'Mind Your Neighbours: Image Annotation With Metadata Neighbourhood Graph Co-Attention Networks', 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.
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Zhang, P, Wu, Q & Xu, J 1970, 'VN-GAN: Identity-preserved Variation Normalizing GAN for Gait Recognition', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary.
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© 2019 IEEE. Gait is recognized as a unique biometric characteristic to identify a walking person remotely across surveillance networks. However, the performance of gait recognition severely suffers challenges from view angle diversity. To address the problem, an identity-preserved Variation Normalizing Generative Adversarial Network (VN-GAN) is proposed for learning purely identity-related representations. It adopts a coarse-to-fine manner which firstly generates initial coarse images by normalizing view to an identical one and then refines the coarse images by injecting identity-related information. In specific, Siamese structure with discriminators for both camera view angles and human identities is utilized to achieve variation normalization and identity preservation of two stages, respectively. In addition to discriminators, reconstruction loss and identity-preserving loss are integrated, which forces the generated images to be the same in view and to be discriminative in identity. This ensures to generate identity-related images in an identical view of good visual effect for gait recognition. Extensive experiments on benchmark datasets demonstrate that the proposed VN-GAN can generate visually interpretable results and achieve promising performance for gait recognition.

Zhang, P, Wu, Q & Xu, J 1970, 'VT-GAN: View Transformation GAN for Gait Recognition Across Views', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary.
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© 2019 IEEE. Recognizing gaits without human cooperation is of importance in surveillance and forensics because of the benefits that gait is unique and collected remotely. However, change of camera view angle severely degrades the performance of gait recognition. To address the problem, previous methods usually learn mappings for each pair of views which incurs abundant independently built models. In this paper, we proposed a View Transformation Generative Adversarial Networks (VT-GAN) to achieve view transformation of gaits across two arbitrary views using only one uniform model. In specific, we generated gaits in target view conditioned on input images from any views and the corresponding target view indicator. In addition to the classical discriminator in GAN which makes the generated images look realistic, a view classifier is imposed. This controls the consistency of generated images and conditioned target view indicator and ensures to generate gaits in the specified target view. On the other hand, retaining identity information while performing view transformation is another challenge. To solve the issue, an identity distilling module with triplet loss is integrated, which constrains the generated images inheriting identity information from inputs and yields discriminative feature embeddings. The proposed VT-GAN generates visually promising gaits and achieves promising performances for cross-view gait recognition, which exhibits great effectiveness of the proposed VT-GAN.

Zhang, X, Zhang, X, Verma, S, Liu, Y, Blumenstein, M & Li, J 1970, 'Detection of Anomalous Traffic Patterns and Insight Analysis from Bus Trajectory Data', PRICAI 2019: Trends in Artificial Intelligence, The 16th Pacific Rim International Conference on Artificial Intelligence, Springer International Publishing, Cuvu, Fiji, pp. 307-321.
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Zhang, Z, Wang, Y, Wu, Q & Chen, F 1970, 'Visual Relationship Attention for Image Captioning', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, HUNGARY.
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© 2019 IEEE. Visual attention mechanisms have been broadly used by image captioning models to attend to related visual information dynamically, allowing fine-grained image understanding and reasoning. However, they are only designed to discover the region-level alignment between visual features and the language feature. The exploration of higher-level visual relationship information between image regions, which is rarely researched in recent works, is beyond their capabilities. To fill this gap, we propose a novel visual relationship attention model based on the parallel attention mechanism under the learnt spatial constraints. It can extract relationship information from visual regions and language and then achieve the relationship-level alignment between them. Using combined visual relationship attention and visual region attention to attend to related visual relationships and regions respectively, our image captioning model can achieve state-of-the-art performances on the MSCOCO dataset. Both quantitative analysis and qualitative analysis demonstrate that our novel visual relationship attention model can capture related visual relationship and further improve the caption quality.

Zhao, M, Shu, Y, Liu, S & Xu, G 1970, 'Electricity Price Forecast using Meteorology data: A study in Australian Energy Market', 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), IEEE, Beijing, China.
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© 2019 IEEE. Electricity price as a fundamental cost for each family which is an essential segment in the electricity market. The adjustment of electricity price can present the change in electricity supply and demand relationship. For the electricity supply companies, an appropriate defined electricity price can eventually determine the level of profit. On the other hand, an accurate prediction can help to seize opportunities in the electricity market. In this paper, we aim to predict the electricity price with more confident accuracy by leveraging data mining techniques. Our experiment on 12 months of electricity prices as well as climate data in the New South Wales has achieved a promising prediction result.

Zhao, M, Zhang, J, Zhang, C & Zhang, W 1970, 'Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting', 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Long Beach, CA, pp. 12728-12737.
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Zhao, M, Zhang, J, Zhang, C & Zhang, W 1970, 'Towards Locally Consistent Object Counting with Constrained Multi-stage Convolutional Neural Networks', COMPUTER VISION - ACCV 2018, PT VI, Asian Conference on Computer Vision, Springer International Publishing, Perth, AUSTRALIA, pp. 247-261.
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High-density object counting in surveillance scenes is challenging mainly due to the drastic variation of object scales. The prevalence of deep learning has largely boosted the object counting accuracy on several benchmark datasets. However, does the global counts really count? Armed with this question we dive into the predicted density map whose summation over the whole regions reports the global counts for more in-depth analysis. We observe that the object density map generated by most existing methods usually lacks of local consistency, i.e., counting errors in local regions exist unexpectedly even though the global count seems to well match with the ground-truth. Towards this problem, in this paper we propose a constrained multi-stage Convolutional Neural Networks (CNNs) to jointly pursue locally consistent density map from two aspects. Different from most existing methods that mainly rely on the multi-column architectures of plain CNNs, we exploit a stacking formulation of plain CNNs. Benefited from the internal multi-stage learning process, the feature map could be repeatedly refined, allowing the density map to approach the ground-truth density distribution. For further refinement of the density map, we also propose a grid loss function. With finer local-region-based supervisions, the underlying model is constrained to generate locally consistent density values to minimize the training errors considering both the global and local counts accuracy. Experiments on two widely-tested object counting benchmarks with overall significant results compared with state-of-the-art methods demonstrate the effectiveness of our approach.

Zheng, C, Cai, Y, Xu, J, Leung, H-F & Xu, G 1970, 'A Boundary-aware Neural Model for Nested Named Entity Recognition', Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, pp. 357-366.
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© 2019 Association for Computational Linguistics In natural language processing, it is common that many entities contain other entities inside them. Most existing works on named entity recognition (NER) only deal with flat entities but ignore nested ones. We propose a boundary-aware neural model for nested NER which leverages entity boundaries to predict entity categorical labels. Our model can locate entities precisely by detecting boundaries using sequence labeling models. Based on the detected boundaries, our model utilizes the boundary-relevant regions to predict entity categorical labels, which can decrease computation cost and relieve error propagation problem in layered sequence labeling model. We introduce multitask learning to capture the dependencies of entity boundaries and their categorical labels, which helps to improve the performance of identifying entities. We conduct our experiments on nested NER datasets and the experimental results demonstrate that our model outperforms other state-of-the-art methods.

Zhou, I, Makhdoom, I, Abolhasan, M, Lipman, J & Shariati, N 1970, 'A Blockchain-based File-sharing System for Academic Paper Review', 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, Australia.
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Zhou, K, Luo, X, Wang, H & Xu, R 1970, 'Multi-task Learning for Relation Extraction', 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, USA, pp. 1480-1487.
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© 2019 IEEE. Distantly supervised relation extraction leverages knowledge bases to label training data automatically. However, distant supervision may introduce incorrect labels, which harm the performance. Many efforts have been devoted to tackling this problem, but most of them treat relation extraction as a simple classification task. As a result, they ignore useful information that comes from related tasks, i.e., dependency parsing and entity type classification. In this paper, we first propose a novel Multi-Task learning framework for Relation Extraction (MTRE). We employ dependency parsing and entity type classification as auxiliary tasks and relation extraction as the target task. We learn these tasks simultaneously from training instances to take advantage of inductive transfer between auxiliary tasks and the target task. Then we construct a hierarchical neural network, which incorporates dependency and entity representations from auxiliary tasks into a more robust relation representation against the noisy labels. The experimental results demonstrate that our model improves the predictive performance substantially over single-task learning baselines.

Zhou, Z, Liu, S, Xu, G & Zhang, W 1970, 'On Completing Sparse Knowledge Base with Transitive Relation Embedding', Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Honolulu, Hawaii USA, pp. 3125-3132.
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Multi-relation embedding is a popular approach to knowledge base completion that learns embedding representations of entities and relations to compute the plausibility of missing triplet. The effectiveness of embedding approach depends on the sparsity of KB and falls for infrequent entities that only appeared a few times. This paper addresses this issue by proposing a new model exploiting the entity-independent transitive relation patterns, namely Transitive Relation Embedding (TRE). The TRE model alleviates the sparsity problem for predicting on infrequent entities while enjoys the generalisation power of embedding. Experiments on three public datasets against seven baselines showed the merits of TRE in terms of knowledge base completion accuracy as well as computational complexity.

Fan, X, Li, B, Sisson, SA, Li, C & Chen, L 2019, 'Scalable Deep Generative Relational Models with High-Order Node Dependence'.

Unanue, IJ, Borzeshi, EZ & Piccardi, M 2019, 'Regressing Word and Sentence Embeddings for Regularization of Neural Machine Translation'.
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In recent years, neural machine translation (NMT) has become the dominant approach in automated translation. However, like many other deep learning approaches, NMT suffers from overfitting when the amount of training data is limited. This is a serious issue for low-resource language pairs and many specialized translation domains that are inherently limited in the amount of available supervised data. For this reason, in this paper we propose regressing word (ReWE) and sentence (ReSE) embeddings at training time as a way to regularize NMT models and improve their generalization. During training, our models are trained to jointly predict categorical (words in the vocabulary) and continuous (word and sentence embeddings) outputs. An extensive set of experiments over four language pairs of variable training set size has showed that ReWE and ReSE can outperform strong state-of-the-art baseline models, with an improvement that is larger for smaller training sets (e.g., up to +5:15 BLEU points in Basque-English translation). Visualizations of the decoder's output space show that the proposed regularizers improve the clustering of unique words, facilitating correct predictions. In a final experiment on unsupervised NMT, we show that ReWE and ReSE are also able to improve the quality of machine translation when no parallel data are available.

Venkata, SK & Musial, K 2019, 'Sub-query Fragmentation for Query Analysis and Data Caching in the Distributed Environment'.
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When data stores and users are distributed geographically, it is essential to organize distributed data cache points at ideal locations to minimize data transfers. To answer this, we are developing an adaptive distributed data caching framework that can identify suitable data chunks to cache and move across a network of community cache locations.

Wahid-Ul-Ashraf, A, Budka, M & Musial, K 2019, 'Simulation and Augmentation of Social Networks for Building Deep Learning Models'.
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A limitation of the Graph Convolutional Networks (GCNs) is that it assumes ata particular $l^{th}$ layer of the neural network model only the $l^{th}$ orderneighbourhood nodes of a social network are influential. Furthermore, the GCNhas been evaluated on citation and knowledge graphs, but not extensively onfriendship-based social graphs. The drawback associated with the dependenciesbetween layers and the order of node neighbourhood for the GCN can be moreprevalent for friendship-based graphs. The evaluation of the full potential ofthe GCN on friendship-based social network requires openly available datasetsin larger quantities. However, most available social network datasets are notcomplete. Also, the majority of the available social network datasets do notcontain both the features and ground truth labels. In this work, firstly, weprovide a guideline on simulating dynamic social networks, with ground truthlabels and features, both coupled with the topology. Secondly, we introduce anopen-source Python-based simulation library. We argue that the topology of thenetwork is driven by a set of latent variables, termed as the social DNA(sDNA). We consider the sDNA as labels for the nodes. Finally, by evaluating onour simulated datasets, we propose four new variants of the GCN, mainly toovercome the limitation of dependency between the order of node-neighbourhoodand a particular layer of the model. We then evaluate the performance of allthe models and our results show that on 27 out of the 30 simulated datasets ourproposed GCN variants outperform the original model.

Wang, S, Cao, L, Wang, Y, Sheng, QZ, Orgun, M & Lian, D 2019, 'A Survey on Session-based Recommender Systems'.