Abdo, P, Huynh, BP, Braytee, A & Taghipour, R 2020, 'An experimental investigation of the thermal effect due to discharging of phase change material in a room fitted with a windcatcher', Sustainable Cities and Society, vol. 61, pp. 102277-102277.
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© 2020 Elsevier Ltd This paper investigates experimentally the effect of the Phase Change Material (PCM) discharging process as a passive cooling technique on the performance of a two sided windcatcher fitted on an acrylic chamber with dimensions 1250 × 1000 × 750 mm3. Four different models with different locations of PCM are studied, and the results are compared with each other and with a fifth model with No PCM. PCM is integrated respectively at the walls of the chamber, its floor and ceiling and also within the windcatcher's inlet channel. Humidity, temperature and air velocity are monitored for each of the models studied. It is noted that with all the models containing PCM, the average humidity inside the chamber changed only slightly compared to the model with No PCM. The difference in humidity ranged between 0 and 3.88 % which indicates that the humidity variations are not significant. The model with the PCM located on the floor, ceiling and walls as well as in the windcatcher's inlet channel has shown the best performance, with a significant minimum reduction of average temperature in the chamber of about 2.75 °C (approximately 9.33 %) compared with the model with No PCM.
Abdulkareem, SA, Augustijn, E-W, Filatova, T, Musial, K & Mustafa, YT 2020, 'Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning', PLOS ONE, vol. 15, no. 1, pp. e0226483-e0226483.
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Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research agenda of various domains. It requires a deep understanding of how individuals perceive risks and communicate about the effectiveness of protective measures, highlighting learning and social interaction as the core mechanisms driving such processes. Methodological approaches that range from purely physics-based diffusion models to data-driven environmental methods rely on agent-based modeling to accommodate context-dependent learning and social interactions in a diffusion process. Mixing agent-based modeling with data-driven machine learning has become popularity. However, little attention has been paid to the role of intelligent learning in risk appraisal and protective decisions, whether used in an individual or a collective process. The differences between collective learning and individual learning have not been sufficiently explored in diffusion modeling in general and in agent-based models of socio-environmental systems in particular. To address this research gap, we explored the implications of intelligent learning on the gradient from individual to collective learning, using an agent-based model enhanced by machine learning. Our simulation experiments showed that individual intelligent judgement about risks and the selection of coping strategies by groups with majority votes were outperformed by leader-based groups and even individuals deciding alone. Social interactions appeared essential for both individual learning and group learning. The choice of how to represent social learning in an agent-based model could be driven by existing cultural and social norms prevalent in a modeled society.
Alfaro-García, VG, Merigó, JM, Alfaro Calderón, GG, Plata-Pérez, L, Gil-Lafuente, AM & Herrera-Viedma, E 2020, 'A citation analysis of fuzzy research by universities and countries', Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5355-5367.
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Alfaro-García, VG, Merigó, JM, Pedrycz, W & Gómez Monge, R 2020, 'Citation Analysis of Fuzzy Set Theory Journals: Bibliometric Insights About Authors and Research Areas', International Journal of Fuzzy Systems, vol. 22, no. 8, pp. 2414-2448.
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Al-Hadhrami, Y & Hussain, FK 2020, 'Real time dataset generation framework for intrusion detection systems in IoT', Future Generation Computer Systems, vol. 108, pp. 414-423.
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© 2020 The Internet of Things (IoT) has evolved in the last few years to become one of the hottest topics in the area of computer science research. This drastic increase in IoT applications across different disciplines, such as in health-care and smart industries, comes with a considerable security risk. This is not limited only to attacks on privacy; it can also extend to attacks on network availability and performance. Therefore, an intrusion detection system is essential to act as the first line of defense for the network. IDS systems and algorithms depend heavily on the quality of the dataset provided. Sadly, there has been a lack of work in evaluating and collecting intrusion detection system related datasets that are designed specifically for an IoT ecosystem. Most of the studies published focus on outdated and non-compatible datasets such as the KDD98 dataset. Therefore, in this paper, we aim to investigate the existing datasets and their applications for IoT environments. Then we introduce a real-time data collection framework for building a dataset for intrusion detection system evaluation and testing. The main advantages of the proposed dataset are that it contains features that are explicitly designed for the 6LoWPAN/RPL network, the most widely used protocol in the IoT environment.
Almasoud, AS, Hussain, FK & Hussain, OK 2020, 'Smart contracts for blockchain-based reputation systems: A systematic literature review', Journal of Network and Computer Applications, vol. 170, pp. 102814-102814.
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© 2020 Elsevier Ltd Reputation systems offer a medium where users can quantify the trustworthiness or reliability of individuals providing online services or products. In the past, researchers have used blockchain technology for reputation systems. Smart contracts are computer protocols which have the primary objective to supervise, implement, or validate performances or negotiations of contracts. However, through a systematic literature review, in this paper, we find that the existing literature has not proposed a framework that facilitates the interchangeable use of smart contracts for blockchain-based reputation systems. We adopt a systematic literature review from 30 relevant studies and the data from them were extracted before identifying the research gaps. As a solution to the research gaps, we propose the FarMed framework for creating an intelligent framework that will execute Ethereum smart contact-based reputation systems and develop reliable blockchain-based protocols for transferring reputation values from one provider to another. We briefly explain our proposed framework before concluding with our future work.
Amirbagheri, K, Merigó, JM, Guitart-Tarrés, L & Nuñez-Carballosa, A 2020, 'OWA operators in the calculation of the average green-house gases emissions', Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5427-5439.
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Amiri, M, Tofigh, F, Shariati, N, Lipman, J & Abolhasan, M 2020, 'Wide-angle metamaterial absorber with highly insensitive absorption for TE and TM modes', Scientific Reports, vol. 10, no. 1.
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AbstractBeing incident and polarization angle insensitive are crucial characteristics of metamaterial perfect absorbers due to the variety of incident signals. In the case of incident angles insensitivity, facing transverse electric (TE) and transverse magnetic (TM) waves affect the absorption ratio significantly. In this scientific report, a crescent shape resonator has been introduced that provides over 99% absorption ratio for all polarization angles, as well as 70% and 93% efficiencies for different incident angles up to $$\theta =80^{\circ }$$θ=80∘ for TE and TM polarized waves, respectively. Moreover, the insensitivity for TE and TM modes can be adjusted due to the semi-symmetric structure. By adjusting the structure parameters, the absorption ratio for TE and TM waves at $$\theta =80^{\circ }$$θ=80∘ has been increased to 83% and 97%, respectively. This structure has been designed to operate at 5 GHz spectrum to absorb undesired signals generated due to the growing adoption of Wi-Fi networks. Finally, the proposed absorber has been fabricated in a $$20 \times 20$$20×20 arr...
Beavan, A, Chin, V, Ryan, LM, Spielmann, J, Mayer, J, Skorski, S, Meyer, T & Fransen, J 2020, 'A Longitudinal Analysis of the Executive Functions in High-Level Soccer Players', Journal of Sport & Exercise Psychology, vol. 42, no. 5, pp. 349-357.
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Introduction: Assessments of executive functions (EFs) with varying levels of perceptual information or action fidelity are common talent-diagnostic tools in soccer, yet their validity still has to be established. Therefore, a longitudinal development of EFs in high-level players to understand their relationship with increased exposure to training is required. Methods: A total of 304 high-performing male youth soccer players (10–21 years old) in Germany were assessed across three seasons on various sport-specific and non-sport-specific cognitive functioning assessments. Results: The posterior means (90% highest posterior density) of random slopes indicated that both abilities predominantly developed between 10 and 15 years of age. A plateau was apparent for domain-specific abilities during adolescence, whereas domain-generic abilities improved into young adulthood. Conclusion: The developmental trajectories of soccer players’ EFs follow the general populations’ despite long-term exposure to soccer-specific training and game play. This brings into question the relationship between high-level experience and EFs and renders including EFs in talent identification questionable.
Ben, X, Gong, C, Zhang, P, Yan, R, Wu, Q & Meng, W 2020, 'Coupled Bilinear Discriminant Projection for Cross-View Gait Recognition', IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 3, pp. 734-747.
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© 1991-2012 IEEE. A problem that hinders good performance of general gait recognition systems is that the appearance features of gaits are more affected-prone by views than identities, especially when the walking direction of the probe gait is different from the register gait. This problem cannot be solved by traditional projection learning methods because these methods can learn only one projection matrix, and thus for the same subject, it cannot transfer cross-view gait features into similar ones. This paper presents an innovative method to overcome this problem by aligning gait energy images (GEIs) across views with the coupled bilinear discriminant projection (CBDP). Specifically, the CBDP generates the aligned gait matrix features for two views with two sets of bilinear transformation matrices, so that the original GEIs' spatial structure information can be preserved. By iteratively maximizing the ratio of inter-class distance metric to intra-class distance metric, the CBDP can learn the optimal matrix subspace where the GEIs across views are aligned in both horizontal and vertical coordinates. Therefore, the CBDP is also able to avoid the under-sample problem. We also theoretically prove that the upper and lower bounds of the objective function sequence of the CBDP are both monotonically increasing, so the convergence of the CBDP is demonstrated. In the terms of accuracy, the comparative experiments on the CASIA (B) and OU-ISIR gait databases show that our method is superior to the state-of-the-art cross-view gait recognition methods. More impressively, encouraging performance is obtained by our method even in matching a lateral-view gait with a frontal-view gait.
Blanco-Mesa, F & Merigó, JM 2020, 'Bonferroni Distances and Their Application in Group Decision Making', Cybernetics and Systems, vol. 51, no. 1, pp. 27-58.
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© 2019, © 2019 Taylor & Francis Group, LLC. The aim of the paper is to develop new aggregation operators using Bonferroni means, ordered weighted averaging (OWA) operators and some distance measures. We introduce the Bonferroni-Hamming weighted distance (BON-HWD), Bonferroni OWA distance (BON-OWAD), Bonferroni OWA adequacy coefficient (BON-OWAAC) and Bonferroni distances with OWA operators and weighted averages (BON-IWOWAD). The main advantages of using these operators are that they allow the consideration of different aggregations contexts to be considered and multiple comparison between each argument and distance measures in the same formulation. An application is developed using these new algorithms in combination with Pichat algorithm to solve a group decision-making problem. Creative personality is taken as an example for forming creative groups. The results show fuzzy dissimilarity relations in order to establish the maximum similarity subrelations and find groups according to each individual’s creative personality similarities.
Blanco-Mesa, F, León-Castro, E & Merigó, JM 2020, 'Covariances with OWA operators and Bonferroni means', Soft Computing, vol. 24, no. 19, pp. 14999-15014.
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Brodka, P, Musial, K & Jankowski, J 2020, 'Interacting Spreading Processes in Multilayer Networks: A Systematic Review', IEEE Access, vol. 8, pp. 10316-10341.
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Buchlak, QD, Esmaili, N, Leveque, J-C, Bennett, C, Piccardi, M & Farrokhi, F 2020, 'Ethical thinking machines in surgery and the requirement for clinical leadership', The American Journal of Surgery, vol. 220, no. 5, pp. 1372-1374.
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Buchlak, QD, Esmaili, N, Leveque, J-C, Farrokhi, F, Bennett, C, Piccardi, M & Sethi, RK 2020, 'Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review', Neurosurgical Review, vol. 43, no. 5, pp. 1235-1253.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Machine learning (ML) involves algorithms learning patterns in large, complex datasets to predict and classify. Algorithms include neural networks (NN), logistic regression (LR), and support vector machines (SVM). ML may generate substantial improvements in neurosurgery. This systematic review assessed the current state of neurosurgical ML applications and the performance of algorithms applied. Our systematic search strategy yielded 6866 results, 70 of which met inclusion criteria. Performance statistics analyzed included area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity. Natural language processing (NLP) was used to model topics across the corpus and to identify keywords within surgical subspecialties. ML applications were heterogeneous. The densest cluster of studies focused on preoperative evaluation, planning, and outcome prediction in spine surgery. The main algorithms applied were NN, LR, and SVM. Input and output features varied widely and were listed to facilitate future research. The accuracy (F(2,19) = 6.56, p < 0.01) and specificity (F(2,16) = 5.57, p < 0.01) of NN, LR, and SVM differed significantly. NN algorithms demonstrated significantly higher accuracy than LR. SVM demonstrated significantly higher specificity than LR. We found no significant difference between NN, LR, and SVM AUC and sensitivity. NLP topic modeling reached maximum coherence at seven topics, which were defined by modeling approach, surgery type, and pathology themes. Keywords captured research foci within surgical domains. ML technology accurately predicts outcomes and facilitates clinical decision-making in neurosurgery. NNs frequently outperformed other algorithms on supervised learning tasks. This study identified gaps in the literature and opportunities for future neurosurgical ML research.
Cao, L 2020, 'Coupling Learning of Complex Interactions', Journal of Information Processing and Management, vol. 51, no. 2, pp. 167-186.
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Complex applications such as big data analytics involve different forms ofcoupling relationships that reflect interactions between factors related totechnical, business (domain-specific) and environmental (includingsocio-cultural and economic) aspects. There are diverse forms of couplingsembedded in poor-structured and ill-structured data. Such couplings areubiquitous, implicit and/or explicit, objective and/or subjective,heterogeneous and/or homogeneous, presenting complexities to existing learningsystems in statistics, mathematics and computer sciences, such as typicaldependency, association and correlation relationships. Modeling and learningsuch couplings thus is fundamental but challenging. This paper discusses theconcept of coupling learning, focusing on the involvement of couplingrelationships in learning systems. Coupling learning has great potential forbuilding a deep understanding of the essence of business problems and handlingchallenges that have not been addressed well by existing learning theories andtools. This argument is verified by several case studies on coupling learning,including handling coupling in recommender systems, incorporating couplingsinto coupled clustering, coupling document clustering, coupled recommenderalgorithms and coupled behavior analysis for groups.
Cao, L 2020, 'Data Science: A Comprehensive Overview', ACM Computing Surveys, 50(3), 43:1-42, 2017, vol. 50, no. 3.
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The twenty-first century has ushered in the age of big data and data economy,in which data DNA, which carries important knowledge, insights and potential,has become an intrinsic constituent of all data-based organisms. An appropriateunderstanding of data DNA and its organisms relies on the new field of datascience and its keystone, analytics. Although it is widely debated whether bigdata is only hype and buzz, and data science is still in a very early phase,significant challenges and opportunities are emerging or have been inspired bythe research, innovation, business, profession, and education of data science.This paper provides a comprehensive survey and tutorial of the fundamentalaspects of data science: the evolution from data analysis to data science, thedata science concepts, a big picture of the era of data science, the majorchallenges and directions in data innovation, the nature of data analytics, newindustrialization and service opportunities in the data economy, the professionand competency of data education, and the future of data science. This articleis the first in the field to draw a comprehensive big picture, in addition tooffering rich observations, lessons and thinking about data science andanalytics.
Cao, L 2020, 'Data Science: Challenges and Directions', Communications of the ACM, vol. 60, no. 8, pp. 8-68.
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While data science has emerged as a contentious new scientific field,enormous debates and discussions have been made on it why we need data scienceand what makes it as a science. In reviewing hundreds of pieces of literaturewhich include data science in their titles, we find that the majority of thediscussions essentially concern statistics, data mining, machine learning, bigdata, or broadly data analytics, and only a limited number of new data-drivenchallenges and directions have been explored. In this paper, we explore theintrinsic challenges and directions inspired by comprehensively exploring thecomplexities and intelligence embedded in data science problems. We focus onthe research and innovation challenges inspired by the nature of data scienceproblems as complex systems, and the methodologies for handling such systems.
Cao, L 2020, 'Data Science: Nature and Pitfalls', IEEE Intelligent Systems, vol. 31, no. 5, pp. 5-75.
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Data science is creating very exciting trends as well as significantcontroversy. A critical matter for the healthy development of data science inits early stages is to deeply understand the nature of data and data science,and to discuss the various pitfalls. These important issues motivate thediscussions in this article.
Cao, L 2020, 'In-Depth Behavior Understanding and Use: The Behavior Informatics Approach', Information Science, 180(17); 3067-3085, 2010, vol. 180, no. 17, pp. 3067-3085.
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The in-depth analysis of human behavior has been increasingly recognized as acrucial means for disclosing interior driving forces, causes and impact onbusinesses in handling many challenging issues. The modeling and analysis ofbehaviors in virtual organizations is an open area. Traditional behaviormodeling mainly relies on qualitative methods from behavioral science andsocial science perspectives. The so-called behavior analysis is actually basedon human demographic and business usage data, where behavior-oriented elementsare hidden in routinely collected transactional data. As a result, it isineffective or even impossible to deeply scrutinize native behavior intention,lifecycle and impact on complex problems and business issues. We propose theapproach of Behavior Informatics (BI), in order to support explicit andquantitative behavior involvement through a conversion from source data tobehavioral data, and further conduct genuine analysis of behavior patterns andimpacts. BI consists of key components including behavior representation,behavioral data construction, behavior impact analysis, behavior patternanalysis, behavior simulation, and behavior presentation and behavior use. Wediscuss the concepts of behavior and an abstract behavioral model, as well asthe research tasks, process and theoretical underpinnings of BI. Substantialexperiments have shown that BI has the potential to greatly complement theexisting empirical and specific means by finding deeper and more informativepatterns leading to greater in-depth behavior understanding. BI creates newdirections and means to enhance the quantitative, formal and systematicmodeling and analysis of behaviors in both physical and virtual organizations.
Cao, L 2020, 'Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting', Engineering, 2: 212-224, 2016, vol. 2, no. 2, pp. 212-224.
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While recommendation plays an increasingly critical role in our living,study, work, and entertainment, the recommendations we receive are often forirrelevant, duplicate, or uninteresting products and services. A criticalreason for such bad recommendations lies in the intrinsic assumption thatrecommended users and items are independent and identically distributed (IID)in existing theories and systems. Another phenomenon is that, while tremendousefforts have been made to model specific aspects of users or items, the overalluser and item characteristics and their non-IIDness have been overlooked. Inthis paper, the non-IID nature and characteristics of recommendation arediscussed, followed by the non-IID theoretical framework in order to build adeep and comprehensive understanding of the intrinsic nature of recommendationproblems, from the perspective of both couplings and heterogeneity. Thisnon-IID recommendation research triggers the paradigm shift from IID to non-IIDrecommendation research and can hopefully deliver informed, relevant,personalized, and actionable recommendations. It creates exciting newdirections and fundamental solutions to address various complexities includingcold-start, sparse data-based, cross-domain, group-based, and shillingattack-related issues.
Cao, L, Yang, Q & Yu, PS 2020, 'Data science and AI in FinTech: An overview', International Journal of Data Science and Analytics, vol. 12, no. 2, pp. 81-99.
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Financial technology (FinTech) has been playing an increasingly critical rolein driving modern economies, society, technology, and many other areas. SmartFinTech is the new-generation FinTech, largely inspired and empowered by datascience and new-generation AI and (DSAI) techniques. Smart FinTech synthesizesbroad DSAI and transforms finance and economies to drive intelligent,automated, whole-of-business and personalized economic and financialbusinesses, services and systems. The research on data science and AI inFinTech involves many latest progress made in smart FinTech for BankingTech,TradeTech, LendTech, InsurTech, WealthTech, PayTech, RiskTech,cryptocurrencies, and blockchain, and the DSAI techniques including complexsystem methods, quantitative methods, intelligent interactions, recognition andresponses, data analytics, deep learning, federated learning,privacy-preserving processing, augmentation, optimization, and systemintelligence enhancement. Here, we present a highly dense research overview ofsmart financial businesses and their challenges, the smart FinTech ecosystem,the DSAI techniques to enable smart FinTech, and some research directions ofsmart FinTech futures to the DSAI communities.
Cao, L, Yuan, G, Leung, T & Zhang, W 2020, 'Special Issue on AI and FinTech: The Challenge Ahead', IEEE Intelligent Systems, vol. 35, no. 3, pp. 3-6.
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Cao, Z, Ding, W, Wang, Y-K, Hussain, FK, Al-Jumaily, A & Lin, C-T 2020, 'Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy', Neurocomputing, vol. 389, pp. 198-206.
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© 2019 Elsevier B.V. Multiscale inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the habituation of brain systems. Entropy dynamics are generally believed to reflect the ability of the brain to adapt to a visual stimulus environment. In this study, we explored repetitive steady-state visual evoked potential (SSVEP)-based EEG complexity by assessing multiscale inherent fuzzy entropy with relative measurements. We used a wearable EEG device with Oz and Fpz electrodes to collect EEG signals from 40 participants under the following three conditions: a resting state (closed-eyes (CE) and open-eyes (OE) stimulation with five 15-Hz CE SSVEPs and stimulation with five 20-Hz OE SSVEPs. We noted monotonic enhancement of occipital EEG relative complexity with increasing stimulus times in CE and OE conditions. The occipital EEG relative complexity was significantly higher for the fifth SSVEP than for the first SSEVP (FDR-adjusted p < 0.05). Similarly, the prefrontal EEG relative complexity tended to be significantly higher in the OE condition compared to that in the CE condition (FDR-adjusted p < 0.05). The results also indicate that multiscale inherent fuzzy entropy is superior to other competing multiscale-based entropy methods. In conclusion, EEG relative complexity increases with stimulus times, a finding that reflects the strong habituation of brain systems. These results suggest that multiscale inherent fuzzy entropy is an EEG pattern with which brain complexity can be assessed using repetitive SSVEP stimuli.
Carmichael, CL, Wang, J, Nguyen, T, Kolawole, O, Benyoucef, A, De Mazière, C, Milne, AR, Samuel, S, Gillinder, K, Hediyeh-zadeh, S, Vo, ANQ, Huang, Y, Knezevic, K, McInnes, WRL, Shields, BJ, Mitchell, H, Ritchie, ME, Lammens, T, Lintermans, B, Van Vlierberghe, P, Wong, NC, Haigh, K, Thoms, JAI, Toulmin, E, Curtis, DJ, Oxley, EP, Dickins, RA, Beck, D, Perkins, A, McCormack, MP, Davis, MJ, Berx, G, Zuber, J, Pimanda, JE, Kile, BT, Goossens, S & Haigh, JJ 2020, 'The EMT modulator SNAI1 contributes to AML pathogenesis via its interaction with LSD1', Blood, vol. 136, no. 8, pp. 957-973.
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Abstract Modulators of epithelial-to-mesenchymal transition (EMT) have recently emerged as novel players in the field of leukemia biology. The mechanisms by which EMT modulators contribute to leukemia pathogenesis, however, remain to be elucidated. Here we show that overexpression of SNAI1, a key modulator of EMT, is a pathologically relevant event in human acute myeloid leukemia (AML) that contributes to impaired differentiation, enhanced self-renewal, and proliferation of immature myeloid cells. We demonstrate that ectopic expression of Snai1 in hematopoietic cells predisposes mice to AML development. This effect is mediated by interaction with the histone demethylase KDM1A/LSD1. Our data shed new light on the role of SNAI1 in leukemia development and identify a novel mechanism of LSD1 corruption in cancer. This is particularly pertinent given the current interest surrounding the use of LSD1 inhibitors in the treatment of multiple different malignancies, including AML.
Casanovas, M, Torres-Martínez, A & Merigó, JM 2020, 'Multi-person and multi-criteria decision making with the induced probabilistic ordered weighted average distance', Soft Computing, vol. 24, no. 2, pp. 1435-1446.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. This paper presents a new approach for selecting suppliers of products or services, specifically with respect to complex decisions that require evaluating different business characteristics to ensure their suitability and to meet the conditions defined in the recruitment process. To address this type of problem, this study presents the multi-person multi-criteria induced ordered weighted average distance (MP-MC-IOWAD) operator, which is an extension of the OWA operators that includes the notion of distances to multiple criteria and expert valuations. Thus, this work introduces new distance measures that can aggregate the information with probabilistic information and consider the attitudinal character of the decision maker. Further extensions are developed using probabilities to form the induced probabilistic ordered weighted average distance (IPOWAD) operator. An example in the management of insurance policies is presented, where the selection of insurance companies is very complex and requires the consideration of subjective criteria by experts in decision making.
Chen, Y, An, P, Huang, X, Yang, C, Liu, D & Wu, Q 2020, 'Light Field Compression Using Global Multiplane Representation and Two-Step Prediction', IEEE Signal Processing Letters, vol. 27, pp. 1135-1139.
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© 1994-2012 IEEE. Due to its spatio-angular structure, light field image allows for a wealth of post-processing techniques like digital refocusing and depth estimation. In order to compress the data of the two domains, the current proposal intends to embed the disparity-based view synthesis method into the decoder. However, predicting each view separately or in local groups means bringing more computational burden to the decoder and destroying the light field structure. Since disparity contains the relationship between all light rays in the light field, the proposed solution is to predict a disparity-based global representation as the first step. In the second step, all the views can be predicted easily based on this representation. In this letter, we use the recently proposed multiplane as the form of this global representation. The experimental results show the effectiveness of the proposed solution, and the better RD performance compared to other schemes especially under low bitrates.
Clark, S, Hyndman, RJ, Pagendam, D & Ryan, LM 2020, 'Modern strategies for time series regression', INTERNATIONAL STATISTICAL REVIEW, vol. 88, no. S1, pp. S179-S204.
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This paper discusses several modern approaches to regression analysisinvolving time series data where some of the predictor variables are alsoindexed by time. We discuss classical statistical approaches as well as methodsthat have been proposed recently in the machine learning literature. Theapproaches are compared and contrasted, and it will be seen that there areadvantages and disadvantages to most currently available approaches. There isample room for methodological developments in this area. The work is motivatedby an application involving the prediction of water levels as a function ofrainfall and other climate variables in an aquifer in eastern Australia.
Cui, L, Wu, J, Pi, D, Zhang, P & Kennedy, P 2020, 'Dual Implicit Mining-Based Latent Friend Recommendation', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 5, pp. 1663-1678.
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IEEE The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user-user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen-Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics.
Curiskis, SA, Drake, B, Osborn, TR & Kennedy, PJ 2020, 'An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit', Information Processing & Management, vol. 57, no. 2, pp. 102034-102034.
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© 2019 Elsevier Ltd Methods for document clustering and topic modelling in online social networks (OSNs) offer a means of categorising, annotating and making sense of large volumes of user generated content. Many techniques have been developed over the years, ranging from text mining and clustering methods to latent topic models and neural embedding approaches. However, many of these methods deliver poor results when applied to OSN data as such text is notoriously short and noisy, and often results are not comparable across studies. In this study we evaluate several techniques for document clustering and topic modelling on three datasets from Twitter and Reddit. We benchmark four different feature representations derived from term-frequency inverse-document-frequency (tf-idf) matrices and word embedding models combined with four clustering methods, and we include a Latent Dirichlet Allocation topic model for comparison. Several different evaluation measures are used in the literature, so we provide a discussion and recommendation for the most appropriate extrinsic measures for this task. We also demonstrate the performance of the methods over data sets with different document lengths. Our results show that clustering techniques applied to neural embedding feature representations delivered the best performance over all data sets using appropriate extrinsic evaluation measures. We also demonstrate a method for interpreting the clusters with a top-words based approach using tf-idf weights combined with embedding distance measures.
Dong, X, Gong, Y & Cao, L 2020, 'e-RNSP: An Efficient Method for Mining Repetition Negative Sequential Patterns', IEEE Transactions on Cybernetics, vol. 50, no. 5, pp. 2084-2096.
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Negative sequential patterns (NSPs), which capture both frequent occurring and nonoccurring behaviors, become increasingly important and sometimes play a role irreplaceable by analyzing occurring behaviors only. Repetition sequential patterns capture repetitions of patterns in different sequences as well as within a sequence and are very important to understand the repetition relations between behaviors. Though some methods are available for mining NSP and repetition positive sequential patterns (RPSPs), we have not found any methods for mining repetition NSP (RNSP). RNSP can help the analysts to further understand the repetition relationships between items and capture more comprehensive information with repetition properties. However, mining RNSP is much more difficult than mining NSP due to the intrinsic challenges of nonoccurring items. To address the above issues, we first propose a formal definition of repetition negative containment. Then, we propose a method to convert repetition negative containment to repetition positive containment, which fast calculates the repetition supports by only using the corresponding RPSP's information without rescanning databases. Finally, we propose an efficient algorithm, called e-RNSP, to mine RNSP efficiently. To the best of our knowledge, e-RNSP is the first algorithm to efficiently mine RNSP. Intensive experimental results on the first four real and synthetic datasets clearly show that e-RNSP can efficiently discover the repetition negative patterns; results on the fifth dataset prove the effectiveness of RNSP which are captured by the proposed method; and the results on the rest 16 datasets analyze the impacts of data characteristics on mining process.
Dong, X, Liu, L, Musial, K & Gabrys, B 2020, 'NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size', IEEE transactions on pattern analysis and machine intelligence, vol. PP, pp. 1-1.
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Neural architecture search (NAS) has attracted a lot of attention and hasbeen illustrated to bring tangible benefits in a large number of applicationsin the past few years. Architecture topology and architecture size have beenregarded as two of the most important aspects for the performance of deeplearning models and the community has spawned lots of searching algorithms forboth aspects of the neural architectures. However, the performance gain fromthese searching algorithms is achieved under different search spaces andtraining setups. This makes the overall performance of the algorithms to someextent incomparable and the improvement from a sub-module of the searchingmodel unclear. In this paper, we propose NATS-Bench, a unified benchmark onsearching for both topology and size, for (almost) any up-to-date NASalgorithm. NATS-Bench includes the search space of 15,625 neural cellcandidates for architecture topology and 32,768 for architecture size on threedatasets. We analyze the validity of our benchmark in terms of various criteriaand performance comparison of all candidates in the search space. We also showthe versatility of NATS-Bench by benchmarking 13 recent state-of-the-art NASalgorithms on it. All logs and diagnostic information trained using the samesetup for each candidate are provided. This facilitates a much larger communityof researchers to focus on developing better NAS algorithms in a morecomparable and computationally cost friendly environment. All codes arepublicly available at: https://xuanyidong.com/assets/projects/NATS-Bench.
Du, X, Yin, H, Chen, L, Wang, Y, Yang, Y & Zhou, X 2020, 'Personalized Video Recommendation Using Rich Contents from Videos', IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 3, pp. 492-505.
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IEEE Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods.
Dun, MD, Mannan, A, Rigby, CJ, Butler, S, Toop, HD, Beck, D, Connerty, P, Sillar, J, Kahl, RGS, Duchatel, RJ, Germon, Z, Faulkner, S, Chi, M, Skerrett-Byrne, D, Murray, HC, Flanagan, H, Almazi, JG, Hondermarck, H, Nixon, B, De Iuliis, G, Chamberlain, J, Alvaro, F, de Bock, CE, Morris, JC, Enjeti, AK & Verrills, NM 2020, 'Shwachman–Bodian–Diamond syndrome (SBDS) protein is a direct inhibitor of protein phosphatase 2A (PP2A) activity and overexpressed in acute myeloid leukaemia', Leukemia, vol. 34, no. 12, pp. 3393-3397.
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Espinoza-Audelo, LF, León-Castro, E, Olazabal-Lugo, M, Merigó, JM & Gil-Lafuente, AM 2020, 'Using Ordered Weighted Average for Weighted Averages Inflation', International Journal of Information Technology & Decision Making, vol. 19, no. 02, pp. 601-628.
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This paper presents the ordered weighted average weighted average inflation (OWAWAI) and some extensions using induced and heavy aggregation operators and presents the generalized operators and some of their families. The main advantage of these new formulations is that they can use two different sets of weighting vectors and generate new scenarios based on the reordering of the arguments with the weights. With this idea, it is possible to generate new approaches that under- or overestimate the results according to the knowledge and expertise of the decision-maker. The work presents an application of these new approaches in the analysis of the inflation in Chile, Colombia, and Argentina during 2017.
Fachrunnisa, O & Hussain, FK 2020, 'A methodology for creating sustainable communities based on dynamic factors in virtual environments', International Journal of Electronic Business, vol. 15, no. 2, pp. 133-133.
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Copyright © 2020 Inderscience Enterprises Ltd. A virtual community is one of communities that exist in an internet economy; however, little research has been conducted on how to make it sustainable. We propose a methodology for creating sustainable virtual communities which depends on the community’s respond to the dynamic factors in its environment such as number of members, shared contents and interaction rules. The methodology proposes the use of iterative negotiation and a panel of expert agents to assess the quality of service (QoS) delivered. This QoS assessment is based on an interaction agreement between the community members and expert agent as the administrator’s representative. The administrators use this QoS assessment to determine whether an individual’s membership will be renewed or terminated after a certain period of time. We present a metric to measure the sustainability index and demonstrate the validity of the methodology by engineering a prototype setup and running simulations under various operational conditions.
Fachrunnisa, O & Hussain, FK 2020, 'Blockchain-based human resource management practices for mitigating skills and competencies gap in workforce', International Journal of Engineering Business Management, vol. 12, pp. 184797902096640-184797902096640.
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Skills gap between company needs and competencies occupied by the workforce can be the source of inefficiencies. The purpose of this research is to develop a blockchain-based human resource (HR) framework to match the needs from the company and workforce competencies This framework will help Corporate Training Centre to standardized the competencies which then used by HR Department to develop the training material. In order to get valid information regarding skills that are needed from the company, we develop a prototype based on Blockchain. Hence, blockchain-based HRM is built to improve the quality of workforce competency in an organization. The current organizations are struggling to fulfil the needs of the workforce in accordance with industry quality standards. Therefore, this will help all parties to create a consensus between the needs of the industry with the labour market. Corporate Training Centre through the competent institution will be the mediator or intermediary to unite the information from companies, training institutions, and Professional Certification Institutions. As a result, in the long term, the needs of the workforce with the qualification required by the company in such industries will always fit the current situation. Blockchain helps to process the information and data needed by each party so that the connection between parties will be assisted efficiently and effectively.
Flores-Sosa, M, Avilés-Ochoa, E & Merigó, JM 2020, 'Induced OWA operators in linear regression', Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5509-5520.
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Hou, W, Liu, Q & Cao, L 2020, 'Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge', Applied Sciences, vol. 10, no. 14, pp. 4893-4893.
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Short text is widely seen in applications including Internet of Things (IoT). The appropriate representation and classification of short text could be severely disrupted by the sparsity and shortness of short text. One important solution is to enrich short text representation by involving cognitive aspects of text, including semantic concept, knowledge, and category. In this paper, we propose a named Entity-based Concept Knowledge-Aware (ECKA) representation model which incorporates semantic information into short text representation. ECKA is a multi-level short text semantic representation model, which extracts the semantic features from the word, entity, concept and knowledge levels by CNN, respectively. Since word, entity, concept and knowledge entity in the same short text have different cognitive informativeness for short text classification, attention networks are formed to capture these category-related attentive representations from the multi-level textual features, respectively. The final multi-level semantic representations are formed by concatenating all of these individual-level representations, which are used for text classification. Experiments on three tasks demonstrate our method significantly outperforms the state-of-the-art methods.
Huang, L, Yang, Q, Wu, J, Huang, Y, Wu, Q & Xu, J 2020, 'Generated Data With Sparse Regularized Multi-Pseudo Label for Person Re-Identification', IEEE Signal Processing Letters, vol. 27, pp. 391-395.
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© 1994-2012 IEEE. Recently, Generative Adversarial Network (GAN) has been adopted to improve person re-identification (person re-ID) performance through data augmentation. However, directly leveraging generated data to train a re-ID model may easily lead to over-fitting issue on these extra data and decrease the generalisability of model to learn true ID-related features from real data. Inspired by the previous approach which assigns multi-pseudo labels on the generated data to reduce the risk of over-fitting, we propose to take sparse regularization into consideration. We attempt to further improve the performance of current re-ID models by using the unlabeled generated data. The proposed Sparse Regularized Multi-Pseudo Label (SRMpL) can effectively prevent the over-fitting issue when some larger weights are assigned to the generated data. Our experiments are carried out on two publicly available person re-ID datasets (e.g., Market-1501 and DukeMTMC-reID). Compared with existing unlabeled generated data re-ID solutions, our approach achieves competitive performance. Two classical re-ID models are used to verify our sparse regularization label on generated data, i.e., an ID-embedding network and a two-stream network.
Huang, Y, Xu, J, Wu, Q, Zhong, Y, Zhang, P & Zhang, Z 2020, 'Beyond Scalar Neuron: Adopting Vector-Neuron Capsules for Long-Term Person Re-Identification', IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 10, pp. 3459-3471.
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Current person re-identification (re-ID) works mainly focus on the short-term scenario where a person is less likely to change clothes. However, in the long-term re-ID scenario, a person has a great chance to change clothes. A sophisticated re-ID system should take such changes into account. To facilitate the study of long-term re-ID, this paper introduces a large-scale re-ID dataset called “Celeb-reID” to the community. Unlike previous datasets, the same person can change clothes in the proposed Celeb-reID dataset. Images of Celeb-reID are acquired from the Internet using street snap-shots of celebrities. There is a total of 1,052 IDs with 34,186 images making Celeb-reID being the largest long-term re-ID dataset so far. To tackle the challenge of cloth changes, we propose to use vector-neuron (VN) capsules instead of the traditional scalar neurons (SN) to design our network. Compared with SN, one extra-dimensional information in VN can perceive cloth changes of the same person. We introduce a well-designed ReIDCaps network and integrate capsules to deal with the person re-ID task. Soft Embedding Attention (SEA) and Feature Sparse Representation (FSR) mechanisms are adopted in our network for performance boosting. Experiments are conducted on the proposed long-term re-ID dataset and two common short-term re-ID datasets. Comprehensive analyses are given to demonstrate the challenge exposed in our datasets. Experimental results show that our ReIDCaps can outperform existing state-of-the-art methods by a large margin in the long-term scenario. The new dataset and code will be released to facilitate future researches.
Hwang, H & Ryan, L 2020, 'Statistical strategies for the analysis of massive data sets', Biometrical Journal, vol. 62, no. 2, pp. 270-281.
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AbstractThe advent of the big data age has changed the landscape for statisticians. Public and private organizations alike these days are interested in capturing and analyzing complex customer data in order to improve their service and drive efficiency gains. However, the large volume of data involved often means that standard statistical methods fail and new ways of thinking are needed. Although great gains can be obtained through the use of more advanced computing environments or through developing sophisticated new statistical algorithms that handle data in a more efficient way, there are also many simpler things that can be done to handle large data sets in an efficient and intuitive manner. These include the use of distributed analysis methodologies, clever subsampling, data coarsening, and clever data reductions that exploit concepts such as sufficiency. These kinds of strategies represent exciting opportunities for statisticians to remain front and center in the data science world.
Islam, MR, Liu, S, Wang, X & Xu, G 2020, 'Deep learning for misinformation detection on online social networks: a survey and new perspectives', Social Network Analysis and Mining, vol. 10, no. 1.
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© 2020, Springer-Verlag GmbH Austria, part of Springer Nature. Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.
Jafarizadeh, S, Tofigh, F, Lipman, J & Abolhasan, M 2020, 'Optimizing synchronizability in networks of coupled systems', Automatica, vol. 112, pp. 108711-108711.
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© 2019 Elsevier Ltd Of collective behaviors in networks of coupled systems, synchronization is of central importance and an extensively studied area. This is due to the fact that it is essential for the proper functioning of a wide variety of natural and engineered systems. Traditionally, uniform coupling strength has been the default choice and the synchronizability measure has been employed for analysis and enhancement of synchronizability. The main drawback of optimizing the synchronizability measure is that it can reach the Pareto frontier but not necessarily a unique point on the Pareto frontier. Additionally, the shortcoming of uniform coupling strength is that it can reach Pareto frontier in specific topologies including edge-transitive graphs. To achieve a unique optimal answer on the Pareto frontier, this paper takes a different approach and addresses the synchronizability in networks of coupled dynamical systems with nonuniform coupling strength and optimizing the synchronizability via maximizing the minimum distance between the nonzero eigenvalues of the Laplacian and the acceptable boundaries for the stability of the system. Furthermore, two solution methods, namely the concave–convex fractional programming and the Semidefinite Programming (SDP) formulations of the problem have been provided. The proposed solution methods have been compared over different topologies and branches of an arbitrary network, where the SDP based approach has shown to be less restricted and more suitable for a wider range of topologies.
Jin, D, Zhang, B, Song, Y, He, D, Feng, Z, Chen, S, Li, W & Musial, K 2020, 'ModMRF: A modularity-based Markov Random Field method for community detection', Neurocomputing, vol. 405, pp. 218-228.
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© 2020 Elsevier B.V. Complex networks are widely used in the research of social and biological fields. Analyzing real community structure in networks is the key to the study of complex networks. Modularity optimization is one of the most popular techniques in community detection. However, due to its greedy characteristic, it leads to a large number of incorrect partitions and more communities than in reality. Existing methods use the modularity as a Hamiltonian at the finite temperature to solve the above problem. Nevertheless, modularity is not formalized as a statistical model in the method, which makes many statistical inference methods limited and cannot be used. Moreover, the method uses the sum-product version of belief propagation (BP) and its performance is not as good as the max-sum version, since it calculates per-variable marginal probabilities rather than the joint probability. To address these issues, we propose a novel Markov Random Field (MRF) method by formalizing modularity as an energy function based on the rich structures of MRF to represent properties and constraints of this problem, and use the max-sum BP to infer model parameters. In order to analyze our method and compare it with existing methods, we conducted experiments on both real-world and synthetic networks with ground-truth of communities, showing that the new method outperforms the state-of-the-art methods.
Kedziora, DJ, Musial, K & Gabrys, B 2020, 'AutonoML: Towards an Integrated Framework for Autonomous Machine Learning'.
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Over the last decade, the long-running endeavour to automate high-levelprocesses in machine learning (ML) has risen to mainstream prominence,stimulated by advances in optimisation techniques and their impact on selectingML models/algorithms. Central to this drive is the appeal of engineering acomputational system that both discovers and deploys high-performance solutionsto arbitrary ML problems with minimal human interaction. Beyond this, an evenloftier goal is the pursuit of autonomy, which describes the capability of thesystem to independently adjust an ML solution over a lifetime of changingcontexts. However, these ambitions are unlikely to be achieved in a robustmanner without the broader synthesis of various mechanisms and theoreticalframeworks, which, at the present time, remain scattered across numerousresearch threads. Accordingly, this review seeks to motivate a more expansiveperspective on what constitutes an automated/autonomous ML system, alongsideconsideration of how best to consolidate those elements. In doing so, we surveydevelopments in the following research areas: hyperparameter optimisation,multi-component models, neural architecture search, automated featureengineering, meta-learning, multi-level ensembling, dynamic adaptation,multi-objective evaluation, resource constraints, flexible user involvement,and the principles of generalisation. We also develop a conceptual frameworkthroughout the review, augmented by each topic, to illustrate one possible wayof fusing high-level mechanisms into an autonomous ML system. Ultimately, weconclude that the notion of architectural integration deserves more discussion,without which the field of automated ML risks stifling both its technicaladvantages and general uptake.
Khlaifat, N, Altaee, A, Zhou, J, Huang, Y & Braytee, A 2020, 'Optimization of a Small Wind Turbine for a Rural Area: A Case Study of Deniliquin, New South Wales, Australia', Energies, vol. 13, no. 9, pp. 2292-2292.
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The performance of a wind turbine is affected by wind conditions and blade shape. This study aimed to optimize the performance of a 20 kW horizontal-axis wind turbine (HAWT) under local wind conditions at Deniliquin, New South Wales, Australia. Ansys Fluent (version 18.2, Canonsburg, PA, USA) was used to investigate the aerodynamic performance of the HAWT. The effects of four Reynolds-averaged Navier–Stokes turbulence models on predicting the flows under separation condition were examined. The transition SST model had the best agreement with the NREL CER data. Then, the aerodynamic shape of the rotor was optimized to maximize the annual energy production (AEP) in the Deniliquin region. Statistical wind analysis was applied to define the Weibull function and scale parameters which were 2.096 and 5.042 m/s, respectively. The HARP_Opt (National Renewable Energy Laboratory, Golden, CO, USA) was enhanced with design variables concerning the shape of the blade, rated rotational speed, and pitch angle. The pitch angle remained at 0° while the rising wind speed improved rotor speed to 148.4482 rpm at rated speed. This optimization improved the AEP rate by 9.068% when compared to the original NREL design.
Khuat, TT & Gabrys, B 2020, 'Accelerated learning algorithms of general fuzzy min-max neural network using a novel hyperbox selection rule', Information Sciences, vol. 547, pp. 887-909.
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This paper proposes a method to accelerate the training process of a generalfuzzy min-max neural network. The purpose is to reduce the unsuitablehyperboxes selected as the potential candidates of the expansion step ofexisting hyperboxes to cover a new input pattern in the online learningalgorithms or candidates of the hyperbox aggregation process in theagglomerative learning algorithms. Our proposed approach is based on themathematical formulas to form a branch-and-bound solution aiming to remove thehyperboxes which are certain not to satisfy expansion or aggregationconditions, and in turn, decreasing the training time of learning algorithms.The efficiency of the proposed method is assessed over a number of widely useddata sets. The experimental results indicated the significant decrease intraining time of the proposed approach for both online and agglomerativelearning algorithms. Notably, the training time of the online learningalgorithms is reduced from 1.2 to 12 times when using the proposed method,while the agglomerative learning algorithms are accelerated from 7 to 37 timeson average.
Khuat, TT & Gabrys, B 2020, 'Random Hyperboxes', IEEE Transactions on Neural Networks and Learning Systems (2021).
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This paper proposes a simple yet powerful ensemble classifier, called RandomHyperboxes, constructed from individual hyperbox-based classifiers trained onthe random subsets of sample and feature spaces of the training set. We alsoshow a generalization error bound of the proposed classifier based on thestrength of the individual hyperbox-based classifiers as well as thecorrelation among them. The effectiveness of the proposed classifier isanalyzed using a carefully selected illustrative example and comparedempirically with other popular single and ensemble classifiers via 20 datasetsusing statistical testing methods. The experimental results confirmed that ourproposed method outperformed other fuzzy min-max neural networks, popularlearning algorithms, and is competitive with other ensemble methods. Finally,we identify the existing issues related to the generalization error bounds ofthe real datasets and inform the potential research directions.
Khuat, TT & Le, MH 2020, 'Evaluation of Sampling-Based Ensembles of Classifiers on Imbalanced Data for Software Defect Prediction Problems', SN Computer Science, vol. 1, no. 2.
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Kieu, L-M, Ou, Y, Truong, LT & Cai, C 2020, 'A class-specific soft voting framework for customer booking prediction in on-demand transport', Transportation Research Part C: Emerging Technologies, vol. 114, pp. 377-390.
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© 2020 Elsevier Ltd Customer booking prediction is essential for On-Demand Transport services, especially for those in rural and suburban areas where the demand is low, variable and often regarded as unpredictable. Existing literature tends to focus more on the prediction of demand for traffic, classical public transport, and urban On-Demand Transport service such as taxi, Uber or Lyft, in areas with higher and less variable demand, in which popular time-series prediction methods can be employed. This paper proposes an ensemble learning framework to predict the customer booking behaviour and demand using the observed data of a suburban On-Demand Transport service where data scarcity is a challenge. The proposed method, which is called as Class-specific Soft Voting, is found to be the most accurate prediction method when compared to popular supervised classification methods such as Logistic Regression, Random Forest, Support Vector Machine and other ensemble techniques.
La Paz, A, Merigó, JM, Powell, P, Ramaprasad, A & Syn, T 2020, 'Twenty‐five years of the Information Systems Journal: A bibliometric and ontological overview', Information Systems Journal, vol. 30, no. 3, pp. 431-457.
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AbstractThe Information Systems Journal (ISJ) published its first issue in 1991, and in 2015, the journal celebrated its 25th anniversary. This study presents an overview of the leading research trends in the papers that the journal has published during its first quarter of a century via a bibliometric and ontological analysis. From a bibliometric perspective, the analysis considers the publication and citation structure of the journal. The study then develops a graphical analysis of the bibliographic material by using visualization of similarities software that employs bibliographic coupling and cocitation analysis. The work produces an ontological framework of impact and analyses the journal papers to assess qualitatively ISJ's impact. The results indicate that the journal has grown significantly over time and is now recognized as one of the leading journals in information systems. Yet challenges remain if the journal is to meet its aims in impacting and setting the agenda for the development of the Information Systems field.
Laengle, S, Merigó, JM, Modak, NM & Yang, J-B 2020, 'Bibliometrics in operations research and management science: a university analysis', Annals of Operations Research, vol. 294, no. 1-2, pp. 769-813.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Many universities around the World have made important contributions in the field of operations research and management science. This article presents the most productive and influential universities between 1991 and 2015. For doing so, we use the Web of Science database in order to search for the information which is usually regarded as the most relevant for scientific research. The results show the country of origin of the leading universities being mainly from North America and Asia and especially from USA and China. The Centre National de la Recherche Scientifique (CNRS) of France is the most productive university while the Massachusetts Institute of Technology (MIT) of USA is the most influential one. The temporal evolution shows that USA is trailing its dominancy while China progressing quickly. The evaluation also reveals that Asian universities outperform North American universities during the last 5 years.
Lee, JYL, Green, PJ & Ryan, LM 2020, 'Analysis of grouped data using conjugate generalized linear mixed models', Biometrika, vol. 107, no. 1, pp. 231-237.
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Summary This article concerns a class of generalized linear mixed models for two-level grouped data, where the random effects are uniquely indexed by groups and are independent. We derive necessary and sufficient conditions for the marginal likelihood to be expressed in explicit form. These models are unified under the conjugate generalized linear mixed models framework, where conjugate refers to the fact that the marginal likelihood can be expressed in closed form, rather than implying inference via the Bayesian paradigm. The proposed framework allows simultaneous conjugacy for Gaussian, Poisson and gamma responses, and thus can accommodate both unit- and group-level covariates. Only group-level covariates can be incorporated for the binomial distribution. In a simulation of Poisson data, our framework outperformed its competitors in terms of computational time, and was competitive in terms of robustness against misspecification of the random effects distributions.
León-Castro, E, Espinoza-Audelo, LF, Merigó, JM, Gil-Lafuente, AM & Yager, RR 2020, 'The ordered weighted average inflation', Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 1901-1913.
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Li, C, Xie, H-B, Mengersen, K, Fan, X, Da Xu, RY, Sisson, SA & Van Huffel, S 2020, 'Bayesian Nonnegative Matrix Factorization With Dirichlet Process Mixtures', IEEE Transactions on Signal Processing, vol. 68, pp. 3860-3870.
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Nonnegative Matrix Factorization (NMF) is valuable in many applications of blind source separation, signal processing and machine learning. A number of algorithms that can infer nonnegative latent factors have been developed, but most of these assume a specific noise kernel. This is insufficient to deal with complex noise in real scenarios. In this paper, we present a hierarchical Dirichlet process nonnegative matrix factorization (DPNMF) model in which the Gaussian mixture model is used to approximate the complex noise distribution. Moreover, the model is cast in the nonparametric Bayesian framework by using Dirichlet process mixture to infer the necessary number of Gaussian components. We derive a mean-field variational inference algorithm for the proposed nonparametric Bayesian model. We first test the model on synthetic data sets contaminated by Gaussian, sparse and mixed noise. We then apply it to extract muscle synergies from the electromyographic (EMG) signal and to select discriminative features for motor imagery single-trial electroencephalogram (EEG) classification. Experimental results demonstrate that DPNMF performs better in extracting the latent nonnegative factors in comparison with state-of-the-art methods.
Li, M, Xu, RY, Xin, J, Zhang, K & Jing, J 2020, 'Fast non-rigid points registration with cluster correspondences projection', Signal Processing, vol. 170, pp. 107425-107425.
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Li, Y, Li, K, Wang, X & Xu, RYD 2020, 'Exploring temporal consistency for human pose estimation in videos', Pattern Recognition, vol. 103, pp. 107258-107258.
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© 2020 In this paper, we introduce a method of exploring temporal information for estimating human poses in videos. The current state-of-the-art methods utilizing temporal information can be categorized into two major branches. The first category is a model-based method that captures the temporal information entirely by using a learnable function such as RNN or 3D convolution. However, these methods are limited in exploring temporal consistency, which is essential for estimating human joint positions in videos. The second category is the posterior enhancement method, where an independent post-processing step (e.g., using optical flow) is applied to enhance the prediction. However, operations such as optical flow estimation can be susceptible to the occlusion and motion blur problems, which will adversely affect the final performance. We propose a novel Temporal Consistency Exploration (TCE) module to address both shortcomings. Compared to previous approaches, the TCE module is more efficient as it captures the temporal consistency at the feature level without having to post-process and calculate extra optical flow. Further, to capture the rich spatial context in video data, we design a multi-scale TCE to explore the time consistency information at multi-scale spatial levels. Finally, a video-based pose estimation network is designed, which is based on the encoder-decoder architecture and extended with the powerful multi-scale TCE module. We comprehensively evaluate the proposed model on two video datasets, Sub-JHMDB and Penn, and our model achieves state-of-the-art performance on both datasets.
Liu, D, Huang, Y, Wu, Q, Ma, R & An, P 2020, 'Multi-Angular Epipolar Geometry Based Light Field Angular Reconstruction Network', IEEE Transactions on Computational Imaging, vol. 6, pp. 1507-1522.
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Liu, L, Zhou, T, Long, G, Jiang, J & Zhang, C 2020, 'Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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We study many-class few-shot (MCFS) problem in both supervised learning andmeta-learning settings. Compared to the well-studied many-class many-shot andfew-class few-shot problems, the MCFS problem commonly occurs in practicalapplications but has been rarely studied in previous literature. It brings newchallenges of distinguishing between many classes given only a few trainingsamples per class. In this paper, we leverage the class hierarchy as a priorknowledge to train a coarse-to-fine classifier that can produce accuratepredictions for MCFS problem in both settings. The propose model,'memory-augmented hierarchical-classification network (MahiNet)', performscoarse-to-fine classification where each coarse class can cover multiple fineclasses. Since it is challenging to directly distinguish a variety of fineclasses given few-shot data per class, MahiNet starts from learning aclassifier over coarse-classes with more training data whose labels are muchcheaper to obtain. The coarse classifier reduces the searching range over thefine classes and thus alleviates the challenges from 'many classes'. Onarchitecture, MahiNet firstly deploys a convolutional neural network (CNN) toextract features. It then integrates a memory-augmented attention module and amulti-layer perceptron (MLP) together to produce the probabilities over coarseand fine classes. While the MLP extends the linear classifier, the attentionmodule extends the KNN classifier, both together targeting the 'few-shot'problem. We design several training strategies of MahiNet for supervisedlearning and meta-learning. In addition, we propose two novel benchmarkdatasets 'mcfsImageNet' and 'mcfsOmniglot' specially designed for MCFS problem.In experiments, we show that MahiNet outperforms several state-of-the-artmodels on MCFS problems in both supervised learning and meta-learning.
Liu, W, Chang, X, Chen, L, Phung, D, Zhang, X, Yang, Y & Hauptmann, AG 2020, 'Pair-based Uncertainty and Diversity Promoting Early Active Learning for Person Re-identification', ACM Transactions on Intelligent Systems and Technology, vol. 11, no. 2, pp. 1-15.
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The effective training of supervised Person Re-identification (Re-ID) models requires sufficient pairwise labeled data. However, when there is limited annotation resource, it is difficult to collect pairwise labeled data. We consider a challenging and practical problem called Early Active Learning, which is applied to the early stage of experiments when there is no pre-labeled sample available as references for human annotating. Previous early active learning methods suffer from two limitations for Re-ID. First, these instance-based algorithms select instances rather than pairs, which can result in missing optimal pairs for Re-ID. Second, most of these methods only consider the representativeness of instances, which can result in selecting less diverse and less informative pairs. To overcome these limitations, we propose a novel pair-based active learning for Re-ID. Our algorithm selects pairs instead of instances from the entire dataset for annotation. Besides representativeness, we further take into account the uncertainty and the diversity in terms of pairwise relations. Therefore, our algorithm can produce the most representative, informative, and diverse pairs for Re-ID data annotation. Extensive experimental results on five benchmark Re-ID datasets have demonstrated the superiority of the proposed pair-based early active learning algorithm.
Liu, X, Song, W, Musial, K, Zhao, X, Zuo, W & Yang, B 2020, 'Semi-supervised stochastic blockmodel for structure analysis of signed networks', Knowledge-Based Systems, vol. 195, pp. 105714-105714.
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© 2020 Elsevier B.V. Finding hidden structural patterns is a critical problem for all types of networks, including signed networks. Among all of the methods for structural analysis of complex network, stochastic blockmodel (SBM) is an important research tool because it is flexible and can generate networks with many different types of structures. However, most existing SBM learning methods for signed networks are unsupervised, leading to poor performance in terms of finding hidden structural patterns, especially when handling noisy and sparse networks. Learning SBM in a semi-supervised way is a promising avenue for overcoming the above difficulty. In this type of model, a small number of labelled nodes and a large number of unlabelled nodes, coupled with their network structures, are simultaneously used to train SBM. We propose a novel semi-supervised signed stochastic blockmodel and its learning algorithm based on variational Bayesian inference, with the goal of discovering both assortative (the nodes connect more densely in same clusters than that in different clusters) and disassortative (the nodes link more sparsely in same clusters than that in different clusters) structures from signed networks. The proposed model is validated through a number of experiments wherein it compared with the state-of-the-art methods using both synthetic and real-world data. The carefully designed tests, allowing to account for different scenarios, show our method outperforms other approaches existing in this space. It is especially relevant in the case of noisy and sparse networks as they constitute the majority of the real-world networks.
Liu, Y, Lan, C, Blumenstein, M & Li, J 2020, 'Bi-Level Error Correction for PacBio Long Reads', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 17, no. 3, pp. 899-905.
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IEEE The latest sequencing technologies such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines can generate long reads at the length of thousands of nucleic bases which is much longer than the reads at the length of hundreds generated by Illumina machines. However, these long reads are prone to much higher error rates, for example 15%, making downstream analysis and applications very difficult. Error correction is a process to improve the quality of sequencing data. Hybrid correction strategies have been recently proposed to combine Illumina reads of low error rates to fix sequencing errors in the noisy long reads with good performance. In this paper, we propose a new method named Bicolor, a bi-level framework of hybrid error correction for further improving the quality of PacBio long reads. At the first level, our method uses a de Bruijn graph-based error correction idea to search paths in pairs of solid < formula > < tex > $k$ < /tex > < /formula > -mers iteratively with an increasing length of < formula > < tex > $k$ < /tex > < /formula > -mer. At the second level, we combine the processed results under different parameters from the first level. In particular, a multiple sequence alignment algorithm is used to align those similar long reads, followed by a voting algorithm which determines the final base at each position of the reads. We compare the superior performance of Bicolor with three state-of-the-art methods on three real data sets. Results demonstrate that Bicolor always achieves the highest identity ratio. Bicolor also achieves a higher alignment ratio ( < formula > < tex > $ & #x003E; 1.3\%$ < /tex > < /formula > ) and a higher number of aligned reads than the current methods on two data sets. On the third data set, our method is closely competitive to the current methods in terms of number of aligned reads and genome coverage. The C++ source codes of our algorithm are freely available at https://github.com/yuansliu/Bicolor.
Liu, Y, Wong, L & Li, J 2020, 'Allowing mutations in maximal matches boosts genome compression performance', Bioinformatics, vol. 36, no. 18, pp. 4675-4681.
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Abstract Motivation A maximal match between two genomes is a contiguous non-extendable sub-sequence common in the two genomes. DNA bases mutate very often from the genome of one individual to another. When a mutation occurs in a maximal match, it breaks the maximal match into shorter match segments. The coding cost using these broken segments for reference-based genome compression is much higher than that of using the maximal match which is allowed to contain mutations. Results We present memRGC, a novel reference-based genome compression algorithm that leverages mutation-containing matches (MCMs) for genome encoding. MemRGC detects maximal matches between two genomes using a coprime double-window k-mer sampling search scheme, the method then extends these matches to cover mismatches (mutations) and their neighbouring maximal matches to form long and MCMs. Experiments reveal that memRGC boosts the compression performance by an average of 27% in reference-based genome compression. MemRGC is also better than the best state-of-the-art methods on all of the benchmark datasets, sometimes better by 50%. Moreover, memRGC uses much less memory and de-compression resources, while providing comparable compression speed. These advantages are of significant benefits to genome data storage and transmission. Availability and implementation https://github.com/yuansliu/memRGC. Supplementary information Supplementary data are available at Bioinformatics online.
Ma, R, Li, T, Bo, D, Wu, Q & An, P 2020, 'Error sensitivity model based on spatial and temporal features', Multimedia Tools and Applications, vol. 79, no. 43-44, pp. 31913-31930.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Packet loss and error propagation induced by it are significant causes of visual impairments in video applications. Most of the existing video quality assessment models are developed at frame or sequence level, which can not accurately describe the impact of packet loss on the local regions in one frame. In this paper, we propose an error sensitivity model to evaluate the impact of a single packet loss. We also make full use of the spatio-temporal correlation of the video and analyze a set of features that directly impact the perceptual quality of videos, based on the specific situation of video packet loss. With the aid of the support vector regression (SVR), these features are used to predict the error sensitivity of the local region. The proposed model is tested on six video sequences. Experimental results show that the proposed model predicts sensitivity of videos to different packet loss cases with certain reasonable accuracy, and provides good generalization ability, which turns out outperform the state-of-art image and video quality assessment methods.
Makhdoom, I, Zhou, I, Abolhasan, M, Lipman, J & Ni, W 2020, 'PrivySharing: A blockchain-based framework for privacy-preserving and secure data sharing in smart cities', Computers & Security, vol. 88, pp. 101653-101653.
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© 2019 Elsevier Ltd The ubiquitous use of Internet of Things (IoT) ranges from industrial control systems to e-Health, e-commerce, smart cities, agriculture, supply chain management, smart cars, cyber-physical systems and a lot more. However, the data collected and processed by IoT systems especially the ones with centralized control are vulnerable to availability, integrity, and privacy threats. Hence, we present “PrivySharing,” a blockchain-based innovative framework for privacy-preserving and secure IoT data sharing in a smart city environment. The proposed scheme is distinct from existing strategies on many aspects. The data privacy is preserved by dividing the blockchain network into various channels, where every channel comprises a finite number of authorized organizations and processes a specific type of data such as health, smart car, smart energy or financial details. Moreover, access to users’ data within a channel is controlled by embedding access control rules in the smart contracts. In addition, data within a channel is further isolated and secured by using private data collection and encryption respectively. Likewise, the REST API that enables clients to interact with the blockchain network has dual security in the form of an API Key and OAuth 2.0. The proposed solution conforms to some of the significant requirements outlined in the European Union General Data Protection Regulation. We also present a system of reward in the form of a digital token named “PrivyCoin” for users sharing their data with stakeholders/third parties. Lastly, the experimental outcomes advocate that a multi-channel blockchain scales well as compared to a single-channel blockchain system.
Maldonado, S, Merigo, J & Miranda, J 2020, 'IOWA-SVM: A Density-Based Weighting Strategy for SVM Classification via OWA Operators', IEEE Transactions on Fuzzy Systems, vol. 28, no. 9, pp. 2143-2150.
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© 1993-2012 IEEE. A weighting strategy for handling outliers in binary classification using support vector machine (SVM) is proposed in this article. The traditional SVM model is modified by introducing an induced ordered weighted averaging (IOWA) operator, in which the hinge loss function becomes an ordered weighted sum of the SVM slack variables. These weights are defined using IOWA quantifiers, while the order is induced via fuzzy density-based methods for outlier detection. The proposal is developed for both linear and kernel-based classification using the duality theory and the kernel trick. Our experimental results on well known benchmark datasets demonstrate the virtues of the proposed IOWA-SVM, which achieved the best average performance compared to other machine learning approaches of similar complexity.
Martínez-López, FJ, Merigó, JM, Gázquez-Abad, JC & Ruiz-Real, JL 2020, 'Industrial marketing management: Bibliometric overview since its foundation', Industrial Marketing Management, vol. 84, pp. 19-38.
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© 2019 Elsevier Inc. Industrial Marketing Management (IMM) is an outstanding journal in the field of business-to-business marketing. This paper focuses on this journal, with an extensive bibliometric analysis of IMM from its foundation in 1971 to 2017, the last year analyzed in this study. It identifies, among others, the annual evolution of publications, the most influential countries, the most relevant authors, the most prominent institutions supporting research, as well as the citations of IMM papers in major marketing, but also other, business and management journals. To do so, this research uses the Web of Science Core Collection and Scopus databases, and analyzes a wide range of bibliometric indicators, including the total number of publications and citations, citations per paper, the h-index, m-value and citation thresholds, and also develops a graphical analysis of the bibliographical material using the visualization of similarities (VOS) viewer software. Finally, by applying a cluster analysis by fractional accounting, this research identifies trends and proposes future topics and research lines, such as: trust, innovation, performance, relationship marketing, the future role of new technologies in industrial marketing research, online marketing and corporate image.
Merigó, JM, Linares-Mustaros, S & Ferrer-Comalat, JC 2020, 'Fuzzy systems in management and information science', Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5319-5322.
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Merigó, JM, Mulet-Forteza, C, Martorell, O & Merigó-Lindahl, C 2020, 'Scientific research in the tourism, leisure and hospitality field: a bibliometric analysis', Anatolia, vol. 31, no. 3, pp. 494-508.
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Modak, NM, Lobos, V, Merigó, JM, Gabrys, B & Lee, JH 2020, 'Forty years of computers & chemical engineering: A bibliometric analysis', Computers & Chemical Engineering, vol. 141, pp. 106978-106978.
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© 2020 Elsevier Ltd Computers & Chemical Engineering (CCE) is one of the premier international journals in the field of chemical engineering. CCE published its first issue in 1977 and completed forty years in 2016. More than four decades of continuous and successful journey influenced us to celebrate its contribution through a comprehensive bibliometric study. Using the Web of Science Core Collection database we depict trends of the journal in terms of papers, topics, authors, institutions, and countries. Networks visualization of co-citation of journals and authors, bibliographic coupling institutions and countries, and co-occurrence of author keywords are prepared using the visualization of similarities (VOS) viewer software. The present analysis explores publication and citation patterns of the journal. Professor Ignacio E. Grossmann, Carnegie Mellon University, and USA respectively appear as the most productive and influential author, institution, and country in CCE publications. Optimization based research topics received most attention in CCE publications.
Modak, NM, Sinha, S, Raj, A, Panda, S, Merigó, JM & Lopes de Sousa Jabbour, AB 2020, 'Corporate social responsibility and supply chain management: Framing and pushing forward the debate', Journal of Cleaner Production, vol. 273, pp. 122981-122981.
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© 2020 Elsevier Ltd Corporate social responsibility (CSR) in supply chain management (SCM) is one of the burgeoning fields of the last decade. Significant interest in this area has led to a large number of publications in recent times. For this reason, this study has been carried out to provide a comprehensive framework and future research directions for this topic. This work presents a bibliometric analysis of relevant publications dealing with CSR in SCM up to April 2019. As well as the presentation of an overview of publications and citation structures, it also explores journals and countries based on a bibliometric study. To collect the relevant data for this study, we have utilized the reliable SCOPUS database. Our results highlight the significant contributions of journals, authors, universities, and countries on this topic. With the help of “Visualization of similarities (VOS)” viewer software, this study investigates bibliographic coupling of sources and countries. It also presents co-occurrence of keywords and graphic representations of the bibliographic materials. Finally, it provides an overview of all relevant review papers in this field and a comprehensive view of related research fields.
Naji, M, Braytee, A, Al-Ani, A, Anaissi, A, Goyal, M & Kennedy, PJ 2020, 'Design of airport security screening using queueing theory augmented with particle swarm optimisation', Service Oriented Computing and Applications, vol. 14, no. 2, pp. 119-133.
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© 2020, Springer-Verlag London Ltd., part of Springer Nature. Designing an efficient and reliable airport security screening system is a critical and challenging task. It is an essential element of airline and passenger safety which aims to provide the expected level of confidence and to ensure the safety of passengers and the aviation industry. In recent years, security at airports has gone through noticeable improvements with the utilisation of advanced technology and highly trained security officers. However, for many airports, it is important to find the best compromise between the capacity of the security area, the number of passengers and the number of screening machines and officers to maintain a high level of security and to ensure that the cost and waiting times for passengers and airlines are at acceptable levels. This paper proposes a novel method based on queueing theory augmented with particle swarm optimisation (QT-PSO) to predict passenger waiting times in a security screening context. This model consists of multiple servers operating in parallel and takes into consideration the complete scenario such as normal, slow and express lanes. Such an approach has the potential to be a reliable model that is able to assimilate variations in the number of passengers, security officers and security machines on the service time. To evaluate our proposed method, we collected real-world security screening data from an Australian airport from December to March for the two consecutive years of 2016 and 2017. The results show that our proposed QT-PSO method is superior to predict the average waiting time of passengers compared to the state of the art.
Naseem, U, Razzak, I, Musial, K & Imran, M 2020, 'Transformer based Deep Intelligent Contextual Embedding for Twitter sentiment analysis', Future Generation Computer Systems, vol. 113, pp. 58-69.
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© 2020 Elsevier B.V. Along with the emergence of the Internet, the rapid development of handheld devices has democratized content creation due to the extensive use of social media and has resulted in an explosion of short informal texts. Although a sentiment analysis of these texts is valuable for many reasons, this task is often perceived as a challenge given that these texts are often short, informal, noisy, and rich in language ambiguities, such as polysemy. Moreover, most of the existing sentiment analysis methods are based on clean data. In this paper, we present DICET, a transformer-based method for sentiment analysis that encodes representation from a transformer and applies deep intelligent contextual embedding to enhance the quality of tweets by removing noise while taking word sentiments, polysemy, syntax, and semantic knowledge into account. We also use the bidirectional long- and short-term memory network to determine the sentiment of a tweet. To validate the performance of the proposed framework, we perform extensive experiments on three benchmark datasets, and results show that DICET considerably outperforms the state of the art in sentiment classification.
Naseer, A, Rani, M, Naz, S, Razzak, MI, Imran, M & Xu, G 2020, 'Refining Parkinson’s neurological disorder identification through deep transfer learning', Neural Computing and Applications, vol. 32, no. 3, pp. 839-854.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Parkinson’s disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life. However, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson’s disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that help as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning-based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that the proposed approach provides better detection of Parkinson’s disease as compared to state-of-the-art work.
Nicolas, C, Valenzuela-Fernández, L & Merigó, JM 2020, 'Research Trends of Marketing: A Bibliometric Study 1990–2017', Journal of Promotion Management, vol. 26, no. 5, pp. 674-703.
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© 2020, © 2020 Taylor & Francis Group, LLC. Interest in the role of marketing has grown in recent decades due to its impact in brand value, value creation for customers, profitability of customer base, and organizational results. The paper shows an overall view on marketing research to explore the development of research trends, showing the high-frequency keywords at different time periods. Using bibliometric methods, the research analyzes publications between 1990 and 2017 found in the Web of Science and Scopus databases. The paper shows the evolution of keywords to reveal emerging topics as demonstrated in the connections network which includes “advertising,” “consumer behavior,” “trust,” “innovation,” and “customer satisfaction.”.
Pang, G & Cao, L 2020, 'Heterogeneous Univariate Outlier Ensembles in Multidimensional Data', ACM Transactions on Knowledge Discovery from Data, vol. 14, no. 6, pp. 1-27.
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In outlier detection, recent major research has shifted from developing univariate methods to multivariate methods due to the rapid growth of multidimensional data. However, one typical issue of this paradigm shift is that many multidimensional data often mainly contains univariate outliers , in which many features are actually irrelevant. In such cases, multivariate methods are ineffective in identifying such outliers due to the potential biases and the curse of dimensionality brought by irrelevant features. Those univariate outliers might be well detected by applying univariate outlier detectors in individually relevant features. However, it is very challenging to choose a right univariate detector for each individual feature since different features may take very different probability distributions. To address this challenge, we introduce a novel Heterogeneous Univariate Outlier Ensembles (HUOE) framework and its instance ZDD to synthesize a set of heterogeneous univariate outlier detectors as base learners to build heterogeneous ensembles that are optimized for each individual feature. Extensive results on 19 real-world datasets and a collection of synthetic datasets show that ZDD obtains 5%–14% average AUC improvement over four state-of-the-art multivariate ensembles and performs substantially more robustly w.r.t. irrelevant features.
Pineda-Escobar, MA & Merigó, JM 2020, 'A bibliometric analysis of the Base/Bottom of the Pyramid research', Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5537-5551.
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Razzak, I, Saris, RA, Blumenstein, M & Xu, G 2020, 'Integrating joint feature selection into subspace learning: A formulation of 2DPCA for outliers robust feature selection', Neural Networks, vol. 121, pp. 441-451.
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© 2019 Elsevier Ltd Since the principal component analysis and its variants are sensitive to outliers that affect their performance and applicability in real world, several variants have been proposed to improve the robustness. However, most of the existing methods are still sensitive to outliers and are unable to select useful features. To overcome the issue of sensitivity of PCA against outliers, in this paper, we introduce two-dimensional outliers-robust principal component analysis (ORPCA) by imposing the joint constraints on the objective function. ORPCA relaxes the orthogonal constraints and penalizes the regression coefficient, thus, it selects important features and ignores the same features that exist in other principal components. It is commonly known that square Frobenius norm is sensitive to outliers. To overcome this issue, we have devised an alternative way to derive objective function. Experimental results on four publicly available benchmark datasets show the effectiveness of joint feature selection and provide better performance as compared to state-of-the-art dimensionality-reduction methods.
Razzak, I, Zafar, K, Imran, M & Xu, G 2020, 'Randomized nonlinear one-class support vector machines with bounded loss function to detect of outliers for large scale IoT data', Future Generation Computer Systems, vol. 112, pp. 715-723.
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© 2020 Elsevier B.V. Exponential growth of large scale data industrial internet of things is evident due to the enormous deployment of IoT data acquisition devices. Detection of unusual patterns from large scale IoT data is important though challenging task. Recently, one-class support vector machines is extensively being used for anomaly detection. It tries to find an optimal hyperplane in high dimensional data that best separates the data from anomalies with maximum margin. However, the hinge loss of traditional one-class support vector machines is unbounded, which results in larger loss caused by outliers affecting its performance for anomaly detection. Furthermore, existing methods are computationally complex for larger data. In this paper, we present novel anomaly detection for large scale data by using randomized nonlinear features in support vector machines with bounded loss function rather than finding optimized support vectors with unbounded loss function. Extensive experimental evaluation on ten benchmark datasets shows the robustness of the proposed approach against outliers such as 0.8239, 0.7921, 0.7501, 0.6711, 0.6692, 0.4789, 0.6462, 0.6812, 0.7271 and 0.7873 accuracy for Gas Sensor Array, Human Activity Recognition, Parkinson's, Hepatitis, Breast Cancer, Blood Transfusion, Heart, ILPD and Wholesale Customers datasets respectively. In addition to this, introduction of randomized nonlinear feature helps to considerably decrease the computational complexity and space complexity from O(N3) to O(Bkn) and O(N2) to O(Bkn). Thus, very attractive for larger datasets.
Razzak, MI, Imran, M & Xu, G 2020, 'Big data analytics for preventive medicine', Neural Computing and Applications, vol. 32, no. 9, pp. 4417-4451.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.
Saki, M, Abolhasan, M & Lipman, J 2020, 'A Novel Approach for Big Data Classification and Transportation in Rail Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 1239-1249.
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This paper introduces a new framework into future data-driven railway condition monitoring systems (RCM). For this purpose, we have proposed an edge processing unit that includes two main parts: a data classification model that classifies Internet of Things (IoT) data into maintenance-critical data (MCD) and maintenance-non-critical data (MNCD) and a data transmission unit that, based on the class of data, employs appropriate communication methods to transmit data to railway control centers. For the transmission of MNCD, we propose a travel pattern method that employs train stations as points of data offloading so that trains can deliver data as well as passengers at stations. The performance of our proposed solution is successfully validated via three various data sets under different operating conditions.
Saki, M, Abolhasan, M, Lipman, J & Jamalipour, A 2020, 'A Comprehensive Access Point Placement for IoT Data Transmission Through Train-Wayside Communications in Multi-Environment Based Rail Networks', IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 11937-11949.
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In this paper, we propose three algorithms for placement of access points (APs) for the purpose of data transportation via train-to-wayside (T2W) communications along a rail network. The first algorithm is proposed to find the minimum number of APs so that the path-loss (PL) does not exceed a desired threshold. Through the second algorithm, the most optimal places for a desired number of APs are determined so that the average PL is minimum. The goal of the third algorithm is to determine the required number and optimal places of APs in a rail network. Furthermore, we propose a model to consider the effects of changes of communication characteristics on the efficiency of the network in different environments. Through such model, the algorithms proposed for placement of APs can be used in different railway scenarios. The proposed algorithms are validated through extensive simulations in Sydney Trains of Australia. The simulation results show that the proposed approach can improve the efficiency of the system at least 21% and up to 165% within 10 different scenarios. We also show that we can approximately transmit over 250 Gigabit data through T2W communications over common WiFi networks.
Salik, B, Yi, H, Hassan, N, Santiappillai, N, Vick, B, Connerty, P, Duly, A, Trahair, T, Woo, AJ, Beck, D, Liu, T, Spiekermann, K, Jeremias, I, Wang, J, Kavallaris, M, Haber, M, Norris, MD, Liebermann, DA, D'Andrea, RJ, Murriel, C & Wang, JY 2020, 'Targeting RSPO3-LGR4 Signaling for Leukemia Stem Cell Eradication in Acute Myeloid Leukemia', Cancer Cell, vol. 38, no. 2, pp. 263-278.e6.
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Signals driving aberrant self-renewal in the heterogeneous leukemia stem cell (LSC) pool determine aggressiveness of acute myeloid leukemia (AML). We report that a positive modulator of canonical WNT signaling pathway, RSPO-LGR4, upregulates key self-renewal genes and is essential for LSC self-renewal in a subset of AML. RSPO2/3 serve as stem cell growth factors to block differentiation and promote proliferation of primary AML patient blasts. RSPO receptor, LGR4, is epigenetically upregulated and works through cooperation with HOXA9, a poor prognostic predictor. Blocking the RSPO3-LGR4 interaction by clinical-grade anti-RSPO3 antibody (OMP-131R10/rosmantuzumab) impairs self-renewal and induces differentiation in AML patient-derived xenografts but does not affect normal hematopoietic stem cells, providing a therapeutic opportunity for HOXA9-dependent leukemia.
Sarin, S, Haon, C, Belkhouja, M, Mas-Tur, A, Roig-Tierno, N, Sego, T, Porter, A, Merigó, JM & Carley, S 2020, 'Uncovering the knowledge flows and intellectual structures of research in Technological Forecasting and Social Change: A journey through history', Technological Forecasting and Social Change, vol. 160, pp. 120210-120210.
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Shukla, N, Merigó, JM, Lammers, T & Miranda, L 2020, 'Half a century of computer methods and programs in biomedicine: A bibliometric analysis from 1970 to 2017', Computer Methods and Programs in Biomedicine, vol. 183, pp. 105075-105075.
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© 2019 Background and Objective: Computer Methods and Programs in Biomedicine (CMPB) is a leading international journal that presents developments about computing methods and their application in biomedical research. The journal published its first issue in 1970. In 2020, the journal celebrates the 50th anniversary. Motivated by this event, this article presents a bibliometric analysis of the publications of the journal during this period (1970–2017). Methods: The objective is to identify the leading trends occurring in the journal by analysing the most cited papers, keywords, authors, institutions and countries. For doing so, the study uses the Web of Science Core Collection database. Additionally, the work presents a graphical mapping of the bibliographic information by using the visualization of similarities (VOS) viewer software. This is done to analyze bibliographic coupling, co-citation and co-occurrence of keywords. Results: CMPB is identified as a leading and core journal for biomedical researchers. The journal is strongly connected to IEEE Transactions on Biomedical Engineering and IEEE Transactions on Medical Imaging. Paper from Wang, Jacques, Zheng (published in 1995) is its most cited document. The top author in this journal is James Geoffrey Chase and the top contributing institution is Uppsala U (Sweden). Most of the papers in CMPB are from the USA followed by the UK and Italy. China and Taiwan are the only Asian countries to appear in the top 10 publishing in CMPB. A keyword co-occurrences analysis revealed strong co-occurrences for classification, picture archiving and communication system (PACS), heart rate variability, survival analysis and simulation. Keywords analysis for the last decade revealed that machine learning for a variety of healthcare problems (including image processing and analysis) dominated other research fields in CMPB. Conclusions: It can be concluded that CMPB is a world-renowned publication outlet for biomedical re...
Skarding, J, Gabrys, B & Musial, K 2020, 'Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey', in IEEE Access, vol. 9, pp. 79143-79168.
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Dynamic networks are used in a wide range of fields, including social networkanalysis, recommender systems, and epidemiology. Representing complex networksas structures changing over time allow network models to leverage not onlystructural but also temporal patterns. However, as dynamic network literaturestems from diverse fields and makes use of inconsistent terminology, it ischallenging to navigate. Meanwhile, graph neural networks (GNNs) have gained alot of attention in recent years for their ability to perform well on a rangeof network science tasks, such as link prediction and node classification.Despite the popularity of graph neural networks and the proven benefits ofdynamic network models, there has been little focus on graph neural networksfor dynamic networks. To address the challenges resulting from the fact thatthis research crosses diverse fields as well as to survey dynamic graph neuralnetworks, this work is split into two main parts. First, to address theambiguity of the dynamic network terminology we establish a foundation ofdynamic networks with consistent, detailed terminology and notation. Second, wepresent a comprehensive survey of dynamic graph neural network models using theproposed terminology
Tofigh, F, Amiri, M, Shariati, N, Lipman, J & Abolhasan, M 2020, 'Crowd Estimation Using Electromagnetic Wave Power-Level Measurements: A Proof of Concept', IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 784-792.
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© 1967-2012 IEEE. Current crowd density estimation technologies that leverage IR depth perception, video and image processing or WiFi/BLE-based sniffing and probing have privacy and deployment issues. This paper presents a novel method for non-intrusive crowd density estimation that monitors variation in EM radiation within an environment. The human body's electrical and magnetic characteristics can be correlated with variations in available EM energy. This allows for the determination of the number of people within a room. Simulations conducted using Comsol to analyse and measure electromagnetic energy levels inside a room containing human bodies. Experimental analysis provides validation of the simulation results by showing $\text{0.8}\;\text{dBm}$ drop on the average level of EM energy per person.
Tofigh, F, Amiri, M, Shariati, N, Lipman, J & Abolhasan, M 2020, 'Polarization-Insensitive Metamaterial Absorber for Crowd Estimation Based on Electromagnetic Energy Measurements', IEEE Transactions on Antennas and Propagation, vol. 68, no. 3, pp. 1458-1467.
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© 2020 IEEE. Noninvasive crowd estimation has remained a challenging issue among researchers. Methods such as image analysis and Wi-Fi/Bluetooth probing can always be used to identify and track people. Lately, authors have introduced a noninvasive method for crowd estimation based on ambient RF energy measurements. In this article, a polarization-insensitive multilayer metamaterial absorber is introduced to measure the variation in the available RF energy levels for crowd estimation purposes. The proposed dual-band absorber is designed to absorb and transfer the maximum of the available Wi-Fi energy to a lumped element to enable proper and accurate measurements. To evaluate the design, the proposed structure is fabricated as an array, and its performance is tested, proving perfect absorption at the desired frequencies, 2.4 and 5 GHz.
Ubaid, A, Hussain, F & Charles, J 2020, 'Modeling Shipment Spot Pricing in the Australian Container Shipping Industry: Case of ASIA-OCEANIA trade lane', Knowledge-Based Systems, vol. 210, pp. 106483-106483.
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Vaughan, N & Gabrys, B 2020, 'Scoring and assessment in medical VR training simulators with dynamic time series classification', Engineering Applications of Artificial Intelligence, vol. 94, pp. 103760-103760.
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© 2020 Elsevier Ltd This research proposes and evaluates scoring and assessment methods for Virtual Reality (VR) training simulators. VR simulators capture detailed n-dimensional human motion data which is useful for performance analysis. Custom made medical haptic VR training simulators were developed and used to record data from 271 trainees of multiple clinical experience levels. DTW Multivariate Prototyping (DTW-MP) is proposed. VR data was classified as Novice, Intermediate or Expert. Accuracy of algorithms applied for time-series classification were: dynamic time warping 1-nearest neighbor (DTW-1NN) 60%, nearest centroid SoftDTW classification 77.5%, Deep Learning: ResNet 85%, FCN 75%, CNN 72.5% and MCDCNN 28.5%. Expert VR data recordings can be used for guidance of novices. Assessment feedback can help trainees to improve skills and consistency. Motion analysis can identify different techniques used by individuals. Mistakes can be detected dynamically in real-time, raising alarms to prevent injuries.
Verhoeven, D, Musial, K, Palmer, S, Taylor, S, Abidi, S, Zemaityte, V & Simpson, L 2020, 'Controlling for openness in the male-dominated collaborative networks of the global film industry', PLOS ONE, vol. 15, no. 6, pp. e0234460-e0234460.
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Studies of gender inequality in film industries have noted the persistence of male domination in creative roles (usually defined as director, producer, writer) and the slow pace of reform. Typical policy remedies are premised on aggregate counts of women as a proportion of overall industry participation. Network science offers an alternative way of identifying and proposing change mechanisms, as it puts emphasis on relationships instead of individuals. Preliminary work on applying network analysis to understand inequality in the film industry has been undertaken. However, in this study we offer a comprehensive approach that enables us to not only understand what inequality in the film industry looks like through the lens of network science but also how we can attempt to address this issue. We offer a data-driven simulation framework that investigates various what-if scenarios when it comes to network evolution. We then assess each of these scenarios with respect to its potential to address gender inequality in the film industry. As suggested by previous studies, inequality is exacerbated when industry networks are most closed. We review evidence from three different national film industries on network relationships in creative teams and identify a high proportion of men who only work with other men. In response to this observation, we test several mechanisms through which industry structures may generate higher levels of openness. Our results reveal that the most critical factor for improving network openness is not simply the statistical improvement of the number of women in a network, nor the removal of men who do not work with women. The most likely behavioural changes to a network will involve the production of connections between women and powerful men.
Verma, R & Merigó, JM 2020, 'A New Decision Making Method Using Interval-Valued Intuitionistic Fuzzy Cosine Similarity Measure Based on the Weighted Reduced Intuitionistic Fuzzy Sets', Informatica, vol. 31, no. 2, pp. 399-433.
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In this paper, we develop a new flexible method for interval-valued intuitionistic fuzzy decision-making problems with cosine similarity measure. We first introduce the interval-valued intuitionistic fuzzy cosine similarity measure based on the notion of the weighted reduced intuitionistic fuzzy sets. With this cosine similarity measure, we are able to accommodate the attitudinal character of decision-makers in the similarity measuring process. We study some of its essential properties and propose the weighted interval-valued intuitionistic fuzzy cosine similarity measure.
Further, the work uses the idea of GOWA operator to develop the ordered weighted interval-valued intuitionistic fuzzy cosine similarity (OWIVIFCS) measure based on the weighted reduced intuitionistic fuzzy sets. The main advantage of the OWIVIFCS measure is that it provides a parameterized family of cosine similarity measures for interval-valued intuitionistic fuzzy sets and considers different scenarios depending on the attitude of the decision-makers. The measure is demonstrated to satisfy some essential properties, which prepare the ground for applications in different areas. In addition, we define the quasi-ordered weighted interval-valued intuitionistic fuzzy cosine similarity (quasi-OWIVIFCS) measure. It includes a wide range of particular cases such as OWIVIFCS measure, trigonometric-OWIVIFCS measure, exponential-OWIVIFCS measure, radical-OWIVIFCS measure. Finally, the study uses the OWIVIFCS measure to develop a new decision-making method to solve real-world decision problems with interval-valued intuitionistic fuzzy information. A real-life numerical example of contractor selection is also given to demonstrate the effectiveness of the developed approach in solving real-life problems.
Verma, R & Merigó, JM 2020, 'Multiple attribute group decision making based on 2-dimension linguistic intuitionistic fuzzy aggregation operators', Soft Computing, vol. 24, no. 22, pp. 17377-17400.
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Wang, S & Cao, L 2020, 'Inferring Implicit Rules by Learning Explicit and Hidden Item Dependency', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 3, pp. 935-946.
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© 2017 IEEE. Revealing complex relations between entities (e.g., items within or between transactions) is of great significance for business optimization, prediction, and decision making. Such relations include not only co-occurrence-based explicit relations but also nonco-occurrence-based implicit ones. Explicit relations have been substantially studied by rule mining-based approaches, including association rule mining and causal rule discovery. In contrast, implicit relations have received much less attention but could be more actionable. In this paper, we focus on the implicit relations between items which rarely or never co-occur while each of them co-occurs with other identical items (link items) with a high probability. A framework integrates both explicit and hidden item dependencies and a corresponding efficient algorithm IRRMiner captures such implicit relations with implicit rule inference. Experimental results show that IRRMiner not only infers implicit rules of various sizes consisting of both frequent and infrequent items effectively, it also runs at least four times faster than IARMiner, a typical indirect association rule mining algorithm which can only mine size-2 indirect association rules between frequent items. IRRMiner is applied to make recommendations and shows that the identified implicit rules can increase recommendation reliability.
Wang, S, Pasi, G, Hu, L & Cao, L 2020, 'The Era of Intelligent Recommendation: Editorial on Intelligent Recommendation with Advanced AI and Learning', IEEE Intelligent Systems, vol. 35, no. 5, pp. 3-6.
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Wang, W & CAO, L 2020, 'Negative Sequence Analysis', ACM Computing Surveys, vol. 52, no. 2, pp. 1-39.
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Negative sequential patterns (NSPs) produced by negative sequence analysis (NSA) capture more informative and actionable knowledge than classic positive sequential patterns (PSPs) due to involving both occurring and nonoccurring items, which appear in many applications. However, the research on NSA is still at an early stage, and NSP mining involves very high computational complexity and a very large search space, there is no widely accepted problem statement on NSP mining, and different settings on constraints and negative containment have been proposed in existing work. Among existing NSP mining algorithms, there are no general and systemic evaluation criteria available to assess them comprehensively. This article conducts a comprehensive technical review of existing NSA research. We explore and formalize a generic problem statement of NSA; investigate, compare, and consolidate the definitions of constraints and negative containment; and compare the working mechanisms and efficiency of existing NSP mining algorithms. The review is concluded by discussing new research opportunities in NSA.
Wang, Y, Zhang, C, Wang, S, Yu, PS, Bai, L, Cui, L & Xu, G 2020, 'Generative temporal link prediction via self-tokenized sequence modeling', World Wide Web, vol. 23, no. 4, pp. 2471-2488.
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Wu, W, Li, B, Chen, L, Gao, J & Zhang, C 2020, 'A Review for Weighted MinHash Algorithms', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Data similarity (or distance) computation is a fundamental research topic which underpins many high-level applications based on similarity measures in machine learning and data mining. However, in large-scale real-world scenarios, the exact similarity computation has become daunting due to "3V" nature (volume, velocity and variety) of big data. In this case, the hashing techniques have been verified to efficiently conduct similarity estimation in terms of both theory and practice. Currently, MinHash is a popular technique for efficiently estimating the Jaccard similarity of binary sets and furthermore, weighted MinHash is generalized to estimate the generalized Jaccard similarity of weighted sets. This review focuses on categorizing and discussing the existing works of weighted MinHash algorithms. In this review, we mainly categorize the weighted MinHash algorithms into quantization-based approaches, "active index"-based ones and others, and show the evolution and inherent connection of the weighted MinHash algorithms, from the integer weighted MinHash ones to the real-valued weighted MinHash ones. Also, we have developed a Python toolbox for the algorithms, and released it in our github. We experimentally conduct a comprehensive study of the standard MinHash algorithm and the weighted MinHash ones in the similarity estimation error and the information retrieval task.
Wu, Z, Wang, R, Li, Q, Lian, X, Xu, G, Chen, E & Liu, X 2020, 'A Location Privacy-Preserving System Based on Query Range Cover-Up or Location-Based Services', IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 5244-5254.
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© 1967-2012 IEEE. Location-based service (LBS) has been widely used in various fields of industry, and become a vital part of people's daily life. However, while providing great convenience for users, LBS results in a serious threat on users' location privacy, due to its more and more untrusted server-side. In this article, we propose a location privacy-preserving system for LBS by constructing 'cover-up ranges' to protect the query ranges associated with a location query sequence. Firstly, we present a client-based system framework for location privacy protection in LBS, which requires no compromise to the accuracy and usability of LBS. Secondly, based on the framework, we introduce a location privacy model to formulate the constraints that ideal cover-up ranges should satisfy, so as to improve the efficiency of location services and the security of location privacy. Finally, we describe an implementation algorithm to well meet the location privacy model. Both theoretical analysis and experimental evaluation demonstrate the effectiveness of our system, which can improve the security of users' location privacy on the untrusted server-side, without compromising the accuracy and usability of LBS.
Xu, G, Duong, TD, Li, Q, Liu, S & Wang, X 2020, 'Causality Learning: A New Perspective for Interpretable Machine Learning', IEEE Intelligent Informatics Bulletin, vol. 20, no. 1, pp. 27-33.
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Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation system, etc. In spite of their great performance in different sectors, researchers still concern about the mechanism under any machine learning (ML) techniques that are inherently blackbox and becoming more complex to achieve higher accuracy. Therefore, interpreting machine learning model is currently a mainstream topic in the research community. However, the traditional interpretable machine learning focuses on the association instead of the causality. This paper provides an overview of causal analysis with the fundamental background and key concepts, and then summarizes most recent causal approaches for interpretable machine learning. The evaluation techniques for assessing method quality, and open problems in causal interpretability are also discussed in this paper.
Yan, Y, Tan, M, Tsang, IW, Yang, Y, Shi, Q & Zhang, C 2020, 'Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis, and Case Study', IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 2, pp. 288-301.
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© 1989-2012 IEEE. Matrix factorization has been widely applied to various applications. With the fast development of storage and internet technologies, we have been witnessing a rapid increase of data. In this paper, we propose new algorithms for matrix factorization with the emphasis on efficiency. In addition, most existing methods of matrix factorization only consider a general smooth least square loss. Differently, many real-world applications have distinctive characteristics. As a result, different losses should be used accordingly. Therefore, it is beneficial to design new matrix factorization algorithms that are able to deal with both smooth and non-smooth losses. To this end, one needs to analyze the characteristics of target data and use the most appropriate loss based on the analysis. We particularly study two representative cases of low-rank matrix recovery, i.e., collaborative filtering for recommendation and high dynamic range imaging. To solve these two problems, we respectively propose a stage-wise matrix factorization algorithm by exploiting manifold optimization techniques. From our theoretical analysis, they are both are provably guaranteed to converge to a stationary point. Extensive experiments on recommender systems and high dynamic range imaging demonstrate the satisfactory performance and efficiency of our proposed method on large-scale real data.
Yao, Y, Shen, F, Xie, G, Liu, L, Zhu, F, Zhang, J & Shen, HT 2020, 'Exploiting Web Images for Multi-Output Classification: From Category to Subcategories', IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 7, pp. 1-13.
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Yao, Y, Zhang, J, Shen, F, Liu, L, Zhu, F, Zhang, D & Shen, HT 2020, 'Towards Automatic Construction of Diverse, High-Quality Image Datasets', IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 6, pp. 1199-1211.
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© 1989-2012 IEEE. The availability of labeled image datasets has been shown critical for high-level image understanding, which continuously drives the progress of feature designing and models developing. However, constructing labeled image datasets is laborious and monotonous. To eliminate manual annotation, in this work, we propose a novel image dataset construction framework by employing multiple textual queries. We aim at collecting diverse and accurate images for given queries from the Web. Specifically, we formulate noisy textual queries removing and noisy images filtering as a multi-view and multi-instance learning problem separately. Our proposed approach not only improves the accuracy but also enhances the diversity of the selected images. To verify the effectiveness of our proposed approach, we construct an image dataset with 100 categories. The experiments show significant performance gains by using the generated data of our approach on several tasks, such as image classification, cross-dataset generalization, and object detection. The proposed method also consistently outperforms existing weakly supervised and web-supervised approaches.
Zhang, D, Yin, J, Zhu, X & Zhang, C 2020, 'Network Representation Learning: A Survey', IEEE Transactions on Big Data, vol. 6, no. 1, pp. 3-28.
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Zhang, P, Xu, J, Wu, Q, Huang, Y & Zhang, J 2020, 'Top-Push Constrained Modality-Adaptive Dictionary Learning for Cross-Modality Person Re-Identification', IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 12, pp. 4554-4566.
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Zhang, X, Zhao, Z, Zheng, Y & Li, J 2020, 'Prediction of Taxi Destinations Using a Novel Data Embedding Method and Ensemble Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 1, pp. 68-78.
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Zhao, M, Zhang, C, Zhang, J, Porikli, F, Ni, B & Zhang, W 2020, 'Scale-Aware Crowd Counting via Depth-Embedded Convolutional Neural Networks', IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 10, pp. 3651-3662.
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© 1991-2012 IEEE. Scale variation of pedestrians in a crowd image presents a significant challenge for vision-based people counting systems. Such variations are mainly caused by perspective-related distortions due to the camera pose relative to the ground plane. Following the density-based counting paradigm, we postulate that generating density values adaptive to object scales plays a critical role in the accuracy of the final counting results. Motivated by this, we distill the underlying information from depth cues to obtain scale-aware representations that can respond to object scales considering the fact that the scale is inversely proportional to the object depth. Specifically, we propose a depth embedding module as add-ons into existing networks. This module exploits essential depth cues to spatially re-calibrate the magnitude of the original features. In this way, the objects, although in the same class, will attain distinct representations according to their scales, which directly benefits the estimation of scale-aware density values. We conduct a comprehensive analysis of the effects of the depth embedding module and validate that exploiting depth cues to perceive object scale variations in convolutional neural networks improves crowd counting performances. Our experiments demonstrate the effectiveness of the proposed approach on four popular benchmark datasets.
Zhao, X, Guo, J, Nie, F, Chen, L, Li, Z & Zhang, H 2020, 'Joint Principal Component and Discriminant Analysis for Dimensionality Reduction', IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 2, pp. 433-444.
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Linear discriminant analysis (LDA) is the most widely used supervised dimensionality reduction approach. After removing the null space of the total scatter matrix St via principal component analysis (PCA), the LDA algorithm can avoid the small sample size problem. Most existing supervised dimensionality reduction methods extract the principal component of data first, and then conduct LDA on it. However, 'most variance' is very often the most important, but not always in PCA. Thus, this two-step strategy may not be able to obtain the most discriminant information for classification tasks. Different from traditional approaches which conduct PCA and LDA in sequence, we propose a novel method referred to as joint principal component and discriminant analysis (JPCDA) for dimensionality reduction. Using this method, we are able to not only avoid the small sample size problem but also extract discriminant information for classification tasks. An iterative optimization algorithm is proposed to solve the method. To validate the efficacy of the proposed method, we perform extensive experiments on several benchmark data sets in comparison with some state-of-the-art dimensionality reduction methods. A large number of experimental results illustrate that the proposed method has quite promising classification performance.
Zhao, Z, Zhang, X, Chen, F, Fang, L & Li, J 2020, 'Accurate prediction of DNA N4-methylcytosine sites via boost-learning various types of sequence features', BMC Genomics, vol. 21, no. 1, p. 627.
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AbstractBackgroundDNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristics and machine learning algorithms have been explored to identify 4mC sites from DNA sequences. However, state-of-the-art methods have limited performance because of the lack of effective sequence features and the ad hoc choice of learning algorithms to cope with this problem. This paper is aimed to propose new sequence feature space and a machine learning algorithm with feature selection scheme to address the problem.ResultsThe feature importance score distributions in datasets of six species are firstly reported and analyzed. Then the impact of the feature selection on model performance is evaluated by independent testing on benchmark datasets, where ACC and MCC measurements on the performance after feature selection increase by 2.3% to 9.7% and 0.05 to 0.19, respectively. The proposed method is compared with three state-of-the-art predictors using independent test and 10-fold cross-validations, and our method outperforms in all datasets, especially improving the ACC by 3.02% to 7.89% and MCC by 0.06 to 0.15 in the independent test. Two detailed case studies by the proposed method have confirmed the excellent overall performance and correctly identified 24 of 26 4mC sites from the C.elegans gene, and 126 out of 137 4mC sites from the D.melanogaster gene.ConclusionsThe results show that the proposed feature space and learning algorithm with feature selection can improve the performance of DNA 4mC prediction on the benchmark datasets. The two case studies prove the effectiveness of our method in practical...
Zhe, T, Huang, L, Wu, Q, Zhang, J, Pei, C & Li, L 2020, 'Inter-Vehicle Distance Estimation Method Based on Monocular Vision Using 3D Detection', IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 4907-4919.
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© 1967-2012 IEEE. Most autonomous vehicles build their perception systems on expensive sensors, such as LIDAR, RADAR, and high-precision Global Positioning System (GPS). However, cameras can provide richer sensing at a considerably lower cost, this makes them a more appealing alternative. A driving assistance system (DAS) based on monocular vision has gradually become a research hotspot, and inter-vehicle distance estimation based on monocular vision is an important technology in DAS. There are still constrains in the existing methods for estimating the inter-vehicle distance based on monocular vision, such as low accuracy when distance is larger, unstable accuracy for different types vehicles, and significantly poor performance on distance estimation for severely occluded vehicles. To improve the accuracy and robustness of ranging results, this study proposes a monocular vision end-to-end inter-vehicle distance estimation method based on 3D detection. The actual area of the rare view of the vehicle and the corresponding projection area in the image are obtained by 3D detection method. An area-distance geometric model is then established on the basis of the camera projection principle to recover distance. Our method shows its potential in complex traffic scenarios by testing the test set data provided on the real-world computer vision benchmark, KITTI. The experimental results have superior performance than the existing published methods. Moreover, the accuracy of occluded vehicle ranging results can reach approximately 98%, while the accuracy deviation between vehicles with different visual angles is less than 2%.
Zheng, J, Li, J & Zheng, Y 2020, 'Guest Editorial for the 29th International Conference on Genome Informatics (GIW 2018)', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 17, no. 3, pp. 726-727.
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Zhou, J, Zogan, H, Yang, S, Jameel, S, Xu, G & Chen, F 2020, 'Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia', IEEE Transactions on Computational Social Systems, pp. 1-10.
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The recent COVID-19 pandemic has caused unprecedented impact across theglobe. We have also witnessed millions of people with increased mental healthissues, such as depression, stress, worry, fear, disgust, sadness, and anxiety,which have become one of the major public health concerns during this severehealth crisis. For instance, depression is one of the most common mental healthissues according to the findings made by the World Health Organisation (WHO).Depression can cause serious emotional, behavioural and physical healthproblems with significant consequences, both personal and social costsincluded. This paper studies community depression dynamics due to COVID-19pandemic through user-generated content on Twitter. A new approach based onmulti-modal features from tweets and Term Frequency-Inverse Document Frequency(TF-IDF) is proposed to build depression classification models. Multi-modalfeatures capture depression cues from emotion, topic and domain-specificperspectives. We study the problem using recently scraped tweets from Twitterusers emanating from the state of New South Wales in Australia. Our novelclassification model is capable of extracting depression polarities which maybe affected by COVID-19 and related events during the COVID-19 period. Theresults found that people became more depressed after the outbreak of COVID-19.The measures implemented by the government such as the state lockdown alsoincreased depression levels. Further analysis in the Local Government Area(LGA) level found that the community depression level was different acrossdifferent LGAs. Such granular level analysis of depression dynamics not onlycan help authorities such as governmental departments to take correspondingactions more objectively in specific regions if necessary but also allows usersto perceive the dynamics of depression over the time.
Zhu, C, Cao, L & Yin, J 2020, 'Unsupervised Heterogeneous Coupling Learning for Categorical Representation', IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, vol. PP, no. 99, pp. 1-1.
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Complex categorical data is often hierarchically coupled with heterogeneousrelationships between attributes and attribute values and the couplings betweenobjects. Such value-to-object couplings are heterogeneous with complementaryand inconsistent interactions and distributions. Limited research exists onunlabeled categorical data representations, ignores the heterogeneous andhierarchical couplings, underestimates data characteristics and complexities,and overuses redundant information, etc. The deep representation learning ofunlabeled categorical data is challenging, overseeing such value-to-objectcouplings, complementarity and inconsistency, and requiring large data,disentanglement, and high computational power. This work introduces a shallowbut powerful UNsupervised heTerogeneous couplIng lEarning (UNTIE) approach forrepresenting coupled categorical data by untying the interactions betweencouplings and revealing heterogeneous distributions embedded in each type ofcouplings. UNTIE is efficiently optimized w.r.t. a kernel k-means objectivefunction for unsupervised representation learning of heterogeneous andhierarchical value-to-object couplings. Theoretical analysis shows that UNTIEcan represent categorical data with maximal separability while effectivelyrepresent heterogeneous couplings and disclose their roles in categorical data.The UNTIE-learned representations make significant performance improvementagainst the state-of-the-art categorical representations and deeprepresentation models on 25 categorical data sets with diversifiedcharacteristics.
Zuo, Y, Fang, Y, Yang, Y, Shang, X & Wu, Q 2020, 'Depth Map Enhancement by Revisiting Multi-Scale Intensity Guidance Within Coarse-to-Fine Stages', IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 12, pp. 4676-4687.
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IEEE Being different from the most methods of guided depth map enhancement based on deep convolutional neural network which focus on increasing the depth of networks, this paper is to improve the effectiveness of intensity guidance when the network goes deep. Overall, the proposed network upsamples the low-resolution depth maps from coarse to fine. Within each refinement stage of certain-scale depth features, the current-scale and all coarse-scales of the guidance features are revisited by dense connection. Therefore, the multi-scale guidance is efficiently maintained as the propagation of features. Furthermore, the proposed network maintains the intensity features in the high-resolution domain from which the multi-scale guidance is directly extracted. This design further improves the quality of intensity guidance. In addition, the shallow depth features upsampled via transposed convolution layer are directly transferred to the final depth features for reconstruction, which is called global residual learning in feature domain. Similarly, the global residual learning in pixel domain learns the difference between the depth ground truth and the coarsely upsampled depth map. Also, the local residual learning is to maintain the low frequency within each refinement stage and progressively recover the high frequency. The proposed method is tested for noise-free and noisy cases which compares against 16 state-of-the-art methods. Our experimental results show the improved performances based on the qualitative and quantitative evaluations.
Zuo, Y, Wu, Q, Fang, Y, An, P, Huang, L & Chen, Z 2020, 'Multi-Scale Frequency Reconstruction for Guided Depth Map Super-Resolution via Deep Residual Network', IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 2, pp. 297-306.
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© 1991-2012 IEEE. The depth maps obtained by the consumer-level sensors are always noisy in the low-resolution (LR) domain. Existing methods for the guided depth super-resolution, which are based on the pre-defined local and global models, perform well in general cases (e.g., joint bilateral filter and Markov random field). However, such model-based methods may fail to describe the potential relationship between RGB-D image pairs. To solve this problem, this paper proposes a data-driven approach based on the deep convolutional neural network with global and local residual learning. It progressively upsamples the LR depth map guided by the high-resolution intensity image in multiple scales. A global residual learning is adopted to learn the difference between the ground truth and the coarsely upsampled depth map, and the local residual learning is introduced in each scale-dependent reconstruction sub-network. This scheme can restore the depth structure from coarse to fine via multi-scale frequency synthesis. In addition, batch normalization layers are used to improve the performance of depth map denoising. Our method is evaluated in noise-free and noisy cases. A comprehensive comparison against 17 state-of-the-art methods is carried out. The experimental results show that the proposed method has faster convergence speed as well as improved performances based on the qualitative and quantitative evaluations.
Zurita, G, Merigó, JM, Lobos-Ossandón, V & Mulet-Forteza, C 2020, 'Bibliometrics in computer science: An institution ranking', Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5441-5453.
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Zurita, G, Shukla, AK, Pino, JA, Merigó, JM, Lobos-Ossandón, V & Muhuri, PK 2020, 'A bibliometric overview of the Journal of Network and Computer Applications between 1997 and 2019', Journal of Network and Computer Applications, vol. 165, pp. 102695-102695.
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Abdollahi, M, Gao, X, Mei, Y, Ghosh, S & Li, J 1970, 'Ontology-Guided Data Augmentation for Medical Document Classification', Artificial Intelligence in Medicine, International Conference on Artificial Intelligence in Medicine, Springer International Publishing, USA, pp. 78-88.
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Extracting meaningful features from unstructured text is one of the most challenging tasks in medical document classification. The various domain specific expressions and synonyms in the clinical discharge notes make it more challenging to analyse them. The case becomes worse for short texts such as abstract documents. These challenges can lead to poor classification accuracy. As the medical input data is often not enough in the real world, in this work a novel ontology-guided method is proposed for data augmentation to enrich input data. Then, three different deep learning methods are employed to analyse the performance of the suggested approach for classification. The experimental results show that the suggested approach achieved substantial improvement in the targeted medical documents classification.
Al-Hadhrami, Y & Hussain, FK 1970, 'A Machine Learning Architecture Towards Detecting Denial of Service Attack in IoT', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 417-429.
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© 2020, Springer Nature Switzerland AG. Internet of thing is part of our everyday life nowadays. Where millions of devices contented to the internet to collect and share data. Although IoT devices are evolving quickly to the consumer market where smart devices and sensors are becoming one of the main components of many households, IoT sensors and actuators have been also heavily used in the industry where thousands of devices are used to collect and share data for different purposes. With the rapid development of the Internet of Things in different areas, IoT is facing difficulty in securing overall availability of the network due to its heterogeneous nature. There are many types of vulnerability in IoT that can be mitigated with further research, however, in this paper, we have concentrated on distributed denial of Service attack (DDoS) on IoT. In this paper, we propose a machine learning architecture to detect DDoS attacks in IoT networks. The architecture collects IoT network traffic and analyzes the traffic through passing to machine learning model for attack detection. We propose the use of real-time data collection tool to dynamically monitor the network.
Al-Hadhrami, Y, Al-Hadhrami, N & Hussain, FK 1970, 'Data Exportation Framework for IoT Simulation Based Devices', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 212-222.
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© Springer Nature Switzerland AG 2020. Internet of things (IoT) is part of everyday life nowadays. Millions of devices are connected to the internet to collect and share data. Although IoT devices are evolving quickly in the consumer market where smart devices and sensors are becoming one of the main components of many households, IoT sensors and actuators are also heavily used in the industry where thousands of devices are used to collect and share data for different purposes. A need for an IoT simulation tool is necessary for development purposes and testing before deployments. One of the widely used tools among IoT researchers is the open-source tool Cooja simulator. Cooja has limitations—one is the lack of a way to export collected data as a data set for further processing. Therefore, this study introduces an extension tool to present and export the data into different forms.
Almansor, EH & Hussain, FK 1970, 'Modeling the Chatbot Quality of Services (CQoS) Using Word Embedding to Intelligently Detect Inappropriate Responses', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 60-70.
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© 2020, Springer Nature Switzerland AG. The rapid growth of intelligent chatbots as conversational agents with the assistance of artificial intelligence has recently attracted much research attention. The major role of a chatbot is to generate appropriate responses to the user, however sometimes the chatbot fails to understand the user’s meaning. Therefore, detecting inappropriate responses from a chatbot is a critical issue. Several studies based on annotated datasets have investigated the problem of inappropriate responses from a chatbots perspective without considering the user’s perspective. Understanding the context of the conversation is an important point in determining whether a response is appropriate or inappropriate. Sentiment analysis is a natural language processing task that supports mining in user behavior. Therefore, we propose an intelligent framework that combines automated sentiment scoring and a word embedding model to detect the quality of chatbot responses considering the end-user’s point of view. We find our model achieves higher quality results than logistic regression.
Almansor, EH & Hussain, FK 1970, 'Survey on Intelligent Chatbots: State-of-the-Art and Future Research Directions', Complex, Intelligent, and Software Intensive Systems, International Conference on Complex, Intelligent, and Software Intensive Systems, Springer International Publishing, Sydney, pp. 534-543.
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Human-computer interaction (HCI) is an area of interest which plays a major role in understanding the interaction between humans and machines. Dialogue systems or conversational systems including chatbots, voice control interfaces and personal assistants are examples of HCI application that have been developed to interact with users using natural language. Chatbots can help customers find useful information for their needs. Thus, numerous organizations are using chatbots to automate their customer service. Thus, the needs for using artificial intelligence has been increasing due to the needs of automated services. However, devolving smart bots that can respond at the human level is challenging. In this paper, we survey the state-of-art chatbot approaches from based on the ability to generate appropriate responses perspective. After summarizing the review from this aspect, we identify the research issues and challenges in chatbots. The findings of this research will highlight directions for future work.
Amirbagheri, K, Merigó, JM & Yang, J-B 1970, 'A Bibliometric Analysis of Leading Countries in Supply Chain Management Research', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 182-192.
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Supply chain management as a newly comer discipline has attracted many attentions of the scholars to do an investigation based on its prominent level of importance for the economy and its influence on the management of the organizations. So, the key point is to understand the trends among the countries throughout the time to have a powerful insight about this issue. To this end, this work does a comprehensive analysis from 1990 to 2017. The purpose of this study is to analyze the leading countries and understand thoroughly their trends during the time. The work has dedicated to three sections. In the first one the countries have studied globally to give a comprehensive overview to academics. Next, the performance of the countries is studied in three periods to understand better the changes of each during the time. Finally, some individual journals and groups of journals are also investigated. The results show that the USA is the leader of the countries while China has experienced an enormous growth and it is predictable that with this trend can reach to the top of the list.
Amiri, M, Tofigh, F, Shariati, N, Lipman, J & Abolhasan, M 1970, 'Ultra Wideband Dual Polarization Metamaterial Absorber for 5G frequency spectrum', 2020 14th European Conference on Antennas and Propagation (EuCAP), 2020 14th European Conference on Antennas and Propagation (EuCAP), IEEE, Copenhagen, Denmark.
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Implementing 5G technology contributes to improve communication quality and facilitate several interesting applications in daily life such as Internet of things. Despite outstanding features of 5G, the amount of ambient electromagnetic waves will be increased significantly in the environment, which may be undesired. Ultra-wideband metamaterial perfect absorber is a promising solution to collect these undesired signals. Using lumped elements in absorber structure to increase the absorption bandwidth leads to design and fabrication process complexity. In this paper, a low profile polarization angle selective metamaterial absorber has been designed to absorb signals in the frequency range of 21.79 GHz to 53.23 GHz with more than 90% efficiency. The relative absorption bandwidth of the final structure is 83.81%. Moreover, the final structure is reasonably insensitive facing different incident angle up to 40 degree.
Bawden, R, Di Nunzio, GM, Grozea, C, Unanue, IJ, Yepes, AJ, Mah, N, Martinez, D, Névéol, A, Neves, M, Oronoz, M, de Viñaspre, OP, Piccardi, M, Roller, R, Siu, A, Thomas, P, Vezzani, F, Navarro, MV, Wiemann, D & Yeganova, L 1970, 'Findings of the WMT 2020 Biomedical Translation Shared Task: Basque, Italian and Russian as New Additional Languages', 5th Conference on Machine Translation, WMT 2020 - Proceedings, Fifth Conference in Machine Translation (WMT 2020), The Association for Computational Linguistics, Online, pp. 660-687.
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Machine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities. In the fifth edition of the WMT Biomedical Task, we addressed a total of eight language pairs. Five language pairs were previously addressed in past editions of the shared task, namely, English/German, English/French, English/Spanish, English/Portuguese, and English/Chinese. Three additional languages pairs were also introduced this year: English/Russian, English/Italian, and English/Basque. The task addressed the evaluation of both scientific abstracts (all language pairs) and terminologies (English/Basque only). We received submissions from a total of 20 teams. For recurring language pairs, we observed an improvement in the translations in terms of automatic scores and qualitative evaluations, compared to previous years.
Betti, F, Ramponi, G & Piccardi, M 1970, 'Controlled Text Generation with Adversarial Learning', INLG 2020 - 13th International Conference on Natural Language Generation, Proceedings, 13th International Conference on Natural Language Generation (INLG 2020), The Association for Computational Linguistics, Dublin, Ireland, pp. 29-34.
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In recent years, generative adversarial networks (GANs) have started to attain promising results also in natural language generation. However, the existing models have paid limited attention to the semantic coherence of the generated sentences. For this reason, in this paper we propose a novel network - the Controlled TExt generation Relational Memory GAN (CTERM-GAN) - that uses an external input to influence the coherence of sentence generation. The network is composed of three main components: a generator based on a Relational Memory conditioned on the external input; a syntactic discriminator which learns to discriminate between real and generated sentences; and a semantic discriminator which assesses the coherence with the external conditioning. Our experiments on six probing datasets have showed that the model has been able to achieve interesting results, retaining or improving the syntactic quality of the generated sentences while significantly improving their semantic coherence with the given input.
Biddle, R, Joshi, A, Liu, S, Paris, C & Xu, G 1970, 'Leveraging Sentiment Distributions to Distinguish Figurative From Literal Health Reports on Twitter', Proceedings of The Web Conference 2020, WWW '20: The Web Conference 2020, ACM, pp. 1217-1227.
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© 2020 ACM. Harnessing data from social media to monitor health events is a promising avenue for public health surveillance. A key step is the detection of reports of a disease (referred to as ĝ€?health mention classification') amongst tweets that mention disease words. Prior work shows that figurative usage of disease words may prove to be challenging for health mention classification. Since the experience of a disease is associated with a negative sentiment, we present a method that utilises sentiment information to improve health mention classification. Specifically, our classifier for health mention classification combines pre-trained contextual word representations with sentiment distributions of words in the tweet. For our experiments, we extend a benchmark dataset of tweets for health mention classification, adding over 14k manually annotated tweets across diseases. We also additionally annotate each tweet with a label that indicates if the disease words are used in a figurative sense. Our classifier outperforms current SOTA approaches in detecting both health-related and figurative tweets that mention disease words. We also show that tweets containing disease words are mentioned figuratively more often than in a health-related context, proving to be challenging for classifiers targeting health-related tweets.
Chen, H, Yin, H, Sun, X, Chen, T, Gabrys, B & Musial, K 1970, 'Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction', Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 1503-1511.
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Cross-platform account matching plays a significant role in social networkanalytics, and is beneficial for a wide range of applications. However,existing methods either heavily rely on high-quality user generated content(including user profiles) or suffer from data insufficiency problem if onlyfocusing on network topology, which brings researchers into an insolubledilemma of model selection. In this paper, to address this problem, we proposea novel framework that considers multi-level graph convolutions on both localnetwork structure and hypergraph structure in a unified manner. The proposedmethod overcomes data insufficiency problem of existing work and does notnecessarily rely on user demographic information. Moreover, to adapt theproposed method to be capable of handling large-scale social networks, wepropose a two-phase space reconciliation mechanism to align the embeddingspaces in both network partitioning based parallel training and accountmatching across different social networks. Extensive experiments have beenconducted on two large-scale real-life social networks. The experimentalresults demonstrate that the proposed method outperforms the state-of-the-artmodels with a big margin.
Chen, T, Zhang, J, Xie, G-S, Yao, Y, Huang, X & Tang, Z 1970, 'Classification Constrained Discriminator For Domain Adaptive Semantic Segmentation', 2020 IEEE International Conference on Multimedia and Expo (ICME), 2020 IEEE International Conference on Multimedia and Expo (ICME), IEEE, London, United Kingdom, pp. 1-6.
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© 2020 IEEE. Unsupervised domain adaptation for semantic segmentation aims to transfer knowledge from label-rich synthetic datasets to real-world images without any annotation. The traditional adversarial learning methods for domain adaptation learn to extract domain-invariant feature representations by aligning the feature distributions of both domains. However, these methods suffer from an imbalance in adversarial training and feature distortion. In this work, we propose a classification constrained discriminator to alleviate these problems. Specifically, we first propose to balance the adversarial training by eliminating any pooling layers or strided convolutions in the discriminator. Then, we propose to constrain the discriminator with an auxiliary classification loss to help the feature generator extract the domain-invariant features that are useful for segmentation rather than just ambiguous features to fool the domain discriminator. Extensive experiments demonstrate the superiority of our proposed approach. The source code and models have been made available at https://github.com/NUSTMachine-Intelligence-Laboratory/ccd.
Du, A, Pang, S, Huang, X, Zhang, J & Wu, Q 1970, 'Exploring Long-Short-Term Context For Point Cloud Semantic Segmentation', 2020 IEEE International Conference on Image Processing (ICIP), 2020 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 2755-2759.
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Golzan, M, Gheisari, S, Shariflou, S, Phu, J, Kennedy, PJ, Agar, A & Kalloniatis, M 1970, 'A combined convolutional and recurrent neural network applied to fundus videos markedly enhances glaucoma detection', INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, Annual Meeting of the Association-for-Research-in-Vision-and-Ophthalmology (ARVO), ASSOC RESEARCH VISION OPHTHALMOLOGY INC, ELECTR NETWORK.
Gong, Y, Li, Z, Zhang, J, Liu, W & Yi, J 1970, 'Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development', Proceedings of the AAAI Conference on Artificial Intelligence, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), Association for the Advancement of Artificial Intelligence (AAAI), New York USA, pp. 4020-4027.
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Recently, practical applications for passenger flow prediction have brought many benefits to urban transportation development. With the development of urbanization, a real-world demand from transportation managers is to construct a new metro station in one city area that never planned before. Authorities are interested in the picture of the future volume of commuters before constructing a new station, and estimate how would it affect other areas. In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems. For example, an accurate PPF predictor can provide invaluable knowledge to designers, such as the advice of station scales and influences on other areas, etc. To address this problem, we propose a multi-view localized correlation learning method. The core idea of our strategy is to learn the passenger flow correlations between the target areas and their localized areas with adaptive-weight. To improve the prediction accuracy, other domain knowledge is involved via a multi-view learning process. We conduct intensive experiments to evaluate the effectiveness of our method with real-world official transportation datasets. The results demonstrate that our method can achieve excellent performance compared with other available baselines. Besides, our method can provide an effective solution to the cold-start problem in the recommender system as well, which proved by its outperformed experimental results.
Gong, Y, Li, Z, Zhang, J, Liu, W, Chen, B & Dong, X 1970, 'A Spatial Missing Value Imputation Method for Multi-view Urban Statistical Data', 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, pp. 1310-1316.
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Large volumes of urban statistical data with multiple views imply rich knowledge about the development degree of cities. These data present crucial statistics which play an irreplaceable role in the regional analysis and urban computing. In reality, however, the statistical data divided into fine-grained regions usually suffer from missing data problems. Those missing values hide the useful information that may result in a distorted data analysis. Thus, in this paper, we propose a spatial missing data imputation method for multi-view urban statistical data. To address this problem, we exploit an improved spatial multi-kernel clustering method to guide the imputation process cooperating with an adaptive-weight non-negative matrix factorization strategy. Intensive experiments are conducted with other state-of-the-art approaches on six real-world urban statistical datasets. The results not only show the superiority of our method against other comparative methods on different datasets, but also represent a strong generalizability of our model.
Gromov, A, Maslennikov, A, Dawson, N, Musial, K & Kitto, K 1970, 'Curriculum profile: modelling the gaps between curriculum and the job market', Proceedings of the 13th International Conference on Educational Data Mining, EDM 2020, Ifrane, Morocco (Fully Virtual Conference), pp. 610-614.
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This study uses skill-based curriculum analytics to mine the curriculum of an entire university. A curriculum profile is constructed, providing insights about university curriculum design and the match between one institution’s curriculum and the job market for a cluster of data-intensive fields. Automating the delivery of diagnostic information like this would enable institutions to ensure that their professionally-oriented degrees meet the needs of industry, so helping to improve learner outcomes and graduate employability.
Huang, C, Jiang, S, Li, Y, Zhang, Z, Traish, J, Deng, C, Ferguson, S & Da Xu, RY 1970, 'End-to-end Dynamic Matching Network for Multi-view Multi-person 3D Pose Estimation', Computer Vision – ECCV 2020, European Conference on Computer Vision, Springer International Publishing, Glasgow, UK, pp. 477-493.
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As an important computer vision task, 3d human pose estimation in a multi-camera, multi-person setting has received widespread attention and many interesting applications have been derived from it. Traditional approaches use a 3d pictorial structure model to handle this task. However, these models suffer from high computation costs and result in low accuracy in joint detection. Recently, especially since the introduction of Deep Neural Networks, one popular approach is to build a pipeline that involves three separate steps: (1) 2d skeleton detection in each camera view, (2) identification of matched 2d skeletons and (3) estimation of the 3d poses. Many existing works operate by feeding the 2d images and camera parameters through the three modules in a cascade fashion. However, all three operations can be highly correlated. For example, the 3d generation results may affect the results of detection in step 1, as does the matching algorithm in step 2. To address this phenomenon, we propose a novel end-to-end training scheme that brings the three separate modules into a single model. However, one outstanding problem of doing so is that the matching algorithm in step 2 appears to disjoint the pipeline. Therefore, we take our inspiration from the recent success in Capsule Networks, in which its Dynamic Routing step is also disjointed, but plays a crucial role in deciding how gradients are flowed from the upper to the lower layers. Similarly, a dynamic matching module in our work also decides the paths in which gradients flow from step 3 to step 1. Furthermore, as a large number of cameras are present, the existing matching algorithm either fails to deliver a robust performance or can be very inefficient. Thus, we additionally propose a novel matching algorithm that can match 2d poses from multiple views efficiently. The algorithm is robust and able to deal with situations of incomplete and false 2d detection as well.
Huang, H, Long, G, Shen, T, Jiang, J & Zhang, C 1970, 'RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion', COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference, pp. 556-567.
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Many graph embedding approaches have been proposed for knowledge graph completion via link prediction. Among those, translating embedding approaches enjoy the advantages of lightweight structure, high efficiency and great interpretability. Especially when extended to complex vector space, they show the capability in handling various relation patterns including symmetry, antisymmetry, inversion and composition. However, previous translating embedding approaches defined in complex vector space suffer from two main issues: 1) representing and modeling capacities of the model are limited by the translation function with rigorous multiplication of two complex numbers; and 2) embedding ambiguity caused by one-to-many relations is not explicitly alleviated. In this paper, we propose a relation-adaptive translation function built upon a novel weighted product in complex space, where the weights are learnable, relation-specific and independent to embedding size. The translation function only requires eight more scalar parameters each relation, but improves expressive power and alleviates embedding ambiguity problem. Based on the function, we then present our Relation-adaptive translating Embedding (RatE) approach to score each graph triple. Moreover, a novel negative sampling method is proposed to utilize both prior knowledge and self-adversarial learning for effective optimization. Experiments verify RatE achieves state-of-the-art performance on four link prediction benchmarks.
Huang, W, Xu, RYD, Du, W, Zeng, Y & Zhao, Y 1970, 'Mean field theory for deep dropout networks: Digging up gradient backpropagation deeply', Frontiers in Artificial Intelligence and Applications, pp. 1215-1222.
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In recent years, the mean field theory has been applied to the study of neural networks and has achieved a great deal of success. The theory has been applied to various neural network structures, including CNNs, RNNs, Residual networks, and Batch normalization. Inevitably, recent work has also covered the use of dropout. The mean field theory shows that the existence of depth scales that limit the maximum depth of signal propagation and gradient backpropagation. However, the gradient backpropagation is derived under the gradient independence assumption that weights used during feed forward are drawn independently from the ones used in backpropagation. This is not how neural networks are trained in a real setting. Instead, the same weights used in a feed-forward step needs to be carried over to its corresponding backpropagation. Using this realistic condition, we perform theoretical computation on linear dropout networks and a series of experiments on dropout networks with different activation functions. Our empirical results show an interesting phenomenon that the length gradients can backpropagate for a single input and a pair of inputs are governed by the same depth scale. Besides, we study the relationship between variance and mean of statistical metrics of the gradient and shown an emergence of universality. Finally, we investigate the maximum trainable length for deep dropout networks through a series of experiments using MNIST and CIFAR10 and provide a more precise empirical formula that describes the trainable length than original work.
Huang, X, Mei, G & Zhang, J 1970, 'Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences', Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, pp. 11363-11371.
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We present a fast feature-metric point cloud registration framework, whichenforces the optimisation of registration by minimising a feature-metricprojection error without correspondences. The advantage of the feature-metricprojection error is robust to noise, outliers and density difference incontrast to the geometric projection error. Besides, minimising thefeature-metric projection error does not need to search the correspondences sothat the optimisation speed is fast. The principle behind the proposed methodis that the feature difference is smallest if point clouds are aligned verywell. We train the proposed method in a semi-supervised or unsupervisedapproach, which requires limited or no registration label data. Experimentsdemonstrate our method obtains higher accuracy and robustness than thestate-of-the-art methods. Besides, experimental results show that the proposedmethod can handle significant noise and density difference, and solve bothsame-source and cross-source point cloud registration.
Ikram, MA, Sharma, N, Raza, M & Hussain, FK 1970, 'Dynamic Ranking System of Cloud SaaS Based on Consumer Preferences - Find SaaS M2NFCP', Advances in Intelligent Systems and Computing, International Conference on Advanced Information Networking and Applications, Springer International Publishing, Japan, pp. 1000-1010.
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Software as a Service (SaaS) is a type of software application that runs and operates over a cloud computing infrastructure. SaaS has grown more dramatically compared to other cloud services delivery models (i.e. PaaS and IaaS) in terms of the number of available services. This rapid growth in SaaS brings a lot of challenges for consumers in selecting the optimum services. The aim of this article is to propose a ranking system for SaaS based on consumer’s preferences called Find SaaS M2NFCP. The proposed ranking system is based on measuring the shortest distance to the minimum and maximum of the selected consumer’s non-functional preferences. In addition, linguistic terms are taken into account to weight the most important non-functional preferences. The proposed system is evaluated against traditional SaaS ranking systems using data collected from online CRM SaaS and achieved improved results.
Jauregi Unanue, I, Esmaili, N, Haffari, G & Piccardi, M 1970, 'Leveraging Discourse Rewards for Document-Level Neural Machine Translation', Proceedings of the 28th International Conference on Computational Linguistics, Proceedings of the 28th International Conference on Computational Linguistics, International Committee on Computational Linguistics, Barcelona, Spain, pp. 4467-4482.
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Jian, S, Hu, L, Cao, L & Lu, K 1970, 'Representation learning with multiple lipschitz-constrained alignments on partially-labeled cross-domain data', AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, pp. 4320-4327.
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The cross-domain representation learning plays an important role in tasks including domain adaptation and transfer learning. However, existing cross-domain representation learning focuses on building one shared space and ignores the unlabeled data in the source domain, which cannot effectively capture the distribution and structure heterogeneities in cross-domain data. To address this challenge, we propose a new cross-domain representation learning approach: MUltiple Lipschitz-constrained AligNments (MULAN) on partiallylabeled cross-domain data. MULAN produces two representation spaces: A common representation space to incorporate knowledge from the source domain and a complementary representation space to complement the common representation with target local topological information by Lipschitzconstrained representation transformation. MULAN utilizes both unlabeled and labeled data in the source and target domains to address distribution heterogeneity by Lipschitzconstrained adversarial distribution alignment and structure heterogeneity by cluster assumption-based class alignment while keeping the target local topological information in complementary representation by self alignment. Moreover, MULAN is effectively equipped with a customized learning process and an iterative parameter updating process. MULAN shows its superior performance on partially-labeled semisupervised domain adaptation and few-shot domain adaptation and outperforms the state-of-the-art visual domain adaptation models by up to 12.1%.
Kacprzyk, J, Merigó, JM, Nurmi, H & Zadrożny, S 1970, 'Multi-agent Systems and Voting: How Similar Are Voting Procedures', Springer International Publishing, pp. 172-184.
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Khan, S & Hussain, FK 1970, 'A SOA Based SLA Negotiation and Formulation Architecture for Personalized Service Delivery in SDN', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 108-119.
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© Springer Nature Switzerland AG 2020. Supporting end-to-end personalized Quality of Services (QoS) delivery in existing network architecture is an ongoing issue. Software Defined Networking (SDN) model has emerged in response to the limitations of traditional network. Integrating Software Defined Network (SDN) architecture with Service Oriented Architecture (SOA) brings new concept for future service oriented delivery in SDN services. Researchers from both academic and industry are working to resolve the QoS limitations of service delivery, however; most of the proposed solutions are application oriented and unable to provide a reliable personalized QoS delivery in future service oriented SDN. This research propose a reliable Service Level Agreement (SLA) oriented Service Negotiation framework that would be able to provide reputation based personalized service delivery and assist in QoS management in SDN for informed decision making. Moreover, potential benefits of the proposed framework are also discussed in this paper in social, scientific and business aspects.
Khan, S & Hussain, FK 1970, 'Evaluation of SLA Negotiation for Personalized SDN Service Delivery', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 579-590.
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© 2020, Springer Nature Switzerland AG. Ensuring the quality of services (QoS) is crucial in a service-oriented business model. A service level agreement (SLA) is an important agreement between a consumer and a provider and is a key element in ensuring QoS. Service negotiation occurs in an initial stage of the SLA where service requirements are agreed upon to avoid conflict situations. Guaranteeing QoS is one of the key challenges in software defined networking (SDN). Several intelligent solutions have been proposed, however most of them are application focused and are unable to provide personalized and reliable QoS delivery in SDN. This paper presents a reputation data-driven SLA negotiation framework that provides personalized and reliable service delivery in SDN and assists in QoS management for informed decision making. In addition, a fuzzy inference system (FIS) is used to implement the framework and the results are discussed in this paper.
Khuat, TT, Chen, F & Gabrys, B 1970, 'An improved online learning algorithm for general fuzzy min-max neural network', Proceedings of the International Joint Conference on Neural Networks, 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Glasgow, UK, pp. 1-9.
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This paper proposes an improved version of the current online learningalgorithm for a general fuzzy min-max neural network (GFMM) to tackle existingissues concerning expansion and contraction steps as well as the way of dealingwith unseen data located on decision boundaries. These drawbacks lower itsclassification performance, so an improved algorithm is proposed in this studyto address the above limitations. The proposed approach does not use thecontraction process for overlapping hyperboxes, which is more likely toincrease the error rate as shown in the literature. The empirical resultsindicated the improvement in the classification accuracy and stability of theproposed method compared to the original version and other fuzzy min-maxclassifiers. In order to reduce the sensitivity to the training samplespresentation order of this new on-line learning algorithm, a simple ensemblemethod is also proposed.
Kieu, T-B, Pham, SB, Phan, X-H & Piccardi, M 1970, 'A Submodular Approach for Reference Recommendation', Communications in Computer and Information Science, International Conference of the Pacific Association for Computational Linguistics, Springer Singapore, Hanoi, Vietnam, pp. 3-14.
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© 2020, Springer Nature Singapore Pte Ltd. Choosing appropriate references for a given topic is an important, yet challenging task. The pool of potential candidates is typically very large, in the order of tens of thousands, and growing by the day. For this reason, this paper proposes an approach for automatically providing a reference list for a given manuscript. The approach is based on an original submodular inference function which balances relevance, coverage and diversity in the reference list. Experiments are carried out using an ACL corpus as a source for the references and evaluated by MAP, MRR and precision-recall. The results show the remarkable comparative performance of the proposed approach.
Kitto, K, Sarathy, N, Gromov, A, Liu, M, Musial, K & Buckingham Shum, S 1970, 'Towards skills-based curriculum analytics', Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, LAK '20: 10th International Conference on Learning Analytics and Knowledge, ACM, ELECTR NETWORK, pp. 171-180.
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Li, Y, Fan, X, Chen, L, Li, B, Yu, Z & Sisson, SA 1970, 'Recurrent Dirichlet Belief Networks for interpretable Dynamic Relational Data Modelling', 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, pp. 2470-2476.
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The Dirichlet Belief Network~(DirBN) has been recently proposed as a promising approach in learning interpretable deep latent representations for objects. In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data. The proposed Recurrent-DBN has the following merits: (1) it infers interpretable and organised hierarchical latent structures for objects within and across time steps; (2) it enables recurrent long-term temporal dependence modelling, which outperforms the one-order Markov descriptions in most of the dynamic probabilistic frameworks; (3) the computational cost scales to the number of positive links only. In addition, we develop a new inference strategy, which first upward-and-backward propagates latent counts and then downward-and-forward samples variables, to enable efficient Gibbs sampling for the Recurrent-DBN. We apply the Recurrent-DBN to dynamic relational data problems. The extensive experiment results on real-world data validate the advantages of the Recurrent-DBN over the state-of-the-art models in interpretable latent structure discovery and improved link prediction performance.
Li, Y, Li, K, Jiang, S, Zhang, Z, Huang, C & Da Xu, RY 1970, 'Geometry-driven self-supervised method for 3D human pose estimation', AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, pp. 11442-11449.
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The neural network based approach for 3D human pose estimation from monocular images has attracted growing interest. However, annotating 3D poses is a labor-intensive and expensive process. In this paper, we propose a novel self-supervised approach to avoid the need of manual annotations. Different from existing weakly/self-supervised methods that require extra unpaired 3D ground-truth data to alleviate the depth ambiguity problem, our method trains the network only relying on geometric knowledge without any additional 3D pose annotations. The proposed method follows the two-stage pipeline: 2D pose estimation and 2D-to-3D pose lifting. We design the transform re-projection loss that is an effective way to explore multi-view consistency for training the 2D-to-3D lifting network. Besides, we adopt the confidences of 2D joints to integrate losses from different views to alleviate the influence of noises caused by the self-occlusion problem. Finally, we design a two-branch training architecture, which helps to preserve the scale information of re-projected 2D poses during training, resulting in accurate 3D pose predictions. We demonstrate the effectiveness of our method on two popular 3D human pose datasets, Human3.6M and MPI-INF-3DHP. The results show that our method significantly outperforms recent weakly/self-supervised approaches.
Li, Y, Shen, T, Long, G, Jiang, J, Zhou, T & Zhang, C 1970, 'Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention', COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference, pp. 1653-1664.
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Wrong labeling problem and long-tail relations are two main challenges caused by distant supervision in relation extraction. Recent works alleviate the wrong labeling by selective attention via multi-instance learning, but cannot well handle long-tail relations even if hierarchies of the relations are introduced to share knowledge. In this work, we propose a novel neural network, Collaborating Relation-augmented Attention (CoRA), to handle both the wrong labeling and long-tail relations. Particularly, we first propose relation-augmented attention network as base model. It operates on sentence bag with a sentence-to-relation attention to minimize the effect of wrong labeling. Then, facilitated by the proposed base model, we introduce collaborating relation features shared among relations in the hierarchies to promote the relation-augmenting process and balance the training data for long-tail relations. Besides the main training objective to predict the relation of a sentence bag, an auxiliary objective is utilized to guide the relation-augmenting process for a more accurate bag-level representation. In the experiments on the popular benchmark dataset NYT, the proposed CoRA improves the prior state-of-the-art performance by a large margin in terms of Precision@N, AUC and Hits@K. Further analyses verify its superior capability in handling long-tail relations in contrast to the competitors.
Li, Z, Zhang, J, Gong, Y, Yao, Y & Wu, Q 1970, 'Field-wise learning for multi-field categorical data', Advances in Neural Information Processing Systems, Conference on Neural Information Processing Systems, On-line.
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We propose a new method for learning with multi-field categorical data. Multi-field categorical data are usually collected over many heterogeneous groups. These groups can reflect in the categories under a field. The existing methods try to learn a universal model that fits all data, which is challenging and inevitably results in learning a complex model. In contrast, we propose a field-wise learning method leveraging the natural structure of data to learn simple yet efficient one-to-one field-focused models with appropriate constraints. In doing this, the models can be fitted to each category and thus can better capture the underlying differences in data. We present a model that utilizes linear models with variance and low-rank constraints, to help it generalize better and reduce the number of parameters. The model is also interpretable in a field-wise manner. As the dimensionality of multi-field categorical data can be very high, the models applied to such data are mostly over-parameterized. Our theoretical analysis can potentially explain the effect of over-parametrization on the generalization of our model. It also supports the variance constraints in the learning objective. The experiment results on two large-scale datasets show the superior performance of our model, the trend of the generalization error bound, and the interpretability of learning outcomes. Our code is available at https://github.com/lzb5600/Field-wise-Learning.
Liu, J, Zhang, JA, Xu, R, Pearce, A, Ni, W & Hedley, M 1970, 'Gaussian Mixture Model based Convolutional Sparse Coding for Radar Heartbeat Detection', 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS), 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, Adelaide, Australia, pp. 1-6.
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Accurate detection of heartbeat through radar has many potential applications in, e.g., security and health. However, it is generally challenging to obtain clear heart-beat signature, due to its weak signal and relatively large interference caused by, e.g., body and respiration movement. In this paper, we propose an advanced algorithm based on convolutional sparse coding (CSC) and Gaussian mixture model (GMM) for suppressing the interference and extracting clear heartbeat signals. In this study, heartbeat signals are modelled by CSC and recovered by exploiting the sparsity of the signal. GMM is introduced to model the unknown noise, which could be a mixture from multiple noise/interference sources. The parameters of GMM, dictionary and codes are computed via the expectation maximization (EM) algorithm. To achieve faster processing, convolution computing is proposed to be processed in the frequency domain. The proposed method is tested and validated by simulation and experiments. The results show that our proposed algorithm can accurately extract the heartbeat components.
Makhdoom, I, Tofigh, F, Zhou, I, Abolhasan, M & Lipman, J 1970, 'PLEDGE: A Proof-of-Honesty based Consensus Protocol for Blockchain-based IoT Systems', 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), IEEE, Toronto, ON, Canada, pp. 1-3.
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Exhibition of malicious behavior during blockchain consensus, threats against reputation systems, and high TX latency are significant issues for blockchain-based IoT systems. Hence, to mitigate such challenges we propose 'Pledge', a unique Proof-of-Honesty based consensus protocol. Initial experimentation shows that Pledge is economical with low computations and communications complexity and low latency in transaction confirmation.
Makhdoom, I, Tofigh, F, Zhou, I, Abolhasan, M & Lipman, J 1970, 'PLEDGE: An IoT-oriented Proof-of-Honesty based Blockchain Consensus Protocol', 2020 IEEE 45th Conference on Local Computer Networks (LCN), 2020 IEEE 45th Conference on Local Computer Networks (LCN), IEEE, Australia, pp. 54-64.
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The existing lottery-based consensus algorithms, such as Proof-of-Work, and Proof-of-Stake, are mostly used for blockchain-based financial technology applications. Similarly, the Byzantine Fault Tolerance algorithms do provide consensus finality, yet they are either communications intensive, vulnerable to Denial-of-Service attacks, poorly scalable, or have a low faulty node tolerance level. Moreover, these algorithms are not designed for the Internet of Things systems that require near-real-time transaction confirmation, maximum fault tolerance, and appropriate transaction validation rules. Hence, we propose 'Pledge, 'a unique Proof-of-Honesty based consensus protocol to reduce the possibility of malicious behavior during blockchain consensus. Pledge also introduces the Internet of Things centric transaction validation rules. Initial experimentation shows that Pledge is economical and secure with low communications complexity and low latency in transaction confirmation.
Marsh, L, Cochrane, M, Lodge, R, Sims, B, Traish, J & Xu, R 1970, 'Autonomous Target Allocation Recommendations', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, Australia, pp. 1403-1410.
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We consider the problem of land vehicles under attack from a number of unmanned aerial systems. As the number of unmanned aerial systems increase, it may become difficult for human operators to coordinate actions across vehicles in a timely manner. In this paper, we study a number of algorithms designed to recommend actions to operators that will maximise the survivability of the vehicle fleet. We present a comparison of several assignment approaches including evolutionary strategies, genetic algorithms, multi-armed bandits, probability trees and basic heuristics. The performance of these algorithms is analysed across six different simulated scenarios. Our findings indicate that while there was no single best approach, Evolution Strategies, Ensemble and Genetic Algorithms were the strongest performers. It was also seen that a number of heuristic algorithms and the multi-armed bandits approach offered reliable performance in a number of scenarios without the need for any training.
Mittal, DA, Liu, S & Xu, G 1970, 'Electricity Price Forecasting using Convolution and LSTM Models', 2020 7th International Conference on Behavioural and Social Computing (BESC), 2020 7th International Conference on Behavioural and Social Computing (BESC), IEEE, pp. 1-4.
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Electricity Market uses Demand and Supply chain strategy. Also, it is prone to random fluctuations that directly impact profit. Therefore forecasting demand becomes very important to mitigate the consequences of price dynamics. This paper proposes a Deep Learning model using Long Short Term Memory (LSTM) and Convolution Neural Network to forecast future electricity prices on the Australian electricity market and compares them with other state of the art models. We have selected evaluation metrics to prove that our model outperforms the other existing models for electricity price prediction.
Naji, M, Braytee, A, Anaissi, A, Sianaki, OA & Al-Ani, A 1970, 'Optimizing the Waiting Time for Airport Security Screening Using Multiple Queues and Servers', Complex, Intelligent, and Software Intensive Systems Proceedings of the 13th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2019), International Conference on Complex, Intelligent and Software Intensive Systems, Springer International Publishing, Australia, pp. 496-507.
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Airport security screening processes are essential to ensure the safety of 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 and can cause delays for passengers and airlines. In optimising a security process it is essential to strike a balance between time delays, security and reduced operation cost. This paper uses queueing theory as a method to study the impact of queue formation and the size of the security area on the average waiting time for the case of multi-lane parallel servers. An experiment is conducted to validate the proposed approach.
Naseem, U & Musial, K 2019, 'DICE: Deep intelligent contextual embedding for twitter sentiment analysis', Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, International Conference on Document Analysis and Recognition, 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.
Naseem, U, Musial, K, Eklund, P & Prasad, M 1970, 'Biomedical Named-Entity Recognition by Hierarchically Fusing BioBERT Representations and Deep Contextual-Level Word-Embedding', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Glasgow, UK, pp. 1-8.
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© 2020 IEEE. Text mining in the biomedical domain is increasingly important as the volume of biomedical documents increases. Thanks to advances in natural language processing (NLP), extracting valuable information from the biomedical literature is gaining popularity among researchers, and deep learning has enabled the development of effective biomedical text mining models. However, directly applying advancements in NLP to biomedical sources often yields unsatisfactory results, due to a word distribution drift from the general language domain corpora to specific biomedical corpora, and this drift introduces linguistic ambiguities. To overcome these challenges, this paper presents a novel method for biomedical named entity-recognition (BioNER) through hierarchically fusing representations from BioBERT, which is trained on biomedical corpora and Deep contextual-level word embeddings to handle the linguistic challenges within biomedical literature. Proposed text representation is then fed to attention-based Bi-directional Long Short Term Memory (BiLSTM) with Conditional random field (CRF) for the BioNER task. The experimental analysis shows that our proposed end-to-end methodology outperforms existing state-of-the-art methods for the BioNER task.
Naseem, U, Razzak, I, Eklund, P & Musial, K 1970, 'Towards Improved Deep Contextual Embedding for the identification of Irony and Sarcasm', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Glasgow, UK.
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Humans use tonal stress and gestural cues to reveal negative feelings that are expressed ironically using positive or intensified positive words when communicating vocally. However, in textual data, like posts on social media, cues on sentiment valence are absent, thus making it challenging to identify the true meaning of utterances, even for the human reader. For a given post, an intelligent natural language processing system should be able to identify whether a post is ironic/sarcastic or not. Recent work confirms the difficulty of detecting sarcastic/ironic posts. To overcome challenges involved in the identification of sentiment valence, this paper presents the identification of irony and sarcasm in social media posts through transformer-based deep, intelligent contextual embedding - T-DICE - which improves noise within contexts. It solves the language ambiguities such as polysemy, semantics, syntax, and words sentiments by integrating embeddings. T-DICE is then forwarded to attention-based Bidirectional Long Short Term Memory (BiLSTM) to find out the sentiment of a post. We report the classification performance of the proposed network on benchmark datasets for #irony and #sarcasm. Results demonstrate that our approach outperforms existing state-of-the-art methods.
Ngo, HH, Guo, W, Ng, HY, Mannina, G & Pandey, A 1970, 'Preface', Conferences in Research and Practice in Information Technology Series, Elsevier, pp. xxi-xxii.
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Nguyen, T-D, Maszczyk, T, Musial, K, Zöller, M-A & Gabrys, B 1970, 'AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), IDA 2020: Advances in Intelligent Data Analysis XVIII, Springer International Publishing, Konstanz, Germany, pp. 352-365.
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© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution.
Panta, A, Khushi, M, Naseem, U, Kennedy, P & Catchpoole, D 1970, 'Classification of Neuroblastoma Histopathological Images Using Machine Learning', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 3-14.
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Neuroblastoma is the most common cancer in young children accounting for over 15% of deaths in children due to cancer. Identification of the class of neuroblastoma is dependent on histopathological classification performed by pathologists which are considered the gold standard. However, due to the heterogeneous nature of neuroblast tumours, the human eye can miss critical visual features in histopathology. Hence, the use of computer-based models can assist pathologists in classification through mathematical analysis. There is no publicly available dataset containing neuroblastoma histopathological images. So, this study uses dataset gathered from The Tumour Bank at Kids Research at The Children’s Hospital at Westmead, which has been used in previous research. Previous work on this dataset has shown maximum accuracy of 84%. One main issue that previous research fails to address is the class imbalance problem that exists in the dataset as one class represents over 50% of the samples. This study explores a range of feature extraction and data undersampling and over-sampling techniques to improve classification accuracy. Using these methods, this study was able to achieve accuracy of over 90% in the dataset. Moreover, significant improvements observed in this study were in the minority classes where previous work failed to achieve high level of classification accuracy. In doing so, this study shows importance of effective management of available data for any application of machine learning.
Peng, X, Long, G, Shen, T, Wang, S, Jiang, J & Zhang, C 1970, 'BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes', 20th IEEE International Conference on Data Mining (ICDM), IEEE International Conference on Data Mining, Sorrento, Italy.
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Electronic health records (EHRs) are longitudinal records of a patient'sinteractions with healthcare systems. A patient's EHR data is organized as athree-level hierarchy from top to bottom: patient journey - all the experiencesof diagnoses and treatments over a period of time; individual visit - a set ofmedical codes in a particular visit; and medical code - a specific record inthe form of medical codes. As EHRs begin to amass in millions, the potentialbenefits, which these data might hold for medical research and medical outcomeprediction, are staggering - including, for example, predicting futureadmissions to hospitals, diagnosing illnesses or determining the efficacy ofmedical treatments. Each of these analytics tasks requires a domain knowledgeextraction method to transform the hierarchical patient journey into a vectorrepresentation for further prediction procedure. The representations shouldembed a sequence of visits and a set of medical codes with a specifictimestamp, which are crucial to any downstream prediction tasks. Hence,expressively powerful representations are appealing to boost learningperformance. To this end, we propose a novel self-attention mechanism thatcaptures the contextual dependency and temporal relationships within apatient's healthcare journey. An end-to-end bidirectional temporal encodernetwork (BiteNet) then learns representations of the patient's journeys, basedsolely on the proposed attention mechanism. We have evaluated the effectivenessof our methods on two supervised prediction and two unsupervised clusteringtasks with a real-world EHR dataset. The empirical results demonstrate theproposed BiteNet model produces higher-quality representations thanstate-of-the-art baseline methods.
Perez-Romero, ME, Alfaro-Garcia, VG, Merigo, JM & Flores-Romero, MB 1970, 'Covariance in Ordered Weighted Logarithm Aggregation Operators', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE.
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Raza, MA, Abolhasan, M, Lipman, J, Shariati, N & Ni, W 1970, 'Statistical Learning-Based Dynamic Retransmission Mechanism for Mission Critical Communication: An Edge-Computing Approach', 2020 IEEE 45th Conference on Local Computer Networks (LCN), 2020 IEEE 45th Conference on Local Computer Networks (LCN), IEEE, Australia, pp. 393-396.
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Mission-critical machine type communication (MC-MTC) systems in which machines communicate to perform various tasks such as coordination, sensing, and actuation, require stringent requirements of ultra-reliable and low latency communications (URLLC). Edge computing being an integral part of future wireless networks, provides services that support URLLC applications. In this paper, we use the edge computing approach and present a statistical learning-based dynamic retransmission mechanism. The proposed approach meets the desired latency-reliability criterion in MC-MTC networks employing framed ALOHA. The maximum number of retransmissions Nr under a given latency-reliability constraint is learned statistically by the devices from the history of their previous transmissions and shared with the base station. Simulations are performed in MATLAB to evaluate a framed-ALOHA system's performance in which an active device can have only one successful transmission in one round composed of (Nr + 1) frames, and the performance is compared with the diversity transmission-based framed-ALOHA.
Shi, L, Li, S, Zheng, Q, Cao, L, Yang, L & Pan, G 1970, 'Maximum Entropy Reinforcement Learning with Evolution Strategies', 2020 International Joint Conference on Neural Networks (IJCNN), 2020 International Joint Conference on Neural Networks (IJCNN), IEEE.
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Shi, Z, Zhang, JA, Xu, R, Cheng, Q & Pearce, A 1970, 'Towards Environment-Independent Human Activity Recognition using Deep Learning and Enhanced CSI', GLOBECOM 2020 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - 2020 IEEE Global Communications Conference, IEEE, Taipei, Taiwan, pp. 1-6.
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© 2020 IEEE. Deep learning has shown a strong potential in device-free human activity recognition (HAR). However, a fundamental challenge is ensuring accuracy, without re-training, when exposing a previously trained architecture to a new or unseen environment. To overcome the aforementioned challenge, this paper proposes an environment-robust channel state information (CSI) based HAR by leveraging the properties of a matching network (MatNet) and enhanced features (HAR-MN-EF). To improve the CSI quality, we propose a CSI cleaning and enhancement method (CSI-CE) that includes two key stages: activity-related information extraction (ARIE) and correlation feature extraction based on principal component analysis (CFE-PCA). The ARIE stage is able to effectively enhance the activity-dependent features whilst mitigating behavior-unrelated information. The CFE-PCA stage further improves the extracted features by filtering out the residual activity-unrelated data and the residual noise contained in signals from the former stage. The extracted features are then sequenced into the MatNet to create an environment-robust HAR. Experimental results confirm that an architecture trained by the proposed HAR-MN-EF can be directly adapted to a new environment, achieving reliable sensing accuracies without requiring additional effort.
Shi, Z, Zhang, JA, Xu, RY & Cheng, Q 1970, 'WiFi-Based Activity Recognition using Activity Filter and Enhanced Correlation with Deep Learning', 2020 IEEE International Conference on Communications Workshops (ICC Workshops), 2020 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, Dublin, Ireland, pp. 1-6.
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Device-free WiFi sensing utilizing channel state information (CSI) is attractive for human activity recognition (HAR). However, several challenging problems are yet to be resolved, e.g., difficulty in extracting proper features from input signals, susceptibility to the phase shift of CSI and difficulty in identifying similar behaviors (e.g., lying and standing). In this paper, we aim to tackle these problems by proposing a novel scheme for CSI-based HAR that uses activity filter-based deep learning network (HAR-AF-DLN) with enhanced correlation features. We first develop a novel CSI compensation and enhancement (CCE) method to compensate for the timing offset between the WiFi transmitter and receiver, enhance activity-related signals and reduce the dimension of inputs to DLN. Then, we design a novel activity filter (AF) to differentiate similar activities (e.g., standing and lying) based on the enhanced CSI correlation features obtained from CCE. Extensive simulation results demonstrate that our proposed HAR-AF-DLN scheme outperforms state-of-the-art methods with significantly improved recognition accuracy (especially for similar activities) and notably reduced training time.
Shu, Y, Sui, Y, Zhang, H & Xu, G 1970, 'Perf-AL', Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), ESEM '20: ACM / IEEE International Symposium on Empirical Software Engineering and Measurement, ACM, pp. 1-11.
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© 2020 IEEE Computer Society. All rights reserved. Context: Many software systems are highly configurable. Different configuration options could lead to varying performances of the system. It is difficult to measure system performance in the presence of an exponential number of possible combinations of these options. Goal: Predicting software performance by using a small configuration sample. Method: This paper proposes PERF-AL to address this problem via adversarial learning. Specifically, we use a generative network combined with several different regularization techniques (L1 regularization, L2 regularization and a dropout technique) to output predicted values as close to the ground truth labels as possible. With the use of adversarial learning, our network identifies and distinguishes the predicted values of the generator network from the ground truth value distribution. The generator and the discriminator compete with each other by refining the prediction model iteratively until its predicted values converge towards the ground truth distribution. Results:We argue that (i) the proposed method can achieve the same level of prediction accuracy, but with a smaller number of training samples. (ii) Our proposed model using seven real-world datasets show that our approach outperforms the state-of-the-art methods. This help to further promote software configurable performance. Conclusion: Experimental results on seven public real-world datasets demonstrate that PERF-AL outperforms state-of-the-art software performance prediction methods.
Sun, X, Jiang, Y & Li, W 1970, 'Residual Attention Based Network for Automatic Classification of Phonation Modes', 2020 IEEE International Conference on Multimedia and Expo (ICME), 2020 IEEE International Conference on Multimedia and Expo (ICME), IEEE, pp. 1-6.
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Sun, Z, Hua, X-S, Yao, Y, Wei, X-S, Hu, G & Zhang, J 1970, 'CRSSC: Salvage Reusable Samples from Noisy Data for Robust Learning', Proceedings of the 28th ACM International Conference on Multimedia, MM '20: The 28th ACM International Conference on Multimedia, ACM, pp. 92-101.
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Thanh, TK, Chen, F & Gabrys, B 1970, 'An Improved Online Learning Algorithm for General Fuzzy Min-Max Neural Network', 2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI), IEEE, ELECTR NETWORK.
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Ubaid, A, Dong, F & Hussain, FK 1970, 'Framework for Feature Selection in Health Assessment Systems', Advances in Intelligent Systems and Computing, International Conference on Advanced Information Networking and Applications, Springer International Publishing, Japan, pp. 313-324.
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Anomaly detection in health assessment systems has gained much attention in the recent past. Various feature selection techniques have been proposed for successful anomaly detection. However, these methods do not cater for the need to select features in health assessment systems. Most of the present techniques are data dependent and do not offer an option for incorporating domain information. This paper proposes a novel domain knowledge-driven feature selection framework named domain-driven selective wrapping (DSW) that can help in the selection of a correlated feature subset. The proposed framework uses an expert’s domain knowledge for the selection of subsets. The framework uses a custom-designed logic-driven anomaly detection block (LDAB) as a wrapper. The experiment results show that the proposed framework is able to select feature subsets more efficiently than traditional sequential selection methods and is very successful in detecting anomalies.
Ubaid, A, Hussain, FK & Charles, J 1970, 'Machine Learning-Based Regression Models for Price Prediction in the Australian Container Shipping Industry: Case Study of Asia-Oceania Trade Lane', Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 52-59.
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© 2020, Springer Nature Switzerland AG. The objective of this paper is to train a data-driven price prediction model for container pricing based on demand and supply for the Australian container shipping industry. The sourcing of demand, supply and pricing data has been done from Australian ports, Sea-Intelligence maritime analysis and the Shanghai Freight Index (SCFI) respectively. Data-driven prediction have been realized by applying three different regression models that include support vector regression (SVR), random forest regression (RFR) and gradient booster regression (GBR) over the gathered datasets after initial feature engineering. A comparison of research outcomes shows that GBR outperforms all the other models by offering a test accuracy of 84%.
Wang, S, Hu, L, Wang, Y, Sheng, QZ, Orgun, M & Cao, L 1970, 'Intention Nets: Psychology-Inspired User Choice Behavior Modeling for Next-Basket Prediction', Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), pp. 6259-6266.
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Human behaviors are complex, which are often observed as a sequence of heterogeneous actions. In this paper, we take user choices for shopping baskets as a typical case to study the complexity of user behaviors. Most of existing approaches often model user behaviors in a mechanical way, namely treating a user action sequence as homogeneous sequential data, such as hourly temperatures, which fails to consider the complexity in user behaviors. In fact, users' choices are driven by certain underlying intentions (e.g., feeding the baby or relieving pain) according to Psychological theories. Moreover, the durations of intentions to drive user actions are quite different; some of them may be persistent while others may be transient. According to Psychological theories, we develop a hierarchical framework to describe the goal, intentions and action sequences, based on which, we design Intention Nets (IntNet). In IntNet, multiple Action Chain Nets are constructed to model the user actions driven by different intentions, and a specially designed Persistent-Transient Intention Unit models the different intention durations. We apply the IntNet to next-basket prediction, a recent challenging task in recommender systems. Extensive experiments on real-world datasets show the superiority of our Psychology-inspired model IntNet over the state-of-the-art approaches.
Wang, S, Hu, L, Wang, Y, Sheng, QZ, Orgun, M & Cao, L 1970, 'Intention2Basket: A Neural Intention-driven Approach for Dynamic Next-basket Planning', 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, pp. 2333-2339.
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User purchase behaviours are complex and dynamic, which are usually observed as multiple choice actions across a sequence of shopping baskets. Most of the existing next-basket prediction approaches model user actions as homogeneous sequence data without considering complex and heterogeneous user intentions, impeding deep under-standing of user behaviours from the perspective of human inside drivers and thus reducing the prediction performance. Psychological theories have indicated that user actions are essentially driven by certain underlying intentions (e.g., diet and entertainment). Moreover, different intentions may influence each other while different choices usually have different utilities to accomplish an intention. Inspired by such psychological insights, we formalize the next-basket prediction as an Intention Recognition, Modelling and Accomplishing problem and further design the Intention2Basket (Int2Ba in short) model. In Int2Ba, an Intention Recognizer, a Coupled Intention Chain Net, and a Dynamic Basket Planner are specifically designed to respectively recognize, model and accomplish the heterogeneous intentions behind a sequence of baskets to better plan the next-basket. Extensive experiments on real-world datasets show the superiority of Int2Ba over the state-of-the-art approaches.
Wang, X, Jin, D, Musial, K & Dang, J 1970, 'Topic enhanced sentiment spreading model in social networks considering user interest', AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, 34th AAAI Conference on Artificial Intelligence / 32nd Innovative Applications of Artificial Intelligence Conference / 10th AAAI Symposium on Educational Advances in Artificial Intelligence, ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE, New York, NY, pp. 989-996.
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Emotion is a complex emotional state, which can affect our physiology and psychology and lead to behavior changes. The spreading process of emotions in the text-based social networks is referred to as sentiment spreading. In this paper, we study an interesting problem of sentiment spreading in social networks. In particular, by employing a text-based social network (Twitter), we try to unveil the correlation between users’ sentimental statuses and topic distributions embedded in the tweets, then to automatically learn the influence strength between linked users. Furthermore, we introduce user interest to refine the influence strength. We develop a unified probabilistic framework to formalize the problem into a topic-enhanced sentiment spreading model. The model can predict users’ sentimental statuses based on their historical emotional status, topic distributions in tweets and social structures. Experiments on the Twitter dataset show that the proposed model significantly outperforms several alternative methods in predicting users’ sentimental status. We also discover an intriguing phenomenon that positive and negative sentiment is more relevant to user interest than neutral ones. Our method offers a new opportunity to understand the underlying mechanism of sentimental spreading in online social networks.
Wang, X, Li, Q, Zhang, W, Xu, G, Liu, S & Zhu, W 1970, 'Joint Relational Dependency Learning for Sequential Recommendation', 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, Singapore, pp. 168-180.
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© Springer Nature Switzerland AG 2020. Sequential recommendation leverages the temporal information of users’ transactions as transition dependencies for better inferring user preference, which has become increasingly popular in academic research and practical applications. Short-term transition dependencies contain the information of partial item orders, while long-term transition dependencies infer long-range user preference, the two dependencies are mutually restrictive and complementary. Although some work investigates unifying both long-term and short-term dependencies for better performance, they still neglect the fact that short-term interactions are multi-folds, which are either individual-level interactions or union-level interactions. Existing sequential recommendations mainly focus on user’s individual (i.e., individual-level) interactions but ignore the important collective influence at union-level. Since union-level interactions can reflect that human decisions are made based on multiple items he/she has already interacted, ignoring such interactions can result in the disability of capturing the collective influence between items. To alleviate this issue, we proposed a Joint Relational Dependency learning (JRD-L) for sequential recommendation that exploits both long-term and short-term preferences at individual-level and union-level. Specifically, JRD-L combines long-term user preferences with short-term interests by measuring short-term pair relations at individual-level and union-level. Moreover, JRD-L can alleviate the sparsity problem of union-level interactions by adding more descriptive details to each item, which is carried by individual-level relations. Extensive numerical experiments demonstrate JRD-L outperforms state-of-the-art baselines for the sequential recommendation.
Wu, Y, Cao, J & Xu, G 1970, 'FAST: A Fairness Assured Service Recommendation Strategy Considering Service Capacity Constraint', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 287-303.
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© Springer Nature Switzerland AG 2020. An excessive number of customers often leads to a degradation in service quality. However, the capacity constraints of services are ignored by recommender systems, which may lead to unsatisfactory recommendation. This problem can be solved by limiting the number of users who receive the recommendation for a service, but this may be viewed as unfair. In this paper, we propose a novel metric Top-N Fairness to measure the individual fairness of multi-round recommendations of services with capacity constraints. By considering the fact that users are often only affected by top-ranked items in a recommendation, Top-N Fairness only considers a sub-list consisting of top N services. Based on the metric, we design FAST, a Fairness Assured service recommendation STrategy. FAST adjusts the original recommendation list to provide users with recommendation results that guarantee the long-term fairness of multi-round recommendations. We prove the convergence property of the variance of Top-N Fairness of FAST theoretically. FAST is tested on the Yelp dataset and synthetic datasets. The experimental results show that FAST achieves better recommendation fairness while still maintaining high recommendation quality.
Wu, Z, Pan, S, Long, G, Jiang, J, Chang, X & Zhang, C 1970, 'Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks', Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 753-763.
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Modeling multivariate time series has long been a subject that has attractedresearchers from a diverse range of fields including economics, finance, andtraffic. A basic assumption behind multivariate time series forecasting is thatits variables depend on one another but, upon looking closely, it is fair tosay that existing methods fail to fully exploit latent spatial dependenciesbetween pairs of variables. In recent years, meanwhile, graph neural networks(GNNs) have shown high capability in handling relational dependencies. GNNsrequire well-defined graph structures for information propagation which meansthey cannot be applied directly for multivariate time series where thedependencies are not known in advance. In this paper, we propose a generalgraph neural network framework designed specifically for multivariate timeseries data. Our approach automatically extracts the uni-directed relationsamong variables through a graph learning module, into which external knowledgelike variable attributes can be easily integrated. A novel mix-hop propagationlayer and a dilated inception layer are further proposed to capture the spatialand temporal dependencies within the time series. The graph learning, graphconvolution, and temporal convolution modules are jointly learned in anend-to-end framework. Experimental results show that our proposed modeloutperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasetsand achieves on-par performance with other approaches on two traffic datasetswhich provide extra structural information.
Xie, H-B, Li, C, Mengersen, K, Wang, S & Xu, RYD 1970, 'Nonparametric Bayesian Nonnegative Matrix Factorization', Modeling Decisions for Artificial Intelligence, International Conference on Modeling Decisions for Artificial Intelligence, Springer International Publishing, Sant Cugat, Spain, pp. 132-141.
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© Springer Nature Switzerland AG 2020. Nonnegative Matrix Factorization (NMF) is an important tool in machine learning for blind source separation and latent factor extraction. Most of existing NMF algorithms assume a specific noise kernel, which is insufficient to deal with complex noise in real scenarios. In this study, we present a hierarchical nonparametric nonnegative matrix factorization (NPNMF) model in which the Gaussian mixture model is used to approximate the complex noise distribution. The model is cast in the nonparametric Bayesian framework by using Dirichlet process mixture to infer the necessary number of Gaussian components. We derive a mean-field variational inference algorithm for the proposed nonparametric Bayesian model. Experimental results on both synthetic data and electroencephalogram (EEG) demonstrate that NPNMF performs better in extracting the latent nonnegative factors in comparison with state-of-the-art methods.
Xu, J, Yu, L, Zhang, J & Wu, Q 1970, 'Automatic Sheep Counting by Multi-object Tracking', 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), IEEE, China, pp. 257-257.
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Animal counting is a highly skilled yet tedious task in livestock transportation and trading. To effectively free up the human labour and provide accurate counts for sheep loading/unloading, we develop an auto sheep counting system based on multi-object detection, tracking and extrapolation techniques. Our system has demonstrated more than 99.9% accuracy with sheep moving freely in a race under optimal visual conditions.
Xu, Y, Chen, L, Fang, M, Wang, Y & Zhang, C 1970, 'Deep Reinforcement Learning with Transformers for Text Adventure Games', 2020 IEEE Conference on Games (CoG), 2020 IEEE Conference on Games (CoG), IEEE, pp. 65-72.
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In this paper, we study transformers for text-based games. As a promising replacement of recurrent modules in Natural Language Processing (NLP) tasks, the transformer architecture could be treated as a powerful state representation generator for reinforcement learning. However, the vanilla transformer is neither effective nor efficient to learn with a huge amount of weight parameters. Unlike existing research that encodes states using LSTMs or GRUs, we develop a novel lightweight transformer-based representation generator featured with reordered layer normalization, weight sharing and block-wise aggregation. The experimental results show that our proposed model not only solves single games with much fewer interactions, but also achieves better generalization on a set of unseen games. Furthermore, our model outperforms state-of-the-art agents in a variety of man-made games.
Xu, Y, Fang, M, Chen, L, Du, Y, Zhou, JT & Zhang, C 1970, 'Deep reinforcement learning with stacked hierarchical attention for text-based games', Advances in Neural Information Processing Systems, Conference on Neural Information Processing Systems, NIPS, Virtual, pp. 1-13.
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We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language. While different methods have been developed to represent the environment information and language actions, existing RL agents are not empowered with any reasoning capabilities to deal with textual games. In this work, we aim to conduct explicit reasoning with knowledge graphs for decision making, so that the actions of an agent are generated and supported by an interpretable inference procedure. We propose a stacked hierarchical attention mechanism to construct an explicit representation of the reasoning process by exploiting the structure of the knowledge graph. We extensively evaluate our method on a number of man-made benchmark games, and the experimental results demonstrate that our method performs better than existing text-based agents.
Yang, H, Chen, L, Lei, M, Niu, L, Zhou, C & Zhang, P 1970, 'Discrete Embedding for Latent Networks', 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, pp. 1223-1229.
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Discrete network embedding emerged recently as a new direction of network representation learning. Compared with traditional network embedding models, discrete network embedding aims to compress model size and accelerate model inference by learning a set of short binary codes for network vertices. However, existing discrete network embedding methods usually assume that the network structures (e.g., edge weights) are readily available. In real-world scenarios such as social networks, sometimes it is impossible to collect explicit network structure information and it usually needs to be inferred from implicit data such as information cascades in the networks. To address this issue, we present an end-to-end discrete network embedding model for latent networks DELN that can learn binary representations from underlying information cascades. The essential idea is to infer a latent Weisfeiler-Lehman proximity matrix that captures node dependence based on information cascades and then to factorize the latent Weisfiler-Lehman matrix under the binary node representation constraint. Since the learning problem is a mixed integer optimization problem, an efficient maximal likelihood estimation based cyclic coordinate descent (MLE-CCD) algorithm is used as the solution. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art network embedding methods.
Yang, X & Liu, W 1970, 'Population Location and Movement Estimation through Cross-domain Data Analysis', 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, Yokahama, Japan.
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Estimations on people movement behaviour within a country can provide valuable information to government strategic resource plannings. In this paper, we propose to utilize multi-domain statistical data to estimate people movements under the assumption that most population tend to move to areas with similar or better living conditions. We design a Multi-domain Matrix Factorization (MdMF) model to discover the underlying consistency patterns from these cross-domain data and estimate the movement trends using the proposed model. This research can provide important theoretical support to government and agencies in strategic resource planning and investments.
Yao, Y, Hua, X, Gao, G, Sun, Z, Li, Z & Zhang, J 1970, 'Bridging the Web Data and Fine-Grained Visual Recognition via Alleviating Label Noise and Domain Mismatch', Proceedings of the 28th ACM International Conference on Multimedia, MM '20: The 28th ACM International Conference on Multimedia, ACM, Virtual, pp. 1735-1744.
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To distinguish the subtle differences among fine-grained categories, a large amount of well-labeled images are typically required. However, manual annotations for fine-grained categories is an extremely difficult task as it usually has a high demand for professional knowledge. To this end, we propose to directly leverage web images for fine-grained visual recognition. Our work mainly focuses on two critical issues including 'label noise' and 'domain mismatch' in the web images. Specifically, we propose an end-to-end deep denoising network (DDN) model to jointly solve these problems in the process of web images selection. To verify the effectiveness of our proposed approach, we first collect web images by using the labels in fine-grained datasets. Then we apply the proposed deep denoising network model for noise removal and domain mismatch alleviation. We leverage the selected web images as the training set for fine-grained categorization models learning. Extensive experiments and ablation studies demonstrate state-of-the-art performance gained by our proposed approach, which, at the same time, delivers a new pipeline for fine-grained visual categorization that is to be highly effective for real-world applications.
Zhang, C, Yao, Y, Zhang, J, Chen, J, Huang, P, Zhang, J & Tang, Z 1970, 'Web-Supervised Network for Fine-Grained Visual Classification', 2020 IEEE International Conference on Multimedia and Expo (ICME), 2020 IEEE International Conference on Multimedia and Expo (ICME), IEEE, London, United Kingdom, pp. 1-6.
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© 2020 IEEE. Fine-grained visual classification (FGVC) is a tough task due to its high annotation cost of the fine-grained subcategories. To build a large-scale dataset at low manual cost, straightforwardly learning from web images for FGVC has attracted broad attention. However, there exist two characteristics in the need of concerning for the web dataset: 1) Noisy images; 2) A large proportion of hard examples. In this paper, we propose a simple yet effective approach to deal with noisy images and hard examples during training. Our method is a pure web-supervised method for FGVC. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is much superior to the state-of-the-art web-supervised methods. The data and source code of this work have been posted available at: https://github.com/NUST-Machine-Intelligence-Laboratory/WSNFG.
Zhang, L, Zhang, J, Li, Z & Xu, J 1970, 'Towards Better Graph Representation: Two-Branch Collaborative Graph Neural Networks For Multimodal Marketing Intention Detection', 2020 IEEE International Conference on Multimedia and Expo (ICME), 2020 IEEE International Conference on Multimedia and Expo (ICME), IEEE, London, United Kingdom, pp. 1-6.
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© 2020 IEEE. Inspired by the fact that spreading and collecting information through the Internet becomes the norm, more and more people choose to post for-profit contents (images and texts) in social networks. Due to the difficulty of network censors, malicious marketing may be capable of harming the society. Therefore, it is meaningful to detect marketing intentions online automatically. However, gaps between multimodal data make it difficult to fuse images and texts for content marketing detection. To this end, this paper proposes Two-Branch Collaborative Graph Neural Networks to collaboratively represent multimodal data by Graph Convolution Networks (GCNs) in an end-to-end fashion. We first separately embed groups of images and texts by GCNs layers from two views and further adopt the proposed multimodal fusion strategy to learn the graph representation collaboratively. Experimental results demonstrate that our proposed method achieves superior graph classification performance for marketing intention detection.
Zhang, S, Luo, L, Li, Z, Wang, Y, Chen, F & Xu, R 1970, 'Simultaneous Customer Segmentation and Behavior Discovery', Neural Information Processing, International Conference on Neural Information Processing, Springer International Publishing, Bangkok, Thailand, pp. 122-130.
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© 2020, Springer Nature Switzerland AG. Customer purchase behavior segmentation plays an important role in the modern economy. We proposed a Bayesian non-parametric (BNP)-based framework, named Simultaneous Customer Segmentation and Utility Discovery (UtSeg), to discover customer segmentation without knowing specific forms of utility functions and parameters. For the segmentation based on BNP models, the unknown type of functions is usually modeled as a non-homogeneous point process (NHPP) for each mixture component. However, the inference of these models is complex and time-consuming. To reduce such complexity, traditionally, economists will use one specific utility function in a heuristic way to simplify the inference. We proposed to automatically select among multiple utility functions instead of searching in a continuous space. We further unified the parameters for different types of utility functions with the same prior distribution to improve efficiency. We tested our model with synthetic data and applied the framework to real-supermarket data with different products, and showed that our results can be interpreted with common knowledge.
Zhang, Z, Da Xu, RY, Jiang, S, Li, Y, Huang, C & Deng, C 1970, 'Illumination Adaptive Person Reid Based on Teacher-Student Model and Adversarial Training', 2020 IEEE International Conference on Image Processing (ICIP), 2020 IEEE International Conference on Image Processing (ICIP), IEEE.
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Zhang, Z, Yu, L, Zhang, J & Wu, Q 1970, 'A Vision Based Fish Processing System', 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), IEEE, Macau, China, pp. 260-260.
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The digital fish provenance and quality tracking system is essential for the seafood supply chain. As a part of this system, we develop a vision-based fish processing system to automatically perform fish freshness estimation, size measurement and species classification. Under the constrained illumination environment, our system is able to auto-process the fish selection, thus greatly reduce the human labour and bring trust and efficiency to the seafood supply chain from catch to market.
Zhang, Z, Yu, L, Zhang, J & Wu, Q 1970, 'A Vision Based Fish Processing System', 2020 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP), IEEE International Conference on Visual Communications and Image Processing (VCIP), IEEE, ELECTR NETWORK, pp. 260-260.
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Zheng, H, Jiang, J, Wei, P, Long, G & Zhang, C 1970, 'Competitive and cooperative heterogeneous deep reinforcement learning', Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, pp. 1656-1664.
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Numerous deep reinforcement learning methods have been proposed, including deterministic, stochastic, and evolutionary-based hybrid methods. However, among these various methodologies, there is no clear winner that consistently outperforms the others in every task in terms of effective exploration, sample efficiency, and stability. In this work, we present a competitive and cooperative heterogeneous deep reinforcement learning framework called C2HRL. C2HRL aims to learn a superior agent that exceeds the capabilities of the individual agent in an agent pool through two agent management mechanisms: one competitive, the other cooperative. The competitive mechanism forces agents to compete for computing resources and to explore and exploit diverse regions of the solution space. To support this strategy, resources are distributed to the most suitable agent for that specific task and random seed setting, which results in better sample efficiency and stability. The other mechanic, cooperation, asks heterogeneous agents to share their exploration experiences so that all agents can learn from a diverse set of policies. The experiences are stored in a two-level replay buffer and the result is an overall more effective exploration strategy. We evaluated C2HRL on a range of continuous control tasks from the benchmark Mujoco. The experimental results demonstrate that C2HRL has better sample efficiency and greater stability than three state-of-the-art DRL baselines.
Zheng, H, Wei, P, Jiang, J, Long, G, Lu, Q & Zhang, C 1970, 'Cooperative heterogeneous deep reinforcement learning', Advances in Neural Information Processing Systems.
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Numerous deep reinforcement learning agents have been proposed, and each of them has its strengths and flaws. In this work, we present a Cooperative Heterogeneous Deep Reinforcement Learning (CHDRL) framework that can learn a policy by integrating the advantages of heterogeneous agents. Specifically, we propose a cooperative learning framework that classifies heterogeneous agents into two classes: global agents and local agents. Global agents are off-policy agents that can utilize experiences from the other agents. Local agents are either on-policy agents or population-based evolutionary algorithms (EAs) agents that can explore the local area effectively. We employ global agents, which are sample-efficient, to guide the learning of local agents so that local agents can benefit from sample-efficient agents and simultaneously maintain their advantages, e.g., stability. Global agents also benefit from effective local searches. Experimental studies on a range of continuous control tasks from the Mujoco benchmark show that CHDRL achieves better performance compared with state-of-the-art baselines.
Zhu, C, Zhang, Q, Cao, L & Abrahamyan, A 1970, 'Mix2Vec: Unsupervised Mixed Data Representation', 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE.
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