Abidi, S, Piccardi, M, Tsang, WH & Williams, M-A 2019, 'Well-M³N: A Maximum-Margin Approach to Unsupervised Structured Prediction', IEEE Transactions on Emerging Topics in Computational Intelligence.
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Unsupervised structured prediction is of fundamental importance for the clustering and classification of unannotated structured data. To date, its most common approach still relies on the use of structural probabilistic models and the expectation-maximization (EM) algorithm. Conversely, structural maximum-margin approaches, despite their extensive success in supervised and semi-supervised classification, have not raised equivalent attention in the unsupervised case. For this reason, in this paper we propose a novel approach that extends the maximum-margin Markov networks (M3N) to an unsupervised training framework. The main contributions of our extension are new formulations for the feature map and loss function of M3N that decouple the labels from the measurements and support multiple ground-truth training. Experiments on two challenging segmentation datasets have achieved competitive accuracy and generalization compared to other unsupervised algorithms such as k-means, EM and unsupervised structural SVM, and comparable performance to a contemporary deep learning-based approach.
Amirbagheri, K, Núñez-Carballosa, A, Guitart-Tarrés, L & Merigó, JM 2019, 'Research on green supply chain: a bibliometric analysis', Clean Technologies and Environmental Policy, vol. 21, no. 1, pp. 3-22.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Abstract: Recently, the emergent concept of green supply chain has received increasing attention. Although popular among scholars, many literature reviews have only examined GSC from a general point of view or focused on a specific issue related to GSC. This study presents a comprehensive analysis of the influence and productivity of research on GSC from 1995 to 2017 by reporting trends among authors, countries and institutions based on a bibliometric approach. To this end, the study analyzes around 1900 papers on GSC. This study uses the Web of Science Core Collection database to analyze the bibliometric data and the visualization of similarities viewer method to graphically map those data. The graphical analysis uses bibliographic coupling, co-citation, co-authorship and co-occurrence of keywords. Graphical abstract: [Figure not available: see fulltext.].
Ben, X, Gong, C, Zhang, P, Jia, X, Wu, Q & Meng, W 2019, 'Coupled Patch Alignment for Matching Cross-view Gaits', IEEE Transactions on Image Processing, pp. 1-1.
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Ben, X, Gong, C, Zhang, P, Yan, R, Wu, Q & Meng, W 2019, 'Coupled Bilinear Discriminant Projection for Cross-view Gait Recognition', IEEE Transactions on Circuits and Systems for Video Technology.
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IEEE A problem that hinders good performance of general gait recognition systems is that the appearance features of gaits are more affected-prone by views than identities, especially when the walking direction of the probe gait is different from the register gait. This problem cannot be solved by traditional projection learning methods, because these methods can learn only one projection matrix, thus for the same subject, it cannot transfer cross-view gait features into similar ones. This paper presents an innovative method to overcome this problem by aligning gait energy images (GEIs) across views with the Coupled bilinear discriminant projection (CBDP). Specifically, CBDP generates the aligned gait matrix features for two views with two sets of bilinear transformation matrices, so that the original GEIs’ spatial structure information can be preserved. By iteratively maximizing the ratio of inter-class distance metric to intra-class distance metric, CBDP can learn the optimal matrix subspace where GEIs across views are aligned in both horizontal and vertical coordinates. Therefore, CBDP is also able to avoid the under-sample problem. We also theoretically prove that the upper and lower bounds of the objective function sequence of CBDP are both monotonically increasing, so the convergence of CBDP is demonstrated. In terms of accuracy, the comparative experiments on the CASIA(B) and OU-ISIR gait databases show that our method is superior to the state-of-the-art cross-view gait recognition methods. More impressively, the encouraging performance is obtained by our method even in matching a lateral-view gait with a frontal-view gait.
Blanco-Mesa, F, León-Castro, E, Merigó, JM & Xu, Z 2019, 'Bonferroni means with induced ordered weighted average operators', International Journal of Intelligent Systems, vol. 34, no. 1, pp. 3-23.
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© 2018 Wiley Periodicals, Inc. The induced ordered weighted average is an averaging aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum. This paper presents some new generalizations by using Bonferroni means (BM) forming induced BM. The main advantage of this approach is the possibility of reordering the results according to complex ranking processes based on order-inducing variables. The work also presents some additional extensions by using the weighted ordered weighted average, immediate weights, and hybrid averages. Some further generalizations with generalized and quasi-arithmetic means are also developed to consider a wide range of particular cases including quadratic and geometric aggregations. The article also considers the applicability of the new approach in-group decision-making developing an application in sales forecasting.
Cao, X, Qiu, B & Xu, G 2019, 'BorderShift: toward optimal MeanShift vector for cluster boundary detection in high-dimensional data', Pattern Analysis and Applications, pp. 1-13.
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© 2018 Springer-Verlag London Ltd., part of Springer Nature We present a cluster boundary detection scheme that exploits MeanShift and Parzen window in high-dimensional space. To reduce the noises interference in Parzen window density estimation process, the kNN window is introduced to replace the sliding window with fixed size firstly. Then, we take the density of sample as the weight of its drift vector to further improve the stability of MeanShift vector which can be utilized to separate boundary points from core points, noise points, isolated points according to the vector models in multi-density data sets. Under such circumstance, our proposed BorderShift algorithm doesn’t need multi-iteration to get the optimal detection result. Instead, the developed Shift value of each data point helps to obtain it in a liner way. Experimental results on both synthetic and real data sets demonstrate that the F-measure evaluation of BorderShift is higher than that of other algorithms.
Cheng, H, Zhang, J, Wu, Q & An, P 2019, 'A computational model for stereoscopic visual saliency prediction', IEEE Transactions on Multimedia, vol. 21, no. 3, pp. 678-689.
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© 2018 IEEE. Depth information plays an important role in human vision as it provides additional cues that distinguish objects from their backgrounds. This paper explores depth information for analyzing stereoscopic saliency and presents a computational model that predicts stereoscopic visual saliency based on three aspects of human vision: 1) the pop-out effect; 2) comfort zones; and 3) background effects. Through an analysis of these three phenomena, we find that most of the stereoscopic saliency region can be explained. Our model comprises three modules, each describing one aspect of saliency distribution, and a control function that can be used to adjust the three models independently. The relationship between the three models is not mutually exclusive. One, two, or three phenomena may appear in one image. Therefore, to accurately determine which phenomena the image conforms to, we have devised a selection strategy that chooses the appropriate combination of models based on the content of the image. Our approach is implemented within a framework based on the multifeature analysis. The framework considers surrounding regions, color/depth contrast, and points of interest. The selection strategy can improve the performance of the framework. A series of experiments on two recent eye-tracking datasets shows that our proposed method outperforms several state-of-the-art saliency models.
Cui, L, Wu, J, Pi, D, Zhang, P & Kennedy, P 2019, 'Dual Implicit Mining-Based Latent Friend Recommendation', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user-user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen-Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics.
Dong, X, Qiu, P, Lu, J, Cao, L & Xu, T 2019, 'Mining Top-k Useful Negative Sequential Patterns via Learning.', IEEE transactions on neural networks and learning systems.
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As an important tool for behavior informatics, negative sequential patterns (NSPs) (such as missing a medical treatment) are sometimes much more informative than positive sequential patterns (PSPs) (e.g., attending a medical treatment) in many applications. However, NSP mining is at an early stage and faces many challenging problems, including 1) how to mine an expected number of NSPs; 2) how to select useful NSPs; and 3) how to reduce high time consumption. To solve the first problem, we propose an algorithm Topk-NSP to mine the k most frequent negative patterns. In Topk-NSP, we first mine the top-k PSPs using the existing methods, and then we use an idea which is similar to top-k PSPs mining to mine the top-k NSPs from these PSPs. To solve the remaining two problems, we propose three optimization strategies for Topk-NSP. The first optimization strategy is that, in order to consider the influence of PSPs when selecting useful top-k NSPs, we introduce two weights, wP and wN, to express the user preference degree for NSPs and PSPs, respectively, and select useful NSPs by a weighted support wsup. The second optimization strategy is to merge wsup and an interestingness metric to select more useful NSPs. The third optimization strategy is to introduce a pruning strategy to reduce the high computational costs of Topk-NSP. Finally, we propose an optimization algorithm Topk-NSP⁺. To the best of our knowledge, Topk-NSP⁺ is the first algorithm that can mine the top-k useful NSPs. The experimental results on four synthetic and two real-life data sets show that the Topk-NSP⁺ is very efficient in mining the top-k NSPs in the sense of computational cost and scalability.
Du, B, Xinyao, T, Wang, Z, Zhang, L & Tao, D 2019, 'Robust Graph-Based Semisupervised Learning for Noisy Labeled Data via Maximum Correntropy Criterion.', IEEE transactions on cybernetics, vol. 49, no. 4, pp. 1440-1453.
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Semisupervised learning (SSL) methods have been proved to be effective at solving the labeled samples shortage problem by using a large number of unlabeled samples together with a small number of labeled samples. However, many traditional SSL methods may not be robust with too much labeling noisy data. To address this issue, in this paper, we propose a robust graph-based SSL method based on maximum correntropy criterion to learn a robust and strong generalization model. In detail, the graph-based SSL framework is improved by imposing supervised information on the regularizer, which can strengthen the constraint on labels, thus ensuring that the predicted labels of each cluster are close to the true labels. Furthermore, the maximum correntropy criterion is introduced into the graph-based SSL framework to suppress labeling noise. Extensive image classification experiments prove the generalization and robustness of the proposed SSL method.
Gaviria-Marin, M, Merigó, JM & Baier-Fuentes, H 2019, 'Knowledge management: A global examination based on bibliometric analysis', Technological Forecasting and Social Change, vol. 140, pp. 194-220.
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© 2018 Knowledge management (KM) is a field of research that has gained wide acceptance in the scientific community and management literature. This article presents a bibliometric overview of the academic research on KM in the business and management areas. Various bibliometric methods are used to perform this overview, including performance analysis and science mapping of the KM field. The performance analysis uses a series of bibliometric indicators, such as the h-index, productivity and citations. In addition, the VOSviewer software is used to map the bibliographic material. Science mapping uses co-citations and the concurrency of keywords. References were obtained from the Web of Science database. We identified and classified the most relevant research in the field according to journals, articles, authors, institutions and countries. The results show that research in this field has increased significantly in the last ten years and that the USA is the most influential country in all aspects in this field. It is important to consider, however, that science continues to advance in this and in all fields and that data rapidly change over time. Therefore, this paper fulfills an informational role that shows that most of the fundamental research of KM is in business and management areas.
Guo, T, Pan, S, Zhu, X & Zhang, C 2019, 'CFOND: Consensus Factorization for Co-Clustering Networked Data', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Networked data are common in domains where instances are characterized by both feature values and inter-dependency relationships. Finding cluster structures for networked instances and discovering representative features for each cluster represent a special co-clustering task usefully for many real-world applications, such as automatic categorization of scientific publications and finding representative key-words for each cluster. To date, although co-clustering has been commonly used for finding clusters for both instances and features, all existing methods are focused on instance-feature values, without leveraging valuable topology relationships between instances to help boost co-clustering performance. In this paper, we propose CFOND, a consensus factorization based framework for co-clustering networked data. We argue that feature values and linkages provide useful information from different perspectives, yet they are not always consistent and therefore need to be carefully aligned for best clustering results. In the paper, we advocate a consensus factorization principle, which simultaneously factorizes information from three aspects: network topology structures, instance-feature content relationships, and feature-feature correlations. The consensus factorization ensures that the final cluster structures are consistent across information from the three aspects with minimum errors. CFOND enjoys sound theoretical basis and proved convergence, and its performance is validated on real-world networks.
He, X, Li, Y & Zheng, M 2019, 'Heterogeneous Agent Models in Financial Markets: A Nonlinear Dynamics Approach', International Review of Financial Analysis.
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Huang, X, An, P, Cao, F, Liu, D & Wu, Q 2019, 'Light-field compression using a pair of steps and depth estimation', Optics Express, vol. 27, no. 3, pp. 3557-3573.
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Huang, Y, Zhong, Y, Wu, Q, Dutkiewicz, E & Jiang, T 2019, 'Cost-Effective Foliage Penetration Human Detection under Severe Weather Conditions based on Auto-Encoder/Decoder Neural Network', IEEE Internet of Things Journal.
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IEEE Military surveillance events and rescue activities are vital missions for the Internet-of-things. To this end, foliage penetration for human detection plays an important role. However, although the feasibility of that mission has been validated, we observe that it still cannot performs promisingly under severe weather conditions such as rainy, foggy, and snowy days. Therefore, in this paper, experiments are conducted under severe weather conditions based on a proposed deep learning approach. We present an Auto-Encoder/Decoder (Auto-ED) deep neural network that can learn the deep representation and conduct classification task concurrently. Since the property of cost-effective, the device-free sensing (DFS) techniques are used to address human detection in our case. As we pursue the signal-based mission, two components are involved in the proposed Auto-ED approach. First, an encoder is utilized that encode signal-based inputs into higher dimensional tensors by fractionally-strided convolution operations. Then, a decoder is leveraged with convolution operations to extract deep representations and learn the classifier simultaneously. To verify the effectiveness of the proposed approach, we compare it with several machine learning approaches under different weather conditions. Also, a simulation experiment is conducted by adding Additive White Gaussian Noise (AWGN) to the original target signals with different Signal to Noise Ratios (SNRs). Experimental results demonstrate that the proposed approach can best tackle the challenge of human detection under severe weather conditions in the high-clutter foliage environment, which indicates its potential application values in the near future.
Jiang, X, Pan, S, Long, G, Xiong, F, Jiang, J & Zhang, C 2019, 'Cost-sensitive Parallel Learning Framework for Insurance Intelligence Operation', IEEE Transactions on Industrial Electronics.
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IEEE Recent advancements in artificial intelligence (AI) are providing the insurance industry with new opportunities to create tailored solutions and services based on newfound knowledge of consumers, and the execution of enhanced operations and business functions. However, insurance data is heterogeneous, and imbalanced class distribution with low frequency and high dimensions presents four major challenges to machine learning in real-world business. Traditional machine learning algorithms can typically only be applied to standard data sets, which are normally homogeneous and balanced. In this paper, we focus on an efficient cost-sensitive parallel learning framework (CPLF) to enhance insurance operations with a deep learning approach that does not require pre-processing. Our approach comprises a novel, unified, end-to-end cost-sensitive parallel neural network that learns real-world heterogeneous data. A specifically-designed cost-sensitive matrix then automatically generates a robust model for learning minority classifications, and the parameters of both the cost-sensitive matrix and the hybrid neural network are alternately but jointly optimized during training. We also study the CPLF-based architecture for a real-world insurance intelligence operation system, and demonstrate fraud detection experiments on this system. The results of comparative experiments on real-world insurance data sets reflecting actual business cases demonstrate the effectiveness of our design.
Kacprzyk, J, Yager, RR & Merigo, JM 2019, 'Towards human-centric aggregation via ordered weighted aggregation operators and linguistic data summaries: A new perspective on zadeh's inspirations', IEEE Computational Intelligence Magazine, vol. 14, no. 1, pp. 16-30.
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© 2005-2012 IEEE. This work presents a new perspective on how Zadeh's ideas related to fuzzy logic and computing with words have influenced the crucial issue of information aggregation and have led to what may be called a human-centric aggregation. We indicate a need to develop tools and techniques to reflect some fine shades of meaning regarding what can be considered the very purpose of human-centric aggregation, notably stated by various modalities in natural language specifications, in particular the usuality. We advocate the use of the ordered weighted average (OWA) operator, which is a formidable tool that can easily be tailored to a user?s intention as to the purpose and method of aggregation, generalizing many simple and natural aggregation types, such as the arithmetic mean, maximum and minimum, and probability. We show some of the most representative extensions and generalizations, including the induced OWA, the generalized OWA, the probabilistic OWA, and the OWA distance. We show their use in the basic case of the aggregation of numerical values and in social choice (voting) results. Then, we claim that linguistic data summaries in Yager?s sense can be considered an »ultimately human consistent» form of human-centric aggregation and show how the OWA operators can be used therein.
Kumar, P, Beck, D, Galeev, R, Thoms, JAI, Talkhoncheh, MS, de Jong, I, Unnikrishnan, A, Baudet, A, Subramaniam, A, Pimanda, JE & Larsson, J 2019, 'HMGA2 promotes long-term engraftment and myeloerythroid differentiation of human hematopoietic stem and progenitor cells.', Blood advances, vol. 3, no. 4, pp. 681-691.
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Identification of determinants of fate choices in hematopoietic stem cells (HSCs) is essential to improve the clinical use of HSCs and to enhance our understanding of the biology of normal and malignant hematopoiesis. Here, we show that high-mobility group AT hook 2 (HMGA2), a nonhistone chromosomal-binding protein, is highly and preferentially expressed in HSCs and in the most immature progenitor cell subset of fetal, neonatal, and adult human hematopoiesis. Knockdown of HMGA2 by short hairpin RNA impaired the long-term hematopoietic reconstitution of cord blood (CB)-derived CB CD34+ cells. Conversely, overexpression of HMGA2 in CB CD34+ cells led to overall enhanced reconstitution in serial transplantation assays accompanied by a skewing toward the myeloerythroid lineages. RNA-sequencing analysis showed that enforced HMGA2 expression in CD34+ cells induced gene-expression signatures associated with differentiation toward megakaryocyte-erythroid and myeloid lineages, as well as signatures associated with growth and survival, which at the protein level were coupled with strong activation of AKT. Taken together, our findings demonstrate a key role of HMGA2 in regulation of both proliferation and differentiation of human HSPCs.
Laengle, S, Merigó, JM, Modak, NM & Yang, JB 2019, 'Bibliometrics in operations research and management science: a university analysis', Annals of Operations Research.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Many universities around the World have made important contributions in the field of operations research and management science. This article presents the most productive and influential universities between 1991 and 2015. For doing so, we use the Web of Science database in order to search for the information which is usually regarded as the most relevant for scientific research. The results show the country of origin of the leading universities being mainly from North America and Asia and especially from USA and China. The Centre National de la Recherche Scientifique (CNRS) of France is the most productive university while the Massachusetts Institute of Technology (MIT) of USA is the most influential one. The temporal evolution shows that USA is trailing its dominancy while China progressing quickly. The evaluation also reveals that Asian universities outperform North American universities during the last 5 years.
Li, X, Zhao, L, Ji, W, Wu, Y, Wu, F, Yang, M-H, Tao, D & Reid, I 2019, 'Multi-Task Structure-Aware Context Modeling for Robust Keypoint-Based Object Tracking.', IEEE transactions on pattern analysis and machine intelligence, vol. 41, no. 4, pp. 915-927.
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In the fields of computer vision and graphics, keypoint-based object tracking is a fundamental and challenging problem, which is typically formulated in a spatio-temporal context modeling framework. However, many existing keypoint trackers are incapable of effectively modeling and balancing the following three aspects in a simultaneous manner: temporal model coherence across frames, spatial model consistency within frames, and discriminative feature construction. To address this problem, we propose a robust keypoint tracker based on spatio-temporal multi-task structured output optimization driven by discriminative metric learning. Consequently, temporal model coherence is characterized by multi-task structured keypoint model learning over several adjacent frames; spatial model consistency is modeled by solving a geometric verification based structured learning problem; discriminative feature construction is enabled by metric learning to ensure the intra-class compactness and inter-class separability. To achieve the goal of effective object tracking, we jointly optimize the above three modules in a spatio-temporal multi-task learning scheme. Furthermore, we incorporate this joint learning scheme into both single-object and multi-object tracking scenarios, resulting in robust tracking results. Experiments over several challenging datasets have justified the effectiveness of our single-object and multi-object trackers against the state-of-the-art.
Liang, H, Wang, H, Li, Q, Wang, J, Xu, G, Chen, J, Wei, JM & Yang, Z 2019, 'A general framework for learning prosodic-enhanced representation of rap lyrics', World Wide Web.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Learning and analyzing rap lyrics is a significant basis for many Web applications, such as music recommendation, automatic music categorization, and music information retrieval, due to the abundant source of digital music in the World Wide Web. Although numerous studies have explored the topic, knowledge in this field is far from satisfactory, because critical issues, such as prosodic information and its effective representation, as well as appropriate integration of various features, are usually ignored. In this paper, we propose a hierarchical attention variational a utoe ncoder framework (HAVAE), which simultaneously considers semantic and prosodic features for rap lyrics representation learning. Specifically, the representation of the prosodic features is encoded by phonetic transcriptions with a novel and effective strategy (i.e., rhyme2vec). Moreover, a feature aggregation strategy is proposed to appropriately integrate various features and generate prosodic-enhanced representation. A comprehensive empirical evaluation demonstrates that the proposed framework outperforms the state-of-the-art approaches under various metrics in different rap lyrics learning tasks.
Liang, T, Chen, L, Wu, J, Xu, G & Wu, Z 2019, 'SMS: A Framework for Service Discovery by Incorporating Social Media Information', IEEE Transactions on Services Computing, pp. 1-1.
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With the explosive growth of services, including Web services, cloud services, APIs and mashups, discovering the appropriate services for consumers is becoming an imperative issue. The traditional service discovery approaches mainly face two challenges: 1) the single source of description documents limits the effectiveness of discovery due to the insufficiency of semantic information; 2) more factors should be considered with the generally increasing functional and nonfunctional requirements of consumers. In this paper, we propose a novel framework, called SMS, for effectively discovering the appropriate services by incorporating social media information. Specifically, we present different methods to measure four social factors (semantic similarity, popularity, activity, decay factor) collected from Twitter. Latent Semantic Indexing (LSI) model is applied to mine semantic information of services from meta-data of Twitter Lists that contains them. In addition, we assume the target query-service matching function as a linear combination of multiple social factors and design a weight learning algorithm to learn an optimal combination of the measured social factors. Comprehensive experiments based on a real-world dataset crawled from Twitter demonstrate the effectiveness of the proposed framework SMS, through some compared approaches.
Lin, L, Xu, L, Huang, Y, Xiang, Y & He, X 2019, 'On exploiting priority relation graph for reliable multi-path communication in mobile social networks', Information Sciences, vol. 477, pp. 490-507.
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© 2018 Elsevier Inc. A mobile social network (MSN) consists of certain amount of mobile users with social characteristics, and it provides data delivery concerning social relationships between mobile users. In MSN, ordinary people contact each other more frequently if they have more social features in common. In this paper, we apply a new topology structure–priority relation graph (PRG) to evaluate the data delivery routing in MSN on the system-level. By using the natural order of nodes’ representation, the diameter, the regular degree and the multi-path technology, we determine the priority relation graph-based social feature routing (PRG-SFR) algorithm to find disjointed multi-paths in MSN. Here, the multi-path technology can be exploited for ensuring that, between each pair of sender and receiver, the important information can be delivered through a highly reliable path. Then we calculate the tolerant ability of ‘faults’ and estimate the availability of MSN on the theoretical level. Finally, we analyze the efficiency of PRG-SFR algorithm from the numerical standpoint in terms of fault tolerance, forwarding number, transmission time and delivery rate. Moreover, we make comparisons between PRG-SFR algorithm and certain state-of-the-art technologies.
Linares-Mustarós, S, Ferrer-Comalat, JC, Corominas-Coll, D & Merigó, JM 2019, 'The ordered weighted average in the theory of expertons', International Journal of Intelligent Systems, vol. 34, no. 3, pp. 345-365.
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© 2018 Wiley Periodicals, Inc. This work presents a data-fusion mathematical object that incorporates the optimism level of a decision-making agent. The new fusion object is constructed by extending the ordered weighted averaging (OWA) operator in the process of creating an experton. The main advantage of this approach is that it can represent the attitudinal character of the decision maker in the construction of the experton. Therefore, this approach represents a new method for addressing multiperson problems by using optimistic and pessimistic perspectives. The work presents different practical examples based on the absolute hierarchical relationships of the “minimum of the bottom end of the intervals,” “minimum of the top end of the intervals,” and “minimum size of the interval.” The work also considers a wide range of particular cases of the OWA-experton, including the minimum experton, the maximum experton, the average experton, and the olympic experton. In addition, the study presents software for the calculation of OWA-expertons. Finally, the paper ends with an application in business decision-making regarding the calculation of expected benefits.
Liu, Y, Lan, C, Blumenstein, M & Li, J 2019, 'Bi-level error correction for PacBio long reads', IEEE/ACM Transactions on Computational Biology and Bioinformatics.
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IEEE The latest sequencing technologies such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines can generate long reads at the length of thousands of nucleic bases which is much longer than the reads at the length of hundreds generated by Illumina machines. However, these long reads are prone to much higher error rates, for example 15%, making downstream analysis and applications very difficult. Error correction is a process to improve the quality of sequencing data. Hybrid correction strategies have been recently proposed to combine Illumina reads of low error rates to fix sequencing errors in the noisy long reads with good performance. In this paper, we propose a new method named Bicolor, a bi-level framework of hybrid error correction for further improving the quality of PacBio long reads. At the first level, our method uses a de Bruijn graph-based error correction idea to search paths in pairs of solid < formula > < tex > $k$ < /tex > < /formula > -mers iteratively with an increasing length of < formula > < tex > $k$ < /tex > < /formula > -mer. At the second level, we combine the processed results under different parameters from the first level. In particular, a multiple sequence alignment algorithm is used to align those similar long reads, followed by a voting algorithm which determines the final base at each position of the reads. We compare the superior performance of Bicolor with three state-of-the-art methods on three real data sets. Results demonstrate that Bicolor always achieves the highest identity ratio. Bicolor also achieves a higher alignment ratio ( < formula > < tex > $ & #x003E; 1.3\%$ < /tex > < /formula > ) and a higher number of aligned reads than the current methods on two data sets. On the third data set, our method is closely competitive to the current methods in terms of number of aligned reads and genome coverage. The C++ source codes of our algorithm are freely available at https://github.com/yuansliu/Bicolor.
Llanos-Herrera, GR & Merigo, JM 2019, 'Overview of brand personality research with bibliometric indicators', Kybernetes.
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© 2018, Emerald Publishing Limited. Purpose: The purpose of this paper is to present a global view of the research that has been conducted regarding brand personality by using the Core Collection of the Web of Science (WoS) as a reference. The main bibliometric indicators considered are number of articles, number of citations, main authors, principal journals, institutions, countries and keywords. Design/methodology/approach: Through a bibliometric investigation, this paper performs an analysis of investigations of brand personality that have been conducted to date. In particular, the analysis focuses on the papers that have generated the greatest impact in the scientific community, the journals that have given the most attention to this concept and the authors who have most strongly influenced the academic world in this field. The analysis reveals a series of relationships between the bases of knowledge considered for different authors and journals and the structure of those relationships based on the keywords considered in each contribution. Findings: This analysis allows to obtain a general and impartial view of brand personality research, and it reveals the most relevant contributions to the academic world in terms of authors, journals, institutions, countries and keywords. The analysis shows that the concept under study seems to still be in an early stage of development and there may well be an important amount of development ahead. Although there have been important contributions to this field, work is still required to consolidate this knowledge. Research limitations/implications: The information provided pertains to a relatively specific subject but is still general when considered within the context of this topic and thus leaves aside elements that could greatly enrich the analysis. However, this work presents some important guidelines for conducting in-depth academic research and publication. Practical implications: This work identifies the most product...
Luo, Y, Wen, Y, Liu, T & Tao, D 2019, 'Transferring Knowledge Fragments for Learning Distance Metric from a Heterogeneous Domain.', IEEE transactions on pattern analysis and machine intelligence, vol. 41, no. 4, pp. 1013-1026.
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The goal of transfer learning is to improve the performance of target learning task by leveraging information (or transferring knowledge) from other related tasks. In this paper, we examine the problem of transfer distance metric learning (DML), which usually aims to mitigate the label information deficiency issue in the target DML. Most of the current Transfer DML (TDML) methods are not applicable to the scenario where data are drawn from heterogeneous domains. Some existing heterogeneous transfer learning (HTL) approaches can learn target distance metric by usually transforming the samples of source and target domain into a common subspace. However, these approaches lack flexibility in real-world applications, and the learned transformations are often restricted to be linear. This motivates us to develop a general flexible heterogeneous TDML (HTDML) framework. In particular, any (linear/nonlinear) DML algorithms can be employed to learn the source metric beforehand. Then the pre-learned source metric is represented as a set of knowledge fragments to help target metric learning. We show how generalization error in the target domain could be reduced using the proposed transfer strategy, and develop novel algorithm to learn either linear or nonlinear target metric. Extensive experiments on various applications demonstrate the effectiveness of the proposed method.
Makhdoom, I, Abolhasan, M, Lipman, J, Liu, RP & Ni, W 2019, 'Anatomy of Threats to The Internet of Things', Communications Surveys and Tutorials, IEEE Communications Society.
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Martin, F, Cahill, A, Wright, E & Stoianoff, N 2019, 'An international approach to establishing a Competent Authority to manage and protect traditional knowledge', Alternative Law Journal.
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Merigó, JM, Cobo, MJ, Laengle, S, Rivas, D & Herrera-Viedma, E 2019, 'Twenty years of Soft Computing: a bibliometric overview', Soft Computing, vol. 23, no. 5, pp. 1477-1497.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The journal Soft Computing was launched in 1997, and it is dedicated to promote advancements in soft computing theories, which includes fuzzy sets theory, neural networks, evolutionary computation, probabilistic reasoning and hybrid theories. 2017 marks the 20th anniversary of the journal. Motivated by this anniversary, this study presents a bibliometric analysis of the current publications in the journal in order to identify the leading trends ruling the journal. The paper also develops a mapping analysis of the bibliographic material by using the visualization of similarities viewer software. The results show that researchers from all over the world publish regularly in the journal. Soft Computing is growing significantly during the last years, becoming one of the leading journals in the field.
Modak, NM, Merigó, JM, Weber, R, Manzor, F & Ortúzar, JDD 2019, 'Fifty years of Transportation Research journals: A bibliometric overview', Transportation Research Part A: Policy and Practice, vol. 120, pp. 188-223.
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© 2018 Elsevier Ltd Transportation Research (TR) was established in 1967 with the vision of promoting multi-disciplinary (economics, engineering, sociology, psychology) research on transport systems. The journal has continuously expanded its wings becoming a world-leading journal, now publishing research work through six parts, A to F, respectively addressing Policy and Practice, Methodological, Emerging Technologies, Transport and Environment, Logistics and Transportation Review, and Traffic Psychology and Behaviour. This study aims to celebrate the first half century of the journal through a bibliometric study of the publications on all six parts between 1967 and 2016. It uses the most reliable database for academic research, the Web of Science Core Collection, to identify the leading trends in all TR journals in terms of impact, topics, authors, universities, and countries. Moreover, it uses the Visualization of Similarities (VOS) viewer software to analyse bibliographic coupling, co-citation, citation, co-authorship, and co-occurrence of keywords.
Nanda, A, Nanda, P, He, X, Jamdagni, A & Puthal, D 2019, 'A hybrid encryption technique for Secure-GLOR: The adaptive secure routing protocol for dynamic wireless mesh networks', Future Generation Computer Systems.
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As we progress in into a digital era where most aspects of our life depend upon a network of computers,it is essential to focus on digital security. Each component of a network, be it a physical network, virtualnetwork or social network requires security when transmitting data. Hence the dynamic wireless meshnetwork must also deploy high levels of security as found in current legacy networks. This paper presentsa secure Geo-Location Oriented Routing (Secure-GLOR) protocol for wireless mesh networks, whichincorporates a hybrid encryption scheme for its multilevel security framework. The hybrid encryptiontechnique improves the network’s overall performance compared to the basic encryption by using acombination of symmetric key as well as asymmetric key encryption. Using the combination of the twoencryption schemes, the performance of the network can be improved by reducing the transmitted datasize, reduced computational overhead and faster encryption–decryption cycles. In this paper discussedmultiple encryption schemes for both symmetric and asymmetric encryption, compare their performancein various experimental scenarios. Proposed security scheme achieves better performance based on theresults obtained with most viable options for our network model.
Qiao, M, Yu, J, Bian, W, Li, Q & Tao, D 2019, 'Adapting Stochastic Block Models to Power-Law Degree Distributions.', IEEE transactions on cybernetics, vol. 49, no. 2, pp. 626-637.
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Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new variant of SBM, termed power-law degree SBM (PLD-SBM), by introducing degree decay variables to explicitly encode the varying degree distribution over all nodes. With an exponential prior, it is proved that PLD-SBM approximately preserves the scale-free feature in real networks. In addition, from the inference of variational E-Step, PLD-SBM is indeed to correct the bias inherited in SBM with the introduced degree decay factors. Furthermore, experiments conducted on both synthetic networks and two real-world datasets including Adolescent Health Data and the political blogs network verify the effectiveness of the proposed model in terms of cluster prediction accuracies.
Seifollahi, S, Bagirov, A, Zare Borzeshi, E & Piccardi, M 2019, 'A simulated annealing‐based maximum‐margin clustering algorithm', Computational Intelligence, vol. 2018, pp. 1-19.
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Maximum‐margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing‐based algorithm that is able to mitigate the issue of local minima in the maximum‐margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k‐means++ and SVM at each step of the annealing process. More precisely, k‐means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms.
van Rijmenam, M, Erekhinskaya, T, Schweitzer, J & Williams, MA 2019, 'Avoid being the turkey: How big data analytics changes the game of strategy in times of ambiguity and uncertainty', Long Range Planning.
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In order for organisations to remain competitive in times of ambiguity and uncertainty, there is a need to detect and anticipate unknown unknowns, also called ‘black swans’. When these are ignored they may lead to competitive struggles. In this paper, we build on this view and suggest that big data analytics can provide necessary insights to help change strategy making. Research suggests that ambidextrous organisations should focus on developing and maintaining their dynamic capabilities. Following on from this, we take a dynamic capabilities perspective and propose a theoretical framework to explain the intricacies of big data analytics. This framework explains the ability of organisations to detect, anticipate and respond strategically in ambiguous and uncertain business environments. For a meta- synthesis of 101 cases of big data analytics, we employ a multi-method approach that incorporates Natural Language Processing, semantic analysis and case analysis, allowing extraction and analysis of structured information from unstructured data. Overall, we find evidence of big data analytics helping to detect, anticipate and respond to industry disruption. We offer six propositions about the relationships between the levels of data analytics capabilities and strategic dynamic capabilities. We find that descriptive data analytics improves the capability of an organisation to understand the business context (sensing) and that predictive data analytics aids in the realisation of business opportunities (seizing). This study contributes to an understanding of big data analytics as a dynamic organisational capability that supports strategic decision-making in times of ambiguity and uncertainty. We conclude by suggesting areas for further investigation, particularly in regard to the strategic application of prescriptive data analytics.
Wang, B, Yuan, X, Gao, X, Li, X & Tao, D 2019, 'A Hybrid Level Set With Semantic Shape Constraint for Object Segmentation.', IEEE transactions on cybernetics, vol. 49, no. 5, pp. 1558-1569.
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This paper presents a hybrid level set method for object segmentation. The method deconstructs segmentation task into two procedures, i.e., shape transformation and curve evolution, which are alternately optimized until convergence. In this framework, only one shape prior encoded by shape context is utilized to estimate a transformation allowing the curve to have the same semantic expression as shape prior, and curve evolution is driven by an energy functional with topology-preserving and kernelized terms. In such a way, the proposed method is featured by the following advantages: 1) hybrid paradigm makes the level set framework possess the ability of incorporating other shape-related techniques about shape descriptor and distance; 2) shape context endows one single prior with semanticity, and hence leads to the competitive performance compared to the ones with multiple shape priors; and 3) additionally, combining topology-preserving and kernelization mechanisms together contributes to realizing a more reasonable segmentation on textured and noisy images. As far as we know, we propose a hybrid level set framework and utilize shape context to guide curve evolution for the first time. Our method is evaluated with synthetic, healthcare, and natural images, as a result, it shows competitive and even better performance compared to the counterparts.
Wang, H, Wu, J, Zhu, X, Chen, Y & Zhang, C 2019, 'Time-Variant Graph Classification', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE Graphs are commonly used to represent objects, such as images and text, for pattern classification. In a dynamic world, an object may continuously evolve over time, and so does the graph extracted from the underlying object. These changes in graph structure with respect to the temporal order present a new representation of the graph, in which an object corresponds to a set of time-variant graphs. In this paper, we formulate a novel time-variant graph classification task and propose a new graph feature, called a graph-shapelet pattern, for learning and classifying time-variant graphs. Graph-shapelet patterns are compact and discriminative graph transformation subsequences. A graph-shapelet pattern can be regarded as a graphical extension of a shapelet--a class of discriminative features designed for vector-based temporal data classification. To discover graph-shapelet patterns, we propose to convert a time-variant graph sequence into time-series data and use the discovered shapelets to find graph transformation subsequences as graph-shapelet patterns. By converting each graph-shapelet pattern into a unique tokenized graph transformation sequence, we can measure the similarity between two graph-shapelet patterns and therefore classify time-variant graphs. Experiments on both synthetic and real-world data demonstrate the superior performance of the proposed algorithms.
Wang, Y, Shuai, Y, Zhu, Y, Zhang, J & An, P 2019, 'Jointly learning perceptually heterogeneous features for blind 3D video quality assessment', Neurocomputing, vol. 332, pp. 298-304.
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© 2018 Elsevier B.V. 3D videos quality assessment (3D-VQA) is essential to various 3D video processing applications. However, it has not been well investigated on how to make use of perceptual multi-channel video information to improve 3D-VQA under different distortion categories and degrees, especially under asymmetrical distortions. In the paper, we propose a new blind 3D-VQA metric by jointly learning perceptually heterogeneous features. Firstly, a binocular spatio-temporal internal generative mechanism (BST-IGM) is proposed to decompose the views of 3D video into multi-channel videos. Then, we extract perceptually heterogeneous features by proposed multi-channel natural video statistics (MNVS) model, which are characterized 3D video information. Furthermore, a robust AdaBoosting Radial Basis Function (RBF) neural network is utilized to map the features to the overall quality of 3D video. On two benchmark databases, the extensive evaluations demonstrate that the proposed algorithm significantly outperforms several state-of-the-art quality metrics in term of prediction accuracy and robustness.
Yao, Y, Shen, F, Zhang, J, Liu, L, Tang, Z & Shao, L 2019, 'Extracting Multiple Visual Senses for Web Learning', IEEE Transactions on Multimedia, vol. 21, no. 1, pp. 184-196.
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© 1999-2012 IEEE. Labeled image datasets have played a critical role in high-level image understanding. However, the process of manual labeling is both time consuming and labor intensive. To reduce the dependence on manually labeled data, there have been increasing research efforts on learning visual classifiers by directly exploiting web images. One issue that limits their performance is the problem of polysemy. Existing unsupervised approaches attempt to reduce the influence of visual polysemy by filtering out irrelevant images, but do not directly address polysemy. To this end, in this paper, we present a multimodal framework that solves the problem of polysemy by allowing sense-specific diversity in search results. Specifically, we first discover a list of possible semantic senses from untagged corpora to retrieve sense-specific images. Then, we merge visual similar semantic senses and prune noise by using the retrieved images. Finally, we train one visual classifier for each selected semantic sense and use the learned sense-specific classifiers to distinguish multiple visual senses. Extensive experiments on classifying images into sense-specific categories and reranking search results demonstrate the superiority of our proposed approach.
Yao, Y, Shen, F, Zhang, J, Liu, L, Tang, Z & Shao, L 2019, 'Extracting Privileged Information for Enhancing Classifier Learning', IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 436-450.
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© 1992-2012 IEEE. The accuracy of data-driven learning approaches is often unsatisfactory when the training data is inadequate either in quantity or quality. Manually labeled privileged information (PI), e.g., attributes, tags or properties, is usually incorporated to improve classifier learning. However, the process of manually labeling is time-consuming and labor-intensive. Moreover, due to the limitations of personal knowledge, manually labeled PI may not be rich enough. To address these issues, we propose to enhance classifier learning by exploring PI from untagged corpora, which can effectively eliminate the dependency on manually labeled data and obtain much richer PI. In detail, we treat each selected PI as a subcategory and learn one classifier for each subcategory independently. The classifiers for all subcategories are integrated together to form a more powerful category classifier. Particularly, we propose a novel instance-level multi-instance learning model to simultaneously select a subset of training images from each subcategory and learn the optimal SVM classifiers based on the selected images. Extensive experiments on four benchmark data sets demonstrate the superiority of our proposed approach.
Zhan, K, Chang, X, Guan, J, Chen, L, Ma, Z & Yang, Y 2019, 'Adaptive Structure Discovery for Multimedia Analysis Using Multiple Features.', IEEE transactions on cybernetics.
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Multifeature learning has been a fundamental research problem in multimedia analysis. Most existing multifeature learning methods exploit graph, which must be computed beforehand, as input to uncover data distribution. These methods have two major problems confronted. First, graph construction requires calculating similarity based on nearby data pairs by a fixed function, e.g., the RBF kernel, but the intrinsic correlation among different data pairs varies constantly. Therefore, feature learning based on such predefined graphs may degrade, especially when there is dramatic correlation variation between nearby data pairs. Second, in most existing algorithms, each single-feature graph is computed independently and then combine them for learning, which ignores the correlation between multiple features. In this paper, a new unsupervised multifeature learning method is proposed to make the best utilization of the correlation among different features by jointly optimizing data correlation from multiple features in an adaptive way. As opposed to computing the affinity weight of data pairs by a fixed function, the weight of affinity graph is learned by a well-designed optimization problem. Additionally, the affinity graph of data pairs from different features is optimized in a global level to better leverage the correlation among different channels. In this way, the adaptive approach correlates the features of all features for a better learning process. Experimental results on real-world datasets demonstrate that our approach outperforms the state-of-the-art algorithms on leveraging multiple features for multimedia analysis.
Zhang, Q, Shi, C, Niu, Z & Cao, L 2019, 'HCBC: A Hierarchical Case-Based Classifier Integrated with Conceptual Clustering', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 1, pp. 152-165.
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© 1989-2012 IEEE. The structured case representation improves case-based reasoning (CBR) by exploring structures in the case base and the relevance of case structures. Recent CBR classifiers have mostly been built upon the attribute-value case representation rather than structured case representation, in which the structural relations embodied in their representation structure are accordingly overlooked in improving the similarity measure. This results in retrieval inefficiency and limitations on the performance of CBR classifiers. This paper proposes a hierarchical case-based classifier, HCBC, which introduces a concept lattice to hierarchically organize cases. By exploiting structural case relations in the concept lattice, a novel dynamic weighting model is proposed to enhance the concept similarity measure. Based on this similarity measure, HCBC retrieves the top-K concepts that are most similar to a new case by using a bottom-up pruning-based recursive retrieval (PRR) algorithm. The concepts extracted in this way are applied to suggest a class label for the case by a weighted majority voting. Experimental results show that HCBC outperforms other classifiers in terms of classification performance and robustness on categorical data, and also works confidently well on numeric datasets. In addition, PRR effectively reduces the search space and greatly improves the retrieval efficiency of HCBC.
Zhang, X, Zhao, Z, Zheng, Y & Li, J 2019, 'Prediction of Taxi Destinations Using a Novel Data Embedding Method and Ensemble Learning', IEEE Transactions on Intelligent Transportation Systems, pp. 1-11.
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Zheng, Y, Peng, H, Ghosh, S, Lan, C & Li, J 2019, 'Inverse similarity and reliable negative samples for drug side-effect prediction.', BMC bioinformatics, vol. 19, no. Suppl 13, p. 554.
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BACKGROUND:In silico prediction of potential drug side-effects is of crucial importance for drug development, since wet experimental identification of drug side-effects is expensive and time-consuming. Existing computational methods mainly focus on leveraging validated drug side-effect relations for the prediction. The performance is severely impeded by the lack of reliable negative training data. Thus, a method to select reliable negative samples becomes vital in the performance improvement. METHODS:Most of the existing computational prediction methods are essentially based on the assumption that similar drugs are inclined to share the same side-effects, which has given rise to remarkable performance. It is also rational to assume an inverse proposition that dissimilar drugs are less likely to share the same side-effects. Based on this inverse similarity hypothesis, we proposed a novel method to select highly-reliable negative samples for side-effect prediction. The first step of our method is to build a drug similarity integration framework to measure the similarity between drugs from different perspectives. This step integrates drug chemical structures, drug target proteins, drug substituents, and drug therapeutic information as features into a unified framework. Then, a similarity score between each candidate negative drug and validated positive drugs is calculated using the similarity integration framework. Those candidate negative drugs with lower similarity scores are preferentially selected as negative samples. Finally, both the validated positive drugs and the selected highly-reliable negative samples are used for predictions. RESULTS:The performance of the proposed method was evaluated on simulative side-effect prediction of 917 DrugBank drugs, comparing with four machine-learning algorithms. Extensive experiments show that the drug similarity integration framework has superior capability in capturing drug features, achieving much better performance tha...
Zuo, Y, Wu, Q, Fang, Y, An, P, Huang, L & Chen, Z 2019, 'Multi-scale Frequency Reconstruction for Guided Depth Map Super-resolution via Deep Residual Network', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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